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*
A Pathway Forward: the Evolution of Intelligent
Vehicles Research on IEEE T-IV
Haoran Wang, Xiao Wang, Xuan Li, Xingtang Wu, Jia Hu, Yuanyuan Chen, Zhongmin Liu, and Ying Li
Abstract—This paper presents a bibliographic and
collaboration pattern analysis of the IEEE TRANSACTIONS ON
INTELLIGENT VEHICLES (T-IV). In this paper, a journal analysis
framework is proposed. The most productive/influential authors,
institutions, and countries/regions are identified. The research
group structure is generated. Hot research topics and trends are
discussed. Analysis results find that: i) The U.S. is now dominating
the IV research field. It contributes 32% of papers in the IEEE T-
IV; ii) Vehicle planning, vehicle perception, and energy
management are the top-three hottest research topics from 2016
to 2021; iii) The Cooperative Automation (CA) is a research trend
that includes vehicle group control, platoon control, intersection
management, cooperation security, and cooperative perception;
iv) The new “3224” processing policy is demonstrated to reduce
processing durations by 90% and attracts nine times more
manuscripts.
Index Terms—Bibliographic analysis, collaboration pattern,
intelligent vehicles (IV), cooperative automation (CA).
I. INTRODUCTION
HE commercialization of driving assistance systems is
leading to the future of Intelligent Vehicle (IV), which
aims at improving the safety, mobility, sustainability,
and comfort of driving [1]. In order to promote the development
of the intelligent vehicle, a growing number of articles have
been published, as illustrated in Fig. 1. The IEEE
TRANSACTIONS ON INTELLIGENT VEHICLES (T-IV) is one of the
stages for these works.
Fig. 1. Articles related to intelligent vehicles
Managed by the IEEE Intelligent Transportation Systems
Society (ITSS), the IEEE T-IV focuses on providing critical
information to the IV community, serving as the dissemination
for ITSS members and others to learn state-of-the-art
developments and progress on research and applications in the
* (Corresponding author: Jia Hu.)
Haoran Wang and Jia Hu are with Key Laboratory of Road and Traffic
Engineering of the Ministry of Education, Tongji University, Shanghai 201804,
China (e-mail: wang_haoran@tongji.edu.cn; hujia@tongji.edu.cn).
Xiao Wang and Yuanyuan Chen are with State Key Laboratory of
Management and Control for Complex Systems, Institute of Automation,
Chinese Academy of Sciences, Beijing 100190, China (e-mail:
x.wang@ia.ac.cn; yuanyuan.chen@ia.ac.cn).
field of IV. The IEEE T-IV was launched in 2016. After six
years, in 2022, it first receives an impact factor of 5.009. This
milestone achievement proves the success of the reform led by
the editorial board. Therefore, the new submission processing
policy shall be discussed as a successful practice for other
publications. Moreover, the six-year history of the IEEE T-IV
reflects the development of the IV research field. A
bibliographic and collaboration pattern analysis on IEEE T-IV
could provide us with a comprehensive understanding of the IV
research.
Therefore, this paper analyses articles and the journal
logistics of the IEEE T-IV. Contributions are summarized as
follows.
1) The research group structure is identified by the co-
authorship analysis.
2) Hot research topics and research trends are discussed by
the keyword co-occurrence analysis.
3) The effectiveness of the new submission processing
policy is demonstrated.
The remainder of the paper is organized as follows. Section
II proposes a journal analysis framework. Section III presents
the bibliographic analysis of papers on the IEEE T-IV. Section
IV presents the collaboration pattern analysis of the IEEE T-IV.
Section V presents the journal logistics analysis of the IEEE T-
IV. Section VI makes a conclusion.
II. JOURNAL ANALYSIS FRAMEWORK
The analysis of a journal consists of three parts: bibliographic
analysis, collaboration pattern analysis, and journal logistics
analysis. The journal analysis framework of this paper is
presented in this section.
The analysis framework is presented in Fig. 2. It consists of
six layers. Each layer is discussed in detail as follows.
1) Metadata: The analysis metadata is collected by two
methods: all articles published by the IEEE ITSS in six years
from IEEE Xplore, the citation data from the Web of Science
Core Collection dataset, and processing data from the IEEE T-
IV editorial board.
Xuan Li is with Peng Cheng Laboratory, Shenzhen 518055, China (e-mail:
lixuan0125@126.com).
Xingtang Wu is with School of Automation Science and Electrical
Engineering, Beihang University, Beijing 100191, China (e-mail:
wuxingtang@bjtu.edu.cn).
Zhongmin Liu is with North Automatic Control Technology Institute in
Taiyuan, Shanxi, 030000, China (tivzmliu@gmail.com).
Ying Li is with School of Mechanical Engineering, Beijing Institute of
Technology, Beijing, 100081, China (e-mail: ying.li@bit.edu.cn).
