evaluation of the safety benefits of existing safety functions - trace

TRACE Deliverable D4.2.2

September 2008 - 1 -

Project No. 027763 – TRACE

D4.2.2

Evaluation of the safety benefits of existing Safety Functions

Contractual Date of Delivery to the CEC: March 2008

Actual Date of Delivery to the CEC (Version 2, final after revision): September 2008

Author(s) - Participant(s): Sophie Cuny, Yves Page (LAB, France), Tobias Zangmeister (TUBS, Germany)

Workpackage: 4

Est. person months: 9

Validated by WP4 Leader: Kosmas Alexopoulos (LMS, Greece)

Validated by TRACE Coordinator: Yves Page (LAB, France)

Reviewed by external reviewer: Philippe Toussaint (CEESAR, France)

Security: CO

Nature: Report

Version: 2 (final)

Total number of pages: 70

Abstract:

The main objective of TRACE – WP4 - task 4.2 is to estimate, by means of statistical calculation, the proportion of injury accidents that could be avoided and/or the proportion of accidents which severity could be mitigated, for existing safety functions (or a combination of functions), selected from the WP6 list, would all cars be equipped with these functions that are already on the market.

This kind of effectiveness is called potential observed effectiveness.

The main striking results show that any increment of a passive or active safety functions selected in this analysis (5th star at EuroNCAP, Emergency Brake Assist, Electronic Stability Control) is producing additional safety benefits. In general, the safety gains are even higher for higher severity levels.

For example, would all cars be five stars fitted with EBA and ESC, compared to four stars without ESC and EBA, injury accidents would be reduced by 47.2%, all injuries would be mitigated by 67.8% and severe + fatal injuries by 69.5%.

Keyword list: Evaluation – Safety Systems – Statistics – Road Accidents – Effectiveness – Safety Benefits



Table of Contents

1 Executive Summary __________________________________________________________________________ 3

2 Introduction_________________________________________________________________________________ 5

3 Methodology ________________________________________________________________________________ 7

3.1 Methodology for the a-posteriori evaluation of accident avoidance due to one safety function. __ 8

3.1.1 Crude Odds Ratio_______________________________________________________________________________ 8

3.1.2 Adjusted Odd ratio______________________________________________________________________________ 9

3.2 Methodology for the a-posteriori evaluation of the effectiveness of multiple safety functions in accident avoidance._____________________________________________________________________________ 9

3.3 Methodology for injury mitigation evaluation ____________________________________________ 10

4 Data ______________________________________________________________________________________ 14

4.1 Description of the French accident national database ______________________________________ 14

4.2 Additional variables ___________________________________________________________________ 15

4.2.1 Car model, EuroNcap rating and safety equipment _________________________________________________ 15

4.2.2 Accidental situations ___________________________________________________________________________ 16

4.3 Data selection_________________________________________________________________________ 19

4.4 Sample description ____________________________________________________________________ 19

4.4.1 Type of vehicles (four or five stars vehicles)________________________________________________________ 21

4.4.2 Emergency Brake Assist ________________________________________________________________________ 27

4.4.3 Electronic Stability Control ______________________________________________________________________ 33

4.4.4 Accidental situations ___________________________________________________________________________ 39

5 Results: Assessment of the effectiveness of existing safety applications in cars. ______________________ 48

5.1 Evaluation of the safety benefit of Emergency Brake Assist_________________________________ 49

5.2 Evaluation of the safety benefit of Electronic Stability Control______________________________ 50

5.2.1 Four stars vehicles _____________________________________________________________________________ 50

5.2.2 Five stars vehicles______________________________________________________________________________ 52

5.3 Evaluation of the safety benefit of EuroNcap star rating____________________________________ 53

5.3.1 Vehicles fitted with EBA ________________________________________________________________________ 53

5.3.2 Vehicles fitted with EBA and ESC ________________________________________________________________ 54

5.4 Evaluation of the safety benefit of the combination of EBA and ESC ________________________ 56

5.5 Evaluation of the safety benefit of the combination of the fifth star and EBA _________________ 57

5.6 Evaluation of the safety benefit of the combination of the fifth star and ESC _________________ 58

5.7 Evaluation of the safety benefit of the combination of the fifth star, EBA and ESC ____________ 59

5.8 Evaluation of the safety benefit of the combination of the fifth star minus ESC _______________ 60

6 Conclusions ________________________________________________________________________________ 62

6.1 Synthesis of results____________________________________________________________________ 62

6.2 Limitations and discussion._____________________________________________________________ 63

References_______________________________________________________________________________________ 68

Annex A (External variables included in the logistic regressions) _______________________________________ 70



1 Executive Summary

In spite of countless amounts of research and development, road safety is still one of the main societal concerns today. It is not only a matter of concern for the European Commission and National Governments but also for the vehicle industry, insurance companies, driving schools, non-governmental organisations and more generally for every single road user. Car manufacturers have made strong efforts and have dramatically improved passive (and also active) safety of their vehicle for the past 15 years. However, current road safety research has shown that an asymptote is about to be reached on this aspect in most countries and many experts agree that preventive (prevention of accidents) and active safety (recovery of an emergency situation) should now, particularly, be brought forward.

In this context, the TRACE project (TRaffic Accident Causation in Europe) has 2 major objectives:

The first one addresses the determination and the continuous up-dating of the aetiology (i.e. analysis of the causes) of road accidents, and the definition of the real needs of the road users as they are deduced from accident and driver behaviour analyses.

The second one aims at identifying and assessing, among possible technology-based safety functions, the most promising solutions that can assist the driver or any other road users in a normal road situation or in an emergency situation, or can mitigate the consequences of a road crash.

The aim of Workpackage 4 is to investigate this impact of advanced safety functions on reducing several types of accidents involving passenger cars or mitigating accident consequences.

The evaluation is performed from two different perspectives:

- Assessment of the potential proportion of injury accidents involving a passenger car that could be avoided and of the potential proportion of injury accidents whose severity could be reduced, for each safety function before they are on the market (this is the so-called a priori effectiveness) (Task 4.1).

- Assessment of the proportion of injury accidents involving a passer car that could be avoided and of the actual proportion of injury accidents whose severity could be reduced, for each safety function (this is the so-called a posteriori effectiveness) once the cars are equipped with existing functions (Task 4.2).

The main objective of this task 4.2 of WP4 was to estimate, by means of statistical calculation, the proportion of injury accidents that could be avoided and/or the proportion of accidents which severity could be mitigated, for existing safety functions (or a combination of functions), selected from the WP6 list, would all cars be equipped with these functions that are already on the market (for instance Electronic Stability Control or Emergency Brake Assist).

The challenges of task 4.2 were the following:

- Identify the right epidemiological methodologies that can help in achieving the objective, integrating the so-called ‘driver adaptation phenomenon’ (the driver can adapt his behaviour to the safety function in a way that it can decrease the expected effectiveness) if any.

Especially it is essential to work on the effectiveness of a package of safety functions and not only on a safety function independently from the others. Moreover, it is also important that the effectiveness can be split into effectiveness due to the preventive or active safety functions, the passive safety functions, and the interaction between the two natures of functions (preventive/active and passive).

- Apply the methodologies to existing accident data, which we know from previous studies that there are a lot of data problems (including absence of information about existence of safety functions in the crashed cars), and lack of exposure data.



We intended to compare the effectiveness of some safety configuration SC I with the effectiveness of some safety configuration SC II. A safety configuration can be understood as a package of safety functions.

This deliverable D4.2.2 presents the final results achieved by applying the TRACE methodology described in Deliverables D.7.4.1 and D.4.2.1. to the available accident data.

The main striking results coming out from the analysis are the following. These are what we call the ‘overall effectiveness’ of the selected safety systems with breakdown by injury severity levels. This ‘overall effectiveness’ represent the percentage of reduction in injury accident and injuries that would be observed if all cars would be fitted with the system(s) under consideration, compared to cars of a reference group. Reference groups are not always the same, the less equipped reference group being 4-stars cars without EBA, without ESC.

Reduction in injury accidents (accident

avoidance)

Reduction in all injuries & fatalities

Reduction in severe injuries and fatalities

Safety benefit of EBA given that the car has four stars.

-3.2% (**) 7.8% 14.6%

Safety benefit of ESC given that the car has four stars and an EBA.

5.2% 10.3% 16,8%

Safety benefit of ESC given that the car has five stars and an EBA.

3.2% 10.7% (*) 23.4% (*)

Safety benefit of the fifth star given that the car has four stars and an EBA.

6,4% 8,3% N.A.

Safety benefit of the fifth star given that the car has four stars, an EBA and an ESC.

19.3% (*) 33,8% (*) 35,1% (*)

Safety benefit of EBA and ESC given that the car has four stars.

18,6% 36,3% (*) 42,3%

Safety benefit of EBA and a fifth star given that the car has four stars.

28,2% (*) 36% (*) 37,5% (*)

Safety benefit of ESC and a fifth star given that the car has four stars and an EBA.

22% (*) 38,6% (*) 37,1% (*)

Safety benefit EBA, ESC and a fifth star given that the car has four stars.

47,2% (*) 67,8% (*) 69,5% (*)

Safety benefit of a fifth star and removing an ESC given that the car has four stars, an EBA and an ESC.

2,1% N.A. N.A.

* statistically significant **: negative sign not significant

Any increment of a passive or active safety function selected in this analysis (5th star, Emergency Brake Assist, Electronic Stability Control) is producing additional safety benefits. In general, the safety gains are higher for higher severity levels.



2 Introduction

In spite of countless amounts of research and development, road safety is still one of the main societal concerns today. It is not only a matter of concern for the European Commission and National Governments but also for the vehicle industry, insurance companies, driving schools, non-governmental organisations and more generally for every single road user. Car manufacturers have made strong efforts and have dramatically improved passive (and also active) safety of their vehicles for the past 15 years. However, current road safety research has shown that an asymptote is about to be reached on this aspect in most countries and many experts agree that preventive (prevention of accidents) and active safety (recovery of an emergency situation) should now, particularly, be brought forward.

In this context, the TRACE project has 2 major objectives:

The first one addresses the determination and the continuous up-dating of the aetiology (i.e. analysis of the causes) of road accidents and injuries, and the definition of the real needs of the road users as they are deduced from accident and driver behaviour analyses.

The second one aims at identifying and assessing, among possible technology-based safety functions, the most promising solutions that can assist the driver or any other road users in a normal road situation or in an emergency situation.

The aim of Workpackage 4 is to investigate this impact of advanced safety functions on reducing several types of accidents involving passenger cars or restricting accident consequences.

The evaluation is performed from two different perspectives:

- Assessment of the potential proportion of injury accidents involving a passenger car that could be avoided and of the potential proportion of accidents whose severity could be reduced, for each safety function (this is the so-called a priori effectiveness) (Task 4.1) before they are introduced on the market.

- Assessment of the actual proportion of injury accidents involving a passenger car that could be avoided and of the actual proportion of accidents whose severity could be reduced, for each safety function (this is the so-called a posteriori effectiveness) once the cars are equipped with existing functions (Task 4.2).

Therefore, the main objective of this task 4.2 of WP4 was to estimate, by means of statistical calculation, the proportion of injury accidents that could be avoided and/or the proportion of accidents which severity could be mitigated, for existing safety functions (or a combination of functions), selected from the WP6 list, would all cars be equipped with these functions that are already on the market (for instance Electronic Stability Control, navigation systems or Emergency Brake Assist).

This kind of effectiveness is called observed effectiveness or a posteriori effectiveness.

This objective is very different from the objective of the WP4 Task 1 which is addressing the expected safety benefit of non existing functions, the ones which are near market, under development, or eventually at a research stage.

There are actually two kinds of observed types of effectiveness. We have to distinguish between the real effect of a safety function since its introduction (which is much depending on the penetration rate of the safety function in the registrations), and the potential safety benefit of an existing safety function would all cars be equipped with such a function. Let’s take an example. Let’s say that the safety function is present in 10% of the car fleet. If the effectiveness of the function is, say, 10% less accident involvement for the cars equipped with such a function, it means that the real effect is 1%:



10% effectiveness out of 10% of the fleet. But the potential benefit is 10% would the whole fleet be fitted with the equipment.

Task 4.2 is working on the second estimation exclusively. This is what we call the potential observed safety benefit.

The challenges of this task have been explained and argued in Deliverable D.4.2.1. In this current report (D.4.2.2.), we apply the methodologies to existing crash data.

Chapter 3 is recalling, for the sake of understanding without looking at Deliverables D.4.2.1. and D.7.4.1 in details, the main aspects of the methodology used for the assessment of the safety benefits of existing safety functions. Chapter 4 describes the data used for the evaluation. Finally chapter 5 presents the results achieved by the TRACE project in this matter.

There are many results available since we wished to assess different packages of safety functions and compare the effectiveness of a specific package vs. another specific package.



3 Methodology

The aim of work package WP4 Task 2 is to achieve an estimation of the safety benefits of existing safety applications, given that these applications are already on the market, thus already fitted on (some) vehicles.

We must first define what an application (or function, both terms will be used alternatively) is. Actually, we must distinguish here between vehicle organs that can bring additional safety compared to what can be considered as normal (for example there are many brake systems, some being more effective than others), and safety applications, which can be considered as an additional system, or another application, additional to the normal system. For example an emergency brake assist is considered as an additional application and not only as an outstanding brake system.

There are currently not so many applications widespread in the European fleet that can be assessed. A quick glance at WP6 deliverable (D.6.1 and D6.2) shows that if some systems are already fitted in cars (night vision, ACC, lane departure warning, etc.), they are usually fitted in luxury cars with a very low penetration rate, and therefore ending up with just a few cars involved in crashes, which makes the analysis difficult.

We had initially selected 5 preventive or active safety systems for our evaluation:

- Electronic Stability Control (ESC)

- Emergency Brake Assist (EBA)

- Manual Speed Limiter

- Tire Pressure Monitoring System (TPMS)

- Navigation systems

Unfortunately, national crash data is limited and does not allow recognizing systematically whether the crashed cars are equipped with such or such device. Even with the help of Vehicle Identification Number files, it is hardly possible to connect these files to the crash dataset and therefore the equipment of optional devices is very often unknown. We then had to reduce the list of the active systems to assess to ESC and EBA for which the information was often connectable between the crashed cars and the vehicle identification files.

As for the passive safety systems, nowadays, the whole car is designed to offer an overall protection. Car structure is stiffer than in the past in order to avoid intrusion in the compartment, which was proved to be one of the major causes of injuries. Load limiters prevent from belt webbing; airbags prevent the head and the chest to hit the steering wheel or another hard element of the compartment; pretensioners couple the occupant to his seat in order to reduce submarining and a hump over the seat and under the base also prevent the pelvis to rotate under the belt. In some cases, knee airbags also prevent from submarining by stopping the legs and then the occupant body displacement under the belt during the crash. Other devices such as padding and non aggressive structures in the door panel, the dashboard, the windshield, the seats, the head rest also participate in supplying more protection. The whole package is then very difficult to evaluate separately, one element independently from the others. We have then decided to consider that we would evaluate in TRACE the safety of the whole package (5 stars against 4 or 3 stars).

We intend to evaluate each safety function individually: for instance, the benefit of having five stars instead of four stars will be calculated. Nevertheless, as several safety systems are on board at the same time on the same vehicle, it is of major interest to asses the overall benefit of adding one or two safety functions. For example, we might be interested in calculating the safety benefit of having an ESC and an EBA compared to having none of these systems. Doing so, it is then possible to estimate the benefit of the combination of active safety function and passive safety function altogether.



