SACA framework combines LLMs' reasoning capabilities with traditional safety systems to enable socially responsible collision avoidance in autonomous vehicles.

• Balances technical and ethical factors by leveraging LLMs to weigh emotional, legal, and ethical considerations
• Overcomes LLM limitations by addressing latency and robustness issues for safety-critical scenarios
• Enables context-aware decisions to navigate complex collision possibilities with social responsibility
• Creates engineering framework for integrating advanced AI reasoning into traditional vehicle safety systems

This research bridges the gap between technical collision avoidance and human-like ethical reasoning, potentially transforming how autonomous vehicles handle extreme scenarios while maintaining safety standards.

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