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TheDetectionofVisualDistractionusingVehicleandDriver-BasedSensors

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DOI:10.4271/2016-01-0114

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AbstractDistracted driving remains a serious risk to motorists in the US and worldwide. Over 3,000 people were killed in 2013 in the US because of distracted driving; and over 420,000 people were injured. A system that can accurately detect distracted driving would potentially be able to alert drivers, bringing their attention back to the primary driving task and potentially saving lives. This paper documents an effort to develop an algorithm that can detect visual distraction using vehicle-based sensor signals such as steering wheel inputs and lane position. Additionally, the vehicle-based algorithm is compared with a version that includes driving-based signals in the form of head tracking data. The algorithms were developed using machine learning techniques and combine a Random Forest model for instantaneous detection with a Hidden Markov model for time series predictions. The AttenD distraction algorithm, based on eye gaze location, was utilized to generate the ground truth for the algorithm development. The data collection at the National Advanced Driving Simulator is summarized, results are presented, and the paper concludes with discussion on the algorithms. This work falls within a program of research on Driver Monitoring of Inattention and Impairment Using Vehicle Equipment (DrIIVE) and is sponsored by NHTSA.

IntroductionDriving impairment poses a serious risk to motorists in the US and worldwide, and distraction is a significant type of impairment. Over 3,000 people were killed in 2013 in the US because of distracted driving; and over 420,000 people were injured [1]. Lives could potentially be saved with an advanced safety system that accurately detects distracted driving and provides countermeasures. This paper

documents an effort to develop an algorithm that can detect visual distraction using vehicle-based sensor signals such as steering wheel inputs and lane position as well as driver-based head pose signals.

The remainder of the introduction reviews the literature on distraction, algorithms, secondary task difficulty, and previous NADS impairment research. The methodology section presents the details of a distraction study that was conducted on the NADS-1 simulator for the development of the algorithm. Methodology is followed by a section on the algorithm design and another on algorithm evaluation. The algorithm evaluation section presents the results of testing and comparing the algorithms. A summary and conclusions from the project end the paper.

Sensors and Feature GenerationIt is reasonable to argue that driver-based sensor signals, including physiological signals like gaze or EEG, offer a more direct measure of impairment generally and of distraction specifically. Substantial research has been done to define input variables for impairment detection algorithms based on eye movements, head position, and even facial expressions. Many algorithms have focused solely on eye tracking [2], and facial image analysis [3], whereas others combine multimodal features or include vehicle-based measures [4]-[6]. In a review of detection systems, Dong et al. [7] suggested that hybrid measures involving multiple modes perform best. Algorithms based on features derived from the driver’s face and eyes show great promise, but the cost, reliability and intrusiveness of camera-based systems undermines their feasibility and may delay their adoption.

The Detection of Visual Distraction using Vehicle and Driver-Based Sensors

2016-01-0114

Published 04/05/2016

Chris Schwarz and Timothy BrownNational Advanced Driving Simulator

John LeeUniversity of Wisconsin

John GasparNational Advanced Driving Simulator

Julie KangUS Dept. of Transportation

CITATION: Schwarz, C., Brown, T., Lee, J., Gaspar, J. et al., "The Detection of Visual Distraction using Vehicle and Driver-Based Sensors," SAE Technical Paper 2016-01-0114, 2016, doi:10.4271/2016-01-0114.

Copyright © 2016 SAE International

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Some measures such as electroencephalography (EEG), heart rate, and skin conductance might provide insight into driver state [8], [9], but they are uncomfortable, expensive and inappropriate for commercial applications. Although many systems have been proposed to analyze eye movements and detect eye position [10]-[12], few have been extensively tested in on-road driving environments. The differences between simulator and naturalistic settings are substantial, and often result in severe reductions in eye measure reliability [13], [14].

An alternative approach is to exploit sensors that already exist in current production vehicles and which generate signals that are already consumed by other vehicle systems. Examples of these vehicle-based signals include steering wheel angle, vehicle speed and acceleration, and pedal position. An intermediate option makes use of cameras or other advanced sensors to measure signals such as lane deviation and time to lane crossing. Ostlund et al. [15] examined many vehicle-based measures in relation to visual and cognitive distraction and recommended several for potential use in driving performance assessment applications. Greenberg et al. [16] found that distraction had effects on lane position, following distance and heading error. Kaber et al. [17] examined the effects of visual and cognitive distraction on steering smoothness and headway time and found that drivers increased their headway time when visually distracted. Liang and Lee have mixed driver-based and vehicle-based signals to train cognitive distraction algorithms [6], [18].