The benefit of a safety function (or a safety configuration) can be expressed as a percentage of avoided injury accidents due to the safety function. As the safety function may not be able to avoid the crash but to mitigate the injury severity sustained by the passengers or the colliding road user, the benefit of the safety function also needs to be expressed as a percentage of avoided injuries.

Actually, because the safety functions under consideration are attached to passenger cars, the safety benefits will be calculated for injury accidents and injuries in injury accidents where a passenger car is involved. This counts for about 85% of injury accidents in France (passenger car alone or impacting another road user, such as pedestrians, pedal cyclist, powered two-wheelers, buses and coaches, or heavy vehicles, etc.)

We are recalling in this chapter the methodologies for single and multiple safety function(s) evaluation, in terms of accident avoidance and injury mitigation.

The complete statistical methodology of the simultaneous evaluation of two safety functions is described and explained in the TRACE report D.7.4.1 (Evaluation of the safety benefits of existing Safety Features Statistical Methodology). This report also explains how to asses the injury mitigation effectiveness of a safety function and a set of safety functions.

As these methods are already detailed in the deliverables quoted above, we will give only an overview of these methodologies in the present report. First, we will present the methodology concerning the effectiveness of one safety function in accident avoidance, and then we will present the approach to evaluate the safety benefit of a set of safety functions (so called safety configuration). The third part will deal with injury mitigation.

3.1 Methodology for the a-posteriori evaluation of accident avoidance due to one safety function.

3.1.1 Crude Odds Ratio

Let’s assume that we need to evaluate the safety benefit of the safety function 1 (SF1), expressed in terms of accident avoidance. The method assumes that a disaggregated accident database is at hands. Crashed vehicles for which the safety equipment is known are selected and the analysis will be performed on this sub-sample. We then have a set of crashed vehicles fitted with SF1 and another set not fitted with SF1.

The accidental situations in which the vehicles are involved also have to be available in the accident database. An accidental situation describes the circumstances a driver has to face with during the accident. It is linked to the driver (or to the vehicle) and there are as many accidental situations in an accident as the number of drivers involved in this accident.

Usually, a safety function is not designed to avoid all the accidental situations. Some accidental situations are not influenced by the presence of SF1 on the vehicle (for example night vision systems are supposed to be ineffective in daylight conditions); these situations are considered as “neutral” to SF1. On the other hand, some accidental situations may be influenced by the effect of SF1, which means that SF1 could have an impact on the accident situation. These situations are called sensitive or non-neutral regarding SF1.

Of course, as always in epidemiology, neutral and non-neutral accident situations have to be carefully identified. Any misclassification of a situation in the wrong group, a bias might occur in the estimation of the safety benefit brought by SF1.

The basic idea of the methodology is to compare the proportion of SF1 equipped vehicles in the neutral accidental situations and in the non-neutral accidental situations. The method relies on the calculation of the so-called Odds-ratio (OR), using a cross tabulation (see Table 1).



Type of accidental situation

Vehicle fitted with SF1 Non Neutral Neutral

Yes a b

No c d

Table 1: Type of accidental situation according to SF1

The effectiveness of SF1 in avoiding non-neutral accidental situations is directly linked to the OR, as shown in Equation 1.

Equation 1

The effectiveness calculated above is the effectiveness of SF1 in preventing the sensitive accidental situations, without taking into account the proportion of these accident situations. The overall effectiveness takes into account the effectiveness calculated on the specific relevant accident situations and the proportion of relevant situations out of the total number of situations. Calculation of the lowest and the highest values for the overall crude effectiveness are also possible in order to obtain a kind of confidence interval.

3.1.2 Adjusted Odd ratio

The calculation of the crude odd-ratio assumed that the two sets of vehicles only differ by the presence of SF1. Hence, the effectiveness observed between the two samples of vehicles is clearly attributed to the safety function. This situation is hardly seen in real life. Different vehicles are not driven by similar drivers in similar conditions and thus, may be involved in different types of accidents. These confounders are not taken into account with the crude effectiveness and actually, this observed crude effectiveness may be due to one of these external factors. A way to account for the possible confounders is to perform a logistic regression. The logistic regression computes the probability of being involved in a sensitive accidental situation given that the vehicle is fitted or not with SF1 and given the influence of the confounding factors. Interaction terms are also tested and if they turn to be significant at the 0.05 level, they are kept in the final regressions. The β coefficients calculated in the logistic regressions are directly connected to the OR. They are adjusted OR as they take into account the influence of the other variables set in the regressions. Having the adjusted OR associated with the presence of SF1, we directly obtain the effectiveness of SF1 in avoiding SF1 sensitive accidental situations, using Equation 1. The overall effectiveness of SF1, adjusted on the confounding factors is generally the result of the complete analysis. The lowest and highest possible values of this overall effectiveness are calculated (see to chapter 3.1.2 and 3.1.3 of the TRACE-D.7.4.1 report for complete formulae).

3.2 Methodology for the a-posteriori evaluation of the effectiveness of multiple safety functions in accident avoidance.

Let’s assume we now need to take into account the fact that some cars are fitted with several safety functions. The methodology for evaluating the safety benefits of a package of safety functions is an

bc

da

d

bc

a

ORSFeff××−=−=−= 111)1(



extension of the methodology applicable for the evaluation of a single safety function. It relies again on the comparison of two groups of vehicles: one group of vehicles equipped with the safety functions of interest and one group not equipped with these safety functions. The proportions of these two sets of vehicles, in the neutral accidental situations and in the sensitive accidental situations, observed in the accidents database are compared.

The specificity of analyzing a set of safety functions instead of a single safety function stands in the choice of the neutral accidental situations. Here the neutral accidental situations have to be chosen in a way that they are neutral to all the safety functions of interest. Inversely, an accidental situation is considered as a sensitive one as soon as one of the safety functions on board on the vehicle might have an influence on the occurrence of the situation. For example, let’s say we want to evaluate the safety benefits of a vehicle equipped with the safety function one (SF1) and the safety function two (SF2), compared to a vehicle not fitted with these safety functions. The neutral accidental situations for such an evaluation are given by the blue cell in Table 2 this is the intersection of all neutral situations. The table shows that the rest of the situations are defined as sensitive, because at least one safety function is sensitive to the situation: the sensitive situation used for the evaluation of multiple safety functions is the union of all sensitive situations, either to SF1 only, SF2 only or both.

Once the accidental situations are described, the methodology for the evaluation of multiple safety functions is exactly the same as the one used when there is only one function in the analysis. We can calculate the crude OR and the adjusted OR via a logistic regression to take into account possible confounders. The adjusted OR will lead to an estimation of the safety benefit of having the safety configuration 1 (SC1) on board compared to having another safety configuration 2 (SC2). SC2 can also mean no safety functions at all. The effectiveness to the sensitive cases only and the overall effectiveness of the safety configuration is then assessed.

SF1

Neutral regarding SF1 Sensitive regarding SF1

Neutral regarding SF2 Neutral for SF1& SF2 Sensitive for SF1 - SF2 SF2

Sensitive regarding SF2 Sensitive for SF1 - SF2 Sensitive for SF1 - SF2

Table 2: Neutral accidental situations in multiple safety functions evaluation.

The neutral group of situations shrinks with the number of safety functions fitted on the vehicle: the more the safety functions we take into account in the analysis, the smallest becomes the neutral accidental situation group. The use of such unbalanced data may lead to inaccuracy when performing logistic regression.

3.3 Methodology for injury mitigation evaluation

As stated before, the safety function implemented on cars may not be able to avoid the crash but to mitigate the severity of the impact and thus the injury severity of the occupants. The accident database must describe the severity level of injuries sustained by the occupants. In our database available for the analysis, the injury severity is coded in four categories: not injured, injured and retained in hospital less than a day, injured and retained in hospital longer than a day, and fatally injured. We are able to get the distribution of injury severity observed for the car occupants according to whether the safety function is fitted or not on the vehicle. Graph 1 displays that distribution, with data example.

Now we assume that the safety function of interest has a positive effectiveness: we make the hypothesis that the severity of the injuries is reduced for the occupants of equipped vehicles compared



to non-equipped vehicles. We want to estimate the proportion of injuries whose level has been reduced.

There are two ways of answering this question, depending on the group of injury we look at. Either we look at the topmost group as defined in the report D.7.4.1 (group with at least a certain level of injury), or we focus on a specific level of injury. Here we will focus on the topmost groups of injuries.

Graph 1: Injury severity distribution according to the safety function

Graph 1 allows us to identify four topmost groups of injuries:

- A0+: group of occupants involved in an injury accident (not injured + injured and retained in hospital less than a day + injured and retained in hospital more than a day + fatal injury)

- A1+: group of occupants sustaining at least an injury needing less than one day in hospital (injured and retained in hospital less than a day + injured and retained in hospital more than a day + fatal injury).

- A2+: group of occupants sustaining at least an injury needing more than one day in hospital (injured and retained in hospital more than a day + fatal injury).

- A3: group of occupants sustaining a fatal injury.

The topmost group A0+ gathers all occupants whatever their injury severity level, as long as there are involved in an injury accident.

If we focus in topmost group A2+, we want to evaluate how many A2+injuries have been avoided and have become A1 injury (slight injury) or A0 (no injury), due to the action of the safety function on board on the vehicle. The Graph 1 becomes Graph 2:



Graph 2: A2+ injury distribution according to the safety function

As well as accident avoidance methodology, there is a need to define neutral and sensitive accidental situation regarding the safety function of interest. The neutral situation should not be influenced by the fact that the vehicles are equipped or not with the safety function. This means that the proportion of safety function vehicle has to be the same at every level of injury severity. The cross tabulation as proposed in Table 3 has to be completed:

Type of accidental situation

Vehicle fitted with SF1 Non Neutral (A2+level) Neutral (all level of injuries)

Yes a b

No c d

Table 3: A2+ injury according to accidental situation and to safety function

We will compare the proportion of A2+ injury within the equipped vehicles with the proportion observed in the non-equipped vehicles. As the distribution of the injury severity level in the neutral group should not be influenced by the safety function, the neutral group is not a sub group of A2+ injury but gather all levels of injuries. The effectiveness of SF1 to avoid A2+ injuries is calculated the same way as in Equation 1 :

Equation 12

This specific effectiveness is extrapolated to get the overall effectiveness if SF1 in avoiding A2+ injury. The final result is obtained after performing a logistic regression to get an estimation of the effectiveness of SF1 adjusted on the possible confounding variables. A minimum and a maximum value for this effectiveness are given to get a kind of confidence interval.

bc

da

d

bc

a

ORSFeff××−=−=−= 111)1(



It is also possible to investigate the injury mitigation effectiveness of a combination of safety functions. Then the neutral group of accidental situations will be the intersection of neutral situations of each safety functions included in the safety configuration. The accidental situation will be defined as sensitive regarding the safety configuration as soon as they are sensitive for at least one of the safety functions included in the safety configuration.



4 Data

In this chapter, we give a description of the database(s) used for the a posteriori evaluation of the safety functions. Some variables had to be created for the purpose of the analysis; this process is explained in a second section of the chapter. Then we describe the specific vehicle selection made for the analysis. Finally, a description of the sample is detailed.

4.1 Description of the French accident national database

The evaluation of the potential safety benefits of existing safety functions is expected to be carried out at the EU25 or EU 27 level. It would mean that:

- either the relevant data is available at that level and the above-mentioned analysis is done with the European data

- or the relevant data is not available at the EU level and the analysis is done with the data available in a selection of countries, the results being expanded at the EU level with an appropriate technique.

The relevant data is actually not available at the EU level (see below for details). We have then chosen to conduct the analysis with the French data and try to expand the results at the EU level if possible.

As explained in D.7.4.1 and D.4.2.2., the data relevant for such an analysis is a macroscopic accident dataset in which we can get information about vehicles involved in crashes (and especially their equipment) and about the crash and the impact configurations. We chose to use the French Injury Crash census.

The French accident national database gathers all information on every injury road accident occurring all over France during a year. This database only focuses on accidents in which at least one road user sustains injuries. No property-damage accident is registered in this database. The information is collected by the Police forces on the scene of the accident. On the basis of the police report, usually used for forensic purpose, they also have to fill in a statistical form called BAAC (Bulletin d’Analyse d’Accident Corporel) bringing together all the characteristic of the accident. This form is made of four parts, resulting in four different tables in the database:

The first part gives information on the general characteristics of the accident (date and localization of the accident, weather and light condition, type of junction, type of collision). The second part focuses on the road(s) where the accident took place (road category, pavement surface, profile and shape of the road). The vehicles involved in the accident are described in the third table: information such as vehicle type, date of first registration, Vehicle Identification Number (VIN), type of obstacle, localization of the main impact and vehicle maneuver prior to the accident are available. Every accident-involved vehicle has to be entered in the database, even those with no injured occupant. The last part of the form refers to the driver, occupants of the vehicle (whatever vehicle, passenger car, moped, motorcycle, pedal cycle, truck, …) and the pedestrian. The following parameters are available: seating position in the vehicle, injury severity, gender and date of birth, use of a seat belt, alcohol detection, and responsibility in the accident. Everybody involved in an accident has to be described at this level, even the non-injured ones.

Before 2005, the injury severity classification was as following: not injured, slightly injured (injured and detained in hospital between 0 and 6 days), severely injured (injured and detained in hospital more than six days), fatally injured (killed within six days after the accident). Since 2005, the injury classification changed. We now consider the non injured occupants, the injured occupants not retained in hospital or retained in hospital less than 24 hours, the injured occupants retained in hospital more than 24 hours and the fatally injured occupants (killed within 30 days after the accident).



One can argue about the relevance, the reliability, the accuracy, the exactness, the completeness and the comprehensiveness of such a national crash data census. In France, this census is established by the National Interministerial Road Safety Observatory of the Ministry in charge of Transport on the basis of the police reports. These reports are filled in by the police forces in case of an injury crash occurred on a public road opened to traffic.

Many studies have regarded the quality of the crash data coming from the police reports. Apart from under reporting and under recording, which are by the way incredibly high in some countries, especially with regards to slight injury crashes, pedal cyclists crashes, or single vehicle crashes, the very important issue is the quality of the coding in the database. It is understandable that the police forces are not fully aware of the complexity of accident analysis and they can misclassify a crash according to some variables. For example, an impact can be classified as a frontal impact if the impact is located in the front of the car whereas the real impact is a side impact in the front of the car or a frontal impact with an angle. Another example is the pre-accidental maneuvers of the involved parties, which are very difficult to determine without an accurate examination of the skid marks, the impacts, the point of impacts, testimonies of the parties, a full reconstruction of the crash, etc. Misclassification or inaccurate classification of the pre-accidental maneuver is also possible in case of a complex event or bad interpretation of the available information on the accident site.

Especially our method relies on the determination of an accident classification on the basis of variables available in the crash data and on the identification of make, model and equipment of the crashed vehicles (see section 4.2. below). If the crash data about these variables is questionable, the subsequent accident classification could also be questionable.

Even though we know that the police data has a limited potential for accident analysis, it was the only way for us to achieve the TRACE objectives. We took care not to use too many variables in the accident census, to use simple variables that are not too much questionable, and to end up with an accident classification which is also simple.

TRACE WP7 has demonstrated that misclassification can lead to under estimation of the effectiveness of a safety functions package. Consequently, we should keep in mind that, if misclassification is somehow present in the data, it would lead to an under estimation of our effectiveness estimates.

