🚗💥 From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios
We propose a novel framework that leverages Large Language Models (LLMs) for:
- 🧠 Evaluation: Assessing safety-criticality of driving scenarios with use cases: Scenario evaluation and Safety inference.
- 🛠️ Generation: Adversarially generating safety-critical scenarios with controllable agent trajectories.
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- [July 2025] Paper accepted at IEEE ITSC 2025
- [May 2025] Project repository initialized
agent/
: Analyze agent-based scenariosagent_normal/
: Analyze normal agent behaviorsscenario/
: Analyze collision scenariosscenario_normal/
: Analyze normal driving scenarios
BEL_Antwerp-1_14_T-1/
: Original normal scenariosBEL_Antwerp-1_14_T-1n/
: Generated adversarial scenariosMetrices/
: Safety metric for these two scenarios
Trajectory_collection/
: Collect vehicle trajectoriesRiskscore_calculation/
: Compute risk scoresSafety_metrics_collection/
: Extract safety metricsCloesdID_identification/
: Identify nearby agentsgenerate_timestep_report.py
: Generate reports for each timestep
normal_scenarios/
: 100 normal scenarios (Frenetix planner)collision_scenarios/
: 100 collision scenarios (Frenetix planner)
LLM/
: Results from LLM evaluationsoutput_validation/
: Validation for collision scenariosoutput_validation_normal/
: Validation for normal scenarios
- Python 3.8+
- CommonRoad
- Frenetix Motion Planner
Create a .env
file in the root directory with your API keys:
OPENAI_API_KEY=your_openai_key
GEMINI_API_KEY=your_gemini_key
DEEPSEEK_API_KEY=your_deepseek_key
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