# Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

arXiv: https://arxiv.org/abs/2306.14131 · HTML: https://mlsanity.com/paper/2306.14131/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior](https://mlsanity.com/paper/2112.05077/) (2021)
- [AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles](https://mlsanity.com/paper/2101.06549/) (2021)
- [BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs](https://mlsanity.com/paper/2203.04050/) (2022)
- [TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios](https://mlsanity.com/paper/2210.06609/) (2022)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving](https://mlsanity.com/paper/1903.10654/) (2019)
- [Autonomy 2.0: Why is self-driving always 5 years away?](https://mlsanity.com/paper/2107.08142/) (2021)
- [Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles](https://mlsanity.com/paper/2103.07403/) (2021)

## Built on by (most cited first, 16 of 16)

- [Generating Out-Of-Distribution Scenarios Using Language Models](https://mlsanity.com/paper/2411.16554/) (2024)
- [WaymoQA: A Multi-View Visual Question Answering Dataset for Safety-Critical Reasoning in Autonomous Driving](https://mlsanity.com/paper/2511.20022/) (2025)
- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [Quantitative Representation of Scenario Difficulty for Autonomous Driving Based on Adversarial Policy Search](https://mlsanity.com/paper/2408.14000/) (2024)
- [Grounding Large Language Models In Embodied Environment With Imperfect World Models](https://mlsanity.com/paper/2410.02742/) (2024)
- [Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL](https://mlsanity.com/paper/2507.19146/) (2025)
- [Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function](https://mlsanity.com/paper/2507.22769/) (2025)
- [Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation](https://mlsanity.com/paper/2605.04366/) (2026)
- [What Will Happen Next: Large Models-Driven Deduction for Emergency Instances](https://mlsanity.com/paper/2605.08599/) (2026)
- [KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution](https://mlsanity.com/paper/2605.18895/) (2026)
- [ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving](https://mlsanity.com/paper/2605.21168/) (2026)
- [From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving](https://mlsanity.com/paper/2606.14032/) (2026)
- [Revealing Safety-Critical Scenarios for UTM via Transformer](https://mlsanity.com/paper/2606.31114/) (2026)
- [NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment](https://mlsanity.com/paper/2608.04776/) (2026)
- [Open-Source Autonomous Driving System Analysis and Multi-Disciplinary Hardware-in-the-Loop Research Paradigm with Reinforcement-Learning Testing and Large Language Models](https://mlsanity.com/paper/2608.30179/) (2026)

## Abstract (arXiv)

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles. Traditional optimization techniques suffer from the curse of dimensionality and limit the search space to fixed parameter spaces. To address these challenges, we propose a deep reinforcement learning approach that generates scenarios by sequential editing, such as adding new agents or modifying the trajectories of the existing agents. Our framework employs a reward function consisting of both risk and plausibility objectives. The plausibility objective leverages generative models, such as a variational autoencoder, to learn the likelihood of the generated parameters from the training datasets; It penalizes the generation of unlikely scenarios. Our approach overcomes the dimensionality challenge and explores a wide range of safety-critical scenarios. Our evaluation demonstrates that the proposed method generates safety-critical scenarios of higher quality compared with previous approaches.