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🚗💥 From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios

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Framework

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.

✨ Highlights

IEEE-ITSC-original 🟢 Original Scenario IEEE-ITSC-modified 🔴 Modified Scenario (Collision)

🔥 Updates

  • [July 2025] Paper accepted at IEEE ITSC 2025
  • [May 2025] Project repository initialized

🗂️ Project Structure

📁 LLM/ – LLM-Based Analysis Scripts

  • agent/: Analyze agent-based scenarios
  • agent_normal/: Analyze normal agent behaviors
  • scenario/: Analyze collision scenarios
  • scenario_normal/: Analyze normal driving scenarios

📁 Generation/ – Scenario Generation & Safety Metrics

  • BEL_Antwerp-1_14_T-1/: Original normal scenarios
  • BEL_Antwerp-1_14_T-1n/: Generated adversarial scenarios
  • Metrices/: Safety metric for these two scenarios

📁 Data_collection/ – Trajectory Data Tools

  • Trajectory_collection/: Collect vehicle trajectories
  • Riskscore_calculation/: Compute risk scores
  • Safety_metrics_collection/: Extract safety metrics
  • CloesdID_identification/: Identify nearby agents
  • generate_timestep_report.py: Generate reports for each timestep

📁 Scenarios/ – Dataset of Driving Scenarios

  • normal_scenarios/: 100 normal scenarios (Frenetix planner)
  • collision_scenarios/: 100 collision scenarios (Frenetix planner)

📁 Results/ – Analysis & Validation

  • LLM/: Results from LLM evaluations
  • output_validation/: Validation for collision scenarios
  • output_validation_normal/: Validation for normal scenarios

⚙️ Setup & Configuration

✅ Requirements

🔐 API Keys

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

📖 Citation

If you find this work helpful in your research, please consider citing us:

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[ITSC'25] LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios

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