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AI Research
arXiv CS.AI·3d ago·5 min read
A Local Agentic AI Framework to Generate Edge Case Scenarios for Safety Assurance
TL;DR
A local agentic AI framework was developed to autonomously generate edge case scenarios for safety assurance in autonomous systems. This approach enhances scenario diversity and provides traceable evidence for safety evaluations.
✦ Why It Matters
Implement this framework to enhance the safety assurance processes in your autonomous systems today.
Key Takeaways
- Utilize local language models for scenario generation.
- Incorporate system and environment descriptions for realism.
- Critique generated scenarios with a second language model.
- Analyze results to identify functional gaps and create new scenarios.
How It Works
The framework operates through a cycle of thinking, acting, observing, updating, and continuing, which allows it to adaptively generate scenarios. It uses lightweight JSON encoding to structure the scenarios and relies on contextual prompts derived from system requirements and descriptions to ensure that the generated scenarios are realistic.
A second independent language model evaluates these scenarios based on domain-specific criteria, enabling iterative refinement before they are tested in simulations.