# Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception

arXiv: https://arxiv.org/abs/2402.04370 · HTML: https://mlsanity.com/paper/2402.04370/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 78 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [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)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [Explainable Object-induced Action Decision for Autonomous Vehicles](https://mlsanity.com/paper/2003.09405/) (2020)
- [Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions](https://mlsanity.com/paper/2208.01166/) (2022)
- [Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior](https://mlsanity.com/paper/2003.11959/) (2020)
- [Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie](https://mlsanity.com/paper/1903.09537/) (2019)
- [Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles](https://mlsanity.com/paper/2310.18891/) (2023)
- [Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?](https://mlsanity.com/paper/2206.11110/) (2022)
- [Modeling human road crossing decisions as reward maximization with visual perception limitations](https://mlsanity.com/paper/2301.11737/) (2023)
- [Models of human behavior for human-robot interaction and automated driving: How accurate do the models of human behavior need to be?](https://mlsanity.com/paper/2202.06123/) (2022)
- [Amortised Experimental Design and Parameter Estimation for User Models of Pointing](https://mlsanity.com/paper/2307.09878/) (2023)

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

- [Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation](https://mlsanity.com/paper/2601.02082/) (2026)
- [CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations](https://mlsanity.com/paper/2604.27354/) (2026)

## Abstract (arXiv)

This paper presents a model of pedestrian crossing decisions, based on the theory of computational rationality. It is assumed that crossing decisions are boundedly optimal, with bounds on optimality arising from human cognitive limitations. While previous models of pedestrian behaviour have been either 'black-box' machine learning models or mechanistic models with explicit assumptions about cognitive factors, we combine both approaches. Specifically, we model mechanistically noisy human visual perception and assumed rewards in crossing, but we use reinforcement learning to learn bounded optimal behaviour policy. The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle. Notably, our findings suggest that behaviours previously framed as 'biases' in decision-making, such as speed-dependent gap acceptance, might instead be a product of rational adaptation to the constraints of visual perception. Our approach also permits fitting the parameters of cognitive constraints and rewards per individual, to better account for individual differences. To conclude, by leveraging both RL and mechanistic modelling, our model offers novel insights about pedestrian behaviour, and may provide a useful foundation for more accurate and scalable pedestrian models.