Computer Vision-Based Multi-Modal Advanced Driver Assistance System

Advanced Driver Assistance Systems (ADAS) play a crucial role in improving road safety and traffic management. The proposed research introduces a vision-based Advanced Driver Assistance System (ADAS) framework that utilizes deep learning and computer vision techniques to enhance situational awareness and driver assistance. Unlike traditional Advanced Driver Assistance System (ADAS) solutions that rely heavily on ultrasonic or LiDAR-based sensors, this framework utilizes Apple's Depth Pro for depth estimation, enabling precise distance measurement for collision warning and avoidance. You Only Look Once (YOLO)-based object detection is employed to quickly and accurately identify vehicles and obstacles, ensuring robust hazard detection. Additionally, Ultra-Fast Lane Detection (UFLD) is integrated to assess lane conditions, assisting drivers in identifying potential risks and maintaining safe lane positioning. The system is designed to operate efficiently across diverse driving environments, seamlessly adapting between autonomous highway operations and driver-assisted functionalities in urban scenarios. The objective of this study is to develop a reliable and efficient Advanced Driver Assistance System (ADAS) solution that enhances driver awareness and road safety while demonstrating the feasibility of vision-based techniques as a core component of modern intelligent transportation systems.