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Citation: Tonec Vranˇci´c, M.; Škorput,
P.; Vidovi´c, K. An Advanced Driver
Information System at Critical Points
in the Multimodal Traffic Network.
Sustainability 2024,16, 372. https://
doi.org/10.3390/su16010372
Academic Editor: Armando Cartenì
Received: 20 November 2023
Revised: 21 December 2023
Accepted: 28 December 2023
Published: 31 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Review
An Advanced Driver Information System at Critical Points in the
Multimodal Traffic Network
Maja Tonec Vranˇci´c * , Pero Škorput and Krešimir Vidovi´c
Department of Intelligent Transport Systems, Faculty of Transport and Traffic Sciences, University of Zagreb,
Vukeli´ceva 4, 10000 Zagreb, Croatia; pskorput@fpz.unizg.hr (P.Š.); kresimir.vidovic@ericsson.hr (K.V.)
*Correspondence: mtonec@fpz.unizg.hr
Abstract: Enhancing traffic safety is one of the fundamental objectives of Intelligent Transport Systems
(ITS), and it aligns closely with the principles of sustainable transport. Due to specific differences
in infrastructure, vehicles, and users’ behavior, places where different modes of traffic intersect are
recognized as critical points of the traffic system, making them crucial areas for the implementation
of Sustainable Urban Mobility Plans (SUMPs). The SUMPs aim to create urban mobility that is
both environmentally friendly and safe for all users. The continuous development and widespread
adoption of innovative ITS technologies have paved the way for a system that can provide drivers
with real-time information about both immediate and potential dangers at these critical points.
This paper presents a comprehensive review of prior research conducted in the field, investigating the
impact of information systems on drivers’ behavior, various detection and communication solutions
that can be effectively integrated into such a system, and a brief overview of the models and solutions
that have been developed to warn drivers in a similar context. A review of the literature found that
warning systems have a significant impact on driver behavior, which contributes to increased traffic
safety. Furthermore, there are numerous solutions applicable to a multimodal environment. Yet,
they mostly refer either to autonomous vehicles or require an additional unit of infrastructure for
communication, which is not realistically applicable to the current state of traffic in most countries of
the world. This paper proposes a system architecture framework for future research that would take
advantage of widely available technologies and make the system accessible to different users in a
multimodal environment.
Keywords: urban mobility; traffic safety; cooperative intelligent transport systems; traffic control
1. Introduction
Sustainable transport refers to ways of moving people and goods that are ecologically,
socially, and economically sustainable in the long term. This concept also promotes safe
transport practices and measures to reduce the number of traffic accidents and injuries.
These include safety initiatives, infrastructure design, the promotion of alternative modes of
transport, education, and raising awareness of risks and safety measures among road users.
The integration of Intelligent Transportation Systems (ITS) is continuously enhancing
the progress of sustainable transport. ITS represent sophisticated applications designed
to offer innovative services to various modes of transport and traffic management. These
applications entail the integration of telecommunications, electronics, and information
technologies with transport engineering for the planning, design, operation, maintenance,
and management of transport systems [
1
]. An ITS aims to enhance traffic efficiency (by
optimizing traffic flow and improving overall operational effectiveness), traffic safety (by
incorporating safety features, such as collision avoidance systems, real-time monitoring,
emergency response coordination, etc.), traffic management and control (by enabling the
real-time monitoring and control of traffic conditions, adaptive signal control, dynamic
route planning, etc.), environmental sustainability (by optimizing traffic flow and reducing
Sustainability 2024,16, 372. https://doi.org/10.3390/su16010372 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 372 2 of 20
congestion), and multimodal integration (by improving connectivity, increasing accessibil-
ity, and enhancing the overall mobility of passengers and goods). While ITSs offer immense
potential to transform transportation, several challenges need to be addressed to achieve
their full benefits, such as interoperability issues (a lack of standardization), privacy and
security concerns (the collection and sharing of sensitive data), and regulatory and policy
challenges (the rapid evolution of technology often outpaces regulatory frameworks), as
well as financial and infrastructure limitations.
In the domain of ITS, a whole range of services is available and divided into several
functional areas, such as passenger information, traffic management, in-vehicle support,
and personal safety. These services use information and communication technologies,
mobile applications, sensors, and data platforms to provide relevant real-time traffic infor-
mation, including information directed to passengers and drivers about road conditions,
public transport, parking, alternative transport, location, road hazards, and more. To
achieve this, the concept of Cooperative Intelligent Transport Systems (C-ITS) has been
developed, where the cooperation between the main actors of the transport network is con-
sidered: vehicles, drivers, and infrastructure [
2
]. With the same purpose, the concept of the
Internet of Vehicles (IoV) is derived from its previous domain, the Internet of Things (IoT).
The IoV represents the developing framework in which vehicles sustain nearly continuous
connectivity to the Internet, enabling the exchange of information between themselves and
with other services [
3
]. It is also considered an extension of Vehicular Ad Hoc Networks
(VANETs), with a significant distinction in that VANET vehicles are not directly connected
to a shared network but require additional information and communication infrastructure
for connectivity [4].
In this paper, the focus on safety is directed toward locations where various traffic
streams or different transportation modes are integrated, such as railway crossings, inter-
sections, pedestrian crossings, etc. Compared to other safety interventions, it is evident
that in-vehicle warning systems have a great potential to increase safety at critical points in
multimodal traffic, in terms of their impact on user behavior and considering that no major
interventions are needed in existing infrastructure. The main guiding idea of this work
was to investigate the available technologies and how they could be applied in a system
that would be reliable and available to a wide range of users. The first step, described in
Section 2, was to investigate the impact of the information system on drivers, i.e., traffic
safety, and thus to justify the need for such a solution. The second step was to research
similar solutions and analyze them to determine whether there is space for improvement.
In Section 3, an overview of the available technologies that have the potential to meet
the three main system requirements is given: to identify a location as a critical point, to
spot a conflicting vehicle in real time, and to transmit this information to the user in a
high-quality and timely manner. The reviewed literature offered valuable insights, upon
which a novel driver information system architecture framework is proposed in Section 4,
which can contribute to the ongoing safety improvement in multimodal transport networks
within the context of sustainable transport. The last section provides an analysis of the
reviewed literature.
2. Driver Information Systems
Driving is a social phenomenon, necessitating interactions between all involved road
users to guarantee smooth traffic flow and the safety of others [
5
]. Such an interaction is
very dynamic and includes tasks such as recognizing other road users, analyzing their
behavior, communicating with them and, when needed, anticipating their future actions,
and selecting an appropriate response accordingly [
6
]. This leads to the conclusion that the
safety of road users largely depends on users’ behavior.
With the development of ITS services, drivers are provided with additional decision
support through various information systems. ITS are growing into systems that are based
on the integration of a wide range of relevant technologies that can collect substantial
amounts of data, process them, and then take appropriate actions in real time [
7
]. Today,
Sustainability 2024,16, 372 3 of 20
vehicles possess the potential for wireless communication with other vehicles and other
entities in their immediate proximity to timely share safety-critical information (warnings)
primarily to avoid or mitigate collisions. Furthermore, vehicles can be connected to traf-
fic management systems via their accompanying network infrastructure to register any
potential road hazards, as well as for guidance, to ensure more efficient traffic flow.
In terms of increasing traffic safety, one of the most used ITS approaches are driver
information systems, which provide real-time information about incidents, possible dan-
gerous conditions on the road, etc. This information increases the driver’s awareness of the
environment and reduces distractions and their reaction time, which subsequently reduces
the risk of accidents.
Driver information systems were mentioned as early as 1975, and with the devel-
opment of sensory and communication technologies numerous driver warning systems
were developed, and studies were conducted on their reliability and efficiency. Only with
the rapid development of technologies in the last decade, and their wide application, is
more specific research involving technologies such as C-ITS and the IoV being carried
out. The swift development of autonomous driving and sophisticated telematic driver
assistance systems have notably enhanced vehicle safety. These systems primarily rely
on sensors integrated into the vehicle, aiding drivers in decision-making to enhance their
self-awareness and mitigate the risk of traffic accidents. While such systems are already
widely used in road traffic across Europe and are paving the way for autonomous vehicles,
there are very few attempts to achieve the same in a multimodal traffic environment. In the
following section, an explanation of why warning systems are essential for increasing traffic
safety in terms of their impact on the user is given, and several papers will be presented
that propose such systems applicable to a multimodal environment.
