Authors - Avisek Sharma, Arpita Dey, Buddhadeb Sau Abstract - The increasing adoption of intelligent transportation systems has high lighted the importance of preventive vehicle safety mechanisms that address critical human factors such as unauthorized access, alcohol impairment, and driver fatigue. This review presents a structured analysis of recent research on automated vehicle access and driver alert systems that integrate biometric au thentication, alcohol sensing, and vision-based drowsiness detection. Embedded platforms, particularly Raspberry Pi– based implementations, are examined alongside computer vision techniques for facial and eye-state analysis and MQ series sensors for alcohol detection. The study reviews and compares commonly used algorithms, including classical feature-based methods and deep learning ap proaches, in terms of detection accuracy, computational requirements, and real time suitability for embedded environments. Communication strategies for alert generation and remote notification are also discussed. The review identifies key challenges related to multi-module system integration, robustness under varying illumination conditions, and long-term sensor calibration. It concludes that an integrated, low-cost, and real-time embedded framework offers a practical and scalable approach to improving vehicular security and reducing road accidents by ensuring that only authorized, sober, and alert drivers operate vehicles.
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