Proxy cooperative awareness message: an infrastructure-assisted V2V messaging

The implementation of security in Cooperative Intelligent Transport Systems (C-ITS) is highly recommended to authenticate and ensure the integrity of Cooperative Awareness Message (CAM) exchanged between vehicles. Modern networks must be reliable and optimized in terms of the use of resources. In Europe, the ETSI standardization institute has defined a PKI (Public Key Infrastructure) for C-ITS that is used to distribute certificates for each vehicle. To ensure that CAMs (Cooperative Awareness Messages) are generated by trusted vehicles, they have to be signed using these certificates. However, integrating signature and certificate within each sent CAM presents a high impact on the overload of the communication channel and the bandwidth, due to the added size to the sent CAMs, which is significant. In this paper, we propose a new lightweight authentication protocol based on the ETSI standard. Our protocol is dynamic and integrates few concepts allowing to smartly reduce the transmitted security information, which often presents the highest cost in network systems. First, we build a cluster to establish a trusted link between vehicles giving them the opportunity to exchange information using unsigned CAMs (without integrating certificate and signature). As a result, the verification and the signing processes are not always necessary, which saves processing time and results in smoother and faster communication. Furthermore, we added a process that aims to check the consistency of the CAMs of each vehicle in the cluster in real time to avoid an attack. Under the Omnet++ simulator and the Artery framework, we have demonstrated that our protocol is more efficient in terms of communication channel usage and latency, while still secured.

In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).

VANETs relies on the repetitive exchange of Cooperative Awareness Messages (CAMs) among vehicle's On­Board Units (OBUs) and vehicles to roadside units (RSUs), thus making CAMs the backbone of the network. This exchange may lead to degradation of network performance caused by the ample amount of data exchange between the vehicles. Therefore, a trade­off between CAM broadcast within the network and optimum utilization of network resources is required. Most of the existing work focuses on retransmission of beacon messages supporting unicast 802.11 models which are not directly suited for the VANET scenario. To deal with this problem, the paper presents a model for non-repetitive single-hop broadcast of CAM within the network, thus supporting IEEE802.11p. For non-repetitive broadcast of CAM, the concept of preemption of messages is introduced in the model. Simulation is carried out using MATLAB 15's Simiilink. The model performance is evaluated by extracting its statistical properties in terms of Channel Utilization, CAM's Average Waiting Time within the queue and Average Queue Length. To validate our model, the simulation is implemented on real world data acquired from wheel speed for all wheels for the duration of a test drive.

Cooperative Intelligent Transport Systems (C-ITS) have seen increased interest in recent years, with several ongoing activities and first practical implementations. While clearly V2X-communication will play a significant role in all C-ITS deployments, several key components, such as the wireless communication technology and the message formats, are still evolving. This calls for a modular approach when designing C-ITS infrastructure, especially when considering a real world urban environment. Such environments are characterized by heterogeneous traffic light hardware and traffic management systems.This paper focuses on the modular architecture of the C-ITS pilot in Dresden. For example, a highly flexible communication stack is developed for this special purpose. It fully supports the message types standardized by European Telecommunications Standards Institute (ETSI) on the one hand, while on the other hand allowing the rapid implementation and test of novel message types and contents. This went hand in hand with the development of a modular roadside unit, enabling the rapid implementation of service applications on its central processing unit. In combination with a cloud-based backend, a real-time C-ITS service platform with a hybrid communication concept is developed.This paper presents a modular architecture and best practice guidelines based on experiences of the C-ITS pilot in Dresden. First results of research topics, such as latency measurements, are shown.The ongoing C-ITS deployment in Dresden is part of the initiative Synchrone Mobilitat 2023, which aims at advancing Intelligent Transport Systems. Focusing on automated and connected driving in urban areas, the research and development projects under its umbrella initiate scientific and technological developments and provide comprehensive test facilities. Automated and connected driving in urban areas requires not only simulations and test drives on test sites but also enormous testing effort under real traffic conditions. The Dresden Testbed offers outstanding conditions to conduct real-world test with a multitude of test kilometers and the experience of different traffic scenarios.