2.1. Influence on the Driver’s Behavior
The complex interaction between the entities of different modes of transport is often
unpredictable because of the road user’s behavior, and the impact of any system primarily
depends on its impact on the road user, specifically, the extent to which the user obeys
the system. Driver disobedience can be intentional (when drivers or pedestrians are
aware of their surroundings and understand the warning signs but still deliberately ignore
them) or unintentional (when they do not notice changes in the environment and/or do
not understand warning signs and consequently approach a critical point even with the
presence of danger). Furthermore, often the focus of the driver’s attention is not on the
location that they are approaching, nor on their observation of the environment; drivers
more often rely on the warning signals installed in the traffic infrastructure and on the
behavior of their surrounding vehicles as a way of warning them of changes in road
conditions, especially drivers with less experience in traffic [
8
]. Recent research shows
that in-vehicle or smartphone warning systems can have a notable influence on driver and
pedestrian behavior, particularly when perceived as credible and reliable. Their impact on
users in different studies was analyzed in simulation environments, through field tests,
or by surveys. Simulator testing revealed that participants are more inclined to adopt ITS
technologies at critical points with passive signaling (traffic signs and protective fences)
than with active signaling (changing lights and sound signals or active barriers) and that
they prefer the systems that are the simplest to use [
9
]. Also, presenting excessive and
unnecessary information can potentially confuse or distract drivers. Therefore, the system
should only display the essential information required to effectively assist the driver in
decision-making [
10
]. Solutions for informing drivers at passive crossings resulted in driver
behavior similar to that at active crossings [
11
]. Also, behavior improves in areas where
drivers are more prone to riskier driving [
12
]. In general, the implementation of a driver
information system significantly improves driver behavior around critical points in terms
of observing the environment (in multiple directions), their braking response, and their
approach speed [
12
]. The approach speed was decreased for all routes with a straight
initial segment. However, for horizontal routes incorporating sharp turns and stop signs,
Sustainability 2024,16, 372 4 of 20
the approach speed increased slightly. Despite this, drivers slow down more intensively
with the application of the system [
13
]. On the negative side, with the active warning
system participants’ obedience to the STOP sign decreased by 16.5%, but in the case of
system failure, most participants had no difficulty in detecting road hazards despite not
receiving any warning message [
14
], meaning that in-vehicle warning systems can have
a sustained impact on driver behavior even after the system has been removed or is no
longer active [
15
]. Field research [
16
] shows that the predicted crash risk decreases as the
approach speed decreases (Figure 1).
Sustainability 2024, 16, 372 4 of 21
decreased for all routes with a straight initial segment. However, for horizontal routes in-
corporating sharp turns and stop signs, the approach speed increased slightly. Despite this,
drivers slow down more intensively with the application of the system [13]. On the negative
side, with the active warning system participants’ obedience to the STOP sign decreased by
16.5%, but in the case of system failure, most participants had no difficulty in detecting road
hazards despite not receiving any warning message [14], meaning that in-vehicle warning
systems can have a sustained impact on driver behavior even after the system has been re-
moved or is no longer active [15]. Field research [16] shows that the predicted crash risk
decreases as the approach speed decreases (Figure 1).
Figure 1. Field test results on the impact of the in-vehicle warning system. Reprinted with permis-
sion from Ref. [16]. Copyright 2019, Xu Wang et al.
Based on the conducted tests, it is evident that a warning system enhances drivers’
behavior over time, but no immediate effect has been confirmed. Moreover, the individ-
ual’s personality significantly influences the effectiveness of the system, therefore, it is
imperative to undertake more comprehensive research across diverse scenarios within the
multimodal traffic network. Despite numerous simulation tests demonstrating impressive
results, field tests at critical multimodal points are still limited. It remains uncertain
whether in-vehicle warning systems can maintain equal reliability in real-world field con-
ditions [17].
2.2. Existing Examples of Driver Information Systems in a Multimodal Environment
Several examples of driver information systems are described below, with an empha-
sis on level crossings as the most critical points in a multimodal environment. In the avail-
able literature, in-vehicle warning systems are mostly related to autonomous driving, and
it is necessary to further investigate the possibilities of applying these technologies at a
higher level.
2.2.1. Critical Crossing Points of Rail and Road Traffic
When observing the interaction of different traffic modes, the most critical points of
the traffic system are level crossings, therefore, they are taken as a reference example for
the analysis of a warning system’s application. Level crossings are characterized as loca-
tions where a railway line or an industrial track intersects with a road at the same level.
This definition may also encompass crossings involving pedestrian and bicycle paths, as
well as other roads designed for the passage of people, animals, vehicles, or machinery
[18]. While accidents at these critical points may not occur frequently, their consequences
are significantly more severe than other traffic accidents, impacting individuals, society,
and economies on a substantial scale. Due to the large disparity between the train’s mass
and the road vehicle, most accidents involve serious injuries and fatalities. Also, second-
ary consequences include damage to vehicles, trains, and infrastructure, the disruption of
critical supply chain links, the environmental impact in the case of the transportation of
hazardous materials, etc. [19]. Even with the abundance of technological systems designed
to enhance safety at these critical points, accidents persist, and their consequences are
Figure 1. Field test results on the impact of the in-vehicle warning system. Reprinted with permission
from Ref. [16]. Copyright 2019, Xu Wang et al.
Based on the conducted tests, it is evident that a warning system enhances drivers’
behavior over time, but no immediate effect has been confirmed. Moreover, the individual’s
personality significantly influences the effectiveness of the system, therefore, it is imperative
to undertake more comprehensive research across diverse scenarios within the multimodal
traffic network. Despite numerous simulation tests demonstrating impressive results, field
tests at critical multimodal points are still limited. It remains uncertain whether in-vehicle
warning systems can maintain equal reliability in real-world field conditions [17].
2.2. Existing Examples of Driver Information Systems in a Multimodal Environment
Several examples of driver information systems are described below, with an emphasis
on level crossings as the most critical points in a multimodal environment. In the available
literature, in-vehicle warning systems are mostly related to autonomous driving, and it
is necessary to further investigate the possibilities of applying these technologies at a
higher level.
2.2.1. Critical Crossing Points of Rail and Road Traffic
When observing the interaction of different traffic modes, the most critical points of
the traffic system are level crossings, therefore, they are taken as a reference example for the
analysis of a warning system’s application. Level crossings are characterized as locations
where a railway line or an industrial track intersects with a road at the same level. This
definition may also encompass crossings involving pedestrian and bicycle paths, as well
as other roads designed for the passage of people, animals, vehicles, or machinery [
18
].
While accidents at these critical points may not occur frequently, their consequences are
significantly more severe than other traffic accidents, impacting individuals, society, and
economies on a substantial scale. Due to the large disparity between the train’s mass and
the road vehicle, most accidents involve serious injuries and fatalities. Also, secondary
consequences include damage to vehicles, trains, and infrastructure, the disruption of
critical supply chain links, the environmental impact in the case of the transportation of
hazardous materials, etc. [
19
]. Even with the abundance of technological systems designed
to enhance safety at these critical points, accidents persist, and their consequences are
consistently deemed the most severe, compared to other traffic-related incidents [
20
].
According to the Annual Safety Report for the year 2021 in the Republic of Croatia, out
of 80 serious accidents and accidents in railway traffic, 35 occurred at level crossings, in
which 6 people died (out of a total of 10) and 7 people were seriously injured [
21
]. At the
Sustainability 2024,16, 372 5 of 20
EU level in the same year, 234 people were killed at level crossings, and an equal number
of people ended up with serious or life-threatening injuries, which makes level crossings
the second largest cause of death in railway traffic [22].
The prevalent safety measures for level crossings typically involve infrastructure
modifications that alter the road or pedestrian crossing levels, along with physical warning
signals for road users. Passive signaling, while a straightforward and cost-effective solution,
is more prone to human disobedience than active signaling. Active signaling systems
commonly rely on sensor devices positioned at a specific distance from the crossing,
detecting train arrival and transmitting the information to sound signaling devices installed
at the level crossings [
23
]. At the EU level, out of approximately 105,000 registered level
crossings, only 45% of them are provided with active signaling [
9
], while in the Republic of
Croatia, it is only slightly more than 20% of the 1500 registered crossings [
9
,
21
]. Most of the
research in the field of level crossing safety deals with the improvement of existing technical
solutions, such as more advanced train detection [
24
], more effective warnings [
25
], better
information transmission within the railway environment [
26
], warnings to train drivers
about obstacles on the track [
27
], etc. Although these and similar studies offer high-quality
solutions, they lead to minor system design changes that have only marginal effects on
safety, considering that accidents at level crossings mostly occur due to irresponsible
drivers’ and pedestrians’ behavior: their wrong decision-making or just their unawareness
of the environment.
According to previous research, driver information systems can be divided into
two basic groups: warning systems about approaching a critical point based on histor-
ical data [
17
,
19
] and warning systems about an approaching train based on real-time
data [13,16,28]
. The first group does not use real-time data on the approaching train, but
does use the location of the vehicle to decide whether to display the warning.
In the study [
17
], the authors designed and evaluated (in an actual real-world setting)
an extensive in-vehicle Decision Support System (DSS), which is presented in Figure 2.
This system offers information about critical points through location analysis applied to
a nationwide dataset of historical accidents, comprising over 266,000 accidents. The in-
vehicle unit uses the Density-Based Clustering Algorithm (DBSCAN) to identify and classify
critical points. The output informs the driver that he is approaching a critical crossing.
