随着汽车制造业蓬勃发展，汽车保有量急剧增加，导致道路交通安全问题日益严重。智能交通系统（Intelligent Transportation System，ITS）是未来交通的发展方向，有利于解决交通安全问题。智能车辆作为ITS的重要组成部分，有助于智能交通的发展，其实现依赖于众多关键技术，其中局部路径规划和路径跟踪控制是重点研究内容。本文设计基于改进RRT的DWA局部路径规划算法和基于状态扩展MPC与转角补偿的路径跟踪控制器，以解决车辆在避障场景下难以跟随全局路径和路径跟踪过程中实时性与跟踪精度难以同时保证的问题。

针对局部路径规划问题，设计RRT全局路径规划算法，在此基础上，通过设置路径中间采样点改进RRT的采样方式，利用三次非均匀B样条曲线对全局规划路径进行平滑处理；建立车辆三自由度运动学模型，设计基于DWA的局部路径规划算法；提取改进RRT所规划的全局路径关键点作为DWA算法的子目标点，并通过改进DWA算法的轨迹评价函数设计基于改进RRT的DWA局部路径规划算法；在四种不同障碍物场景下验证所设计局部路径规划算法的有效性。针对路径跟踪控制问题，建立采用魔术公式的轮胎动力学模型并分析轮胎特性，在此基础上，建立车辆横摆动力学模型，根据状态轨迹方法对模型进行线性化；将模型状态变量进行扩展和转换，设计状态扩展MPC控制器，通过设计目标函数和约束条件求解前轮转角以跟踪目标路径；建立车辆-路径跟踪误差模型，根据车辆横向和航向偏差设计转角补偿模糊控制器校正前轮转角；验证三种不同车速下路径跟踪控制器的实时性和跟踪精度。搭建MATLAB/CarSim联合仿真平台，在单、多障碍物避障场景下验证设计的规划和跟踪方法的有效性。

仿真结果表明：在路径规划的四种障碍物场景下，相比于基于A*的DWA算法，基于改进RRT的DWA局部路径规划算法的规划路径距离和规划时长均减小，其中在复杂障碍物场景和室内空间场景下规划路径距离分别减少7.04%和5.1%，规划时长分别减少16.6%和13.3%。在路径跟踪的标准双移线工况下，相比于传统MPC控制器，基于状态扩展MPC与转角补偿的路径跟踪控制器具有较好的跟踪性能，路径跟踪横向和航向偏差最大值分别降低23%和17%以上；状态扩展MPC控制器的控制增量求解时间平均值降低14%以上。在单、多障碍物避障仿真实验中，所设计局部路径规划算法的避障路径平稳光滑，路径跟踪控制器保持车辆行驶最大侧向偏差小于0.5m，最大侧向加速度在0.03g左右。

With the vigorous development of automobile manufacturing industry, car ownership has increased dramatically, resulting in increasingly serious road traffic safety problems. Intelligent Transportation System (ITS) is the development direction of future transportation, which is conducive to solving traffic safety problems. As an important part of ITS, intelligent vehicles contribute to the development of intelligent transportation, and its realization depends on many key technologies. Local path planning and path tracking control are the key research contents. In this paper, the DWA local path planning algorithm based on improved RRT and the path tracking controller based on state extended MPC and corner compensation are designed to solve the problem that the vehicle is difficult to follow the global path in the obstacle avoidance scene and the real-time and tracking accuracy are difficult to guarantee in the process of path tracking.

Aiming at the local path planning problem, the RRT global path planning algorithm is designed. On this basis, the sampling method of RRT is improved by setting the middle sampling points of the path, and the global planning path is smoothed and optimized by cubic non-uniform B-spline curve. The 3-DOF kinematics model of vehicle is established, and the local path planning algorithm based on DWA is designed. The global path key points planned by the improved RRT are extracted as the sub-goal points of the DWA algorithm, and the DWA local path planning algorithm based on the improved RRT is designed through the trajectory evaluation function of the improved DWA algorithm. The effectiveness of the designed local path planning algorithm is verified in four different obstacle scenarios. For the problem of path tracking control, the tire dynamics model using magic formula is established and the tire characteristics are analyzed. On this basis, the vehicle yaw dynamics model is established and linearized according to the state trajectory method. The model state variables are extended and converted, and the state extended MPC controller is designed. The front wheel angle is solved by designing the objective function and constraint conditions to track the target path. The vehicle-path tracking error model is established, and the angle compensation fuzzy controller is designed to correct the front wheel angle according to the vehicle lateral and heading deviation. Verify the real-time performance and tracking accuracy of the path tracking controller under three different speeds. The MATLAB/CarSim co-simulation platform is built to verify the effectiveness of the planning and tracking method in single and multiple obstacle avoidance scenarios.