Defining Driver DistractionA clear definition of driver distraction is central to the process of distraction detection and mitigation. Distraction ground truth is important for training algorithms and requires the interpretation of distraction to obtain a gold standard set of data. Unfortunately, considerable uncertainty surrounds the definition of driver distraction. Studies have proposed a wide variety of definitions, centering on multiple aspects of the phenomenon. Regan, Lee and Young [19] attempted to unify these divergent definitions by defining driver distraction as, “the diversion of attention away from activities critical for safe driving toward a competing activity.” Implicit in this definition is the relationship between the attentional demands of the driving environment and the attention devoted to this environment by the driver. Distraction represents inadequate attention to the driving environment relative to the roadway demands, exceeding a driver’s attentional capacity. The interaction between roadway demand and task intensity has been considered in a limited fashion [20], [21].

Many previously developed algorithms consider distraction as a state that is independent of the environment [2], [4], [7], [22]. Thus, distraction is defined only by a state of mind. The consequences of distraction may be more severe when roadway demand is greater. However, just because roadway demand is low does not mean that distraction is tolerable. NHTSA distraction guidelines are designed to test driver-vehicle interfaces (DVI) in a low demand environment [23]. Task interactions that require too much attention in the low-demand environment constitute a sufficient condition for redesign.

Distraction can be linked to drivers’ glance patterns, and glances away from the road at inopportune times can increase crash risk. Data from the 100-Car Naturalistic Driving Study were analyzed during the period of 2001-2003. These data only include driver behavior

immediately before a safety-critical event that triggered the recording device in the car, such as a sudden deceleration, swerving, or a crash. It was observed that 93% of all lead-vehicle crashes occurred when the driver was inattentive, and four of the top five types of inattention were linked to glances away from the roadway [24]. Glance times exceeding more than two seconds away from the road were estimated to increase near-crash/crash risk by at least twice [25]. Recent analysis of the SHRP2 naturalistic data found that glances away from the road longer than two seconds significantly increased the odds of a crash or near crash [26]. Interestingly, a protective effect of talking on the phone prior to near crashes was also observed. These results motivate the consideration of a glance-based metric as ground truth for visual distraction

Algorithm DesignSimilar to the variety of distraction definitions and sensors, many different algorithms have been employed to detect impairment. These include traditional machine learning algorithms such as support vector machines (SVM) [6], [22], Neural Networks [27], graph based models such as Hidden Markov Models (HMM) [28], temporal graph based models [29], [30] and deep learning approaches [31]. All of these methods have been demonstrated with some degree of success and offer several promising directions for further development.

Ensemble techniques use combinations of algorithms to detect impairment. Random forests are a very successful example of ensemble techniques that combines the results of hundreds of simple decision trees to make a classification [32]. Combining data from multiple sensors is more effective than relying on a single sensor; and likewise, combining estimates from multiple algorithms can be more robust than relying on one.

The deep learning approach is particularly interesting because it redefines the relationship between feature identification and algorithm development. Rather than considering these as two separate steps, deep learning approaches build the feature engineering process into the model training process [31]. Deep learning goes beyond simply detecting a difference from baseline behavior, it develops a model that generates expected behavior and thus can detect impairment by a failure of drivers to produce the expected behavior [22]. In such a system, the focus becomes one of predicting drivers’ maneuvers and using divergence from the predicted outcome as indicators of impairment [20]. This approach increases computational difficulty and complexity, but it promises to enhance algorithm accuracy and robustness [33].

Secondary TasksThe secondary tasks selected for this study were representative of ones that drivers perform in the real world, increasing face validity and promoting well-learned interaction with the task. Secondary task difficulty has the potential to greatly impact driving performance; therefore, two levels of task difficulty were used in this study to vary the amount of distraction.

While Dingus et al. [24] and Klauer et al. [25] showed that tasks of different difficulties have varying safety impacts, other experimental research has explored the relationship between task difficulty and driving performance more directly. Lanseown, Brook-Carter and Kersloot [34] showed compensatory changes in speed and lateral

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vehicle control in the presence of secondary tasks as additional secondary tasks were added. Chisholm, Caird, Lockhard, Fern and Teteris [35] showed that task difficulty affected performance and although repeated exposure reduced dual-task costs for simple tasks, performance under more difficult tasks did not improve with practice.

Blanco, Biever, Gallagher and Dingus [36] manipulated the level of cognitive engagement by requiring interpretation and planning in responding to some questions (e.g. “Select the quickest route after being shown distance and speed limits for three alternative routes”). They also varied the information density of the displays by using tables, paragraphs, and graphics. The Human machine interface And the Safety of Traffic in Europe (HASTE) project tasks also varied in difficulty: Jamson and Merat [37] demonstrated a direct effect on driving performance measures by increasing the complexity of the arrows and auditory continuous memory task.

NADS Driving Impairment ResearchThe National Advanced Driving Simulator (NADS) has been involved in driving impairment research for many years and with many types of impairment. A standard database and set of scenarios were developed and have been used for all impairment studies conducted since around 2008. The scenario mimics a nighttime drive from an urban area to a rural setting via a freeway, and is described in more detail in the Methodology section.

