Real-Time Visual Navigation Framework for Edge-Level Systems Using Classical Vision Algorithms - Kaya, Baris; Tekelioglu, Melih Ilter; Sarioglu, Baykal (Institute of Electrical and Electronics Engineers Inc., 2025)
This paper presents a novel real-time navigation assistance system that enhances driver awareness by visually embedding directional guidance directly into the roadway scene. Instead of displaying instructions on a separate map interface, our system renders navigational cues - such as arrows - within the actual road view captured by a forward-facing camera. These cues are aligned with the detected lane geometry, enabling drivers to follow directions more intuitively without shifting focus away from the road. The system relies on lightweight computer vision techniques using OpenCV, avoiding the need for complex neural networks or high-performance hardware. It operates efficiently on minimal CPU resources and requires only a simple camera. Real-time integration with Google Maps provides up-to-date route information, which is seamlessly transformed into visual overlays projected within the driving lane. This design significantly improves driver focus and reaction time, particularly at higher speeds or in unfamiliar environments. By bridging map data with on-road visual guidance, our method offers a practical step forward in intelligent driver assistance systems. The approach supports future extensions such as windshield-projected interfaces, but its current implementation already demonstrates how smart, vision-based systems can make navigation safer, more accessible, and more human-centered. © 2025 IEEE.
19th International Conference on Innovations in Intelligent Systems and Applications, INISTA 2025 -- 29 October 2025 through 31 October 2025 -- Ras Al Khaimah -- 217522
Keywords: Augmented Navigation Cues; Computer Vision; Driver Assistance; Embedded Directions; Intelligent Transportation Systems; Lane Detection; Map Integration; Opencv; Real-Time Navigation