# Intelligent Transportation Systems With The Use of External Infrastructure:
A Literature Survey

1st Christian Creß 2nd Alois C. Knoll  Chair of Robotics, Artificial
Intelligence and Real-time Systems
Technical University of Munich
Munich, Germany
<EMAIL_ADDRESS>
###### Abstract

Increasing problems in the transportation segment are accidents, bad traffic
flow and pollution. The Intelligent Transportation System with the use of
external infrastructure (ITS) can tackle these problems. To the best of our
knowledge, there exists no current systematic review of the existing
solutions. To fill this knowledge gap, this paper provides an overview about
existing ITS which use external infrastructure. Furthermore, this paper
discovers the currently not adequately answered research questions. For this
reason, we performed a literature review to documents, which describes
existing ITS solutions since 2009 until today. We categorized the results
according to his technology level and analyzed their properties. Thereby, we
made the several ITS comparable and highlighted the past development as well
as the current trends. According to the mentioned method, we analyzed more
than 346 papers, which includes 40 test bed projects. In summary, the current
ITS can deliver high accurate information about individuals in traffic
situations in real-time. However, further research in ITS should focus on more
reliable perception of the traffic with the use of modern sensors, plug and
play mechanism as well as secure real-time distribution in decentralized
manner for a high amount of data. With addressing these topics, the
development of Intelligent Transportation Systems is in a correction direction
for the comprehensive roll-out.

###### Index Terms:

Intelligent Transportation Systems, Intelligent Infrastructure, Distributed
Systems, Autonomous Driving, C-ITS, Test Field, Test Bed

##  I Introduction

As a result of traffic accidents due 94 % human error, 1.25 - 1.3 million
people die worldwide [1, 2]. Besides the accidents, in the transportation
segment exists capacity problems and pollution problems too [3]. Only in the
United States, people spend in total 6.9 billion hours in traffic jams [2].
Furthermore, 50 % of the world population live in cities. There are the
mentioned issues significantly noticeable. Unfortunately, this situation will
be more critical: The share of the population in cities will be risen to two
thirds until 2050. Therefore, we have to consider especially cities for
optimal mobility [4].

The Intelligent Transportation Systems with the use of infrastructure (ITS)
are a possible solution to tackle these problems [1, 2, 5, 3, 6]. Only in the
United States, these systems can prevent approximately 400,000 to 600,000
accidents with parallel reduced travel time by around 42 %. In addition,
modern ITS can reduce the fuel consumption for passenger vehicles up to 44 %
and for trucks up to 18 % [2]. This fact results in reduction of around 15 %
of the greenhouse gases [7]. To fulfill these goals, two examples: 1.) The ITS
can implement a real-time synchronization of individual traffic with the
public transport system with the support of "Park and Ride"-Parking. 2.) The
ITS can exchange data with vehicles which are equipped with intelligent driver
assistance systems. This V2X technology would enable a further foresight which
results in an accurate warning against hazards and obstacles.

Of course, manufacturers of ITS and the automotive industry have also business
opportunities: In 2014, the worldwide annual turnover was around 30 billion
Euros for connected driving applications, it increased to 170 - 180 billion
Euros in the year 2020. The application "Collaborative Cruise Control" which
enables continuous exchange between vehicles and infrastructure is an
important example. This function is already used by Tesla for its feature
AutoPilot excessively in 4 billion miles on the road. Personalized on-board
entertainment, fleet management and platooning [8] are further influential
value-added services of ITS. So, these systems can make a valuable
contribution in the transportation segment.

To the best of our knowledge, there exists no other current peer-reviewed
systematic literature survey which provides technical details about existing
Intelligent Transportation Systems which use external sensors and other
infrastructures. However, the papers [9, 10, 11, 12] and [13] list ITS in
general, they do not give technical details nor the exact selection criterion
of their listed systems. Hence, we aim to fill this gap by a systematic
literature review from the last decade. Furthermore, we make for better
understanding a historical summary of such systems. While this review does not
cover a deep entry into the specific topics in the field of ITS (e.g.
communication, architecture, object detection or tracking), it provides an
overview about existing solutions, trends and current open research questions
in the field.

In this paper, we select documents about implemented ITS from 2009 until
today. Additionally, we accept for the historical insertion documents the
first known ITS. Then, we categorize the test fields according to their
general functionality. To make the several systems comparable, we extract the
important characteristic as well as the novelty of each ITS. Lastly, we
highlight and visualize the results and show the trends of the development in
the area of ITS.

The paper is organized as follows: First, section II explains the methods for
collecting and reviewing the papers about Intelligent Transportation Systems
which use external infrastructure. Then, the section III shows the results of
the literature survey. Furthermore, this section contains a detailed analysis
of each listed ITS. The section IV discuss the results, the requirements and
the trends of modern ITS. Last, but not least, a conclusion and outlook for
further research are given in Section V.

##  II Methodology

In this paper, we perform a literature review about implemented Intelligent
Transportation Systems which use external infrastructure. The purpose of this
review is to give an overview about the growing landscape of ITS. For that, we
categorize the collected systems according their features, analyze their
properties as well as mention their scientific contributions in the field.
Furthermore, we would like to discover the open questions and research gaps in
the field of ITS with external infrastructure. To answer the questions above,
first we look unsystematic into the literature to get an overview in the
dynamic field of ITS. Then, to achieve the highest possible coverage of
existing literature, we performed a systematic review according the method by
Kitchenham [14]:

  1. 1.

Identification of research: We collect documents with the known databases IEEE
Xplore, Google Scholar and Google with the keywords "Testfeld ITS", "Test
Field ITS", "Testbed ITS as well as "C-ITS". We also consider the sources of
the collected publications. With this strategy, we get a kind of pyramid
scheme in our research. Unfortunately, a lot of Intelligent Transportation
System projects do not publish their results in peer reviewed papers.
Therefore, we accept in our literature survey papers as well as gray
literature (e.g. official websites about the projects). For sure, we delete
duplicates after collecting all documents.

  2. 2.

Study selection: We follow several rules for the assessment respectively the
filtering of collected sources. We select only ITS between 2009 until today.
For the historical insertion, we choose exceptionally important first known
test field projects too. As an additional criterion, the ITS in this paper has
to use external sensors or other infrastructure. The systems have to be
implemented in real world, only a conceptual idea is not enough.

  3. 3.

Study quality assessment: After the assessment of all documents, we categorize
the ITS solutions according their stage of development. Already [7] propose
the categories "Semi-intelligent infrastructure", "Intelligent or semismart
infrastructure" and "Smart infrastructure ". Here, the categories of the ITS
are in terms of real-time behavior, accuracy and independence working without
human intervention. However, we suggest in our work the three categories
"Early Days", "Situation-related Analysis" and "Creation Digital Twins". These
categories should describe the stage of development according the general
features and functionality of the system. The meaning of the categories are
described in detail in table I.