The first study to make use of the standard scenario was an alcohol-intoxication study [38]. Several machine learning algorithms were explored to estimate intoxication from vehicle-based sensors. These included logistic regression, decision trees and SVMs. Drowsiness and distraction studies were conducted under the Driver Monitoring of Inattention and Impairment Using Vehicle Equipment (DrIIVE) program for NHTSA [39]-[42]. The distraction studies tested several vision-based distraction-detection algorithms and implemented one into the simulator environment. Mitigation results compared drivers’ acceptance of real-time and post-drive systems [43]. The drowsiness study tested drivers during daytime, early night and late night conditions and tested several types of algorithms including Bayesian Networks and Random Forests with different notions of drowsiness ground truth [42]. This project produced a Random Forest algorithm that was successful at estimating episodic drowsiness at least six seconds in advance of a drowsy-induced lane departure [44].

This paper fits into the second, final phase of DrIIVE, the overarching theme of which is to detect and differentiate multiple types of impairment using vehicle-based sensors. A second-phase DrIIVE project implemented a real-time drowsiness mitigation system on top of the Random Forest detection algorithm and tested the effectiveness of different alert types and modalities on driver performance [45]. The basic framework for the multiple impairment detection was conceived as a hierarchical combination of Random Forests and Hidden Markov Models [46].

MethodologyA driving simulator study was conducted to collect a large array of data from drivers in distracted and undistracted states. This section describes the methodology used in collecting the data, including the apparatus, secondary tasks and experimental design.

ApparatusThe National Advanced Driving Simulator (NADS) is located at The University of Iowa. The main simulator is called the NADS-1. It consists of a 24-foot dome in which an entire car cab is mounted. All participants drove the same vehicle-a 1996 Malibu sedan. The motion system, on which the dome sits, provides 400 square meters of horizontal and longitudinal travel and ±330 degrees of rotation. The driver feels acceleration, braking, and steering cues much as if he or she were actually driving a real vehicle. High frequency road vibration up to 40 Hz is reproduced from vibration actuators placed in each wheel well of the cab. A picture of the NADS-1 simulator and an image from the interior of the dome are shown in Figure 1.

The NADS-1 displays graphics by using sixteen high definition (1920×1200) LED projectors. These projectors provide a 360 degree horizontal 40 degree field of view. The visual system also features a custom-built Image Generator (IG) system that is capable of generating graphics for 20 channels (16 for the dome and 4 additional for task-specific displays), and which performs warping and blending of the image to remove seams between projector images and display scenery properly on the interior wall of the dome. The NADS produces a thorough record of vehicle state (e.g., lane position) and driver inputs (e.g., steering wheel position), sampled at 240 Hz.

The cab is equipped with a Face Lab™ 5.0 eye-tracking system that is mounted on the dash in front of the driver’s seat above the steering wheel. The worst-case head-pose accuracy is estimated to be about 5°. In the best case, where the head is motionless and both eyes were visible, a fixated gaze may be measured with an error of about 2°. The eye tracker samples at a rate of 60 Hz.

Figure 1. NADS-1 driving simulator (left) with a nighttime driving scene inside the dome (right).

Participants drove a set of nighttime scenarios that have been developed as standard environments for NADS driving impairment research. Each drive was composed of three driving segments. The drives started with an urban segment composed of a two-lane road through a city with posted speed limits of 25 to 45 mph with signal-controlled and uncontrolled intersections. An interstate segment followed that consisted of a four-lane divided expressway with a speed limit of 70 mph. After merging onto the interstate segment, drivers made lane changes to pass several slower-moving trucks. The drives concluded with a rural segment composed of a two-lane undivided road with curves and a section of gravel road. These three segments mimicked a drive home from an urban parking spot to a rural location via an interstate. Nineteen separate events (e.g. yellow light dilemma, left turn) combined to provide a representative trip home. Drivers encountered situations that might be encountered in a real drive. Each drive was approximately 25 minutes long.

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Secondary TasksTwo secondary tasks were designed to create varying levels of distraction for the driver. Both tasks were presented on the same display screen. The location of the display screen conformed to Alliance Guidelines (Driver Focus-Telematics Working Group, 2006) [47]. The display screen was on the center stack near the heating and air conditioning controls of the vehicle. The downward viewing angle was less than 30° and the lateral viewing angle was less than 40°. However, the location required a head turn by the driver to interact with the display. Each task was implemented at moderate and high levels of difficulty.

The purpose of the secondary tasks was to provide clearly marked driving segments with measureable task engagement. The tasks were self-paced; that is, the drivers were given the freedom to delay task engagement and to determine their own task completion pace. This allowed natural patterns of task chunking and interruptions to be observed in the data.

