The evolution of Driver Assistance Systems(DAS)is shifting focus from mere safety to integrating emotional and psychological well-being,1 transforming intelligent connected vehicles(ICVs)from passive tools into cognit...The evolution of Driver Assistance Systems(DAS)is shifting focus from mere safety to integrating emotional and psychological well-being,1 transforming intelligent connected vehicles(ICVs)from passive tools into cognitive partners that require complex,bidirectional interaction.1 Affective computing(AC),which enables machines to recognize and interpret human emotions,provides a crucial foundation for this shift.2 Large Language Models(LLMs)can significantly advance AC by processing multimodal data,enabling a transition from functional execution to empathetic human-machine interaction.1,3 Despite early applications like mandated fatigue monitoring,current systems are limited by passive responsiveness and opacity.4 While LLM-enhanced AC promises to address these issues,this integration creates a Collingridge's Dilemma(Figure 1).This commentary examines this paradox,focusing on the technical potential,limitations of LLM-empowered AC and the associated governance complexities,aiming to foster discussion on responsible innovation in next-generation intelligent driving.展开更多
基金funded by the National Natural Science Foundation of China(72525009,72431009,72171210,72350710798)Zhejiang Provincial Natural Science Foundation of China(LZ23E080002).
文摘The evolution of Driver Assistance Systems(DAS)is shifting focus from mere safety to integrating emotional and psychological well-being,1 transforming intelligent connected vehicles(ICVs)from passive tools into cognitive partners that require complex,bidirectional interaction.1 Affective computing(AC),which enables machines to recognize and interpret human emotions,provides a crucial foundation for this shift.2 Large Language Models(LLMs)can significantly advance AC by processing multimodal data,enabling a transition from functional execution to empathetic human-machine interaction.1,3 Despite early applications like mandated fatigue monitoring,current systems are limited by passive responsiveness and opacity.4 While LLM-enhanced AC promises to address these issues,this integration creates a Collingridge's Dilemma(Figure 1).This commentary examines this paradox,focusing on the technical potential,limitations of LLM-empowered AC and the associated governance complexities,aiming to foster discussion on responsible innovation in next-generation intelligent driving.
