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<!DOCTYPE html>
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<title> Event-Aided Time-to-Collision Estimation for Autonomous Driving </title>
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<h1 class="title is-1 publication-title">Event-Aided Time-to-Collision Estimation <br> for Autonomous Driving</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://scholar.google.com/citations?hl=en&user=csfnTk4AAAAJ/" target="_blank"> Jinghang Li</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com.tw/citations?user=0z2qluIAAAAJ&hl=zh-TW&oi=ao/" target="_blank"> Bangyan Liao</a><sup>2*</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com.tw/citations?user=my5DtVMAAAAJ&hl=zh-TW&oi=ao/" target="_blank"> Xiuyuan LU</a><sup>3</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=XZczNEEAAAAJ&hl=en&oi=ao/" target="_blank"> Peidong Liu</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://uav.hkust.edu.hk/group/" target="_blank"> Shaojie Shen</a><sup>3</sup>, and</span>
<span class="author-block">
<a href="https://sites.google.com/view/zhouyi-joey/home/" target="_blank"> Yi Zhou</a><sup>1✉</sup>
</span>
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<span class="author-block">
<sup>1</sup> Neuromorphic Automation and Intelligence Lab (NAIL), Hunan University <br>
<sup>2</sup> School of Engineering, Westlake University <br>
<sup>3</sup> Aerial Robotics Group, HKUST <br>
European Conference on Computer Vision (ECCV), 2024
</span>
<span class="eql-cntrb"><small><br><sup>*</sup> Equal Contribution <sup>✉</sup>Corresponding author: <a href="mailto:[email protected]">[email protected]</a></small></span>
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<span>arXiv</span>
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<span>Code</span>
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<source src="https://nail-hnu.github.io/EventAidedTTC/static/videos/experiment_result.mp4"
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<h2 class="subtitle has-text-centered">
Real-world Experiments.
</h2>
</div>
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</section>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Predicting a potential collision with leading vehicles is an essential functionality of any autonomous/assisted driving system. One bottleneck of existing vision-based solutions is that their updating rate is limited to the frame rate of standard cameras used. In this paper, we present a novel method that estimates the time to collision using a neuromorphic event-based camera, a biologically inspired visual sensor that can sense at exactly the same rate as scene dynamics. The core of the proposed algorithm consists of a two-step approach for efficient and accurate geometric model fitting on event data in a coarse-to-fine manner. The first step is a robust linear solver based on a novel geometric measurement that overcomes the partial observability of event-based normal flow. The second step further refines the resulting model via a spatio-temporal registration process formulated as a nonlinear optimization problem. Experiments on both synthetic and real data demonstrate the effectiveness of the proposed method, outperforming other alternative methods in terms of efficiency and accuracy.
</p>
</div>
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</section>
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<h2 class="title is-3">Datasets Overview</h2>
<div style="text-align: center;">
<img src="https://nail-hnu.github.io/EventAidedTTC/static/images/setup_overview.png" alt="sys" width="1280" height="720" />
</div>
<h2 class="subtitle has-text-centered">
Illustration of our datasets and devices for data collection.
(a) A selected snapshot of the synthetic dataset, on each side of which the intensity information and the events (with a naive accumulation) are illustrated, respectively.
(b) The configuration of the small-scale test platform.
(c) The multi-sensor suite mounted on a car.
</h2>
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</div>
</section>
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<h2 class="subtitle has-text-centered">
The details of three datasets we collected and their corresponding data collection hardware.