Visual Distraction TaskThis visual-only task required drivers to read text aloud from a display. For each task, an auditory prompt, “Read Message,” alerted the driver that the message was ready to be read. Each message was roughly the length of a SMS text message (not exceeding 160 characters). Messages contained “interesting facts” and were written for a Flesch-Kincaid grade level between 6.5 and 8. The participant read the message aloud. The task contained two levels of difficulty. The high level of difficulty was achieved by increasing the message length, removing grammar and line breaks from the text and causing phrases to run together. An example of the moderate and difficult text reading tasks is shown in Table 1.

Task engagement began when the participant spoke the first word of the message and ended with the last word of the message. Voice key software was used to detect speech from the driver, and a researcher also marked the beginning and end points of speech in the data stream. The researcher cleared the message from the screen when the participant completed reading the message.

Table 1. Difficult message task

Visual-Manual Distraction TaskThe visual-manual task required drivers to search through a list of songs and select a target song from the list. Each target song was presented to drivers only once and was not repeated. The moderate difficulty used a list of five songs on a single menu page. The high level of difficulty was achieved by using a longer list of 15 sings

spanning three menu pages, and possibly requiring one or more page scrolls to find the target title. An example of the moderate and difficult list-selection tasks is shown in Table 2

Table 2. Difficult list-selection task

Drivers received an audio prompt, “Find [song title],” instructing them to begin the task. The beginning of task engagement was recorded as the driver’s first touch to the display screen to initiate a scroll. Drivers responded by manually scrolling to the page with the target song and selecting it, marking the end of task engagement.

ExperimentA 3 (distraction levels) × 3 (order) × 2 (gender) mixed design exposed participants to three distraction levels in three different orders. Between-subjects independent variables were gender and order of the distraction drives. The within-subject independent variable was distraction level: no distraction, moderate distraction, and high distraction. The experimental matrix for the study is shown in Table 3

Table 3. Experimental Conditions

ParticipantsParticipants were recruited from the NADS participant database and through newspaper ads, internet postings, and referrals. An initial telephone interview determined eligibility for the study. Potential participants were screened for health history and current health status.

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Pregnancy, disease, sleep disorders, or evidence of substance abuse resulted in exclusion from the study. Potential participants taking prescription medications that cause or prevent drowsiness also were excluded from the study. If all telephone-screening criteria were met, individuals were scheduled for a screening visit. If all eligibility requirements were met during the screening visit, participants were scheduled for the data collection visit.

A single age group of 21-34 years was selected for this study. This group represented adult drivers who have the longest expected number of years remaining as drivers and who are more likely to multitask while driving.

ProceduresEach participant drove the simulator three times - once in the baseline distraction condition, once in a moderate distraction condition, and once in a high distraction condition. For each of the three scenarios, there were the same number of curves and turns, but their order varied. For example, the position of the left turn in the urban section varied so that it was located at a different position for each drive. Additionally, the order of left and right rural curves varied between drives. These examples of order variations mitigated the learning effect experienced on the second and third drives.

Additionally, the order of the drives for the different levels of distraction were counterbalanced using a Latin Square. Driving sessions in the simulator alternated between participants to reduce carryover effects from one distraction level to another as well as to reduce simulator-induced fatigue.

Algorithm DesignGeneral impairment-detection algorithms and warning systems that use low-cost vehicle-based sensor suites would be attractive as they could be adopted quickly and avoid drivers’ privacy objections. The challenge to such an approach lies in the creation of an algorithm that is effective at detecting the impairment. Specific challenges in algorithm design include choosing inputs (or features) that are sensitive to the impairment, selecting an appropriate ground truth signal, and choosing from among many machine learning models.

Input SignalsThe data from each drive were segmented into consecutive one-second windows. Raw simulator data were aggregated in each window by calculating an appropriate statistic on the segment, such as the mean. If a variable took on only integer values then the mode was used instead to ensure that an admissible value was obtained. While further aggregation into coarser segments was done for other impairment algorithms, the distraction data were left as one-second segments. This is an appropriate timescale on which to measure and classify the distraction impairment.

A large set of signals were collected from the simulator data to be used as inputs to the algorithm. For algorithm development, there isn’t really a downside to including many inputs, and it is possible to examine the relative importance of inputs in a trained Random Forest model. The signals that were used, as well as the statistics used to

aggregate the segments are listed in Table 4. The descriptive statistics that were used included the following: mean, standard deviation (sd), maximum (max), mode, and peak-to-peak (p2p).

Table 4. Algorithm input signals

Common driving maneuvers like turning and driving on curves complicate the use of steering wheel angle as an input to the algorithm. A trend in the steering signal may be caused by an artifact from the driving environment, such as the gradual appearance of steering adjustments (a signal bias) on a curve. These trends can be characterized by their lower frequency content. On one hand, the presence of steering trends might confound the training of an algorithm, causing it to detect biased variations in the trend rather than informative steering signal content. On the other hand, the process of removing trends from the signal could potentially strip vital information`, causing driver state classification to suffer.

