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<!DOCTYPE html>
<html>
<head lang="en">
<meta charset="UTF-8">
<meta http-equiv="x-ua-compatible" content="ie=edge">
<title>Sim-on-Wheels</title>
<meta name="description" content="">
<meta name="viewport" content="width=device-width, initial-scale=1">
<!-- <base href="/"> -->
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<script src="./js/jquery.min.js"></script>
<script src="./js/bootstrap.min.js"></script>
<script src="./js/app.js"></script>
</head>
<body>
<div class="container" id="main">
<div class="row">
<h2 class="col-md-12 text-center">
<b>Sim-on-Wheels</b>: Physical World in the Loop Simulation for Autonomous Driving
</h2>
<h3 class="col-md-12 text-center">
Accepted to <b>IEEE RA-L</b>, Presenting at <b>ICRA '24</b>
</h3>
<h4 class="col-md-12 text-center">
ICRA '24 Oral: <b>Thursday 1:30-3, Room CC-311 (Autonomous Agents I)</b> <br>
ICRA '24 Poster: <b>#02.01 on Thursday 4:30-6 </b>
</h4>
</div>
<div class="row">
<div class="col-sm-10 col-sm-offset-1 text-center">
<ul class="list-inline">
<li> <a href="https://yshen47.github.io/">Yuan Shen<sup>1*</sup></a> </li>
<li> <a href="https://bchandaka.github.io/">Bhargav Chandaka<sup>1*</sup></a> </li>
<li> <a href="https://zhihao-lin.github.io/"> Zhi-Hao Lin<sup>1</sup></a> </li>
<li> <a href="https://ajzhai.github.io/"> Albert Zhai<sup>1</sup></a> </li>
<li> <a href="https://hangpersonal.com"> Hang Cui<sup>1</sup></a> </li>
<li> <a href="http://luthuli.cs.uiuc.edu/~daf/">David Forsyth<sup>1</sup></a> </li>
<li> <a href="https://shenlong.web.illinois.edu/">Shenlong Wang<sup>1</sup></a> </li>
</ul>
<ul class="list-inline">
<li> <sup>1</sup>University of Illinois at Urbana-Champaign </li>
<br />
<li> <sup>*</sup> Equal Contribution</li>
</ul>
</div>
</div>
<div class="row">
<div class="col-sm-10 col-sm-offset-1 text-center">
<span class="link-block">
<a href="https://ieeexplore.ieee.org/document/10287403" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-ieee ai-2x" aria-hidden="true"></i>
</span>
<span>IEEE <em>Xplore</em></span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/abs/2306.08807" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<!-- <i class="fa fa-file-pdf-o fa-2x" aria-hidden="true"></i> -->
<i class="ai ai-arxiv ai-2x" aria-hidden="true"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/Sim-on-Wheels" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fa fa-github fa-2x" aria-hidden="true"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
<!-- Abstraction, Overview figure-->
<div class="row">
<div class="col-md-10 col-md-offset-1">
<div class="text-center">
<image src="./assets/teaser.png" class="img-responsive" alt="teaser"/>
</div><br>
<h3>
Abstract
</h3>
<p class="text-justify">
We present Sim-on-Wheels, a safe, realistic, and
vehicle-in-loop framework to test autonomous vehicles’ performance
in the real world under safety-critical scenarios. Sim-on-wheels
runs on a self-driving vehicle operating in the physical
world. It creates virtual traffic participants with risky behaviors
and seamlessly inserts the virtual events into images perceived
from the physical world in real-time. The manipulated images
are fed into autonomy, allowing the self-driving vehicle to react
to such virtual events. The full pipeline runs on the actual vehicle
and interacts with the physical world, but the safety-critical
events it sees are virtual. Sim-on-Wheels is safe, interactive,
realistic, and easy to use. The experiments demonstrate the
potential of Sim-on-Wheels to facilitate the process of testing
autonomous driving in challenging real-world scenes with high
fidelity and low risk.
</p>
</div>
<div class="col-md-10 col-md-offset-1">
<h3>
Supplementary Video
</h3>
<div class="text-center">
<video width="720" height="540" controls>
<source src="./assets/Sim-on-Wheels_Supplementary_Video.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
</div><br>
</div>
<div class="col-md-10 col-md-offset-1">
<h4>
Scenario Diversity
</h4>
<p class="text-justify">
Sim-on-Wheels is highly flexible and generates scenarios with different difficulty levels. The scenarios include static human/objects, a traffic intersection, and jaywalkers with different speeds and occluding walls.
