Article
A Unified Multiple-Target Positioning Framework for
Intelligent Connected Vehicles
Zhongyang Xiao
1
, Diange Yang
1,
*, Fuxi Wen
1,2
and Kun Jiang
1
1
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University,
Beijing 100084, China; xiaozy15@mails.tsinghua.edu.cn (Z.X.); fuxi@chalmers.se (F.W.);
jiangkun@mail.tsinghua.edu.cn (K.J.)
2
Department of Electrical Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
* Correspondence: ydg@mail.tsinghua.edu.cn
Received: 11 April 2019; Accepted: 24 April 2019; Published: 26 April 2019
Abstract:
Future intelligent transport systems depend on the accurate positioning of multiple
targets in the road scene, including vehicles and all other moving or static elements. The existing
self-positioning capability of individual vehicles remains insufficient. Also, bottlenecks in
developing on-board perception systems stymie further improvements in the precision and integrity
of positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming a
standard component of intelligent and connected vehicles, renders new sources of information
such as dynamically updated high-definition (HD) maps accessible. In this paper, we propose a
unified theoretical framework for multiple-target positioning by fusing multi-source heterogeneous
information from the on-board sensors and V2X technology of vehicles. Numerical and theoretical
studies are conducted to evaluate the performance of the framework proposed. With a low-cost
global navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-board
sensors, and a normally equipped HD map, the precision of multiple-target positioning attained
can meet the requirements of high-level automated vehicles. Meanwhile, the integrity of target
sensing is significantly improved by the sharing of sensor information and exploitation of map
data. Furthermore, our framework is more adaptable to traffic scenarios when compared with
state-of-the-art techniques.
Keywords:
vehicular localization; target positioning; high-definition map; vehicle-to-everything;
intelligent and connected vehicles; intelligent transport system
1. Introduction
The intelligent transportation system (ITS) is one of the most indispensable components of the
smart city concept that integrates sensing, control, information, and communication technologies
into transportation [
1
]. In recent years, with the emergence of cutting-edge applications of ITS,
the positioning of multiple targets, including vehicles and other elements has been playing an
increasingly important role in improving safety, mobility, and efficiency [
2
–
4
]. For example, future
intelligent connected vehicles (ICVs) require the positioning of their own real-time location with
centimeter-level precision [
5
], and the awareness of all objects such as surrounding vehicles and
vulnerable road users with significant integrity and confidence. In ITS, the positioning of vehicles and
other targets are usually referred to as vehicular self-positioning and target localization, respectively.
Although attention has been paid in these topics [
6
–
9
], there still exist many limitations that need to
be eliminated.
Sensors 2019, 19, 1967; doi:10.3390/s19091967 www.mdpi.com/journal/sensors