T
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
2
Fig. 2. Journal analysis framework
2) Data extraction: The metadata is processed to extract the
following data: titles, citations, views, keywords, authors,
institutions, countries, and processing durations.
3) Key problems: The analysis mainly focuses on four
problems. i) Research topics are found as a guide for future
studies. ii) The productivity and impact of authors, institutions,
and countries are discussed to highlight the main contributors.
iii) Collaboration patterns are discussed to understand the field-
specific shaping of scientific communication practices. iv) The
processing effectiveness is discussed to provide a reference for
the editorial board of the IEEE T-IV.
4) Bibliographic analysis: The bibliographic analysis
includes the statistical analysis of paper, keyword, author,
institution, and country. It focuses on two aspects: productivity
and impact.
5) Collaboration pattern: The collaboration pattern among
authors is clustered and visualization on UCINET software [2].
Top-three largest research groups are recognized. Hot research
topics and research trends of IV are found by the co-occurrence
analysis of keywords. Co-occurrence networks are generated by
a text mining method [3].
6) Journal logistics: The submission processing data is
analyzed to evaluate the reform of the IEEE T-IV. Processing
duration and manuscript number are compared before and after
Jan 2022.
III. THE BIBLIOGRAPHIC ANALYSIS
A bibliographic analysis is conducted for the IEEE T-IV by
a statistic method in this section. The most productive and
influential authors, institutions, and countries/regions in the
IEEE T-IV are recognized in the past six years.
A. Statistic Measures
In order to measure the productivity of authors, institutions,
and countries/regions, the Adjusted Productivity Score (APS)
is introduced in this paper. It has been widely adopted in
previous bibliographic analyses [4, 5]. It consists of Author
Adjusted Productivity Score (AAPS), Institution Adjusted
Productivity Score (IAPS), and Country/region Adjusted
Productivity Score (CAPS). They are computed as follows:
AAPS ≝∑1
=1 (1)
IAPS ≝∑1
=1 (2)
CAPS ≝∑1
=1 (3)
where AAPS is the AAPS of the author ; IAPS is the
IAPS of the institution ; CAPS is the CAPS of the
country/region ;
is the number of papers of the
author ;
is the number of papers from the institution
;
is the number of papers of the country/region ;
is the number of authors in paper ;
is the number of
institutions in paper ;
is the number of countries/regions
in paper .
In order to measure the impact of authors, institutions, and
countries/regions. The Adjusted Citation Score (ACS) and the
Adjusted View Score (AVS) are proposed in this paper. They
follow a similar method as APS. ACS is detailed as Author
Adjusted Citation Score (AACS), Institution Adjusted Citation
Score (IACS), and Country/region Adjusted Citation Score
(CACS). AVS is detailed as Author Adjusted View Score
(AAVS), Institution Adjusted View Score (IAVS), and
Country/region Adjusted View Score (CAVS).
AACS ≝∑
=1 (4)
AAVS ≝∑
=1 (5)
IACS ≝∑
=1 (6)
IAVS ≝∑
=1 (7)
CACS ≝∑
=1 (8)
CAVS ≝∑
=1 (9)
where AACS is the AACS of the author ; AAVS is
the AAVS of the author ; IACS is the IACS of the
institution ; IAVS is the IAVS of the institution ;
CACS is the CACS of the country/region ; CAVS is
the CAVS of the country/region ;
is the cites of paper
;
is the views of paper .
B. Paper Statistics
As shown in Fig. 3, from 2016 to 2021, IEEE T-IV has
published 286 papers. The most cited and viewed papers are
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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analyzed statistically in this section.
Fig. 3. Statistic of IEEE T-IV articles in the data set
TABLE I
MOST CITED PAPERS IN THE IEEE T-IV
Rank
Title
Year
Cites
1
A Survey of Motion Planning and Control
Techniques for Self-Driving Urban Vehicles
2016 544
2
Simultaneous Localization and Mapping: A
Survey of Current Trends in Autonomous
Driving
2017 222
3
A Review of Truck Platooning Projects for
Energy Savings
2016 101
4
Looking at Humans in the Age of Self-Driving
and Highly Automated Vehicles
2016 99
5
Lane Change and Merge Maneuvers for
Connected and Automated Vehicles: A Survey
2016 95
6
How Would Surround Vehicles Move? A
Unified Framework for Maneuver Classification
and Motion Prediction
2018 89
7
Intelligence Testing for Autonomous Vehicles:
A New Approach
2016 81
8
Understanding Pedestrian Behavior in Complex
Traffic Scenes
2018 72
9
Automated Driving in Uncertain Environments:
Planning With Interaction and Uncertain
Maneuver Prediction
2018 63
10
Mixed-Integer Linear Programming for Optimal
Scheduling of Autonomous Vehicle Intersection
Crossing
2018 57
TABLE II
MOST VIEWED PAPERS IN THE IEEE T-IV
Rank
Title
Year
Views
1
A Survey of Motion Planning and Control
Techniques for Self-Driving Urban Vehicles
2016 24283
2
Simultaneous Localization and Mapping: A
Survey of Current Trends in Autonomous
Driving
2017 11841
3
Test Your Self-Driving Algorithm: An
Overview of Publicly Available Driving
Datasets and Virtual Testing Environments
2019 8840
4
Looking at Humans in the Age of Self-Driving
and Highly Automated Vehicles
2016 6956
5
Intelligence Testing for Autonomous Vehicles:
A New Approach
2016 5896
6
Automated Driving in Uncertain
Environments: Planning With Interaction and
Uncertain Maneuver Prediction
2018 5406
7
Lane Change and Merge Maneuvers for
Connected and Automated Vehicles: A Survey
2016 5015
8
Comparison of Path Tracking and Torque-
Vectoring Controllers for Autonomous Electric
Vehicles
2018 4866
9
A Review of Truck Platooning Projects for
Energy Savings
2016 3990
10
The Role of Machine Vision for Intelligent
Vehicles
2016 3465
TABLE I presents the most cited papers in the IEEE T-IV.
