Space-based Collision Avoidance Framework for Autonomous Vehicles

High confidence in the safe operation of autonomous systems remains a critical hurdle on their path to becoming ubiquitous. Recent accidents of Uber and Google driverless cars illustrate the difficulty ahead. Leading collision avoidance framework for autonomous systems fail to properly capture and account for the high variability of geometries, shapes, and sizes of the agents (e.g., 18 wheels truck vs. 4 doors sedan), capabilities that are critical in situations with high risk of accident (e.g., intersection crossing). The authors introduce a simple and efficient multi-agent collision avoidance framework for Autonomous Vehicles (AV) in various collision configurations (i.e., glancing, away, clipping). Machine learning techniques are proposed to properly train the autonomous systems involved. Vehicle-to-Vehicle (V2V) communication technologies and shape-based spatial-temporal collision avoidance algorithms are leveraged to ensure the accurate prediction of the collision and correct decision on the appropriate steps to avoid its occurrence. A prototype implementation and simulation is currently under development for a clipping collision problem at a lightless intersection crossing using the AirSim platform.

• Record URL:
  https://doi.org/10.1016/j.procs.2018.10.290
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S187705091831963X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2018 Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Yu, Jinke
  • Petnga, Leonard
• Conference:
  • Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning (CAS 2018)
  • Location: Chicago Illinois, United States
  • Date: 2018-11-5 to 2018-11-7
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 37-45
• Serial:
  • Procedia Computer Science
  • Volume: 140
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Crash avoidance systems; Highway safety; Highway traffic control; Intelligent vehicles; Vehicle to vehicle communications
• Subject Areas: Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01687084
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 27 2018 9:25AM