-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathcav-localization.html
267 lines (241 loc) · 18 KB
/
cav-localization.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
<!DOCTYPE HTML>
<!--
Editorial by Ding Zhao
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>Ding Zhao's Website</title>
<meta name="author" content="Ding Zhao">
<meta name="keywords" content="ding,zhao,ding zhao,zhaoding,cmu,carnegie,mellon,umich,michigan,umtri,transportation,automated,automated vehicle,av,intelligent,accelerated evaluation,connected,safety,DSRC">
<meta name="description" content="Ding Zhao umich Accelerated Evaluation">
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<!--[if lte IE 8]><script src="assets/js/ie/html5shiv.js"></script><![endif]-->
<link rel="stylesheet" href="assets/css/main.css" />
<!--[if lte IE 9]><link rel="stylesheet" href="assets/css/ie9.css" /><![endif]-->
<!--[if lte IE 8]><link rel="stylesheet" href="assets/css/ie8.css" /><![endif]-->
<link rel="icon" href="assets/icon/cmu.ico" />
</head>
<body>
<!-- Wrapper -->
<div id="wrapper">
<!-- Main -->
<div id="main">
<div class="inner">
<!-- Header -->
<header id="header">
<a href="index.html" class="logo"><strong>Highlighed
Research</strong></a>
<ul class="icons">
<li><a href="https://www.linkedin.com/in/ding-zhao-01130730/" class="icon fa-linkedin"><span class="label">LinkedIn</span></a></li>
<li><a href="https://scholar.google.com/citations?user=uirPPuYAAAAJ&hl=en" class="icon fa-graduation-cap"><span class="label">Scholar</span></a></li>
<li><a href="https://github.com/zhao-ding" class="icon fa-github"><span class="label">Github</span></a></li>
<li><a href="https://www.strava.com/athletes/7560016" class="icon fa-heartbeat"><span class="label">Run</span></a></li>
</ul>
</header>
<!-- Content -->
<section>
<header class="main">
<h1>Cooperative Localization in the Connected Vehicle Network</h1>
</header>
<span class="image main"><img src="research/LocalizationCVN/TestingAtMcity.jpg" alt="" /></span>
<p>Good localization is essential in the deployment of autonomous vehicles. Currently, high accuracy GNSS devices are still too expensive for passenger cars. Based on the booming V2X or Connected Vehicle technology, we are exploring approaches that
can enhance localization quality for a network of vehicles on average but with no extra cost, as long as the vehicles are connected by exchanging their raw GNSS information.</p>
<h2>Improving Localization Accuracy in the Connected Vehicle Network</h2>
<p><span class="image right"><img src="research/LocalizationCVN/Illustration_of_CMM.png" alt="" /></span>
<b>Macheng Shen, <u>Ding Zhao</u>, Jing Sun,
''Enhancement of Low-cost GNSS Localization in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters,
'' <i>Proceedings of the IEEE 19th International Intelligent Transportation Systems Conference (<b>ITSC</b>)</i>, Rio de Janeiro, Brazil, Nov 1-4, 2016.</b> |
<a href="https://arxiv.org/abs/1606.03736" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=uirPPuYAAAAJ&citation_for_view=uirPPuYAAAAJ:WF5omc3nYNoC"><i class="icon fa-graduation-cap"></i></a>
<br /><br />
<b><li>Macheng Shen, <u>Ding Zhao</u>, Jing Sun, Huei Peng,
''Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments,''
<i>IEEE Transactions on Intelligent Transportation Systems</i>, 2017.</b> |
<a href="https://arxiv.org/abs/1702.05792" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=Improving+Localization+Accuracy+in+Connected+Vehicle+Networks+Using+Rao-Blackwellized+Particle+Filters%3A+Theory%2C+Simulations%2C+and+Experiments&btnG="><i class="icon fa-graduation-cap"></i></a>
<a href="https://github.com/cav-localization/V2V-network-RBPF"><i class="fa fa-github"></i></a>
<br /><br />
<i>Abstract</i> — Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. It has been shown, in our previous work, that
the GNSS error can be reduced from several meters to sub-meter level by matching the biased GNSS positioning to a digital map with road constraints. While further error reduction is expected by increasing the number of participating vehicles, fundamental
questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This work presents a theoretical study that establishes
a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule.
