  Understanding the interaction between different road users is critical for
road safety and automated vehicles (AVs). Existing mathematical models on this
topic have been proposed based mostly on either cognitive or machine learning
(ML) approaches. However, current cognitive models are incapable of simulating
road user trajectories in general scenarios, and ML models lack a focus on the
mechanisms generating the behavior and take a high-level perspective which can
cause failures to capture important human-like behaviors. Here, we develop a
model of human pedestrian crossing decisions based on computational
rationality, an approach using deep reinforcement learning (RL) to learn
boundedly optimal behavior policies given human constraints, in our case a
model of the limited human visual system. We show that the proposed combined
cognitive-RL model captures human-like patterns of gap acceptance and crossing
initiation time. Interestingly, our model's decisions are sensitive to not only
the time gap, but also the speed of the approaching vehicle, something which
has been described as a "bias" in human gap acceptance behavior. However, our
results suggest that this is instead a rational adaption to human perceptual
limitations. Moreover, we demonstrate an approach to accounting for individual
differences in computational rationality models, by conditioning the RL policy
on the parameters of the human constraints. Our results demonstrate the
feasibility of generating more human-like road user behavior by combining RL
with cognitive models.

Machine Learning