A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

By Yanjun Huang, Haitao Ding, Yubiao Zhang, Hong Wang, Dongpu Cao, N. Xu, Chuan Hu. IEEE transactions on industrial electronics (1982. Print), 2020

317 citations recorded by the source corpus.

This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely, comfortably, economically, human-like, etc. In this paper, the APF method is used to assign different potential functions to different obstacles and road boundaries; while the drivable area is meshed and assigned resistance values in each edge based on the potential functions. A local current comparison method is employed to find a collision-free path. As opposed to a path, the vehicle motion or trajectory should be planned spatiotemporally. Therefore, the entire planning process is divided into two spaces, namely the virtual and actual. In the virtual space, the vehicle trajectory is predicted and executed step by step over a short horizon with the current vehicle speed. Then, the predicted trajectory is evaluated to decide if the speed should be kept or changed. Finally, it will be sent to the actual space, where an experimentally validated Carsim model controlled by a model predictive controller is used to track the planned trajectory. Several case studies are presented to demonstrate the effectiveness of the proposed framework.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/6b859e32e23dbb5ac866837557f34f5c9ddd3a20

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Siyu Teng
2. Peng Deng
3. Bai Li
4. Yunfeng Ai
5. Dongsheng Yang
6. Lingxi Li
7. Zhe Xuanyuan
8. Xuemin Hu

Citations in the corpus

• Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning (2020, background): “In contrast to artificial potential field based collision-free path planning methods [22]–[24], which require comprehensive knowledge about the environment (e.”
• Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives (2023, background): “a trajectory rather than a path [13], [14], and the trajectory”