  4. 4.

Data extraction: The several ITS projects focus on different aspects. For
instance, some test fields focus on the communication between vehicles and
infrastructure, other test fields focus on the data fusion or object
detection. To make the test fields comparable, we analyze the used sensors,
the length of the test stretch, the overall architecture as well as the
scientific novelty of each ITS. This give the purpose and the technical
details about the numerous projects in the last decade.

  5. 5.

Data synthesis: In the last step in our method, we highlight and visualize the
qualitative and quantitative results of the collected ITS. We summarize the
hard facts in a table and show the trend of the past development in suitable
diagrams. Therefore, this synthesis gives a sufficient impression about the
current development in the segment of ITS as well as anchor points for open
research questions.

TABLE I: Development stages of the ITS. Category |  Description
---|---
Early Days |  This category gives a historical summarize about the concepts, challenges and successes of the first known ITS.
Situation-related Analysis |  The ITS in this category can analyze and interpret individual traffic situations. Therefore, they can improve the safety and the flow of the traffic.
Creation Digital Twins |  These systems are able to create complex digital twins of all traffic participants. They are characterized by high accuracy and real-time capacity. Such systems can have a high impact on autonomous and connected driving.

##  III Results

According to our proposed method, we analyzed 346 papers about Intelligent
Transportation Systems with the use of external infrastructure, which were
findable in the search engines of IEEE Xplore, Google Scholar as well as
Google with our mentioned keywords. Since the search results from Google and
Google Scholar were more than one million entries, only the first three result
pages were taken into account our research. The exact number of documents for
each keyword in the several search engines are listed in table II.

TABLE II: Number of the analyzed documents. Search Engine | Keyword | Number of results
---|---|---
IEEE Xplorea | Testfeld ITS | 0
Test Field ITS | 78
C-ITS | 180
Test Bed ITS | 14
Google Scholar | Testfeld ITS | 12
Test Field ITS | 0
C-ITS | 6
Test Bed ITS | 1
Google | Testfeld ITS | 25
Test Field ITS | 17
C-ITS | 6
Test Bed ITS | 7
$\sum$ |  | 346
aWith topics filter "intelligent transportation systems".

In the next step, we propose the details about the found ITS which fulfills
our mentioned requirements. The feature categories "Early Days", "Situation-
related Analysis" and "Creation Digital Twins" are described in the respective
sections. The table III gives a complete overview about the results.

###  III-A Early Days

The origins of using external infrastructure in Intelligent Transportation
Systems are in the 80s and 90s. Here, the projects Prometheus and Path were
decisive in the beginning development of ITS. In 1988, the PROMETHEUS project
aimed to increase significantly the safety in the future road traffic.
Furthermore, the project focused on improvements in traffic efficient and
comfort too. The subproject Pro-Road concentrated on communication between
infrastructure and vehicles with the goal to optimize the road traffic.
Relevant sensors (e.g. radar, laser scanner, ultrasonic and cameras) in the
cars should provide information about the conditions in the environment. With
this, the first ideas about hazard warning (e.g. traffic jam, accidents, ice)
were created. It should be emphasized that the project not aimed to dominate
the drivers’ activity with autonomous driving [15, 16].

In contrast, for optimization of the traffic flow in the year 1986, the
project PATH tried to influence the control of vehicles on highways. For this
purpose, the highways should be equipped with sensors and control units. For
instance, in the year 1992, the first platooning experiments of vehicles with
support of Doppler radar systems were successfully performed. However, it was
already recognized at that time that an implementation until at least the turn
of the millennium will not be possible. The reasons are technical deficiencies
and the political acceptance by the population [17, 18].

After the first concepts and prototypes, the next ITS generation were able to
control and optimize the general traffic flow. For this, the systems did not
influence the traffic participants directly. They controlled it, for instance,
with intelligent traffic light logic [19] or with electronic gantries. The
necessary data input were achieved by induction loops in the asphalt. The
first ITS of this type came in the late 1990s. They are already widely
available in modern road networks. For instance, in the year 1998, researcher
in the DynaMIT (Dynamic Network Assignment for the Management of Information
to Travelers) project in Los Angeles created a concept and simulation for an
ITS, which should improve the traffic flow in case of accidents or other
emergencies. In 2006, the concept was realized with the use of counting loops
in the freeway. The sensors sent the data to a computer for further analysis.
In summary, the system could measure the traffic load and generated forecasts
in real time. Based on this, the system offered dynamic route guidance, which
has improved the traffic flow [20, 21].

In 2006, the SAFESPOT project researched on the interaction between ITS and
vehicles with the goal of increasing the road safety (e.g. warning about
"black spots") and optimizing the traffic flow. The ITS was deployed in the
following places: 1.) Sweden: Stockholm and Goteborg. 2.) Germany: Dortmund.
3.) Italy: Torino and Brescia-Padova-Freeway. 4.) France and Spain: West
Europe Test Site. 5.) Netherlands: Corridor Rotterdam-Brabant-Antwerp.
Therefore, the locations covers the scenarios city center, country road as
well as freeway. In this huge European project the infrastructures were
equipped with Road Side Units (RSU), dynamic cooperative networks were
created. These systems allowed communication between vehicles and
infrastructure. With the shared information, the driver of vehicles has got
enhance perception of the surrounded vehicles. The ITS has created "local
dynamic map" and a "safety margin assistant", which detected critical
situations in advance [22, 23].

###  III-B Situation-related Analysis

The focus of ITS solutions in this section are in the detailed analysis of
specific traffic situations. Furthermore, they allow an interaction with the
individual participants. This gives a clear added value in comparison to the
ITS which give a rough statement about the general traffic flow. In the
European cities Bordeaux (France), Copenhagen (Denmark), Helmond
(Netherlands), Newcastle (England), Thessaloniki (Greece), Verona (Italy) and
Vigo (Spain) were installed ITS in the project Compass4D (2013). A total of
147 Road Side Units and 5 backend offices were set up. The aim was to develop
a warning system against road hazards. The use cases were construction sites,
slippery roads, wrong-way drivers and red light violations. In addition, the
ITS should optimize the fuel consumption in intersection areas to achieve
energy efficient. For this, the ITS has introduced a situation-adapted traffic
light system control. If cars have arrived the traffic light, they have got a
speed recommendation [24].