Eskandarian et al. [48] subtracted the mean steering angle over the length of a curve. This approach is not appropriate for real-time implementation because it uses steering samples that would be collected in the future. Alternatively, road curvature from a GIS database, such as from a navigation system, might be used. However, drivers do not always follow the curvature of the road, especially when entering or exiting a curve, so GIS data also does not offer a perfect solution.

Brown’s Double Exponential Smoothing (DES) method [49] can remove low steering frequencies at the scale of geographic features. Moreover, its frequency cutoff can be easily adapted to different road curvatures with a single parameter. A scheme to adapt this parameter for gentle curves as well as tight turns was developed and tested on simulator data. The steering signal was first low-pass filtered to remove noise. This filtered signal, as well as steering wheel angle detrended using the DES filter, are shown in Figure 2. The steering signals for four types of curve in the figure illustrate the effect of the DES filter in removing steering bias, though the short duration and

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low radius of curvature in the left turn make that case particularly challenging. De-trended versions of steering and steering rate were computed and are denoted as str_des and strd_des in Table 4.

Figure 2. Original (blue) and de-trended (green) steering wheel angle for four roadway types: a left turn (upper left), curved roads in urban setting (upper right), curved roads on highway (lower left) and curved roads in rural setting (lower right).

The lanedevmod signal is based on the modified standard deviation of lateral position (MSDLP) measure evaluated in the AIDE program [15]. The signal is calculated from a 0.1 Hz high-pass filtered lane deviation. Table 4 specifies the use of mean and standard deviation of the one-second segments. The latter corresponds to MSDLP. While the statistics can be calculated on one-second segments, the high pass filter should be applied to windows of at least ten seconds.

Some environmental signals are included in the list. The curvature signal reports the radius of curvature of the roadway in that segment. The events signal reports the active event number in the segment. The q measure reports the roadway demand metric that combines several features of the roadway environment [50]. These signals are included as proxy measures for a number of environmental inputs that might realistically be included in a production system.

Driver-based sensor signals may provide the most direct measure of impairment generally and could be expected to benefit a distraction-detection algorithm. Visual distraction would especially benefit from sensors that monitor the drivers gaze direction; however, there are obstacles to the approach. One problem is that robust eye-tracking is difficult to achieve in all realistic lighting conditions, and drivers may not accept this type of monitoring technology. Another is that an eye-tracking algorithm was used to establish ground truth for distraction so an eye-based detection algorithm would have an unfair advantage.

A compromise is to consider head pose as a driver-based signal since it is easier to detect than gaze location, but does not duplicate the information gleaned from gaze location. Examples of head pose signals are shown in Figure 3. Notice the clear pattern of horizontal head

rotation that is associated with task engagement. The secondary tasks in this study were in one fixed location; but in reality, there are infinite ways for a driver to look away from the road leading to distraction.

Figure 3. Mean vertical and horizontal head pose signals with task engagement indicated by shaded regions.

Ground TruthAs discussed above, the definition of ground truth for distracted driving is a challenge. Does task engagement equal distraction? If not, when does distraction begin and what constitutes the threshold that separates distraction from alertness? NHTSA’s distraction guidelines penalize glances longer than two seconds, as well as total task time greater than 12 seconds [23]. Analysis of crashes and near crashes from the SHRP 2 dataset reveals the particularly risky nature of long glances [26].

A slightly modified version of the AttenD [51] algorithm was used to define ground truth for distraction. AttenD uses gaze location and requires an eye tracker to collect. It defines a 90 degree horizontal field of view in which gaze is interpreted to be on the road. The allowed vertical field of view is 22.5 degrees downward from center. Glances outside of the front field of view are tested to see whether they are aimed at any of the mirrors, which are also allowed. Failing to meet the mirror exception, AttenD accumulates eyes-off-road time and indicates distraction when a threshold is crossed. When the driver returns their gaze to the front, the output begins to drop, after a short delay, at the same rate until it reaches zero. Distraction is indicated when the output exceeds a nominal threshold of two seconds. Finally, the output is limited at a maximum value, set to 2.05 in this project. The higher this limit is set, the longer it takes for the output to fall below the distraction threshold; and the selected value implies that it takes at most 100 ms for the algorithm to output ‘undistracted’.

AttenD detects single long glances. Due to its accumulation mechanism, it also penalizes densely spaced glances separated by short glances to the front. The published algorithm initializes the output at two seconds and then drops it towards zero when distracted, while the modified version starts at zero and raises the output towards two. Figure 4 shows an example of the AttenD output, whose units are in seconds. The shaded regions indicate periods of task engagement, and distraction occurs when the AttenD value exceeds a threshold of two seconds. Note that it is possible to be engaged in a task and not distracted, as in the first part of the third task on the right side Figure 4. It is also possible to be distracted while not engaged in the tasks, as in the short periods on the left side of Figure 4. Ambient distractions may be caused by the driver looking around to acclimatize to the simulation, or for other unexplained reasons.