The simulation results show that in the four obstacle scenarios of path planning, compared with the DWA algorithm based on A*, the planning path distance and planning time of DWA local path planning algorithm based on improved RRT are reduced. Among them, the planning path distance is reduced by 7.04% and 5.1% in complex obstacle scenarios and indoor space scenarios, and the planning time is reduced by 16.6% and 13.3%, respectively. Compared with the traditional MPC controller, the path tracking controller based on state-extended MPC and angle compensation has better tracking performance under the standard double lane change condition of path tracking, and the maximum lateral and heading deviation of path tracking are reduced by 23% and 17% respectively. The average control increment solving time of state extended MPC controller is reduced by more than 14%. In the single and multiple obstacle avoidance simulation experiments, the designed local path planning algorithm has smooth and smooth obstacle avoidance path. The path tracking controller maintains that the maximum lateral deviation of the vehicle is less than 0.5m and the maximum lateral acceleration is about 0.03g.

[1]中国汽车工业协会. 2021汽车工业运行情况[EB/OL]. http://www.caam.org.cn/, 2022- 01-12.

[2]欧阳明高. 我国节能与新能源汽车与新能源汽车发展战略与对策[J]. 汽车工程, 2006, 28(4): 317-318.

[3]国家统计局. 2021中国统计年鉴[M]. 北京: 中国统计出版社, 2021.

[4]杨明. 无人自动驾驶车辆研究综述与展望[J]. 哈尔滨工业大学学报, 2006, 38(8): 1259-1262.

[5]Welde M, Odeck J. Evaluating the Economic Impacts of Intelligent Transport Systems[C]. Proceedings of the 14th World Congress on Intelligent Transport System, 2007, 2(5): 20-28.

[6]张迎春, 黄志红. 智能交通系统(ITS)的发展[J]. 科技信息, 2009, 2(26): 485-486.

[7]National Intelligent Transportation Systems Program Plan. Intelligent Transportation Society of America, 1995.

[8]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.

[9]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.

[10]崔胜民.智能网联汽车新技术[M], 北京: 化学工业出版社, 2016.

[11]常昊. 无人驾驶汽车现状的分析和意见建议[J]. 内燃机与配件, 2018, 2(9): 2-4.

[12]Tsugawa S, Vatabe T, Hirose T, et al. An automobile with artificial intelligence[J]. In: Proceedings of the 6th International Joint Conference on Artificial Intelligence, 1979, 2(2): 1147-1149.

[13]Tsugawa S. Vision-based Vehicles in Japan: Machine Vision Systems and Driving Control System[J]. IEEE Trans. on Industrial Elcetronics, 1994, 41(4): 398-495.

[14]Thorpe C, Coulter R C, Hebert M, et al. Smart Cars: The CMU Navlab[J]. Proceedings of World Med, 1993.

[15]杨晨. 智能车控制系统的设计与路径跟踪算法的研究[D]. 北京: 北京工业大学, 2016.

[16]Broggi A, Buzzoni M, Felisa M, et al. Stereo obstacle detection in challenging environments: The VISC experience[J]. IEEE International Conference on Intelligent Robotics and System, 2011: 1599-1604.

[17]Buzzoni M, Broggi A, Cardarelli E, et al. VISC expedition toward autonomous mobility[J]. IEEE Robotics and Autonomous Magazine, 2011,18(3): 120-124.

[18]Volpe. R, Khosla. P. Artificial potrntials with elliptical isopotential contours for obstacle avoidance[J]. 26th IEEE Conference on Decision and Control, Los Angeles, USA, 1987: 180-185.

[19]Parent M. Intelligent transportation in cities with CTS[J]. In: Proceeding of the IEEE 5th International Conference on Intelligent Transportation Systems, Piscataway: IEEE Operations Center, 2002, 8(12): 826-830.

[20]Buzzoni M, Broggi A, Cardarelli E,et al. VIAC expedition toward autonomous mobility[J]. IEEE Robotics and Autonomous Magazine, 2011, 18(3): 120-124.