In the year 2012, the project INSIGHT (Intelligent Synthesis and Realtime
Response using Massive Streaming of Heterogeneous Data) set up a real world
ITS in Dublin, Ireland. Here, numerous of cameras in certain sections as well
as GPS of the local transport has given information about the current traffic
situation in real-time. They have bundled with messages from Twitter, radio
and possible callers. The purpose of this big data application was the
automated management of resources to enable efficient actions in intelligent
and networked cities in case of a disaster [25, 26, 27].

In 2015, the follow-up project VaVel (Variety, Veracity, VaLue: Handling the
Multiplicity of Urban Sensors) extended the INSIGHT system in Dublin and
implemented it in Warsaw, Poland too. The ITS has collected the same data, as
well as data from trams and bicycles. The project had the same goal as the
mentioned INSIGHT project [25, 26, 28].

In the 2016 European project ICSI (Intelligent Cooperative Sensing for
Improved Traffic Efficiency) an ITS were implemented in the cities of Lisbon,
Portugal and Pisa, Italy. The research goal was to find a high scalable
distributed ITS for data acquisition and analysis. The uses cases were traffic
flow optimization and reduction of the emission level. On an 8 km long section
of the A5 freeway in Lisbon, three measuring stations with cameras have
analyzed the traffic flow, as well as have detected anomalies (e.g.
construction sites, traffic jams, accidents). Parallel in the city center of
Pisa, the focus was on a parking guidance system and the reduction of air
pollution. For this purpose, on a stretch of 1 km, 5 measuring stations with
sensor were set up. The charm of these systems lies in the decentralized
horizontal architecture, so that a high level of scalability has guaranteed
[29].

TABLE III: This table gives an overview about ITS which use external infrastructure. Here, the test beds are divided into the sections Early Days, Situation-related Analysis and Creation of Digital Twins. It should be mentioned that the ITS, which does not have own sensors, use the data from the connected vehicles. Year | Name | Length | Camera | Radar | Lidar | Induction loop | Special sensors / other remarks | Architecture
---|---|---|---|---|---|---|---|---
1986 | PROMETHEUS | N/A | - | - | - | - | Focusing V2X | N/A
1986 | PATH | N/A | - | - | - | - | Focusing V2X | N/A
1998 | DynaMIT | N/A | - | - | - | X | - | Centralized
2006 | SAFESPOT | > 20 km | X | - | X | - | V2X/Weather/RFID/IR camera | Cloud
2013 | Compass4D | > 10 km | X | - | - | - | Focusing V2X | Edge-Cloud
2012 | INSIGHT | N/A | X | - | - | - | V2X/Use social networks | Cloud
2015 | VaVeL | N/A | X | - | - | - | V2X/Use social networks | Cloud
2016 | ICSI | 9 km | X | - | - | - | - | Decentralized
2018 | Fed4Fire+ | 3 km | - | - | - | - | Focusing V2X | Edge-Cloud
2017 | DIGINET-PS | 3.7 km | X | - | - | - | Weather/parking/road | Edge-Cloud
2016 | ConVeX | 30 km | - | - | - | X | V2X | Cloud
2017 | DRIVE Hessen | 200 km | X | - | - | - | - | Edge-Cloud
2012 | Testfeld Kassel | 9 km | - | - | - | - | Focusing V2X | Edge-Cloud
2018 | Midlands Future Mobility | 300 km | - | - | - | - | Focusing V2X | Edge-Cloud
2016 | ECo-AT | N/A | - | - | - | - | Focusing V2X | Centralized
2016 | InterCor | > 100 km | - | - | - | - | Focusing V2X | Cloud-Edge
2017 | Safe Strip | 79 km | - | - | - | - | V2X/Pavement sensors | Edge-Cloud
2016 | AUTOCITS | > 17 km | - | - | - | - | V2X/Internet | Edge-Cloud
2019 | C-Roads CZ | > 100 km | - | - | - | - | Focusing V2X | Edge-Cloud
2014 | SCOOP@F | 2000 km | - | - | - | - | Focusing V2X | Edge-Cloud
2017 | SAFARI | 16 km | - | - | - | - | Focusing V2X | Cloud-Edge
2018 | Testfeld Friedrichshafen | > 5.5 km | - | - | - | - | Focusing V2X | N/A
2019 | ZalaZONE | > 100 km | - | - | - | - | Focusing V2X | Edge-Cloud
2016 | CARISSMA | N/A | - | - | - | - | Focusing V2X | N/A
2014 | AstaZero | 7.4 km | - | - | - | - | Focusing V2X | N/A
2017 | MEC-View | < 1 km | X | - | X | - | Stereo camera, Laser scanner | Cloud
2019 | Testfeld Ger.-Fran.-Lux. | > 100 km | X | - | - | - | Focusing V2X | Edge-Cloud
2017 | KoRA9 | 225 m | - | X | - | - | - | Edge-Cloud
2018 | 5GMOBIX | > 100 km | X | X | X | - | V2X/Weather/road/pedestrian | Edge-Cloud
2017 | Providentia | 3.5 km | X | X | X | - | Event camera | Edge-Cloud
2014 | AIM Braunschweig | 7 km | X | X | X | - | Stereo camera, Laser scanner | Edge-Cloud
2016 | Testfeld Niedersachsen | 280 km | X | - | - | - | Stereo camera on 7.5 km | Edge-Cloud
2017 | Testfeld Düsseldorf | 24 km | X | - | X | - | V2X | Edge-Cloud
2016 | Testfeld Bad.-Württem. | > 100 km | X | - | - | - | V2X/Weather | Edge-Cloud
2009 | Ko-PER | < 1 km | X | - | X | - | - | Cloud
2017 | ALP.Lab | 23 km | X | X | X | - | - | Edge-Cloud
2020 | SMLL London | 24 km | X | - | - | - | - | Edge-Cloud
2021 | IN2Lab | 2 km | X | X | X | - | - | Edge-Cloud
2016 | Testfeld Dresden | 20km | X | - | - | - | Focusing V2X | Edge-Cloud
2019 | HEAT | 1.8 km | - | X | X | - | V2X | Edge-Cloud

Within the scope of the project FED4FIRE+, the "Smart Highway: V2X Testbed"
was created in the year 2018. The ITS had 7 RSU on a length of 3 km at the
E313 highway in Antwerp, Belgium. The system was part of the CityLab test bed
too. The research topic of the project was the communication between vehicles
and other vehicles or infrastructures (V2X). For this, the system used direct
antennas and a backend respectively a cloud solution which is connected via
4G/5G internet. The system has achieved latency times of 10-15 ms. The
infrastructure had not its own sensors, it used only vehicle data. For data-
intensive applications, the ITS used high-performance computers. The FED4FIRE+
project has presented a new approach for collocation of Multi-Access Edge
Computing platforms with the support of RSU. This has improved the Quality of
Service in infotainment services for vehicles on the highway [30, 31].

