Accelerated Training for Connected and Automated Vehicles Based on Adaptive Evaluation Method

This project focuses on resolving the inefficiency problem caused by the long-tail phenomena in the development of connected and automated vehicles (CAVs) to accelerate the training process. The training of CAV model can be divided into two stage: in the first stage, the model is trained with naturalistic driving data, while in the second stage, when the training efficiency is greatly compromised by the long-tail phenomena, a reinforcement learning-based mechanism with critical scenarios is proposed. The critical scenarios, which contain vulnerabilities of the CAV model, can be generated by the adaptive evaluation method. An incremental learning mechanism is designed and a discount factor will be introduced according to the probability of the critical scenarios. Importance sampling technologies will be applied to guarantee the accuracy of the discount factor. Meanwhile, a training and testing platform will be designed and built to validate the proposed accelerated training framework.

Language

• English

Project

• Status: Completed
• Funding: $149860
• Contract Numbers:

  69A3551747105

• Sponsor Organizations:

  Department of Transportation

  Intelligent Transportation Systems Joint Program Office
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Managing Organizations:

  Center for Connected and Automated Transportation

  University of Michigan Transportation Research Institute
  Ann Arbor, MI  United States  48109
• Project Managers:

  Tucker-Thomas, Dawn

• Performing Organizations:

  University of Michigan Transportation Research Institute

  2901 Baxter Road
  Ann Arbor, Michigan  United States  48109
• Principal Investigators:

  Liu, Henry

• Start Date: 20190401
• Expected Completion Date: 20240229
• Actual Completion Date: 20200228
• USDOT Program: University Transportation Centers Program
• Subprogram: Research

Subject/Index Terms

• TRT Terms: Adaptive control; Autonomous vehicles; Connected vehicles; Data analysis; Driving; Learning; Technology; Training; Vehicles
• Subject Areas: Passenger Transportation; Policy; Research; Transportation (General); Vehicles and Equipment;

Filing Info

• Accession Number: 01742584
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3551747105
• Files: UTC, RIP
• Created Date: Jun 18 2020 8:22AM