ConsciousDriver: A Context-Aware Multimodal Personalized Autonomous Driving System

Current multimodal large language model (MLLM)-based autonomous driving systems struggle with deep contextual understanding, fine-grained personalization, and transparent risk assessment in complex real-world scenarios. This paper introduces ConsciousDriver, a novel context-aware multimodal personalized autonomous driving system designed to address these limitations. Our system integrates a Context-Awareness Module for richer environmental understanding and a Dynamic Risk Adaptation Mechanism to flexibly modulate driving behaviors based on real-time user prompts and situational risks. Built upon an extended MLLM architecture, ConsciousDriver processes environmental inputs and user prompts to generate deep contextual understanding, context-adaptive danger levels, optimal action decisions, and explicit decision intent explanations. Evaluated on an extended PAD-Highway benchmark, ConsciousDriver demonstrates superior performance in driving safety, efficiency, lane-keeping, and traffic density adaptation. Furthermore, it exhibits robust adaptability to diverse personalized prompts and enhanced performance in challenging traffic scenarios, with lower collision and higher completion rates. Human evaluation confirms the high quality of its explanations. ConsciousDriver represents a significant advancement towards intelligent, adaptive, and trustworthy autonomous driving.