Cooperative Intelligent Transport Systems Research Articles

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.

AI-based efficient wireless technologies and infrastructure-based networks with VANET for smart transportation high performance

VANETs (Vehicular Ad-hoc NETworks) were deemed the most suitable communication network for supporting the dissemination of alert messages due to their low dissemination delays as well as extensive vehicle coverage in vicinity of an emergency. With the introduction of cooperative ITS services, it is envisaged that emerging vehicular networks will progressively rely on Vehicle to Infrastructure (V2I) communication lines, which are expected to be nominally accessible with certain temporary as well as time-limited connectivity losses. This study proposes a novel method for VANET-based efficient vehicle clustering and routing based on network infrastructure for high-performance smart transportation. The vehicle clustering is using infrastructure-based fuzzy K-means convolutional neural networks. Then the energy-efficient cluster-based multi-hop distributed routing has been carried out. The experimental analysis in terms of latency, network lifetime, throughput, QoS, energy efficiency, and packet delivery ratio. In addition, empirical equations that can be used to predict speed recommendations for drivers are derived from the result.

A Novel Cloud Approach for Connected Vehicles

Cooperative intelligent transport systems (C-ITSs) are being deployed all around the world. Shortly, in addition to vehicles, bicycles, pedestrians, buses, and all moving equipment will be compatible with C-ITS. These systems are connected through wireless local area networks based on WIFI IEEE 802.11p. The large number of C-ITSs and services will lead to a glut in the bandwidth of wireless networks. To overcome this limitation, we propose in this paper a new approach using the information-centric networking (ICN) paradigm which allows vehicles to communicate with the cloud environment. This scheme is denoted by vehicular central data networking (GeoVCDN). Our approach aims to reduce bandwidth consumption and improve data freshness by taking benefit from the existing application beacons and the geographical routing used by C-ITS actors. We have compared the performances (in terms of the network overhead and data freshness) of our solution to two other well-known ICN-based solutions. Each of them represents one of ICN categories, in particular, rendez-vous network (RENE) and named data networking (NDN). To do so, we have proposed a probabilistic model that allows us to evaluate the freshness and the load of the network. Furthermore, we have implemented these methods in a simulator. Our proposal outperforms the other methods in terms of network overhead and data freshness.

Intelligent Proof-of-Trustworthiness-Based Secure Safety Message Dissemination Scheme for Vehicular Ad Hoc Networks Using Blockchain and Deep Learning Techniques

Vehicular ad hoc networks have emerged as the main building block for the future cooperative intelligent transportation system (cITS) to improve road safety and traffic efficiency and to provide passenger comfort. However, vehicular networks are decentralized, characterized by high mobility and dynamicity, and vehicles move in a hostile environment; such characteristics make VANET applications suffer many security and communication issues. Recently, blockchain has been suggested to solve several VANET issues including the dissemination of trustworthy life-threatening information. However, existing dissemination schemes are inefficient for safety messages and are vulnerable to malicious nodes and rely on the majority of honest assumptions. In the VANET context, adversaries may collude to broadcast false information causing serious safety threats. This study proposes an intelligent proof-of-trustworthiness-based secure safety message dissemination scheme (PoTMDS) to efficiently share only trustworthy messages. The consistency and plausibility of the message were evaluated based on a predictive model developed using a convolutional neural network and signal properties such as the received signal strength and angle of arrival. A blockchain-based data dissemination scheme was developed to share critical messages. Each vehicle calculates the proof of trustworthiness of the disseminated messages by comparing the received message with the output of the prediction model. The results showed that the proposed scheme reduced the consensus delay by 58% and improved the detection accuracy by 7.8%. Therefore, the proposed scheme can have an important role in improving the applications of future cITS.

Evaluation of Cooperative Intelligent Transportation System scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR

There is a critical need for research in proactive and predictive management of the resilience of transportation systems implementing new technologies. Cooperative Intelligent Transportation System (C-ITS) uses wireless technology to allow vehicles and infrastructure to talk to each other in real-time. This makes it easier for people to work together on the road and makes it possible to make safer and more efficient traffic flows. Significant progress may be made in the transportation industry as a result of the incorporation of self-powered sensors into C-ITS providing resilience in transportation operation. One advantageous feature is that these sensors, which generate their power, could be deployed in a variety of C-ITS implementation scenarios. To assist decision-makers in making the most informed choice possible concerning investments and implementations, a type-2 neutrosophic number (T2NN) based VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is used to perform advantage prioritization. To accomplish this goal, a case study is carried out to determine which of the three alternatives is the most suitable based on a set of twelve criteria that is divided into four aspects. According to the findings, the applicability and short-term benefits are the most crucial factors in determining which option is the most advantageous for the use of self-powered sensors in C-ITS. This is because both of these factors have an immediate impact on the system.

A Roadside and Cloud-Based Vehicular Communications Framework for the Provision of C-ITS Services

Road infrastructure plays a critical role in the support and development of the Cooperative Intelligent Transport Systems (C-ITS) paradigm. Roadside Units (RSUs), equipped with vehicular communication capabilities, traffic radars, cameras, and other sensors, can provide a multitude of vehicular services and enhance the cooperative perception of vehicles on the road, leading to increased road safety and traffic efficiency. Moreover, the central C-ITS system responsible for overseeing the road traffic and infrastructure, such as the RSUs, needs an efficient way of collecting and disseminating important information to road users. Warnings of accidents or other dangers, and other types of vehicular services such as Electronic Toll Collection (ETC), are examples of the types of information that the central C-ITS system is responsible for disseminating. To remedy these issues, we present the design of an implemented roadside and cloud architecture for the support of C-ITS services. With the main objectives of managing Vehicle-to-Everything (V2X) communication units and network messages of a public authority or motorway operator acting as a central C-ITS system, the proposed architecture was developed for different mobility testbeds in Portugal, under the scope of the STEROID research project and the pan-European Connected Roads (C-Roads) initiative. RSUs, equipped with ETSI ITS-G5 communications, are deployed with a cellular link or fiber optics connection for remote control and configuration. These are connected to a cloud Message Queuing Telemetry Transport (MQTT) broker where communication is based on a geographical tiling scheme, which allows the selection of the appropriate coverage areas for the dissemination of C-ITS messages. The architecture is deployed in the field, on several Portuguese motorways, where road traffic and infrastructure are monitored through a C-ITS platform with visualization and event reporting capabilities. The provided architecture is independent of the underlying communication technology and can be easily adapted in the future to support Cellular-V2X (PC5 interface) or 5G RSUs. Performance results of the deployed architecture are provided.

CITS-MEW: Multi-Party Entangled Watermark in Cooperative Intelligent Transportation System

Federated learning is good for building better cooperative intelligent transportation system (C-ITS). Intellectual property protection in C-ITS brings many benefits to all vehicles. Although the protection of model intellectual property by watermark has received much research attention, the existing works only deploy watermark in centralized models. Due to the difference of watermark distribution among vehicles, the global model accuracy of watermark in federated learning is significantly reduced or the local watermark is invalid. To solve these problems, we propose a multi-party entangled watermark algorithm in federated learning. Specifically, in the local training, we propose a watermark enhancement algorithm, which solves the problem of local watermark failure. Then, in the global aggregation, we propose an entanglement aggregation algorithm, which solves the problem of a great loss of global model accuracy. We conduct extensive experiments on public datasets to show the superiority of our proposal. The results show that our scheme can obtain more than 16% and 31% advantages in model accuracy and watermark success rate, respectively, compared with existing watermark schemes in federated learning.