Sustainability 2024, 16, 372 5 of 21
consistently deemed the most severe, compared to other traffic-related incidents [20]. Ac-
cording to the Annual Safety Report for the year 2021 in the Republic of Croatia, out of 80
serious accidents and accidents in railway traffic, 35 occurred at level crossings, in which
6 people died (out of a total of 10) and 7 people were seriously injured [21]. At the EU level
in the same year, 234 people were killed at level crossings, and an equal number of people
ended up with serious or life-threatening injuries, which makes level crossings the second
largest cause of death in railway traffic [22].
The prevalent safety measures for level crossings typically involve infrastructure
modifications that alter the road or pedestrian crossing levels, along with physical warn-
ing signals for road users. Passive signaling, while a straightforward and cost-effective
solution, is more prone to human disobedience than active signaling. Active signaling sys-
tems commonly rely on sensor devices positioned at a specific distance from the crossing,
detecting train arrival and transmiing the information to sound signaling devices in-
stalled at the level crossings [23]. At the EU level, out of approximately 105,000 registered
level crossings, only 45% of them are provided with active signaling [9], while in the Re-
public of Croatia, it is only slightly more than 20% of the 1500 registered crossings [9,21].
Most of the research in the field of level crossing safety deals with the improvement of
existing technical solutions, such as more advanced train detection [24], more effective
warnings [25], beer information transmission within the railway environment [26], warn-
ings to train drivers about obstacles on the track [27], etc. Although these and similar stud-
ies offer high-quality solutions, they lead to minor system design changes that have only
marginal effects on safety, considering that accidents at level crossings mostly occur due
to irresponsible drivers’ and pedestrians’ behavior: their wrong decision-making or just
their unawareness of the environment.
According to previous research, driver information systems can be divided into two
basic groups: warning systems about approaching a critical point based on historical data
[17,19] and warning systems about an approaching train based on real-time data
[13,16,28]. The first group does not use real-time data on the approaching train, but does
use the location of the vehicle to decide whether to display the warning.
In the study [17], the authors designed and evaluated (in an actual real-world seing)
an extensive in-vehicle Decision Support System (DSS), which is presented in Figure 2.
This system offers information about critical points through location analysis applied to a
nationwide dataset of historical accidents, comprising over 266,000 accidents. The in-ve-
hicle unit uses the Density-Based Clustering Algorithm (DBSCAN) to identify and classify
critical points. The output informs the driver that he is approaching a critical crossing.
Figure 2. Decision support system based on DBSCAN. Reprinted with permission from Ref. [17].
Copyright 2017, Elsevier.
The authors in [19] proposed an early warning system for oncoming trains based on
a wide range of available data, including the train’s schedule. The data sets were incorpo-
rated into a Geographic Information System (GIS), with various analyses conducted to
evaluate and characterize different locations more thoroughly. The system notifies
Figure 2. Decision support system based on DBSCAN. Reprinted with permission from Ref. [
17
].
Copyright 2017, Elsevier.
The authors in [
19
] proposed an early warning system for oncoming trains based on a
wide range of available data, including the train’s schedule. The data sets were incorporated
into a Geographic Information System (GIS), with various analyses conducted to evaluate
and characterize different locations more thoroughly. The system notifies drivers, using the
onboard navigation unit, of an oncoming train, allowing users to efficiently assess the best
available route.
In research involving real-time data, the system architecture consists of two main
elements: on-board equipment and infrastructure equipment. In study [
16
], the device
installed on the Roadside Equipment (RSE) utilizes static information, encompassing
geometric characteristics and positioning accuracy parameters for collision risk assessment.
The On-Board Equipment (OBE) obtains location data from its Global Navigation Satellite
Sustainability 2024,16, 372 6 of 20
System (GNSS) module, enabling the calculation of the approach speed and travel direction.
Combining this information with data stored in the RSE, the system initially estimates
the actual location, accounting for latency and user behavior. Subsequently, the system
assesses the collision probability using a mathematical collision risk assessment model. If
the probability surpasses a predetermined threshold, a warning is activated and promptly
transmitted to road users. Simultaneously, the road user can receive a time estimate for
how long it will take the train to pass (Figure 3). Communication between the devices takes
place via Dedicated Short-Range Communication (DSRC).
Sustainability 2024, 16, 372 6 of 21
drivers, using the onboard navigation unit, of an oncoming train, allowing users to effi-
ciently assess the best available route.
In research involving real-time data, the system architecture consists of two main
elements: on-board equipment and infrastructure equipment. In study [16], the device in-
stalled on the Roadside Equipment (RSE) utilizes static information, encompassing geo-
metric characteristics and positioning accuracy parameters for collision risk assessment.
The On-Board Equipment (OBE) obtains location data from its Global Navigation Satellite
System (GNSS) module, enabling the calculation of the approach speed and travel direc-
tion. Combining this information with data stored in the RSE, the system initially esti-
mates the actual location, accounting for latency and user behavior. Subsequently, the
system assesses the collision probability using a mathematical collision risk assessment
model. If the probability surpasses a predetermined threshold, a warning is activated and
promptly transmied to road users. Simultaneously, the road user can receive a time es-
timate for how long it will take the train to pass (Figure 3). Communication between the
devices takes place via Dedicated Short-Range Communication (DSRC).
Figure 3. Information system framework based on real-time GNSS data. Reprinted with permis-
sion from Ref. [16]. Copyright 2019, Xu Wang et al.
The authors of studies [13] and [28] investigated the safety impact of the C-ITS service
as part of the SAFER-LC project. The main elements of this system are a module for train
monitoring and arrival time estimation, and a module for vehicle tracking and communi-
cation, with a dedicated web service that enables data exchange and storage (Figure 4).
This system gives the driver two forms of messages: a static alert about a level crossing
ahead, and a dynamic alert about the approaching train, including the estimated time of
arrival. The calculation of the train’s anticipated arrival time relies on factors such as the
trains’ current position and speed, with predictions generated through the utilization of
machine learning algorithms, i.e., neural networks. The warning system uses mobile com-
munication, delivering alerts through a pop-up window on navigation devices located
within the vehicle. The critical area is determined using two predefined polygons: the road
network polygon includes all sections of the road leading to the level crossing within a
radius of 80 m from the railway, while the railway polygon includes railway tracks at a
distance of approx. one kilometer from the level crossing in both directions. An audio-
Figure 3. Information system framework based on real-time GNSS data. Reprinted with permission
from Ref. [16]. Copyright 2019, Xu Wang et al.
The authors of studies [
13
,
28
] investigated the safety impact of the C-ITS service
as part of the SAFER-LC project. The main elements of this system are a module for
train monitoring and arrival time estimation, and a module for vehicle tracking and
communication, with a dedicated web service that enables data exchange and storage
(Figure 4). This system gives the driver two forms of messages: a static alert about a level
crossing ahead, and a dynamic alert about the approaching train, including the estimated
time of arrival. The calculation of the train’s anticipated arrival time relies on factors such
as the trains’ current position and speed, with predictions generated through the utilization
of machine learning algorithms, i.e., neural networks. The warning system uses mobile
communication, delivering alerts through a pop-up window on navigation devices located
within the vehicle. The critical area is determined using two predefined polygons: the road
network polygon includes all sections of the road leading to the level crossing within a
radius of 80 m from the railway, while the railway polygon includes railway tracks at a
distance of approx. one kilometer from the level crossing in both directions. An audio-
visual warning is generated when the vehicle enters the polygon, or when the train and the
vehicle are in the same group of polygons.
The LeCross study [
29
] analyzed the concept of a satellite system that enables reliable
information about approaching trains at level crossings with passive signaling. The service
uses satellite communication and navigation systems. Instead of a trackside detection
system, information about the train’s arrival is delivered to the level crossing equipment
using wireless communication systems. Its implementation requires a back-end server for
data transfer, as well as a communication platform that can distribute information across
Sustainability 2024,16, 372 7 of 20
remote areas. The server maintains an up-to-date database on the train’s position and
calculates the train’s arrival time. Warnings are transmitted via a satellite link whenever
the train is within a certain distance of the crossing. The system requires the installation
of a smaller satellite terminal unit on the level crossing with an interface for users, which
enables a two-way connection with the central server. By default, the system runs in failsafe
mode: the assessment is made by the trackside unit independently of other systems and is
triggered by the lack of the train’s position message when expected. This architecture has
the added advantage of a centralized data model and communication platform that can be
used to deliver additional information.
Sustainability 2024, 16, 372 7 of 21
visual warning is generated when the vehicle enters the polygon, or when the train and
the vehicle are in the same group of polygons.
Figure 4. SAFER-LC system architecture. Reprinted with permission from Ref. [28]. Copyright
2020, Elsevier.
