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 and Shuo Cheng
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Pengwei Wang: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Song Gao: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Liang Li: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Haidian District, Beijing 100084, China
Binbin Sun: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Shuo Cheng: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Haidian District, Beijing 100084, China

Energies, 2019, vol. 12, issue 12, 1-14

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; obstacle avoidance; path planning; improved artificial potential field (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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