Embedded Lane Detection for Korea's Traffic Infrastructure

This paper introduces a lane detection module optimized for embedded systems, addressing the significant challenge of adapting to different road conditions that arise from the over-reliance on datasets from a single nation, primarily China. Traditional lane detection algorithms are often hindered by their dependency on extensive and diverse data that may not be available in all regions. By developing and utilizing a tailored Korean dataset, we effectively overcome the domain adaptation problem, ensuring that our model performs reliably under the unique road conditions found in Korea. Our approach not only utilizes depth images derived from camera-captured data, enhancing the training process without the need for multiple sensor types but also significantly reduces the resource demands typical of conventional sensor fusion techniques. Consequently, we present a highly efficient, Korean-specific lane detection model that demonstrates robust performance and is particularly suited for implementation within embedded systems, offering a scalable and practical solution in the realm of autonomous driving technologies.