The top-five cited papers are all reviews. “A Survey of Motion
Planning and Control Techniques for Self-Driving Urban
Vehicles”[6] is the most cited paper in the past six years. “How
Would Surround Vehicles Move? A Unified Framework for
Maneuver Classification and Motion Prediction” [7] is the
most cited paper except for reviews.
TABLE II presents the most viewed papers in the IEEE T-
IV. “A Survey of Motion Planning and Control Techniques for
Self-Driving Urban Vehicles” [6] is the most cited and viewed
paper in the past six years. “Intelligence Testing for
Autonomous Vehicles: A New Approach” [8] is the most viewed
paper except for reviews. All the top ten most cited and viewed
papers are published before 2019.
C. Keyword Statistics
TABLE III lists the most frequent keywords in the IEEE T-
IV. Road, intelligent vehicles, and autonomous vehicles are the
top three. This is in accordance with the aim of IEEE T-IV –
raising awareness of pressing research and application
challenges in areas of intelligent vehicles in a roadway
environment. Besides the top three keywords, vehicle dynamics
and trajectory are also frequent keywords. It indicates that
intelligent vehicle is the hottest subject of interest in IEEE T-
IV.
TABLE III
MOST FREQUENT KEYWORDS IN THE IEEE T-IV
Rank
Keywords
Frequency
1
Intelligent vehicle
72
2
Autonomous vehicle
62
3
Vehicle dynamics
58
4
Roads
47
5
Trajectory
41
6
Safety
29
7
Vehicles
28
8
Automobiles
27
9
Predictive models
27
9
Sensors
27
D. Author Statistics
From 2016 to 2021, the IEEE T-IV has received papers from
232 authors. The top-ten productive authors are listed in Table
IV. In the past six years, Mohan Manubhai Trivedi (University
of California (UC), San Diego) published 14 papers with a 5.53
AAPS score. He is the most productive author in the IEEE T-
IV. The top-ten authors are mostly involved in more than three
papers with AAPS larger than 1.20. Moreover, they mostly
come from the U.S., especially in California. It confirms the
leading position of the U.S. in the IV research field.
TABLE V lists the most cited authors in the IEEE T-IV.
Mohan Manubhai Trivedi is the most cited author with 368
citations and 142.97 AACS score. Five authors from MIT are
the second to sixth most-cited authors, due to their most cited
paper “A Survey of Motion Planning and Control Techniques
for Self-Driving Urban Vehicles” [6]. The top-six authors are
from institutions in the U.S. Other authors are from institutions
in Europe.
TABLE VI lists the most viewed authors in the IEEE T-IV.
Mohan Manubhai Trivedi is the most viewed author with 19658
views and 8237.63 AAVS score. This rank is nearly the same
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
4
as the most cited author rank.
