|Table of Contents|

Lane changing trajectory planning of intelligent vehicle based on multiple objective optimization(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年02期
Page:
232-242
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Lane changing trajectory planning of intelligent vehicle based on multiple objective optimization
Author(s):
ZHAO Shu-en;  WANG Jin-xiang;  LI Yu-ling
(School of Mechanotronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
Keywords:
intelligent vehicle;  lane changing;  path planning;  multi-objective optimization;  whale optimization algorithm
PACS:
U491.2
DOI:
10.19818/j.cnki.1671-1637.2021.02.020
Abstract:
To improve the anthropomorphism and real-time performance of lane changing trajectory planning for intelligent vehicles, a lane changing trajectory planning algorithm based on the multi-objective collaborative optimization of safety, comfort, and energy saving was proposed. The adaptation of proposed trajectory planning method depended on the constraints of key variables such as lane changing time, longitudinal and lateral velocities, and accelerations. Based on the theory of vehicle kinematics and dynamics, the safe area of vehicle lane changing in dynamic unknown environments was analyzed, and the ideal lane-changing trajectory model of a sixth-degree polynomial was established. A genetic algorithm-back propagation neural network was used to predict the end time and target position of lane changing, and lane changing trajectory clusters in complex scenes were obtained. The performance evaluation functions of safety, comfort, and economy of vehicle lane changing based on feasible solution space were analyzed, and the objective function and constraint conditions of multi-objective collaborative optimization were constructed. The whale optimization algorithm was used to optimize the lane changing trajectory clusters to achieve an optimal lane changing trajectory planning of intelligent vehicles with multi-performance objectives. To further verify the accuracy of the multi-objective optimization trajectory planning algorithm, an L3-level intelligent vehicle test platform was used to test the algorithm for intelligent vehicles in structured road scenes. Simulation and experimental results show that the proposed algorithm can successfully achieve smooth and safe lane changing under various constraints. Compared with traditionallane changing of driver, the safety, comfort, and multi-objective comprehensive performance of the method are improved by 5.1%, 3.3%, and 1.7%, respectively, which effectively improves the personification of intelligent vehicle lane-changing trajectory planning in dynamic environments. 2 tabs, 11 figs, 30 refs.

References:

