LAPSE:2023.20978
Published Article
LAPSE:2023.20978
Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm
Pengwei Wang, Song Gao, Liang Li, Binbin Sun, Shuo Cheng
March 21, 2023
Abstract
Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field range of obstacles are rebuilt. Finally, based on the improved artificial potential field, a collision-free path for autonomous driving vehicles is generated. To verify the performance of the proposed algorithm, co-simulation and real vehicle tests are carried out. Results show that the generated path satisfies the constraints of roads, dynamics, and kinematics. The real time performance, effectiveness, and feasibility of the proposed path planning approach for obstacle avoidance scenarios are also verified.
Keywords
autonomous driving vehicle, improved artificial potential field, obstacle avoidance, path planning
Suggested Citation
Wang P, Gao S, Li L, Sun B, Cheng S. Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm. (2023). LAPSE:2023.20978
Author Affiliations
Wang P: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China [ORCID]
Gao S: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Li L: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Haidian District, Beijing 100084, China
Sun B: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Cheng S: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Haidian District, Beijing 100084, China [ORCID]
[Login] to see author email addresses.
Journal Name
Energies
Volume
12
Issue
12
Article Number
E2342
Year
2019
Publication Date
2019-06-19
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en12122342, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.20978
This Record
External Link

https://doi.org/10.3390/en12122342
Publisher Version
Download
Files
Mar 21, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
192
Version History
[v1] (Original Submission)
Mar 21, 2023
 
Verified by curator on
Mar 21, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.20978
 
Record Owner
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version