TABLE IV
MOST PRODUCTIVE AUTHORS IN THE IEEE T-IV
Rank
Name
Institution
Counts
AAPS
1
Mohan Manubhai
Trivedi
University of
California, San Diego
14 5.53
2
Takahiro Wada
Ritsumeikan University
6
2.17
2
Kohei Sonoda
Ritsumeikan University
6
2.17
4 Akshay Rangesh
University of
California, San Diego
5 2.17
5
J. Christian
Gerdes
Stanford University 4 1.70
6 Matthias Althoff
Technical University of
Munich
4 1.67
7
Seng W. Loke
Deakin University
2
1.50
8 Kevan Yuen
University of
California, San Diego
3 1.25
9 Wenshuo Wang
Beijing Institute of
Technology
4 1.23
10 Henk Nijmeijer
Eindhoven University
of Technology
4 1.20
TABLE V
MOST CITED AUTHORS IN THE IEEE T-IV
Rank
Name
Institution
Cites
AACS
1
Mohan
Manubhai
Trivedi
University of California,
San Diego 368 142.97
2 Sze Zheng Yong
Massachusetts Institute
of Technology
545 109.13
3 Michal Čáp
Massachusetts Institute
of Technology
544 108.80
3 Emilio Frazzoli
Massachusetts Institute
of Technology
544 108.80
3 Dmitry Yershov
Massachusetts Institute
of Technology
544 108.80
3 Brian Paden
Massachusetts Institute
of Technology
544 108.80
7 Akshay Rangesh
University of California,
San Diego
156 58.17
8 Eshed Ohn-Bar
University of California,
San Diego
120 56.50
9
Zayed Alsayed
VEDECOM
222
55.50
9
Sébastien Glaser
VEDECOM
222
55.50
9
Li Yu
Mines ParisTech
222
55.50
9
Guillaume
Bresson
VEDECOM 222 55.50
TABLE VI
MOST VIEWED AUTHORS IN THE IEEE T-IV
Rank
Name
Institution
Views
AAVS
1
Mohan
Manubhai
Trivedi
University of
California, San Diego 19658 8237.63
2 Sze Zheng Yong
Massachusetts Institute
of Technology
24684 4990.27
3 Michal Čáp
Massachusetts Institute
of Technology
24283 4856.60
3 Emilio Frazzoli
Massachusetts Institute
of Technology
24283 4856.60
3 Dmitry Yershov
Massachusetts Institute
of Technology
24283 4856.60
3 Brian Paden
Massachusetts Institute
of Technology
24283 4856.60
7 Eshed Ohn-Bar
University of
California, San Diego
7794 3757.33
8
Christoph Stiller
FZI
10124
3126.95
9
Zayed Alsayed
VEDECOM
11841
2960.25
9
Sébastien Glaser
VEDECOM
11841
2960.25
9
Li Yu
Mines ParisTech
11841
2960.25
9
Guillaume
Bresson
VEDECOM 11841 2960.25
E. Institution Statistics
There are 336 institutions contributed to the IEEE T-IV in the
past six years. TABLE VII shows the top-ten productive
institutions in IEEE T-IV. It confirms that the University of
California at San Diego is the most productive institution with
a 13.80 IAPS score. It nearly doubles the second institution.
Four American universities are on the list. Four European
universities are on the list. Ritsumeikan University in Japan and
Tongji University in China are also on the list.
TABLE VIII lists the most cited institutions in the IEEE T-
IV ranked by IACS. Massachusetts Institute of Technology is
the most cited institution with a 544.00 IACS score. In the rank,
five institutions are from the U.S. Four institutions are from
Europe. One institution is from Canada.
TABLE IX presents the most viewed institutions ranked by
IAVS. Massachusetts Institute of Technology is the most
viewed institution. In this rank, five institutions are from the
U.S. Four institutions are from Europe. One institution is from
Japan.
TABLE VII
MOST PRODUCTIVE INSTITUTIONS IN THE IEEE T-IV
Rank
Institution
IAPS
1
University of California at San Diego
13.80
2
Ritsumeikan University
6.25
3
Stanford University
4.83
4
Tongji University
4.50
4
Linköping University
4.50
6
University of California at Riverside
4.37
6
Eindhoven University of Technology
4.37
8
Technical University of Munich
4.33
9
Chalmers University of Technology
4.22
10
University of Michigan
4.14
TABLE VIII
MOST CITED INSTITUTIONS IN THE IEEE T-IV
Rank
Institution
IACS
1
Massachusetts Institute of Technology
544.00
2
University of California at San Diego
363.20
3
VEDECOM
111.00
4
University of California at Berkeley
108.93
5
University of California at Riverside
95.83
6
Clemson University
92.00
7
York University
72.00
8
BMW Group
67.60
9
Mines ParisTech
55.50
10
Zenuity
55.50
TABLE IX
MOST VIEWED INSTITUTIONS IN THE IEEE T-IV
Rank
Institution
IAVS
1
Massachusetts Institute of Technology
24283.00
2
University of California at San Diego
19459.20
3
VEDECOM
8880.75
4
Chalmers University of Technology
6921.15
5
University of California at Berkeley
6214.24
6
Ritsumeikan University
5716.00
7
Zenuity
5645.23
8
BMW Group
5393.73
9
Stanford University
5040.50
10
University of California at Riverside
4423.83
F. Country/Region Statistics
In the past six years, the IEEE T-IV has received papers from
33 countries/regions. Fig. 4 and TABLE X illustrate the most
productive countries/regions in the IEEE T-IV. It shows that the
U.S. is the most productive country/region in the IEEE T-IV by
taking 32% of CAPS scores. China is the second most
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
5
productive country/region. Besides the top-ten
countries/regions, the other countries/regions only take 12% of
CAPS scores.
TABLE X
MOST PRODUCTIVE COUNTRIES/REGIONS IN THE IEEE T-IV
Rank
Country/region
CAPS
1
U.S.
