Translating Images to Road Network: A Sequence-to-Sequence Perspective

• URL: http://arxiv.org/abs/2402.08207v2
• Date: Sat, 31 Aug 2024 06:35:17 GMT
• Title: Translating Images to Road Network: A Sequence-to-Sequence Perspective
• Authors: Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Feng Wen, Wei Zhang, Li Zhang,
• Abstract summary: Road network is essential for the generation of high-definition maps. Existing methods struggle to merge the two types of data domains effectively. We propose a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
• Score: 32.39335559663393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. However, generating road network poses a significant challenge due to the conflicting underlying combination of Euclidean (e.g., road landmarks location) and non-Euclidean (e.g., road topological connectivity) structures. Existing methods struggle to merge the two types of data domains effectively, but few of them address it properly. Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence. Further than modeling an auto-regressive sequence-to-sequence Transformer model to understand RoadNet Sequence, we decouple the dependency of RoadNet Sequence into a mixture of auto-regressive and non-autoregressive dependency. Building on this, our proposed non-autoregressive sequence-to-sequence approach leverages non-autoregressive dependencies while fixing the gap towards auto-regressive dependencies, resulting in success on both efficiency and accuracy. We further identify two main bottlenecks in the current RoadNetTransformer on a non-overfitting split of the dataset: poor landmark detection limited by the BEV Encoder and error propagation to topology reasoning. Therefore, we propose Topology-Inherited Training to inherit better topology knowledge into RoadNetTransformer. Additionally, we collect SD-Maps from open-source map datasets and use this prior information to significantly improve landmark detection and reachability. Extensive experiments on nuScenes dataset demonstrate the superiority of RoadNet Sequence representation and the non-autoregressive approach compared to existing state-of-the-art alternatives.

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