The Diginet-PS test field in Berlin, Germany, covers the scenarios federal
road and secondary road in inner-cities areas. The ITS uses cameras, weather
sensors and other traffic sensors (e.g. for measurement of the traffic flow,
parking stores, pollution) for detection of the environment. On the several
RSU, local data processing is implemented as edge computing. Furthermore, a
cloud system receives the results from the RSU. The main purposes of the cloud
are to store, analyze and predict test bed wide data. Based on these ITS, the
researcher focus on the development of services and applications to increase
traffic safety for automated driving. For instance, the applications are
evaluating parking space utilization, analyzing the road conditions and
recognizing the traffic situations [25, 32, 33].

In 2016, the ConVeX project created two ITS solutions. The first ITS was
located on the A9 Freeway near Nuremberg, Germany. Over a length of 30 km, it
was equipped with 6 RSU. The second ITS was located in Rosenheim, Germany and
had two RSU. The data inputs of the systems were induction loops from existing
infrastructure and the sensor information of the connected cars. The data
processing was in the several RSU and in a traffic control center. The
research focus of the project were value-added services, as well as the V2V
and V2X communication. The project has implemented the value-added services
"Emergency Electronic Brake Light", "Do Not Pass Warning", "Blind Spot
Warning" and "Vulnerable Road User Collision Warning". To influence the
traffic flow, the system has sent the relevant information directly to the
road user or to the electronic gantries. Furthermore, the ITS has notified
vulnerable groups (e.g. cyclists, pedestrians) with a smartphone app [34].

The ITS DRIVE-Testfeld Hessen (Dynamic Road Infrastructure Vehicle
Environment) is located in the region of Frankfurt, Germany. The overall goal
is the intelligent management of traffic and construction sites. Furthermore,
the ITS focuses on new techniques for traffic data acquisition with the use of
V2V and V2X communication as well as data fusion between vehicle and
infrastructure. The DRIVE-Testfeld Hessen has implemented intelligent
applications which should support automated driving as well as the general
traffic flow. Therefore, the test bed has more than 120 RSU. The applications
have been traffic hazard warnings, shockwave damping and Green Light Optimal
Speed Advisory (GLOSAR) [25, 32, 35, 36]. The mentioned ITS was platform of
several projects. The project simTD set up many RSU and an ITS Central
Station. The valued-added services of this project were obstacle warning,
electronic brake light and warning about arriving emergency vehicles and
construction sites [37]. Then, the project CVIS (Cooperative Vehicle-
Infrastructure Systems) has focused on V2V and V2X communication issues for
enhanced driver awareness in an inter-urban environment [38, 11, 23]. Last,
but not least, the project DRIVE C2X has performed a comprehensive assessment
of cooperative systems with field operational tests [11, 39].

Another ITS exists in the city center of Kassel, Germany. Here, the traffic
lights are equipped with 15 RSU and an existing traffic management center is
used. The system allows priority treatment of public transport and emergency
vehicles at traffic lights. Furthermore, the ITS warns road users about
approaching emergency vehicles. For this purpose, the system uses the so-
called DENM (Decentraliced Environmental Notification Messages) for V2V and
V2X communication. As value-added services, the researchers have developed a
forecast for the green phase and an alternative route control [25, 32, 40,
41].

The Midlands Future Mobility test field in the United Kingdom is used to
research on connected and autonomous vehicles. Over a length of 300
kilometers, the ITS is located between the cities Coventry, Birmingham and
Solihull. Therefore, the ITS covers the scenarios city center, country road
and motorway. The test field use RSU, which offer various value-added
services. For instance, the services could be in-vehicle signage, roadworks
warnings as well as GLOSAR. Current research results on the ITS have achieved
in the cooperative perception for 3D object detection with the use of
infrastructure sensors [42, 43].

In the context of the European Cooperative ITS Corridor from 2013, vehicles
communicated with support of the infrastructure on the Rotterdam-Frankfurt-
Vienna route. The Eco-AT project (2016) in Austria, a follow-up project of
Telematik, set up a central ITS station as well as a number of RSU. The
architectural approach was more centralized. The researchers have generated
the necessary specifications. The use cases were decentralized environmental
notification message applications (e.g. road works warnings), aggregation of
cooperative awareness messages, in-vehicle information (e.g. speed
restrictions) and intersection safety (e.g. red light detection) [6, 44].

In the year 2016, the overall goals of the European project InterCor
(Interoperable Corridors) were improving the safety, the traffic flow as well
as increasing the cybersecurity of the ITS system itself. The project aimed to
link several ITS corridors to exchange innovation and best practice in solving
common problems. Therefore, the project included the ITS operations in
Belgium, France, Netherlands and United Kingdom. The environment scenarios of
the ITS were in Belgium, Netherlands and France on freeways. Furthermore, the
ITS in France was an extension of the SCOOP@F project, which is mentioned
below. The environment scenario in the United Kingdom were urban roads and
motorways too. The project installed RSU which allowed V2X communication. For
instance, the project has achieved the value-added services road works
warnings, in-vehicle signage, on-board signalling of hazardous and unexpected
events, multi-modal cargo optimization, green light optimization, probe
vehicle data, parking information services for trucks and tunnel management
[45, 46, 47, 48].

The project SAFE STRIP (SAFE and green Sensor Technologies for self-explaining
and forgiving Road Interactive aPplications) implements an unconventional new
approach to increase the traffic safety. The ITS consists of intelligent
infrastructure sensors in the form of strips with networking capabilities.
They are on the road pavement surface and analyze the individual situations.
For instance, the system measures environmental parameters (e.g. temperature,
ice, ambient light), passing vehicles (speed, lateral position, vehicle type)
as well as pedestrian crossings. Furthermore, these sensors communicate
bidirectional with vehicles as well as external infrastructure (e.g. traffic
management centers). For test and implementation, the project installed ITS in
a 9 km section near Rovereto on the A22 freeway in Italy as well as in an 70
km section in the Attiki Odos urban freeway in Athen, Greece. Here, the
project has placed sensors on 39 toll barriers in Athen and several strip
sensors as well as 5 RSU in Rovereto [49, 50, 51].

The deployment of ITS solutions in Europe were aimed in the European Project
AUTOCITS in the year 2016. The overall goal of the project is to boost the
role of ITS as a catalyst for the implementation of autonomous driving.
Therefore, several ITS in Lisbon (Portugal), Madrid (Spain) and Paris (France)
were created. The test bed in Madrid consisted of 15 RSU which were set up at
a high frequented freeway on a length of 10 km. On a total length of 8 km on
freeway and urban road, the ITS in Lisbon used 5 RSU. Last, but not least, in
a urban highway near to Paris, the ITS consisted of 1 RSU. The project has
achieved various day 1 services. For instance, road works warning, slow or
stationary vehicle warning as well as the providing of information about
weather conditions has been implemented [52, 53, 54, 55].

