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L2T-BEV: Local Lane Topology Prediction from Onboard Surround-View Cameras in Bird’s Eye View Perspective

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14427))

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Abstract

High definition maps (HDMaps) serve as the foundation for autonomous vehicles, encompassing various driving scenario elements, among which lane topology is critically important for vehicle perception and planning. Existing work on lane topology extraction predominantly relies on manual processing, while automated methods are limited to road topology extraction. Recently, road representation learning based on surround-view with bird’s-eye view (BEV) has emerged, which directly predicts localized vectorized maps around the vehicle. However, these maps cannot represent the topological relationships between lanes. As a solution, we propose a novel method, L2T-BEV, which learns local lane topology maps in BEV. This method utilizes the EfficientNet to extract features from surround-view images, followed by transforming these features into the BEV space through the Inverse Perspective Mapping (IPM). Nonetheless, the IPM transformation often suffers from distortion issues. To alleviate this, we add a learnable residual mapping function to the features after the IPM transformation. Finally, we employ a transformer network with learnable positional embedding to process the fused images, generating higher-precision lane topology. We validated our method on the NuScenes dataset, and the experimental results demonstrate the feasibility and excellent performance.

This work was supported by the Key Research and Development Program of Zhejiang Province in China (No. 2023C01237), and the Natural Science Foundation of China(No.U22A202101).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Shanding Ye, Tao Li, Ruihang Li & Zhijie Pan

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  1. Shanding Ye
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  2. Tao Li
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Corresponding author

Correspondence to Zhijie Pan .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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Ye, S., Li, T., Li, R., Pan, Z. (2024). L2T-BEV: Local Lane Topology Prediction from Onboard Surround-View Cameras in Bird’s Eye View Perspective. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14427. Springer, Singapore. https://doi.org/10.1007/978-981-99-8435-0_29

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  • DOI: https://doi.org/10.1007/978-981-99-8435-0_29

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