Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

Du, Guodong (ETH Zürich; Beijing Institute of Technology)
Zou, Yuan (Beijing Institute of Technology)
Zhang, Xudong (Beijing Institute of Technology)
Li, Z. (TU Delft Transport and Planning)
Liu, Qi (Beijing Institute of Technology)

2023

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.

Autonomous vehicle
Autonomous vehicles
global heuristic based potential field
Heuristic algorithms
motion planning
Planning
Prediction algorithms
Real-time systems
Reinforcement learning
reinforcement learning based predictive control
Tracking
tracking control

http://resolver.tudelft.nl/uuid:45b77942-97f1-4494-bd8f-d3df9cd9766b

https://doi.org/10.1109/TITS.2023.3266195

2023-10-17

1524-9050

IEEE Transactions on Intelligent Transportation Systems, 24 (8), 8304-8323

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Guodong Du, Yuan Zou, Xudong Zhang, Z. Li, Qi Liu