In the year 2019, the project C-Roads CZ focused on the implementation of ITS
units as well as the test and evaluation of its functionalities. The features
of the ITS were situated related analysis (e.g. road works warning, in-vehicle
information, slow and stationary vehicles, traffic jam ahead warning,
hazardous location notification). For testing under real conditions, seven
test fields has built in the Czech Republic. All this ITS solutions has been
equipped with RSU which communicates with vehicles and a central processing
system [56].

In the project SCOOP@F, an ITS was deployed in a nationwide scale in 2014. For
this, in the project were equipped 2000 km of the road with RSU as well as
3000 vehicles with on-board units for V2X communication. The RSU were
connected to traffic management centers. The main data sources of the test
field were the vehicles itself. They deliver data about their own position,
speed and direction. Furthermore, vehicles received information about
unexpected events (e.g. construction, slippery road, reduced visibility) from
external sources. The main goals of the ITS were increasing the safety and
improving the travel quality. The researchers have implemented a decentralized
communication to alert road users about unexpected events. In addition, the
enhancing of the coverage of VANETs, a secure cloud environment for connected
vehicles as well as a novel detailed security protocol were achieved [57, 58,
59, 60, 61, 62].

Another important test bed is SAFARI in Berlin. The ITS covers the scenarios
main road, secondary road and a junction of a freeway. The overall goal of the
project is the exchange of information and the dynamic update mechanism of the
dynamic maps, which are basic requirements for automated driving. In detail,
the project research on the handling of construction sites and unexpected
events for automated and connected driving. The test field uses cloud
platforms as well as mobile edge computing for the V2X processing [25, 32,
63].

In the test bed Friedrichshafen, traffic lights were equipped with RSU and
V2X. In total, the ITS covers the traffic scenarios federal road, country
road, city center and pedestrian zones. The research topic is the information
exchange vehicles to infrastructure as well vehicles to each other. The
current research project on the test field is Alfried [25, 32, 64].

The project ZalaZONE in Hungary has a cordoned-off proving ground as well as a
real world ITS to the border to Austria. In the real world, the ITS covers the
highway scenario. In the cordoned-off proving ground, it covers the scenarios
of smart cities, high and low speed handling as well as high speed freeways
and rural roads. The purposes are test and research in the field of autonomous
and connected vehicles. For this, the test field is equipped V2X communication
technique [65]. Another cordoned-off test facility for re-enacting and
simulating traffic scenarios is implemented in the CARISSMA project in
Ingolstadt. The test facility offers 4000 square meter experimental for
researching on V2X communication, accident detection, accident consequences
reduction as well as the automated driving functions [66]. The next cordoned-
off ITS is AstaZero (Active Safety Test Area). It was set up in 2014 in
Sweden, and it is an open environment where vehicle OEMs, research institutes
and universities perform development and research. The research topics are new
active safety functions for road vehicles. The test field contains a city
area, 700 m multilane road, 5.7 km long rural road and 1 km high-speed area
[67].

###  III-C Creation Digital Twins

Autonomous vehicles are developed from Google [68], Tesla [69] as well as many
other automotive manufactures. They can tackle the known problems in the
transportation segment, because they optimize the traffic flow and can
increase parallel the safety of the traffic participants. According to the
current approaches in autonomous driving, the primary responsibility, which is
associated with replacing human perception and control, lies on vehicle
manufacturers. However, the ITS on the infrastructure can create high
precision digital twins. The vehicles can use this data as ground truth
information. Therefore, the responsibility can re-balanced between vehicles
and infrastructure. So, high precision ITS can be an essential enabler for
autonomous driving [70]. This section presents several ITS solutions which
aims to the mentioned properties.

In the year 2017, the MEC-View project implemented an ITS in the city center
of Ulm, Germany. Across of roads and an intersection, the system contained 8
cameras, 4 stereo-cameras, 4 laser scanners as well as 8 Lidars. For object
prediction and data fusion, the sensors were connected with a server. Vehicles
communicated with the server via 5G. In addition, a 3D-HD map of the relevant
road sections were created. The aim of the project was to create a digital
twin of the road traffic. The researchers have achieved data transfer in real
time [71, 72].

The digital test bed "Germany-France-Luxembourg" was created in 2016. The
focus was on testing of automated and connected driving functions. The special
charm of this project was in the challenges of national border crossing. As
part of the mentioned test bed, the project ITeM (ITS Testfeld Merzig) in
Merzig, Germany, covered the environments city and rural read. The research on
this ITS was on connected driving functions via C2V and C2X too. For traffic
perception, the infrastructure was extended with RSU, C2X technology and
cameras. The RSU communicated with vehicles and a centralized server. The
project have implemented applications for hybrid access technologies and
safety functions [25, 32, 73, 74].

In 2017 on the freeway A9 in Germany, researchers of the project KoRA9
(Kooperative Radarsensoren für das digitale Testfeld A9. English: Cooperative
radar sensors for the digital test field A9) installed 10 novel 77 GHz radar
sensors on five masts along the freeway with a mutual distance of 45 m.
Furthermore, 2 Road Side Units were installed in a gantry bridge for the V2X
communication. The project goal was to evaluate the potential of seamless
sensory recording of traffic with radars. For this purpose, the infrastructure
sensors performed automotive radar applications. The obtained data streams
were merged to a digital twin. The researchers have analyzed the digital twin
and have made it available to third parties via the developed cloud platform.
Furthermore, they have developed services for infrastructure support during
overtaking maneuvers and traffic jam warnings [32, 75].

With the European Project 5GMOBIX (5G for cooperative & connected automated
MOBIlity on X-border corridors), several ITS in Europe and Asia were created
respectively reused in 2018. The project showcased the benefits of 5G
technology for cooperative, connected and automated mobility. The Spain-
Portugal cross-border ITS between Vigo and Porto focused on automated driving
maneuvers. It was equipped with 2 MEC nodes, 2 center clouds, 5 RSU, 4 traffic
radars and 2 pedestrian detectors. The ITS Greece-Turkey researched on
platooning and the use of external sensors. The external infrastructure
contains 6 Edge computer, 2 clouds, 4 RSU as well as 2 Lidars. The ITS in
Eindhoven-Helmond in the Netherlands focused on cooperative collision
avoidance with extensive use of cameras. Therefore, the ITS used 2 MEC
servers, 2 cloud systems and more than 50 cameras. The ITS in France were
located in Versailles and Paris. They focused on infrastructure-assisted
driving with 4 MEC nodes, 1 cloud, 2 Lidars as well as 3 cameras. The ITS in
Berlin and Stuttgart, Germany, researched on RSU-assisted platooning and
surround view generation. Therefore, the system used 9 RSU, 1 Cloud, 18
cameras and 10 environmental condition sensors. The ESPOO urban ITS in Finland
focused on extended sensor processing as well as on remote driving. The ITS
were set up with 2 MEC nodes, 1 cloud, 1 Lidars and 1 camera. The researcher
of the Jinan ITS in China focused on cloud assisted advanced driving, cloud-
assisted platooning as well as remote driving. The used equipment were 5 RSU
nodes, 1 cloud, 5 Lidars, 6 cameras and 5 radars. Last, but not least, the
cordoned-off ITS Yeonggwang in South Korea focuses on remote driving as well.
The system used 1 cloud [76, 77].

