The Optimal Maneuver Decision of Collision Avoidance for Autonomous Vehicle in Emergency Conditions

Emergency Braking Maneuver (EBM) and Emergency Lane-Changing Maneuver (ELCM) are the most common collision avoidance maneuvers when obstacles suddenly appear in front of the vehicle. In order to establish a more effective collision avoidance decision model, based on the analysis of the critical collision condition under emergency scenes, this paper proposes a decision algorithm for collision avoidance in view of the minimum Longitudinal Safety Distance (LSD). Firstly, the authors have determined the decision principle that the effect of collision avoidance is positively correlated with the minimum LSD. By comparing the minimum LSD of EBM and ELCM, the internal decision-making rules are designed for the Emergency Maneuver Decision System module of autonomous vehicles. Then by analyzing the critical safety state between autonomous vehicle and obstacle, the minimum LSD models of EBM and ELCM are established based on road adhesion coefficient, the types of obstacle, etc. Finally, taken the scene that stationary obstacle suddenly present in front of the autonomous vehicle as the case, the authors compare the minimum LSD of EBM and ELCM and analyze the effect of different factors, and then the dynamic simulation is performed to verify the feasibility and effectiveness of the above decision algorithm. The results show that the velocity, road adhesion coefficient and the types of obstacle have a great impact on the emergency decision-making, and the simulation results are consistent with the theoretical results, which show that the decision algorithm in this paper can be effectively applied to the autonomous vehicle system.

• Record URL:
  http://www.atsinternationaljournal.com/index.php/2020-issues/lii-november-2020/1163-the-optimal-maneuver-decision-of-collision-avoidance-for-autonomous-vehicle-in-emergency-conditions
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18245463
• Supplemental Notes:
  • © 2020, Aracne Editrice. All rights reserved.
• Authors:
  • Wang, S F
  • Chen, M X
  • Zhang, D W
  • Zhang, J Y
• Publication Date: 2020-11

Language

• English

Media Info

• Media Type: Print
• Features: References;
• Pagination: pp 55-70
• Serial:
  • Advances in Transportation Studies
  • Volume: 52
  • Publisher: University Roma Tre
  • ISSN: 1824-5463
  • Serial URL: http://www.atsinternationaljournal.com/

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Braking; Crash avoidance systems; Decision making; Emergencies; Lane changing; Proximity detectors
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01760005
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 19 2020 11:21AM