92.07
2
China
37.62
3
Germany
27.47
4
Japan
24.25
5
Sweden
16.00
6
France
14.65
7
Canada
12.13
8
Australia
11.55
9
Netherlands
9.63
10
U.K.
6.60
Fig. 4. The productivity of countries/regions
TABLE XI
MOST CITED COUNTRIES/REGIONS IN THE IEEE T-IV
Rank
Country/region
CACS
1
U.S.
1917.76
2
France
390.08
3
Germany
336.33
4
China
333.43
5
Japan
210.86
6
Sweden
181.37
7
Canada
146.93
8
Australia
97.73
9
U.K.
74.05
10
Netherlands
72.20
TABLE XII
MOST VIEWED COUNTRIES/REGIONS IN THE IEEE T-IV
Rank
Country/region
CAVS
1
The U.S.
112566.62
2
China
29534.98
3
Germany
27078.47
3
France
23601.65
3
Sweden
21899.17
3
Japan
16340.52
7
U.K.
11486.80
8
Canada
11341.78
9
Australia
7621.60
9
Netherlands
5757.13
TABLE XI lists the most cited countries/regions in the IEEE
T-IV. The U.S. is the most cited country. Its CACS score is
nearly five times that of the second country, France. TABLE
XII lists the most viewed countries/regions in the IEEE T-IV.
China ranks second in the most viewed countries/regions list,
higher than fourth in the most cited countries/regions list.
IV. THE COLLABORATION PATTERN ANALYSIS
In this section, the collaboration pattern is found for authors
and keywords. Three networks are constructed, including the
co-authorship network, the keyword co-occurrence network for
the IEEE T-IV, and the keyword co-occurrence network in the
field of Cooperative Automation (CA) in 2022. The top-three
largest research groups are recognized. Hot research topics and
research trends are discussed.
A. Research Groups
Fig. 5. The co-authorship network of the top-three largest research groups
Co-authorship relations directly reflect collaboration
between researchers [9]. By clustering authors who publish
papers together, a co-authorship network could be constructed.
It could visually reflect whether the authors would like to work
alone or collaborate in a group.
Co-authorship networks of the top-three largest research
groups are illustrated in Fig. 5. A node represents an author. The
size of nodes represents the number of papers published by the
author in the IEEE T-IV. The line between two nodes represents
the collaboration between two authors. As illustrated by Fig. 5,
Matthew J. Barth, from the University of California at
Riverside, is leading the largest research group contributing to
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
6
the IEEE T-IV. Li Li, from Tsinghua University, is a
representative of a research group in China. Mohan Manubhai
Trivedi, from the University of California San Diego, is leading
a most productive group.
B. Hot Research Topics
512 keywords have been utilized in the past six years in the
IEEE T-IV. To find hot research topics in the IEEE T-IV, the
collaboration pattern of keywords is analyzed.
Fig. 6. Six-year keywords co-occurrence network for the IEEE T-IV
A six-year keywords co-occurrence network is generated for
the IEEE T-IV as shown in Fig. 6. A node represents a keyword.
The size of nodes reflects the frequency of keywords. Black
lines between nodes reflect the cooccurrence relationship of
keywords. The nodes with the same color are clustered into a
group, which represents a research topic. As shown in Fig. 6,
there are mainly 13 research topics in the IEEE T-IV. The top
three hot topics are summarized as follows.
1) Vehicle planning: Orange nodes represent the hottest
research topic in the IEEE T-IV. It includes research on vehicle
route planning, path planning, trajectory planning, and motion
planning. Predictive models and hidden Markov models are
correlative methods.
2) Vehicle perception: Blue nodes are correlative to vehicle
perception. It focuses on the adoption of sensors in steering
systems and Advanced Driving Assistant Systems (ADAS).
3) Energy management: Green nodes are correlative to the
energy management of vehicles. This research topic focuses on
the management of fuel consumption and battery energy,
especially for electric vehicles.
C. Research Trends
Cooperative Automation (CA) is becoming a new research
trend. The number of papers on CA in IEEE ITSS is shown in
Fig. 7. There is a significant increase in the number of papers
on CA in recent two years. In 2020, there are only 48 papers on
CA. In 2021, the number of papers on CA is 113. While, from
Jan. to Jul. 2022, the number of papers on CA is already 173.
To catch up with this trend, an analysis is conducted in this
paper to provide a roadmap for future research on IV.
Fig. 7. The number of papers on CA in IEEE ITSS from 2017 to Jul. 2022
Fig. 8. 2022 keywords co-occurrence network in the field of CA
Keywords from the 173 CA-related papers of the year 2022
in IEEE ITSS are clustered into a network as shown in Fig. 8.
