Improving Bounds on Occluded Vehicle States for Use in Safe Motion Planning

Sensor occlusions pose a large risk to autonomous vehicles due to the unknown nature of what lies within. Previous research has introduced methods for predicting the future occupancy of hidden vehicles. Potential hidden vehicles are represented as continuous intervals on position, angle, and speed. These intervals are defined based on sensor occlusions, but over-represent true possible hidden vehicle state. The resulting occupancy predictions are restrictive to motion planning, and cause unwanted over-cautious behavior. Therefore, we introduce our method for reducing the bounds on the state interval used in prediction. We detail how tracking occlusion motion can be used to restrict the position interval, and observation over time can be used to find an upper limit on the exit velocity. The smaller initial intervals yield much smaller predicted occupancies, especially over long prediction horizons. Our method is evaluated in simulation alongside the previous method, using a basic provably-safe planner. Over three simulated scenarios, we show that our approach is able to navigate intersections up to 3 seconds faster, and avoid unnecessary braking maneuvers.