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Chengxin Li

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2 papers

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ICRA Conference 2024 Conference Paper

Bi 2 Lane: Bi-Directional Temporal Refinement with Bi-Level Feature Aggregation for 3D Lane Detection

Chengxin Li
Yihui Hu
Zewen Zheng
Xiang Gao
Yongqiang Mou
Peng Nie
Jun Li

Monocular 3D lane detection has recently received increasing research attention in autonomous driving due to its application effectiveness and simplicity. However, depending solely on the limited semantic information from a single image makes current monocular detection methods unable to deal with complex scenarios, such as occluded, blurred, and unaligned scenes. In this study, we introduce an end-to-end framework named Bi 2 Lane which models temporal dependency in a continuous sequence. It recurrently utilizes detected lanes within historical frames as prior information to achieve robust lane detection. Additionally, Bi 2 Lane employs temporal reverse refinement together with temporal forward refinement to achieve bi-directional temporal refinement (BDTR) while maintaining a robust temporal dependency. For the refined features of different frames, we design a bi-level feature aggregation module (BLFA) to fuse them in both point-level and line-level manners, enabling a comprehensive feature representation to deal with complicated road scenes. Extensive experiments conducted on the OpenLane dataset demonstrate the superiority of Bi 2 Lane, achieving a notable F1 score of 63. 8% using a simple ResNet50 backbone, surpassing the performance of existing state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

PVALane: Prior-Guided 3D Lane Detection with View-Agnostic Feature Alignment

Zewen Zheng
Xuemin Zhang
Yongqiang Mou
Xiang Gao
Chengxin Li
Guoheng Huang
Chi-Man Pun
Xiaochen Yuan

Monocular 3D lane detection is essential for a reliable autonomous driving system and has recently been rapidly developing. Existing popular methods mainly employ a predefined 3D anchor for lane detection based on front-viewed (FV) space, aiming to mitigate the effects of view transformations. However, the perspective geometric distortion between FV and 3D space in this FV-based approach introduces extremely dense anchor designs, which ultimately leads to confusing lane representations. In this paper, we introduce a novel prior-guided perspective on lane detection and propose an end-to-end framework named PVALane, which utilizes 2D prior knowledge to achieve precise and efficient 3D lane detection. Since 2D lane predictions can provide strong priors for lane existence, PVALane exploits FV features to generate sparse prior anchors with potential lanes in 2D space. These dynamic prior anchors help PVALane to achieve distinct lane representations and effectively improve the precision of PVALane due to the reduced lane search space. Additionally, by leveraging these prior anchors and representing lanes in both FV and bird-eye-viewed (BEV) spaces, we effectively align and merge semantic and geometric information from FV and BEV features. Extensive experiments conducted on the OpenLane and ONCE-3DLanes datasets demonstrate the superior performance of our method compared to existing state-of-the-art approaches and exhibit excellent robustness.

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