With the support of external infrastructure, the Providentia project created a
digital twin of each traffic participant in real time in the year 2017. This
digital twin should provide a foresight for autonomous vehicles as well as
vehicles with conventional driver assistance systems. For this purpose, an ITS
was built on the A9 freeway in Munich, Germany. The ITS contained 2
measurement stations on gantry bridges which collected data of the traffic.
Each station were equipped with a RSU, 4 cameras and 4 radars. Furthermore, a
backend computer realized the global data fusion as well as the connection to
the 5G network and the internet. The sensors have detected the individual
traffic participants and have sent the data to the RSU. Then, the RSU has
tracked the detections over several time steps. The tracks have been merged
into a local digital twin for each RSU. In the end, the backend receives the
local twins of the RSU and has combined it again to a global digital twin
across all measuring points. With this, the ITS of Providentia has created
digital twins which combine the advantages of radar and camera from different
perspectives [78, 79, 80]. In 2020, the follow-up project Providentia++ has
extended the ITS into urban areas. Thus enabled the analysis of complex
traffic scenarios in intersections and pedestrian crossings. In total, the
test bed had a length of 3.5 km, contained 7 measuring stations with 75
sensors (e.g. cameras, event-based cameras, radars and Lidars). The figure 1
shows the hardware setup on a public road intersection in an urban area. At
the moment of writing this paper, the researchers of Providentia++ have
established a stable data stream of the high precision digital twin for the
broad public. To the best of our knowledge, this feature is unique in the
current development of ITS [81].

Figure 1: An equipped intersection in the Intelligent Transportation System of
the project Providentia++.

In the year 2014 starts the ITS AIM (Application Platform for Intelligent
Mobility) in Braunschweig, Germany. The ITS infrastructure is designed as a
long-term platform for research projects in the area of autonomous and
connected driving in an urban environment. The test bed has a length of 7 km
and is equipped with RSU for V2X communication across 35 intersections with
traffic lights. The ITS enables information sharing between traffic lights,
vehicles as well as other infrastructure. For perception, the several RSU are
connected with radar, camera, stereo-camera and Lidars respectively Laser
scanners. The goal of the overall system is the delivery of digital twins with
corresponding video scenes under real-time conditions [25, 82, 83, 84, 85].

The project Testfeld Niedersachsen (Englisch: Lower Saxony test bed) in
Germany is an extension of the mentioned AIM test bed in Braunschweig. The
test bed runs on a total length of 280 km of freeways and connects the cities
Hannover, Hildesheim, Braunschweig and Wolfsburg. On a 7.5 km long section,
the ITS has 71 measuring stations for high-precision recording of the traffic
situations. The test bed is equipped with stereo cameras, 3 RSU which provides
V2X communication as well as a backend server. The ITS aims to offer an open
research platform which delivers ground truth data for autonomous and
connected driving. The overall goals are reduction of accidents, improvement
of perception and optimization of the traffic flow. The researchers have
realized the value-added services’ maintenance vehicle warning, in-vehicle
signage as well as lane management [86, 87, 88, 89].

The digital test bed "Düsseldorf" in Germany runs on a length of 20 km on the
A57 freeway from the Meerbusch junction near Kreefeld via the Kaarst junction
onto the A52 into downtown Düsseldorf. Therefore, the ITS covers the scenarios
freeway, tunnel, bridge, city center and parking. The test bed grows with
several projects. The project KoMoD researched on the interaction between
infrastructure and vehicles in the scope of connected and automated driving
functions. The ITS used vehicles as mobile sensors for the detection of
obstacles on the road (e.g. pedestrians, wrong-way drivers). The project has
developed the value-added services autonomous parking, prioritization of
public transport system and cooperative traffic light [90]. Then, the follow-
up project KoMoDnext should prepare numerous sections of the ITS for
autonomous driving (level 4). The project has designed and implemented a high-
resolution environment detection on the A57. This component has merged
stationary and vehicle-generated data [91, 32]. Last, but not least, the
project ACCorD expanded the corridor for New Mobility Aachen-Düsseldorf with
additional 4 km. The central component of the project activity was the
generation of a digital twin. The charming aspect was the extensive usage of
the Lidar-technology: The ITS contained on 68 measurement stations. Each
station has 2 cameras and 2 Lidars, so that 136 Lidars were set up. This data
has been locally processed in various RSU and a central database, which
offered the collected data to other research activities and simulation [92].

The ITS Testfeld Autonomes Fahren Baden-Württemberg (English: Test Bed
Autonomous Driving Baden-Württemberg), which is located in Karlsruhe and
Heilbronn, uses cameras, several RSU and a backend server. The test bed allows
a sensor extension with radars and Lidars. Based on camera object tracking,
the ITS aims to generate digital twins of the traffic. With this low-cost
sensor setup, real-time recording in various traffic situations (e.g. traffic
with vehicle, bicycle and pedestrian) are implemented. The purpose of the
infrastructure is the testing of vehicle systems for automated and connected
driving under real traffic conditions [93, 32, 94]. For instance, a current
research on the test field is the CARAMEL project. The project research on the
challenges in cybersecurity of modern vehicles. For this, CARAMEL applies
advanced artificial intelligence and machine learning techniques [95, 96].

In the year 2009, the project Ko-PER of the research initiative Ko-FAS aimed
to increase road safety. The goal was a precise creation of a digital twin of
the traffic participants in real-time. Therefore, intersections in Germany
were equipped with cameras and Lidars. The intersection in Ulm contained 2
Laser scanners and 2 cameras, in Alzenau 8 Laser scanners and 7 cameras and
last, but not least, the intersection in Aschaffenburg contained 14 Laser
scanners and 10 cameras. The data streams of the sensors on each intersection
were combined locally. Then, the fusion result were combined with vehicle
information. Therefore, V2X communication technology were implemented too.
With this, the researchers has created a digital twin of the traffic [97, 98,
99].

