# Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling

arXiv: https://arxiv.org/abs/2306.07525 · HTML: https://mlsanity.com/paper/2306.07525/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Socially Aware Motion Planning with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.08862/) (2017)
- [Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.11689/) (2019)
- [The social dilemma of autonomous vehicles](https://mlsanity.com/paper/1510.03346/) (2015)
- [Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving](https://mlsanity.com/paper/2107.02326/) (2021)

## Built on by (most cited first, 4 of 4)

- [INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation](https://mlsanity.com/paper/2502.00262/) (2025)
- [Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios](https://mlsanity.com/paper/2404.19087/) (2024)
- [Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving](https://mlsanity.com/paper/2504.18931/) (2025)
- [Customized Generative AI Agent for Transportation Engineering Practice: A Development and Continued Pre-training Guideline](https://mlsanity.com/paper/2606.29014/) (2026)

## Abstract (arXiv)

Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases. To address this, specialized pedestrian behavior algorithms are needed. Current research focuses on realistic trajectories using social force models and reinforcement learning based models. However, we propose a reinforcement learning algorithm that specifically targets collisions and better uncovers unique failure modes of automated vehicle controllers. Our algorithm is efficient and generates more severe collisions, allowing for the identification and correction of weaknesses in autonomous driving algorithms in complex and varied scenarios.