Robust Lane Detection via Expanded Self Attention

TL;DR

This paper introduces the Expanded Self Attention (ESA) module, a novel self-attention mechanism designed to improve lane detection robustness in challenging conditions by capturing global context without increasing inference time.

Contribution

The paper proposes the ESA module, a simple and effective self-attention mechanism optimized for lane detection, which can be integrated into existing models to enhance performance under difficult scenarios.

Findings

Achieves state-of-the-art results on CULane and BDD100K benchmarks.

Shows significant improvements on the TuSimple dataset.

Demonstrates robustness to occlusion and extreme lighting conditions.

Abstract

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.