The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected
vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the Safety Pilot Model Deployment and Pillar dataset of Ann Arbor traffic flow collected over three years period. The contributions of this
work include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.</p>
<br />
<hr class="major" />
<h2>The Impact of Road Configuration on V2V-based Cooperative Localization</h2>
<p><span class="image right"><img src="research/LocalizationCVN/composite_hour.png" alt="" /></span>
<b>Macheng Shen, <u>Ding Zhao</u>, Jing Sun,
''The Impact of Road Configuration on V2V-based Cooperative Localization,''
<i>IEEE 85th Vehicular Technology Conference (<b>VTC</b>)</i>, Sydney, Australia, June 4-7, 2017.
</b> |
<a href="https://arxiv.org/abs/1703.02098" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=uirPPuYAAAAJ&citation_for_view=uirPPuYAAAAJ:IRz6iEL74y4C"><i class="icon fa-graduation-cap"></i></a>
<br /><br />
<b>Macheng Shen, Jing Sun, <u>Ding Zhao</u> ''The Impact of Road Configuration in V2V-based Cooperative Localization: Mathematical Analysis and Real-world Evaluation,''
<i>IEEE Transactions on Intelligent Transportation Systems</i>, 2017.</b> |
<a href="https://arxiv.org/abs/1705.00568" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=The+Impact+of+Road+Configuration+in+V2V-based+Cooperative+Localization%3A+Mathematical+Analysis+and+Real-world+Evaluation&btnG="><i class="icon fa-graduation-cap"></i></a>
<a href="https://github.com/cav-localization/V2V-network-sensitivity-analysis"><i class="fa fa-github"></i></a>
<br /><br />
<i>Abstract</i> — Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. It has been shown, in our previous work, that
the GNSS error can be reduced from several meters to sub-meter level by matching the biased GNSS positioning to a digital map with road constraints. While further error reduction is expected by increasing the number of participating vehicles, fundamental
questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This work presents a theoretical study that establishes
a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule.
The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected
vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the Safety Pilot Model Deployment and Pillar dataset of Ann Arbor traffic flow collected over three years period. The contributions of this
work include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.</p>
<hr class="major" />
<h2>Optimization of Vehicle Connections in V2V-based Cooperative Localization</h2>
<p><span class="image right"><img src="research/LocalizationCVN/CPU_time_ratio.png" alt="" /></span>
<b>Macheng Shen, Jing Sun, <u>Ding Zhao</u>,
''Optimization of Vehicle Connections in V2V-based Cooperative Localization, ''
<i>Proceedings of the IEEE 20th International Intelligent Transportation Systems Conference (<b>ITSC</b>)</i>, Yokohama, Japan, October 16-19, 2017.</b>
|
<a href="https://arxiv.org/abs/1703.08818" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<a href="https://scholar.google.com/citations?view_op=view_citation&hl=en&user=uirPPuYAAAAJ&citation_for_view=uirPPuYAAAAJ:IUKN3-7HHlwC"><i class="icon fa-graduation-cap"></i></a>
<a href="https://github.com/cav-localization/V2V-network-optimization"><i class="fa fa-github"></i></a>
<br /><br />
<i>Abstract</i> — Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) positioning of a group of vehicles to improve the standalone localization accuracy. It has been shown to reduce GNSS error from several meters
