Intelligent Speed Control System for Automated Vehicles with Real-Time Hazard Detection and Response

This research introduces an intelligent driver assistance framework aimed at enhancing vehicular safety under various traffic and environmental conditions. The system employs a lightweight deep learning architecture to interpret driving scenarios, such as low-light or rainy environments, and notifies drivers to activate essential features like headlights or windshield wipers. In addition, it integrates multi-angle object detection and proximity analysis to warn drivers of potential collisions when nearby vehicles are too close. Despite limitations in computational resources, experimental results show that the proposed method maintains both accuracy and efficiency. By merging environmental awareness with real-time hazard detection, this approach offers a practical contribution to the development of next-generation Advanced Driver Assistance Systems (ADAS). The system not only supports safer driving habits but also enhances driver focus by continuously monitoring and reporting vehicle surroundings.