4.2 Additional variables

To assess the benefit of the safety function, we need to know for every vehicle included in the database, whether or not it was equipped with the safety function of interest. We also need to know the number of stars assigned to the vehicles. The information is not available in the national accident database so it has to be completed using other sources.

Accidental situations also need to be described, and each driver has to be characterized with the situation he was confronted to just before the crash. This variable had also been developed for the purpose of the analysis.

4.2.1 Car model, EuroNcap rating and safety equipment

First it was necessary to identify the make, model and model year of the vehicles in the accident database to be able to assign the Euro NCAP rating and the safety equipment to the cars. Make and model of the car are not directly available in the accident database. The Police forces have to code the VIN related to each vehicle involved in an accident. This code is hardly ever entered as it should be. Instead, the Police forces use the CNIT code or the ‘type mine’ code to characterize vehicles; these two codes are describing only the French vehicles. In a substantial amount of the cases, they choose to write the actual name of the vehicle (for instance “CITROEN C3”).



Therefore, we had to identify car models among a list of several VIN, CNIT, type mine and manual writings, i.e. make the connection between these codes and the car models. For this, we made use of a data set which reports all car registrations done in France per year. This database makes it possible to know which VIN connects with which car model; this data set also allows making the link between the CNIT and the car model.

Model year was simple to obtain from the accident database, as the month and year of first registration is reported for each involved car.

With the information on car model and year of first registration, we had been able to attribute a number of EuroNcap stars to each different type of vehicles identified in the accident census. For that purpose, we worked with the EuroNcap website to assign the number of stars for each vehicle. For vehicles that have been tested several times due to the change of generation or improvement in the safety devices, model year information was used to attribute the right number of stars.

The second type of information that we needed, concerns the safety equipment of the cars. The connection between the model and model year of the car and the characteristics of the safety equipments had been made. For that purpose, we investigated three methods to get the information. First, we used an Renault internal database, in which vehicles equipment is described regarding the make and model, the type mine, the version and the model year of the car. This database is not a comprehensive one, not all the cars sold in France are included in it. Moreover, the type mine information is reliable only for French cars, meaning that we are able to characterize safety equipment only for French cars but not for all of them.

The second source of information we used was commercial documents of vehicles. This source of information is not comprehensive and concerns only French cars, and mostly cars built before 2000. It has been used to complete the information provided by the internal database.

The third supply of information was the French car manufacturers database, given a complete VIN number, those database provide the safety equipment of the car selected by the VIN number. This method requires at first that the 18 digits of the VIN are known but this is hardly ever the case.

Finally, about thirty different car models among the French vehicles have been characterized regarding EuroNcap rating and safety equipment.

4.2.2 Accidental situations

The accidental situation is a brief description of the driver/user maneuver and eventually the conditions under which the maneuver is performed during the crash process. It does not refer to the crash itself but to the couple vehicle/driver.

This variable is not entirely available directly in the national accident database. We had to create it by using other variables in addition to the variable describing the maneuver prior to the accident. Variables such as the responsibility of the driver in the accident, the presence of a junction, a straight road or a bend, the type of collision and the point of impact on the vehicle and on the opposite vehicle, were used to describe these accidental situations more precisely.

Because of the way the accidents are registered in the national database, this method is appropriate for single vehicle accidents and for accidents between two road users only. It was not possible to assign a precise accidental situation for drivers involved in an accident with three or more road users (which represent only 6-7% of the injury crashes). These multiple-users crashes are not excluded from the analysis but the situation attached to each driver involved in such an accident is simply quoted as ‘multiple collision accidents’ without further details.

49 accidental situations have been created, identified and fully described. We grouped these situations into four classes, as follows:

1. Driver in loss of control or guidance problems accidents, all kind of obstacle (except pedestrians).



2. Accidental situation involving pedestrians.

3. Accidental situation involving two vehicles (not at intersections).

4. Accidental situation involving two vehicles (at intersections).

As detailed in the methodology section above, the methodology requires specifying if an accidental situation is neutral or not regarding the safety functions we’re assessing. Table 4 specifies the different accidental situations and whether they are neutral or sensitive regarding the fitment of EBA and ESC on board.

Accidental situations Neutral or Sensitive

regarding EBA Neutral or Sensitive

regarding ESC

Single vehicle loss of control / guidance accident in a straight line (not at intersections).

N if lateral impact, else S S

Two vehicles loss of control / guidance accident in a straight line (not at intersections).


Single vehicles loss of control / guidance accident in a bend (not at intersections).


Two vehicles loss of control / guidance accident in a bend (not at intersections). N if lateral impact, else S S

Single vehicle loss of control / guidance intersection accident. N if lateral impact, else S S

Single vehicle / Collision against parked vehicle. S S

Collision against an animal S S

Collision with a pedestrian walking along the road. S N

Collision with a pedestrian crossing the road. S N

Collision with a pedestrian initially hidden from view. S N

Collision with a pedestrian while reversing. N N

Collision with a pedestrian running or playing on the road. S N

Driving through a bend, confronted with an oncoming vehicle out of control or badly positioned.

S N if frontal or lateral impact, else S

Driving along a straight road, confronted with an oncoming vehicle out of control or badly positioned.

S N if frontal or lateral impact, else S

Front to rear collision, impacted vehicle. S N

Front to rear collision, impacting vehicle. S S

Driver involved in a collision while changing travel lanes. S N

Driver involved in a collision while avoiding an obstacle on the road. S S

Driver involved in a collision during an overtaking manoeuvre. S S

Driver involved in a collision during a parking manoeuvre. N N

Driver involved in a collision while making a left or right turn manoeuvre (not at intersection).

N N

Driver involved in a collision while opening a car door. N N

Driver involved in a collision while making a U-turn manoeuvre on the carriageway (not at intersection).

N N

Driver involved in a collision while crossing the carriageway (not at intersection).

N N

Driver involved in a collision with a parked vehicle. N N

At fault driver in roundabout accident. No turn or entry manoeuvre in the accident.

N N

Not at fault driver in roundabout accident. No turn or entry manoeuvre in the S N



accident.

Driver confronted with a vehicle turning left or right in a roundabout or entering a roundabout.

S N

Driver involved in a collision when entering a roundabout. N N

Driver involved in a collision when turning right in a roundabout. N N

Driver involved in a collision when turning left in a roundabout. N N

At fault driver in a straight crossing path (SCP) intersection accident. N N

Not at fault driver in an SCP intersection accident. S N

Driver confronted with a vehicle turning left or right at an intersection. N if at fault, else S N

Driver turning left at an intersection. N if at fault, else S N

Driver turning right at an intersection. N if at fault, else S N

At fault driver in a crossing accident at an intersection. No turn manoeuvre in the accident.

N N

Not at fault driver in a crossing accident at an intersection. Left, right or no turn manoeuvre of the opposing vehicle.

S N

At fault driver in left turn across path intersection accident. N N

At fault driver in right turn intersection accident involving oncoming vehicle. N N

At fault driver in intersection accident involving two vehicles traveling in the same direction without turn manoeuvre.

S N

Not at fault driver in intersection accident involving two vehicles traveling in the same direction without turn manoeuvre.

S N

Driver involved in a collision with a vehicle turning left at an intersection and traveling in the same direction.

S N

Driver turning left at an intersection involved in an accident with a vehicle traveling in the same direction.

S N

Driver involved in a collision with a vehicle turning right at an intersection and traveling in the same direction.

S N

Driver turning right at an intersection involved in an accident with a vehicle traveling in the same direction.

S N

Driver involved in a collision while making a U-turn manoeuvre at an intersection.

N N

Driver involved in a collision with a vehicle making a U-turn manoeuvre at an intersection.

S N

Driver involved in an accident at an intersection – other types S N

3 road users and + S N if not at fault, else S

unknown Unknown Unknown

Table 4: Accidental situations and their neutrality regarding EBA and ESC

To asses the benefit of the star rating, the neutral and sensitive accidental situations have been defined regarding the type of impact on the vehicle. The rear-impacted cars are considered as neutral and the remaining types of impacts (frontal, lateral, and rollover) as the sensitive cases.

This is of course questionable since the rear impact might also gain form a 5-stars vehicle compared to a 3 or 4-stars vehicle. The gain, if positive, is assumed to be anyway much less than the gain expected in the other types of impact. This is why the assumption, even slightly questionable, does not introduce too much bias in the analysis.



4.3 Data selection

Among all the vehicles within our injury accidents database, a selection has been made in order to retain only crashed vehicles that were pertinent for the analysis.

Firstly, we selected French vehicles whose model year stands between 2000 and 2006. We restricted our analysis to four and five stars vehicles, excluding three stars vehicles. It was useless to keep vehicles with model years prior to year 2000 since considerable improvements have been brought to car crashworthiness since the late nineties and the additional benefits of newer passive or active safety devices must be compared to vehicles built just prior to these improvements and not a long time ago.

We also selected cars fitted with ABS since this is now standard equipment.

The presence of an EBA and an ESC in the car also had to be stated. The vehicles with optional equipment were not taken into account, as we could not be sure if the safety function was really on board. There were some special cases where the optional equipment has been considered as if it was not present on the vehicle (ESC equipment for the Megane for instance since the equipment rate for some vehicles was known to be very low).

We already enlightened that the injury severity codification has changed in 2005. There is not any evident correlation between the new and the former classification. It becomes impossible to aggregate data of accidents occurred before 2005 with those concerning accidents from 2005 on, at least if the analysis deals with injury severity. Therefore, we had to perform our analysis on the accident cases occurred in 2005 and 2006.

The last selection concerned the use of the seat belt and the seating position in the vehicle; only the belted driver and front passenger were selected for the analysis.

4.4 Sample description

The data sample includes a selection of 28 French cars awarded four or five stars at EuroNcap, equipped with ABS as standard and for which the safety equipment is known for at least one safety function. A total of 20 076 belted drivers and front passengers of passenger cars have been included in the final dataset. The list of the different cars models and star rating is detailed in Table 5:

Car model EuroNcap rating Frequency Percent

RENAULT CLIO 2 4 4244 21.14%

RENAULT CLIO 3 5 220 1.1%

CITROËN C2 4 259 1.29%

CITROËN C3 4 714 3.56%

CITROËN C4 5 197 0.98%

CITROËN C5 4 289 1.44%

CITROËN C5 5 180 0.9%

CITROËN C6 5 4 0.02%

CITROËN C8 5 62 0.31%

RENAULT ESPACE 4 5 224 1.12%

RENAULT KANGOO 4 452 2.25%

RENAULT LAGUNA 2 5 966 4.81%



RENAULT MEGANE 1 cabriolet 4 32 0.16%

RENAULT MEGANE 1 4 841 4.19%

RENAULT MEGANE 2 cabriolet 5 48 0.24%

RENAULT MEGANE 2 5 1046 5.21%

RENAULT MODUS 5 264 1.32%

PICASSO 4 921 4.59%

RENAULT SCENIC 1 4 810 4.03%

RENAULT SCENIC 2 5 926 4.61%

RENAULT VEL SATIS 5 56 0.28%

PEUGEOT 1007 5 44 0.22%

PEUGEOT 206 4 4761 23.7%

PEUGEOT 307 CC 4 49 0.24%

PEUGEOT 307 4 1960 9.76%

PEUGEOT 407 5 275 1.37%

PEUGEOT 607 4 134 0.67%

PEUGEOT 807 5 98 0.49%

Table 5: Car models and their number of EuroNcap stars

There are 15 466 four stars vehicles and 4 610 five stars vehicles in our sample. Table 6 gives an overview of the combination of the safety functions according to the number of stars.

EuroNcap rating EBA ESC Frequency Percent

4 not fitted unknown 177 0.88%

4 not fitted not fitted 4728 23.55%

4 not fitted optional 96 0.48%

4 standard unknown 6427 32.01%

4 standard not fitted 3085 15.37%

4 standard optional 488 2.43%

4 standard standard 465 2.32%

5 standard unknown 1948 9.7%

5 standard not fitted 159 0.79%

5 standard optional 819 4.08%

5 standard standard 1684 8.39%

Table 6: Number of cars according to EuroNcap rating, EBA and ESC characteristics



Even though the sample is large, the distribution of cars among the different classes is really unbalanced. Especially some classes are over represented or small sized. That might lead to unstable or inaccurate estimates of effectiveness or just simply to the impossibility to estimate some types of effectiveness (especially on fatalities).

About 75% of the vehicles are equipped with an EBA. All French five stars cars were fitted with this safety function. The methodology for the evaluation of a safety function requires a control group of vehicles not fitted with the function. As we do not have any five stars vehicles without EBA, the benefit of adding an EBA to a five stars vehicle cannot be assessed with our data. To our knowledge all the five stars vehicles on the French market were sold with that safety function on board, this condition does not allow any evaluation of EBA given that we have a five stars car.

According to Table 6 ten comparisons can be made and the following evaluations will then be performed:

- Evaluation of safety benefit of EBA given that the car has four stars.

- Evaluation of safety benefit of ESC given that the car has four stars and an EBA.

- Evaluation of safety benefit of ESC given that the car has five stars and an EBA.

- Evaluation of safety benefit of the fifth star given that the car has four stars and an EBA.

- Evaluation of safety benefit of the fifth star given that the car has four stars, an EBA and an ESC.

- Evaluation of safety benefit of EBA and ESC given that the car has four stars.

- Evaluation of safety benefit of EBA and a fifth star given that the car has four stars.

- Evaluation of safety benefit of ESC and a fifth star given that the car has four stars and an EBA.

- Evaluation of safety benefit of EBA, ESC and a fifth star given that the car has four stars.

- Evaluation of safety benefit of a fifth star and removing an ESC given that the car has four stars, an EBA and an ESC.

Variables describing the drivers, the localization of the accident and other characteristics of the cars are available in the database and will be used in the logistic regressions as possible confounders. But prior to the statistical analysis, we have performed cross tabulations of these variables according to the safety functions and the neutral/sensitive accidental situations in order to provide a description of our sample and allow us to characterize the different groups of vehicles.

4.4.1 Type of vehicles (four or five stars vehicles)

Table 7 and Table 8 display the gender and age of the drivers and front passengers of the selected cars. The tables show differences between the two types of cars regarding these two characteristics: the proportion of male drivers/front passengers is larger in five stars cars than in four stars vehicle (66.49% compared to 54.95%). The part of young drivers/front passengers is larger in four stars vehicles then in five stars (21.1% compared to 8.52%).



EuroNcap rating Gender (N)

(Percent) 4 5

Total

6968 1545 Female

45.05% 33.51% 8513

8498 3065 Male

54.95% 66.49% 11563

Total 15466 4610 20076

Table 7: Gender according to the number of stars of the vehicle

EuroNcap rating Age (N)

(Percent) 4 5

Total

3263 393 18-24 years old

21.10% 8.52% 3656

3690 1006 25-34 years old

23.86% 21.82% 4696

2522 1114 35-44 years old

16.31% 24.16% 3636

2360 918 45-54 years old

15.26% 19.91% 3278

1711 658 55-64 years old

11.06% 14.27% 2369

1920 521 Over 65 years old

12.41% 11.30% 2441

Total 15466 4610 20076

Table 8: Age according to the number of stars of the vehicle

When evaluating the benefit of EuroNcap stars rating, it should be considered that the five and four stars vehicles are not used by similar people with regards to age and gender, and these two variables should be taken into account in the logistic regression as they can also explain differences in the accident and injury risk.

