| 复杂动态环境下智能汽车局部路径规划与跟踪算法研究 |
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| 引用本文： | 张智能,李以农,余颖弘,张紫微,郑玲.复杂动态环境下智能汽车局部路径规划与跟踪算法研究[J].中国公路学报,2022,35(9):372-386. |
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| 作者姓名： | 张智能 李以农 余颖弘 张紫微 郑玲 |
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| 作者单位： | 1. 重庆大学 机械与运载工程学院, 重庆 400044;2. 上海汽车集团股份有限公司, 上海 201804 |
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| 基金项目： | 国家重点研发计划项目(2017YFB0102603-3);国家自然科学基金项目(51875061) |
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| 摘 要： | 路径规划及路径跟踪控制是智能汽车研究的关键技术，而复杂、时变的交通环境给智能汽车的路径规划与跟踪提出严苛要求。针对现有局部路径规划方法只适用于较为简单的工况，无法应对多车道、多静/动态障碍等复杂工况的问题，提出一种基于离散优化思想的动态路径规划算法。该算法利用样条曲线曲率变化均匀的特性，在s-ρ曲线坐标系中生成了一组参数化候选路径簇；考虑动态碰撞安全影响，在碰撞带约束下结合道路法规限制及车辆动态安全要求，规划车辆速度；此外，综合考虑静态安全性、舒适性、目标车道、道路占用率等影响因素，以选择最优路径。在路径跟踪层面，基于预瞄理论设计鲁棒性好、跟踪精度高的分数阶PID路径跟踪控制器，以跟踪误差最小为目标，采用粒子群优化算法对分数阶PID控制器参数进行整定。最后，基于Simulink/CarSim建立联合仿真平台，设计多车道，多静/动态障碍的复杂工况以验证该算法的有效性。研究结果表明：由于在评价函数中引入动态安全评价指标、目标车道评价指标以及道路占用率指标，极大地提升了规划器性能，使车辆在行驶过程中根据驾驶环境自主调整速度，降低换道次数，从而保证智能汽车的主动安全性能，提升了通行效率，使该算法能够较好地处理复杂动态环境下的避障问题。
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| 关 键 词： | 汽车工程 路径规划 粒子群优化 路径跟踪 智能汽车 分数阶PID |
| 收稿时间： | 2021-07-31 |
Study on Local Path Planning and Tracking Algorithm of Intelligent Vehicle in Complex Dynamic Environment |
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| ZHANG Zhi-neng,LI Yi-nong,YU Ying-hong,ZHANG Zi-wei,ZHENG Ling.Study on Local Path Planning and Tracking Algorithm of Intelligent Vehicle in Complex Dynamic Environment[J].China Journal of Highway and Transport,2022,35(9):372-386. |
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| Authors: | ZHANG Zhi-neng LI Yi-nong YU Ying-hong ZHANG Zi-wei ZHENG Ling |
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| Affiliation: | 1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China;2. SAIC Motor Co., Ltd., Shanghai 201804, China |
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| Abstract: | Path planning and path tracking control are key technologies in intelligent vehicle research. However, the complex and time-varying traffic environment puts forward strict requirements for path planning and tracking of intelligent vehicles. In view of the fact that the existing local path planning methods are only suitable for relatively simple working conditions and cannot cope with the problems of complex working conditions such as that of multi-lane and multi-static/dynamic obstacles, a dynamic path planning algorithm based on a discrete optimization method was proposed. Based on the continuity of the first and second derivatives and the uniform curvature variation of the spline curve, a set of parameterized spline candidate path clusters were generated in the s-ρ coordinate system. Considering the impact of dynamic collision safety, the velocity of the vehicle was planned under the constraint of the collision zone, combined with the speed limit of road regulations and the requirements of vehicle dynamic safe speed. In addition, considering factors such as static safety, comfort, target lane, and road occupancy, the optimal path was selected. In the path-tracking level, based on the preview theory, a fractional-order PID path-tracking controller with good robustness and high tracking accuracy was designed. With the minimum tracking error as the objective, the parameters of the fractional-order PID controller were adjusted by the particle swarm optimization algorithm. Finally, a joint simulation platform was established based on Simulink/CarSim to design multi-lane and multi-static/dynamic obstacles to verify the effectiveness of the algorithm. The results show that because of the introduction of the dynamic safety evaluation index, target lane evaluation index, and road occupancy index, the performance of the planner is significantly improved so that the vehicle can adjust the speed according to the driving environment during the driving process and can effectively avoid frequent lane-changing behavior. Therefore, the algorithm not only ensures the safety of riding but also improves the traffic efficiency to be able to deal with the obstacle avoidance problem in a complex dynamic environment. |
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| Keywords: | automotive engineering path planning particle swarm optimization path tracking intelligent vehicle fractional order PID control |
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