A Learning-Based Model Predictive Trajectory Planning Controller for Automated Driving in Unstructured Dynamic Environments

This paper presents a learning-based model predictive trajectory planning controller for automated driving in unstructured, dynamic environments with obstacle avoidance. The authors first address the problem of lacking prior knowledge in unstructured environments by introducing a risk map that maps the density and motion of obstacles and the road to an occupancy risk. Model predictive control is then used to integrate trajectory planning and tracking control into one framework to bridge the gap between planning and control. Meanwhile, the authors use Gaussian Process (GP) regression to learn the residual model uncertainty for improving the model accuracy. An objective function considering both risks within the feasible region and vehicle dynamics is carefully formulated to obtain collision-free and kinematically-feasible local trajectories. Field experiments are performed on real unstructured environments with the authors' automated vehicle. Experimental results demonstrate the effectiveness of the proposed algorithm for successful obstacle avoidance in various complex unstructured scenarios.

• Record URL:
  • https://doi.org/10.1109/TVT.2022.3159994
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00189545
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Li, Zhiyuan
  • Zhao, Pan
  • Jiang, Chunmao
  • Huang, Weixin
  • Liang, Huawei
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 5944-5959
• Serial:
  • IEEE Transactions on Vehicular Technology
  • Volume: 71
  • Issue Number: 6
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 0018-9545
  • Serial URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=25

Subject/Index Terms

• TRT Terms: Machine learning; Motor vehicle dynamics; Predictive models; Trajectory control
• Subject Areas: Data and Information Technology; Vehicles and Equipment;

Filing Info

• Accession Number: 01852440
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 21 2022 11:42AM