Multi-Model-Based Local Path Planning Methodology for Autonomous Driving: An Integrated Framework

Autonomous driving systems (ADSs) need to be able to respond quickly to changes in the dynamic traffic scenario. However, regardless of the changes occurring in traffic scenes, the current local path planning frameworks of ADSs are based on the fixed frequency re-planning path (i.e., running their planning algorithms repeatedly). This planning method makes it difficult to provide a reasonable traveling path, agility, and comfort for driverless vehicles in changing traffic scenarios. Therefore, this article performs an in-depth analysis of the problems of traditional planning frameworks which use a fixed frequency to replan the path and proposes a novel path planning framework that is universal based on multiple-models. The proposed framework divides the planning process into several layers, each of which has different functions. With this framework, the ADS can adaptively adjust the planning process according to the changes in traffic scenes and then provide different path planning algorithms to ensure its safety and flexibility in the process of driving. Moreover, the problems caused by the traditional planning framework can be solved. This framework has been applied to the autonomous vehicle “Pioneer”, which won first place in the 2019 China Intelligent Vehicle Future Challenge (IVFC). The effectiveness and rationality of the integrated framework of local path planning proposed in this article were verified by a large number of tests in real-world traffic scenarios.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2020.3042603
• Record URL:
  https://ieeexplore.ieee.org/document/9296563
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Jian, Zhiqiang
  • Chen, Shukai
  • Zhang, Songyi
  • Chen, Yu
  • Zheng, Nanning
• Publication Date: 2022-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 4187-4200
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 5
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Acceleration (Mechanics); Autonomous vehicles; Crash avoidance systems; Mobile robots; Systems analysis; Trajectory control; Vehicle dynamics
• Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01850351
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 27 2022 5:19PM