Emotion Based Autonomous Driving Control Using Multi Sensor Integration for Enhanced EV Experience

This paper presents an emotion-based autonomous driving control system that integrates real-time physiological monitoring with autonomous vehicle technology to enhance driving safety and personalization. The system continuously evaluates the driver’s emotional state using sensors that measure pulse rate, oxygen saturation, and body temperature, dynamically adjusting the vehicle’s driving mode between manual and autonomous according to the detected condition. By autonomously assuming control during stress or fatigue, the system mitigates the risk of human error, particularly in high-stress scenarios, while providing real-time feedback to maintain driver trust. Experimental results demonstrate high accuracy in emotional state classification and reliable autonomous driving performance. However, external factors affecting sensor readings and minor delays in mode transitions at high speeds highlight areas for further optimization. Future improvements, including enhanced sensor precision, faster mode-switching algorithms, and a more robust classification model, could further increase system effectiveness. This emotion-aware approach represents a significant advancement in human-centered autonomous driving, offering safer, adaptive, and more comfortable driving experiences.

Driver fatigue detection, Stress-aware vehicle systems, Sensor fusion algorithms, Adaptive human–machine interaction, Real-time mode switching

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