The proportions of drivers and front passengers are similar in four and five stars vehicles of our sample, as shown in Table 9.



EuroNcap rating Position in the car (N)

(Percent) 4 5

Total

13367 3987 Driver

86.43% 86.49% 17354

2099 623 Front seat passenger 13.57% 13.51%

2722

Total 15466 4610 20076

Table 9: Proportion of drivers and front passengers according to the number of stars of the vehicle

The three following tables (Table 10 to Table 12) give information on the class of vehicles and on their model year and age. The five stars vehicles tend to be newer and heavier cars than the four stars vehicles.

EuroNcap rating Class vehicle 1 (N)

(Percent) 4 5

Total

259 0 I1

1.67% 0.00% 259

10171 528 I2

65.76% 11.45% 10699

4613 2217 M1

29.83% 48.09% 6830

289 1421 M2

1.87% 30.82% 1710

134 444 S1

0.87% 9.63% 578

Total 15466 4610 20076

Table 10: Vehicle class according to the number of stars

1 Examples : I1 class : Citroën C2 – I2 class : Renault Clio – M1 class : Renault Mégane, Peugeot 307 – M2 class: Renauilt Laguna, Peugeot 407 – S Class: Renault Espace, Peugeot 807.



EuroNcap rating Vehicle age (N)

(Percent) 4 5

Total

2145 1731 Less than a year

13.87% 37.55% 3876

2385 1260 1 year old

15.42% 27.33% 3645

2648 833 2 years old

17.12% 18.07% 3481


17.43% 10.67% 3188


17.02% 4.60% 2844


19.14% 1.78% 3042

Total 15466 4610 20076

Table 11: Age of the vehicle according to the number of stars

EuroNcap rating Model year (N)

(Percent) 4 5

Total

1617 0 2000

10.46% 0.00% 1617

3826 269 2001

24.74% 5.84% 4095

2771 355 2002

17.92% 7.70% 3126

2698 865 2003

17.44% 18.76% 3563

2416 1271 2004

15.62% 27.57% 3687

1675 1246 2005

10.83% 27.03% 2921

463 604 2006

2.99% 13.10% 1067

Total 15466 4610 20076

Table 12: Model year of the vehicle according to the number of stars



The next tables (Table 13 to Table 19) deal with the crash circumstances and the road characteristics. Overall, just a few differences are observed in the distribution of these factors according to the number of stars; the only distinctions concern a lower proportion of single vehicle crashes for five stars vehicles compared to four stars vehicles (6.83% vs. 12.41%) and a lower proportion of frontal impact observed on five stars vehicles (58.61% vs. 62.03% for four stars cars). The proportion of rear impact is higher for five stars cars than four stars vehicles (24.86% compared to 19.91%).

EuroNcap rating Road configuration (N)

(Percent) 4 5

Total

12753 3906 Straight line

82.46% 84.73% 16659

2713 704 In a bend

17.54% 15.27% 3417

Total 15466 4610 20076

Table 13: Road configuration according to the number of stars

EuroNcap rating Road surface (N)

(Percent) 4 5

Total

2812 848 Wet

18.18% 18.39% 3660

12654 3762 Dry

81.82% 81.61% 16416

Total 15466 4610 20076

Table 14: Road surface according to the number of stars

EuroNcap rating Junction (N)

(Percent) 4 5

Total

4315 1264 At a junction

27.90% 27.42% 5579

11151 3346 Out of a junction 72.10% 72.58%

14497

Total 15466 4610 20076

Table 15: Presence of a junction according to the number of stars



EuroNcap rating Urban/Rural (N)

(Percent) 4 5

Total

9256 2681 Urban area

59.85% 58.16% 11937

6210 1929 Rural area

40.15% 41.84% 8139

Total 15466 4610 20076

Table 16: Type of area according to the number of stars

EuroNcap rating Type of road (N)

(Percent) 4 5

Total

1843 609 Highway

11.92% 13.21% 2452

478 148 Others

3.09% 3.21% 626

6439 1843 Local road

41.63% 39.98% 8282

4390 1294 Secondary Road 28.38% 28.07%

5684

2316 716 National Road

14.97% 15.53% 3032

Total 15466 4610 20076

Table 17: Category of road according to the number of stars

EuroNcap rating Number of road users in the accident (N)

(Percent) 4 5

Total

1919 315 Single vehicle crash

12.41% 6.83% 2234

13547 4295 2 road users and +

87.59% 93.17% 17842

Total 15466 4610 20076

Table 18: Number of road users involved according to the number of stars



EuroNcap rating Main impact location (N)

(Percent) 4 5

Total

3079 1146 Rear

19.91% 24.86% 4225

9594 2702 Frontal

62.03% 58.61% 12296

857 232 Lateral - right side

5.54% 5.03% 1089

1168 365 Lateral - left side

7.55% 7.92% 1533

768 165 Rollover - others

4.97% 3.58% 933

Total 15466 4610 20076

Table 19: Main impact location according to the number of stars

Finally, five stars and four stars vehicles differ not only in the number of stars, but also in the characteristics of their drivers, in the age and in the class of the vehicles. Four stars vehicles tend also to experience a greater proportion of single vehicle crashes than five stars vehicles and also a higher proportion of frontal impacts. Therefore, there is an absolute need for a statistical model taking into consideration these variables as confounders of the injury risk.

4.4.2 Emergency Brake Assist

The following tables present the potential confounders (and their distribution) to the estimation of the impact of Emergency Brake Assist on crash risk.

As stated in Table 20, the front seat occupants of EBA fitted cars are more often males than females. We observe 59.63% of male in EBA equipped vehicles and 51.47% in vehicles not equipped with EBA. The drivers and front passengers are slightly older in EBA vehicles than in non-EBA vehicles (Table 21).

The distribution of passengers according to their position in the car is similar for equipped vehicles and non equipped vehicles, as shown in Table 22.



EBA Gender

(N) (Percent)

Not avalailable

Standard Total

2427 6086 Female

48.53% 40.37% 8513

2574 8989 Male

51.47% 59.63% 11563

Total 5001 15075 20076

Table 20: Gender according to EBA

EBA Age (N)

(Percent) Not

avalailable Standard

Total

1234 2422 18-24 years old 24.68% 16.07%

3656

1223 3473 25-34 years old 24.46% 23.04%

4696

754 2882 35-44 years old 15.08% 19.12%

3636

694 2584 45-54 years old 13.88% 17.14%

3278

495 1874 55-64 years old 9.90% 12.43%

2369

601 1840 Over 65 years old 12.02% 12.21%

2441

Total 5001 15075 20076

Table 21: Age according to EBA

EBA Position in the car

(N) (Percent)

Not avalailable Standard Total

4325 13029 Driver

86.48% 86.43% 17354

676 2046 Front seat passenger

13.52% 13.57% 2722

Total 5001 15075 20076

Table 22: Proportion of drivers and front-seat passengers according to EBA



The tables below enlighten the fact that the cars do not only differ in EBA equipment, but also in their age and class. The vehicles where EBA is not available are all in the inferior 2 and medium 1 class whereas the upper classes vehicles (M2 and S1) are all fitted with EBA (see Table 23).

EBA Class vehicle

(N) (Percent)

Not avalailable

Standard Total

0 259 I1

0.00% 1.72% 259

3509 7190 I2

70.17% 47.69% 10699

1492 5338 M1

29.83% 35.41% 6830

0 1710 M2

0.00% 11.34% 1710

0 578 S1

0.00% 3.83% 578

Total 5001 15075 20076

Table 23: Vehicle class according to EBA

The distribution of vehicle age is also depending on the presence of EBA on board. As stated in Table 24, about 50% of the fitted vehicles are less than two years old, while 49% of the non-fitted cars are older than 5 years old. In addition, all the five stars vehicles and all the four stars after model year 2003 are equipped with EBA (detailed in Table 25).



EBA Vehicle age (N)

(Percent) Not avalailable Standard

Total

0 3876 < 1 year

0.00% 25.71% 3876

0 3645 1 year old

0.00% 24.18% 3645


5.12% 21.39% 3481


16.72% 15.60% 3188

1455 1389 4 years old

29.09% 9.21% 2844


49.07% 3.90% 3042

Total 5001 15075 20076

Table 24: Age of the vehicle according to EBA

EBA Model year

(N) (Percent)

Not avalailable Standard Total

1617 0 2000

32.33% 0.00% 1617

2297 1798 2001

45.93% 11.93% 4095

1087 2039 2002

21.74% 13.53% 3126

0 3563 2003

0.00% 23.64% 3563

0 3687 2004

0.00% 24.46% 3687

0 2921 2005

0.00% 19.38% 2921

0 1067 2006

0.00% 7.08% 1067

Total 5001 15075 20076

Table 25: Model year of the vehicle according to EBA



The next seven tables are related to the crash characteristics. Overall, they show no difference between the vehicles fitted with EBA and the ones not fitted with EBA. The major variations in the distribution are again connected to the number of road users involved in the accident and the type of impact. While 9.73% of the front seat occupants of cars fitted with EBA are involved in single vehicle crashes, the equivalent figure is 15.34% for the cars non-fitted with EBA (Table 31). Cars fitted with EBA also show a decrease in the percentage of frontal impacts and an increase in the percentage of rear impacts, compared to the distribution observed for non-fitted cars (Table 32).

EBA Road configuration

(N) (Percent)

Not avalailable

Standard Total

4076 12583 Straight line

81.50% 83.47% 16659

925 2492 In a bend

18.50% 16.53% 3417

Total 5001 15075 20076

Table 26: Road configuration according to EBA

EBA Road surface

(N) (Percent)

Not avalailable

Standard Total

936 2724 Wet

18.72% 18.07% 3660

4065 12351 Dry

81.28% 81.93% 16416

Total 5001 15075 20076

Table 27: Road surface according to EBA

EBA Junction

(N) (Percent)

Not avalailable

Standard Total

1389 4190 At a junction

27.77% 27.79% 5579

3612 10885 Out of a junction

72.23% 72.21% 14497

Total 5001 15075 20076

Table 28: Presence of a junction according to EBA



EBA Urban/Rural (N)

(Percent) Not avalailable Standard

Total

2943 8994 Urban area

58.85% 59.66% 11937

2058 6081 Rural area

41.15% 40.34% 8139

Total 5001 15075 20076

Table 29: Type of area according to EBA

EBA Category of road

(N) (Percent)

Not avalailable

Standard Total

571 1881 Highway

11.42% 12.48% 2452

169 457 Others

3.38% 3.03% 626

2079 6203 Local road

41.57% 41.15% 8282

1479 4205 Secondary road

29.57% 27.89% 5684

703 2329 National road

14.06% 15.45% 3032

Total 5001 15075 20076

Table 30: Category of road according to EBA

EBA Number of road users in the accident

(N) (Percent)

Not avalailable

Standard Total

767 1467 1

15.34% 9.73% 2234

4234 13608 2 and above

84.66% 90.27% 17842

Total 5001 15075 20076

Table 31: Number of road users involved according to EBA



EBA Main impact location

(N) (Percent)

Not avalailable

Standard Total

861 3364 Rear

17.22% 22.32% 4225

3215 9081 Frontal

64.29% 60.24% 12296

276 813 Lateral - right side

5.52% 5.39% 1089

358 1175 Lateral - left side

7.16% 7.79% 1533

291 642 Rollover - others

5.82% 4.26% 933

Total 5001 15075 20076

Table 32: Main impact location according to EBA

Finally, the EBA fitted cars in the sample are characterized by different front seat passengers (more often male and middle-aged), a higher proportion of newer and heavier vehicles and a decrease in the proportion of single vehicles crashes and frontal impacts, compared to vehicles were EBA is not at hand.

4.4.3 Electronic Stability Control

The determination of ESC equipment was impossible in 8552 cases out 20076. ESC was optional for 1403 other cases. These observations should not be taken into account in the analysis, as the presence of ESC in the cars is therefore not confirmed. Nevertheless, they still appear in the tables in order to make sure that the proportion of the external variables observed for these cases are not too different from the proportions observed for the cases where ESC presence is known. If the characteristics of the unknown cases deviate from the characteristics of the others cases, it would mean that there is bias in the determination of ESC.

The cross tabulation are done according to gender and age of occupants and their seating position. The tables state that there is a link between the presence of ESC on board and gender (Table 33) and age of the driver/front seat passenger (Table 34). Male and middle aged (35-54 years old) driver/front seat passengers are more often found in cars fitted with ESC than in cars where ESC is not available. This is in accordance with the findings concerning the star rating and EBA, where five stars vehicles or vehicles fitted with EBA had a higher proportion of males and middle-aged front occupants compared to four stars cars or non EBA vehicles.



ESC Gender

(N) (Percent)

Unknown Not

available Option Standard

Total

3515 3786 531 681 Female

41.10% 47.49% 37.85% 31.69% 8513

5037 4186 872 1468 Male

58.89% 52.51% 62.15% 68.31% 11563

Total 8552 7972 1403 2149 20076

Table 33: Gender of occupants according to ESC

ESC Age (N)

(Percent) Unknown

Not available

Option Standard

Total

1458 1844 223 131 18-24 years old

17.05% 23.13% 15.89% 6.10% 3656

2014 1964 332 386 25-34 years old

23.55% 24.64% 23.66% 17.96% 4696

1529 1241 265 601 35-44 years old

17.88% 15.57% 18.89% 27.97% 3636

1391 1158 253 476 45-54 years old

16.27% 14.53% 18.03% 22.15% 3278

1065 795 159 350 55-64 years old

12.45% 9.97% 11.33% 16.29% 2369

1095 970 171 205 Over 65 years old

12.80% 12.17% 12.19% 9.54% 2441

Total 8552 7972 1403 2149 20076

Table 34: Age according to ESC

Vehicles equipped with ESC as standard equipment are newer cars and they belong to higher-class vehicles than non-equipped vehicles or vehicles with an optional ESC: 31.22% of the non-equipped vehicles are at least 5 years old, whereas equipped vehicles all are less than 5 years old (Table 37). Table 38 illustrates that vehicle of model years above 2004 are overrepresented in cars fitted with ESC compared to cars without ESC on board. ESC is hardly ever seen on inferior class vehicles, while the medium2 and superior class vehicles are mostly fitted with ESC (for the cases where the ESC equipment is known). The M1 class is overrepresented for the cars where ESC is optional (see Table 36).