As part of the project ALP.Lab - Austrian-Public Test Track Highway A2, an ITS
near Graz was implemented in 2017. The test bed covered a distance of 23 km
and was equipped with more than 100 sensors. For the creation of digital
twins, the ITS used cameras, radars and Lidars. The goals of the ITS were
extension of the vehicles’ perception rang and offering ground truth data for
plausibility checks of vehicles sensor data. With this, the ITS offers
comprehensive testing functions for automated driving and driver assistance
systems. Interestingly, the researchers have achieved tracking of vehicles for
more than 2 km [100, 101].

The Smart Mobility Living Lab in London is a real world environment for
testing and developing future mobility solutions. For this purpose, the 24 km
long test bed has 276 cameras, 40 RSU and 2 data centers, which are connected
to each other via fiber optics. The RSU provides V2X communication service.
With this hardware, the ITS collects high resolution and high confidence
information about the road traffic. The overall goal is the support of
development for autonomous vehicles to market-ready [102].

As extension of the mentioned cordoned-off test facility CARISSMA, the project
IN2Lab offers a real world test bed in Ingolstadt, Germany. The ITS has a
total length of approximate 2 km. Every 200 m exists a measurement stations
with a 4.5 m high mast. So that, there are 13 measuring stations which are
connected to a central computing unit via optical fiber. On each measuring
station are a RSU, cameras, Lidars, radar as well as V2X communication
modules. The purpose of the test field is the research and the test of
autonomous vehicles [103, 104].

In the scope of the project Testfeld Dresden (English: Dresden test bed), a
cooperative ITS is deployed in the urban and suburban areas of Dresden,
Germany. The test field provides on 20 km stretch various driving scenarios
(e.g. urban roads, connection to freeways, complex junctions, tram tracks, bus
lanes and separate cyclists lanes). Therefore, the test bed contains several
camera systems, 10 RSU for research and 25 RSU for productive work.
Furthermore, the ITS has a backend which hosts a centralized cloud service.
The overall goal is the testing of automated and connected driving in a
realistic traffic environment. The project has proposed a reference platform
for heterogeneous ITS communication as well as several value-added services
(e.g. GLOSAR, probe vehicle data and a cooperative lane change) [105, 106,
107].

In addition, further ITS solutions for connected and autonomous driving were
implemented in numerous cities in Germany. For instance, the project HEAT
(Hamburg Electric Autonomous Transportation) installed in 2019 a test bed with
a length of 2 km in Hamburg. The ITS contained of several RSU, radars and
Lidars. The purpose of the sensors was to support the environment perception
of an autonomous electric minibus in a real world scenario. So, the research
goal of the HEAT project was the networking of an automated fleet of minibuses
with traffic infrastructure. The researchers on this project has achieved
successfully this goal [25, 108, 109].

##  IV Discussion

The Intelligent Transportation Systems which use infrastructure offer great
potential and possibility to support connected and autonomous driving. To
fulfill this future use cases, the sensors and services of ITS becomes more
demanding and more complex. To achieve political acceptance, the ITS must also
be taken privacy and security aspects into account. Based on the mentioned
literature survey, we investigate and discuss 1.) the trend of the general
purpose of ITS, 2.) the necessary sensor setup, 3.) existing value-added
services as well as 4.) the nonfunctional requirements of ITS. The open
research questions that have not yet been adequately answered are identified.

###  IV-A General Purpose

The claim on the tasks of ITS increase significantly. In the Early Days, the
ITS systems have improved safety and efficiency of the traffic flow.
Therefore, the systems have allowed a general statement about the current
traffic flow. Furthermore, prototypes of the first V2X have been developed.
This development started in 1986 and ended in the first decade of the 2000s.
In the next step, the ITS of the category Situation-related Analysis have
analyzed and interpreted individual traffic situations. Furthermore, these ITS
solutions could share their results with other traffic participants. With
this, these systems have contributed extreme to more safety and a better
traffic flow. This category grew exponentially from 2010 to 2018 and then
stagnated. Presumably, the functional scope of these systems is now no longer
sufficient. Since 2012, more and more test beds exist which can generate
highly accurate digital twins of road traffic. They allow a complete, high
granularity analysis of the traffic. So far, there is no sign of stagnation.
Because future ITS solutions should give control recommendations to autonomous
vehicles, the current development is absolutely necessary. The figure 2 shows
the accumulated number of test beds in each category and therefore the general
trend of ITS solutions.

Figure 2: Feature trend in Intelligent Transportation Systems.

###  IV-B Sensors

The first step in the process chain of ITS are the several sensors. They
collect information about the real world. So, they have the main impact on the
precision and quality of the services. In the first test beds, the sensors for
analyzing the traffic flow were induction loops. Unfortunately, only induction
loops do not allow an individual situation analysis. Therefore, cameras have
to be used in the most ITS since 2006. Cameras can create a bird’s-eye view of
what is happening on the road so that they allow complex situation analysis.
Weakness points are obvious: The camera does not deliver reliably data in case
of night or extreme weather conditions (e.g. strong rain, fog or snow). For
compensation, the modern ITS use more and more frequent a combination of
cameras with radars, Lidars and other special sensors (e.g. IR camera, stereo
camera, event camera or weather sensor). These hardware setup can percept the
environment in any weather conditions. Most of the test beds use camera and
Lidar. The Figure 3 includes the full trend of sensor setups in ITS.

Figure 3: The several sensors in ITS.

Modern vehicles contain cameras, radars and Lidars too. Strictly speaking, the
moving vehicles could be part of the ITS sensor setup. However, the ITS use
extensive V2X technology, the sensors of vehicles receive too little attention
in the overall fusion for generating digital twins. The integration of such
"mobile sensor stations" into modern ITS needs further research. In
particular, the early sensor fusion with vehicles should be more considered.

###  IV-C Value-added services

The capture of the traffic and the creation of digital twins must not end in
self-purpose. Otherwise, the ITS would be useless to the society. Instead, the
ITS has to offer meaningful services which support people in their daily life.
For instance, an ITS can decrease maintenance costs, reducing pollution,
improving transfer speed as well as protecting human life [7, 4]. Additional
to cover the high costs for installation and maintenance, the ITS services
offer potentials for high sales too [110, 8]. The table IV shows the value-
added services in the mentioned test beds.

