自动驾驶车辆速度控制：安全通过遮挡人行横道
Autonomous Vehicle Speed Control for Safe Navigation of Occluded Pedestrian Crosswalk
AI总结：
本文提出基于部分可观测马尔可夫决策过程的速度控制方法，用于安全通过遮挡人行横道，通过动态规划计算控制策略以应对感知限制。
摘要：
人类和自动驾驶车辆传感器的感知能力有限。当这些限制与涉及易受伤害道路使用者的场景重合时，必须在运动规划器中考虑这些限制。在遮挡人行横道的场景中，接近车辆的速度应是道路上不确定性量的函数。在本工作中，纵向控制器被建模为部分可观测马尔可夫决策过程，并使用动态规划计算控制策略。该控制策略将速度剖面传递给模型预测转向控制器。
英文摘要：
Both humans and the sensors on an autonomous vehicle have limited sensing capabilities. When these limitations coincide with scenarios involving vulnerable road users, it becomes important to account for these limitations in the motion planner. For the scenario of an occluded pedestrian crosswalk, the speed of the approaching vehicle should be a function of the amount of uncertainty on the roadway. In this work, the longitudinal controller is formulated as a partially observable Markov decision process and dynamic programming is used to compute the control policy. The control policy scales the speed profile to be used by a model predictive steering controller.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
