Wireless vehicular communications and sensing technologies are key to enabling more advanced intelligent transportation systems (ITSs) with improved safety and efficiency. Within the realm of wireless communication, millimeter-wave (mmwave) technology has recently received much attention, providing rich spectrum resources to support the timely transmission of large amounts of data. This is especially important for vehicular applications because the number of sensors on modern vehicles is rapidly increasing and thus generating large amounts of data. To fully exploit this potential, understanding mm-wave vehicle-to-vehicle (V2V) propagation channels is crucial. In this article, we review the state of the art in mm-wave V2V channel measurements and modeling, describe recent directional V2V channel measurements performed in the 60-GHz band, and discuss future challenges to be addressed in mm-wave V2V channel measurements and modeling.
Benedikt Brecht, D. Therriault, A. Weimerskirch
et al.
The U.S. Department of Transportation (USDOT) issued a proposed rule on January 12, 2017 to mandate vehicle-to-vehicle safety communications in light vehicles in the U.S. Cybersecurity and privacy are major challenges for such a deployment. We present a Security Credential Management System (SCMS) for vehicle-to-everything (V2X) communications in this paper, which has been developed by the Crash Avoidance Metrics Partners LLC under a cooperative agreement with the USDOT. This system design is currently transitioning from research to proof-of-concept and is a leading candidate to support the establishment of a nationwide Public Key Infrastructure for V2X security. It issues digital certificates to participating vehicles and infrastructure devices for trustworthy communications among them, which is necessary for safety and mobility applications that are based on V2X communications. The SCMS supports four main use cases, namely, bootstrapping, certificate provisioning, misbehavior reporting, and revocation. The main design goal is to provide both security and privacy to the largest extent reasonable and possible. To achieve a reasonable level of privacy in this context, vehicles are issued pseudonym certificates, and the generation and provisioning of those certificates are divided among multiple organizations. Given the large number of pseudonym certificates per vehicle, one of the main challenges is to facilitate efficient revocation of misbehaving or malfunctioning vehicles, while preserving privacy against attacks from insiders. The proposed SCMS supports all identified V2X use-cases and certificate types necessary for V2X communication security. This paper is based upon work supported by the USDOT. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors (“we”) and do not necessarily reflect the view of the USDOT.
Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.
Decentralized federated learning, inherited from decentralized learning, enables the edge devices to collaborate on model training in a peer-to-peer manner without the assistance of a server. However, existing decentralized learning frameworks usually assume perfect communication among devices, where they can reliably exchange messages, e.g., gradients or parameters. But the real-world communication networks are prone to packet loss and transmission errors. Transmission reliability comes with a price. The commonly-used solution is to adopt a reliable transportation layer protocol, e.g., transmission control protocol (TCP), which however leads to significant communication overhead and reduces connectivity among devices that can be supported. For a communication network with a lightweight and unreliable communication protocol, user datagram protocol (UDP), we propose a robust decentralized stochastic gradient descent (SGD) approach, called Soft-DSGD, to address the unreliability issue. Soft-DSGD updates the model parameters with partially received messages and optimizes the mixing weights according to the link reliability matrix of communication links. We prove that the proposed decentralized training system, even with unreliable communications, can still achieve the same asymptotic convergence rate as vanilla decentralized SGD with perfect communications. Moreover, numerical results confirm the proposed approach can leverage all available unreliable communication links to speed up convergence.
As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literature relevant to each of the scenarios is compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.
Road safety is an active area of research for the automotive industry, and certainly one of ongoing interest to governments around the world. The intelligent transportation system (ITS) is one of several viable solutions with which to improve road safety, where the communication medium (e.g., among vehicles and between vehicles and the other components in an ITS environment, such as roadside infrastructure) is typically wireless. A typical communication standard adopted by car manufacturers is IEEE 802.11p for communications. Thus, this paper presents an overview of IEEE 802.11p, with a particular focus on its adoption in an ITS setting. Specifically, we analyze both MAC and PHY layers in a dedicated short-range communication (DSRC) environment.
