  Motion planning under uncertainty is one of the main challenges in developing
autonomous driving vehicles. In this work, we focus on the uncertainty in
sensing and perception, resulted from a limited field of view, occlusions, and
sensing range. This problem is often tackled by considering hypothetical hidden
objects in occluded areas or beyond the sensing range to guarantee passive
safety. However, this may result in conservative planning and expensive
computation, particularly when numerous hypothetical objects need to be
considered. We propose a reinforcement learning (RL) based solution to manage
uncertainty by optimizing for the worst case outcome. This approach is in
contrast to traditional RL, where the agents try to maximize the average
expected reward. The proposed approach is built on top of the Distributional RL
with its policy optimization maximizing the stochastic outcomes' lower bound.
This modification can be applied to a range of RL algorithms. As a
proof-of-concept, the approach is applied to two different RL algorithms, Soft
Actor-Critic and DQN. The approach is evaluated against two challenging
scenarios of pedestrians crossing with occlusion and curved roads with a
limited field of view. The algorithm is trained and evaluated using the SUMO
traffic simulator. The proposed approach yields much better motion planning
behavior compared to conventional RL algorithms and behaves comparably to
humans driving style.
