Path planning and path following are key technologies for autonomous vehicles. Strong nonlinearity, coupling characteristics, parametric uncertainties, external disturbances, and complicated driving scenarios put forward great challenges in estimation, control, and optimization of autonomous vehicles. Multi-dimension information utilization offers an effective solution to cope with the foregoing challenges. The presented research proposes a path planning and tracking framework for optimal design and regulation of control variables for collision avoidance and active guidance system, aiming to improve tracking performance and system robustness. For path planning approach, a virtual harmful potential field in 3 dimension (3D) is built to provide a collision-free trajectory as a reference path for path following, depending on vehicle kinematic model and boundary conditions of roads. A linear parameter-varying, polytopic vehicle lateral model is developed to address the issues of time-variable longitudinal speeds and preview distance on vehicle lateral stability management. Further, a strong robust gain-scheduling path following control approach based on linear matrix inequality methodology is proposed to tackle problems relating to characteristics of temporal variations, parametric uncertainties and external disturbances. Comparisons study under the TruckMaker/Xpack4-RapidECU joint HIL platform demonstrate that both tracking precision and high robustness to parametric uncertainties and external disturbances are superior than model predictive control method. The presented path following method provides an important insight into exploiting the robust control method's favorable merits while guaranteeing its computing efficiency.

中文翻译：

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时变参数下针对参数不确定性和外部干扰的自主公交车路径规划与跟随控制


路径规划和路径跟踪是自动驾驶汽车的关键技术。强非线性、耦合特性、参数不确定性、外部干扰以及复杂的驾驶场景对自动驾驶车辆的估计、控制和优化提出了巨大的挑战。多维信息利用为应对上述挑战提供了有效的解决方案。本研究提出了一种路径规划和跟踪框架，用于优化设计和调节防撞和主动制导系统的控制变量，旨在提高跟踪性能和系统鲁棒性。对于路径规划方法，根据车辆运动学模型和道路边界条件，构建 3 维（3D）虚拟有害势场，以提供无碰撞轨迹作为路径跟踪的参考路径。开发了线性参数变化的多面体车辆横向模型，以解决车辆横向稳定性管理中的时变纵向速度和预览距离问题。此外，提出了一种基于线性矩阵不等式方法的强鲁棒增益调度路径跟踪控制方法，以解决与时间变化、参数不确定性和外部干扰特性相关的问题。在TruckMaker/Xpack4-RapidECU联合HIL平台下的对比研究表明，该方法无论是跟踪精度还是对参数不确定性和外部干扰的鲁棒性都优于模型预测控制方法。所提出的路径跟踪方法为利用鲁棒控制方法的有利优点同时保证其计算效率提供了重要的见解。