ESC Position in the car

(N) (Percent)

Unknown Not


Total

7373 6910 1183 1888 Driver

86.21% 86.68% 84.32% 87.85% 17354

1179 1062 220 261 Front seat passenger

13.79% 13.32% 15.68% 12.15% 2722

Total 8552 7972 1403 2149 20076

Table 35: Proportion of driver and front passenger according to ESC

ESC Class vehicle

(N) (Percent)

Unknown Not


Total

115 144 0 0 I1

1.34% 1.81% 0.00% 0.00% 259

4349 6087 188 75 I2

50.85% 76.35% 13.40% 3.49% 10699

3234 1718 1173 705 M1

37.82% 21.55% 83.61% 32.81% 6830

778 15 42 875 M2

9.10% 0.19% 2.99% 40.72% 1710

76 8 0 494 S1

0.89% 0.10% 0.00% 22.99% 578

Total 8552 7972 1403 2149 20076

Table 36: Vehicle class according to ESC



ESC Vehicle age (N)

(Percent) Unknown Not available Option Standard

Total

2482 374 349 671 Less than a year

29.02% 4.69% 24.88% 31.22% 3876

1955 697 358 635 1 year old

22.86% 8.74% 25.52% 29.55% 3645

1486 1243 314 438 2 years old

17.38% 15.59% 22.38% 20.38% 3481

1092 1519 271 306 3 years old

12.77% 19.05% 19.32% 14.24% 3188

984 1650 111 99 4 years old

11.51% 20.70% 7.91% 4.61% 2844

553 2489 0 0 Over 5 years old

6.47% 31.22% 0.00% 0.00% 3042

Total 8552 7972 1403 2149 20076

Table 37: Age of the vehicle according to ESC

ESC Model year (N)

(Percent) Unknown Not available Option Standard

Total

0 1617 0 0 2000

0.00% 20.28% 0.00% 0.00% 1617

1627 2459 0 9 2001

19.02% 30.85% 0.00% 0.42% 4095

816 1609 339 362 2002

9.54% 20.18% 24.16% 16.85% 3126

1407 1429 334 393 2003

16.45% 17.93% 23.81% 18.29% 3563

2167 602 288 630 2004

25.33% 7.55% 20.53% 29.32% 3687

1648 227 419 627 2005

19.27% 2.85% 29.86% 29.18% 2921

887 29 23 128 2006

10.37% 0.36% 1.64% 5.96% 1067

Total 8552 7972 1403 2149 20076

Table 38: Model year of the vehicle according to ESC



Table 39 enlightens that there is a link between the road configuration at the location of the crash and the fact that the car is equipped or not with an ESC. We observe 18.44% of curve for cars without ESC and 13.96% for cars fitted with ESC. There is a relation too between the number of road users involved in the crash and the presence of an ESC on the car (see Table 44): we observe 4.37% of single vehicle crashes for cars with ESC and 13.71% for cars without ESC. For ESC fitted cars we note a decrease in the proportion of frontal impact and rollover accidents whereas the rear impact proportion increases, compared to vehicles non-fitted with ESC, as shown in Table 45.

ESC Road configuration

(N) (Percent)

Unknown Not


Total

7162 6502 1146 1849 Straight line

83.74% 81.56% 81.68% 86.04% 16659

1390 1470 257 300 In a bend

16.25% 18.44% 18.32% 13.96% 3417

Total 8552 7972 1403 2149 20076

Table 39: Road configuration according to ESC

ESC Road surface

(N) (Percent)

Unknown Not


Total

1549 1472 263 376 Wet

18.12% 18.46% 18.75% 17.50% 3660

7003 6500 1140 1773 Dry

81.88% 81.54% 81.25% 82.50% 16416

Total 8552 7972 1403 2149 20076

Table 40: Road surface according to ESC

ESC Junction

(N) (Percent)

Unknown Not


Total

2375 2229 387 588 At a junction

29.73% 27.96% 27.58% 27.36% 5579

6177 5743 1016 1561 Out of a junction

70.27% 72.04% 72.42% 72.64% 14497

Total 8552 7972 1403 2149 20076

Table 41: Presence of a junction according to ESC



ESC Urban/Rural

(N) (Percent)

Unknown Not


Total

5169 4723 779 1266 Urban area

60.44% 59.24% 55.52% 58.91% 11937

3383 3249 624 883 Rural area

39.56% 40.76% 44.48% 41.09% 8139

Total 8552 7972 1403 2149 20076

Table 42: Type of area according to ESC

ESC Category of road

(N) (Percent)

Unknown Not


Total

1082 906 186 278 Highway

12.65% 11.36% 13.26% 12.94% 2452

268 251 39 68 Others

3.13% 3.15% 2.78% 3.16% 626

3562 3342 526 852 Local road

41.65% 41.92% 37.49% 39.65% 8282

2303 2340 412 629 Secondary road

26.93% 29.35% 29.37% 29.27% 5684

1337 1133 240 322 National road

15.63% 14.21% 17.11% 14.98% 3032

Total 8552 7972 1403 2149 20076

Table 43: Category of road according to ESC

ESC Number of road users in the accident

(N) (Percent)

Unknown Not


Total

893 1093 154 94 Single vehicle crash

10.44% 13.71% 10.98% 4.37% 2234

7659 6879 1249 2055 2 road users and +

89.56% 86.29% 89.02% 95.63% 17842

Total 8552 7972 1403 2149 20076

Table 44: Number of road users involved according to ESC



ESC Main impact location

(N) (Percent)

Unknown Not


Total

1904 1445 300 576 Rear

22.26% 18.13% 21.38% 26.80% 4225

5163 5046 858 1229 Frontal

60.37% 63.30% 61.15% 57.19% 12296

452 458 71 108 Lateral - right side

5.28% 5.75% 5.06% 5.03% 1089

660 587 112 174 Lateral - left side

7.71% 7.36% 7.98% 8.10% 1533

373 436 62 62 Rollover - others

4.36% 5.47% 4.42% 2.89% 933

Total 8552 7972 1403 2149 20076

Table 45: Main impact location according to ESC

Finally, the ESC equipped vehicles differ from the non-equipped in their age, their class and in the gender and age of the occupants. We also notice a lower proportion of single vehicle crashes, frontal impact and rollover accidents and less accidents occurring in a bend for the cars with ESC. The very preliminary suggest would be that ESC is efficient in preventing loss of control crashes, as these accidents are often single vehicle accidents, occurring in bends, with frontal or side impacts, in wet road conditions, eventually ending with a rollover.

The tables also illustrate that the “unknown” and “optional” ESC cars tend to be distributed the same way and that they generally seem closer to the non-equipped ESC vehicles than to the ESC equipped vehicles.

4.4.4 Accidental situations

In this section, the distribution of the external factors will be analyzed in correlation with the accidental situations related to EBA, ESC and stars rating. The unknown accidental situations will be dropped from the final analysis, but as for ESC, their distributions are detailed in order to make sure that no critical bias occurs when these cases are excluded.

The following tables present gender, age and position of front seat occupants according to the neutral or sensitive definitions of accidental situations. The accidental situations regarding EBA are distributed in a relatively homogeneous way, if considering gender and age of the occupants. This is not the case for the ESC and the stars rating situations. Gender appears to be linked with the ESC accidental situations and with the stars accidental situations (Table 46). In both situations, we observe an over representation of male occupants in the sensitive categories (60.44% of male in ESC sensitive situations vs. 56.42% for neutral ESC situations). ESC sensitive situations are characterized by a larger proportion of young front passengers than the proportion observed for neutral situations (26.45% compared to 15.40%, see Table 47). The remark is valid also for the stars related situations (19.27% of young people in sensitive situations compared to 14.25% in neutral situations).

In Table 48 we also notice a higher proportion of front seat occupants in EBA and ESC relevant accidental situations.



Accidental situation / EBA Accidental situation / ESC Accidental

situation / Stars Gender (N)

(Percent) Neutral Sensitive Unk. Neutral Sensitive Unk. Neutral Sensitive

Total

1924 6238 351 6148 1961 404 1942 6571 Female

43.17% 42.35% 39.53% 43.58% 39.56% 39.96% 45.96% 41.45% 8513

2533 8493 537 7960 2996 607 2283 9280 Male

56.83% 57.65% 60.47% 56.42% 60.44% 60.04% 54.04% 58.55% 11563

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 46: Gender according to accidental situations


situation / Stars Age (N)

(Percent) Neutral Sensitive Unk. Neutral Sensitive Unk. Neutral Sensitive

Total

793 2703 160 2172 1311 173 602 3054 18-24 years old 17.79% 18.35% 18.02% 15.40% 26.45% 17.11% 14.25% 19.27%

3656

977 3507 212 3242 1212 242 1089 3607 25-34 years old 21.92% 23.81% 23.87% 22.98% 24.45% 23.94% 25.78% 22.76%

4696

817 2646 173 2749 681 206 905 2731 35-44 years old 18.33% 17.96% 19.48% 19.49% 13.74% 20.38% 21.42% 17.23%

3636

716 2415 147 2492 615 171 761 2517 45-54 years old 16.06% 16.39% 16.55% 17.66% 12.41% 16.91% 18.01% 15.88%

3278

519 1749 101 1809 444 116 527 1842 55-64 years old 11.64% 11.87% 11.37% 12.82% 8.96% 11.47% 12.47% 11.62%

2369

635 1711 95 1644 694 103 341 2100 over 65 years old 14.25% 11.61% 10.70% 11.65% 14.00% 10.19% 8.07% 13.25%

2441

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 47: Age according to accidental situations




situation / Stars Position

in the car –

(N) (Percent)

Neutral Sensitive Unk. Neutral Sensitive Unk. Neutral Sensitive

Total

3968 12609 777 12345 4122 887 3634 13720 Driver

89.03% 85.60% 87.50% 87.50% 83.16% 87.73% 86.01% 86.56% 17354

489 2122 111 1763 835 124 591 2131 Front seat passenger 10.97% 14.40% 12.50% 12.50% 16.84% 12.27% 13.99% 13.44%

2722

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 48: Proportion of driver and front passenger according to accidental situations

The distributions of vehicle class and vehicle age categories do not show any association with the accidental situation related with EBA. In Table 49 we can notice that class I2 is overrepresented in ESC sensitive situations (60.64% compared to 50.79% in neutral situations), and that the superior classes are less frequent in these situations compared to the neutral ones (1.63% vs. 3.31% for class S1).

We observe about the same thing when looking at the accidental situations connected with EuroNcap stars rating. The proportion of lower class vehicles is larger in sensitive situations than in neutral situations (54.49% compared to 48.78% for I2 class) and the observed proportion of S1 vehicles is higher in neutral situations than in the sensitive one (4.02% compared to 2.57%).

The vehicles involved in neutral accident situations regarding ESC and stars rating seem newer than those involved in sensitive situations (Table 50).

The vehicles aged less than a year represent the most frequent age category in those neutral accidental situations (19.66% and 21.70%). While the 5 year-old vehicles are as frequent as the new vehicles (less than a year) in ESC sensitive situation (18.40% and 18.32%).

EBA related accidental situations seem independent of the model year of the car, but we notice in Table 51 some difference in the distribution of model year according to ESC and star rating accidental situations, with an overrepresentation of 2000&2001 model years’ vehicles in sensitive situations.



Accidental situation / EBA Accidental situation / ESC Accidental situation / Stars

Class Vehicle

(N) (Percent) Neutral Sensitive Unk. Neutral Sensitive Unk. Neutral Sensitive

Total

57 193 9 189 59 11 41 218 I1

1.28% 1.31% 1.01% 1.34% 1.19% 1.09% 0.97% 1.38%

259

2388 7848 463 7165 3006 528 2061 8638 I2

53.58% 53.28% 52.14% 50.79% 60.64% 52.23% 48.78% 54.49%

10699

1520 5007 303 5021 1466 343 1533 5297 M1

34.10% 33.99% 34.12% 35.59% 29.57% 33.93% 36.28% 33.42%

6830

366 1257 87 1266 345 99 420 1290 M2

8.21% 8.53% 9.80% 8.97% 6.96% 9.79% 9.94% 8.14%

1710

126 426 26 467 81 30 170 408 S1

2.83% 2.89% 2.93% 3.31% 1.63% 2.97% 4.02% 2.57%

578

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 49: Vehicle class according to accidental situations


situation / Stars Vehicle

age

(N) (Percent)


Total

875 2834 167 2774 912 190 917 2959 Less than a year 19.63% 19.24% 18.81% 19.66% 18.40% 18.79% 21.70% 18.67%

3876

814 2642 189 2632 799 214 811 2834 1 year old 18.26% 17.93% 21.28% 18.66% 16.12% 21.17% 19.20% 17.88%

3645

761 2592 128 2546 790 145 753 2728 2 years old 17.07% 17.60% 14.41% 18.05% 15.94% 14.34% 17.82% 17.21%

3481

706 2325 157 2222 794 172 650 2538 3 years old 15.84% 15.78% 17.68% 15.75% 16.02% 17.01% 15.38% 16.01%

3188

624 2104 116 1956 754 134 589 2255 4 years old 14.00% 14.28% 13.06% 13.86% 15.21% 13.25% 13.94% 14.23%

2844

677 2234 131 1978 908 156 505 2537 Over 5 years old 15.19% 15.17% 14.75% 14.02% 18.32% 15.43% 11.95% 16.01%

3042

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 50: Age of the vehicle according to accidental situations




situation / Stars Model year

(N) (Percent)


Total

357 1188 72 1011 522 84 259 1358 2000

8.01% 8.06% 8.11% 7.17% 10.53% 8.31% 6.13% 8.57% 1617

889 3018 188 2746 1130 219 800 3295 2001

19.95% 20.49% 21.17% 19.46% 22.80% 21.66% 18.93% 20.79% 4095

717 2289 120 2219 777 130 619 2507 2002

16.09% 15.54% 13.51% 15.73% 15.67% 12.86% 14.65% 15.82% 3126

795 2603 165 2561 816 186 738 2825 2003

17.84% 17.67% 18.58% 18.15% 16.46% 18.40% 17.47% 17.82% 3563

822 2703 162 2704 795 188 880 2807 2004

18.44% 18.35% 18.24% 19.17% 16.04% 18.60% 20.83% 17.71% 3687

669 2121 131 2117 655 149 694 2227 2005

15.01% 14.40% 14.75% 15.01% 13.21% 14.74% 16.43% 14.05% 2921

208 809 50 750 262 55 235 832 2006

4.67% 5.49% 5.63% 5.32% 5.29% 5.44% 5.56% 5.25% 1067

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 51: Model year of the vehicle according to accidental situations

The next tables deal with the characteristics of the road and the localization of the accident. All sensitive situations show a higher proportion of accidents occurring in a bend than the proportion observed in neutral situations: for instance in Table 52 we observed 29.88% of ESC sensitive situations occurring in a bend and only 12.51% bend configuration for the neutral situations.

The road surface condition is linked to the accidental situation related to ESC and EBA, as detailed in Table 53: the proportion of degraded road surfaces turns out to be reduced in neutral situations compared to sensitive situations. There is no observed difference for the stars accidental situations.

The main part of the EBA and ESC sensitive situations occurs out of intersections and the proportions are higher than those observed for the EBA and ESC neutral situations. The opposite is observed when focusing on stars accidental situation, where the neutral situations show a higher proportion of accidents occurring out of junction than the sensitive (82.22% vs. 69.54%, see Table 54).

Table 55 underlines the link between rural area and the sensitive accidental situations, whatever the safety function we look at. The overrepresentation of rural area in sensitive accidental situations is particularly clear for ESC sensitive situations (65.32% of front occupants observed in rural area compared to 31.97% when considering the neutral situations). Potentially the overrepresentation of rural area means that the violence of impact may not be the same between the neutral and sensitive accidental situations.

The type of accidental situation appears to be linked to the category of road, for the three safety functions of interest (see Table 56). Table 57 and Table 58 clearly show that accidental situations are correlated to the number of vehicles involved in the crash and to the location of the main impact on the vehicle.



