Identifying Deviations from Normal Driving Behavior
Advanced driver assistance systems (ADAS) have significantly improved safety on today’s roadways but their impact may be limited by driver errors. Understanding and identifying these driver errors will require the integration of multi-domain datasets through predictive modeling and data integration approaches. The goals of this project are to identify relevant datasets for ADAS error prediction, evaluate modeling approaches for predicting driver errors during ADAS use, and developing models to proactively predict driver errors. Results from the project will be used to guide data collection system design at automakers and develop predictive modeling benchmarks.
Language
- English
Project
- Status: Active
- Funding: $49493
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Contract Numbers:
69A3551747115
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
Safety through Disruption University Transportation Center (Safe-D)
Virginia Tech Transportation Institute
Blacksburg, VA United States 24060 -
Project Managers:
Glenn, Eric
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Performing Organizations:
Texas A&M Transportation Institute (TTI)
400 Harvey Mitchell Parkway South
Suite 300
College Station, TX United States 77845-4375 -
Principal Investigators:
Manser, Michael
- Start Date: 20201020
- Expected Completion Date: 20211015
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Behavior; Data collection; Data files; Driver performance; Driver support systems; Drivers; Driving; Errors; Predictive models
- Subject Areas: Data and Information Technology; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01769144
- Record Type: Research project
- Source Agency: Safety through Disruption University Transportation Center (Safe-D)
- Contract Numbers: 69A3551747115
- Files: UTC, RIP
- Created Date: Apr 6 2021 2:11PM