Enhancing Road Safety with AI: A Multi-Module Driving Assistant for Drowsiness

Muhammad Izzul Roslan, Norzihani Yusof, Nuzulha Khilwani Ibrahim and Zuraini Othman
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Muhammad Izzul Roslan: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100, Malacca, Malaysia
Norzihani Yusof: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100, Malacca, Malaysia
Nuzulha Khilwani Ibrahim: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100, Malacca, Malaysia
Zuraini Othman: Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, 76100, Malacca, Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 9, 651-658

Abstract: One of the main causes of traffic accidents is sleepy driving, which has serious implications for drivers, passengers, and pedestrians. In this study an AI-powered multi-module driving assistant is presented with the goal of improving road safety through driver monitoring. The three main modules of the system are: AI Companion, which offers interactive engagement through natural language processing; Drowsiness Detection, which uses facial recognition techniques; and Music Recommendation, which uses machine learning for emotional state analysis. The Drowsiness Detection Module uses OpenCV and dlib's facial landmark detection to track driver alertness in real time which implement alerts when fatigue symptoms are identified. While the Music Recommendation Module assesses emotional states to recommend suitable music for sustaining alertness and the AI Companion Module offers voice-based interaction and real-time assistance. In the system development Python, Flask, and a number of APIs that include OpenAI and OpenWeather. Result obtained in the testing phase revealed that dependable emotional state recognition, responsive conversational AI, and effective drowsiness detection accuracy. Future research will concentrate on increasing offline capabilities and enhancing performance in low light.

Date: 2025
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