The size of nodes reflects the frequency of keywords. Black
lines between nodes reflect the cooccurrence relationship of
keywords. The nodes with the same color are a group, which
represents a research topic. As illustrated by Fig. 8, there are
mainly five research topics in the field of CA:
1) Vehicle Group Control: This research topic is illustrated
by yellow nodes in Fig. 8. In a connected environment, traffic
flow is handled as a vehicle group. The group control could be
realized by a multi-agent method [10]. Moreover, this group
could be mixed with Connected and Automated Vehicles
(CAVs) and Human-driven Vehicles (HVs). It leads to a
nascent research topic, the new-type Mixed Vehicle Group
Control (MVGC). The new type is a concept compared with the
conventional mixed traffic which refers to the mixing of various
types of vehicles, such as cars and trucks. State-of-the-art
studies have made efforts on the new-type MVGC. An et al.
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
7
[11] modeled the system dynamics of mixed traffic. Guo et al.
[12] proposed a CAV controller with the capability of
predicting the behavior of HVs. Wang et al. [13] proposed an
autopilot controller with the consideration of mixed traffic
stochasticity. Xu et al. [14] developed a CAV controller with
the consideration of the uncertain reaction time of HVs.
Valiente et al. [15] adopted reinforcement learning into the CA
in mixed traffic. However, these pioneering studies are still
safety oriented. Future studies could make efforts on the
running mobility of CAVs in the new-type mixed traffic. It may
cover strategies like human-machine shared control, human-
like driving, and game-based planning.
2) Platoon Control: This research topic is illustrated by
green nodes in Fig. 8. As a specific form of group, the vehicle
platoon refers to a longitudinal string of vehicles [16, 17]. It is
regarded as a tactic to accelerate the application of autonomous
driving. Platoon control is a hot research topic in the field of
CA. In 2022, 24 papers in the IEEE ITSS made efforts on
platoon control. They mainly focus on developing distributed
or centralized controllers for the stable cruising of platoons.
Cooperative Adaptive Cruise Control (CACC) is a typical
longitudinal platoon controller. Zhang et al. [18] proposed a
human-lead-platooning CACC method. It has been
implemented by a “5G+L4” truck platooning practice in
Donghai Bridge, Shanghai, China. The commercial operation
has reached the capacity of 100,000 TEU a year. Wang et al.
[19] further added the lateral control of the platoon and
developed a Platoon Lane-Change (PLC) controller. Li et al.
[20] and Zhang et al. [21] developed platooning methods for
mixed traffic. Xiao et al. [22] and Yan et al. [23] made efforts
on developing communication and control networks for the
platoon. However, distance still exists before the application of
platooning technology. How to enhance the mobility of the
platoon in the context of traffic is a problem for researchers.
3) Intersection Management: This research topic is
illustrated by purple nodes in Fig. 8. Intersection management
is another hot topic in CA. In 2022, 17 papers are relevant to
the management of intersections. Most of them focus on the
automation of CAVs at non-signalized intersections [24-27]. Lu
et al. [28] proposed an optimization-based approach for
crossing control. Wang et al. [29] proposed a traffic stabilizing
method at signalized intersections. Bai et al. [30] proposed an
eco-driving strategy at signalized intersections based on hybrid
reinforcement learning. However, these studies are still based
on hypotheses that there are no HVs or HVs are disciplined.
Therefore, future studies may make efforts on developing
controllers with the capability of handling the interaction
between vehicles.
4) Cooperation Security: This research topic is illustrated
by red nodes in Fig. 8. In the connected environment, cyber-
attacks have to be considered to ensure the security of
cooperation. State-of-the-art studies mainly adopt security
measures from the perspective of detection, location, and
control. Gao et al. [31] proposed a cooperative target tracking
method under attacks. Ju et al. [32] made a survey on attack
detection and resilience from vehicle dynamics and control
perspectives. Since communication security is a must for the
implementation of CA, more work is appreciated. Robust CA
controllers may be one of the research topics.
5) Cooperative Perception: This research topic is illustrated
by blue nodes in Fig. 8. Cooperation perception focuses on the
perception fusing of multiple sensors. Cooperative positioning,
cooperative object detection, and tracking are the main research
topics [33]. Gao et al. [34] developed a cooperative positioning
method by integrating GPS and UWB. Bai et al. [35] made a
survey on infrastructure-based object detection and tracking.
Future studies could focus on the interaction protocol and the
fusion of multi-source perception.
To facilitate the implementation of CA, a roadmap is
presented as follows [36]. It consists of four classes. We are at
the stage of Class B or Class C. More creative works are
appreciated.
1) Status-Sharing Cooperation (Class A): This class is the
most straightforward application of cooperation. Vehicles, road
users, and roadside units would broadcast location, speed,
event, and other status or perception information. This type of
cooperation could significantly enhance perception range. It
prompts applications, such as cooperative collision avoidance,
vehicle platooning, etc.
2) Intent-Sharing Cooperation (Class B): This class of
cooperation extends beyond Class A by including information
about planned actions. It would benefit the prediction of the
actions of other traffic participants. Intent-sharing cooperation
could further enhance driving safety. In addition, this class may
lead to a game between participants.
