Cooperative Intelligent Transport Systems Research Articles

An adjusted propagation model for ITS-G5 communications for improving the location of RSUs in real V2I deployments

The future of mobility is cooperative, connected, and autonomous leading to new technological challenges in the development of Cooperative Intelligent Transport Systems (C-ITS). Therefore, Vehicle to Everything (V2X) and, more specifically, Vehicle to Infrastructure (V2I) deployments are key to enabling these features around the highways as well as along the cities. The communication range of the RoadSide Units (RSUs) is one of the most important aspects when implementing Vehicle-to-Infrastructure (V2I) communications as it has a direct impact on efficiency and the economy of the installation. The aim is to maximise the communication range with the minimum number of RSUs and to optimise the deployments, thus having a realistic simulation tool is key. To be realistic, simulations rely on adequate propagation models, which ideally would adapt to the environment without a high computational need. Therefore, an appropriate characterisation of the different V2X environments as well as a simple and versatile propagation model is an important instrument for deciding the location of the RSUs. In this paper, we characterise different environments for ITS-G5 communications and provide an adjusted propagation model with an α parameter that depends on the environment. Thus, eradicating the need to model the environment and the obstacles in it. For that purpose, a methodology for the modeling and characterisation of the ITS-G5 propagation model is proposed,after that the methodology is applied and the results validated. The methodology is presented and the characterisation of the ITS-G5 environments is made. Later, tests were carried out in different environments to measure how the signal power decreases with the distance. After that, the propagation model for ITS-G5 communications, specifically V2I communications, is presented along with the methodology applied to obtain it. Then, an α value is assigned to each environment. Finally, the validation is made by comparing our adjusted propagation model with other propagation models and applying the adjusted propagation model to a new RSU installation.

Guest Editorial Special Issue on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV)–Part I

Guest Editorial Special Issue on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV)–Part I

Protecting Hybrid ITS Networks: A Comprehensive Security Approach

Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures can lead to privacy violations or even fatalities. Moreover, a high fatality rate is correlated with soft-mobility road users. Therefore, when developing C-ITS systems, it is important to broaden the focus beyond connected vehicles to include soft-mobility users and legacy vehicles. This work presents a new approach developed in the context of emerging hybrid networks, combining intelligent transport systems operating in 5.9 GHz (ITS-G5) and radio-mobile cellular technologies. Two protocols were implemented and evaluated to introduce security guarantees (such as privacy and integrity) in communications within the developed C-ITS hybrid environment. As a result, this work securely integrates G5-connected ITS stations and soft-mobility users through a smartphone application via cellular networks. Commercial equipment was used for this goal, including on-board and roadside units. Computational, transmission and end-to-end latency were used to assess the system’s performance. Implemented protocols introduce an additional 11% end-to-end latency in hybrid communications. Moreover, workflows employing hybrid communications impose, on average, an extra 28.29 ms of end-to-end latency. The proposal shows promise, as it reaches end-to-end times below the latency requirements imposed in most C-ITS use cases.

V2X-Equipped Smart Intersections − Survey of Surveys, Use Cases, and Deployments

The interaction between Vulnerable Road Users (VRUs) and smart intersection participants, such as vehicles and drivers in Cooperative Intelligent Transportation Systems (C-ITS), is one of the main tools in solutions for increasing road safety and minimizing the number of possible road accidents. Vehicle-to-Everything (V2X) communication of advanced C-ITS architectures includes Vehicle-to-Pedestrian (V2P) context for extensive information exchange between road users. V2P employs various direct, indirect, and hybrid communication technologies and uses different techniques to interact with multiple traffic participants, infrastructure, traffic light controllers, and especially VRUs, during unpredictable situations. To address emerging technologies and applications of collaborative smart intersections and explore novel C-ITS solutions designed for this domain, the paper analyses available surveys and introduces the relevant communication schemes, use cases, and recent deployment efforts.