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Figure 4. The AttenD distraction metric defines the ground truth of distraction as values over 2.0, as indicated by the bold horizontal line

FrameworkSince data are all important in the machine learning paradigm, it is important to take proper care to use the available data wisely. A standard procedure is to use a piece of data to train an algorithm, and reserve another piece to test the performance of the algorithm fairly. A good training/testing regime prevents common pitfalls of the machine learning approach like overfitting to the training data. The method that was used is represented graphically in Figure 5. 75% of the data was used to train the model using 10-fold cross validation. Then the remaining 25% was used to run the model and test its performance on data it had not seen before.

Given that there were many ways to split the training and test sets, a conservative approach was used that created distinct sets of participants. Each participant was assigned either to the training set or the test set. The set allocation was random; however, an allocation could be rejected if it did not adequately preserve the ratio between impaired cases and normal cases. If rejected, the allocation was randomized again. This avoided the situation in which the training participants could have had many more (or fewer) cases of distraction, as a percentage of their total driving time, in relation to the participants in the test set. A less conservative approach could have involved splitting participant samples between the training and test set, but then the training phase might have had unfair knowledge of a participant’s performance who was also used in the test set.

Figure 5. Training and testing procedure using 10-fold cross validation and a reserved test set.

The distraction-detection algorithm uses a framework that has developed over the course of the DrIIVE and alcohol-intoxication projects, and after several exploratory efforts that considered various alternatives. MacDonald, et al. reported that a Random Forest model was able to use 54 second windows of the raw steering wheel angle signal to classify drowsiness six seconds in advance of drowsy lane departures. Recent DrIIVE work on drowsiness extended this model by feeding the output of the Random Forest model(s) into a Hidden Markov Model to take advantage of the time series estimation capabilities of the HMM. The algorithm framework adopted for the distraction-detection algorithm, as well as all the impairment algorithms developed in DrIIVE used the two stage combination of Random Forest and Hidden Markov Model.

The first stage in the algorithm framework used the Random Forest. A Random Forest that classifies time windows of input data as impaired or not impaired. This ensemble method works by training many individual decision trees, each with a different sample of data, different subsets of features, and different branching conditions. Each decision tree classifies the driver state and these predictions decide the forest’s classification according to a majority vote. The output of the Random Forest is the state predicted by the greatest number of decision trees. Figure 6 shows how predictions from many decision trees are combined through voting to indicate driver state. However, it is also useful to count up the number of votes for and against distraction and use that information rather than the resulting classification.

Figure 6. Random Forest Model

The second component of the algorithm was a Hidden Markov Model (HMM) that took its inputs from the Random Forest model(s) and performed time series estimation on the value of the driver state. The HMM assigns probabilities regarding whether the system remains in a given state or transitions to a new state at each moment in time. Moreover, it makes use of state history to estimate the current driver’s state. This history is particularly important in estimating driver state assuming that a driver who was recently distracted is likely to still be distracted. Figure 7 shows how the HMM combines a series of inputs (or observations) to better predict driver state in a way that takes into account past states.

Figure 7. Hidden Markov Model

The algorithms were implemented using the R statistics software [52]. The caret package was used to train and test the Random Forests [53]. The mhsmm package was used to build the Hidden Markov Models [54]. The mhsmm package provides two innovations over the traditional HMM as described above. First, it allows the creation of Hidden Semi-Markov Models in which the time step is treated as a variable. This capability was not used. Second, it allows the specification of observation data as a distribution rather than a simple table of observed frequencies. The vote count of the Random Forest over all one-segment segments provided such a distribution. The package has built-in support for Poisson and gamma distributions, but the lognormal distribution was found to provide a better fit. The necessary functions were added to allow integration of the lognormal distribution into the mshmm functions for training and evaluating HMMs.

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Algorithm EvaluationThe algorithms evaluated here consist of a single Random Forest that generates a number of votes for distraction. These votes are passed into a Hidden Markov Model which estimates the value of the distraction state and issues a posterior probability of that state being distracted. This section reports on the measures used to evaluate the algorithms and the results obtained.

Performance MeasuresThe measures of algorithm performance are taken from the theory of receiver operating characteristics (ROC) and the ROC curve, which in turn is built upon signal detection theory. Algorithm classifications either line up with ground truth or they do not. There are four possible options, as represented in the confusion matrix of Table 5.

Table 5. Confusion Matrix

The sensitivity index, also known as d’, measures the ability of an algorithm to produce true positive estimates. Denoting the true and false positives, and true and false negatives in Table 5 as TP, FP, TN and FN respectively, the sensitivity is expressed as

(1)

Its counterpart is specificity which measures the algorithm’s ability to estimate true negatives and is given by

(2)

Suppose that an algorithm is parameterized using a single parameter and let that parameter vary from one of its extremes to the other. Its sensitivity and specificity will naturally vary with the parameter. The result is a whole family of parameterized algorithms. An ROC curve is generated by plotting the sensitivity variable versus one minus the specificity. The resulting curve should have a concave shape for a performant algorithm. The entire area under the ROC curve (AUC) may be calculated, and will have a value of one for a perfect algorithm and 0.5 for an algorithm that performs no better than chance. An ROC curve is presented in the results in Figure 9. All measures in this section other than the AUC are obtained by selecting one value of the parameter, i.e. selecting an operating point on the ROC curve.