3) Agreement-Seeking Cooperation (Class C): This class
provides a negotiation cooperation strategy for participants to
reach an agreement on driving. It aims at reducing conflicts and
achieving global optimality. This class enables cooperative
merging, cooperative lane-change, cooperative traffic
assignment, etc. However, to make cooperative strategies, the
yielding of some entities is inevitable.
4) Prescriptive Cooperation (Class D): This class is
asymmetrical cooperation. It is proposed for the traffic
participants with privilege, such as buses with authority to
demand traffic signal priority and emergency vehicles
disobedient to speed limits.
V. THE JOURNAL LOGISTICS ANALYSIS
Since 2021, reform is conducted in the IEEE T-IV under the
leadership of the Editor-in-Chief (EIC), Fei-Yue Wang. The
reform has obtained a milestone success. In 2022, the IEEE T-
IV receives an impact factor of 5.009. This section aims at
revealing the secret of success from the perspective of
submission processing.
A. Submission Processing Policy
The new “3224” processing policy has been adopted in the
IEEE T-IV, as presented in Fig. 9. According to the new policy,
EIC handles and assigns tasks within a day. AE has 3 weeks for
the first decision and 2 weeks for the final decision. Authors
have 2 weeks for a minor revision and 4 weeks for a major
revision.
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
8
Fig. 9. The new “3224” processing policy in the IEEE T-IV
B. Submission Processing Efficiency Analysis
By adopting the new processing policy as shown in Fig. 9,
the IEEE T-IV is confirmed with enhanced processing
efficiency after Jan. 2022. Statistics analysis results are
presented in this section.
Fig. 10. Processing durations of submissions
Fig. 10 illustrates submission processing durations before
and after Jan. 2022. It confirms that the processing duration is
reduced by 90% in 2022. Before 2022, the submissions are
processed following the old policy. It took about 263 days on
average to complete the entire review process and reach a final
decision. In the worst case, an AE spent 449 days assigning
first-round reviewers. In 2022, all received manuscripts could
be handled within 15 weeks. The average time duration from
submission to decision is only 21 days. The longest duration is
43 days which is due to the delay of a reviewer.
C. Reform Achievements
In 2022, the IEEE T-IV receives an impact factor of 5.009.
Due to the new efficient processing policy, the IEEE T-IV now
attracts eight times more submissions.
Fig. 11 illustrates the annual number of manuscripts received
by the IEEE T-IV. From Nov.2015 to Dec.2021, there are 822
manuscripts submitted to the IEEE T-IV. The number of
submissions per year is 126. 44.8% of these manuscripts are
accepted. However, from Jan.2022 to Apr.2022, the IEEE T-IV
editorial board received 435 manuscripts and completed 376
manuscripts. The number of submissions per year is now
estimated at over 1200. It may make the IEEE T-IV a monthly
publication from the quarterly publication. Moreover, only
19.4% of received manuscripts in 2022 are accepted for
publication. It means that only excellent papers could be
published. Therefore, this new policy is believed to enhance the
influence of IEEE T-IV.
Fig. 11. The manuscript number of IEEE T-IV
The continent distribution of IEEE T-IV submissions in 2022
is illustrated in Fig. 12. It indicates that Asian researchers are
now making primary contributions to the IEEE T-IV. More than
half of the submissions are from Asia. North America accounts
for less than a quarter. It reveals the trend of relay handover
from North America to Asia in the field of IV.
Fig. 12. The continent distribution of IEEE T-IV submissions from Jan.1 to
Apr.22
VI. CONCLUSION
This paper provides a journal analysis framework. Following
this framework, data of papers and submission processing are
collected from 2016 to 2022. Bibliographic analysis and
collaboration pattern analysis are conducted for the IEEE T-IV.
The major findings are as follows.
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
9
The bibliographic analysis of the IEEE T-IV shows that the
U.S. dominates the IV research field. 32% of papers are from
the U.S. Massachusetts Institute of Technology is the most
influential institution. University of California at San Diego is
the most productive institution. Mohan Manubhai Trivedi from
the University of California at San Diego is the most productive
and influential author. After the U.S., China, and Europe are in
pursuing status.
The collaboration pattern analysis of authors provides
community structures of the top-three largest research groups
in the IEEE T-IV. Matthew J. Barth, from the University of
California at Riverside, is leading the largest research group. Li
Li, from Tsinghua University, is a representative of a research
group in China.
The collaboration pattern analysis of keywords indicates
research topics and trends in IV. Vehicle planning, vehicle
perception, and energy management are the top-three hottest
research topics in the past six years. CA is now receiving
significantly increased attention in the publications of IEEE
ITSS. In 2022, research trends are vehicle group control,
platoon control, intersection management, cooperation security,
and cooperative perception. To facilitate the implementation of
CA, a four-classes roadmap is presented in this paper.