The LeCross study [29] analyzed the concept of a satellite system that enables reliable
information about approaching trains at level crossings with passive signaling. The ser-
vice uses satellite communication and navigation systems. Instead of a trackside detection
system, information about the train’s arrival is delivered to the level crossing equipment
using wireless communication systems. Its implementation requires a back-end server for
data transfer, as well as a communication platform that can distribute information across
remote areas. The server maintains an up-to-date database on the train’s position and cal-
culates the train’s arrival time. Warnings are transmied via a satellite link whenever the
train is within a certain distance of the crossing. The system requires the installation of a
smaller satellite terminal unit on the level crossing with an interface for users, which ena-
bles a two-way connection with the central server. By default, the system runs in failsafe
mode: the assessment is made by the trackside unit independently of other systems and
is triggered by the lack of the train’s position message when expected. This architecture
has the added advantage of a centralized data model and communication platform that
can be used to deliver additional information.
The authors of [30] proposed a system that combines Vehicle-to-Vehicle (V2V) and
Vehicle-to-Infrastructure (V2I) communication. The system is based on DSRC, and it
works in two types of cases. In the first case, the driver receives the warning directly from
the train. This type of communication is favored in situations where the radio channel
between the train and the vehicle has a strong line of sight. In the second case, a DSRC
receiver positioned at the level crossing intercepts the warning from the train and may
subsequently resend it to the vehicle. Alternatively, it produces a warning in the form of
light and sound for vehicles lacking a DSRC radio.
A step further was taken by the authors of [31]. In their work, they presented a
DSRC/Wi-Fi hybrid system that acts as a one-way broadcast mechanism for transmiing
important information to drivers whose On-Board Units (OBUs) are designed for Wi-Fi
reception only. Essential information is routed through the Road Side Unit (RSU) from the
trains’ DSRC OBU to the Wi-Fi OBU in the vehicle. The Wi-Fi transmission is based on
custom beams that serve a similar function to the roadside warnings in DSRCs but with a
configurable repeat interval.
2.2.2. Extended Driver Information Systems—The Smart City Concept
Researchers [32] presented the 5G mobile technology-based concept of an IoT archi-
tecture for collision avoidance systems in smart cities. The cloud-based system functions
as traffic management, and it collects data about the environment through distributed
Figure 4. SAFER-LC system architecture. Reprinted with permission from Ref. [
28
]. Copyright 2020,
Elsevier.
The authors of [
30
] proposed a system that combines Vehicle-to-Vehicle (V2V) and
Vehicle-to-Infrastructure (V2I) communication. The system is based on DSRC, and it works
in two types of cases. In the first case, the driver receives the warning directly from the
train. This type of communication is favored in situations where the radio channel between
the train and the vehicle has a strong line of sight. In the second case, a DSRC receiver
positioned at the level crossing intercepts the warning from the train and may subsequently
resend it to the vehicle. Alternatively, it produces a warning in the form of light and sound
for vehicles lacking a DSRC radio.
A step further was taken by the authors of [
31
]. In their work, they presented a
DSRC/Wi-Fi hybrid system that acts as a one-way broadcast mechanism for transmitting
important information to drivers whose On-Board Units (OBUs) are designed for Wi-Fi
reception only. Essential information is routed through the Road Side Unit (RSU) from the
trains’ DSRC OBU to the Wi-Fi OBU in the vehicle. The Wi-Fi transmission is based on
custom beams that serve a similar function to the roadside warnings in DSRCs but with a
configurable repeat interval.
2.2.2. Extended Driver Information Systems—The Smart City Concept
Researchers [
32
] presented the 5G mobile technology-based concept of an IoT architec-
ture for collision avoidance systems in smart cities. The cloud-based system functions as
traffic management, and it collects data about the environment through distributed applica-
tions and sensors and manages all traffic control procedures. With modern vehicles able to
communicate with RSUs, the system encompasses all traffic entities, including vulnerable
road users. The system architecture (Figure 5) can be divided into three segments: data
input, transmission and processing, and data output. The first segment represents the data
input from different sources, such as cameras, radars, and users’ applications, which are
employed for the identification and categorization of individual road users. The data is
transferred through a 5G network and processed in the cloud. AI-based processing enables
the identification of the location, velocity, and direction of users, and based on this infor-
mation it predicts collision probability. Processed data are transmitted through optical or
Sustainability 2024,16, 372 8 of 20
acoustic devices or users’ warning applications. The advantages of this architecture are that
even vehicles without communication technology or other road users can be incorporated
into the system. The authors emphasize the particular importance of using the 5G network
due to its low latency, high bandwidth, larger number of networked participants, and data
processing beyond the capabilities of previous wireless technology.
Sustainability 2024, 16, 372 8 of 21
applications and sensors and manages all traffic control procedures. With modern vehicles
able to communicate with RSUs, the system encompasses all traffic entities, including vul-
nerable road users. The system architecture (Figure 5) can be divided into three segments:
data input, transmission and processing, and data output. The first segment represents
the data input from different sources, such as cameras, radars, and users’ applications,
which are employed for the identification and categorization of individual road users. The
data is transferred through a 5G network and processed in the cloud. AI-based processing
enables the identification of the location, velocity, and direction of users, and based on
this information it predicts collision probability. Processed data are transmied through
optical or acoustic devices or users’ warning applications. The advantages of this architec-
ture are that even vehicles without communication technology or other road users can be
incorporated into the system. The authors emphasize the particular importance of using
the 5G network due to its low latency, high bandwidth, larger number of networked par-
ticipants, and data processing beyond the capabilities of previous wireless technology.
Figure 5. Generic IoT architecture for smart cities [32].
In study [33], the authors propose an in-vehicle warning system to avoid collisions
with cyclists in the area of a bicycle path at an intersection in a Connected Vehicles (CVs)
environment. Based on the 118 collected trajectories of vehicles turning to the right, the
behavior of drivers when turning was investigated. The authors proposed an algorithm
for calculating the time of entry into the critical area in different circumstances and iden-
tifying potential places of collision between vehicles and bicycles around the bicycle path.
The critical point can be predicted based on vehicle and bicycle speeds and their location
information. The warning system is primarily built on the conceptual foundation of Vehi-
cle-to-Bicycle (V2B) communication, operating under the assumption that the driver or
vehicle is aware of the bicycle’s position through GNSS data. The proposed system aims
to enhance driver readiness for impending right-turn maneuvers, contributing to the over-
all improvement of traffic safety at intersections.
Study [34] considers a pedestrian collision avoidance system designed for low-speed
autonomous shules, relying on Vehicle-to-Pedestrian (V2P) communication. This system
is particularly relevant in situations where pedestrians are not detectable by line-of-sight
sensors such as cameras, radar, and Light Detection and Ranging (LiDAR); V2P commu-
nication based on pedestrian smartphones and DSRC is used for their detection and posi-
tioning. In such scenarios, the vehicle takes the action of either coming to a stop or, if
feasible, maneuvering around the pedestrian. In study [35], the authors investigated the
extension of vehicle crash avoidance systems to smartphone-equipped participants. Given
the limited functionalities of smartphones in contrast to OBUs, the authors proposed the
support of Multi-access Edge Computing (MEC). The MEC-based system architecture in-
cludes three main segments: users (i.e., vehicles and vulnerable users), the access points
of various technologies, and collision detection servers. They conclude that, thanks to
MEC, a system traditionally used in vehicles can be extended to vulnerable users. In their
working paper [36], the authors consider communication between vehicles and vulnerable
users through an Android smartphone application based on Bluetooth technology. The
proposed system analyses real-time data from a smart intersection close to vehicles and
vulnerable users and informs them of having trajectories with potential collision risk. Sys-
tems based on smart devices open up the possibility of wide application of warning sys-
tems in challenging areas of the multimodal transport network.
Figure 5. Generic IoT architecture for smart cities [32].
In study [
33
], the authors propose an in-vehicle warning system to avoid collisions
with cyclists in the area of a bicycle path at an intersection in a Connected Vehicles (CVs)
environment. Based on the 118 collected trajectories of vehicles turning to the right, the
behavior of drivers when turning was investigated. The authors proposed an algorithm for
calculating the time of entry into the critical area in different circumstances and identifying
potential places of collision between vehicles and bicycles around the bicycle path. The
critical point can be predicted based on vehicle and bicycle speeds and their location
information. The warning system is primarily built on the conceptual foundation of
Vehicle-to-Bicycle (V2B) communication, operating under the assumption that the driver or
vehicle is aware of the bicycle’s position through GNSS data. The proposed system aims to
enhance driver readiness for impending right-turn maneuvers, contributing to the overall
improvement of traffic safety at intersections.
Study [
34
] considers a pedestrian collision avoidance system designed for low-speed
autonomous shuttles, relying on Vehicle-to-Pedestrian (V2P) communication. This system
is particularly relevant in situations where pedestrians are not detectable by line-of-sight
sensors such as cameras, radar, and Light Detection and Ranging (LiDAR); V2P com-
munication based on pedestrian smartphones and DSRC is used for their detection and
positioning. In such scenarios, the vehicle takes the action of either coming to a stop or,
if feasible, maneuvering around the pedestrian. In study [
35
], the authors investigated
the extension of vehicle crash avoidance systems to smartphone-equipped participants.