</p>
</div>
<div class="col-md-8 col-md-offset-2">
<table width="100%" class="table-spaced">
<tbody>
<tr>
<td align="center" width="33%">
<img src="./assets//scenario_diversity/straight_human.png" class="img-responsive">
</td>
<td align="center" width="33%">
<img src="./assets/scenario_diversity/straight_obj.png" class="img-responsive"> </td>
</tr>
<tr>
<td align="center" width="50%">
<font size="2" color="black">Scanned Human</font>
</td>
<td align="center" width="50%">
<font size="2" color="black">Virtual Objects</font>
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/scenario_diversity/jaywalker_group.png" class="img-responsive"> </td>
<td align="center" width="50%">
<img src="./assets/scenario_diversity/jaywalker_occ_group.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<font size="2" color="black">Jaywalkers</font>
</td>
<td align="center" width="50%">
<font size="2" color="black">Jaywalkers with occlusion</font>
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/scenario_diversity/intersection_2.png" class="img-responsive"> </td>
<td align="center" width="50%">
<img src="./assets/scenario_diversity/mix.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<font size="2" color="black">Intersection</font>
</td>
<td align="center" width="50%">
<font size="2" color="black">Mixed Scenarios</font>
</td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-10 col-md-offset-1">
<h4>
Generalization to Other Environments
</h4>
<p class="text-justify">
We can easily adapt to any environment as long as the scenario is compatible with the environment mapping. For example, we ran our Sim-on-Wheels on KITTI-360 data to simulate scenarios on the street roads as shown below:
</p>
</div>
<div class="col-md-8 col-md-offset-2">
<h5><strong>1. KITTI-360</strong></h5>
<table width="100%" class="table2-spaced">
<tbody>
<tr>
<td align="center" width="50%">
<font size="3" color="black">Original</font>
</td>
<td align="center" width="50%">
<font size="3" color="black">Sim-on-Wheels</font>
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/kitti/1130-00016-origin.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/kitti/1130-00016-insert.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/kitti/1538-00014-origin.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/kitti/1538-00014-insert.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/kitti/250-00030-origin.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/kitti/250-00030-insert.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/kitti/250-00031-origin.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/kitti/250-00031-insert.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/kitti/610-00018-origin.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/kitti/610-00018-insert.png" class="img-responsive">
</td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-8 col-md-offset-2">
<h5><strong>2. Tests in Street Scenarios</strong></h5>
<p class="text-justify">
Additionally, to showcase the generalization, we ran Sim-on-Wheels on our GEM vehicle in daily streets beyond agent-free setting, e.g., empty testbed. Due to our university’s policy, we are not allowed to test outside our test track at large scale. We plan to test in more complicated real-world environments in future.
</p>
<table width="100%" class="table2-spaced">
<tbody>
<tr>
<td align="center" width="50%">
<font size="3" color="black">Original</font>
</td>
<td align="center" width="50%">
<font size="3" color="black">Sim-on-Wheels</font>
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/street1.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/street1_inserted.png" class="img-responsive">
</td>
</tr>
<tr>
<td align="center" width="50%">
<img src="./assets/street2.png" class="img-responsive">
</td>
<td align="center" width="50%">
<img src="./assets/street2_inserted.png" class="img-responsive">
</td>
</tr>
</tbody>
</table>
</div>
<div class="col-md-10 col-md-offset-1">
<h4>
Scalibility to Large Amount of Agents
</h4>
<p class="text-justify">
Unlike real-world road testing, Sim-on-Wheels can insert agents itself into the background and can scale to insert many agents without significant loss in rendering speed. We conducted the following experiment under multiple resolution and various number of virtual objects. Our experiment shows that under the same resolution, the rendering speed is not affected much by the increasing number of virtual objects.
</p>
<div class="col-md-6 col-md-offset-3 text-center">
<img src="./assets/rendering.png" class="img-responsive" />
</div>
</div>
<div class="row">
<div class="col-md-10 col-md-offset-1">
<h3>
Citation
</h3>
<div class="form-group col-md-10">
If you find our work useful in your research, please consider citing:
<textarea id="codemirror" class="form-control" readonly style="white-space: pre; overflow-x: auto;">
@inproceedings{shen2023simonwheels,
author={Shen, Yuan and Chandaka, Bhargav and Lin, Zhi-Hao and Zhai, Albert and Cui, Hang and Forsyth, David and Wang, Shenlong},
journal={IEEE Robotics and Automation Letters},
title={Sim-on-Wheels: Physical World in the Loop Simulation for Self-Driving},
year={2023},
volume={8},
number={12},
pages={8192-8199},
doi={10.1109/LRA.2023.3325689}
}
</textarea>
</div>
</div>
</div>
<div class="row">
<div class="col-md-10 col-md-offset-1">
<h3>
Acknowledgements
</h3>
This work was supported by the Amazon Research Award, Nvidia Hardware Grants, the Insper-Illinois Innovative Grant, the NCSA Faculty Fellow, NSF Award #2331878, and the Illinois Smart Transportation Infrastructure Initiative STII-21-07.
<br>
The website template was borrowed from <a href="https://climatenerf.github.io/">ClimateNerf</a>.
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