Research on Active Collision Avoidance Control Algorithm for Blind Zone Based on Potential Energy Field

In order to solve the problem that a single longitudinal control cannot meet the active collision avoidance demand of intelligent vehicles, the authors firstly investigated the trajectory planning of intelligent driving. They established the risk potential energy field for blind zone collision avoidance, which is simple and easy to realize the path smoothing of the simultaneously generated paths. The authors then verified the linear time-varying model prediction control (LTV-MPC) and the general model predictive control (LTI-MPC), respectively, on the planned trajectory tracking effects produced. Finally, a finite state machine active collision avoidance model with lateral and longitudinal combination is designed. The simulation results show that the algorithms designed in this paper all have better collision avoidance comfort compared with the traditional algorithms.

• Record URL:
  • https://doi.org/10.1061/9780784485484.036
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784485484
• Supplemental Notes:
  • © 2024 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Wang, Qi
  • Luo, Jiezhong
  • Li, Heyan
  • Peng, Paoshan
  • Meng, Zhenji
• Conference:
  • 24th COTA International Conference of Transportation Professionals
  • Location: Shenzhen , China
  • Date: 2024-7-23 to 2024-7-26
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 370-380
• Monograph Title: CICTP 2024: Resilient, Intelligent, Connected, and Low-Carbon Multimodal Transportation

Subject/Index Terms

• TRT Terms: Advanced vehicle control systems; Blindness; Crash avoidance systems; Highway safety; Intelligent vehicles; Trajectory control
• Identifier Terms: Model Predictive Control
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01942222
• Record Type: Publication
• ISBN: 9780784485484
• Files: TRIS, ASCE
• Created Date: Jan 13 2025 10:24AM