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Hyper-Anchor Based Lane Detection
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DOI：10.1109/TITS.2024.3410376.png)
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摘要
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As a critical task in autonomous driving, lane detection has caught increasing attention. Due to the inherently thin and long structure of lanes and complex external environments, current lane detection methods may not perform well, particularly in challenging driving scenarios, where lanes might be hardly visible because of extreme illumination, occlusion, absence of lane marks, and so on. To tackle the lane detection problem, we propose a novel hyper-anchor, which can take flexible shapes and provide coarse estimates of lane points. Based on hyper-anchors, multi-level lane-aware feature aggregations are proposed to integrate local lane details with global context. Specifically, lane descriptors of an adaptive receptive field are initially constructed by extracting local lane features from neighboring rows and columns. Intra-lane aggregation establishes an information flow across rows and columns within each lane descriptor. By aggregating local lane features, accurate offsets between hyper-anchors and lane points can be obtained to fine-tune the lane locations. Additionally, inter-lane feature aggregation is conducted to capture the global structural relationship of lanes. Subsequently, lane confidence is predicted to measure the overall reliability of a lane prediction and reduce false positive predictions. Extensive experiments demonstrate that our hyper-anchor based method outperforms some state-of-the-art lane detection methods, especially in challenging driving scenarios. Meanwhile, our method also achieves desirable speed due to its efficient hyper-anchor generation and lane-aware feature aggregations.
Keyword:
Anchor-based lane detection
hyper-anchor
self-attention
convolutional neural networks
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9.5K
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