Modern automated driving solutions utilize trajectory planning and control components with numerous parameters that need to be tuned for different driving situations and vehicle types to achieve optimal performance. This paper proposes a method to automatically tune such parameters to resemble expert demonstrations. We utilize a cost function which captures deviations of the closed-loop operation of the controller from the recorded desired driving behavior. Parameter tuning is then accomplished by using local optimization techniques. Three optimization alternatives are compared in a case study, where a trajectory planner is tuned for lane following in a real-world driving scenario. The results suggest that the proposed approach improves manually tuned initial parameters significantly even with respect to noisy demonstration data.

翻译：现代自动驾驶解决方案采用轨迹规划与控制组件，这些组件包含大量参数，需要针对不同驾驶场景和车辆类型进行调优以实现最佳性能。本文提出一种自动调优此类参数以匹配专家示范的方法。我们构建了一个成本函数，用于捕捉控制器闭环运行与记录的理想驾驶行为之间的偏差。随后通过局部优化技术实现参数调优。在案例研究中比较了三种优化方案，其中针对现实驾驶场景中的车道保持任务对轨迹规划器进行了参数调优。结果表明，即使面对存在噪声的示范数据，所提方法仍能显著改进手动设置的初始参数。