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High Resolution In Vehicle SensingNagui M. RouphailDirector, ITREProfessor of Civil EngineeringNC State University1DriveSense14October 30-31, 2014

Institute for Transportation Research and Education N.C. State Universityhttp://www.itre.ncsu.eduThe challenge21 billion highway vehicles

SAFETY1.2 million traffic fatalities per year

ENERGY30% of world Energy

EMISSIONS25% of world CO2 Emissions

TRAFFIC1.5 hours per day on a vehiclehttp://www.itre.ncsu.eduOutlineDescription of in-vehicle sensor

Data description and demonstration

Research questions and hypotheses

Planned capabilities (VIV)

3http://www.itre.ncsu.eduIn-Vehicle Sensor: BackgroundPartnership with TUL(Technical University of Lisbon) and ITds (software company in Lisbon)Funded collaboration through NSF international supplement for a just concluded NSF awardSensor developed in Portugal by ITds and TUL through an Innovation co-fund awardInitial prototype was to provide feedback to driver on fuel use and emissions via a secure websiteOngoing prototype testing through funding from the University of Maryland National UTC

4http://www.itre.ncsu.eduThe In-Vehicle Sensor5

i2D INTELLIGENCETO DRIVE http://www.itre.ncsu.edu5How it Works6

GPRS/GSMhttp://www.itre.ncsu.eduThe Data Levels (min 1Hz Resolution)71st LevelRaw DataAll 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 Datafuel 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 triptrip mapping and reconstruction, benchmarking, driving indicators, driving learning support, Driving Profiling, why and where are you spending fuel, http://www.itre.ncsu.eduRaw Data: OBD Speed vs. Acceleration8

http://www.itre.ncsu.eduRaw Data: GPS vs. OBD Speed9

http://www.itre.ncsu.eduRaw Data: Lateral Acceleration Distribution10

http://www.itre.ncsu.eduDatabase 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 formatHard to perform queries, changes, etc.NCSU broke down the table into several tables connected to each other in a SINGLE databaseMore efficient query and searchIs needed to perform faster visualization11http://www.itre.ncsu.eduOther DatabasesIndividual users and fleet managers access website https://app.i2d.co/Basic configuration of unit, vehicle, passwordUser 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.xvwShows the overall performance of drivers and vehicles anonymously

NCSU Website under construction http://www.redconverge.com/i2dBased on SQL databasePerforms faster search and visualization

12http://www.itre.ncsu.eduPrivate Driver Website View (1)13

Eventshttp://www.itre.ncsu.eduPrivate Driver Website View (2)14

Trip Summaries, benchmarking and fuel waste reportshttp://www.itre.ncsu.eduSAFETY ApplicationsQueue WarningEvent / Exception Trigger Based alert; each generating n customized messages that are automatically delivered by the system to identified vehiclesPreventing accidents and traffic jams15i2DDual CommunicationSystem (M2M) for VIVM2M communication establishes an IP connection2 independent, parallel, communication channels are created1st Level communicationChannel DATAM2MData may be associated withFleet Mangmt.Individual usageUBI (insurance)2nd Level communicationChannelDATAM2MData just for VIV purposes:SAFETY ApplicationsTRAFFIC Applicationsa Random ID is generated for each trip > No Privacy issuesPLANNED CAPABILITY December 2014VIV Vehicle to Infrastructure to VehiclePriorityReal Timehttp://www.itre.ncsu.eduResearch Questions / HypothesesGenerating driving profiles from Hi Res dataDeveloping 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 recordDistinguishing contributing factors to crashes (driver behavior, road/ traffic control effects)Testing impact of feedback on eco-driving perform.Long term driving trends vs. economic factors16http://www.itre.ncsu.eduResearch Questions / HypothesesReal time data quality checks and imputationsTesting 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 modelsFeasibility of PHYD or PAYD tolling schemesPrivacy issues

17http://www.itre.ncsu.eduQuestions

Thank you !18http://www.itre.ncsu.edu