SACA framework combines LLMs' reasoning capabilities with traditional collision avoidance systems to enable socially responsible, context-aware decisions in extreme situations.

• Addresses a critical gap in autonomous vehicle safety by incorporating ethical, legal, and emotional factors
• Overcomes LLM limitations (latency and robustness) through strategic integration with existing systems
• Enhances decision-making by providing scenario-aware responses to complex collision scenarios
• Creates more socially acceptable autonomous vehicles that can reason like humans in life-critical situations

This engineering breakthrough matters because it moves autonomous vehicles beyond purely technical collision avoidance toward systems that can make nuanced, ethically sound decisions that align with human values and social expectations.

SACA: A Scenario-Aware Collision Avoidance Framework for Autonomous Vehicles Integrating LLMs-Driven Reasoning