Leveraging Context-Aware Emotion and Fatigue Recognition Through Large Language Models for Enhanced Advanced Driver Assistance Systems (ADAS)

The automotive sector is swiftly advancing, focusing on driving experiences that prioritize safety and integrate with human emotions and well-being. This chapter explores the transformative potential of combining large language models (LLMs) with context-aware emotion and fatigue recognition techniques in Advanced Driver Assistance Systems (ADAS). The primary objective is to enhance driving experiences and overall well-being through real-time emotional and fatigue assessments. Grounded in Active and Assisted Living (AAL) principles, this chapter emphasizes the practical implementation of bio-signal-based emotion and fatigue estimation techniques within the ADAS framework. Utilizing formal knowledge representation techniques, such as ontologies, demonstrates how contextual modeling can facilitate optimal support services in dynamic driving contexts. Central to this approach is the integration of LLMs with emotion and fatigue recognition methodologies. The chapter details using non-intrusive bio-signal sensors to analyze facial expressions through video, EEG measurements, and voice analysis. This synergy between language comprehension and bio-signal insights enables real-time emotional assessments and fatigue estimations, empowering safer and more responsive driving experiences. LLMs act as the cognitive bridge, enhancing human driver assistance with context-aware emotion and fatigue recognition. The chapter reveals the potential of language models to decode emotional cues and detect fatigue levels, which are crucial for shaping proactive and adaptive driver assistance strategies. Integrating LLMs within ADAS showcases their ability to anticipate driver needs, provide timely alerts, and improve decision-making processes. Importantly, this chapter offers a roadmap for integrating LLMs with context-aware emotion and fatigue recognition into ADAS. Leveraging the capabilities of LLMs presents a scalable method for embedding bio-signal-based emotion and fatigue estimation techniques into ADAS, highlighting the AAL principles while demonstrating the transformative potential of LLMs. This work envisions an advanced iteration of ADAS, fostering a safer, more intuitive, and supportive driving experience.