Road-Side Unit Anomaly Detection

Actors of the Cooperative Intelligent Transport Systems (C-ITS) generate various amounts of data. Useful information on various issues such as anomalies, failures, road profiles, etc., could be revealed from the analysis of these data. The analysis, could be managed by operators and vehicles, and its output could be very helpful for future decision making. In this study, we collected real data extracted from road operators. We analyzed these streams in order to verify whether abnormal behaviors could be observed in the data. Our main target was a very sensitive C-ITS failure, which is when a road-side unit (RSU) experiences transmission failure. The detection of such failure is to be achieved by end users (vehicles), which in turn would inform road operators which would then recover the failure. The data we analyzed were collected from various roads in Europe (France, Germany, and Italy) with the aim of studying the RSUs’ behavior. Our mechanism offers compelling results regarding the early detection of RSU failures. We also proposed a new C-ITS message dedicated to raise alerts to road operators when required.

Disaggregate Modelling for Estimating Location Choice of Safe and Secure Truck Parking Areas: A Case Study

Responding to the increasing need for safety and security in road freight transport, and to targeted legislation specifying the availability of freight drivers’ rest areas, this paper proposes a plan and a model for deployment of safe and secure parking areas for truck drivers. Disaggregate analysis within a stated preference and conjoint analysis framework leads to the modelling of truck parking area selection by each truck driver that registers in the system proposed in this research. The concept builds upon the Cooperative Intelligent Transport Systems (C-ITS) upgrading of the Trans-European Transport Network (TEN-T) infrastructure systems while adapting to novel transport and logistics needs in an operationally safe, secure, and efficient environment for the supply chain. The analysis is applied in the Orient/East-Med Corridor of the TEN-T and is supported by the clustering of available truck parking areas for each truck route in the application subnetwork. The personalised approach is scalable and can be integrated into platforms for safe and secure truck parking areas, thus facilitating their acceptance and increasing awareness by the end-users. From pilot implementation on the Hellenic motorways, functional evaluation of use cases indicates 94.4% estimated choice probability of the most suitable parking area by the pilot drivers.

SoC-VRP: A Deep-Reinforcement-Learning-Based Vehicle Route Planning Mechanism for Service-Oriented Cooperative ITS

With the rapid development of emerging information technology and its increasing integration with transportation systems, the Intelligent Transportation System (ITS) is entering a new phase, called Cooperative ITS (C-ITS). It offers promising solutions to numerous challenges in traditional transportation systems, among which the Vehicle Routing Problem (VRP) is a significant concern addressed in this work. Considering the varying urgency levels of different vehicles and their different traveling constraints in the Service-oriented Cooperative ITS (SoC-ITS) framework studied in our previous research, the Service-oriented Cooperative Vehicle Routing Problem (SoC-VRP) is firstly analyzed, in which cooperative planning and vehicle urgency degrees are two vital factors. After examining the characteristics of both VRP and SoC-VRP, a Deep Reinforcement Learning (DRL)-based prioritized route planning mechanism is proposed. Specifically, we establish a deep reinforcement learning model with Rainbow DQN and devise a prioritized successive decision-making route planning method for SoC-ITS, where vehicle urgency degrees are mapped to three priorities: High for emergency vehicles, Medium for shuttle buses, and Low for the rest. All proposed models and methods are implemented, trained using various scenarios on typical road networks, and verified with SUMO-based scenes. Experimental results demonstrate the effectiveness of this hybrid prioritized route planning mechanism.

Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis

Cars and the transportation industry are undergoing a radical transformation. In the past, cars were independent means of transportation, and motor vehicles could not communicate with each other or the nearby environment. Nowadays, the Internet of Vehicles (IoV) technology and autonomous driving vehicles are gradually becoming a reality. Thanks to its high data throughput, fast data transmission rate, and low latency, 5G mobile communication technology lies at the heart of this technological transformation. The Internet of Vehicles service promoted by the 5G Automotive Association (5GAA) is recognized as a market‑leading technological development strategy. In this service, which integrates 5G mobile data with the Internet of Vehicles, Connected Autonomous Vehicles (CAV) will play a key role in the next generation of Cooperative Intelligent Transport Systems (C-ITS).In this paper, we apply a series of patent analyses to investigate the global 5G-V2X (vehicle-to-everything) patent distribution, compare patents of the major technology assignees, and assess the technological growth curves for eight different 5G-V2X applications in the field of autonomous driving. The results of this study indicate that R&D on 5G-V2X and autonomous driving applications and technologies began growing in 2018, and development has remained at a high level, with the United States, Korea, and China as the three leading patented technology-holders. Analysis of the patent holding share of patent right-holding companies reveals that Qualcomm, LG, and Ericsson have relatively large shares of patented technology in the field of autonomous driving applications. The network analysis of IPC 3-digit symbols indicates that H04W, H04B, G08G, and H04L are important relevant technology areas. Our findings can contribute valuable information concerning 5G-V2X autonomous driving applications to government, industry, and academia.