to sub-meter level by matching the biased GNSS positioning of four vehicles to a digital map with road constraints in our previous work. While further error reduction is expected by increasing the number of participating vehicles, fundamental questions
on how the vehicle membership of the CMM affects the performance of the GNSS-based localization results need to be addressed to provide guidelines for design and optimization of the vehicle network. The quantitative relationship between the estimation
error and the road constraints has to be systematically investigated to provide insights. In this work, a theoretical study is presented that aims at developing a framework for quantitatively evaluating effects of the road constraints on the CMM
accuracy and for eventual optimization of the CMM network. More specifically, a closed form expression of the CMM error in terms of the road angles and GNSS error is first derived based on a simple CMM rule. Then a Branch and Bound algorithm and
a Cross Entropy method are developed to minimize this error by selecting the optimal group of vehicles under two different assumptions about the GNSS error variance.</p>
<hr class="major" />
<h2>Interpenetrating Cooperative Localization</h2>
<p><span class="image right"><img src="research/LocalizationCVN/location-net.png" alt="" /></span>
<b>Huajing Zhao, Zhaobin Mo, Macheng Shen, Jing Sun, <u>Ding Zhao</u>, ''Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks,''
<i>IEEE Transactions on Intelligent Vehicles</i>, 2017.</b> |
<a href="https://arxiv.org/abs/1804.10064" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<!-- <a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C23&q=Improving+Localization+Accuracy+in+Connected+Vehicle+Networks+Using+Rao-Blackwellized+Particle+Filters%3A+Theory%2C+Simulations%2C+and+Experiments&btnG="><i class="icon fa-graduation-cap"></i></a>
<a href="https://github.com/cav-localization/V2V-network-RBPF"><i class="fa fa-github"></i></a> -->
<b>Macheng Shen, Huajing Zhao, Jing Sun, <u>Ding Zhao</u>, ''Semi-Interpenetrating Cooperative Localization in Connected Vehicle Networks, ''
<i>IEEE International Conference on Robotics and Automation (<b>ICRA</b>)</i>, Brisbane, May 21-25,2018.</b>
|
<a href="https://arxiv.org/abs/1709.05457" target="_blank"><i class="icon fa-file-pdf-o"></i></a>
<a href="https://scholar.google.com/scholar?q=Semi-Interpenetrating+Cooperative+Localization+in+Connected+Vehicle+Networks&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0ahUKEwiDlvy1mLzWAhWM0YMKHboGDfUQgQMIKzAA"><i class="icon fa-graduation-cap"></i></a>
<a href="https://github.com/cav-localization/network-propagation"><i class="fa fa-github"></i></a>
<br /><br />
<i>Abstract</i> — We proposed a fusion mechanism for the distributed cooperative map matching (CMM) within the vehicular ad-hoc network. This mechanism makes the information from each node reachable within the network by other nodes without
direct communication, thus improving the overall localization accuracy and robustness. Each node runs a Rao-Blackwellized particle filter (RBPF) that processes the Global Navigation Satellite System (GNSS) measurements of its own and its neighbors,
followed by a map matching step that reduces or eliminates the GNSS atmospheric error. Then each node fuses its own filtered results with those from its neighbors for a better estimation. In this work, the complicated dynamics and fusion mechanics
of these RBPFs are represented by a linear dynamical system. We proposed a distributed optimization framework that explores the model to improve both robustness and accuracy of the distributed CMM. The effectiveness of this distributed optimization
