基于动态运动基元的车辆高速公路换道轨迹规划

doi:10.3901/JME.2024.10.192

摘要/Abstract

摘要： 为提高驾乘人员对自动驾驶车辆换道轨迹的接受度，解决轨迹规划中路径与速度解耦的问题，以高速公路自然驾驶数据集为依托，提出一种基于动态运动基元(Dynamic movement primitives，DMPs)的车辆高速公路换道轨迹规划方法。首先，利用极大似然估计对车辆高速公路历史换道轨迹信息进行分析与处理，得到能够表征自然驾驶员换道特性的换道示教轨迹；其次，采用DMPs算法对换道示教轨迹进行学习，完成对示教轨迹的复现并构建运动基元库；之后，基于运动基元库及换道需求，可泛化出多条适应高速公路驾驶场景的换道轨迹；最后，基于Prescan/Matlab/Carsim联合仿真和实车试验对规划轨迹的实时性和可跟踪性进行验证。结果表明，所提出的换道轨迹规划算法具有计算效率高、泛化能力强、驾乘人员接受度好的优点，并实现了换道路径与速度的协同规划。

关键词: 自动驾驶车辆, 轨迹规划, 动态运动基元, 示教轨迹

Abstract: To enhance drivers’ acceptance of autonomous vehicles while also addressing the decoupling path and velocity in trajectory planning, an expressway lane-changing trajectory planning method based on dynamic movement primitives(DMPs) is proposed utilizing natural expressway driving data. First, the expressway’s historical lane change trajectory is analyzed and processed using the maximum likelihood estimation to obtain the teaching lane change trajectory, which can characterize the natural driver’s lane change characteristics. Second, the DMPs algorithm is adopted to learn the teaching lane change trajectory, complete the trajectory replication, and construct a library of movement primitives. Then, based on the library of movement primitives and lane change requirements, multiple lane change trajectories can be generalized that can suit expressway driving scenarios. Finally, the planned trajectory’s real-time and traceability is verified based on the joint Prescan/Matlab/Carsim simulation and vehicle experiment. The results demonstrate that the proposed DMPs algorithm offers efficient computation, excellent generalization capability, and high acceptance by drivers. Moreover, it enables cooperative planning of lane change path and velocity.

Key words: autonomous vehicle, lane changing trajectory planning, dynamic movement primitives, teaching trajectory

中图分类号:

U461

梁凯冲, 赵治国, 颜丹姝, 赵坤. 基于动态运动基元的车辆高速公路换道轨迹规划[J]. 机械工程学报, 2024, 60(10): 192-206.

LIANG Kaichong, ZHAO Zhiguo, YAN Danshu, ZHAO Kun. Expressway Lane Changing Trajectory Planning Method of Autonomous Vehicle Based on Dynamic Movement Primitives[J]. Journal of Mechanical Engineering, 2024, 60(10): 192-206.

