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

Details

• Creators:
  Dong, Jiqian ; Chen, Sikai ; Labi, Samuel
• Corporate Creators:
  Center for Connected and Automated Transportation (CCAT) ; Purdue University
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Autonomous vehicles
  • Artificial intelligence
  • Image processing
  • Transformers
  • Automated vehicle control
  • Cameras
  • Computer vision
  • Connected vehicles
  • Image analysis
  • Explainable Artificial Intelligence (XAI)
  More +
• Publication/ Report Number:
  74
• DOI:
  http://dx.doi.org/10.5703/1288284317701
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report, Jan 2022-May 2023
• Corporate Publisher:
  University of Michigan. Center for Connected and Automated Transportation
• Abstract:
  User trust is pivotal to autonomous vehicle (AV) operations which are driven by artificial intelligence (AI). A promising way to build user trust is to use explainable artificial intelligence (XAI) which requires the AI system to provide the user with the underlying explanations for its decisions. Motivated by the need to enhance user trust and the promise of novel XAI technology in this context, this study strives to enhance trustworthiness in autonomous driving systems through the development of explainable Deep Learning (DL) models. The study casts the AV decision-making process not as a classification task (which is the traditional process) but rather as an image-based language generation (image captioning) task. As such, the proposed approach makes driving decisions by first generating textual descriptions of the driving scenarios which serve as explanations that humans can understand.
• Format:
  PDF
• Funding:
  69A3551747105
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/73330/dot_73330_DS1.pdf
• File Type:
  [PDF - 5.30 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; Autonomous Vehicles ; University Transportation Centers
• Main Document Checksum:
  urn:sha-512:9e277de3fa132c6beb6ff4c5199d85d8709e3a86d59410f7495fa0ed9e39d8aeea5d4e0617d1da5f2dff80ebb25f90967d75805e21d49fc3144ce1b7cd265259