  Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial
components of safe autonomous vehicles (AVs). The nature of the
vehicle-pedestrian interaction where decisions of one agent directly affect the
other agent's optimal behavior, and vice versa, is a challenging yet often
neglected aspect of such systems. We address this issue by modeling a Markov
decision process (MDP) for a simulated AV-pedestrian interaction at an unmarked
crosswalk. The AV's PCAM decision policy is learned through deep reinforcement
learning (DRL). Since modeling pedestrians realistically is challenging, we
compare two levels of intelligent pedestrian behavior. While the baseline model
follows a predefined strategy, our advanced pedestrian model is defined as a
second DRL agent. This model captures continuous learning and the uncertainty
inherent in human behavior, making the AV-pedestrian interaction a deep
multi-agent reinforcement learning (DMARL) problem. We benchmark the developed
PCAM systems according to the collision rate and the resulting traffic flow
efficiency with a focus on the influence of observation uncertainty on the
decision-making of the agents. The results show that the AV is able to
completely mitigate collisions under the majority of the investigated
conditions and that the DRL pedestrian model learns an intelligent crossing
behavior.
