http://www.itre.ncsu.edu 1

Institute for Transportation Research and Education – N.C. State University

High Resolution In Vehicle Sensing

Nagui M. RouphailDirector, ITRE

Professor of Civil EngineeringNC State University

DriveSense14October 30-31, 2014

http://www.itre.ncsu.edu 2

The challenge1 billion highway vehicles

SAFETY 1.2 million traffic fatalities per year

ENERGY 30% of world Energy

EMISSIONS 25% of world CO2 Emissions

TRAFFIC 1.5 hours per day on a vehicle

 

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Outline

• Description of in-vehicle sensor

• Data description and demonstration

• Research questions and hypotheses

• Planned capabilities (VIV)

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In-Vehicle Sensor: Background• Partnership with TUL(Technical University of

Lisbon) and ITds (software company in Lisbon)• Funded collaboration through NSF international

supplement for a just concluded NSF award• Sensor developed in Portugal by ITds and TUL

through an Innovation co-fund award• Initial prototype was to provide feedback to driver

on fuel use and emissions via a secure website• Ongoing prototype testing through funding from

the University of Maryland National UTC

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The In-Vehicle Sensor

i2D INTELLIGENCE

TO DRIVE

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How it Works

GPRS/GSM

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The Data Levels (min 1Hz Resolution)

1st LevelRaw Data

• All available PIDs from OBD as: speed (odometer), rpm, engine temperature, accelerator position, error codes, VIN… (most of them on a 1 Hz basis)

• From additional sensors: location (GPS), 3 axis accelerometer (up to 50 Hz local), altitude (barometer) …

2nd LevelProcessed

Data

• fuel consumption (i2D algorithms), CO2 and other pollutant emissions, engine cold temperature points, slope, distances, driving periods, driving events (Stops, predefined alerts over speed, rpm, accelerations…), average speed, energy efficiency for each trip…

• trip mapping and reconstruction, benchmarking, driving indicators, driving learning support, Driving Profiling, why and where are you spending fuel, …

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Raw Data: OBD Speed vs. Acceleration

-25

-20

-15

-10

-5

0

5

10

15

0 10 20 30 40 50 60 70 80 90 100

Acc

eler

atio

n (M

PH

/S)

Speed (MPH)

OBD Speed vs. Acceleration on Freeway

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Raw Data: GPS vs. OBD Speed

0

10

20

30

40

50

60

0 10 20 30 40 50 60

GPS

Spee

d (M

PH)

OBD Speed (MPH)

OBD Speed vs GPS Speed on Arterial

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Raw Data: Lateral Acceleration Distribution

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Database at NC State • ~2 million records of data seconds are collected each

month, each having 40 data fields from about 10-15 vehicles driven by student/ staff volunteers – About ~3,500 miles of travel (low use)– Consumes ~200MB of memory (xlsx format)

• Available to NC State in a SINGLE table format– Hard to perform queries, changes, etc.

• NCSU broke down the table into several tables connected to each other in a SINGLE database– More efficient query and search– Is needed to perform faster visualization

http://www.itre.ncsu.edu 12

Other Databases• Individual users and fleet managers access website https://app.i2d.co/

– Basic configuration of unit, vehicle, password– User friendly reports, visualizations, etc

• Research Website http://research.i2d.co – Simple web access for researchers to download raw data

• i2D public website https://www.i2d.co/i2dpubportal/login.xvw– Shows the overall performance of drivers and vehicles anonymously

• NCSU Website under construction http://www.redconverge.com/i2d– Based on SQL database– Performs faster search and visualization

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Private Driver Website View (1)Events…

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Private Driver Website View (2)

Trip Summaries, benchmarking and fuel waste reports

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SAFETY ApplicationsQueue Warning

• “Event / Exception Trigger Based” alert; each generating “n” customized messages that are automatically delivered by the system to identified vehicles

• Preventing accidents and traffic jams

i2DDual CommunicationSystem (M2M) for VIV

M2M communication establishes an IP connection2 independent, parallel, communication channels are created

1st Level communicationChannel

DATAM2M

Data may be associated with Fleet Mangmt. Individual usage UBI (insurance)

2nd Level communicationChannel

DATAM2M

Data just for VIV purposes: SAFETY

Applications TRAFFIC

Applicationsa Random ID is generated for each trip –> No Privacy issues

PLANNED CAPABILITY –December 2014VIV – Vehicle to Infrastructure to Vehicle

PriorityReal Time

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Research Questions / Hypotheses

• Generating driving profiles from Hi Res data• Developing micro-scale vehicle interaction models

based on driver profiles (car-following, lane changing, gap acceptance)

• Testing hypotheses of micro-scale driver behavior vs. long term safety record

• Distinguishing contributing factors to crashes (driver behavior, road/ traffic control effects)

• Testing impact of feedback on eco-driving perform.• Long term driving trends vs. economic factors

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Research Questions / Hypotheses

• Real time data quality checks and imputations• Testing regional and national travel demand model

route choice assumptions (UE vs. SE vs. SO)• Testing assumptions about traffic signal timing • Testing the value of and compliance with travel

information to calibrate/ validate ATIS models• Feasibility of PHYD or PAYD tolling schemes• Privacy issues…

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Questions

Thank you !