Emotion Recognition and Multimodal Fusion in Smart Cockpits: Advancements, Challenges, and Future Directions

This paper reviews the latest developments in emotion recognition and multimodal fusion technologies for smart cockpits, a critical area in the evolution of intelligent transportation systems. With the convergence of advanced sensing, machine learning, and human–computer interaction, smart cockpits have evolved from basic interfaces to comprehensive platforms capable of interpreting a driver’s emotional state. The study examines the theoretical foundations—such as affective computing, multimodal data fusion, and deep learning algorithms—that underpin current systems. It provides a detailed analysis of various fusion strategies, including early, late, and hierarchical fusion, and discusses innovative models like Transformer-based methods and contrastive learning approaches that have significantly enhanced the precision of emotion recognition. Despite notable progress, challenges remain, particularly in achieving robust adaptability in dynamic driving environments and reconciling heterogeneous data from diverse modalities. The paper also highlights concerns regarding privacy, data misuse, and hardware adoption barriers in mid- to low-end vehicle models. Through a systematic review of current methodologies and system architectures, the study identifies critical gaps in existing technologies and offers insights into optimization strategies such as adaptive weight adjustment, deep learning–based optimization, and continuous learning approaches. As breakthroughs in AI and sensor technologies continue, multimodal fusion in smart cockpits is poised to deliver safer and more personalized driving experiences.