Energy-Efficient Lane Identification Module with Sliding-Based Parallel Segment Recognition Accelerator for FPGA

Lane detection is an emerging technology integrated into vehicles to facilitate autonomous navigation. Most existing Lane recognition systems are developed for well-structured roads, trusting on the presence of lane patterns. To address this limitation, we propose a sliding-based K-means parallel segment image dispensation approach. Since most onboard systems in autonomous vehicles are lightweight, the method is optimized to minimize computational load and power consumption. Lane identification is achieved by applying a Hough transform to an image where edge finding has been performed concurrently. The primary goal of edge detection is to recognize and pinpoint abrupt changes in pixel intensity, which define object boundaries and distinct elements within a scene. Various implementations of the Canny algorithm exist, as boundary detection serves as a fundamental step in many computer vision applications, such as edge-based obstacle recognition, facial recognition, target detection, and image compression. These computational techniques help distinguish vertical, flat, step, and corner edges. The effectiveness of edge detection depends on factors like lighting conditions, noise levels, edge density, and intensity variations within the image. Consequently, achieving precise boundary localization is crucial. Therefore, computational methods must account for these intensity fluctuations to establish accurate edge detection correlations. Ultimately, the process ensures lane detection with reduced noise, improved contrast boundaries, and optimized processing time.