Automotive Radar for Collision Avoidance

Abstract: This project introduces a low-cost prototype of an Advanced Driver Assistance System (ADAS) that integrates Doppler radar with computer vision to enhance vehicle safety. The system is designed to detect obstacles in real time, estimate the speed of nearby vehicles, and recognize road lanes, thereby assisting drivers in navigating complex traffic situations. The Doppler radar sensor provides reliable velocity measurements even in poor visibility conditions such as fog or rain, while the vision module, based on lane detection algorithms, helps in maintaining proper road alignment. Together, these components form a sensor fusion framework that reduces accident risks by delivering accurate and timely driving assistance. The hardware implementation is based on affordable and widely available components, including the HB100 Doppler radar module, a standard camera, and a Raspberry Pi 4. The Raspberry Pi acts as the central processing unit, executing both radar signal processing and computer vision tasks. By eliminating the need for expensive automotive-grade systems, this design makes ADAS functionalities more accessible for low-cost applications, particularly in regions where advanced safety systems are not yet common. Testing in a simulated environment has shown promising results, demonstrating that the system performs effectively across varied weather and lighting conditions. Radar ensures robust speed detection regardless of external factors, while the camera enables contextual awareness of road structures. This synergy enhances reliability and sets the stage for future improvements. Further advancements may include machine learning-based object classification, integration of additional sensors like LiDAR, and optimization for real-world deployment. The outcomes suggest that combining Doppler radar and embedded vision can provide a practical pathway toward safer and smarter driving technologies..