Given the limited functionalities of smartphones in contrast to OBUs, the authors proposed
the support of Multi-access Edge Computing (MEC). The MEC-based system architecture
includes three main segments: users (i.e., vehicles and vulnerable users), the access points
of various technologies, and collision detection servers. They conclude that, thanks to
MEC, a system traditionally used in vehicles can be extended to vulnerable users. In their
working paper [
36
], the authors consider communication between vehicles and vulnerable
users through an Android smartphone application based on Bluetooth technology. The
proposed system analyses real-time data from a smart intersection close to vehicles and vul-
nerable users and informs them of having trajectories with potential collision risk. Systems
based on smart devices open up the possibility of wide application of warning systems in
challenging areas of the multimodal transport network.
3. Driver Information System Architectures—Components and Technologies
Based on the reviewed system models in the previous chapter, it was concluded that an
advanced system, applicable in a multimodal environment, must meet three main require-
ments: the identification of critical points, the detection of conflicting entities, and real-time
and reliable communication between vehicles and/or users (Figure 6). The identification
of critical points enables the driver to be informed of approaching a potentially danger-
ous location, thereby increasing his awareness of the environment, while the detection of
conflicting vehicles provides information on the real immediate danger, enabling a better
reaction of the user and thereby avoiding a potential collision. Both requirements must
be based on reliable real-time data transfer to influence the user at the right time, thus
increasing traffic safety.
Sustainability 2024,16, 372 9 of 20
Sustainability 2024, 16, 372 9 of 21
3. Driver Information System Architectures—Components and Technologies
Based on the reviewed system models in the previous chapter, it was concluded that
an advanced system, applicable in a multimodal environment, must meet three main re-
quirements: the identification of critical points, the detection of conflicting entities, and
real-time and reliable communication between vehicles and/or users (Figure 6). The iden-
tification of critical points enables the driver to be informed of approaching a potentially
dangerous location, thereby increasing his awareness of the environment, while the de-
tection of conflicting vehicles provides information on the real immediate danger, ena-
bling a beer reaction of the user and thereby avoiding a potential collision. Both require-
ments must be based on reliable real-time data transfer to influence the user at the right
time, thus increasing traffic safety.
Figure 6. Driver information system’s basic features.
For a high-quality information system solution, it is necessary to combine several
segments from different research areas, such as different methods for data analysis and
processing, identification and classification, and available technologies for detection and
communication that could be applicable in a multimodal environment. Also, regarding
architecture definition, it is essential to define the physical, logical, and communication
components of the system, which are presented below.
3.1. Identification of Critical Points in a Multimodal Environment
Currently, numerous solutions in the transportation sector rely on consistent and de-
pendable spatial data. For the high-quality identification and classification of critical
points, it is necessary to connect their safety features with the spatial component. So, the
first step is to list potentially dangerous locations, which requires location data for all
points where multiple modes of transport meet at the same level. Furthermore, classifica-
tion according to the hazard criteria requires data on technical equipment and compre-
hensive historical data on accidents, specific to each location [23]. The available data in the
EU about transport are quite limited. According to the Official European Data Portal, the
subject of traffic occupies only 3.75% of the total number of available data sets, and their
quality and quantity vary depending on the source [23]. The data sources are also different
in terms of their functionality, characteristics, and quality of service, and the main chal-
lenge, besides the lack of publicly available data sets, is their uneven distribution across
subdomains [37].
In the last six decades, the analysis of critical points’ accidents has been thoroughly
carried out, leading to the development of numerous methods. However, classical meth-
ods often overlook the accidents’ spatial aspects [17]. In modern computer science, nu-
merous methods and algorithms have been developed that can collect and process a large
amount of data (location, time, number of fatalities, severity of injuries, type of vehicle,
and other relevant information) and perform qualification according to the given condi-
tions. Many studies are devoted to identifying different paerns of traffic accidents, con-
sidering temporal, spatial, and other influences (holidays, days of the week, peak hours,
location of the accident, road geometry, type of accident, cause of accidents and liability,
weather conditions, etc.). The authors of these studies use methods such as different clus-
tering algorithms [38,39], fuzzy paern recognition [40], random forest analysis [41], etc.
Figure 6. Driver information system’s basic features.
For a high-quality information system solution, it is necessary to combine several
segments from different research areas, such as different methods for data analysis and
processing, identification and classification, and available technologies for detection and
communication that could be applicable in a multimodal environment. Also, regarding
architecture definition, it is essential to define the physical, logical, and communication
components of the system, which are presented below.
3.1. Identification of Critical Points in a Multimodal Environment
Currently, numerous solutions in the transportation sector rely on consistent and
dependable spatial data. For the high-quality identification and classification of critical
points, it is necessary to connect their safety features with the spatial component. So, the
first step is to list potentially dangerous locations, which requires location data for all points
where multiple modes of transport meet at the same level. Furthermore, classification
according to the hazard criteria requires data on technical equipment and comprehensive
historical data on accidents, specific to each location [
23
]. The available data in the EU about
transport are quite limited. According to the Official European Data Portal, the subject of
traffic occupies only 3.75% of the total number of available data sets, and their quality and
quantity vary depending on the source [
23
]. The data sources are also different in terms of
their functionality, characteristics, and quality of service, and the main challenge, besides
the lack of publicly available data sets, is their uneven distribution across subdomains [
37
].
In the last six decades, the analysis of critical points’ accidents has been thoroughly
carried out, leading to the development of numerous methods. However, classical methods
often overlook the accidents’ spatial aspects [
17
]. In modern computer science, numerous
methods and algorithms have been developed that can collect and process a large amount
of data (location, time, number of fatalities, severity of injuries, type of vehicle, and
other relevant information) and perform qualification according to the given conditions.
Many studies are devoted to identifying different patterns of traffic accidents, considering
temporal, spatial, and other influences (holidays, days of the week, peak hours, location
of the accident, road geometry, type of accident, cause of accidents and liability, weather
conditions, etc.). The authors of these studies use methods such as different clustering
algorithms [38,39], fuzzy pattern recognition [40], random forest analysis [41], etc.
Considering the limitations of the available data and their incompatibility with more
advanced processing methods, simpler risk assessment methods would be more suitable
for identifying critical points, especially for parts of traffic networks where accidents are
not frequent. According to the example of basic risk assessment at level crossings [
42
],
the first step in identifying critical or high-risk points is to create a list of all crossings
where incidents have been recorded in at least the last five years. For each transition, the
frequency or number of accidents is determined, and divided by the number of years
considered. After determining the frequency, the consequence is determined, i.e., the
number of fatalities in one accident. Using the equation [43]
ℜ=X∗Y(1)
the frequency factor Xand the consequence factor Yare multiplied to obtain the risk
index
ℜ
. Depending on the values of the risk index, based on qualitative methods, the
authors determine the intervals of the indices that represent an acceptable, marginal, and
unacceptable degree of risk. In a more advanced analysis of the safety level [
44
], all level
Sustainability 2024,16, 372 10 of 20
crossings on one section of the railway were observed, including individual level crossings
with passive signaling, which have a history of accidents. A multi-criteria fuzzy model
consisting of 15 criteria and 8 alternatives was formed, and data on serious accidents,
accidents, incidents, and the number of deceased and injured persons were considered.
Based on the obtained results, the authors proposed measures to increase the safety of
individual level crossings.
The two mentioned risk assessment methods determine the level of danger at certain
critical points but do not attach a spatial component to them. In study [
19
], the features of
critical places, with their geospatial components, were analyzed through the Geographic
Information System (GIS) to determine the causes of the reduction in the effectiveness of
traditional security measures. The proposed method evaluates data from several sources,
which relate to geospatial information about the multimodal transport network in the
observed area, the safety features of the crossings, train schedules, historical information
about accidents (frequency, financial damage, injuries, and deaths), daily population migra-
tions, etc. Data analysis revealed patterns in transport traffic and the historical frequency
of accidents, based on which locations were singled out as potential candidates for the
installation of an advanced warning system.
The authors of [
17
] used the DBSCAN to identify critical points using geolocated
accident records. The DBSCAN classifies the elements into clusters by organizing them
so that the density of the elements inside each cluster is higher than outside the cluster.
Elements not associated with any group are designated as noise or considered “forests”.
