Bayesian Artificial Intelligence-Based Driver for Fully Automated Vehicle with Cognitive Capabilities

At automation level 5 as defined by the Society of Automotive Engineers (SAE), a driver will not be in the loop even in a complex driving environment featuring among other challenges, the presence of vehicles with automation levels ranging from 1 (no automation) to 5 (fully automated). This paper defines the safety and ride quality requirements that a fully automated vehicle should meet when operating in a mixed traffic environment featuring vehicles of various automation levels and proposes a Bayesian AI-based driver algorithm as a solution. Design advances that can potentially overcome the safety and ride quality issues are described. Microscopic level data sourced from driving simulator studies are used in applications. Finally, conclusions are presented on the abilities of the Bayesian AI-based driver to meet safety and ride quality criteria while operating in driving environment characterized by uncertainties. The Bayesian AI-based driver is likely to enhance consumer and safety regulator acceptance.

• Record URL:
  https://doi.org/10.1007/978-3-030-20503-4
• Record URL:
  https://link.springer.com/chapter/10.1007/978-3-030-20503-4_6
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9783030205027
• Supplemental Notes:
  • © Springer Nature Switzerland AG 2020.
• Corporate Authors:

  Springer International Publishing

  ,    
• Authors:
  • Khan, Ata
• Conference:
  • International Conference on Human Factors in Transportation (AHFE 2019)
  • Location: Washington DC, United States
  • Date: 2019-7-24 to 2019-7-28
• Publication Date: 2019-6

Language

• English

Media Info

• Media Type: Web
• Edition: 1
• Features: References;
• Pagination: pp 57-66
• Monograph Title: Advances in Human Factors of Transportation: Proceedings of the AHFE 2019 International Conference on Human Factors in Transportation, July 24-28, 2019, Washington D.C., USA
• Serial:
  • Advances in Intelligent Systems and Computing
  • Volume: 964
  • Publisher: Springer International Publishing
  • ISSN: 2194-5357
  • Serial URL: http://link.springer.com/bookseries/11156

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Cognition; Level 5 driving automation; Ride quality; Traffic safety
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01880594
• Record Type: Publication
• ISBN: 9783030205027
• Files: TRIS
• Created Date: Apr 21 2023 9:51AM