[21]Montemerlo M, Becker J, Bhat S, et al. Junior: The Stanford entry in the Urban Challenge[J]. Journal of Field Robotics, 2008, 25(9): 569-597.

[22]刘凯. 无人驾驶车辆体系结构与定位导航技术研究[D]. 北京: 北京理工大学, 2010.

[23]Thrun S, Montemerlo M, Dahlkamp H, et al. Stanley: the robot that won the darpa grand challenge[J]. Journal of Field Robotics, 2006, 23(2): 661-692.

[24]章江. Waymo开启无人驾驶商业化时代[J]. 轻型汽车技术, 2019, 3(3): 50-54.

[25]陈念航. 挑战特斯拉FSD, 百度Apollo推出领航辅助驾驶ANP[J]. 企业观察家, 2020, 6(12): 66-67.

[26]乔维高, 徐学进. 无人驾驶汽车的发展现状及方向[J]. 上海汽车, 2007,13(7): 40-43.

[27]赵耕云. 基于MC9S12XS128的智能车辆的关键技术研究[D]. 西安: 长安大学, 2013.

[28]Michailovich O, Rathi Y, Dolui S. Spatially Regularized Compressed Sensing for High Angular Resolution Diffusion Imaging[J]. IEEE Trans Med Imaging, 2011, 30(3): 1100-1115.

[29]王翔. 园区内自动驾驶车辆局部路径规划与控制算法研究[D]. 长春: 吉林大学, 2020.

[30]何佳, 戎辉, 王文扬, 等. 百度谷歌无人驾驶汽车发展综述[J]. 汽车电器, 2017, 3(12): 19-21.

[31]刘依卓. 无人车全局路径规划与局部避障方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2021.

[32]Glaser S, Vanholme B, Saïd Mammar, et al. Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 589-606.

[33]董桂丽. 人工势场法和蚁群算法在轨迹规划上的比较[J]. 信息记录材料, 2019, 20(12): 220-222.

[34]修彩靖, 陈慧. 基于改进人工势场法的无人驾驶车辆局部规划的研究. 汽车工程, 2013, 35(9): 808-811.

[35]郭枭鹏. 基于改进人工势场法的路径规划算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2017.

[36]Miller I, Campmell M, Huttenlocher D, et al. Team cornell’s skynet: robust perception and planning in an urban environment[J]. Journal of field robotics, 2008, 25(8): 493-527.

[37]姜岩, 龚建伟, 熊光明, 等. 基于运动微分约束的无人车辆纵横向协同规划算法的研究[J]. 自动化学报, 2013, 39(12): 2012-2020.

[38]盖荣丽, 王允森, 孙一兰, 等. 样条曲线插补方法综述[J]. 小型微型计算机系统, 2012, 33(12): 2744-2749.

[39]孙浩, 邓伟文, 张素民, 等. 考虑全局最优性的汽车微观动态轨迹规划[J]. 吉林大学学报(工学版), 2014, 6(4): 918-924.

[40]刘涛, 唐玲, 袁宝峰, 等. 基于能量约束的火星车路径规划研究[J]. 北京邮电大学学报, 2019, 42(5): 107-112.

[41]David Gonzàlez. Continuous curvature planning with obstacle avoidance capabilities in urban scenarios[C]. IEEE ITSS Society, 2014: 958-963.

[42]屈盼让, 李林, 任晓琨, 等. 基于B样条曲线的无人车路径规划算法[J]. 电脑知识与技术, 2016, 12(26): 235-237.

[43]周兵, 万希, 吴晓建, 等. 紧急避撞工况下的路径规划与跟踪[J]. 湖南大学学报(自然科学版), 2020, 47(10): 10-18.

[44]王永雄, 田永永, 李璇, 等. 穿越稠密障碍物的自适应动态窗口法[J]. 控制与决策, 2019, 34(5): 927-936.

[45]Zhu, Z, Xie J, Wang Z. Global Dynamic Path Planning Based on Fusion of A* Algorithm and Dynamic Window Approach[C]. 2019 Chinese Automation Congress(CAC), 2019: 5572-5576.

[46]王洪斌, 尹鹏衡, 郑维, 等. 基于改进的 A*算法与动态窗口法的移动机器人路径规划[J]. 机器人, 2020, 42(3): 346-353.

[47]程传奇, 郝向阳, 李建胜, 等. 融合改进A*算法和动态窗口法的全局动态路径规划[J]. 西安交通大学学报, 2017, 51(11): 137-143.

