Trajectory Planning Method of Intelligent Vehicle Based on Sampling Area Optimization

doi:10.3901/JME.2022.14.276

Abstract

Abstract: In order to solve the problem of computational time waste of existing intelligent vehicle uniform sampling trajectory planning methods in structured road, medium and high speed scene, a trajectory planning method based on sampling area optimization was proposed. The road environment information and obstacle information are considered, the sampling area is divided into the base cost area and obstacle cost area, the cost value of the sampling point in the two regions is calculated and the sampling point according to the value is screened. The high cost points will be ignored, and the low cost points will be used to calculate the optimal trajectory, so as to avoid the calculation waste caused by the uniform sampling. In order to further reduce the planning time and improve the rationality of trajectory selection, the candidate trajectory is sorted according to the cost value, and then carried out collision detection on the trajectory in turn. The trajectory that did not meet the detection were removed and the first trajectory that passed the detection was selected as the optimal trajectory. To test the reliability of the algorithm, the simulation path is constructed and multiple scenes are designed to simulate the planning algorithm. The simulation results show that the proposed method can effectively reduce the single-step planning time and ensure the security, rationality and reliability of the optimal trajectory.

Key words: intelligent vehicle, trajectory planning, structured road, sampling area, multi-scene simulation

CLC Number:

U469

ZHANG Lipeng, SU Tai, YAN Yong. Trajectory Planning Method of Intelligent Vehicle Based on Sampling Area Optimization[J]. Journal of Mechanical Engineering, 2022, 58(14): 276-287.

References

[1] 李克强,戴一凡,李升波,等.智能网联汽车(ICV)技术的发展现状及趋势[J].汽车安全与节能学报, 2017, 8(1):1-14. LI Keqiang, DAI Yifan, LI Shengbo, et al. State-of-the-art and technical trends of intelligent and connected vehicles[J]. Journal of Automotive Safety and Energy, 2017, 8(1):1-14.
[2] 江浩斌,沈峥楠,马世典,等.基于信息融合的自动泊车系统车位智能识别[J].机械工程学报, 2017, 53(22):125-133. JIANG Haobin, SHEN Zhengnan, MA Shidian, et al. Intelligent identification of automatic parking system based on information fusion[J]. Journal of Mechanical Engineering, 2017, 53(22):125-133.
[3] DAVID G, JOSHUE P, VICENTE M, et al. A review of motion planning techniques for automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(4):1135-1145.
[4] 王艺,蔡英凤,陈龙,等.基于模型预测控制的智能网联汽车路径跟踪控制器设计[J].机械工程学报, 2019, 55(8):136-144, 153. WANG Yi, CAI Yingfeng, CHEN Long, et al. Design of intelligent and connected vehicle path tracking controller based on model predictive control[J]. Journal of Mechanical Engineering, 2019, 55(8):136-144, 153.
[5] LIN F, ZHANG Y, ZHAO Y, et al. Trajectory tracking of autonomous vehicle with the fusion of DYC and longitudinal-lateral control[J]. Chinese Journal of Mechanical Engineering, 2019, 32(1):212-227.
[6] 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 Hongbin, 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.
[7] MALONE N, CHIANG H T, LESSER K, et al. Hybrid dynamic moving obstacle avoidance using a stochastic reachable set-based potential field[J]. IEEE Transactions on Robotics, 2015, 33(5):1-15.
[8] DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische Mathematik, 1959, 1(1):269-271.
[9] HART P E, NILSSON N J, RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science&Cybernetics, 1972, 4(2):28-29.
[10] BOHREN J, FOOTE T, KELLER J, et al. Little Ben:The Ben Franklin racing Team's entry in the 2007 DARPA urban challenge[J]. Journal of Field Robotics, 2008, 25(9):598-614.
[11] 王红卫,马勇,谢勇,等.基于平滑A^*算法的移动机器人路径规划[J].同济大学学报, 2010, 38(11):1647-1650, 1655. WANG Hongwwei, MA Yong, XIE Yong, et al. Mobile robot optimal path planning based on smoothing A^* algorithm[J]. Journal of Tongji University, 2010, 38(11):1647-1650, 1655.
[12] DOLGOV D, THRUN S, MONTEMERLO M, et al. Path planning for autonomous vehicles in unknown semi-structured environments[J]. The International Journal of Robotics Research, 2010, 29(5):485-501.
[13] 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.
[14] LI X, SUN Z, CAO D, et al. Real-time trajectory planning for autonomous urban driving:Framework, algorithms, and verifications[J]. IEEE/ASME Transactions on Mechatronics, 2016, 21(2):740-753.
[15] 刘凯,龚建伟,陈舒平,等.高速无人驾驶车辆最优运动规划与控制的动力学建模分析[J].机械工程学报, 2018, 54(14):141-151. LIU Kai, GONG Jianwei, CHEN Shuping, et al. Dynamic modeling analysis of optimal motion planning and control for high-speed self-driving vehicles[J]. Journal of Mechanical Engineering, 2018, 54(14):141-151.
[16] YANG X, LI H. Model predictive motion planning for autonomous vehicle in mid-high overtaking scene[C]//IEEE 91st Vehicular Technology Conference, Antwerp, Belgium, 2020:1-5.
[17] MOHAMED E, MILAN S. Sampling-based robot motion planning:A review[J]. IEEE Access, 2014, 2(1):56-77.
[18] KARAMAN S, FRAZZOLI E. Sampling based algorithms for optimal motion planning[J]. The International Journal of Robotics Research, 2011, 30(7):846-894.
[19] 冯来春,梁华为,杜明博,等.基于A^*引导域的RRT智能车辆路径规划算法[J].计算机系统应用, 2017, 26(8):127-133. FENG Laichun, LIANG Huawei, DU Mingbo, et al. Guiding-area rrt path planning algorithm based on A^* for intelligent vehicle[J]. Computer Systems&Applications, 2017, 26(8):127-133.
[20] JEONG H, RAGHVENDRA V, STEVEN C, et al. Optimal motion planning with the half-car dynamical model for autonomous high-speed driving[C]//American Control Conference, Washington, DC, USA, 2013:188-193.
[21] 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, Anchorage, AK, USA, 2010:987-993.
[22] GU T, SNIDER J, DOLAN J, et al. Focused Trajectory Planning for autonomous on-road driving[C]//2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia, 2013:547-552.
[23] 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 Ronghui, YOU Feng, CHU Xinnan, 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.
[24] 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):1-14.
[25] WANG H, KEARNEY J, ATKINSON K. Arc-length parameterized spline curves for real-time simulation[C]//5th International Conference on Curves and Surfaces, 2002:387-396.
[26] ZIEGLER J, STILLER C. Fast collision checking for intelligent vehicle motion planning[C]//2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA, 2010:518-522.