</h2>
</div>
</div>
</section>
</div>
</div>
<!-- Dataset links -->
<!-- Carla dataset Links -->
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<h2 class="title is-3">Dataset Links</h2>
<h3 class="title is-4 has-text-centered">Carla Synthetic Data Sequences</h3>
<div class="columns is-centered is-vcentered">
<div class="column is-half">
<figure class="image">
<img src="https://nail-hnu.github.io/EventAidedTTC/static/images/carla.gif" alt="Left Sequence GIF" width="425" height="240">
</figure>
</div>
<div class="column is-half">
<div class="columns is-multiline">
<div class="column is-full">
<h4 class="title is-6">Suburban Const. Vel</h4>
<ul>
<li>rawdata with 8 types of leading vehicles: <a href="https://drive.google.com/drive/folders/1BdNs93O-P8CuCdzP3Tz-K2Qrxia-S2qL?usp=sharing">rosbag (197M)</a> </li>
<li>calibration: <a href="https://drive.google.com/file/d/1N5amhdVXkf9aQZx1ke15XnfYjQd91PWI/view?usp=sharing">mat (1K)</a> </li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Urban Const. Vel</h4>
<ul>
<li>rawdata with 8 types of leading vehicles: <a href="https://drive.google.com/drive/folders/1QVPaVk0YszNkUv7PzA4LHNkc8HZ36oak?usp=sharing">rosbag (223M)</a> </li>
<li>calibration: <a href="https://drive.google.com/file/d/1x1x6Fng_nsngNpOhTerJhghXKVZvICiU/view?usp=sharing">mat (1K)</a> </li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Suburban Accelerate</h4>
<ul>
<li>rawdata with 8 types of leading vehicles: <a href="https://drive.google.com/drive/folders/1TEEtbbJRYn12i65wKBv1YL2p-aHNIsmK?usp=sharing">rosbag (202M)</a> </li>
<li>calibration: <a href="https://drive.google.com/file/d/1apxuvuhi7c7vyE9iM9S1LX4NqAgFcLk0/view?usp=sharing">mat (1K)</a> </li>
</ul>
</div>
</div>
</div>
</div>
</div>
<!-- End Slider Dataset links -->
<!-- slider dataset Links -->
<div class="hero-body">
<div class="container">
<h3 class="title is-4 has-text-centered">Small-scale Data Sequences</h3>
<div class="columns is-centered is-vcentered">
<div class="column is-half">
<figure class="image">
<img src="https://nail-hnu.github.io/EventAidedTTC/static/images/slider.gif" alt="Left Sequence GIF" width="425" height="240">
</figure>
</div>
<div class="column is-half">
<div class="columns is-multiline">
<div class="column is-full">
<h4 class="title is-6">Slider 500</h4>
<ul>
<li>rawdata: <a href="https://drive.google.com/file/d/1pIMngg_zyveweRGhUh4rV1vJrniVGEj3/view?usp=sharing">rosbag (445M)</a>
| bounding box: <a href="https://drive.google.com/file/d/1pkuIpNEfJQo67OnI-k6o6pyQbnr31lhY/view?usp=sharing">csv (3K)</a></li>
<li>groundtruth TTC: <a href="https://drive.google.com/file/d/1VOlZnZMh1dAnw6zN2KLl32pszVdXxYBn/view?usp=sharing">csv (3K)</a>
| calibration: <a href="https://drive.google.com/file/d/1h5YUogbsrotCdib_ghHDwJKtnp6T7RVa/view?usp=sharing">mat (61K)</a></li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Slider 750</h4>
<ul>
<li>rawdata: <a href="https://drive.google.com/file/d/1RblCeHK1akuPjcZjGy-N9IsPL9a4Jrmi/view?usp=sharing">rosbag (613M)</a>
| bounding box: <a href="https://drive.google.com/file/d/1ie8gfp2keiVHXIjwzX_h7Qs6ceyExK8b/view?usp=sharing">csv (2K)</a></li>
<li>groundtruth TTC: <a href="https://drive.google.com/file/d/1mrhJplOKqOKwsOqqDe1fFcoXkbNLk1pP/view?usp=sharing">csv (2K)</a>
| calibration: <a href="https://drive.google.com/file/d/1EaGqz9j8qaor678T7n4IU2iKJd4io__5/view?usp=sharing">mat (61K)</a></li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Slider 1000</h4>
<ul>
<li>rawdata: <a href="https://drive.google.com/file/d/1xiLSPXHeoBdbvdO201OUx3IUnJGXzWUH/view?usp=sharing">rosbag (484M)</a>
| bounding box: <a href="https://drive.google.com/file/d/1sFqz2tREbi07Jd4lS1nRzLFBnSpjIjr3/view?usp=sharing">csv (1K)</a></li>
