ADAS and OpenCV Implementation in Heavy Vehicles using MATLAB

This research successfully developed a comprehensive, vision-based safety framework that bridges the gap between external environment monitoring and internal driver state analysis. By unifying Advanced Driver Assistance Systems (ADAS)—specifically Lane Departure Warning (LDW) and Forward Collision Warning (FCW)—with a Driver Monitoring System (DMS), the project created a singular, cohesive architecture. The integration was achieved through a hybrid software stack: MATLAB and Simulink efficiently handled high-speed road video processing and image tracking, while OpenCV and Dlib provided the precision necessary for facial landmark detection to monitor eye closures and head tilts. Under simulated driving conditions, the system demonstrated high reliability and feasibility. The image processing modules for lane and obstacle tracking generated consistent warnings, while the driver-monitoring sensors effectively identified fatigue and distraction. This performance validates the core objective: proving that an integrated ADAS+DMS solution can function reliably within a unified framework. While the current camera-only setup operates best under controlled conditions, it serves as a robust proof-of-concept for real-time safety interventions in the heavy-vehicle sector. Summary of Unified Tools: ●            MATLAB/Simulink: Road environment, lane detection, and collision logic. ●            OpenCV/Dlib: Facial landmarking and driver behavior analysis. ●            Integrated Framework: A single-system approach to heavy-vehicle safety.

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