Advancing Safety in Vehicles with AI-Driven Emotion Recognition

Jadhav, Sonali Subhash (2024) Advancing Safety in Vehicles with AI-Driven Emotion Recognition. Masters thesis, Dublin, National College of Ireland.

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Abstract

Enhancing road safety in automobiles takes a thorough analysis of the driver's role, as human error is responsible for most accidents. A pivotal aspect in bolstering safety lies in the field of emotion recognition, which can effectively detect and manage emotional states to ensure a more stable driving experience. Past study limitations show the importance of developing a comprehensive system capable of identifying emotions from audio and image only. Improving road safety extends beyond outside influences to include a driver's mental well-being. The integration of Artificial Intelligence (AI) technologies in this manner creates an environment that not only promotes safety but also ensures a comfortable passenger experience. The purpose of this research is to make use of AI technology, specifically deep learning models like CNNs Long Short-term Memory (LSTM) on publicly available datasets to understand driver’s emotions through speech, text, and facial expressions. The identified emotions include happy, sad, neutral, fear, disgust, surprise, and anger. The system generates real-time alerts based on the detected emotions to enhance safety on the road. These alerts include audio, text, and visual cues to capture the driver's attention and prompt appropriate responses. To improve assurance, technology generates recommendations depending on these feelings. A music recommendation method offers songs and some quotes to help the driver's emotional state. test accuracy for image, text, and audio emotion detection is reported at 89%, 96%, and 85%, respectively.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Uncontrolled Keywords:	Artificial Intelligence; Long Short-term Memory; Deep Learning; Recommendation system
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence B Philosophy. Psychology. Religion > Psychology > Emotions
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Tamara Malone
Date Deposited:	04 Apr 2025 11:56
Last Modified:	04 Apr 2025 11:56
URI:	https://norma.ncirl.ie/id/eprint/7367

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