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ARETE paper details new method for HD map generation using vehicle fleet data

Researchers have developed ARETE, a new method for generating High-Definition (HD) maps for autonomous driving using crowdsourced vehicle data. The approach employs a Detection Transformer (DETR) model to predict vectorized lane representations, including centerlines and lane dividers, from rasterized vehicle trajectories. This technique aggregates local trajectory data, transforms it into a rasterized format encoding presence and direction, and then predicts geometrically constrained lanes. Experiments were conducted on internal datasets and public benchmarks like nuScenes and nuPlan. AI

影响 Improves HD map generation for autonomous vehicles, potentially enhancing safety and efficiency.

排序理由 Academic paper detailing a new method for HD map generation using computer vision techniques.

在 arXiv cs.CV 阅读 →

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ARETE paper details new method for HD map generation using vehicle fleet data

报道来源 [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data

    The continuous advancement of autonomous driving (AD) introduces challenges across multiple disciplines to ensure safe and efficient driving. One such challenge is the generation of High-Definition (HD) maps, which must remain up to date and highly accurate for downstream automot…

  2. arXiv cs.CV TIER_1 English(EN) · Daniel Fritz, Dimitrios Lagamtzis, Michael Mink, Markus Enzweiler, Steffen Schober ·

    ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data

    arXiv:2604.24353v1 Announce Type: new Abstract: The continuous advancement of autonomous driving (AD) introduces challenges across multiple disciplines to ensure safe and efficient driving. One such challenge is the generation of High-Definition (HD) maps, which must remain up to…

  3. arXiv cs.CV TIER_1 English(EN) · Steffen Schober ·

    ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data

    The continuous advancement of autonomous driving (AD) introduces challenges across multiple disciplines to ensure safe and efficient driving. One such challenge is the generation of High-Definition (HD) maps, which must remain up to date and highly accurate for downstream automot…