# Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation

arXiv: https://arxiv.org/abs/2601.02082 · HTML: https://mlsanity.com/paper/2601.02082/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 8 of its 39 references that are in the map.
- First posted on arXiv 5 Jan 2026.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 8 of 8)

- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior](https://mlsanity.com/paper/2003.11959/) (2020)
- [Dynamic Game-Theoretical Decision-Making Framework for Vehicle-Pedestrian Interaction with Human Bounded Rationality](https://mlsanity.com/paper/2409.15629/) (2024)
- [Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning](https://mlsanity.com/paper/2102.06765/) (2021)
- [Realistic Extreme Behavior Generation for Improved AV Testing](https://mlsanity.com/paper/2409.10669/) (2024)
- [Modeling Pedestrian Crossing Behavior: A Reinforcement Learning Approach with Sensory Motor Constraints](https://mlsanity.com/paper/2409.14522/) (2024)
- [Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception](https://mlsanity.com/paper/2402.04370/) (2024)
- [Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2309.00249/) (2023)

## Abstract (arXiv)

Autonomous vehicles (AVs) are rapidly advancing and are expected to play a central role in future mobility. Ensuring their safe deployment requires reliable interaction with other road users, not least pedestrians. Direct testing on public roads is costly and unsafe for rare but critical interactions, making simulation a practical alternative. Within simulation-based testing, adversarial scenarios are widely used to probe safety limits, but many prioritise difficulty over realism, producing exaggerated behaviours which may result in AV controllers that are overly conservative. We propose an alternative method, instead using a cognitively inspired pedestrian model featuring both inter-individual and intra-individual variability to generate behaviourally plausible adversarial scenarios. We provide a proof of concept demonstration of this method's potential for AV control optimisation, in closed-loop testing and tuning of an AV controller. Our results show that replacing the rule-based CARLA pedestrian with the human-like model yields more realistic gap acceptance patterns and smoother vehicle decelerations. Unsafe interactions occur only for certain pedestrian individuals and conditions, underscoring the importance of human variability in AV testing. Adversarial scenarios generated by this model can be used to optimise AV control towards safer and more efficient behaviour. Overall, this work illustrates how incorporating human-like road user models into simulation-based adversarial testing can enhance the credibility of AV evaluation and provide a practical basis to behaviourally informed controller optimisation.