# GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation

arXiv: https://arxiv.org/abs/2406.03870 · HTML: https://mlsanity.com/paper/2406.03870/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2024.

## Built on by (most cited first, 10 of 10)

- [SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation](https://mlsanity.com/paper/2409.10320/) (2024)
- [Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2505.00972/) (2025)
- [CrashAgent: Crash Scenario Generation via Multi-modal Reasoning](https://mlsanity.com/paper/2505.18341/) (2025)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving](https://mlsanity.com/paper/2603.15221/) (2026)
- [Controllable Latent Diffusion for Traffic Simulation](https://mlsanity.com/paper/2503.11771/) (2025)
- [KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution](https://mlsanity.com/paper/2605.18895/) (2026)
- [From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving](https://mlsanity.com/paper/2606.14032/) (2026)
- [Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving](https://mlsanity.com/paper/2608.11498/) (2026)
- [Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation](https://mlsanity.com/paper/2609.06433/) (2026)

## Abstract (arXiv)

Scenario-based testing is considered state-of-the-art for verifying and validating Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). However, the practical application of scenario-based testing requires an efficient method to generate or collect the scenarios that are needed for the safety assessment. In this paper, we propose Goal-conditioned Scenario Generation (GOOSE), a goal-conditioned reinforcement learning (RL) approach that automatically generates safety-critical scenarios to challenge ADASs or ADSs. In order to simultaneously set up and optimize scenarios, we propose to control vehicle trajectories at the scenario level. Each step in the RL framework corresponds to a scenario simulation. We use Non-Uniform Rational B-Splines (NURBS) for trajectory modeling. To guide the goal-conditioned agent, we formulate test-specific, constraint-based goals inspired by the OpenScenario Domain Specific Language(DSL). Through experiments conducted on multiple pre-crash scenarios derived from UN Regulation No. 157 for Active Lane Keeping Systems (ALKS), we demonstrate the effectiveness of GOOSE in generating scenarios that lead to safety-critical events.