[48]严浙平, 黄俊儒, 吴迪. 基于RRT和DWA的欠驱动UUV路径规划[J]. 数字海洋与水下攻防, 2020, 3(3): 258-264.

[49]李宁. 面向家庭环境的移动机器人局部路径规划算法研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.

[50]明廷友. 智能汽车的轨迹跟随控制研究[D]. 长春: 吉林大学, 2016.

[51]杨慧杰. 智能车辆局部轨迹规划与路径跟踪控制研究[D]. 西安: 西安科技大学, 2021.

[52]Rui L, Duan J. A path tracking algorithm of intelligent vehicle by preview strategy[C]. Chinese Control Conference. China: IEEE Press, 2013: 5630-5635.

[53]Duan J, Yao J, Liu D. A path tracking control algorithm with speed adjustment for intelligent vehicle[C]. 2013 IEEE International Conference on Robotics and Biomimetics. USA: IEEE Press, 2013: 2397-2402.

[54]邱少林, 钱立军, 陆建辉. 基于最优预瞄的智能车变道控制[J]. 中国机械工程, 2019, 30(23): 2778-2783.

[55]蔡英凤, 李健, 孙晓强, 等. 智能汽车路径跟踪混合控制策略研究[J]. 中国机械工程, 2020, 31(3): 289-298.

[56]赵凯, 朱愿, 冯明月, 等. 基于多点序列预瞄的自动驾驶汽车路径跟踪算法研究[J]. 汽车技术, 2018, 15(11): 1-5.

[57]Liu K, Gong J W, Arda K, et al. Dynamic modeling and control of high-speed automated vehicles for lane change maneuver[J]. IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 329-339.

[58]Ji J, Khajepour A, Melek W W, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints[J]. IEEE Transactions on Vehicular Technology, 2017, 66(2): 952-964.

[59]Falcone P. Nonlinear model predictive control for autonomous vehicles[D]. Benevento: University of Sannio, 2007.

[60]潘世举, 李华, 苏致远, 等. 基于跟踪误差模型的智能车辆轨迹跟踪方法[J]. 汽车工程, 2019, 41(9): 1021-1027.

[61]毛丁丁, 邓亚东. 四轮转向智能车辆轨迹跟踪及稳定控制研究[J]. 机械科学与技术, 2020, 39(7): 1094-1099.

[62]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.

[63]张亮修, 张铁柱, 吴光强. 考虑误差校正的智能车辆路径跟踪鲁棒预测控制[J]. 西安交通大学学报, 2020, 54(3): 20-27.

[64]刘凯, 陈慧岩, 龚建伟, 等. 高速无人驾驶车辆的操控稳定性研究[J]. 汽车工程, 2019, 41(5): 514-521

[65]白国星, 刘丽, 孟宇, 等.基于非线性模型预测控制的移动机器人实时路径跟踪[J]. 农业机械学报, 2020, 51(9) : 47-60.

[66]杨阳阳, 何志刚, 汪若尘, 等.基于转角补偿的智能车辆循迹控制系统[J].仪表技术与传感器, 2019, 28(5): 73-77.

[67]喻凡, 林逸. 汽车系统动力学[M]. 北京：机械工业出版社, 2008.

[68]李松焱, 闵永军, 王良模, 等. 轮胎动力学模型的建立与仿真分析[J]. 南京工程学院学报: 自然科学版, 2009, 7(3): 34-38.

[69]王聪. 基于预瞄的车辆路径跟踪控制研究[D]. 哈尔滨: 哈尔滨工业大学, 2014.

[70]宋金泽. 自主泊车系统关键技术研究[D]. 长沙: 国防科学技术大学, 2009.

[71]杜明博, 梅涛, 陈佳佳, 等. 复杂环境下基于RRT的智能车辆运动规划算法[J]. 机器人, 2015, 37(4): 443-450.

[72]Fox D, Burgand W, Thrun S. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23-33.

[73]高帅. 基于ROS的未知环境移动机器人导航研究[D]. 沈阳: 东北大学, 2015.

[74]王建勋. 移动机器人视觉 SLAM 闭环检测与路径规划研究[D]. 武汉: 武汉科技大学, 2018.

[75]王艺. 基于模型预测控制的智能车辆横向运动控制研究[D]. 镇江: 江苏大学，2018.

[76]Van Zanten A T, Erhardt R, Landesfeind K, et al. VDC systems development and perspective[J]. SAE transactions, 1998, 107(6): 424-444.