framework is illustrated by simulation results on realistic vehicular networks drawn from data, compared with the centralized one and a decentralized one with random fusion weights.</p>
</section>
</div>
</div>
<!-- Sidebar -->
<div id="sidebar">
<div class="inner">
<!-- Search -->
<section id="search" class="alt">
<form method="post" action="#">
<input type="text" name="query" id="query" placeholder="Search" />
</form>
</section>
<!-- Menu -->
<nav id="menu">
<header class="major">
<h2>Menu</h2>
</header>
<ul>
<li><a href="index.html">Homepage</a></li>
<li><a href="research.html">Research</a></li>
<li><a href="publications.html">Publication</a></li>
<!--<li>-->
<!--<span class="opener">Submenu</span>-->
<!--<ul>-->
<!--<li><a href="#">Lorem Dolor</a></li>-->
<!--<li><a href="#">Ipsum Adipiscing</a></li>-->
<!--<li><a href="#">Tempus Magna</a></li>-->
<!--<li><a href="#">Feugiat Veroeros</a></li>-->
<!--</ul>-->
<!--</li>-->
<!--<li><a href="#">Teaching</a></li>-->
<li><a href="talk.html">Talk</a></li>
<li><a href="student.html">Student</a></li>
<li><a href="calendar.html">Calendar</a></li>
<!--<li>-->
<!--<span class="opener">Another Submenu</span>-->
<!--<ul>-->
<!--<li><a href="research.html">Lorem Dolor</a></li>-->
<!--<li><a href="#">Ipsum Adipiscing</a></li>-->
<!--<li><a href="#">Tempus Magna</a></li>-->
<!--<li><a href="#">Feugiat Veroeros</a></li>-->
<!--</ul>-->
<!--</li>-->
<!--<li><a href="#">Maximus Erat</a></li>-->
<!--<li><a href="#">Sapien Mauris</a></li>-->
<!--<li><a href="#">Amet Lacinia</a></li>-->
</ul>
</nav>
<!-- Section -->
<!--<section>-->
<!--<header class="major">-->
<!--<h2>Ante interdum</h2>-->
<!--</header>-->
<!--<div class="mini-posts">-->
<!--<article>-->
<!--<a href="#" class="image"><img src="images/pic07.jpg" alt="" /></a>-->
<!--<p>Aenean ornare velit lacus, ac varius enim lorem ullamcorper dolore aliquam.</p>-->
<!--</article>-->
<!--<article>-->
<!--<a href="#" class="image"><img src="images/pic08.jpg" alt="" /></a>-->
<!--<p>Aenean ornare velit lacus, ac varius enim lorem ullamcorper dolore aliquam.</p>-->
<!--</article>-->
<!--<article>-->
<!--<a href="#" class="image"><img src="images/pic09.jpg" alt="" /></a>-->
<!--<p>Aenean ornare velit lacus, ac varius enim lorem ullamcorper dolore aliquam.</p>-->
<!--</article>-->
<!--</div>-->
<!--<ul class="actions">-->
<!--<li><a href="#" class="button">More</a></li>-->
<!--</ul>-->
<!--</section>-->
<!-- Section -->
<section>
<header class="major">
<h2>Get in touch</h2>
</header>
<p>The best way to reach me, generally, is via email. During the day, I am usually in my office.</p>
<ul class="contact">
<li class="fa-envelope-o">[email protected]</li>
<li class="fa-phone">412-268-3348</li>
<li class="fa-map-marker">5000 Forbes Avenue<br /> Scaife Hall 315<br /> Pittsburgh, PA 15213</li>
</ul>
</section>
<!-- Footer -->
<footer id="footer">
<p class="copyright">© Ding Zhao. All rights reserved. Design: <a href="http://html5up.net">HTML5 UP</a>.</p>
</footer>
</div>
</div>
</div>
<!-- Scripts -->
<script src="assets/js/jquery.min.js"></script>
<script src="assets/js/skel.min.js"></script>
<script src="assets/js/util.js"></script>
<!--[if lte IE 8]><script src="assets/js/ie/respond.min.js"></script><![endif]-->
<script src="assets/js/main.js"></script>
<script>
(function(i, s, o, g, r, a, m) {
i['GoogleAnalyticsObject'] = r;
i[r] = i[r] || function() {
(i[r].q = i[r].q || []).push(arguments)
}, i[r].l = 1 * new Date();
a = s.createElement(o),
m = s.getElementsByTagName(o)[0];
a.async = 1;
a.src = g;
m.parentNode.insertBefore(a, m)
})(window, document, 'script', 'https://www.google-analytics.com/analytics.js', 'ga');
ga('create', 'UA-85426991-2', 'auto');
ga('send', 'pageview');
</script>
</body>
</html>