Cooperative Intelligent Transport Systems (C-ITS) aim to enhance road safety, traffic efficiency and environmental performance of road transportation by enabling communication between the vehicles and the road infrastructure. Today, vehicle manufacturers and road operators are investing significantly in C-ITS technology and infrastructure implementation to realise its potential benefits. In Europe, the C-Roads platform aims to harmonise the deployment of C-ITS infrastructure and services across Europe. As part of the C-Roads platform, Transport Infrastructure Ireland implemented C-ITS pilot infrastructure to deliver C-ITS services to road users via ITS-G5 and cellular communications. For the deployment of ITS-G5 Roadside Units along a 60 km distance of the pilot routes in Ireland, a radio coverage planning exercise was undertaken to optimise the deployment. Several technical and practical factors were taken into consideration during this exercise. This paper outlines Transport Infrastructure Ireland’s C-ITS pilot infrastructure, discusses the key technical and practical factors taken into consideration and summarises the results achieved. As a result of this radio coverage planning exercise, we were able to achieve over 50% cost savings on infrastructure deployment for C-ITS services via ITS-G5 technology on the M50 and M1 motorways.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

Global misbehavior detection is an important backend mechanism in Cooperative Intelligent Transport Systems (C-ITS). It is based on the local misbehavior detection information sent by Vehicle's On-Board Units (OBUs) and by Road-Side Units (RSUs) called Misbehavior Reports (MBRs) to the Misbehavior Authority (MA). By analyzing these reports, the MA provides more accurate and robust misbehavior detection results. Sybil attacks pose a significant threat to the C-ITS systems. Their detection and identification may be inaccurate and confusing. In this work, we propose a Machine Learning (ML) based solution for the internal detection process of the MA. We show through extensive simulation that our solution is able to precisely identify the type of the Sybil attack and provide promising detection accuracy results.

This paper aims to tackle the complex challenge of channel assignment and joint power-energy allocation within a cellular-vehicle-to-everything (C-V2X) network, which is deployed to manage vehicular dynamics at an urban traffic intersection. The primary function of the C-V2X network is to facilitate the coordination of multiple vehicle platoons formed by closely spaced same-lane vehicles. This coordination involves two critical communication tasks including the timely update of platoon states to a roadside unit (RSU) and the reliable exchange of cooperative awareness messages (CAMs) among vehicles within the same platoon. The main objective of this paper is to minimise the average age of information (AoI) to ensure the timely update between vehicle platoons and the RSU, maximise the CAM delivery probability (CDP) to guarantee the successful exchange of CAMs among vehicles, and promote sustainable, green communication practices through the implementation of our optimal power-energy management strategies. Recognising the intricate and dynamic nature of this challenge, we adopt a multi-agent deep reinforcement learning (MADRL) approach based on the Markov decision process (MDP). Two innovative algorithms based on the multi-agent deep deterministic policy gradient (MADDPG) and twin delayed deep deterministic policy gradient (TD3) algorithms are proposed to address this optimisation problem effectively. Finally, comprehensive simulation results are presented, which demonstrate the remarkable performance of our proposed schemes, particularly in terms of energy efficiency when compared to existing research. Importantly, these gains in energy efficiency are achieved while maintaining competitive algorithm convergence speeds, low AoI levels, and high CDP, showcasing the practical viability of the developed methods.

Current transportation systems face great challenges due to the increasing mobility. Traffic accidents, congestion, air pollution, etc., are all calling for new methods to improve the transportation system. With the US legislation in progress over vehicle communications and EU’s finalization of the basic set of standards over cooperative intelligent transportation systems (C-ITS), vehicular ad hoc network (VANET) based applications are expected to address those challenges and provide solutions for a safer, more efficient and sustainable future intelligent transportation systems (ITS). In this chapter, transportation challenges are firstly summarized in respect of safety, efficiency, environmental threat, etc. A brief introduction of the VANET is discussed along with state of the art of VANET-based applications. Based on the current progress and the development trend of VANET, a number of new features of future VANET are identified, together with a set of potential future ITS applications. The on-going research and field operational test projects, which are the major enabling efforts for the future VANET-based C-ITS, are presented. The chapter is of great interest to readers working within ITS for current development status and future trend within the C-ITS area. It is also of interest to general public for an overview of the VANET enabled future transportation system.