参考文献

[1] Bergman K，Ljungqvist O，Axehill D. Improved path planning by tightly combining lattice-based path planning and optimal control[J]. IEEE Transactions on Intelligent Vehicles，2020，6(1):57-66.
[2] Wang Y，Pan D，Deng H，et al. Dynamic trajectory planning of autonomous lane change at medium and low speeds based on elastic soft constraint of the safety domain[J]. Automotive Innovation，2020，3(1):73-87.
[3] 王国栋，刘立，孟宇，等. 一体式车辆避撞轨迹规划与跟踪控制[J]. 交通运输系统工程与信息，2022，22(2):127. Wang Guodong，Liu Li，Meng Yu，et al. Integrated control of trajectory planning and tracking for vehicle collision avoidance[J]. Journal of Transportation Systems Engineering and Information Technology，2022，22(2):127.
[4] 李荣粲，庄伟超，殷国栋，等.自动驾驶赛车路径与车速协同规划方法[J]. 机械工程学报，2022，58(10):200-208. Li Rongcan，Zhuang Weichao，Yin Guodong，et al. Simultaneous path and speed planning of driverless racing car[J]. Journal of Mechanical Engineering，2022，58(10):200-208.
[5] CLAUSSMANN L，REVILLOUD M，GRUYER D，et al. A review of motion planning for highway autonomous driving[J]. IEEE Transactions on Intelligent Transportation Systems，2019，21(5):1826-1848.
[6] Khatib O，Le Maitre J. Dynamic controlof manipulators operating in a complex environment[C]//On Theory and Practice of Robots and Manipulators，3rd CISM-IFToMM Symp，1978:1-16.
[7] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The International Journal of Robotics Research，1986，5(1):90-98.
[8] Wang P，Gao S，Li L，et al. Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm[J]. Energies，2019，12(12):1-14.
[9] Xie S，Hu J，Bhowmick P，et al. Distributed motion planning for safe autonomous vehicle overtaking via artificial potential field[J]. IEEE Transactions on Intelligent Transportation Systems，2022，23(11):21531-21547.
[10] Meng Y，Wu Y，Gu Q，et al. A decoupled trajectory planning framework based on the integration of lattice searching and convex optimization[J]. IEEE Access，2019，7:130530-130551.
[11] Sheng W，Li B，Zhong X. Autonomous parking trajectory planning with tiny passages:A combination of multistage hybrid A-star algorithm and numerical optimal control[J]. IEEE Access，2021，9:102801-102810.
[12] Tang G，Tang C，Claramunt C，et al. Geometric A-star algorithm:An improved A-star algorithm for AGV path planning in a port environment[J]. IEEE Access，2021，9:59196-59210.
[13] González D，Pérez J，Milanés V，et al. A review of motion planning techniques for automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems，2015，17(4):1135-1145.
[14] Feng Z，Song W，Fu M，et al. Decision-making and path planning for highway autonomous driving based on spatio-temporal lane-change gaps[J]. IEEE Systems Journal，2021，16(2):3249-3259.
[15] Liu S W，Xie J Y，Wang J Y. Design of collision avoidance method for vehicle emergency lane change based on Bezier curve[J]. Advances in Transportation Studies，2022，1(2):35-46.
[16] Suzuki T，Usami R，Maekawa T. Automatic two-lane path generation for autonomous vehicles using quartic B-spline curves[J]. IEEE Transactions on Intelligent Vehicles，2018，3(4):547-558.
[17] Levy R，Haddad J. Path and trajectory planning for autonomous behicles on roads without lanes[C]//2021 IEEE International Intelligent Transportation Systems Conference (ITSC). New York:IEEE，2021:3871-3876.
[18] 余嘉星，裴晓飞，过学迅. 考虑路径平滑性和避撞稳定性的智能汽车弯道轨迹规划研究[J]. 汽车工程，2022，44(5):656-663. YU Jiaxing，PEI Xiaofei，GUO Xuexun. Research on cornering trajectory planning for intelligent vehicle considering trajectory smoothness and stability for collision avoidance[J]. Automotive Engineering，2022，44(5):656-663.
[19] 张卫波，肖继亮. 改进RRT算法在复杂环境下智能车路径规划中的应用[J]. 中国公路学报，2021，34(3):225. Zhang Weibo，Xiao Jiliang. Application of improved RRT algorithm in intelligent vehicle path planning under complicated environment[J]. China Journal of Highway and Transport，2021，34(3):225.
