Traffic Mirror-Aware POMDP Behavior Planning for Autonomous Urban Driving

Driving autonomous vehicles safely through a complex urban environment remains a difficult task. The sensor limitations, as well as the various occlusions in the urban environment caused by static and dynamic objects, make the decision-making task even more complex. To improve the autonomous vehicle's ability to handle various occlusion driving scenarios, we propose a behavior planner with traffic mirror awareness based on the partially observable Markov decision process (POMDP). Our approach is based on the concept of phantom road users, which allows us to reason about the potentially occluded traffic participants and estimate the appearance probability in risky areas based on contextual information. A confidence modifier is introduced to either increase or decrease the appearance probability by utilizing the uncertain road users tracking results from available traffic mirror detections. Furthermore, we present an active traffic mirror perceiving method for encouraging the ego vehicle to explore the environment and plan driving policies that support perception. Finally, in the POMDP model, the detected real road users and inferred phantom traffic participants are represented in the state space. The driving policies are obtained by using the anytime Monte Carlo tree search (MCTS) algorithm to solve the POMDP model online. In various simulation scenarios with static and dynamic obstacles in an urban environment, the proposed approach is compared to the baseline approach. Our planner successfully uses the uncertain objects tracking information from traffic mirrors and provides safer and more efficient driving policies.