Figure 4: The purpose of the developed value-added services in ITS. TABLE IV: This table gives an overview about the value-added services of ITS which use external infrastructure. Value-added service |  Warning in operational maneuvers |  Warning in
road conditions |  Warning in
traffic situations |  Controlling of
vehicles |  Controlling of
traffic flow
---|---|---|---|---|---
Description |  Black spot, emergency electronic brake light, do not pass, road collision |  Construction sites, slow or stationary vehicle, slippery road |  Wrong-way driver, traffic jams, accidents, red light violation, emergency vehicles arriving |  Platooning, remote driving, cooperative lane change, optimize overtaking maneuvers, autonomous parking |  Parking guidance, alternative route control, speed advisory on traffic lights, in-vehicle signage, Shockwave damping, priority for public transport or emergency vehicles, lane management
PROMETHEUS | - | X | X | - | -
PATH | - | - | - | X | -
DynaMIT | - | - | - | - | X
SAFESPOT | X | - | X | - | -
Compass4D | - | X | X | - | X
INSIGHT | - | - | - | - | -
VaVeL | - | - | - | - | -
ICSI | - | X | X | - | X
Fed4Fire+ | - | - | - | - | -
DIGINET-PS | - | X | X | - | X
ConVeX | X | - | - | - | -
DRIVE Hessen | X | X | X | - | X
Testfeld Kassel | - | - | - | - | X
Midlands Future Mobility | - | X | - | - | X
ECo-AT | - | X | X | - | X
InterCor | - | X | X | - | X
Safe Strip | - | - | - | - | -
AUTOCITS | - | X | - | - | -
C-Roads CZ | - | X | X | - | X
SCOOP@F | - | X | X | - | -
SAFARI | - | X | X | - | -
Testfeld Friedrichshafen | - | - | - | - | -
ZalaZONE | - | - | - | - | -
CARISSMA | - | - | - | - | -
AstaZero | - | - | - | - | -
MEC-View | - | - | - | - | -
Testfeld Ger.-Fran.-Lux. | - | - | - | - | -
KoRA9 | - | - | X | X | -
5GMOBIX | - | - | - | X | -
Providentia | - | - | - | - | -
AIM Braunschweig | - | - | - | - | -
Testfeld Niedersachsen | - | X | - | - | X
Testfeld Düsseldorf | - | - | X | X | X
Testfeld Bad.-Württem. | - | - | - | - | -
Ko-PER | - | - | - | - | -
ALP.Lab | - | - | - | - | -
SMLL London | - | - | - | - | -
IN2Lab | - | - | - | - | -
Testfeld Dresden | - | - | - | X | X
HEAT | - | - | - | X | -

Unfortunately, the figure 4 shows a deficiency in the development of services,
which are needs for operational maneuvers. This value-added services require
the complete potential of the high accurate sensors. Most of the other value-
added services do not need this accuracy. Therefore, further research should
focus on the development of services which use the high data density of the
digital twins. However, the ITS projects [75, 91, 94, 100, 102, 103] create
digital twins with high data density to support the development of autonomous
driving, there are still gaps of using this information in market-ready
products to a broad public. This step is still necessary to cover the high
costs of such systems, as well as to provide real added value to the users of
ITS solutions.

###  IV-D Nonfunctional requirements

The ITS will be involved in the decision-making process for specific maneuver
actions of traffic participants. The system must be reliable in its operation
at all times, in extreme scenarios such as natural disasters, too. Therefore,
the ITS are critical infrastructure and needs several mechanisms for safety
and cybersecurity. To fulfill the safety requirement, the systems must have
functionality for self-diagnostic as well as for self-healing. Then, the ITS
would be safe in operation against malfunction [3, 7, 111].

In addition, we need mechanisms to protect the ITS again cyberattacks. In
particular with fatal consequences, the RSU are very exposed to this kind of
attacks. Unfortunately, there exists still research gaps. For instance, the
communication between vehicles and the test beds are most of the time
unencrypted. This makes the system very vulnerable. To tackle these problems,
the researchers have developed communication encryption with cloud support
[2], priority-based authentication techniques [8] and real-time secure
communication without overloading the network with security overhead [112].
Fortunately, some test bed projects have addressed the cybersecurity too: The
project InterCor have introduced a cyber-physical blockchain cryptographic
architecture [46] and the project SCOOP@F a secure cloud environments as well
as a novel security protocol [58, 61]. Furthermore, in the project CARAMEL on
the ITS Baden-Württemberg are activities on advanced artificial intelligence
and machine learning techniques to fulfill cybersecurity requirements, too
[95]. However, it seems the challenges in cybersecurity have not yet been
definitively solved. In particular, the secure transmission of high volumes of
data, as occurs with detailed twins, still seems to require research.
Therefore, further research on these security aspects are needed.

In case of a national wide rollout, the nonfunctional requirements’
scaleability, adaptability as well as real-time capability are important for
ITS solutions [113, 7]. This high demands are big challenges in the software
architecture: Centralized software solutions (e.g. with a cloud) are available
for ITS, but they have numerous disadvantages in scalability, energy
efficiency and failure tolerance. A scalable architecture to solve the
mentioned problems could be a distributed approach. According to [114], this
approach for intelligent sensor networks was not available, respectively was
not adequately researched. Indeed, with the use of edge computing, the works
of [110, 1] have presented distributed approaches for ITS architectures.
Furthermore, the mentioned ICSI test bed have focused on high scalable
distributed ITS, too [29]. However, this test bed has analyzed only specific
situations. It has not processed digital twins in real-time. To the best of
our knowledge, it is still unclear how this distributed systems performs in a
high data density environment with digital twins. For this reason, further
research in scaleability as well as adaptability in real world test beds is
warranted.

##  V Conclusion

In the scope of this work, 346 papers and documents have been collected with a
systematic review to provide a summary of the current Intelligent
Transportation Systems. After passing the filtering, 114 documents with 40
test beds have been analyzed. Here, this paper has showed the fascinating
development in the field of Intelligent Transportation Systems with the use of
external infrastructure from the Early Days until the Creation of Digital
Twins. So, this literature survey accompanies the reader from the beginning of
ITS until the current state of the art.

According to the mentioned history, the complexity as well as the
functionality of ITS have increased immensely. The current systems can deliver
high accurate information about individuals in traffic situations in real-
time. In the field of ITS, this development is a great success. However, there
are still improvements in the reliability, scalability and security of the
overall system needed. For this reason, further research in the ITS should
focus on more reliable perception of the traffic with the use of modern
sensors, plug and play mechanism as well as secure real-time distribution in
decentralized manner for a high amount of data. Furthermore, to ensure the
sense of such systems, services with a real added value for the broad public
with autonomous as well as non-autonomous cars have to be developed.

In the end, the development of Intelligent Transportation Systems with the use
of external infrastructure is in a correct direction. For the comprehensive
roll-out, however, a few questions still need to be answered.

## Acknowledgment

This research was funded by the Federal Ministry of Transport and Digital
Infrastructure of Germany in the project Providentia++. We would like to
express our gratefulness to have made this paper possible.

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