Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles
Abstract Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-09-01
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| Series: | Chinese Journal of Mechanical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s10033-022-00790-5 |
| _version_ | 1818021621678473216 |
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| author | Xiaolin Tang Kai Yang Hong Wang Wenhao Yu Xin Yang Teng Liu Jun Li |
| author_facet | Xiaolin Tang Kai Yang Hong Wang Wenhao Yu Xin Yang Teng Liu Jun Li |
| author_sort | Xiaolin Tang |
| collection | DOAJ |
| description | Abstract Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover. First, a 4-degree of freedom vehicle dynamics model, and a rollover risk index are introduced. Besides, the uncertainty of surrounding vehicles' position is processed and propagated based on the Extended Kalman Filter method. Then, the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles. In addition, the model predictive controller is designed as the motion planning framework which accounts for the rollover risk, the position uncertainty of the surrounding vehicles, and vehicle dynamic constraints of autonomous vehicles. Furthermore, two edge cases, the cut-in scenario, and merging scenario are designed. Finally, the safety, effectiveness, and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench. |
| first_indexed | 2024-04-14T08:20:30Z |
| format | Article |
| id | doaj.art-ee3e85c69c82499abafb6a8d84cfc6a1 |
| institution | Directory Open Access Journal |
| issn | 1000-9345 2192-8258 |
| language | English |
| last_indexed | 2024-04-14T08:20:30Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Chinese Journal of Mechanical Engineering |
| spelling | doaj.art-ee3e85c69c82499abafb6a8d84cfc6a12022-12-22T02:04:14ZengSpringerOpenChinese Journal of Mechanical Engineering1000-93452192-82582022-09-0135111410.1186/s10033-022-00790-5Driving Environment Uncertainty-Aware Motion Planning for Autonomous VehiclesXiaolin Tang0Kai Yang1Hong Wang2Wenhao Yu3Xin Yang4Teng Liu5Jun Li6College of Mechanical and Vehicle Engineering, Chongqing UniversityCollege of Mechanical and Vehicle Engineering, Chongqing UniversityTsinghua Intelligent Vehicle Design and Safety Research Institute, Tsinghua UniversityTsinghua Intelligent Vehicle Design and Safety Research Institute, Tsinghua UniversityCollege of Mechanical and Vehicle Engineering, Chongqing UniversityCollege of Mechanical and Vehicle Engineering, Chongqing UniversityTsinghua Intelligent Vehicle Design and Safety Research Institute, Tsinghua UniversityAbstract Autonomous vehicles require safe motion planning in uncertain environments, which are largely caused by surrounding vehicles. In this paper, a driving environment uncertainty-aware motion planning framework is proposed to lower the risk of position uncertainty of surrounding vehicles with considering the risk of rollover. First, a 4-degree of freedom vehicle dynamics model, and a rollover risk index are introduced. Besides, the uncertainty of surrounding vehicles' position is processed and propagated based on the Extended Kalman Filter method. Then, the uncertainty potential field is established to handle the position uncertainty of autonomous vehicles. In addition, the model predictive controller is designed as the motion planning framework which accounts for the rollover risk, the position uncertainty of the surrounding vehicles, and vehicle dynamic constraints of autonomous vehicles. Furthermore, two edge cases, the cut-in scenario, and merging scenario are designed. Finally, the safety, effectiveness, and real-time performance of the proposed motion planning framework are demonstrated by employing a hardware-in-the-loop experiment bench.https://doi.org/10.1186/s10033-022-00790-5Position uncertaintyRollover preventionAutonomous vehiclesMotion planningModel predictive control |
| spellingShingle | Xiaolin Tang Kai Yang Hong Wang Wenhao Yu Xin Yang Teng Liu Jun Li Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles Chinese Journal of Mechanical Engineering Position uncertainty Rollover prevention Autonomous vehicles Motion planning Model predictive control |
| title | Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles |
| title_full | Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles |
| title_fullStr | Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles |
| title_full_unstemmed | Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles |
| title_short | Driving Environment Uncertainty-Aware Motion Planning for Autonomous Vehicles |
| title_sort | driving environment uncertainty aware motion planning for autonomous vehicles |
| topic | Position uncertainty Rollover prevention Autonomous vehicles Motion planning Model predictive control |
| url | https://doi.org/10.1186/s10033-022-00790-5 |
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