Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors

• URL: http://arxiv.org/abs/2411.17161v1
• Date: Tue, 26 Nov 2024 07:05:05 GMT
• Title: Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors
• Authors: Peijin Jia, Ziang Luo, Tuopu Wen, Mengmeng Yang, Kun Jiang, Le Cui, Diange Yang,
• Abstract summary: In this paper, we investigate prior augmentation from a novel perspective of trajectory priors. We design a confidence-based fusion module that takes alignment into account during the fusion process. The results indicate that our method's performance significantly outperforms the current state-of-the-art methods.
• Score: 12.333249510969289
• License:
• Abstract: In autonomous driving, recent advances in lane segment perception provide autonomous vehicles with a comprehensive understanding of driving scenarios. Moreover, incorporating prior information input into such perception model represents an effective approach to ensure the robustness and accuracy. However, utilizing diverse sources of prior information still faces three key challenges: the acquisition of high-quality prior information, alignment between prior and online perception, efficient integration. To address these issues, we investigate prior augmentation from a novel perspective of trajectory priors. In this paper, we initially extract crowdsourcing trajectory data from Argoverse2 motion forecasting dataset and encode trajectory data into rasterized heatmap and vectorized instance tokens, then we incorporate such prior information into the online mapping model through different ways. Besides, with the purpose of mitigating the misalignment between prior and online perception, we design a confidence-based fusion module that takes alignment into account during the fusion process. We conduct extensive experiments on OpenLane-V2 dataset. The results indicate that our method's performance significantly outperforms the current state-of-the-art methods.

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