This paper proposes a new, low-cost overtaking support system for two-lane roads, based on a single front camera and a virtual heads-up display (HUD) to improve driver safety without requiring high-cost multi-sensor advanced driver assistance systems (ADAS). Tackling the endemic problem of accidents arising from overtaking, which accounts for a large proportion of the World Health Organization’s cited 1.35 million yearly road deaths, the research offers a vision-based approach specially designed for low-budget vehicles and the developing world. The system analyzes pre-recorded dashcam clips using computer vision methods such as Canny edge detection and Hough Transform for lane detection, and contour analysis for vehicle detection, with a 92% lane detection rate in frames and 88% vehicle detection rate, and an overall accuracy of 85% in 900 frames at 18.2 FPS. A heuristic distance estimation model determines safe points for overtaking, marking frames with “Clear to overtake” or “Wait” suggestions superimposed as HUD-like indicators, beyond actual real-time processing capacity of 15 FPS. The method is cost-effective, using Python, OpenCV, and NumPy on standard hardware, eliminating the need for radar or LIDAR. Despite being successful, there are limitations such as compromised performance in bad weather or worn lane markings, where detection accuracy reduces to 85% in shaded regions and 12% failure rates for far-away vehicles. Future development entails the inclusion of real-time video streams, deep learning algorithms such as YOLO for better object detection, and 5G for low-latency processing. Scalability of the system extends to other applications such as motorcycle safety, traffic monitoring, and driver training simulators. Through its evidence of strong performance with low hardware, this work closes the accessibility divide for ADAS, providing an economically scalable framework that potentially can minimize overtaking accidents all over the world, especially in areas with high rates of crashes because they lack exposure to advanced safety technology.