# FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality

arXiv: https://arxiv.org/abs/2406.02983 · HTML: https://mlsanity.com/paper/2406.02983/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors](https://mlsanity.com/paper/2101.06557/) (2021)
- [AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles](https://mlsanity.com/paper/2101.06549/) (2021)
- [Language-Guided Traffic Simulation via Scene-Level Diffusion](https://mlsanity.com/paper/2306.06344/) (2023)
- [A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective](https://mlsanity.com/paper/2202.02215/) (2022)
- [PlanT: Explainable Planning Transformers via Object-Level Representations](https://mlsanity.com/paper/2210.14222/) (2022)
- [KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients](https://mlsanity.com/paper/2204.13683/) (2022)
- [Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method](https://mlsanity.com/paper/2003.01197/) (2020)
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022)
- [Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model](https://mlsanity.com/paper/2401.10700/) (2024)
- ["Curse of rarity" for autonomous vehicles](https://mlsanity.com/paper/2207.02749/) (2022)
- [Reachability Constrained Reinforcement Learning](https://mlsanity.com/paper/2205.07536/) (2022)
- [Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation](https://mlsanity.com/paper/2009.08311/) (2020)
- [CAT: Closed-loop Adversarial Training for Safe End-to-End Driving](https://mlsanity.com/paper/2310.12432/) (2023)
- [Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning](https://mlsanity.com/paper/2111.07695/) (2021)
- [CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation](https://mlsanity.com/paper/2110.13939/) (2021)
- [Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate](https://mlsanity.com/paper/2210.07553/) (2022)

## Built on by (most cited first, 13 of 13)

- [Steerable Adversarial Scenario Generation through Test-Time Preference Alignment](https://mlsanity.com/paper/2509.20102/) (2025)
- [CrashAgent: Crash Scenario Generation via Multi-modal Reasoning](https://mlsanity.com/paper/2505.18341/) (2025)
- [RIFT: Group-Relative RL Fine-Tuning for Realistic and Controllable Traffic Simulation](https://mlsanity.com/paper/2505.03344/) (2025)
- [LinguaSim: Interactive Multi-Vehicle Testing Scenario Generation via Natural Language Instruction Based on Large Language Models](https://mlsanity.com/paper/2510.08046/) (2025)
- [SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling](https://mlsanity.com/paper/2603.04071/) (2026)
- [Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios](https://mlsanity.com/paper/2503.23708/) (2025)
- [ScenePilot: Controllable Boundary-Driven Critical Scenario Generation for Autonomous Driving](https://mlsanity.com/paper/2605.21168/) (2026)
- [EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents](https://mlsanity.com/paper/2606.03678/) (2026)
- [From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving](https://mlsanity.com/paper/2606.14032/) (2026)
- [CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation](https://mlsanity.com/paper/2607.04451/) (2026)
- [End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation](https://mlsanity.com/paper/2607.18637/) (2026)
- [Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning](https://mlsanity.com/paper/2608.10403/) (2026)
- [Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation](https://mlsanity.com/paper/2609.06433/) (2026)

## Abstract (arXiv)

Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the naturalness of scenarios, aiming to achieve a balance through data-driven approaches. However, without an appropriate upper bound for adversariality, the scenarios might exhibit excessive adversariality, potentially leading to unavoidable collisions. In this paper, we introduce FREA, a novel safety-critical scenarios generation method that incorporates the Largest Feasible Region (LFR) of AV as guidance to ensure the reasonableness of the adversarial scenarios. Concretely, FREA initially pre-calculates the LFR of AV from offline datasets. Subsequently, it learns a reasonable adversarial policy that controls the scene's critical background vehicles (CBVs) to generate adversarial yet AV-feasible scenarios by maximizing a novel feasibility-dependent adversarial objective function. Extensive experiments illustrate that FREA can effectively generate safety-critical scenarios, yielding considerable near-miss events while ensuring AV's feasibility. Generalization analysis also confirms the robustness of FREA in AV testing across various surrogate AV methods and traffic environments.