# Modeling human road crossing decisions as reward maximization with visual perception limitations

arXiv: https://arxiv.org/abs/2301.11737 · HTML: https://mlsanity.com/paper/2301.11737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior](https://mlsanity.com/paper/2003.11959/) (2020)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)

## Built on by (most cited first, 2 of 2)

- [Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception](https://mlsanity.com/paper/2402.04370/) (2024)
- [Predicting Multitasking in Manual and Automated Driving with Optimal Supervisory Control](https://mlsanity.com/paper/2503.17993/) (2025)

## Abstract (arXiv)

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.