Trajectory Planning for Autonomous Driving Featuring Time-Varying Road Curvature and Adhesion Constraints
文献类型：期刊论文
| 作者 | Gao, Yifan1; Li, Wei2; Hu, Yu2 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2024-06-25 |
| 页码 | 18 |
| 关键词 | Roads Trajectory Adhesives Planning Accidents Vehicle dynamics Trajectory planning Road curvature road adhesion local trajectory planning autonomous driving varying road conditions |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2024.3416289 |
| 英文摘要 | Among the various driving situations, there are challenging road conditions where both the texture and curvature are variables over time (e.g., mountainous area). However, it is found that the characteristics of road texture and curvature have been respectively considered in some of the existing studies to determine the vehicle speed for trajectory planning, but the complementary effect of these two factors is still yet to be incorporated. This could lead to unsafe vehicle behaviour. This limitation has led us to develop a trajectory planning method that gives a systematic consideration of road conditions and leverages the complementary effect of road curvature and adhesion on the vehicle speed. It prioritises the trajectory safety through a preview of road constraints (i.e., waypoints, curvature and adhesion) in a look-ahead distance and the real-time computation of the vehicle speed that satisfies the constraints. In the experiment, our method was compared with the state-of-the-art techniques in a simulated mountainous driving environment, namely Model Predictive Control (MPC), Deep Reinforcement Learning (DRL) and Hybrid A*. The environment was built with abundant variation in road curvature and adhesion. The results showed that our approach was able to generate safe and comfort trajectories in both sharp turn and ice-covered driving scenarios, in which the vehicle successfully passed through the whole length of the global path without producing large deviations and exceeding lane boundaries. Whereas, the MPC, DRL and Hybrid A* approaches resulted in the vehicle exceeding lanes at some point with completeness levels of 77.72%, 75.31% and 79.53%, respectively. |
| 资助项目 | Key Research Project of Zhejiang Lab[2022PC0AC01] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001258806800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39870]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Wei; Hu, Yu |
| 作者单位 | 1.Zhejiang Lab, Res Ctr Frontier Fundamental Studies, Hangzhou 311121, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Intelligent Comp Syst, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Yifan,Li, Wei,Hu, Yu. Trajectory Planning for Autonomous Driving Featuring Time-Varying Road Curvature and Adhesion Constraints[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2024:18. |
| APA | Gao, Yifan,Li, Wei,&Hu, Yu.(2024).Trajectory Planning for Autonomous Driving Featuring Time-Varying Road Curvature and Adhesion Constraints.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,18. |
| MLA | Gao, Yifan,et al."Trajectory Planning for Autonomous Driving Featuring Time-Varying Road Curvature and Adhesion Constraints".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024):18. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。