Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field
In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial pot...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/4/1626 |
| _version_ | 1827344199886831616 |
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| author | Rui Mu Wenhao Yu Zhongxing Li Changjun Wang Guangming Zhao Wenhui Zhou Mingyue Ma |
| author_facet | Rui Mu Wenhao Yu Zhongxing Li Changjun Wang Guangming Zhao Wenhui Zhou Mingyue Ma |
| author_sort | Rui Mu |
| collection | DOAJ |
| description | In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections. |
| first_indexed | 2024-03-07T22:43:32Z |
| format | Article |
| id | doaj.art-68e05e9fa40b4f31899efda2f138f26b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-07T22:43:32Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-68e05e9fa40b4f31899efda2f138f26b2024-02-23T15:06:36ZengMDPI AGApplied Sciences2076-34172024-02-01144162610.3390/app14041626Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential FieldRui Mu0Wenhao Yu1Zhongxing Li2Changjun Wang3Guangming Zhao4Wenhui Zhou5Mingyue Ma6School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaSchool of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, ChinaResearch Institute for Road Safety of the Ministry of Public Security, Beijing 100062, ChinaResearch Institute for Road Safety of the Ministry of Public Security, Beijing 100062, ChinaResearch Institute for Road Safety of the Ministry of Public Security, Beijing 100062, ChinaResearch Institute for Road Safety of the Ministry of Public Security, Beijing 100062, ChinaIn unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.https://www.mdpi.com/2076-3417/14/4/1626autonomous vehiclesmotion planningtraffic lawviolation costartificial potential fieldmodel predictive control |
| spellingShingle | Rui Mu Wenhao Yu Zhongxing Li Changjun Wang Guangming Zhao Wenhui Zhou Mingyue Ma Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field Applied Sciences autonomous vehicles motion planning traffic law violation cost artificial potential field model predictive control |
| title | Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field |
| title_full | Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field |
| title_fullStr | Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field |
| title_full_unstemmed | Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field |
| title_short | Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field |
| title_sort | motion planning for autonomous vehicles in unanticipated obstacle scenarios at intersections based on artificial potential field |
| topic | autonomous vehicles motion planning traffic law violation cost artificial potential field model predictive control |
| url | https://www.mdpi.com/2076-3417/14/4/1626 |
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