Cooperative Intelligent Transport System (CITS) is a modern approach to safety and efficient traffic operation over Vehicle Ad-hoc Networks (VANETs). This approach is based on vehicles communication each with other or infrastructure communication through wireless connections. The security of VANETs has been identified as a vital component in C-ITS, because there may some attacks in such network that can expose the safety of the driver and passengers or other road users. Intrusion Detection Systems (IDS) is a crucial instrument that assists to defend the vehicular network by detecting the misbehaving vehicles. In this paper, a new ML technique that capitalizes on three new features basically related to the sender location, which in turn improves the performances in position transgression attacks is developed. In the final, the output of each considered K-Nearest Neighbor (KNN), Random Forest (RF) and Decision Tree (DT) is given to Stacking method such as Generalized Linear Model (GLM) in order to bring superiority in the detection performance. An IDS is constructed allowing vehicles to detect misbehavior in a distributed way, while the detection mechanism is trained centrally. The findings show that the proposed system outperforms the existing techniques using classification with an accuracy of $\mathbf{9 9. 8 2 \%}$, precision of $\mathbf{9 7. 3 4 \%}$, recall of $\mathbf{9 7. 5 6 \%}$, and F1-score of $\mathbf{9 7. 2 8 \%}$.

In this demo, we introduce a Human-Interactive Hardware-In-the-Loop Simulation (HI2LS) framework for evaluating Cooperative Intelligent Transportation Systems (C-ITS) and road safety services. C-ITS is a complex system consisting of Wireless Access in Vehicular Environments (WAVE) communication devices, Human-Machine Interface (HMI), and computing elements to evaluate collision risk. Furthermore, C-ITS and services require the high reliability in a complicated operating environments including the human factors, and thus system testing is very important and essential process. In this demo, we propose a HI2LS framework for designing and evaluating road safety systems based on vehicle-to-vehicle/-infrastructure (V2X) communications. It enables developers to evaluate their road safety system designs in a wide range of virtual traffic environments and road conditions, which can replace dangerous and time-consuming field tests while guaranteeing the high reliability.

Cooperative intelligent transportation system (C-ITS) applications are generally susceptible to position spoofing-dependent attacks such as Sybil and DDoS attacks due to a lack of established solutions. This paper presents a novel cyber-physical blockchain cryptographic architecture to help prevent position spoofing attackers from becoming validated nodes in C-ITS applications. The solution also guarantees security requirements including the non-trivial non-repudiation in light of these and other attacks. With a use case of electronic toll collection (ETC), our architecture implements techniques based on Received Signal Strength Indication (RSSI) measurements in conjunction with blockchain authentication methods such as Proof-of-Location and smart contracts to determine the legitimacy of a node. We demonstrate our solution in experiments using ITS-G5 Cohda Wireless technology (a Road Side Unit and two On-Board Units programmed with the ITS Vanetza stack) with functionalities specified by the European Telecommunications Standardization Institute (ETSI). From our experimental results from several driving-based data gathering tests, we discovered that our solution is able to cope with noise and relative velocity challenges because it incorporates both OBUs and RSUs in the Proof of Location computation steps. In light of this, the proposed architecture may also be applicable to govern V2X in general.

Global misbehavior detection in Cooperative Intelligent Transport Systems (C–ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle’s On–Board Units (OBUs) and by Road–Side Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML)-based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.

Cooperative Intelligent Transport Systems (C-ITS) communication technology is expected to be the near-future pioneer in the traffic management and road-safety control area by provisioning timely accurate and location-aware information. The data generated by connected vehicles may be privacy-sensitive and could be hacked by intrusive receivers. In order to prevent malicious sources from injecting untrusted data content, relevant ITS standards included security processes and protocols to deal with the potential architecture-imposed security vulnerabilities. In this paper we study the impact of these processes on time-sensitive and safety related applications. In this regard, we deeply investigate the ITS architecture integrating the security components and evaluate its performance through extensive simulations for sparse to dense network of vehicles in terms of delay and packet delivery ratio. We consider this work as an important step towards understanding the tradeoff between security and communication efficiency in V2X networks.

Roadside infrastructure, equipped with traffic and environmental sensing technologies and leveraged by vehicle-to-everything (V2X) communications, plays a key role in Cooperative Intelligent Transportation Systems (C-ITS). By providing additional road state information to Cooperative, Connected and Automated Vehicles (CCAVs), as well as to drivers of connected vehicles and to road operators, better decision-making outcomes can be obtained, thus improving overall traffic safety and efficiency. This work proposes a modular and scalable road-side C-ITS architecture, by including road sensors for weather, environmental, infrastructure, and traffic data collection, which communicate with roadside units (RSUs) for data processing and alert dissemination. A new message type is introduced, the Road Sensor Messages (RSMs), to aggregate data coming from sensors localized in the vicinity of the RSUs and transmit such information to remote Traffic Management Systems (TMSs) and, eventually, to the local V2X environment via short-range wireless communications.