Parking Planning with Genetic Algorithm for Multiple Autonomous Vehicles

The past decade has witnessed the rapid development of autonomous parking technology, since it has promising capacity to improve traffic efficiency and reduce the burden on drivers. However, it is prone to the trap of self-centeredness when each vehicle is automated parking in isolation. And it is easy to cause traffic congestion and even chaos when multiple autonomous vehicles require of parking into the same lot. In order to address the multiple vehicle parking problem, we propose a parking planning method with genetic algorithm. Firstly, an optimal mathematic model is established to describe the multiple autonomous vehicle parking problem. Secondly, a genetic algorithm is designed to solve the optimization problem. Thirdly, illustrative examples are developed to verify the parking planner. The performance of the present method indicates its competence in addressing parking multiple autonomous vehicles problem.

• Record URL:
  • https://doi.org/10.4271/2022-01-0087
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Luo, Chagen
  • Xu, Feng
  • Zeng, Dequan
  • Hu, Yiming
  • Deng, Zhenwen
  • Fu, Zhiqiang
  • Li, Zhuoren
  • Zhang, Peizhi
• Conference:
  • WCX SAE World Congress Experience
  • Location: Detroit & Online Michigan, United States
  • Date: 2022-4-5 to 2022-4-7
• Publication Date: 2022-3-29

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Drivers; Mathematical models; Optimization; Traffic congestion
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01841559
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2022-01-0087
• Files: TRIS, SAE
• Created Date: Apr 6 2022 2:18PM