iVehicles: Spatial Feature Aggregation Network for Lane Detection Assistance System

In recent years, the development of intelligent and electric vehicles has placed an increased emphasis on autonomous driving systems, which have the potential to improve safety, reduce traffic congestion, and increase accessibility. One of the key technologies in these systems is lane detection. However, existing technology faces many challenges, particularly under complex scenes and adverse lighting conditions, leading to inconsistent detection results and discouraging consumers from using the system. To address this problem, we propose a novel lane detection model based on deep learning, which introduces a multifeature aggregation module to improve the model's ability to extract explicit and implicit spatial features and accurately locate severe occlusion and ambiguous lane lines. We deploy the proposed technology on the vehicle control platform to further implement the lane-keeping assist system and analyze the experimental results using public TuSimple and Taiwan scene datasets. Our analysis demonstrates the high and stable detection performance of the proposed method in different field environments, with improvements over existing models. This work shows the effectiveness and feasibility of our proposed model in addressing the challenges of lane detection in automated driving systems.