Although this variable should not always be used in the analysis because of its complete or almost complete overlap with the ESC sensitive situations and with the EBA and stars neutral situations. The same comment is also appropriate concerning the type of impact and stars rating accidental situations: the neutral and sensitive situations for EuroNcap evaluation are directly based on the type of impact.


situation / Stars Road

configuration

(N) (Percent)


Total

3944 11986 729 12343 3476 840 3815 12844 Straight line

88.49% 81.37% 82.09% 87.49% 70.12% 83.09% 90.30% 81.03% 16659

513 2745 159 1765 1481 171 410 3007 In a bend

11.51% 18.63% 17.91% 12.51% 29.88% 16.91% 9.70% 18.97% 3417

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 52: Road configuration according to accidental situations


situation / Stars Road

surface

(N) (Percent)


Total

713 2803 144 2387 1115 158 750 2910 Wet

16.00% 19.03% 16.22% 16.92% 22.49% 15.63% 17.75% 18.36% 3660

3744 11928 744 11721 3842 853 3475 12941 Dry

84.00% 80.97% 83.78% 83.08% 77.51% 84.37% 82.25% 81.64% 16416

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 53: Road surface according to accidental situations


situation / Stars Junction


Total

1944 3398 237 5101 237 241 751 4828 At a junction 43.62% 23.07% 26.69% 36.16% 4.78% 23.84% 17.78% 30.46%

5579

2513 11333 651 9007 4720 770 3474 11023 Out of a junction 56.38% 76.93% 73.31% 63.84% 95.22% 76.16% 82.22% 69.54%

14497

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 54: Presence of a junction according to accidental situations




situation / Stars Urban/Rural


Total

3271 8146 520 9598 1719 620 2633 9304 Urban area

73.39% 55.30% 58.56% 68.03% 34.68% 61.33% 62.32% 58.70% 11937

1186 6585 368 4510 3238 391 1592 6547 Rural area

26.61% 44.70% 41.44% 31.97% 65.32% 38.67% 37.68% 41.30% 8139

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 55: Type of area according to accidental situations


situation / Stars Category of road

(N) (Percent)


Total

144 2156 152 1287 1010 155 821 1631 Highway

3.23% 14.64% 17.12% 9.12% 20.38% 15.33% 19.43% 10.29% 2452

214 390 22 506 85 35 115 511 Others

4.80% 2.65% 2.48% 3.59% 1.71% 3.46% 2.72% 3.22% 626

2362 5596 324 6835 1052 395 1739 6543 Local road

53.00% 37.99% 36.49% 48.45% 21.22% 39.07% 41.16% 41.28% 8282

1271 4164 249 3553 1864 267 768 4916 Secondary road 28.52% 28.27% 28.04% 25.18% 37.60% 26.41% 18.18% 31.01%

5684

466 2425 141 1927 946 159 782 2250 National road 10.46% 16.46% 15.88% 13.66% 19.08% 15.73% 18.51% 14.19%

3032

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 56: Category of road according to accidental situations




situation / Stars Number of road users in

the accident


Total

237 1906 91 0 2138 96 71 2163 Single vehicle crash 5.32% 12.94% 10.25% 0.00% 43.13% 9.50% 1.68% 13.65%

2234

4220 12825 797 14108 2819 915 4154 13688 2 road users and + 94.68% 87.06% 89.75% 100.00% 56.87% 90.50% 98.32% 86.35%

17842

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 57: Number of road users involved according to the accidental situations


situation / Stars Main impact location

– (N)

(Percent)


Total

832 3308 85 3914 162 149 4225 0 Rear

18.67% 22.46% 9.57% 27.74% 3.27% 14.74% 100.00% 0.00% 4225

2323 9388 585 8092 3581 623 0 12296 Frontal

52.12% 63.73% 65.88% 57.36% 72.24% 61.62% 0.00% 77.57% 12296

557 482 50 696 337 56 0 1089 Lateral - right side 12.50% 3.27% 5.63% 4.93% 6.80% 5.54% 0.00% 6.87%

1089

670 792 71 1049 404 80 0 1533 Lateral - left side 15.03% 5.38% 8.00% 7.44% 8.15% 7.91% 0.00% 9.67%

1533

75 761 97 357 473 103 0 933 Rollover - others 1.68% 5.17% 10.92% 2.53% 9.54% 10.19% 0.00% 5.89%

933

Total 4457 14731 888 14108 4957 1011 4225 15851 20076

Table 58: Main impact location according to accidental situations

We have noticed a difference in the characteristics of the occupants involved in ESC neutral and sensitive situations, as well for those involved in stars neutral and sensitive situations. We also detected a difference in the age of the vehicles and the class of the vehicles for those two safety functions, whereas EBA situations look independent of these variables.



The tables also stress the fact that neutral and sensitive accidental situations do not occur in the same conditions. The road characteristics, the localizations are different. This may have an influence on the severity of the impact and thus on the severity of injuries sustained by the occupants.

The distribution of the variables for the “unknown” category of ESC or EBA neutral accidental situations are in line with the distributions observed for the neutral and sensitive situations. The EBA “unknown” categories behaves more like the EBA sensitive category, whereas the ESC “unknown” situation looks close to the ESC neutral category. Excluding these “unknown” situations should not bring any critical bias in the analysis.



5 Results: Assessment of the effectiveness of existing safety applications in cars.

All the results will be presented in the same way. They concern accidental situations avoidance and injury mitigation. First, a cross tabulation (see example Table 59) of the injury severity distribution of front seat occupants according to the type of accidental situations and to the safety function(s) in the car, is computed. This table also summarizes the kind of cars that are compared to conduct the evaluation.

Accidental situation related to SF1

Injury severity for belted front passengers

SF1 on board

SF1 not on board

not injured N00 N10

Injured, retained in hospital less than a day

N01 N11

Injured, retained in hospital more than a day

N02 N21

Neutral

Fatally injured N03 N31

Not injured S00 S10


S01 S11


S02 S12

Sensitive

Fatally injured S03 S13

Table 59: Injury severity according to accidental situations and to the safety function

Second, we present a table (see example Table 60) with the estimation of the specific and the overall effectiveness of the safety function(s), with their confidence interval.

The specific effectiveness expresses the reduction (in percent) of injuries of at least a certain level of severity, in sensitive accidental situations only, given that all cars are fitted with SF1. The overall effectiveness expresses the reduction (in percent) of injuries of at least a certain level of severity, in the whole set of accidental situations, given that all cars are fitted with SF1.

Several logistic regressions were performed, for every effectiveness, with some external variables used as confounders. The best regression model was selected by the means of the likelihood ratio chi-square, the AIC criterion. The predictive power of the model was also taken into account while selecting the best model. We attribute a number to each evaluation (first column of the table). In annex A, the variables used to adjust the OR via the logistic regression are listed, each evaluation being defined by its number. The p values associated to the OR are also provided in that table.



Effectiveness_low Effectiveness Effectiveness_high

Specific E_sl E_s E_sh N° of regression

A0+ : All occupants Overall E_ol E_o E_oh

Specific … … … N° of regression

A1+ : At least an injured occupant retained in hospital

less than a day Overall … … …

Specific … … … N° of regression


more than a day Overall … … …

Table 60: Estimation of safety benefit of the safety function

The specific effectiveness and its confidence interval is calculated via the use of the OR as stated in Equation 1. The overall effectiveness is calculated as stated in formula 1.5 of the TRACE report D.7.4.1, and the confidence interval as in formula 1.8 of the same report.

If we focus on the first level (uninjured + injured + killed occupants), the effectiveness expresses a reduction in accidental situations. This figure may be understood as an estimation of the accident avoidance effectiveness.

For a better understanding, the injury severity level ‘At least an injured occupant retained in hospital less than a day’ will be called A1+’, and the level ‘At least an injured occupant retained in hospital more than a day’ will be called A2+. Accordingly, the term A0+ stands for all occupants.

5.1 Evaluation of the safety benefit of Emergency Brake Assist

The safety benefit brought by EBA is estimated on four stars vehicles. As we are interested in the pure effectiveness of EBA, four stars vehicles fitted with ESC are not taken into account here: we selected four stars cars fitted neither with EBA nor with ESC and four stars cars fitted with EBA but not with ESC. The neutral accidental situations regarding EBA are defined as declared in Table 4.

Table 61 displays the distribution of injury severity for EBA neutral and sensitive accidental situations, according to the type of cars.

Accidental situations

related to EBA Injury severity for belted front passengers

4 stars cars fitted with

EBA

4 stars cars not fitted with

EBA

Not injured 656 700

Injured, retained in hospital less than a day 212 232

Injured, retained in hospital more than a day 72 104 Neutral

Fatally injured 12 39

Not injured 1623 1759


Injured, retained in hospital more than a day 405 604 Sensitive


Table 61: Injury severity according to EBA, and to EBA accidental situations



Table 62 below provides us the estimations of the benefit of adding an EBA, given that the car is a four stars vehicle.


Specific -19.5% -4.2% 9.2% 1 A0+ : All occupants

Overall -14.6% -3.2% 7.1%

Specific -4.6% 9.4% 21.5% 2


less than a day Overall -3.8% 7.8% 18.1%

Specific -8.8% 17.3% 37.2% 3


more than a day Overall -7.3% 14.6% 32.0%

Table 62: Safety benefit of EBA for four stars vehicles

We observe no reduction in accidental situations, would all four stars be fitted with EBA. The overall effectiveness comes out to be even negative, but thus not statistically significant (p=0.55). Given that all four stars cars would be fitted with EBA, the percentage of injuries at A1+ level would be reduced by 7.8% and the percentage of injuries at the A2+ level would be reduced by 14.6%. As all the lower possible values for the effectiveness are negative, the effectiveness appears to be not statistically significant.

The effectiveness of EBA in preventing crashes seems then to be slightly negative. This was also observed for ABS when ABS was evaluated in the late 1980’s or early 1990’s. The assumption was that ABS could increase rear-end accidents in a world where not all cars are equipped with ABS, the front car equipped with ABS being able to brake better that the rear car. We are unable to validate this assumption. The accident avoidance effectiveness is very close to 0, with a large confidence interval, which rather suggests an absence of avoidance power.

On the other hand, even if statistical significance is still lacking, EBA seems to be very effective in mitigating injuries, especially for severe injuries.

As the proportion of sensitive accidental situations is large compared to the neutral group, the different specific types of effectiveness are almost equivalent with each’s overall effectiveness.

5.2 Evaluation of the safety benefit of Electronic Stability Control

The safety benefit of ESC is assessed for four stars and for five stars vehicles seperately.

5.2.1 Four stars vehicles

The analysis was performed with four stars vehicles fitted with EBA and ESC, compared to four stars vehicles fitted with EBA but not with ESC.

Table 63 and Table 64 describe the number of cases included in the analysis and the result of the effectiveness calculation.




related to ESC Injury severity for belted front passengers

4 stars cars fitted with EBA

and ESC

4 stars cars fitted with EBA and not with

ESC





Not injured 45 348




Table 63: Injury severity according to ESC, and to ESC accidental situations (four stars vehicles)


Specific -18.8% 11.8% 34.5% 4 A0+ : All occupants

Overall -4.8% 3.1% 9.2%

Specific -7.5% 27.7% 51.4% 5



Specific -76.4% -3.4% 39.4% 6


more than a day Overall -43.3% -2.0% 23.1%

Table 64: Safety benefit of ESC for four stars vehicles

Four stars vehicles fitted with ESC show a reduction of 11.8% in their involvement in ESC sensitive accidental situations, which corresponds to an overall effectiveness of ESC of 3.1%. Regarding injury mitigation due to ESC, all four stars vehicles would be fitted with ESC, the proportion of injury at A1+ level would be reduced by 11.2% (p=0.1). If we consider the specific effectiveness of ESC at this injury severity level, the safety benefit reaches 27.7%. No positive result is observed when focusing on the higher level of injury severity (injured people retained in hospital more than a day and fatally injured occupants). However the large confidence interval suggests a very low statistical power for that kind of injuries. The non positive results should then not be regarded too closely and consequently not interpreted wrongly.

Four stars vehicles in our sample are mainly small and medium sized vehicles. The rate of ESC presence on the small sized vehicles is very low (Table 36). If we exclude this category of vehicles for the sake of statistical analysis and focus the analysis on medium and large sized vehicles, the results are as follow (Table 65).





Overall -1.06% 5.23% 10.81%

Specific -7.60% 31.30% 56.10% 8



Specific -16.00% 35.60% 64.20% 9


more than a day Overall -6.86% 16.81% 34.40%

Table 65: Safety benefit of ESC for four stars vehicles (exclusion of inferior class vehicles)

The figures of overall effectiveness of accidental situations avoidance and injury mitigation at A1+ level are on line with the effectiveness calculated on the whole sample. For medium and large sized vehicles, awarded four stars, we then observe a positive effect of ESC on injury mitigation at the A2+ level (16.81%).

This does not mean that the effectiveness for smaller vehicles is negative. It just may be interpreted such that the sample size of smaller vehicles fitted with ESC is too restricted to provide us with accurate and stable results about this car segment.

5.2.2 Five stars vehicles

Table 66 and Table 67 display the figures of the safety benefit of ESC for five stars vehicles.


related to ESC Injury severity for belted front passengers


and ESC


ESC




Fatally injured 7 4

Not injured 202 134




Table 66: Injury severity according to ESC, and to ESC accidental situations (five stars vehicles)





Overall -0.9% 3.2% 7.2%

Specific 8.0% 28.6% 44.5% 11


less than a day Overall 2.7% 10.7% 18.3%

Specific 21.3% 43.2% 59.1% 12


more than a day Overall 10.6% 23.4% 35.0%

Table 67: Safety benefit of ESC for five stars vehicles

Given that all five stars vehicles would be fitted with ESC as standard, the accidental situations would be reduced by 3.2% (p=0.12), the reduction of A1+ injuries would be of 10.7% (p=0.009) and the injuries of higher severity level would be reduced by 23.4% (p=0.0007).

The safety benefit of ESC seems equivalent for four and five stars cars, as long as we focus on accidental situation avoidance and on A1+ injury mitigation. However, if we are interested in A2+ injury mitigation, ESC benefit is higher for five stars vehicles than for four stars cars overall.

If we consider four stars vehicles of medium and superior sized only, ESC benefit is comparable to what is observed for five stars vehicle although a bit lower (16.81% vs. 23.4%).

When looking at statistical significance, ESC benefit appears to be statistically significant for five stars vehicles but not for four stars cars.

5.3 Evaluation of the safety benefit of EuroNcap star rating

This section concerns the safety benefit of the EuroNcap stars, i.e. the potential benefit of having a five stars vehicle compared to a four stars vehicle. The safety equipment on board will be taken into account and two analyses are performed: one concerns the benefit of a fifth star when EBA is on board, the second assesses the advantage of the fifth star when EBA and ESC are already fitted on the car.

5.3.1 Vehicles fitted with EBA

Four stars vehicles fitted with EBA and five stars vehicles also fitted with EBA are compared to assess the benefit of having five stars instead of four. The figures are presented in Table 68 and Table 69.




related to EuroNcap rating



EBA and not with ESC


ESC

Not injured 209 471



Fatally injured 0 5





Table 68: Injury severity according to the number of stars, and to accidental situations (vehicles

fitted with EBA)



Overall -5.9% 6.4% 17.2%

Specific -12.8% 10.3% 28.6% 14



Specific N.A. N.A. N.A. 15


more than a day Overall N.A. N.A. N.A.

Table 69: Safety benefit of the fifth stars (vehicles fitted with EBA)

The tables enlighten that if all EBA fitted cars had five stars, instead of four stars, the accidental situations would be reduce by 6.4% (p=0.29) and a reduction of A1+ injury of 8.3% (p=0.35) would be observed. However, these results are not statistically significant.

No effect of the fifth star can be computed for A2+ injury level, given that the car is already fitted with EBA, as the small sample size for neutral situations disturbs the analysis and the analysis becomes too instable.

5.3.2 Vehicles fitted with EBA and ESC

The benefit of having five stars instead of four is now calculated for vehicles already fitted with EBA and ESC as standard (Table 71).



Accidental situations related to

EuroNcap rating



and ESC


and ESC

Not injured 427 68


120 28


20 6

Neutral

Fatally injured 3 0



276 79


142 48

Sensitive


Table 70: Injury severity according to the number of stars, and to accidental situations (vehicles

fitted with EBA and ESC)


Specific 3.7% 24.8% 41.3% 16 A0+ : All occupants

Overall 2.7% 19.3% 33.6%

Specific 18.9% 40.7% 56.7%

17

A1+ : At least an injured occupant

retained in hospital less than a day

Overall 15.0% 33.8% 49.1%

Specific 2.3% 38.3% 61.0%

18


retained in hospital more than a day

Overall 2.0% 35.1% 57.4%

Table 71: Safety benefit of the fifth stars (vehicles fitted with EBA and ESC)

In that configuration, specific and overall effectiveness of the EuroNcap fifth star appear to be very high and each time statistically significant.

If all EBA and ESC fitted cars had five stars instead of four stars, the accidental situations would be reduced by 19.3% (p=0.02), the amount of A1+ injuries would be reduced by 33.8% (p=0.001) and the A2+ injuries would decrease by 35.1% (p=0.03).

If we look back at table 69, it appears that the addition of the fifth star to a car already equipped with only EBA is less effective than the addition of the fifth star to a car equipped with both EBA and ESC. At first glance, it is hardly explainable unless we consider that the combination of a fifth star and the ESC is multiplying the effects of the systems instead of adding only the effects of the systems taken separately. This multiplying effect is not a statistical surprise as it often happens in the real world. If we for example consider traffic safety, driving under the influence of alcohol is producing disasters as



well as driving under the influence of drugs. But combining drugs and alcohol and youth is increasing dramatically the risk to get involved in an injury or a fatal crash.

Now the question is: what are the physical reasons for such a multiplying effect? Apparently, the fifth star offers a better protection when ESC is on board? Why?

Actually, there are four reasons that can lead to a decrease in injury accidents and injuries due a passive safety improvement:

1. the decrease of injury risk at the same impact speed

2. the passive safety improvement gives an additional benefit, even higher than the one which would have been expected in the absence of active safety, if the impact speed is decreased by an active safety system

3. the injury accident is no longer injurious and become a property-damage only accident because the occupants are overall better protected, especially at lower impact speeds

4. changes in crash configuration (type of impact, angle, hit area, etc.) can also lead to changes in injury risk

ESC is supposed to decrease the number of side impacts (by decreasing the losses of control) and to decrease further the impact speed (in addition to EBA). Some of the side impacts are fully avoided, some of the side impacts occur at a lesser speed and some of the side impacts are turned into frontal impacts. The fifth star can be even more effective at lower speed and in frontal impacts. This can explain the combined benefit of ESC and 5 stars altogether.

5.4 Evaluation of the safety benefit of the combination of EBA and ESC

In this section, we consider the safety benefit brought by adding an EBA and an ESC to a four stars vehicle. We defined the neutral situations as stated in section 2.2. They have to be neutral to ESC and EBA at the same time, which is dramatically reducing the sample size in the neutral group.


related to EBA and ESC



and ESC

4 stars cars without EBA

and ESC

Not injured 85 656


20 198


6 67

Neutral




79 1145


45 641

Sensitive


Table 72: Injury severity according to the number of EBA and ESC, and to accidental situations

(four stars vehicles)





Overall -5.1% 18.6% 36.1%

Specific 17.7% 41.5% 58.5%

20


retained in hospital less than a day

Overall 15.4% 36.3% 51.5%

Specific -0.2% 46.5% 71.4%

21


retained in hospital more than a day

Overall -0.2% 42.3% 65.5%

Table 73: Safety benefit of EBA and ESC (four stars vehicles)

The benefit of having a four stars vehicle fitted with EBA and ESC instead of a four stars car without any of these safety functions is clearly assessed by the results of the Table 73. We observe a reduction of 18.6% (p=0.11) of the accidental situations, would all four cars be fitted with ESC and EBA. Around 36.3% of A1+ injuries and more than 40% of A2+ injuries would be saved.

The benefits observed in that configuration are higher than those observed if we only add one of these safety functions at the same time to a four star vehicle (section 5.1 and 5.2.1)

5.5 Evaluation of the safety benefit of the combination of the fifth star and EBA

To assess the benefit of the fifth star and EBA, we have to compare five stars vehicles fitted with EBA to four stars vehicles non-fitted with EBA. None of these cars are implemented with ESC.

Accidental situations related to EBA and EuroNcap rating


5 stars cars fitted with EBA and

without ESC


and ESC

Not injured 44 112



Fatally injured 0 0





Table 74: Injury severity according to the number of stars and EBA, and to accidental situations

(vehicles without ESC)





Overall 3.2% 28.2% 46.7%

Specific 7.7% 36.7% 56.7% 23



Specific 6.9% 37.6% 58.1% 24



Table 75: Safety benefit of the fifth star and EBA (vehicles without ESC)

All results of Table 75 are statistically significant at the 0.05 level and they underline high safety benefit of the combination of a fifth star and EBA. The safety benefits concern the avoidance of accidental situations as well as injury mitigation: if all four stars cars were five stars fitted with EBA, the accidental situations would be reduced by 28.2% (p=0.03), and the injuries would be mitigated by 36% (p=0.01) for A1+ level and by 37.5% (p=0.02) for A2+ level.

If we compare these results with the safety benefits obtained when adding only the EBA (paragraph 5.1), the additional gain of the stars is clearly affirmed.

5.6 Evaluation of the safety benefit of the combination of the fifth star and ESC

Table 76 and Table 77 allow the evaluation of the benefit of adding a fifth star and an ESC to four stars vehicles fitted with EBA.


related to ESC and EuroNcap rating

Injury severity for belted front passengers 5 stars cars fitted with

EBA and ESC

4 stars cars with EBA and without ESC

Not injured 405 450



Fatally injured 2 3





Table 76: Injury severity according to the number of stars and ESC, and to accidental situations

(vehicles fitted with EBA)





Overall 9.6% 22.0% 32.6%

Specific 32.3% 47.1% 58.7% 26



Specific 13.5% 40.7% 59.4% 27



Table 77: Safety benefit of ESC and the fifth star (vehicles fitted with EBA)

The figures show that if all four stars vehicles, already fitted with EBA, would be five stars cars fitted with ESC, the accidental situations would be reduced by 22% (p=0.001). The A1+ injuries sustained by front occupants would be mitigated by 38.6% (p<0.0001) and an equivalent reduction would be observed for A2+ injuries (37.1% p=0.006).

The safety benefits are here again higher than those observed when adding only one of the safety functions separately (sections 5.2.1 and 5.3.1)

5.7 Evaluation of the safety benefit of the combination of the fifth star, EBA and ESC

In this section, we analyze the safety benefit of five stars vehicles fitted with EBA and ESC compared to four stars vehicles without EBA and ESC. We compare the most equipped vehicles to the less equipped ones. Neutral situations are neutral for all the safety functions in concern, and thus this group is extremely reduced (see Table 78).


related to EBA, ESC and EuroNcap

rating

Injury severity for belted front passengers 5 stars cars

fitted with EBA and ESC


and ESC

Not injured 87 99

Injured. retained in hospital less than a day 20 37

Injured. retained in hospital more than a day 1 3 Neutral

Fatally injured 0 0


Injured. retained in hospital less than a day 355 1306

Injured. retained in hospital more than a day 157 705 Sensitive


Table 78: Injury severity according to the number of stars, EBA and ESC, and to accidental

situations





Overall 32.0% 47.2% 58.9%

Specific 58.8% 68.9% 76.5% 29

A1 : At least an injured occupant retained in hospital less than a

day Overall 57.8% 67.8% 75.5%

Specific 51.7% 69.7% 81.0% 30

A2 : At least an injured occupant retained in hospital more than a

day Overall 51.5% 69.5% 80.8%

Table 79: Safety benefit of EBA, ESC and the fifth star

The different types of effectiveness reported in Table 79 are important. Specific and overall effectiveness are nearly identical. Would all cars be five stars fitted with EBA and ESC instead of four stars without ESC and EBA, accidental situations would be lowered down by 47.2% (p ≤ 0.0001). Injury mitigation benefits are equivalent for A1+ and A2+ injury severity level. They reach 67.8% (p ≤ 0.0001) and 69.5% (p ≤ 0.0001) respectively.

5.8 Evaluation of the safety benefit of the combination of the fifth star minus ESC

In the last comparison, we focus on five stars cars equipped only with EBA and on four cars fitted with EBA and ESC. Here, the four stars vehicles have more safety functions than the five stars cars. We estimate the benefit of adding a star and losing the ESC, given that the car has four stars, and EBA and an ESC (Table 80 and Table 81).


related to ESC and EuroNcap rating



EBA without ESC

4 stars cars with EBA and

ESC

Not injured 194 59



Fatally injured 0 0

Not injured 633 216




Table 80: Injury severity according to the number of stars and ESC, and to accidental situations

(vehicles fitted with EBA)





Overall -21.9% 2.1% 22.0%


A1+ : At least an injured occupant retained in hospital less

than a day Overall N.A. N.A. N.A.



more than a day Overall N.A. N.A. N.A.

Table 81: Safety benefit of the fifth star with or without ESC (vehicles fitted with EBA)

When adding a fifth stars but deleting the ESC, compared to fully equipped four stars vehicles, all results become non statistically significant. The accidental situations avoidance effectiveness appears to be close to zero, and injury mitigation benefits are in favor of the four stars vehicle equipped with EBA and ESC.

Once again, the sample sizes are not sufficient to make a proper and robust analysis. We chose not to go on any further and to drop this last analysis



6 Conclusions

6.1 Synthesis of results

The main objective of task 4.2 of WP4 was to estimate, by means of statistical calculation, the proportion of injury accidents that could be avoided and/or the proportion of injury accidents where the severity could be mitigated, for existing safety functions (or a combination of functions), selected from the WP6 list, would all cars be equipped with these functions that are already on the market.

Depending on the availability of crash data and also considering the actual low penetration rate of active safety functions, we have selected for evaluation:

- Electronic Stability Control (ESC)

- Emergency Brake Assist (EBA)

As for the passive safety systems, newer cars are designed to offer an overall protection. Car structure, load limiters, front airbags, side airbags, knee airbags, pretensioners, padding and non aggressive structures in the door panel, the dashboard, the windshield, the seats, the head rest also participate in supplying more protection. The whole package is then very difficult to evaluate separately, one element independently from the others. We have then decided to consider that we would evaluate in TRACE the safety of the whole package, this package being, for the sake of simplicity, the number of stars awarded at the Euro NCAP testing.

The challenges of task 4.2 were to compare the effectiveness of some safety configuration SC I with the effectiveness of some safety configuration SC II. A safety configuration can be understood as a package of safety functions.

Ten comparisons have been carried out and the following evaluations are now available:

- Evaluation of safety benefit of EBA given that the car has four stars.

- Evaluation of safety benefit of ESC given that the car has four stars and an EBA.

- Evaluation of safety benefit of ESC given that the car has five stars and an EBA.

- Evaluation of safety benefit of the fifth star given that the car has four stars and an EBA.

- Evaluation of safety benefit of the fifth star given that the car has four stars, an EBA and an ESC.

- Evaluation of safety benefit of EBA and ESC given that the car has four stars.

- Evaluation of safety benefit of EBA and a fifth star given that the car has four stars.

- Evaluation of safety benefit of ESC and a fifth star given that the car has four stars and an EBA.

- Evaluation of safety benefit EBA, ESC and a fifth star given that the car has four stars.

- Evaluation of safety benefit of a fifth star and removing an ESC given that the car has four stars, an EBA and an ESC.

This deliverable D4.2.2 presents the final results achieved by applying the TRACE methodology (described in Deliverables D.7.4.1 and D.4.2.1.) to the available accident data.

The main striking results coming out from the analysis are what we call the ‘overall effectiveness’ of the selected safety systems with breakdown by injury severity levels. This ‘overall effectiveness’ represents the percentage of reduction in injury accident and injuries that would be observed if all cars would be fitted with the system(s) under consideration, compared to cars of a reference group. Reference groups are not always the same, the less equipped reference group being 4-stars cars without EBA, without ESC.



Reduction in injury accidents (accident

avoidance)

Reduction in all injuries & fatalities

Reduction in severe injuries and fatalities

Safety benefit of EBA given that the car has four stars.

-3.2% 7.8% 14.6%

Safety benefit of ESC given that the car has four stars and an EBA.

5.2% 10.3% 16,8%

Safety benefit of ESC given that the car has five stars and an EBA.

3.2% 10.7% (*) 23.4% (*)

Safety benefit of the fifth star given that the car has four stars and an EBA.

6,4% 8,3% N.A.

Safety benefit of the fifth star given that the car has four stars, an EBA and an ESC.

19.3% (*) 33,8% (*) 35,1% (*)

Safety benefit of EBA and ESC given that the car has four stars.

18,6% 36,3% (*) 42,3%

Safety benefit of EBA and a fifth star given that the car has four stars.

28,2% (*) 36% (*) 37,5% (*)

Safety benefit of ESC and a fifth star given that the car has four stars and an EBA.

22% (*) 38,6% (*) 37,1% (*)

Safety benefit EBA, ESC and a fifth star given that the car has four stars.

47,2% (*) 67,8% (*) 69,5% (*)

Safety benefit of a fifth star and removing an ESC given that the car has four stars, an EBA and an ESC.

2,1% N.A. N.A.

* Statistically significant

Table 82: Synthesis of findings

This overall effectiveness is derived from the specific effectiveness which is the effectiveness of the safety configurations which applies only to accident types or impact types for which the safety systems are designed for.

The main outcome of this analysis is that any increment of a passive or active safety function selected in this analysis (5 stars, Emergency Brake Assist, Electronic Stability Control) is producing additional safety benefits. In general, the safety gains are higher for higher severity levels. For example, would all cars be five stars fitted with EBA and ESC, compared to four stars without ESC and EBA, injury accidents would be reduced by 47.2%, all injuries would be mitigated by 67.8% and severe + fatal injuries by 69.5%.

6.2 Limitations and discussion.

The results are very positive and encouraging, showing great potential for the generalization of the selected safety applications and validating the choices made so far by the various stakeholders who have been pushing the installation of safety technologies in the passenger cars for years.

However, a few questions remain and need to be addressed in this last section.



Highly effective estimates

Some of the above results sound at first glance highly questionable. A reduction in injury accidents by 47.2%, in injuries by 67.8% and in severe + fatal injuries by 69.5% if we compare 5 star cars + EBA + ESC to four star cars only is just unbelievable.

Apart from the limitations expressed below, can we consider these results as plausible or just as a statistical trick coming out of these limitations and restricted data?