Consequently, the identified clusters can be recognized as critical points with a substantially
higher accident density compared to other regions. Noise elements signify “random” acci-
dents, indicating minimal or no spatial dependence on other accidents. The effectiveness
of DBSCAN in recognizing clusters is highly dependent on the chosen distance between
points within the same cluster and the minimum number of points required to form a clus-
ter. There is no universally optimal selection for these parameters, and relying on domain
expertise is recommended to determine the most suitable values based on the objectives of
the analysis. According to the recommendation of experts, the authors defined conditions
for the identification of critical points: at least ten accidents must have occurred within
15 months at the observed location over five years. With these parameter settings, the
authors successfully identified the critical points. Their further classification was based on
the output message to the user, consisting of three pieces of information: “What”, “Why”,
and “Where”, where more than 50% of the involved accidents must have shared the same
predominant contextual detail information.
The aforementioned studies proposed methods for the identification and classification
of critical points in specific examples of a multimodal environment. However, there
are numerous other papers in the field of artificial intelligence that study methods such
as machine learning [
45
–
48
] or data mining [
49
] applicable in the field of traffic. These
methods have huge application potential in the driver warning system due to the possibility
of processing a large amount of data, including historical and real-time data.
Through the mentioned studies, it can be concluded that the choice of the identification
and classification methods of critical points depends on what part of the multimodal
network is being observed. For example, level crossings are fixed points in space; their
number is far less than, for example, the number of intersections in urban areas, and
accidents at these locations do not occur as often. Therefore, a simple method of risk
assessment is sufficient for such locations. On the other hand, the urban multimodal
environment is extremely dynamic, and critical points are not necessarily connected to
intersections, but can appear anywhere on the traffic network. For their identification and
classification, it is necessary to collect and process large amounts of data from different
sources, which requires a more complex method from the field of artificial intelligence.
Sustainability 2024,16, 372 11 of 20
3.2. Detection of Conflicting Entities in a Multimodal Environment
Obstacle detection is one of the key aspects of research in the field of driver DSSs.
Reliable detection includes the analysis of different types of obstacles, sensor characteristics,
and environmental conditions. While roadside driver assistance systems and autonomous
driving systems are well-researched in this regard, the methods developed for structured
urban roads may fail in a multimodal environment due to their uncertainty and diversity.
In principle, there are two sources of data about the environment: those from the vehicle’s
built-in sensors and other vehicles or from nearby infrastructure. Autonomous vehicles use
a variety of sensors to perceive their surroundings and navigate safely. The main sensors
employed in autonomous vehicles include LiDAR, radar, cameras, ultrasonic sensors,
and Inertial Measurement Unit (IMU) sensors [
50
,
51
]. LiDAR sensors use laser beams to
measure distances and create detailed 3D maps of the vehicle’s surroundings and are very
effective in detecting objects and obstacles [
52
]. Radar sensors use radio waves to determine
the range, angle, and velocity of objects around the vehicle and are commonly used for
object detection and collision avoidance [
53
]. Cameras capture visual information, enabling
the vehicle to recognize and interpret traffic signs, lane markings, and other objects [
54
].
Ultrasonic sensors use sound waves to detect objects in close proximity to the vehicle and
are often used for parking assistance and low-speed obstacle detection [
55
]. IMU sensors
measure the vehicle’s acceleration, angular rate, and sometimes magnetic field orientation
so that they can give information on the vehicle’s motion and orientation [51].
Unlike typical object detection, detection that refers to a vehicle of a different mode
of transport, such as a train, has different limitations. The communication environment
near level crossings is similar to road intersections for vehicles, but the line of sight is
a function of geometry for which greater visual blockages are possible [
30
]. Therefore,
commercial sensors installed on vehicles are not reliable for train detection. LIDAR sensors
have a typical detection range of 120 m, but their range is limited by technical factors
such as power requirements and target reflectivity. The intensity of infrared beams is also
limited by eye safety regulations. Therefore, currently available automotive LIDAR sensors
cannot detect a train early enough [
56
]. The sensors capable of detecting objects up to
one kilometer away are cameras and radars. Radars intended for adaptive cruise control
applications have a detection range of up to 250 m but have a narrow beam of detection,
usually
±
6–9
◦
, and vegetation or weather conditions can limit visibility [
56
]. So, relying
only on sensors does not provide sufficiently reliable data for the detection of conflicting
entities from different modes of transport, especially because they cannot detect vulnerable
users in obstructed areas.
The installation of GNSS devices in vehicles enables real-time monitoring, thus giving
car drivers a reliable warning about the presence of other vehicles, but this technology
also encounters difficulties in a multimodal environment. In the simulation of a railway
environment [
29
], one of the main investigated questions was the impact of long-delay
satellite communication on the warning time for road users. The first simulation test was
performed using terrestrial communication (with an average latency of 5 s), and the second
using satellite communication (latency varies statistically between 15 and 40 s). The results
show that the developed communication protocol successfully manages delay problems,
and the difference in warning time and reliability is negligible. The positioning accuracy
was reduced by adding a worst-case error component to the original measured positioning
data. The results show that the system is resistant to the degradation of its positioning
performance, and standard GNSS accuracy (<30 m) is sufficient for timely train detection.
Higher-level systems such as C-ITS and the IoV rely on several components for data
processing, communication, decision-making, and information projection, including GNSS,
LIDAR, camera, radar, and electronic control unit systems. Considering the complexity of
the multimodal environment, all available data sources must be used for a reliable driver
warning system. Looking at the global level, the existing fleet is still, to a lesser extent,
equipped with advanced sensors for detecting obstacles, therefore the basis of the solution
for detecting conflicting entities in traffic must be real-time position monitoring. Although
Sustainability 2024,16, 372 12 of 20
the average accuracy of a GNSS is about 10–15 m, the mentioned sensor technologies can
improve the accuracy of determining the position of the vehicle/user [57].
3.3. Communication Technologies
For the collected and processed data to have a purpose, it is necessary to reliably and
timely transfer them to the user. Today, numerous wireless communication technologies
can be applied in a multimodal environment, but one of the key implementation challenges
is propagation effects—especially channel statistics and their correlation with obstacles [
58
].
In addition, as vehicles move quickly, the physical layers of the communication solution
must support very high speeds.
C-ITS research is mostly based on two main solutions: DSRC and Cellular Vehicle
to Everything (C-V2X). Both DSRC and C-V2X share the common objective of improving
road safety and enhancing the efficiency of journeys for road users. They enable rapid
communication between vehicles, roadside infrastructure, and pedestrians through direct
communication, utilizing the same frequency band for short-range wireless communication.
DSRC represents the first and the main protocol for V2V and V2I communication, and
the key technological driver for the development of ITS applications. It utilizes WLAN
technology to create dedicated short-range communication channels, enabling vehicles to
communicate directly with other entities within short to medium ranges, typically up to
300 m [
59
]. The initial allocation of frequency bands contained 10 MHz channels between
5.855 and 5.925 GHz, but, because of the progress of C-V2X, a change in the operating
band of DSRC to 5.895–5.905 GHz was adopted [
30
]. At these operating frequencies, the
Doppler spread is around 2700 Hz, resulting in rapid channel fluctuations and much more
challenging channel estimation [60].
On the other hand, C-V2X is defined by 3rd Generation Partnership Projects (3GPP),
and it employs cellular radio instead of WLAN, using the same set of cellular radio technol-
ogy as cell phones [
61
]. A significant distinction from DSRC is that C-V2X allows both direct
and indirect communication [
62
]. In direct C-V2X, vehicles communicate directly with
other vehicles (V2V) and roadside units (V2I), similar to DSRC. In indirect C-V2X, vehicles
communicate with other entities indirectly via the cellular network (V2N), a capability that
DSRC lacks.
In the comparison of the two mentioned technologies, according to [
60
], in all aspects,
C-V2X outperforms DSRC or performs equally. A crucial advantage of C-V2X from the
point of vehicle safety is its greater communication range. However, the communication
technology that is most often mentioned today in the context of C-ITS is DSRC. The lack of
research on the use of C-V2X in the context of C-ITS could be attributed to several factors. C-
V2X is a relatively new and evolving technology and there is a lack of standardized protocols
or established compatibility between C-V2X and existing C-ITSs. Research efforts often
prioritize areas that are more mature or have immediate practical applications, and DSRC,
as the first standard for V2X communication, is already used in certain implementations.
Also, supporters of DSRC believe that switching to C-V2X would delay the rollout of
autonomous driving because DSRC is a more mature standard, proven to work in large
commercial settings. Study [
63
] is an example of using DSRC in a multimodal environment.
The authors analyzed it in a railway–road environment. One of the notable characteristics
of the DSRC system is its capability to detect possible collisions and issue a timely warning
about them. While DSRC systems typically have a nominal range of one kilometer, this
range is highly influenced by the surroundings and can be notably diminished in crowded
or obstructed conditions. The DSRC waveform incorporates features that facilitate the
establishment of communication systems characterized by high reliability and robustness.
More than 10,000 measurements were conducted on two distinct test tracks. The results
indicate that signals operating at the designated 5.8GHz frequency maintain operational
integrity over considerable distances with minimal fading or spectral distortion, even in
somewhat crowded surroundings. In a related study [
64
], concentrating on direct DSRC
between vehicles and trains, simulations and field tests of DSRCs were conducted. The
Sustainability 2024,16, 372 13 of 20
authors determined that about 80% of the sent warnings arrived on time, about 10% arrived
early, and the same amount arrived too late.
