<![CDATA[This paper proposes an integrated artificial intelligence–based driver assistance system for electric vehicles (EVs) that combines computer vision–based drowsiness detection with a generative artificial intelligence (GenAI)–driven conversational interaction framework to enhance driver safety and human–vehicle interaction. The primary objective of this work is to reduce fatigue-related driving risks while enabling natural, hands-free, and context-aware communication between the driver and the vehicle. The core idea is to tightly couple real-time driver state monitoring with intelligent conversational feedback, allowing safety alerts and voice interactions to adapt dynamically to the driver’s condition. Driver drowsiness is detected using non-intrusive visual indicators, namely eye closure duration and blink rate, extracted from an in-vehicle camera. A drowsy state is identified when eye closure exceeds 10 s or when the blink rate exceeds 6 blinks within a 6 s interval. Upon detection, the system generates multi-modal alerts consisting of audio warnings and vibration feedback, while a GenAI-based natural language processing module provides real-time, hands-free voice interaction. Experimental evaluation was conducted on an ESP32-based embedded prototype across five predefined driving scenarios representing normal and fatigued conditions. The results show stable face and eye detection under normal driving and achieved 100% correct alert triggering in all drowsiness-related cases (3 out of 5 scenarios), with zero false positives observed during non-drowsy conditions (2 out of 5 scenarios). The system demonstrated consistent real-time response and reliable alert activation under fatigue conditions. The main contribution and novelty of this research lie in the real-time integration of generative AI–driven conversational intelligence with embedded computer vision–based drowsiness detection within a unified, resource-constrained platform, which is rarely addressed jointly in existing systems. Overall, the proposed framework provides a practical, scalable, and human-centered solution for intelligent driver assistance in semi-autonomous and future autonomous EV environments.]]>