Prospecter l’acceptation située d’une technologie en contexte professionnel – une étude qualitative exploratoire sur l’exploitation de la route

This paper proposes to prospect the future activity of road operation professionals in relation to cooperative intelligent transport systems in three phases that use the dimensions of situated acceptance as a reading framework. We propose a protocol that can be used to prospect future activity before confronting the technology with its future users. This protocol starts with the analysis of the technological functionalities, continues with an analysis of the current activity and ends with a confrontation of the two. The application of this protocol has enabled us to highlight, at each stage, complementary elements useful for anticipating the impacts of the technology and their acceptability by the actors involved. We will conclude this article by discussing the role of this methodological proposal in a more global approach to the transformation of work in relation to technology, to be used in association with simulation workshops, for example.

A Gap Analysis of the Adoption Maturity of Certificateless Cryptography in Cooperative Intelligent Transportation Systems

Cooperative Intelligent Transport Systems (C-ITSs) are an important development for society. C-ITSs enhance road safety, improve traffic efficiency, and promote sustainable transportation through interconnected and intelligent communication between vehicles, infrastructure, and traffic-management systems. Many real-world implementations still consider traditional Public Key Infrastructures (PKI) as the underlying trust model and security control. However, there are challenges with the PKI-based security control from a scalability and revocation perspective. Lately, certificateless cryptography has gained research attention, also in conjunction with C-ITSs, making it a new type of security control to be considered. In this study, we use certificateless cryptography as a candidate to investigate factors affecting decisions (not) to adopt new types of security controls, and study its current gaps, key challenges and possible enablers which can influence the industry. We provide a qualitative study with industry specialists in C-ITSs, combined with a literature analysis of the current state of research in certificateless cryptographic in C-ITS. It was found that only 53% of the current certificateless cryptography literature for C-ITSs in 2022–2023 provide laboratory testing of the protocols, and 0% have testing in real-world settings. However, the trend of research output in the field has been increasing linearly since 2016 with more than eight times as many articles in 2022 compared to 2016. Based on our analysis, using a five-phased Innovation-Decision Model, we found that key reasons affecting adoption are: availability of proof-of-concepts, knowledge beyond current best practices, and a strong buy-in from both stakeholders and standardization bodies.

A Taxonomy and Analysis of Misbehaviour Detection in Cooperative Intelligent Transport Systems: A Systematic Review

Cooperative Intelligent Transport Systems (C-ITS) is one of the proposed solutions to improve the safety and efficiency of road transport. However, C-ITS is prone to misbehaviours that can cause devastating effects such as road accidents and potential loss of life. While there are several research studies including technical studies and surveys that discuss misbehaviour detection in C-ITS, they tend to focus on specific aspects of misbehaviour detection and do not provide a comprehensive and unified coverage. With the objective of serving as a reference for future researchers, this study provides a comprehensive survey of misbehaviour detection in C-ITS. It identifies and discusses the key causes of misbehaviour in C-ITS, and the mechanisms used to detect them. Additionally, it proposes a thematic taxonomy on misbehaviour detection based on a comparative analysis of the technical studies. Furthermore, the existing solutions from the state-of-the-art and their shortcomings are also presented. Finally, this study highlights several significant research challenges and discusses future research directions in the area of misbehaviour detection in C-ITS. In doing so, this study contributes to the existing literature in an important field of study.

Raising security of first responders with C-ITS?

Abstract This article recommends social science research-based recommendations for improving first responder safety and occupational health by assessing cooperative intelligent transport systems (C-ITS) for use by emergency services and law enforcement. This involves vehicles using digital technologies to inform each other or via objects such as roadworks signs about traffic situations and dangerous circumstances. The application of information and communication technologies to the road transport sector and its interfaces with other modes of transport shall make a significant contribution to improving environmental performance, efficiency, including energy efficiency, safety, and security of road transport. This article investigates which social science aspects must be considered when implementing C-ITS, so that emergency vehicles such as ambulances can be given automated priority. Since emergency vehicles can communicate with traffic signal systems or other vehicles, safety requirements play a particularly important role. This leads to the question of which challenges subjective safety and technology perceptions regarding first responders are essential for the introduction of C-ITS for first responder organizations and which factors can lead to rejection.

