DriveSafe: A Hierarchical Risk Taxonomy for Safety-Critical LLM-Based Driving Assistants

1. 1Identify domain-specific risks in LLM-based driving assistants
2. 2Develop a hierarchical risk taxonomy to categorize and prioritize risks
3. 3Evaluate LLM responses against the taxonomy to detect safety-critical errors
4. 4Refine LLM training data and models to minimize risks and improve safety

AI engineers and researchers working on LLM-based driving assistants can benefit from this taxonomy to ensure safety and regulatory compliance, while product managers can use it to inform design decisions

💡 A domain-specific risk taxonomy is essential for ensuring the safety and reliability of LLM-based driving assistants

🚗💻 DriveSafe: A new risk taxonomy for LLM-based driving assistants to improve safety and compliance