Other signal detection measures are Accuracy, Kappa and Positive Predictive Value. Accuracy is calculated as

(3)

and Positive Predictive Value (PPV) is calculated as

(4)

Kappa is a statistic that compares an observed accuracy with an expected accuracy. For example, accuracy may be observed at 75%, but that is less impressive if the expected accuracy is actually 80%. Kappa is a more reliable measure than accuracy when the positive and negative cases are very unbalanced. The expected accuracy depends on the expected values for TP and TN, given as

(5)

and

(6)

Then the expected accuracy may be written as

(7)

Observe how the expected accuracy mirrors accuracy in Equation (3). Finally, the kappa statistic is given by

(8)

ResultsA Random Forest was trained using several vehicle-based and environmental-based signals as inputs from the one-second data segments. Then the output of the Random Forest was used as an input to an HMM. Raw output signals from the algorithm for six participants are shown in Figure 8. The vote ratio and posterior probability range between 0 and 1, while the AttenD metric has units of seconds and a maximum value of 2. Shaded engagement periods have height scaled to 2.

The performance summary of just the Random Forest (RF) stage as well as the total algorithm (HMM) is given in Table 6. Each set of parentheses in Table 6, and throughout the section, presents the estimated value of the measure (center value) as well as the 95% confidence interval (left and right values). The confidence interval (CI) for the area under the ROC curve (AUC) was estimated using the Delong method in the pROC package [55] using the R statistics software. The confidence intervals for all other measures were estimated using the Wilson score interval [56], [57]. An alternate version of the distraction detection algorithm was trained and tested with driver-based signals for head pose added to the input data. The performance statistics are summarized in Table 7.

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Figure 8. Raw algorithm output showing the Random Forest vote ratio as well as the HMM posterior probability overlaid on a plot of the AttenD ground truth and task engagement. RF vote ratio is displayed as blue dots. HMM posterior probability is a red dotted line. AttenD output is a solid black line. Periods of task engagement are shaded regions.

Table 6. Distraction-detection algorithm statistics

The top five most important measures in the vehicle-based RF were the following: steer_sd, speed_mean, str_des_sd, strd_des_sd and lanedev_sd. The top five most important measures in the driver and vehicle-based RF were the following: headhoriz_sd, headhoriz_mean, headvert_mean, headvert_sd and headconf_mean. Significantly, the top five most important measures were all related to head pose.

Table 7. Distraction-detection algorithm statistics with head pose

Random Forest ROC curves were parameterized by the number of votes for distraction, while the ROC curves for the HMMs were parameterized by the posterior probability threshold for estimating distraction. The operating point for the Random Forest was obtained

by setting the vote threshold to 50% of the trees, while that of the HMM was obtained by setting the posterior probability threshold for distraction to 0.5. It is important to note that the HMM algorithm uses vote count distribution from the Random Forest (RF) as an input; therefore, it was not necessary to set an RF operating point. The ROC curve from the best-performing model is shown in Figure 9. It shows the results for the combination of vehicle-based and driver-based sensors summarized in the HMM column of Table 7.

Figure 9. ROC plot of best-performing model, an RF-HMM algorithm using both vehicle and driver-based input signals. The 95% CI of AUC is shown as the blue shaded region.

Summary/ConclusionsA distraction algorithm was successfully developed using the AttenD distraction metric as ground truth. This choice is appropriate for visual distraction and matches well with the current wisdom on the risk of glances away from the road, except that it does not consider the total engagement time that is part of NHTSA’s distraction guidelines. As total engagement time increases, a gradual loss of situational awareness would be expected, however this is more difficult to quantify. Rather, we have chosen a binary classification of distraction here instead of one with multiple levels of severity. This choice for ground truth would not work for cognitive distraction or mind wandering, which can actually cause an increased concentration of gaze at the forward roadway. A cognitive distraction algorithm could be developed using the appropriate ground truth metric.

The addition of a Hidden Markov Model to the algorithm resulted in modest improvements to its performance. A key benefit of the HMM is its ability to consider the time series evolution of the impairment, and it is this property that is responsible for the observed performance improvement. Additionally, the HMM provides a flexibility to the framework. It can accept inputs from multiple observation sources, for example several Random Forest models instead of just one. It also provides a way to create hierarchical models with multiple layers of HMMs. Related research in the DrIIVE project used two Random Forests with an HMM for a drowsiness-detection algorithm, and the HMM provided a more dramatic increase in performance [58].

