Learning to Drive Autonomously
The research team aims to develop innovative adaptive learning algorithms to tackle the combined longitudinal and lateral control of autonomous vehicles and its extension to optimal cooperative adaptive cruise control (CACC) of connected and autonomous vehicles. Learning-based suboptimal vehicle controllers will be designed, along with robustness analysis in the presence of human reaction time and exogenous disturbances.
Language
- English
Project
- Status: Completed
- Funding: $98608
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Contract Numbers:
USDOT 69A3551747124
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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 4201 Wilson Boulevard
Arlington, VA United States 22230 -
Managing Organizations:
Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
New York University
Tandon School of Engineering
Brooklyn, NY United States -
Performing Organizations:
Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
New York University
Tandon School of Engineering
Brooklyn, NY United States -
Principal Investigators:
Jiang, Zhong-Ping
- Start Date: 20200301
- Expected Completion Date: 20210228
- Actual Completion Date: 20210228
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Algorithms; Automated vehicle control; Autonomous intelligent cruise control; Autonomous vehicles; Connected vehicles; Drivers; Machine learning; Reaction time
- Subject Areas: Data and Information Technology; Design; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01737125
- Record Type: Research project
- Source Agency: Connected Cities for Smart Mobility towards Accessible and Resilient Transportation Center (C2SMART)
- Contract Numbers: USDOT 69A3551747124
- Files: UTC, RIP
- Created Date: Apr 23 2020 8:29AM