Deep Learning-Based AI model for Road Detection

In the rapidly advancing domain of autonomous vehicles, ensuring robust and realtime lane detection is pivotal for safe navigation. This paper addresses the challenges of existing lane detection methods, marked by computational inefficiencies and limited adaptability to changing lane configurations. Our innovative approach treats lane detection as an instance segmentation problem, utilizing the LaneNet architecture and incorporating metric learning to enhance the model's understanding of lane features. A crucial contribution lies in integrating H-Net for homography prediction during the forward pass, dynamically adjusting for changes in road plane geometry, and ensuring robust lane fitting. This overcomes the limitations of fixed homography matrices in traditional methods. LaneNet instance segmentation and DBSCAN clustering facilitate effective lane grouping, particularly in scenarios with variable lane numbers and complex lane changes. Tested on the TuSimple dataset comprising 6,408 road images, our results demonstrate superior performance compared to conventional lane detection approaches. The approach's adaptability to diverse road scenarios, including curved lanes and ground plane variations, positions it as a significant advancement in the open literature. As the automotive industry leans towards autonomous solutions, our methodology stands poised to contribute to the evolution of precise and efficient lane detection systems.