Safe-D: Safety through Disruption

Identifying Deviations from Normal Driving Behavior

 

Abstract

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.

Project Highlights

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Final Report

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EWD & T2 Products

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Presentations/Publications

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Research Investigators (PI*)

Michael Manser (TTI/TAMU)*
Tony McDonald (TTI/TAMU)*
Eva Shipp (TTI/TAMU)*
Hananeh Alambeigi (TTI/TAMU-Student)

Project Information

Start Date: 2020-10-20
End Date: 2021-10-15
Status: Active
Grant Number: 69A3551747115
Total Funding: $49,493
Source Organization: Safe-D National UTC
Project Number: TTI-Student-08

Safe-D Theme Areas

Big Data Analytics
Automated Vehicles

Safe-D Application Areas

Performance Measures
Vehicle Technology

More Information

RiP URL
UTC Project Information Form

Sponsor Organization

Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States

Performing Organization

Texas A&M University
Texas A&M Transportation Institute
3135 TAMU
College Station, Texas 77843-3135
USA

 

Image Collected from Freight Wave, & Prevost, C. (2018, May 7). [Semi-Trucks congested in a dense parking lot.]. What Is the Solution to Trucking’s Stubborn Parking Problem? https://www.freightwaves.com/news/driver-issues/what-is-the-solution-to-truckings-stubborn-parking-problem