<li>groundtruth TTC: <a href="https://drive.google.com/file/d/1iX5GeyhrnxixolBY2U40cfo3DHTRL9HB/view?usp=share_link">csv (2K)</a>
| calibration: <a href="https://drive.google.com/file/d/1cTWeDiqCjmsoyFz5j015VOMIX4fDO4eK/view?usp=sharing">mat (61K)</a></li>
</ul>
</div>
</div>
</div>
</div>
</div>
<!-- End Slider Dataset links -->
<!-- FCWD Dataset links -->
<div class="hero-body">
<div class="container">
<h3 class="title is-4 has-text-centered">Forward Collision Warning Data (FCWD) Sequences</h3>
<div class="columns is-centered is-vcentered">
<div class="column is-half">
<figure class="image">
<img src="https://nail-hnu.github.io/EventAidedTTC/static/images/fcwd.gif" alt="Left Sequence GIF" width="425" height="240">
</figure>
</div>
<div class="column is-half">
<div class="columns is-multiline">
<div class="column is-full">
<!-- <h4 class="title is-6">Calibration File</h4> -->
<ul>
<li>Camera intrinsic: <a href="https://drive.google.com/drive/folders/1RH8MnHrv_L_Z_EQLivvwmZ4gHGLY79Lf?usp=sharing">mat (112K)</a>
| Lidar2RGB extrinsic: <a href="https://drive.google.com/drive/folders/1Wpgm6pMLHus4JQ4NTEYWk29xQY8aDA6X?usp=sharing">txt (1K)</a> </li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Sequence 1</h4>
<ul>
<li>rawdata: <a href="https://drive.google.com/file/d/1GoYA4pveeEVePT6JN0lZH35cVKThaOpq/view?usp=sharing">rosbag (1.01G)</a>
| <a href="https://drive.google.com/file/d/1UP7mGo9Sm-FBe8cw1j51ej2hwlYa-E9Y/view?usp=sharing">hdf5 (2.22G)</a></li>
<li>bounding box: <a href="https://drive.google.com/file/d/160p6IE2BTUJG_4Pw3NoTYKjupV_OtfZm/view?usp=sharing">csv (6K)</a>
| groundtruth TTC: <a href="https://drive.google.com/file/d/1Nq7YwqAvua4N2FZqBFq4CLHRQKdHN0om/view?usp=sharing">csv (7K)</a> </li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Sequence 2</h4>
<ul>
<li>rawdata: <a href="https://drive.google.com/file/d/1aqD-UBiWcAdd8qms1NJyw2Xf2MivUs-l/view?usp=sharing">rosbag (518M)</a>
| <a href="https://drive.google.com/file/d/1fQmlxH5JMgV6JzoxjV4XcRQ2uSkLT4fs/view?usp=sharing">hdf5 (2.01G)</a></li>
<li>bounding box: <a href="https://drive.google.com/file/d/1bXw7DTz0symg-liO_t4EsQJQ1cSUoxe2/view?usp=sharing">csv (7K)</a>
| groundtruth TTC: <a href="https://drive.google.com/file/d/1-MTOlgYlXSa7v7MTSdux0mw2smlUd25V/view?usp=sharing">csv (7K)</a> </li>
</ul>
</div>
<div class="column is-full">
<h4 class="title is-6">Sequence 3</h4>
<ul>
<li>rawdata: <a href="https://drive.google.com/file/d/1Y1Ls68a3LJmr76EF27c24hCocUn0UY9E/view?usp=sharing">rosbag (503M)</a>
| <a href="https://drive.google.com/file/d/1o0TWqkc6KCbHsEGwwxiVI-XyZeM5FUZZ/view?usp=sharing">hdf5 (1.99G)</a></li>
<li>bounding box: <a href="https://drive.google.com/file/d/1obM4PKJyX4VPhpJ8NUJ1Q3snR1StDPxw/view?usp=sharing">csv (6K)</a>
| groundtruth TTC: <a href="https://drive.google.com/file/d/1gH80TDTE-rLW55fkHPAzdGCPXR5yVhn1/view?usp=sharing">csv (7K)</a> </li>
</ul>
</div>
</div>
</div>
</div>
</div>
<!-- End FCWD Dataset links -->
</div>
<h4 class="subtitle has-text-centered">
<small>If you have any questions about using the data, please contact Jinghang Li <a href="mailto:[email protected]">([email protected])</a></small>
</h4>
</div>
<!--BibTex citation -->
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<h2 class="title">BibTeX</h2>
<pre><code>
@misc{li2024eventaidedtimetocollisionestimationautonomous,
title = {Event-Aided Time-to-Collision Estimation for Autonomous Driving},
author = {Jinghang Li and Bangyan Liao and Xiuyuan LU and Peidong Liu and Shaojie Shen and Yi Zhou},
year = 2024,
eprint = {2407.07324},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}
</code></pre>
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