Autonomous Vehicle Speed Control for Safe Navigation of Occluded Pedestrian Crosswalk
This work addresses safety for autonomous vehicles and pedestrians in occluded crosswalk scenarios, but it is incremental as it builds on existing motion planning and control methods.
The paper tackled the problem of autonomous vehicle speed control at occluded pedestrian crosswalks by formulating a partially observable Markov decision process and using dynamic programming to compute a control policy that scales speed based on uncertainty, resulting in a method that adjusts vehicle speed to enhance safety in such scenarios.
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.