[1] 李 立,徐志刚,赵祥模,等.智能网联汽车运动规划方法研究综述[J].中国公路学报,2019,32(6):20-33.
LI Li, XU Zhi-gang, ZHAO Xiang-mo, et al. Review of motion planning methods of intelligent connected vehicles[J]. China Journal of Highway and Transport, 2019, 32(6): 20-33.(in Chinese)
[2] YANG Da, ZHENG Shi-yu, WEN Cheng, et al. A dynamic lane-changing trajectory planning model for automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 228-247.
[3] 陆 建,李英帅.车辆换道行为建模的回顾与展望[J].交通运输系统工程与信息,2017,17(4):48-55.
LU Jian, LI Ying-shuai. Review and outlook of modeling of lane changing behavior[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(4): 48-55.(in Chinese)
[4] 赵 鑫,胡广地.平滑ARA*算法在智能车辆路径规划的应用[J].机械科学与技术,2017,36(8):1272-1275.
ZHAO Xin, HU Guang-di. Application of smoothing ARA* algorithm in intelligent vehicles path planning[J]. Mechanical Science and Technology for Aerospace Engineering, 2017, 36(8): 1272-1275.(in Chinese)
[5] KIM S, LIKHACHEV M. Path planning for a tethered robot using multi-heuristic A* with topology-based heuristics[C]∥IEEE. IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2015: 4656-4663.
[6] JANSON L, SCHMERLING E, CLARK A, et al. Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions[J]. International Journal of Robotics Research, 2015, 34(7): 883-921.
[7] 阮晓钢,周 静,张晶晶,等.基于子目标搜索的机器人目标导向RRT路径规划算法[J].控制与决策,2020,35(10):2543-2548.
RUAN Xiao-gang, ZHOU Jing, ZHANG Jing-jing, et al. Robot goal guide RRT path planning based on sub-target search[J]. Control and Decision, 2020, 35(10): 2543-2548.(in Chinese)
[8] LUO Y, XIANG Y, CAO K. A dynamic automated lane change maneuver based on vehicle-to-vehicle communication[J]. Transportation Research Part C: Emerging Technologies, 2016, 62: 87-102.
[9] 陈 成,何玉庆,卜春光,等.基于四阶贝塞尔曲线的无人车可行轨迹规划[J].自动化学报,2015,41(3):486-496.
CHEN Cheng, HE Yu-qing, BU Chun-guang, et al. Feasible trajectory generation for autonomous vehicles based on Quartic Bezier Curve[J]. Acta Automatica Sinica, 2015, 41(3): 486-496.(in Chinese)
[10] CAO Hao-tian, SONG Xiao-lin, HUANG Zheng-yu. Simulation research on emergency path planning of an active collision avoidance system combined with longitudinal control for an autonomous vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2016, 230(12): 1624-1653.
[11] LEFEVRE S, CARVALHO A, BORRELLI F, et al. A learning-based framework for velocity control in autonomous driving[J]. IEEE Transactions on Automation Science and Engineering, 2016, 13(1): 32-42.
[12] 安林芳,陈 涛,成艾国,等.基于人工势场算法的智能车辆路径规划仿真[J].汽车工程,2017,39(12):1451-1456.
AN Lin-fang, CHEN Tao, CHENG Ai-guo, et al. A simulation on the path planning of intelligent vehicles based on artificial potential field algorithm[J]. Automotive Engineering, 2017, 39(12): 1451-1456.(in Chinese)
[13] 王洪斌,郝 策,张 平,等.基于A*算法和人工势场法的移动机器人路径规划[J].中国机械工程,2019,30(20):2489-2496.
WANG Hong-bin, HAO Ce, ZHANG Ping, et al. Path planning of mobile robots based on A* algorithm and artificial potential field algorithm[J]. China Mechanical Engineering, 2019, 30(20): 2489-2496.(in Chinese)
[14] BOROUJENI Z, GOEHRING D, ULBRICH F, et al.
Flexible unit A-star trajectory planning for autonomous vehicles on structured road maps[C]∥IEEE. 2017 IEEE International Conference on Vehicular Electronics and Safety. New York: IEEE, 2017: 7-12.
[15] SAZGAR H, AZADI S, KAZEMI R. Trajectory planning
and combined control design for critical high-speed lane change manoeuvres[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019, 234(2/3): 823-839.
[16] 冀 杰,唐志荣,吴明阳,等.面向车道变换的路径规划及模型预测轨迹跟踪[J].中国公路学报,2018,31(4):172-179.
JI Jie, TANG Zhi-rong, WU Ming-yang, et al. Path planning and tracking for lane changing based on model predictive control[J]. China Journal of Highway and Transport, 2018, 31(4): 172-179.(in Chinese)
[17] RASEKHIPOUR Y, KHAJEPOUR A, CHEN S K, et al.
A potential field-based model predictive path-planning controller for autonomous road vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(5): 1255-1267.
[18] 郭应时,蒋拯民,白 艳,等.无人驾驶汽车路径跟踪控制方法拟人程度研究[J].中国公路学报,2018,31(8):189-196.
GUO Ying-shi, JIANG Zheng-min, BAI Yan, et al. Investigation of humanoid level of path tracking methods based on autonomous vehicles[J]. China Journal of Highway and Transport, 2018, 31(8): 189-196.(in Chinese)
[19] 付骁鑫,江永亨,黄德先,等.一种新的实时智能汽车轨迹规划方法[J].控制与决策,2015,30(10):1751-1758.
FU Xiao-xin, JIANG Yong-heng, HUANG De-xian, et al. A novel real-time trajectory planning algorithm for intelligent vehicles[J]. Control and Decision, 2015, 30(10): 1751-1758.(in Chinese)
[20] 张荣辉,游 峰,初鑫男,等.车-车协同下无人驾驶车辆的换道汇入控制方法[J].中国公路学报,2018,31(4):180-191.
ZHANG Rong-hui, YOU Feng, CHU Xin-nan, et al.Lane change merging control method for unmanned vehicle under V2V cooperative environment[J]. China Journal of Highway and Transport, 2018, 31(4): 180-191.(in Chinese)
[21] 孙 扬,熊光明,陈慧岩,等.基于混沌理论的无人驾驶车辆行驶轨迹量化分析[J].机械工程学报,2016,52(2):127-133.
SUN Yang, XIONG Guang-ming, CHEN Hui-yan, et al. Quantitative analysis of unmanned ground vehicles trajectories based on Chaos Theory[J]. Journal of Mechanical Engineering, 2016, 52(2): 127-133.(in Chinese)
[22] SHIM T, ADIREDDY G, YUAN Hong-liang. Autonomous vehicle collision avoidance system using path planning and model-predictive-control-based active front steering and wheel torque control[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2012, 226(6): 767-778.
[23] 杨 刚,张东好,李克强,等.基于车车通信的车辆并行协同自动换道控制[J].公路交通科技,2017,34(1):120-129,136.
YANG Gang, ZHANG Dong-hao, LI Ke-qiang, et al. Cooperative same-direction automated lane-changing based on vehicle to vehicle communication[J]. Journal of Highway and Transportation Research and Development, 2017, 34(1): 120-129, 136.(in Chinese)
[24] 赵树恩,冷 姚,邵毅明.车辆多目标自适应巡航显式模型预测控制[J].交通运输工程学报,2020,20(3):206-216.
ZHAO Shu-en, LENG Yao, SHAO Yi-ming. Explicit model predictive control of multi-objective adaptive cruise of vehicle[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 206-216.(in Chinese)
[25] 张颖达,邵春福,李慧轩,等.基于NGSIM轨迹数据的换道行为微观特性分析[J].交通信息与安全,2015,33(6):19-24,32.
ZHANG Ying-da, SHAO Chun-fu, LI Hui-xuan, et al. Microscopic characteristics of lane-change maneuvers based on NGSIM[J]. Journal of Transport Information and Safety, 2015, 33(6): 19-24, 32.(in Chinese)
[26] SHARMA R C, HARA K, HIRAYAMA H. A machine
learning and cross-validation approach for the discrimination of vegetation physiognomic types using satellite based multispectral and multitemporal data[J]. Scientifica, 2017, DOI: 10.1155/2017/9806479.
[27] SONG Xiao-lin, CAO Hao-tian, HUANG Jiang. Vehicle path planning in various driving situations based on the elastic band theory for highway collision avoidance[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2013, 227(12): 1706-1722.
[28] 隗海林,王劲松,王云鹏,等.基于城市道路工况的汽车燃油消耗模型[J].吉林大学学报(工学版),2009,39(5):1146-1150.
KUI Hai-lin, WANG Jin-song, WANG Yun-peng, et al. Vehicle fuel consumption model based on urban road operations[J]. Journal of Jilin University(Engineering and Technology Edition), 2009, 39(5): 1146-1150.(in Chinese)
[29] GENG Guo-qing, WU Zheng, JIANG Hao-bin, et al. Study on path planning method for imitating the lane changing operation of excellent drivers[J]. Applied Sciences, 2018, 8:1-19.
[30] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.

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Last Update: 2021-06-01