Improved LaneNet for Lane Detection

Lane detection is a critical component in autonomous vehicles and advanced driver assistance systems (ADAS), enabling accurate lane tracking and vehicle positioning. While traditional lane detection methods based on handcrafted features and heuristics have limitations in challenging environments, the adoption of machine learning (ML) techniques has shown promise. However, many existing ML models struggle with detecting a variable number of lanes, making them less effective in complex driving scenarios. A simple solution involves the use of High Definition (HD) Maps. HD Maps offer comprehensive road information necessary for autonomous driving, but their high cost and inflexibility pose challenges for frequent updates and modifications. This research proposes an innovative approach, the Improved LaneNet (ILaneNet) network, to strike a balance between ML techniques and HD maps. By augmenting input images with a lane parameter namely the number of lanes, we aim to enhance lane detection accuracy without incurring the prohibitive costs of HD maps. ILaneNet seeks to achieve real-time precision in locating and tracking lane markings, even in challenging conditions like inadequate lighting and intricate road layouts. The objective of this study is to develop a flexible, cost-effective, and robust lane detection system that adapts to diverse driving scenarios. By incorporating pertinent information into the network, we demonstrate improved adaptability and potential advancements in autonomous driving technologies. We also introduce new evaluation metrics namely capacity, lost capacity and unsafe driving measure to assess lane detection techniques more comprehensively. We also propose evaluation of lane detection techniques by using a lane abstraction approach instead of the traditional line abstraction method. Through extensive evaluation and comparisons, we showcase the superiority of ILaneNet over LaneNet in detecting lanes. This research contributes to bridging the gap between ML techniques and HD maps, offering a viable solution for effective and efficient lane detection in autonomous vehicles and ADAS

Robust Lane-Detection Method for Low-Speed Environments

Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles