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<!DOCTYPE html>
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<title>Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving</title>
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<h1 class="title is-1 publication-title">Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank" rel="noopener noreferrer">Xinyu Zhang</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://zewei-zhou.github.io/" target="_blank" rel="noopener noreferrer">Zewei Zhou</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://wi11ione.github.io/" target="_blank" rel="noopener noreferrer">Zhaoyi Wang</a>,</span>
<span class="author-block">
<a href="FOURTH AUTHOR PERSONAL LINK" target="_blank" rel="noopener noreferrer">Yangjie Ji</a>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/yanjun-huang-46099b82/" target="_blank" rel="noopener noreferrer">Yanjun Huang</a>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=zh-CN&user=n_eA148AAAAJ" target="_blank" rel="noopener noreferrer">Hong Chen</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Tongji University</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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<div class="has-text-centered" style="margin-top: 10px;">
<strong class="is-size-4", style="color: red;">ICRA 2025</strong>
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<span>Paper</span>
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<a href="https://github.com/xiaomiaozhang/Co-MTP" target="_blank"
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<i class="fab fa-github"></i>
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<span>Code</span>
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<a href="https://arxiv.org/abs/2502.16589" target="_blank" rel="noopener noreferrer" class="external-link button is-normal is-rounded is-dark">
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<span>arXiv</span>
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</section>
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<h2 class="subtitle has-text-centered">
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<h2 class="title is-3">Abstract</h2>
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<p>
Vehicle-to-everything technologies (V2X) have become an ideal paradigm to extend the perception range and see through the occlusion. Exiting efforts focus on single-frame cooperative perception, however, how to capture the temporal cue between frames with V2X to facilitate the prediction task even the planning task is still underexplored. In this paper, we introduce the Co-MTP, a general cooperative trajectory prediction framework with multi-temporal fusion for autonomous driving, which leverages the V2X system to fully capture the interaction among agents in both history and future domains to benefit the planning. In the history domain, V2X can complement the incomplete history trajectory in single-vehicle perception, and we design a heterogeneous graph transformer to learn the fusion of the history feature from multiple agents and capture the history interaction. Moreover, the goal of prediction is to support future planning. Thus, in the future domain, V2X can provide the prediction results of surrounding objects, and we further extend the graph transformer to capture the future interaction among the ego planning and the other vehicles' intentions and obtain the final future scenario state under a certain planning action. We evaluate the Co-MTP framework on the real-world dataset V2X-Seq, and the results show that Co-MTP achieves state-of-the-art performance and that both history and future fusion can greatly benefit prediction.
</p>
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</section>
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<!-- Method -->
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<h2 class="title is-3">Co-MTP Framework</h2>
<div class="content has-text-justified">
<img src="static/images/overview.jpg" alt="MY ALT TEXT"/>
<p>
The overall architecture of Co-MTP. In this framework, infrastructures share the history and their prediction results to ego CAV. Then, we construct a heterogeneous scene graph with the processed trajectory data and map information, categorizing them according to the types of objects and map elements. Next, we initialize the features of nodes and edges in the relative coordinate system of each object. The CTCA Fusion is used to update the features of the nodes and edges selected by the STSA module over K Transformer layers. Finally, we take the nodes' hidden features from the last layer and input them into the Multimodal Decoder to obtain the multimodal trajectory prediction results.
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<style>
/* 保证整个模块居中 */
.full-container {
display: flex;
justify-content: center; /* 水平居中 */
align-items: center; /* 垂直居中 */
}
/* 设置内容列的最大宽度和位置 */
.column.is-four-fifths {
width: 80%; /* 可以根据需要调整列的宽度 */
}
/* 设置标题居左 */
.title.is-4 {
text-align: left; /* 使标题左对齐 */
}
/* 图片居中 */
.content img {
display: block;
margin: 0 auto; /* 使图片居中 */
}
/* 文字居中并且文本两端对齐 */
.content.has-text-justified {
text-align: justify;
}
</style>
<!-- Method -->
<section class="hero teaser">
<div class="container full-container">
<div class="column is-four-fifths">
<h2 class="title is-4">Co-MTP Framework</h2>
<div class="content has-text-justified">
<img src="static/images/overview.jpg" alt="MY ALT TEXT"/>
<p>
<strong>The overall architecture of Co-MTP.</strong> In this framework, infrastructures share the history and their prediction results to ego CAV. Then, we construct a heterogeneous scene graph with the processed trajectory data and map information, categorizing them according to the types of objects and map elements. Next, we initialize the features of nodes and edges in the relative coordinate system of each object. The CTCA Fusion is used to update the features of the nodes and edges selected by the STSA module over K Transformer layers. Finally, we take the nodes' hidden features from the last layer and input them into the Multimodal Decoder to obtain the multimodal trajectory prediction results.
</p>
</div>
</div>
</div>
</section>
<!-- End Method -->
<!-- Experiment -->
<section class="hero teaser">
<div class="container full-container">
<div class="column is-four-fifths">
<h2 class="title is-4">Experiment</h2>
<div class="content has-text-justified">
<div class="has-text-centered">
<img src="static/images/main_result.png" alt="Description of the new image" class="new-image_1"/>
<p style="text-align: justify;">
<strong>Performance comparison on the V2X-Seq dataset.</strong> TNT, HiVT and V2X-Graph are existing methods on the V2X-Seq dataset. Co-HTTP is the baseline model, simplified from our Co-MTP model. The framework Co-MTP ranks first across minADE/minFDE/MR in the benchmark of the dataset.
</p>
</div>
<!-- 增加空行 -->
<br>
<div class="has-text-centered">
<img src="static/images/ablation_study.png" alt="Description of the new image" class="new-image_2"/>
<p style="text-align: justify;">
<strong>Results of model ablation study.</strong> We examine the effectiveness of multiview data processing strategies and the decoder, assessing Co-MTP variations separately in history and future time dimensions.
</p>
</div>
<!-- 增加空行 -->
<br>
<div class="has-text-centered">
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<div style="display: flex; justify-content: center; gap: 20px;">
<img src="static/images/noise.png" alt="Description of image 1" style="max-width: 47%;"/>
<img src="static/images/time_delay.png" alt="Description of image 2" style="max-width: 47%;"/>
</div>
<p style="text-align: justify; margin-top: 0px;">
<strong>Robustness assessment.</strong> We conduct robustness assessments by introducing noise and communication delays, assuming a positional deviation of 0.2 meters and a time delay of 0.5 seconds. We design experiments using the same Co-MTP model base, alongside two variants: Co-MTP-no fusion, which excludes the future fusion, and Co-HTTP-nofut, which simply stitches the trajectory without future information.
</p>
</div>
<div class="gif-container">
<!-- 每个 GIF 和标题的容器 -->
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<img src="static/images/visualize_pra_1001.gif" alt="MY ALT TEXT" class="gif-image"/>
<p class="gif-caption">(a)</p>
</div>
<div class="gif-item">
<img src="static/images/visualize_pra_10025.gif" alt="MY ALT TEXT" class="gif-image"/>
<p class="gif-caption">(b)</p>
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<img src="static/images/visualize_pra_10058.gif" alt="MY ALT TEXT" class="gif-image"/>
<p class="gif-caption">(c)</p>
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<img src="static/images/visualize_pra_10078.gif" alt="MY ALT TEXT" class="gif-image"/>
<p class="gif-caption">(d)</p>
</div>
<p>
<strong>Qualitative examples of Co-MTP on V2X-Seq dataset.</strong> The red box are AV, while the orange ones are the predicted targets. The history ground-truth are shown in blue, the predicted trajectories are shown in green, , and the future ground-truth are shown in brown.
<p>
</div>
<p>
<!-- 其他内容 -->
</p>
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</section>
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<style>
.gif-container {
display: flex;
flex-wrap: wrap;
gap: 0px; /* 调整 GIF 之间的间距 */
}
.new-image_1 {
max-width: 53%; /* 图片最大宽度 */
margin: 20px 0; /* 上下边距 */
}
.new-image_2 {
max-width: 100%; /* 图片最大宽度 */
margin: 20px 0; /* 上下边距 */
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.gif-item {
flex: 1 1 calc(50% - 0px); /* 每行两个 GIF，减去间距 */
max-width: calc(50% - 0px); /* 确保每行只有两个 GIF */
text-align: center; /* 标题居中 */
}
.gif-image {
width: 100%; /* 让 GIF 填满容器宽度 */
height: auto; /* 保持比例 */
max-height: 600px; /* 设置最大高度，根据需要调整 */
}
.gif-caption {
margin-top: 0px; /* 标题与 GIF 的间距 */
font-size: 14px; /* 标题字体大小 */
color: #333; /* 标题颜色 */
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<!--BibTex citation -->
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@misc{zhang2025comtpcooperativetrajectoryprediction,
title={Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving},
author={Xinyu Zhang and Zewei Zhou and Zhaoyi Wang and Yangjie Ji and Yanjun Huang and Hong Chen},
year={2025},
eprint={2502.16589},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.16589},
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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