论文标题
概率密度功能的预测和最佳反馈转向用于安全自动驾驶
Prediction and Optimal Feedback Steering of Probability Density Functions for Safe Automated Driving
论文作者
论文摘要
我们提出了一个随机预测控制框架，通过直接控制通过轨迹级别状态反馈进行车辆动力学的关节状态概率密度函数（PDF），以促进自动驾驶的安全性。为了说明主要思想，我们专注于多车道的高速公路驾驶场景，尽管拟议的框架可以适应其他环境。计算管道由PDF预测层组成，然后是PDF控制层。预测层对自我和非EGO车辆随机状态进行移动视野非参数预测，从而为自我提供了安全的目标PDF。后者基于预测的碰撞概率，并促进了自我的概率安全。 PDF控制层设计了一个反馈，该反馈最佳地引导关节状态PDF受控制的自我动态，同时满足端点PDF约束。我们对PDF预测层的计算利用受控的liouville PDE的结构发展了关节PDF值，而不是经验近似PDF。我们对PDF控制层的计算利用了车辆动力学中的差异平坦结构。我们利用最佳大众传输和施罗丁桥的最新理论和算法进步。数值模拟说明了提出的框架的功效。
We propose a stochastic prediction-control framework to promote safety in automated driving by directly controlling the joint state probability density functions (PDFs) subject to the vehicle dynamics via trajectory-level state feedback. To illustrate the main ideas, we focus on a multi-lane highway driving scenario although the proposed framework can be adapted to other contexts. The computational pipeline consists of a PDF prediction layer, followed by a PDF control layer. The prediction layer performs moving horizon nonparametric forecasts for the ego and the non-ego vehicles' stochastic states, and thereby derives safe target PDF for the ego. The latter is based on the forecasted collision probabilities, and promotes the probabilistic safety for the ego. The PDF control layer designs a feedback that optimally steers the joint state PDF subject to the controlled ego dynamics while satisfying the endpoint PDF constraints. Our computation for the PDF prediction layer leverages the structure of the controlled Liouville PDE to evolve the joint PDF values, as opposed to empirically approximating the PDFs. Our computation for the PDF control layer leverages the differential flatness structure in vehicle dynamics. We harness recent theoretical and algorithmic advances in optimal mass transport, and the Schrödinger bridge. The numerical simulations illustrate the efficacy of the proposed framework.