The broader term for the dynamic network infrastructure that connects vehicles,
users, and other smart devices to the Internet is the IoV. A growing number of vehicles are
integrated into IoV environments, wherein each vehicle serves as a node in the network [
65
].
These vehicles engage in information exchange within an open wireless mode. Different
communication activities occur between IoV users, in which they share crucial information
like their identification, position, velocity, and other data that are essential for the operation
of the network [
57
]. Numerous authors have proposed a three-layered architecture designed
to integrate various technologies into the IoV [
57
]. Built-in vehicle sensors for collecting
local information and detecting critical driving situations represent the first layer. The
e-communications layer is the second level, which ensures that existing and emerging
networks are seamlessly connected through communication standards. The third layer
shapes the IoV’s intelligence, providing a substantial data processing capability. This
layer consists of statistical hardware, a processing unit, and storage capacity. The IoV
necessitates vehicles’ continuous connection to the Internet, operating within an ad hoc
network environment, where public Internet connection is also available. Vehicles can also
be equipped with local data storage for future utilization, allowing information sharing
through the IoV network [
66
]. The IoV combines several directions of communication
(Figure 7): V2V communication involves a wireless vehicle connection in which they
exchange information about their position, velocity, and other useful data. On the other
hand, communication between vehicles and pedestrians (V2P) enables the vehicle to track,
check, and exchange information with pedestrians and other vulnerable users, which is
instrumental to accident prevention through user awareness systems.
Sustainability 2024, 16, 372 13 of 21
efforts often prioritize areas that are more mature or have immediate practical applica-
tions, and DSRC, as the first standard for V2X communication, is already used in certain
implementations. Also, supporters of DSRC believe that switching to C-V2X would delay
the rollout of autonomous driving because DSRC is a more mature standard, proven to
work in large commercial seings. Study [63] is an example of using DSRC in a multi-
modal environment. The authors analyzed it in a railway–road environment. One of the
notable characteristics of the DSRC system is its capability to detect possible collisions and
issue a timely warning about them. While DSRC systems typically have a nominal range
of one kilometer, this range is highly influenced by the surroundings and can be notably
diminished in crowded or obstructed conditions. The DSRC waveform incorporates fea-
tures that facilitate the establishment of communication systems characterized by high
reliability and robustness. More than 10,000 measurements were conducted on two dis-
tinct test tracks. The results indicate that signals operating at the designated 5.8GHz fre-
quency maintain operational integrity over considerable distances with minimal fading
or spectral distortion, even in somewhat crowded surroundings. In a related study [64],
concentrating on direct DSRC between vehicles and trains, simulations and field tests of
DSRCs were conducted. The authors determined that about 80% of the sent warnings ar-
rived on time, about 10% arrived early, and the same amount arrived too late.
The broader term for the dynamic network infrastructure that connects vehicles, us-
ers, and other smart devices to the Internet is the IoV. A growing number of vehicles are
integrated into IoV environments, wherein each vehicle serves as a node in the network
[65]. These vehicles engage in information exchange within an open wireless mode. Dif-
ferent communication activities occur between IoV users, in which they share crucial in-
formation like their identification, position, velocity, and other data that are essential for
the operation of the network [57]. Numerous authors have proposed a three-layered ar-
chitecture designed to integrate various technologies into the IoV [57]. Built-in vehicle
sensors for collecting local information and detecting critical driving situations represent
the first layer. The e-communications layer is the second level, which ensures that existing
and emerging networks are seamlessly connected through communication standards. The
third layer shapes the IoV’s intelligence, providing a substantial data processing capabil-
ity. This layer consists of statistical hardware, a processing unit, and storage capacity. The
IoV necessitates vehicles’ continuous connection to the Internet, operating within an ad
hoc network environment, where public Internet connection is also available. Vehicles can
also be equipped with local data storage for future utilization, allowing information shar-
ing through the IoV network [66]. The IoV combines several directions of communication
(Figure 7): V2V communication involves a wireless vehicle connection in which they ex-
change information about their position, velocity, and other useful data. On the other
hand, communication between vehicles and pedestrians (V2P) enables the vehicle to track,
check, and exchange information with pedestrians and other vulnerable users, which is
instrumental to accident prevention through user awareness systems.
Figure 7. IoV communication. Reprinted with permission from Ref. [57]. Copyright 2022, Sulaiman
M. Karim et al.
There is a constant exchange of information between RSUs through V2I communi-
cation that provides numerous services to the vehicle and the data center of the road
service provider. Lastly, Vehicle-to-Cloud communication (V2C) enables the vehicles’ data
collection and cloud storage, providing system access for obtaining even more information
through the Application Programming Interface (API) [57].
A review of the literature indicates the different characteristics and advantages of each
described technology. DSRC stands out as an established technology that is already used in
certain implementations. Its advantage lies in its ability to enable quick communication
between the vehicle and the infrastructure. Despite its reliability, DSRC can experience
challenges in densely populated areas and requires adequate infrastructure. C-V2X stands
out for its ability to use the cellular network, providing wider coverage and improved con-
nectivity. This technology has the potential for enabling communication between different
vehicles and devices on the road. A mobile network that supports C-V2X also enables
advanced features, such as communication with the cloud, which opens up possibilities
Sustainability 2024,16, 372 14 of 20
for diverse applications. The IoV represents a broader concept that can integrate different
technologies, including DSRC and C-V2X. This integration enables complete connectivity
between vehicles, infrastructure, and other entities in the transport system. The strength of
the IoV lies in its ability to use different technologies depending on its needs, which makes
it the most acceptable choice for the real-time communication required for a reliable driver
information system.
4. Challenges and Future Research Directions
ITSs bring advances to various fields, including in-vehicle warning systems. However,
there are some hidden issues and key technologies that deserve further investigation. The
C-ITS environment needs to address several future challenges before it becomes successful,
such as:
•
Interoperability and standardization—considering the variety of the systems and
equipment in the field of ITSs, the absence of standardization could causeincompatibility
between different components, making it difficult for them to interact with each other.
The development of common standards is crucial for optimal system integration.
•
Data security and privacy—with the increased exchange of data between vehicles
and infrastructure, data security becomes crucial. Implementing strong cyber security
measures and privacy policies is necessary to ensure user confidence in these systems.
•
Reliability and redundancy—reliability is essential to a warning system. An intense
dependence on communication networks poses challenges, so the implementation of
a redundancy system is necessary to ensure continuous functionality even in the case
of a failure of certain parts of the network.
•
Human factors—the growing complexity of information provided by ITSs can lead to
driver overload or misunderstandings in the interpretation of information. Additional
care is needed when designing the interface to reduce the possibility of misinterpreta-
tion and ensure effective communication with drivers.
By solving these hidden problems and leveraging key technologies, in-vehicle warning
systems can become more reliable, safer, and more efficient parts of ITSs.
In the available literature, in-vehicle warning systems are mostly related to autonomous
driving. In terms of multimodal transport, several quality solutions have been developed
for level crossings specifically, while solutions for other critical points of the multimodal
network have been very poorly researched, so it is necessary to further investigate the
possibilities of applying these technologies at a more advanced level. The primary focus
of this paper was to study the available research in the field of C-ITS and the IoV that can
be used to create a robust solution, designed to deliver timely and reliable warnings to all
drivers, and potentially other users of the traffic system, as they approach critical points of
the multimodal network environment. The focus was on synthesizing the available research
findings to develop a solution that seamlessly integrates into the existing framework of
sustainable transport.
Based on the reviewed literature, a framework for a refined system architecture is
proposed, on which future research will be based. This framework combines two existing
models of in-vehicle warning systems: the ones based on historical data and the ones based
on real-time data, as shown in Figure 8. The guiding idea was that users of the system have
the possibility of obtaining information about both potential and immediate danger when
approaching critical points.
The first segment of the proposed architecture uses historical data and can be applied to
all critical locations that data are accessible for. Historical data provide valuable information
for conducting the identification and classification of locations with a history of traffic
accidents. They are crucial for identifying patterns, assessing risks, and informing the
system’s response strategies. Traffic is a highly intricate system consisting of numerous
independent subsystems. Therefore, most of these subsystems employ specific information
system architectures and data collection methods, so, for high-quality and usable data, it is
necessary to connect the data sets available from multiple sources, e.g., the government,
Sustainability 2024,16, 372 15 of 20
scientific community, industry, and the public [
23
], therefore, there is a need to establish
stable and open access to these data.
Sustainability 2024, 16, 372 15 of 21
focus of this paper was to study the available research in the field of C-ITS and the IoV
that can be used to create a robust solution, designed to deliver timely and reliable warn-
ings to all drivers, and potentially other users of the traffic system, as they approach crit-
ical points of the multimodal network environment. The focus was on synthesizing the
available research findings to develop a solution that seamlessly integrates into the exist-
ing framework of sustainable transport.
