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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping.">
<meta name="keywords" content="Vector HD mapping, Autonomous driving">
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<title>Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping</title>
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<section class="hero">
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<div class="container is-max-desktop">
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<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping</h1>
<div class="is-size-4 publication-authors">
<span class="author-block">
</div>
<div class="is-size-4 publication-authors">
<span class="author-block">
Shuang Zeng<sup>1,2*†</sup>,</span>
<span class="author-block">
Xinyuan Chang<sup>1†</sup>,</span>
<span class="author-block">
Xinran Liu<sup>1</sup>,
</span>
<span class="author-block">
Zheng Pan<sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com.hk/citations?user=KNyC5EUAAAAJ&hl=zh-CN&oi=ao/">Xing Wei</a><sup>2‡</sup>
</span>
</div>
<div class="is-size-5 publication-affiliations">
<span class="author-block"> <sup>1</sup> Amap, Alibaba Group </span>
   
<span class="author-block"> <sup>2</sup> Xi’an Jiaotong University </span>
</div>
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<div class="publication-links">
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<span >Code </span>
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</section>
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<img src="./static/images/pipeline.jpg" alt="Description of the image" style="max-width: 100%; height: auto;" />
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<p>
Overview of PriorDrive. Our unified vector encoder (UVE) directly encodes diverse vector prior maps and
seamlessly integrates them into existing online mapping frameworks. This integration enhances the final
predictions, making them more complete and accurate than those generated without prior information.
</p>
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</section>
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<h2 class="title is-2">Abstract</h2>
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<p>
High-Definition Maps (HD maps) are essential for the precise navigation and decision-making of autonomous vehicles,
yet their creation and upkeep present significant cost and timeliness challenges. The online construction of HD maps
using on-board sensors has emerged as a promising solution; however, these methods can be impeded by incomplete data
due to occlusions and inclement weather. This paper proposes the PriorDrive framework to addresses these limitations
by harnessing the power of prior maps, significantly enhancing the robustness and accuracy of online HD map construction.
</p>
<p>
Our approach integrates a variety of prior maps, such as OpenStreetMap's Standard Definition Maps (SD maps), outdated HD maps
from vendors, and locally constructed maps from historical vehicle data. To effectively encode this prior information into
online mapping models, we introduce a Hybrid Prior Representation (HPQuery) that standardizes the representation of diverse map elements.
</p>
<p>
At the core of PriorDrive is the Unified Vector Encoder (UVE), which employs a dual encoding mechanism to process vector data.
The intra-vector encoder captures fine-grained local features, while the inter-vector encoder integrates global context. Furthermore,
we propose a segment-level and point-level pre-training strategy that enables the UVE to learn the prior distribution of vector data,
thereby improving the encoder's generalizability and performance.
</p>
<p>
Through extensive testing on the nuScenes dataset, we demonstrate that PriorDrive is highly compatible with various online mapping models
and substantially improves map prediction capabilities. The integration of prior maps through the PriorDrive framework offers a robust solution
to the challenges of single-perception data, paving the way for more reliable autonomous vehicle navigation.
</p>
</div>
</div>
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<h2 class="title is-2">Video</h2>
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<h2 class="title is-2">Qualitative results</h2>
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<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
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<p>
Qualitative results with and w/o online local prior. Prior maps can help restore obscured map
elements and make predictions more complete and accurate.
</p>
</div>
</div>
</div>
</section>
<section class="section">
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<h2 class="title is-2">Main results</h2>
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<img src="./static/images/result.png" alt="Description of the image" style="max-width: 100%; height: auto;" />
</div>
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<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="content has-text-justified">
<p>
Performance of various online mapping models with different prior maps on nuScenes within a 60m x 30m perception range.
“C” denotes camera input. The best results using the same backbone are highlighted in bold. The SD map is sourced from
OpenStreetMap. The online local map refers to the historical prediction results. As the
nuScenes dataset lacks existing HD maps, we followed the approach of MapEX to create an HD map-EX*, simulating
existing HD maps by removing pedestrian crossings and lane dividers, while retaining only the road boundaries.
</p>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<p>
If you find our work useful in your research, please cite our paper:
</p>
<pre><code>@article{zeng2024driving,
title={Driving with Prior Maps: Unified Vector Prior Encoding for Autonomous Vehicle Mapping},
author={Zeng, Shuang and Chang, Xinyuan and Liu, Xinran and Pan, Zheng and Wei, Xing},
journal={arXiv preprint arXiv:2409.05352},
year={2024}
}</code></pre>
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