# CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening

arXiv: https://arxiv.org/abs/2411.16996 · HTML: https://mlsanity.com/paper/2411.16996/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 26 Nov 2024.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior](https://mlsanity.com/paper/2112.05077/) (2021)
- [Risk Averse Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1904.00511/) (2019)
- [Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration](https://mlsanity.com/paper/2202.04628/) (2022)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2102.03483/) (2021)
- [Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies](https://mlsanity.com/paper/2112.11937/) (2021)
- [Comparison of Waymo Rider-Only Crash Data to Human Benchmarks at 7.1 Million Miles](https://mlsanity.com/paper/2312.12675/) (2023)
- [Critical concrete scenario generation using scenario-based falsification](https://mlsanity.com/paper/2208.13329/) (2022)

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

- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [Sharpening the Spear: Adaptive Expert-Guided Adversarial Attack Against DRL-based Autonomous Driving Policies](https://mlsanity.com/paper/2506.18304/) (2025)

## Abstract (arXiv)

Ensuring the safety of autonomous vehicles (AVs) requires identifying rare but critical failure cases that on-road testing alone cannot discover. High-fidelity simulations provide a scalable alternative, but automatically generating realistic and diverse traffic scenarios that can effectively stress test AV motion planners remains a key challenge. This paper introduces CRASH - Challenging Reinforcement-learning based Adversarial scenarios for Safety Hardening - an adversarial deep reinforcement learning framework to address this issue. First CRASH can control adversarial Non Player Character (NPC) agents in an AV simulator to automatically induce collisions with the Ego vehicle, falsifying its motion planner. We also propose a novel approach, that we term safety hardening, which iteratively refines the motion planner by simulating improvement scenarios against adversarial agents, leveraging the failure cases to strengthen the AV stack. CRASH is evaluated on a simplified two-lane highway scenario, demonstrating its ability to falsify both rule-based and learning-based planners with collision rates exceeding 90%. Additionally, safety hardening reduces the Ego vehicle's collision rate by 26%. While preliminary, these results highlight RL-based safety hardening as a promising approach for scenario-driven simulation testing for autonomous vehicles.