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Why did sensitivity, kappa and PPV not perform as well as the other measures? This relates directly to the frequency of false positive cases. PPV is interpreted as the probability that a positive classification from the algorithm corresponds to a true positive case. It and specificity are both penalized by having large numbers of false positive cases. Similarly, false positives raise the expected accuracy which has the indirect effect of lowering kappa. It may be that much of this effect can be attributed to the strict adherence to distraction ground truth. In reality many instances of false positives and false negatives are edge cases that could go either way.

Observe the raw algorithm outputs in Figure 8 above. False positives are present in the third subplot at around 140 seconds as well as the sixth subplot at around 125 seconds. The first false positive occurs during a task engagement that was not labeled as truly distracted. This could be a failing of the eye tracker data, or of the choice of ground truth itself, as the driver could actually be distracted. The second false positive occurs shortly after a task engagement. It is reasonable that the driver needs some time after task engagement completes to regain stable vehicle control. However, the relatively noisy distribution of Random Forest votes for this driver seems to dispose the algorithm towards more frequent distraction classifications. This could be an example for which an individualized algorithm would take into account the driving style and adjust the detection setting accordingly.

Incorporating head-tracking hardware and software into a production vehicle may be feasible in the short term, as it requires less resolution and accuracy than eye tracking. Not surprisingly, the use of head pose data improved the performance of the distraction-detection algorithm. One fixed location was used for both tasks so a concern was that the algorithm learned a specific pattern of head movement. This distraction-detection algorithm was also applied to alcohol and drowsiness datasets that had no specific tasks, but had periods of miscellaneous distraction. It was observed there that the algorithm with head pose generalized to those datasets better than the one with only vehicle-based signals [59]. It may be that the vehicle-based algorithm trained too narrowly to the specific nature of the visual and visual-manual tasks described in this paper. For example, tasks that involve looking and reaching to the right may create a bias in lane deviation to the right. These types of associations were not explored.

Development of a commercial distraction-detection and mitigation system should consider a wider variety of visually distracting tasks. Moreover, an understanding of current roadway demand and its interaction with task complexity would allow designers to adapt the urgency and timing of a mitigation system to alert the driver sooner in more demanding situations. Alternatively, the presence of distraction and a high-demand environment could be used to alter the behavior of other advanced driving assistance systems such as forward collision warning (FCW) system to adjust its timing.

The increasing use of automation in vehicles provides opportunities and challenges for driver-state monitoring systems. Such systems will have to make use of driver-based sensor signals while automation is in control of vehicle handling and speed. On the other hand, vehicle-based or driver-based systems provide information during manual control that could recommend the automation should take over control to maintain safety. When the automation needs to shift control back to the driver, assessing driver state will be even more important

than it is with conventional vehicles to ensure that the driver is still engaged in monitoring the road situation. The design of these types of automation transfers is an active area of research.

The research conducted in the DrIIVE program as well as other impairment studies at the NADS represents an important step in establishing a database of impaired driving data based on a common set of scenarios. These datasets should ultimately facilitate additional efforts to understand the effects of driver impairment and aid in the development of methods to assess driver state and enhance safety. Other driving-safety researchers would benefit from the availability of carefully controlled simulator data that complements broad naturalistic datasets lacking experimentally controlled conditions.

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Contact InformationThe corresponding author may be contacted using the following information:

Chris SchwarzNational Advanced Driving Simulator2401 Oakdale BlvdIowa City, IA 52242chris-schwarz@uiowa.edu319-335-4642

AcknowledgmentsThis research reported here is part of a program of research sponsord by the National Highway Traffic Safety Administration (NHTSA) under the leadership of Julie Kang. The authors would like to acknowledge the help of Eric Nadler of Volpe and the research staff at the NADS for their diligent efforts.

Definitions/AbbreviationsAttenD - Gaze-based distraction detection algorithm used for ground truth in this project.

AUC - Area Under an ROC Curve.

CARET - Classification And REgression Training. An R package for creating predictive models.

DrIIVE - Driver Monitoring of Inattention and Impairment Using Vehicle Equipment

DES - Double Exponential Smoothing

DVI - Driver Vehicle Interface

EEG - Electroencephalography

FCW - Forward Collision Warning

HASTE - Human machine interface And the Safety of Traffic in Europe

HMM - Hidden Markov Model

GIS - Geographic Information System

IG - Image Generator

LED - Light Emitting Diode

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MHSMM - R package for inference of Hidden Markov and Semi-Markov models

MSDLP - Modified Standard Deviation of Lane Position

NADS - National Advanced Driving Simulator

NHTSA - National Highway Transportation Safety Administration

P2P - Peak to Peak

PPV - Positive Predictive Value

R - R open source statistical software

RF - Random Forest

ROC - Receiver Operator Characteristic

SD - Standard Deviation

SHRP2 - Strategic Highway Research Program 2

SMS - Short Message Service

SVM - Support Vector Machine

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