Robust Lane Detection via Expanded Self Attention
This addresses a key problem for autonomous vehicles by improving lane detection robustness in real-world scenarios, though it is an incremental advancement over existing deep learning methods.
The paper tackles robust lane detection in challenging conditions like occlusion and extreme lighting by proposing an Expanded Self Attention module that extracts global contextual information, achieving state-of-the-art performance on CULane and BDD100K benchmarks with distinct improvement on TuSimple.
The image-based lane detection algorithm is one of the key technologies in autonomous vehicles. Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions. To be robust on these challenging situations, it is important to extract global contextual information even from limited visual cues. In this paper, we propose a simple but powerful self-attention mechanism optimized for lane detection called the Expanded Self Attention (ESA) module. Inspired by the simple geometric structure of lanes, the proposed method predicts the confidence of a lane along the vertical and horizontal directions in an image. The prediction of the confidence enables estimating occluded locations by extracting global contextual information. ESA module can be easily implemented and applied to any encoder-decoder-based model without increasing the inference time. The performance of our method is evaluated on three popular lane detection benchmarks (TuSimple, CULane and BDD100K). We achieve state-of-the-art performance in CULane and BDD100K and distinct improvement on TuSimple dataset. The experimental results show that our approach is robust to occlusion and extreme lighting conditions.