The journal logistics analysis of the IEEE T-IV demonstrates
that the new “3224” processing policy reduces the processing
duration by 90% and attracts nearly nine times more
manuscripts in 2022. 51% of these submissions are from China.
ACKNOWLEDGMENT
The authors would like to thank the editorial board of the
IEEE T-IV for encouraging them to conduct the reported
analysis.
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content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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Haoran Wang received the bachelor’s degree in
transportation engineering from Tongji University,
Shanghai, China, in 2017, and the Ph.D. degree from
Tongji University in 2022. He is currently a Research
Associate with the College of Transportation
Engineering, Tongji University. He is a researcher on
vehicle engineering, majoring in intelligent vehicle
control and cooperative automation.
Dr. Wang served the IEEE TRANSACTIONS ON
INTELLIGENT VEHICLES, Journal of Intelligent
Transportation Systems, and IET Intelligent
Transport Systems as peer reviewers with a good reputation.
Xiao Wang (Member, IEEE) received the bachelor’s
degree in network engineering from the Dalian
University of Technology, Dalian, Liaoning, China, in
2011, and the Ph.D. degree in social computing from
the University of Chinese Academy of Sciences,
Beijing, China, in 2016. She is currently an Associate
Professor with The
State Key Laboratory for Management and Control of
Complex Systems, Institute of Automation, Chinese
Academy of Sciences. Her research interests include
social transportation, cyber movement organizations, artificial intelligence, and
social network analysis. She has authored or co-authored more than a dozen of
SCI/EI articles and translated three technical books (English to Chinese).
Dr. Wang served the IEEE TRANSACTIONS ON INTELLIGENT
TRANSPORTATION SYSTEMS, IEEE/CAA JOURNAL OF AUTOMATION SINICA,
and ACM Transactions of Intelligent Systems and Technology as peer reviewers
with a good reputation.
Xuan Li received his Ph.D. degree in Control Science
and Engineering from the Beijing Institute of
Technology, Beijing, China, in 2020. After that, he
joined the Peng Cheng Laboratory and became an
Assistant Professor at the Virtual Reality Laboratory.
From October 2018 to October 2019, he was a Visiting
Scholar at the Department of Computer Science, Stony
Brook University, Stony Brook, NY, USA. His
research interests include image synthesis, computer
vision, bionic vision computing, intelligent
transportation systems, and machine learning.
Xingtang Wu received the Ph.D. degree in traffic
information engineering and control from Beijing
Jiaotong University, Beijing, China in 2020. He was a
Research Assistant with the Hong Kong Polytechnic
University in 2016 and 2017. He is currently a
postdoctoral research fellow at the School of
Automation Science and Electrical Engineering,
Beihang University, Beijing, China. His current
research interests include railway network modeling
and performance optimization, delay propagation, deep
learning application.
Jia Hu (Member, IEEE) is currently working as a
Zhongte Distinguished Chair of Cooperative
Automation with the College of Transportation
Engineering, Tongji University. Before joining
Tongji University, he was a Research Associate with
the Federal Highway Administration (FHWA), USA.
He is an Editorial Board Member of the Journal of
Intelligent Transportation Systems and the
International Journal of Transportation Science and
Technology. He is a member of TRB (a Division of
the National Academies) Vehicle Highway
Automation Commit-tee, the Freeway Operations Committee, Simulation
subcommittee of Traffic Signal Systems Committee, and the Advanced
Technologies Committee of the ASCE Transportation and Development
Institute. He is the Chair of the Vehicle Automation and Connectivity
Committee of the World Transport Convention. He is an Associate Editor of
the American Society of Civil Engineers Journal of Transportation
Engineering and IEEE OPEN JOURNAL OF INTELLIGENT
TRANSPORTATION SYSTEMS.
Yuanyuan Chen received the B.E. degree in
automation from Tongji University, Shanghai, China,
and the Ph.D. degree in control theory and control
engineering from the University of Chinese Academy
of Sciences, Beijing, China. He is currently an
Associate Professor with the Institute of Automation,
Chinese Academy of Sciences, Beijing, China. His
main research interests include intelligent vehicles,
social transportation systems, and data-driven traffic
modeling and prediction.
Zhongmin Liu is a Senior Researcher at North
Automatic Control Technology Institute in Taiyuan,
Shanxi, China. His focus is training, validating, and
certificating of intelligent systems and personals,
especially for heavy duty smart machineries and
operations.
Ying Li received the Ph.D. degree from the University
of Waterloo, Waterloo, ON, Canada. She is currently
an Assistant Professor with the School of Mechanical
Engineering, Beijing Institute of Technology, Beijing,
China. Her research interests include autonomous
driving, environmental perception, computer vision,
mobile laser scanning, geometric and semantic
modeling, and high definition mapping.
This article has been accepted for publication in IEEE Transactions on Intelligent Vehicles. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TIV.2022.3215784
© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
