Wang, Y, Guo, Q, Lin, P, Cheng, G 
ORCID: 0000-0001-8686-9513 and Wu, J
(2022)
Spatio-Temporal Fusion-based Monocular 3D Lane Detection.
In: BMVC 2022.
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Abstract
The monocular 3D lane detection (Lane3D) methods are increasingly proposed to address the issue of inaccurate bird-eye-view (BEV) results in various complex scenarios (e.g. up and downhills, bumps). However, there are a few restrictions on existing Lane3D methods. Primarily, only single-frame input is considered, which leads to poor results in no visual cues scenarios (e.g. obscured by surrounding vehicles). The other is that they rely on the camera pose to the road surface to translate to the road coordinate system. To address these issues and better exploit the spatio-temporal continuity of the lanes, we propose a novel Spatial-Temporal Lane3D model abbreviated as STLane3D. First, we propose a novel multi-frame pre-alignment layer under BEV, which uniformly projects features from different frames onto the same ROI region. Afterward, we propose a plug- and-play spatio-temporal attention module and a new 3DLane IOULoss. Experiments on the ONCE and OpenLane datasets demonstrate that our single-frame model, independent of camera extrinsic, can achieve close detection accuracy compared to the current state-of-the-art. And our multi-frame model improves the F1 score by 3.5% compared to the single-frame model on the ONCE dataset, which demonstrates the effectiveness of the multi-frame fusions strategy. Moreover, with multi-frame information, our model achieves satisfying performance in complex scenes lacking enough visual information and meets the real-time requirements on autonomous vehicles.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 14 Mar 2023 09:08 |
| Last Modified: | 24 Apr 2024 11:30 |
| Open Access URL: | https://bmvc2022.mpi-inf.mpg.de/0314.pdf |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169034 |
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