Based on the reviewed literature, a framework for a refined system architecture is
proposed, on which future research will be based. This framework combines two existing
models of in-vehicle warning systems: the ones based on historical data and the ones
based on real-time data, as shown in Figure 8. The guiding idea was that users of the
system have the possibility of obtaining information about both potential and immediate
danger when approaching critical points.
Figure 8. Proposed system architecture framework.
The first segment of the proposed architecture uses historical data and can be applied
to all critical locations that data are accessible for. Historical data provide valuable infor-
mation for conducting the identification and classification of locations with a history of
traffic accidents. They are crucial for identifying paerns, assessing risks, and informing
the system’s response strategies. Traffic is a highly intricate system consisting of numer-
ous independent subsystems. Therefore, most of these subsystems employ specific infor-
mation system architectures and data collection methods, so, for high-quality and usable
data, it is necessary to connect the data sets available from multiple sources, e.g., the gov-
ernment, scientific community, industry, and the public [23], therefore, there is a need to
establish stable and open access to these data.
Simultaneously, the second segment deals with real-time location data and provides
dynamic and timely information about the current state of the driver’s environment. The
sources are system users from different modes of transport. Based on the precise data
about their location, the system calculates the collision probability of conflicting traffic
entities. This process can also consider the results of the critical point classification pro-
cess, and vice versa, to make a more reliable decision. Based on the outputs of these two
segments, the system will decide whether to warn the driver of potential or immediate
danger.
Combining real-time data with historical data not only enhances the reliability of the
system but also extends its applicability to locations lacking a history of incident situa-
tions. Integrating these system segments requires a comprehensive understanding of their
functioning, mechanisms, and how they can complement each other to create a more ro-
bust and effective system.
Figure 8. Proposed system architecture framework.
Simultaneously, the second segment deals with real-time location data and provides
dynamic and timely information about the current state of the driver’s environment. The
sources are system users from different modes of transport. Based on the precise data about
their location, the system calculates the collision probability of conflicting traffic entities.
This process can also consider the results of the critical point classification process, and vice
versa, to make a more reliable decision. Based on the outputs of these two segments, the
system will decide whether to warn the driver of potential or immediate danger.
Combining real-time data with historical data not only enhances the reliability of
the system but also extends its applicability to locations lacking a history of incident
situations. Integrating these system segments requires a comprehensive understanding of
their functioning, mechanisms, and how they can complement each other to create a more
robust and effective system.
For a truly sustainable intelligent driver information system, the integration of the
following technologies within the ITS is required:
•
Edge Computing—processing data at the source (computing at the edge of the net-
work) reduces latency in the transmission of information, which is essential for pro-
viding fast and accurate warnings to drivers.
•
Cloud computing—data storage and analysis in the cloud provides the necessary
infrastructure to further improve warning system algorithms based on large amounts
of data.
•
Machine learning and artificial intelligence—machine learning and artificial intelli-
gence algorithms enable ITS to learn from experience, adapt to changing conditions,
and improve the accuracy of their predictions and alerts.
•
Sensors and detection systems—advanced sensors such as LiDAR, radar, and cameras
enable vehicles to accurately identify potential hazards in the environment, providing
important information to the warning system.
•V2V communication—vehicle-to-vehicle communication ensures the rapid exchange
of information about the speed, position, and condition of vehicles in real-time, thus
contributing to the prevention of collisions and improving overall traffic safety.
•
Cybersecurity measures—protecting communication networks and data from cyber
threats is essential to maintaining the integrity of the in-vehicle warning system.
Sustainability 2024,16, 372 16 of 20
•
Blockchain technology—the integration of blockchain technology can improve the
security and transparency of data exchange between vehicles and infrastructure
within ITSs.
•
Human–Machine Interface (HMI)—a quality human–machine interface helps drivers
understand and respond appropriately to alerts, increasing the overall efficiency of
the system.
Furthermore, within these key technologies, it is necessary to investigate which ap-
plications are most suitable for the proposed information system. One type of sensor can
hardly meet the needs for obstacle detection in a multimodal environment due to the
sensor’s limitations in range, signal characteristics, and detection operating conditions, and
it is necessary to investigate in detail the methodology of combining multiple sensors and
their system integration. The combination of real-time monitoring and sensor technologies
is proving to be a sustainable solution.
Regarding the transfer of information, the emphasis of future research should be on
direct communication between transport entities, excluding the RSU if possible. A reliable
warning system requires a stable network connection with low latency and global coverage.
Today, numerous wireless communication technologies can be deployed in a multimodal
environment, but any mobile vehicle or device may face network disconnection, wireless
bottlenecks, and security threats in different geographic locations. Over the years, the
speed and efficiency of wireless networks have improved, but there are still some areas in
which current wireless networks struggle to perform efficiently. New generations of mobile
networks are expected to be able to meet the performance criteria for low latency, high
speed, and improved system reliability. The IoV combines different technologies depending
on its needs, which makes it the most acceptable choice for the real-time communication
required for a reliable driver information system.
In conclusion, the good design of a driver warning system is crucial to achieving
its purpose, which is to provide relevant information that contributes to safer and more
efficient driving. This is why it is important to carefully choose the most appropriate
technologies on which the system will be based.
5. Conclusions
Traffic safety is a critical issue that concerns all sectors involved worldwide. At present,
in the transport system, there are numerous high-quality solutions used to increase safety,
but in the field of multimodal transport networks such solutions are very limited. Critical
points in a multimodal environment represent challenging situations in which different
modes of transport are in “physical conflict”. Due to specific differences in the infrastructure,
vehicles, and users’ behavior, places where different modes of traffic intersect are recognized
as critical points of the traffic system, making them crucial aspects of the implementation of
Sustainable Urban Mobility Plans (SUMPs). The unpredictable nature of these interactions
is a result of road users’ behavior. Various passenger and driver information systems
are already widely used within ITSs, and recent research indicates that warning systems
implemented within vehicles or smartphones can have a substantial influence on users’
behavior, provided they are perceived as conceivable and trustworthy.
The existing literature predominantly associates in-vehicle warning systems with
autonomous driving, necessitating further exploration of the potential applications of
these technologies at an elevated level. Several examples of driver information systems
were described in this paper, with an emphasis on level crossings as the most critical
points in the multimodal environment. All the mentioned examples showed a certain
impact on the driver through simulations and/or field tests, while providing guidelines for
designing a more advanced system. The conclusion was that the system must fulfill three
key requirements: the identification of critical points, the detection of conflicting entities,
and real-time and reliable communication.
Identifying and classifying critical points requires linking security aspects with spatial
components, for which reliable and consistent spatial data play a key role. The first step is
Sustainability 2024,16, 372 17 of 20
to identify potentially dangerous places, considering different modes of transport at the
same level. Detailed technical and historical accident data are essential for risk assessment.
Classical methods do not consider spatial components, while more advanced methods
include analyses where geospatial elements are combined with accident data to identify
critical points. The choice of method depends on the specific parameters of the system and
the degree of risk at the crossing points for different forms of traffic.
Regarding the detection of conflicting entities, commercial sensors, such as LIDAR,
cameras, and radars are not reliable due to their limitations in range and reflections. The
installation of GNSS devices enables the monitoring and warning of drivers in real time,
and research shows that the standard performance of the GNSS meets the needs of the
system, reducing its complexity and costs. Alongside the GNSS, the mentioned sensor
technologies can improve the accuracy of detection.
Wireless communication technologies play a key role in the multimodal environment
but face numerous challenges due to the effects of propagation and vehicle speed. The
main technologies used for communication are DSRC and C-V2X. C-V2X shows advantages
in terms of its communication range and security applications, but DSRC is more often
mentioned in the context of C-ITSs. The IoV enables complete connectivity between vehi-
cles, infrastructure, and other entities in the transport system, and is presented as the most
acceptable choice for real-time communication. Since the integration of communication sys-
tems between multiple transportation modes is still in its infancy, there is limited evidence
within current research to completely recognize the potential benefits and drawbacks for
user safety that this technology might present.
The proposed framework for advanced driver information systems combines two
existing models of in-vehicle warning systems found in the literature: the ones based on
historical data and the ones based on real-time data. Most of the research so far focuses
on systems that require additional devices for detection and data processing, while the
proposed system relies on methods for direct communication between users and cloud-
based processing. The implementation of the system proposed in this paper would be
technologically and financially less demanding than classic solutions and could alleviate
the stagnation of the effectiveness of existing security measures.
The effectiveness of driver warning systems depends on their design, implementation,
and driver education on their proper use. Each driver warning system has its advantages
and disadvantages, and its effectiveness depends on various factors. The advantages
of driver warning systems are manifested in increasing the driver’s awareness of the
environment, providing them with information about potential and immediate dangers;
shortening the reaction time, which reduces the risk of