Scalable Decision Making with Sensor Occlusions for Autonomous Driving

Abstract

Autonomous driving in urban areas requires avoiding other road users with only partial observability of the environment. Observations are only partial because obstacles can occlude the field of view of the sensors. The problem of robust and efficient navigation under uncertainty can be framed as a partially observable Markov decision process (POMDP). In order to bypass the computational cost of scaling the formulation to avoiding multiple road users, this paper demonstrates a decomposition method that leverages the optimal avoidance strategy for a single user. We evaluate the performance of two POMDP solution techniques augmented with the decomposition method for scenarios involving a pedestrian crosswalk and an intersection.