Promoting CAV Deployment by Enhancing the Perception Phase of the Autonomous Driving Using Explainable AI

The perception phase, the weak link in the driving task, has been identified as the key cause of most autonomous vehicle (AV) accidents. This has been attributed to the relative infancy of computer vision (CV), the key technology in perception. Deep learning (DL) approaches have been used widely in computer vision applications, from object detection to semantic understanding, but are generally considered as black boxes due to their lack of interpretability which exacerbates user distrust and hinders their deployment in autonomous driving. It has been argued that explainable AI (XAI), an emerging concept in contemporary computer science literature where model outputs can be understood by humans, offers an opportunity to address this issue. Thus, this research project is developing an explainable end-to-end autonomous driving system as an improvement to existing autonomous driving systems. To do this, the team is using a state-of-the-art self-attention-based model that generates driving actions with corresponding explanations using visual features from images from onboard cameras. The model will imitate human peripheral vision by performing soft attention over the images’ global features.


  • English


  • Status: Completed
  • Funding: $240000
  • Contract Numbers:


  • 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:

    Center for Connected and Automated Transportation

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

    Tucker-Thomas, Dawn

    Bezzina, Debra

  • Performing Organizations:

    Purdue University, Lyles School of Civil Engineering

    550 Stadium Mall Drive
    West Lafayette, IN  United States  47907
  • Principal Investigators:

    Chen, Sikai

    Labi, Samuel

  • Start Date: 20220401
  • Expected Completion Date: 20240229
  • Actual Completion Date: 20240207
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01846010
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RIP
  • Created Date: May 21 2022 7:51AM