Lane-changing Trajectory Planning for Autonomous Vehicles on Structured Roads

doi:10.3901/JME.2023.24.271

Abstract

Abstract: Automated lane changing plays a crucial role in the advancement of autonomous driving technology. A layered trajectory planning method is present that separates path planning and speed planning into independent processes. The path planning phase involves establishing potential fields for the road, static obstacles, and surrounding vehicles, followed by generating path clusters using the quintic polynomial method. The environmental potential field is determined to derive the optimal lane-changing path. The speed planning process simultaneously considers influencing factors such as lane change efficiency, ride comfort, safety, vehicle dynamics response, time window, and road constraints, and a convex optimization-based method is proposed. To evaluate the proposed scheme, a Prescan-Simulink co-simulation environment is established, and the trajectory planning algorithm is tested under diverse scenarios. The results demonstrate the efficient handling of complex constraints during the lane-changing process using the proposed method, while simultaneously ensuring safety, ride comfort, and lane change efficiency.

Key words: trajectory planning, polynomial curve, environment potential field, speed planning, convex optimization

CLC Number:

TP216

LIU Peng, JIA Hanbing, ZHANG Lei, WANG Zhenpo. Lane-changing Trajectory Planning for Autonomous Vehicles on Structured Roads[J]. Journal of Mechanical Engineering, 2023, 59(24): 271-281.

References

[1] WANG C,WANG Z,ZHANG L,et al. Post-impact motion planning and tracking control for autonomous vehicles[J]. Chinese Journal of Mechanical Engineering,2022,35(1):54.
[2] ZHANG Z,ZHANG L,WANG C,et al. Integrated decision making and motion control for autonomous emergency avoidance based on driving primitives transition[J]. IEEE Transactions on Vehicular Technology,2022,72(4):4207-4221.
[3] 刘梓林,黎予生,郑玲. 基于非结构化环境点云稀疏表示的无人驾驶汽车局部路径规划方法[J]. 机械工程学报,2020,56(2):163-173. LIU Zilin,LI Yusheng,ZHENG Ling. Local path planning for autonomous vehicles based on sparse representation of point cloud in unstructured environments[J]. Journal of Mechanical Engineering,2020,56(2):163-173.
[4] ZENG D,YU Z P,XIONG L,et al. A novel robust lane change trajectory planning method for autonomous vehicle[C]//2019 IEEE Intelligent Vehicles Symposium (IV),IEEE,2019:486-493.
[5] 高振海,朱乃宣,高菲,等. 考虑驾驶员特性的自学习换道轨迹规划系统[J]. 汽车工程,2020,42(12):1710-1717. GAO Zhenhai,ZHU Naixuan,GAO Fei,et al. Self-learning lane-change trajectory planning system with driver characteristics[J]. Automotive Engineering,2020,42(12):1710-1717.
[6] XIE Dongfan,FANG Zhezhe,JIA Bin,et al. A data-driven lane-changing model based on deep learning[J]. Transportation Research Part C:Emerging Technologies,2019,106:41-60.
[7] AN H I,JUNG J I. Decision-making system for lane change using deep reinforcement learning in connected and automated driving[J]. Electronics,2019,8(5):543.
[8] HORST J,BARBERA A. Trajectory generation for an on-road autonomous vehicle[J]. Proc. Spie.,2006,6230:82.
[9] 张一鸣,周兵,吴晓建,等. 基于前车轨迹预测的高速智能车运动规划[J]. 汽车工程,2020,42(5):574-580,587. ZHANG Yiming,ZHOU Bing,WU Xiaojian,et al. Motion planning of high speed intelligent vehicle based on front vehicle trajectory prediction[J]. Automotive Engineering,2020,42(5):574-580,587.
[10] KATRAKAZAS C,MOHAMMED Q,Chen W,et al. Real-time motion planning methods for autonomous on-road driving:State-of-the-art and future research directions[J]. Transportation research Part C:Emerging Technologies,2015,60:416-442.
[11] WEI C,LI S. Planning a continuous vehicle trajectory for an automated lane change maneuver by nonlinear programming considering car-following rule and curved roads[J]. Journal of Advanced Transportation,2020(4):1-17.
[12] DING Y,ZHUANG W,WANG L,et al. Safe and optimal lane-change path planning for automated driving[J]. Proceedings of the Institution of Mechanical Engineers Part D:Journal of Automobile Engineering,2020,235(4):1-14.
[13] ZHANG Z,ZHANG L,DENG J,et al. An enabling trajectory planning scheme for lane change collision avoidance on highways[J]. IEEE Transactions on Intelligent Vehicles,2023,8(1):147-158.
[14] 黄晶,蓟仲勋,彭晓燕,等. 考虑驾驶人风格的换道轨迹规划与控制[J]. 中国公路学报,2019,32(6):226-239. HUANG Jing,JI Zhongxun,PENG Xiaoyan,et al. Driving style adaptive lane-changing trajectory planning and control[J]. China J. Highw. Transp.,2019,32(6):226-239.
[15] ZHENG L,ZENG P,YANG W,et al. Bézier curve-based trajectory planning for autonomous vehicles with collision avoidance[J]. IET Intelligent Transport Systems,2020,14(13):1882-1891.
[16] WANG M,WANG Z,ZHANG L,et al. Speed planning for autonomous driving in dynamic urban driving scenarios[C/CD]//2020 IEEE Energy Conversion Congress and Exposition (ECCE),IEEE,2020.
[17] 滕儒民,李玉鑫,王欣,等. 基于凸优化的举高消防车时间最优轨迹规划[J]. 机械工程学报,2019,55(6):138-144. TENG Rumin,LI Yuxin,WANG Xin,et al. Time optimal trajectory planning of elevating fire truck based on convex optimization[J]. Journal of Mechanical Engineering,2019,55(6):138-144.
[18] BEVAN G,GOLLEE H,REILLY J. Trajectory generation for road vehicle obstacle avoidance using convex optimization[J]. Proceedings of the Institution of Mechanical Engineers Part D:Journal of Automobile Engineering,2010,224(4):454-473.
[19] 方晨曦,张辉,叶佩青,等. 数控速度规划中的时间最优凸优化方法[J]. 计算机集成制造系统,2015,21(1):187-194. FANG Chenxi,ZHANG Hui,YE Peiqing,et al. Convex optimization approach in time-optimal feed-rate planning for CNC[J]. Computer Integrated Manufacturing Systems,2015,21(1):187-194.
[20] 张玉. 自动驾驶车辆混合运动规划研究[D]. 北京:北京理工大学,2018. ZHANG Yu. Hybrid motion planning for autonomous vehicles[D]. Beijing:Beijing Institute of Technology,2018.