An Amalgamated Novel IDS Model for Misbehaviour Detection using VeReMiNet

Vehicle ad hoc networks (VANETs) are gaining popularity because they can potentially increase traffic efficiency with road safety in cooperative intelligent transport systems (C-ITS). Effective vehicle deployment requires the identification of nodes providing false data. Researchers have focused much of their attention on an important issue called misbehaviour detection for disseminating false data about vehicle information (position, time, speed and vehicle id) in VANETs. It can threaten the security and privacy of the network and cause some problems, including accidents, collisions, and traffic congestion. There are more works have been proposed based on machine learning methods. However, all of the machine learning-based detection models used currently are signature-based, necessitating previous knowledge of the attacks to identify them and also requiring numerous features, which increases the computational time needed to detect attacks and to select the high-performing features; this works a marine predator algorithm based on teaching-learning. The article provides a unique deep learning strategy to recognize the position falsification attacks in VANETs to find hidden patterns from the data for fighting against known and emerging threats. We evaluate our method using a publicly accessible VeReMi dataset, demonstrating that it achieves high detection accuracy of 99.55% for various attacks. According to our findings, our method can be useful for enhancing VANET security and assuring vehicular communications' reliable and secure functioning.

Spatial-Temporal Aware Inductive Graph Neural Network for C-ITS Data Recovery

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several different types of missing entries in spatial-temporal traffic data to meet the high-quality demand of data science applied in Cooperative-ITS (C-ITS) since accurate data recovery is critical to many downstream tasks in ITSs, such as traffic monitoring and decision making. For such, it is proposed in this article solutions to three kinds of data recovery tasks in a unified model via spatial-temporal aware Graph Neural Networks (GNNs), named Spatial-Temporal Aware Data Recovery Network (STAR), enabling a real-time and inductive inference. A residual gated temporal convolution network is designed to permit the proposed model to learn the temporal pattern from long sequences with masks and an adaptive memory-based attention model for utilizing implicit spatial correlation. To further exploit the generalization power of GNNs, a sampling-based method is adopted to train the proposed model to be robust and inductive for online servicing. Extensive numerical experiments on two real-world spatial-temporal traffic datasets are performed, and results show that the proposed STAR model consistently outperforms other baselines at 1.5-2.5 times on all kinds of imputation tasks. Moreover, STAR can support recovery data for 2 to 5 hours, with its performance barely unchanged, and has comparable performance in transfer learning and time-series forecast. Experimental results demonstrate that STAR provides adequate performance and rich features for multiple data recovery tasks under the C-ITS scenario.

Cooperative Vehicles versus Non-Cooperative Traffic Light: Safe and Efficient Passing

Connected and automated vehicles (CAVs) will be a key component of future cooperative intelligent transportation systems (C-ITS). Since the adoption of C-ITS is not foreseen to happen instantly, not all of its elements are going to be connected at the early deployment stages. We consider a scenario where vehicles approaching a traffic light are connected to each other, but the traffic light itself is not cooperative. Information about indented trajectories such as decisions on how and when to accelerate, decelerate and stop, is communicated among the vehicles involved. We provide an optimization-based procedure for efficient and safe passing of traffic lights (or other temporary road blockage) using vehicle-to-vehicle communication (V2V). We locally optimize objectives that promote efficiency such as less deceleration and larger minimum velocity, while maintaining safety in terms of no collisions. The procedure is computationally efficient as it mainly involves a gradient decent algorithm for one single parameter.