[20] Werling M，Ziegler J，Kammel S，et al. Optimal trajectory generation for dynamic street scenarios in a frenet frame[C]//2010 IEEE International Conference on Robotics and Automation. New York:IEEE，2010:987-993.
[21] 胡杰，张敏超，徐文才，等. 自动驾驶车辆的平行泊车轨迹规划[J]. 汽车工程，2022，44(3):330-339. Hu jie，Zhang Minchao，Xu Wencai，et al. Parallel parking trajectory planning for autonomous vehicles[J]. Automotive Engineering，2022，44(3):330-339.
[22] Fan H，Zhu F，Liu C，et al. Baidu apollo em motion planner[J]. arXiv preprint arXiv:1807.08048，2018.
[23] Lim W，Lee S，Sunwoo M，et al. Hierarchical trajectory planning of an autonomous car based on the integration of a sampling and an optimization method[J]. IEEE Transactions on Intelligent Transportation Systems，2018，19(2):613-626.
[24] 王博洋，龚建伟，张瑞增，等. 基于真实驾驶数据的运动基元提取与再生成[J]. 机械工程学报，2020，56(16):155-165. Wang Boyang，Gong Jianwei，Zhang Ruizeng，et al. Motion primitives extraction and regeneration based on real driving data[J]. Journal of Mechanical Engineering，2020，56(16):155-165.
[25] Ijspeert A J，Nakanishi J，Schaal S. Movement imitation with nonlinear dynamical systems in humanoid robots[C]//Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292). New York:IEEE，2002，2:1398-1403.
[26] 王健发，王耀南，陈文锐，等. 基于改进动态运动基元的6D轨迹规划[J]. 控制理论与应用，2022，39(5):809-818. Wang Jianfa，Wang Yaonan，Chen Wenrui，et al. 6D trajectories planning based on improved dynamic movement[J]. Control Theory & Applications，2022，39(5):809-818.
[27] Kober J，Mülling K，Krömer O，et al. Movement templates for learning of hitting and batting[C]//2010 IEEE International Conference on Robotics and Automation. New York:IEEE，2010:853-858.
[28] Kulvicius T，Ning K J，Tamosiunaite M，et al. Joining movement sequences:Modified dynamic movement primitives for robotics applications exemplified on handwriting[J]. IEEE Transactions on Robotics，2011，28(1):145-157.
[29] Wang B，Gong J，Chen H. Motion primitives representation，extraction and connection for automated vehicle motion planning applications[J]. IEEE Transactions on Intelligent Transportation Systems，2019，21(9):3931-3945.
[30] 陆瑶敏，龚建伟，王博洋，等. 基于多重示范的智能车辆运动基元表征与序列生成[J]. 兵工学报，2021，42(4):851-861. LU Yaomin，Gong Jianwei，Wang Boyang，et al. Representation of motion primitives of intelligent vehicle based on multiple demonstrations and generation of rheir sequences[J]. Acta Armamentarii，2021，42(4):851-861.
[31] Krajewski R，Bock J，Kloeker L，et al. The highd dataset:A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems[C]//2018 21st International Conference on Intelligent Transportation Systems (ITSC). New York:IEEE，2018:2118-2125.
[32] Wu Z，Liang K，Liu D，et al. Driver lane change intention recognition based on attention enhanced residual-MBi-LSTM network[J]. IEEE Access，2022，10:58050-58061.
[33] Yao W，Zeng Q，Lin Y，et al. On-road vehicle trajectory collection and scene-based lane change analysis:Part II[J]. IEEE Transactions on Intelligent Transportation Systems，2016，18(1):206-220.
[34] 姚峰，刘崇德，王玉甲，等. 基于动态运动基元的轨迹学习方法[J]. 机器人，2018(4):560-568. Yao Feng，Liu Chongde，Wang Yujia，et al. Trajectory learning based on dynamic movement primitives[J]. Robot，2018，40(4):560-568.
[35] 赵治国，周良杰，朱强. 无人驾驶车辆路径跟踪控制预瞄距离自适应优化[J]. 机械工程学报，2018，54(24):166-173. Zhao Zhiguo，Zhou Liangjie，Zhu Qiang. Preview distance adaptive optimization for the path tracking control of unmanned vehicle[J]. Journal of Mechanical Engineering，2018，54(24):166-173.
[36] Yang H，Li H，Liu K，et al. Research on path planning based on improved RRT-Connect algorithm[C]//2021 33rd Chinese Control and Decision Conference (CCDC). New York:IEEE，2021:5707-5712.
[37] He X，Liu Y，LÜ C，et al. Emergency steering control of autonomous vehicle for collision avoidance and stabilisation[J]. Vehicle System Dynamics，2019，57(8):1163-1187.