Robust Lane Detection via Spatial and Temporal Fusion

Lane detection is a crucial and challenging task in autonomous driving. Most existing detection methods only have good results in common scenes, but they have detected poorly in extreme scenarios such as occlusion and strong illumination. To address this problem, this paper introduces a robust lane detection network based on spatial-temporal fusion LSTnet for extreme scenarios like occlusion. LSTnet incorporates a detachable local and global memory component as an external storage unit. Through the fusion, read, and update operations on memory features, the component captures temporal information to compensate for the lack of information in extreme detection scenarios. Additionally, LSTnet uses a memory alignment loss function to guide the memory component to update the memory effectively, so as to obtain temporal consistency between the feature maps outputted by the memory component and the ground truth feature maps.Extensive experiments on two commonly used datasets demonstrate that the network achieves an F1 score of 79.49 on CULane and 97.31 on the TuSimple dataset.