# AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests

arXiv: https://arxiv.org/abs/2502.21100 · HTML: https://mlsanity.com/paper/2502.21100/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## 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, 20 of 20)

- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [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)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning](https://mlsanity.com/paper/2001.08726/) (2020)
- [Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method](https://mlsanity.com/paper/2003.01197/) (2020)
- [GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving](https://mlsanity.com/paper/2101.06543/) (2021)
- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarios](https://mlsanity.com/paper/2004.06531/) (2020)
- [Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World](https://mlsanity.com/paper/2003.07739/) (2020)
- [CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning](https://mlsanity.com/paper/2106.13364/) (2021)
- [CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios](https://mlsanity.com/paper/1910.00099/) (2019)
- [Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California](https://mlsanity.com/paper/1705.01187/) (2017)
- [Mcity Data Collection for Automated Vehicles Study](https://mlsanity.com/paper/1912.06258/) (2019)
- [Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm](https://mlsanity.com/paper/2107.12940/) (2021)
- [Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments](https://mlsanity.com/paper/2107.09614/) (2021)
- [Boundary State Generation for Testing and Improvement of Autonomous Driving Systems](https://mlsanity.com/paper/2307.10590/) (2023)

## Built on by (most cited first, 1 of 1)

- [NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment](https://mlsanity.com/paper/2608.04776/) (2026)

## Abstract (arXiv)

Generating adversarial safety-critical scenarios is a pivotal method for testing autonomous driving systems, as it identifies potential weaknesses and enhances system robustness and reliability. However, existing approaches predominantly emphasize unrestricted collision scenarios, prompting non-player character (NPC) vehicles to attack the ego vehicle indiscriminately. These works overlook these scenarios' authenticity, rationality, and relevance, resulting in numerous extreme, contrived, and largely unrealistic collision events involving aggressive NPC vehicles. To rectify this issue, we propose a three-layer relative safety region model, which partitions the area based on danger levels and increases the likelihood of NPC vehicles entering relative boundary regions. This model directs NPC vehicles to engage in adversarial actions within relatively safe boundary regions, thereby augmenting the scenarios' authenticity. We introduce AuthSim, a comprehensive platform for generating authentic and effective safety-critical scenarios by integrating the three-layer relative safety region model with reinforcement learning. To our knowledge, this is the first attempt to address the authenticity and effectiveness of autonomous driving system test scenarios comprehensively. Extensive experiments demonstrate that AuthSim outperforms existing methods in generating effective safety-critical scenarios. Notably, AuthSim achieves a 5.25% improvement in average cut-in distance and a 27.12% enhancement in average collision interval time, while maintaining higher efficiency in generating effective safety-critical scenarios compared to existing methods. This underscores its significant advantage in producing authentic scenarios over current methodologies.