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Conference Paper
Emotion-Aware Autonomous Driving: A VR-Based Multimodal Analysis Using Gaze and Physiological Signals
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- Authors
- Mi Chang, Jiwoo Han, Woojin Kim, Daesub Yoon
- Issue Date
- 2025-12
- Citation
- ACM SIGGRAPH Asia (SA) 2025, pp.1-2
- Language
- English
- Type
- Conference Paper
- Abstract
- While autonomous driving technology continues to advance, understanding passengers’ emotional experiences remains limited. This study examines emotion recognition in fully autonomous scenarios using a virtual reality (VR) simulation with two contrasting driving styles— Calm and Tense. Multimodal data analysis revealed significant behavioral and physiological differences between the two modes. A Random Forest classifier achieved 97.1% accuracy in distinguishing passenger emotional states, demonstrating the feasibility of emotionaware autonomous driving systems.
- KSP Keywords
- Autonomous driving system, Data analysis, Driving styles, Emotion Recognition, Emotion-aware, Emotional states, Multimodal analysis, Physiological signals, Random Forest Classifier, Virtual Reality, multimodal data