Previous studies have shown high potential for ESC (Lie et al., 2004), Page et Cuny, 2006, Kreiss et al., 2006; Groempig, 2005), and EBA (Page et al., 2005). Each of the systems is considered to potentially reduce the number of car injury accidents by 10 % to 15 %. Other studies have also shown the great potential of front and side airbags or overall restraint systems (load limitors, pretensioners, airbags). Sometimes the effectiveness of newer cars in preventing serious or fatal injuries to thorax for example reach 80 % (LAB unpublished, 2005). Some other studies have also shown a correlation between the Ncap star ratings and the real world injury risk (see SARAC project for example). In addition, the injury risk curves (injury risk by severity levels as a function of impact speed or EES, equivalent energy speed) often show high potential of systems able to decrease the impact speed, especially at highest or medium speeds. Also, the in-depth accident investigations also showed a high reduction in the risk to sustain severe injuries at reasonable impact speeds.

As a whole, we expected a high effectiveness of the combination of the systems. However, some of the estimates above are really high, higher than expected. The calculations were run, re-run checked and re-checked several times, without changing the results much which remained incredibly stable.

We do think that each of the systems separately (EBA and ESC) are effective in preventing road crashes and in mitigating injuries. We do think that passive safety measures have the same potential. Combining these safety packages altogether is simply safer than the simple addition of them.

Statistical Significance

A few results are actually either not statistically significant or present large confidence intervals. This might appear disappointing at a first glance. Actually, it is not. Significant results show that the safety benefits brought by the systems are positive, even though the exact quantification of the benefits are estimated in a quite large interval. These are the best estimates achievable, regarding the data available and should be regarded as such. Best estimate just means that the likelihood that this estimate is the best one, is the highest, given the study conditions.

As for non significant results, it does not mean that they are ignorable. To a certain extent, statistically non significant does not mean insignificant. Once again, these are the best estimates we can calculate in TRACE. They have indeed to be validated by further studies, with additional data. They must be considered as a valuable starting point that tells us that the benefits are more likely to be positive than negative and that the only remaining problem to be solved is the sample size. One of the ‘principles’ in Epidemiology is that a result is validated once several studies have concluded with similar (or close) results. In that sense, further work in this field should be strongly encouraged.

Data Limitations

French Crash data limitations have been explained in the data chapter. There are basically four issues:

- Vehicle equipment in crashed cars is hardly accessible directly in the accident data sets. A connection has to be made between the crash data and the vehicle equipment files. These files are supposed to provide, for each car identified by its VIN (or any kind of identification code), all important technical characteristics of the car. They are hardly accessible for non industrial bodies. Therefore, the only solution is to get general commercial description of the make and model one by one. In that case, it is impossible to state, for optional equipment, whether or not it was fitted in the car recorded in the crash file. For standard equipment, this solution applies very well though.



But this limitation in the data considerably reduces the sample size and the extent of the vehicles studied. Because most of the information was accessible for French cars only, we limited our focus on French cars. Collection of information about German cars was also attempted but finally failed.

- The method demands the determination of accident situations which can be sensitive or neutral to the safety function or the safety configurations (package of functions) under study. We proposed a classification of accident situations on the basis of the information available in the crash data. We then stated whether or not the situation is neutral or sensitive to the safety function. These statements where sometimes difficult since only sub-parts of the class are sensitive and some other parts are not. For example, the Single vehicle loss of control/guidance accident in a straight line (not at intersections) combines loss of control and guidance crashes. We cannot distinguish the two classes in the data file. Information available in this file is fully helpless. Lost of control accidents are sensitive to ESC but not guidance accidents. Therefore we had to live with it even though we know from WP7 that misclassification can lead to bias in the analysis and to an underestimation of the safety benefits. This is something we cannot avoid.

- Severity definitions in the French accident data file changed in 2005. Therefore, it was impossible to work on more crash years to increase the data sample. We worked only with 2005 and 2006 national accident files. On the other hand, severity levels used in the national file are rather vague regarding the real severity of the injuries (less than one day or more than one day of hospitalization) and we know that they are not fully representative for the real severity levels that can be measured by the AIS or ISS for example. Unfortunately these details are accessible only in in-depth data, not used here because we need thousands of cases, available only in national accident files.

- We considered only a selection of French cars because the above mentioned information was more easily accessible for these cars. French cars are by no means representative of the car fleet in France even though 2/3 of the market is made of French vehicles. However, we consider that, overall, the bias introduced by this restriction is not damageable. Systems under study are very likely different from a car manufacturer to another one, and the intrinsic performance of vehicles are also different from a manufacturer to another one. They are also different from a model to another one in the same make. Adding other types of vehicles would have likely modified the estimates, but probably to a very small extent, variety of models counting probably more than variety of make.

Limitations have been considered the best we could. The second data limitation reported above is certainly the most important one and lets us think that we ended up with conservative estimates rather than excessive estimates.

General results vs. Specific results

Evaluation studies are usually looking at the benefits of such or such safety measures both on the total number of injury accidents and also on specific accidents. These ‘specific accidents’ may vary according to the scope of the study, it can be accidents involving specific user groups (powered two wheelers, small or large cars, young drivers, etc.), accident types (accidents at night or in the rain for example), or specific variables of interests (accidents with breakdown by road types, by regions, by of impact, by area, urban, or rural, etc.).

In our report we worked only on the so called sensitive injury accident and on the total number of injury accidents, without being able to work on specificities. As for accident prevention and eventually segmentation of the different target groups to prevent, it would have been interesting though. For example, ESC might be more effective for medium sized cars, or 5th star for large cars, or EBA on rural roads, etc. In our study, we can just simply state that, overall, all safety applications are relevant and effective in preventing road injuries, without being able to more accurately target prevention actions on specific groups or specific issues.



Safety Applications

We had to restrict our analysis to two well-known active safety applications: ESC and EBA. We also had to group all passive safety improvements brought by the car industry into two restrictive classes (4 stars and 5 stars) which actually poorly reflects the diversity of these improvements.

These restrictions have been argued in the data section. Firstly there are not so many safety applications widespread in the car fleet so far. Secondly, the available data is not able to provide reliable information about the presence/absence of the other safety applications that we had initially planned to evaluate (Tire Pressure Monitoring and Speed Limiter).

This is indeed a pity but we have already ended up with interesting results concerning the combination of passive and active safety systems, which is a step forward compared to the state-of-the-art in this field. On the other hand, the method is now ready to be applied on other types of systems and can be used as soon as the data becomes available.

Europe 27 level

To what extent are the findings achieved in France with the French data expandable to whole Europe?

First of all, our results are based on a virtual world where all cars are at least rated 4 stars at the EuroNCAP and at most rated 5 stars and equipped with EBA and ESC. Therefore this world does not take into account the real distributions of equipment in the car fleets in the 27 countries of Europe. As we stated earlier in the report, we did not focus our interest in the actual observed effectiveness of the safety functions (i.e. how many lives these safety applications have saved so far) but rather in the potential safety benefits of these functions, would all cars be equipped with such safety applications.

Such benefits can also be regarded the other way around. Let’s assume a country has the whole fleet equipped with 5 stars cars + EBA + ESC. Our calculation can give insights into the potential disbenefits that a virtual downgrade of the fleet towards less safe vehicles could produce.

Reasoning in such a virtual world automatically prevents us from calculating the potential benefits of safety applications for the whole Europe in terms of absolute figures of fatalities or injuries that have been saved or could be saved or mitigated thanks to the safety applications. It could of course be done with the availability of the frequency of safety applications in the car fleets for each and every country, which is unfortunately not available. And it is out of the scope of TRACE.

Second, are we sure that this potential safety benefits calculated for France, also applies to the rest of Europe? It actually depends on a single issue: if the distributions of neutral accident situations and the sensitive accident situations do not vary much from a country to another one, the overall potential safety benefits estimated for France are valid for the rest of Europe.

We haven’t checked this assumption as the accident situation classification that we created for France is strictly not available in the other countries the same way. But, looking at the former studies about ESC effectiveness carried out in Sweden (Lie et al., 2004), France (Page et Cuny, 2006) and Germany (Kreiss et al., 2006; Groempig, 2005), with similar results and similar methodologies, we can conclude that, even as we know or suppose that accident types frequencies are variable in all European countries, data from France should not deviate very much from the average. Therefore, the findings achieved for France should be a good ‘rough estimate’ for what would have been observed in Europe if data would have been available.

Innovations in TRACE and recommendations

Now, we must enlighten the innovations brought by TRACE in the field of the assessment of safety benefits of safety applications based on technologies and propose a few recommendations.

First, TRACE proposed a new methodology to evaluate the potential safety benefits of a package of safety applications, so-called safety configurations, including passive, preventive and active safety.



Second, this methodology has been applied successfully to French accident data. It produced results of high interest. As it can be seen in Table 82, we investigated 10 variants of safety benefits that can be expected from installation of safety technologies in cars for the prevention of car accidents.

Third, this methodology is extensively explained in report D.7.4.1 and applied in details in this current report. Advantages and limitations of the methodology are also comprehensively reported. It can then be considered as a ready-to-apply methodology, which can be helpful for forthcoming similar research, with eventually an extent to other European data and other safety configurations.

Finally, the results can be used for explaining the safety benefits achieved so far in the countries where the penetration rate of the safety applications is high enough to have produced visible effects. They can also be used to forecast the evolution of safety where the penetration of such systems is increasing rapidly.

We have not conducted any cost benefit analysis of such systems. It was beyond the scope of our project. However, the evaluation of a combination of systems altogether and/or the assessment of the additional safety benefits of a system given the presence of other systems can be valuable for these costs-benefits analysis, in which, currently, systems are too often considered as acting independently.



References

Aga, M, Okada A. Analysis of vehicle stability control (VSC)´s effectiveness from crash data. ESV paper 541.18th ESV Conference. Nagoya 2003.

Agresti, A. (1996). An Introduction to Categorical Data Analysis.

Wiley Series in probability and Statistics (New York).

Broughton, Baughan. The effectiveness of antilock braking systems in reducing accidents in Great Britain. Accident Analysis and Prevention 34 (2002) 347-355.

Cooper, P. and Pinili, M. (1994) Assessment of ABS Effectiveness Using ICBC Claim Data. Insurance Corporation of British Columbia, North Vancouver, British Columbia, Canada.

Evans L, ABS and relative crash risk under different roadway, weather, and other conditions. SAE paper 950353. Warrendale, PA: Society of Automotive Engineers; February 1995. (Also included in: Accident Reconstruction: Technology and Animation V, SAE Special Publication SP-1083, p. 177-186; 1995).

Evans, Gerrish. Antilock brakes and risk of front and rear impact in two-vehicle crashes. Accident Analysis and Prevention 28 (1996) 315-323.

Evans. Antilock brake systems and risk of different types of crashes in traffic. 1999. Crash Prevention and Injury control. pp. 445-461.

Farmer, C.M., Lund, A.K., Trempel, R.E., Braver, E.R., 1997. Fatal crashes of passenger vehicles before and after adding antilock braking systems. Accident Analysis and Prevention 29, 745– 757.

Farmer. New evidence concerning fatal crashes of passenger vehicles before and after adding antilock braking systems. Accident Analysis and Prevention 33 (2001) 361-369.

Farmer C. Effect of electronic stability control on automobile crash risk. Traffic Injury Prevention Vol 5:317-325. 2004.

Hertz, E., Hilton, J., Johnson, D.M., 1998. Analysis of the crash experience of vehicles equipped with antilock braking systems (ABS) – an update. NHTSA paper 98-S2-O-07. Paper presented at the 16th ESV Conference, February 1998 in Windsor, Canada.

Kahane, C.J. 1994. Preliminary evaluation of the effectiveness of antilock brake systems for passenger cars. Report no. DOT HS-808-206. Washington, DC: National Highway Traffic Safety Administration

Kreiss, J.-P., Langwieder, K. and Schüler, L. (2005). The Effectiveness of Primary Safety Features in Passenger Cars in Germany. ESV-paper No. 05-145. 19th-ESV-Conference,Washington D.C. (USA).

Kreiss, J.-P., Schüler, L., Zangmeister, T. (2006). Statistical Evaluation of the Effectiveness of Safety Functions in Vehicles based on Real-World Accidents (State-of-the-Art). TRACE-paper.

Kullgren A, Lie A, Tingvall C. The effectiveness of ABS in real life crashes. ESV Conf paper no 94-S4-O-07. Munich 1994.

Lie, Tingvall, Krafft and Kullgren. The effectiveness of ESP (Electronic Stability Program) in reducing real life accidents. Traffic Injury Prevention, paper n°05-0135, 2006.

Lie, Tingvall, Krafft and Kullgren. The effectiveness of ESC (Electronic Stability Control) in reducing real life crashes and injuries. Traffic Injury Prevention, 5:37-41, 2004.

Padmanaban, J. and Lau, E. 1996. Accident experience of passenger vehicles with four-wheel antilock braking systems. Proceedings of the 40th Annual Conference of the Association for the Advancement of Automotive Medicine, 111-25. Des Plaines, IL: Association for the Advancement of Automotive Medicine.



Page, Forêt-Bruno, Cuny. Are expected and observed effectiveness of emergency brake assist in preventing road injury accidents consistent ? ESV paper n° 05-0268.

Page Y, Cuny, S. Is ESP efficient on French Roads? First Expert Symposium on Accident Research, ESAR, Hannover.

Page Y, Cuny, S. Is ESP efficient on French Roads? Accident Analysis and Prevention 38 (2006) 357-364.

Page, Y, Riviere C., Cuny, S., Zangmeister, T. (2008) A posteriori evaluation of safety functions effectiveness. Methodologies. TRACE Project Deliverable D.4.2.1. Report. February 2008.

Unselt T, Breuer J, Eckstein L, Frank P. Avoidance of “loss of control crashes” through the benefit of ESP. FISITA Conference paper no F2004V295. Barcelona 2004.

Zangmeister T., Kreiß J.P., Schüler L., Page Y., Cuny S. (2007) Simultaneous evaluation of multiple safety functions in passenger vehicles Tobias. 20th ESV Conference Paper Number 07-0174

Zangmeister T., Kreiß J.P., Schüler L. (2008) Evaluation of existing safety features. TRACE Project Deliverable D.7.4.1 Report. February 2008.



Annex A (External variables included in the logistic regressions)

N° of evalu

ation

p

Safety function

Gender

Age

Class

vehicle

Vehicle age

Road surfac

e

Road category

Road configura

tion

Junction

Area

model yea

r

No. of ro

ad use

rs

type of im

pact

Sea

ting position

interaction term

s

1 0.55 X X X X X X X X X X X X

2 0.25 X X X X X X X X X X X X X

3 0.17 X X X X X X X X X X X X

4 0.40 X X X X X X X X X

5 0.10 X X X X X X X X

6 0.90 X X X X X X X

7 0.09 X X X X X

8 0.10 X X X X X X X

9 0.14 X X X

10 0.12 X X X X X X X X

11 0.009 X X X X X X

12 0.0007 X X X X

13 0.29 X X X X X X X X

14 0.35 X X X X X X X X X

15 0.46 X X X X X X X X X X

16 0.02 X X X X X X X

17 0.001 X X X X X X X X

18 0.03 X X X X X X X X

19 0.11 X X X X X X X X X X

20 0.002 X X X X X X X X X X X X

21 0.05 X X X X X X X X X X X

22 0.03 X X X X X X X

23 0.01 X X X X X X X X

24 0.02 X X X X X X

25 0.001 X X X X X X X X X

26 <0.0001 X X X X X X X X X X

27 0.006 X X X X X X X X X X X

28 <0.0001 X X X X X

29 <0.0001 X X X X X X

30 <0.0001 X X X X X X X

31 0.84 X X X X X X X X X

32 0.59 X X X X X X X

33 0.52 X X X X X X

evaluation of the safety benefits of existing safety functions - trace

Documents