Real-Life Traffic Data Based ITS-G5 Channel Load Simulations of a Major Hungarian C-ITS Deployment Site

Transportation efficiency and safety are crucial development areas nowadays. Cooperative Intelligent Transport Systems (C-ITSs), relying on Vehicle-to-Everything (V2X) communication, are a promising group of technologies and applications aimed at solving several issues related to road safety or efficiency. The C-Roads Platform was brought to life to ensure the cross-border harmonization of C-ITS at a European level, guiding several pilot activities in national deployment projects and providing a harmonized pan-European C-ITS service perspective. Because of the safety relevance of V2X technologies, it is essential to ensure that the crucial parameters of wireless communication are within an acceptable range to serve C-ITS applications appropriately. In this work, we developed a simulation pipeline to evaluate future V2X deployments using the real-world traffic and map data of a C-Roads harmonized major Hungarian C-ITS deployment site. First, we selected three time periods representing different traffic patterns. Then, we reconstructed the flow-based traffic data from the real-world traffic counters for the selected time periods. We developed an approach based on linear equations to perform the conversion. Eventually, we used the real-world data to simulate the effects of various DSRC (ITS-G5-based) C-ITS services and V2X penetration rates on the Channel Busy Ratio (CBR) parameter of the radio access environment.

In their own words: A qualitative study of users’ acceptance of connected vehicle technology after nine months of experience with the technology

The Ipswich Connected Vehicle Pilot (ICVP) was the largest on-road trial of connected vehicle technology in Australia. In the ICVP, 355 privately-owned vehicles were outfitted with connected vehicle technology and driven for 9 months with 326 participant vehicles remaining in the study until completion. User acceptance is as a key determinant of the future effectiveness of connected and co-operative Intelligent Transport systems (C-ITS) given it impacts likely uptake of these vehicles and the optimal functioning of such systems is contingent on widespread uptake by the public. This paper presents the findings of two qualitative studies conducted as part of the ICVP, comprising 53 individual interviews and 14 focus groups/interviews with 47 participants. The purpose of the qualitative studies was to understand users’ perceptions of the system as well as their acceptance of C-ITS in general. Overall, participants were generally positive towards the system, could appreciate the safety benefits it offers, and expressed interest in adopting the technology presuming future systems would be refined. Some participants reported that they missed the warnings provided by the system once the technology had been removed. Participants wanted the system to be seamlessly integrated in their vehicle and have features that enabled personalisation. Participants identified that some of the warnings, most notably the advanced red light warning, were presented too late or at inaccurate times indicating that these issues must be remedied to ensure successful future implementation of C-ITS. Participants appreciated that data sharing would optimise the system but wanted controls to be put in place to guard against misuse; participants did not want their data to be used for sales or marketing purposes. Users’ experience and perceptions are key to ensuring wider-spread adoption of C-ITS. The findings thus have important implications for future C-ITS implementation to ensure that the benefits of C-ITS are optimised to facilitate wide-spread penetration of the technology.

A brief survey on cooperative intelligent transportation systems and applications

Cooperative Intelligent Transport Systems (C-ITS) have emerged to provide a range of intel-ligent transport services that optimize traffic management, improve the travel experience of road users, increase road safety and security, and reduce the negative environmental impacts of transport. C-ITS, which aim to ensure that intelligent transport services function effec-tively and efficiently, communicate and exchange data with vehicles, road users, traffic con-trol centers, and road infrastructure as well. In this study, an overview of C-ITS related ac-tivities in the world and in Türkiye are discussed. C-ITS studies in literature and the objec-tives and benefits of C-ITS, its stakeholders, its relationship with ITS, its components and services are examined. After summarizing C-ITS studies in the world, policies and strategies in Europe, USA and Asian countries, and the current situation in Türkiye is provided. In addition, since there are limited studies on C-ITS in Türkiye, where ITS is still in its devel-opment stage, it is aimed to contribute to the literature by presenting recommendations for Türkiye to support the dissemination of C-ITS.

Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

This paper presents an early prediction framework to classify drivers’ intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers’ intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users’ safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers’ intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.

Road-Network-Based Event Information System in a Cooperative ITS Environment

This study proposes and implements an intelligent transportation system (ITS)-based system that collects road event information via a vehicle’s on-board unit (OBU) and roadside unit (RSU), integrates and processes information based on the database and the road network, and provides information to vehicles. This system enables the collection of road unit information without calculating the exact location of event information recognized by the vehicle and facilitates duplicate processing of information recognized from multiple sources. This information can then be provided to other vehicles or road operators via apps or the web, enabling immediate response to emergency situations or changes in road conditions. To verify the practical applicability of this system, we developed a prototype and validated its functions through experiments. Using this system and methods, general drivers, autonomous vehicles, and infrastructure can cooperate in an ITS environment to recognize and propagate various road situations, contributing to the creation of safer and more efficient roads.