The new mobile technology, 5G, challenges the current scenario in communications by overcoming the flaws of currently working 4G. Such new technology offers to smart cities and intelligent transportation systems a new way to become fully integrated by allowing massive simultaneous connections and ubiquity of network, even under high mobility situations or dense populated areas. In this way, 5G will become a key enabler for real Internet of Things and its corresponding Internet of Vehicles. This discussion is aimed at providing, in a comprehensive manner, how 5G technology will impact on smart cities, intelligent transportation systems –including autonomous or semi-autonomous vehicles– and vehicular communications, its technical, economic and legal challenges, in the following years.
Torkel Bjørnskau, Ingeborg Storesund Hesjevoll, Rikke Ingebrigtsen
et al.
This study explores the characteristics of bicycle, e-bike, and e-scooter accidents that are not included in official Norwegian accident statistics, focusing on findings from the ReCyCLIST project in Agder County. Traditional accident reporting systems overlook most incidents involving vulnerable road users (VRUs), particularly single accidents, which represent the majority of such cases. ReCyCLIST introduced a digital self-reporting tool deployed in hospitals and clinics, collecting 671 accident cases between June 2022 and April 2024. The study analyses 487 incidents that occurred in traffic environments, revealing that 73% were single accidents, predominantly caused by infrastructure issues or loss of balance, rather than collisions. The data also highlight demographic differences in accident patterns by age, gender, and vehicle type. Notably, women were more frequently involved in e-scooter accidents, and men were overrepresented in racing bike collisions. Multivariate analysis shows that vehicle type, especially racing bikes, is a strong predictor of collisions. The findings emphasize the critical role of underreported single accidents and provide actionable insights for urban planning and policy development aimed at improving micromobility safety.
Transportation engineering, Transportation and communications
Cancer screening is a high priority within the Métis community in Alberta. Despite comparable cancer incidence rates to non-Métis Albertans, Métis people face barriers to accessing cancer screening programs. This study used community-based research approaches informed by Métis ways of knowing to engage 31 individuals across Alberta about their experiences with accessing cancer screening services. Data collection was completed through two in-person Métis gatherings and six telephone interviews. Gatherings included talking circles and cultural activities, with discussions lasting approximately three hours. Topics discussed included experiences with accessing screening services, the quality of care received during appointments, and the supports needed to improve access to screening programs. Discussions were audio-recorded, transcribed, de-identified, and thematically analyzed using NVivo Software. Four prominent themes emerged from this study: (1) the impact of patient-provider communications on cancer experiences, (2) a broken healthcare system and access to care, (3) a need for support and safety, and (4) health promotion behaviours. An overarching theme of discrimination as a social determinant of health emerged throughout the findings. Tangible barriers, including geographical, transportation, and financial, were also identified by study participants. This study provides an increased understanding of Métis experiences related to cancer screening and offers direction for improvements.
The applications of Intelligent Transportation System (ITS) and autonomous driving in the 6G era heavily rely on the massive information exchange of ultra-wide bandwidth, high reliability, and low latency to guarantee the safety and experience. On the other hand, the signals in high frequency bands including Millimeter Wave (mmWave) and Terahertz (THz) can be easily blocked by the obstacles. To address this problem, Intelligent Reflecting Surface (IRS) has attracted a lot of attention since it can reflect the signals and change propagation directions the propagation directions in a highly intelligent and energy efficient way, which creates smart radio environments. Due to its easy deployment and low expense, it has been widely recognized as a promising technology for ground and aerial vehicular networks to enhance signal strength, physical layer security, and positioning accuracy. In this paper, we give a comprehensive survey of current research works on different IRS applications in ground-based vehicular communications and aerial vehicular communications after discussing its basic knowledge and main characteristics. To improve current works and spark more ideas, we summarize the challenges and discuss the future perspectives of IRS-aided 6G vehicular communications.
Electrification turned over a new leaf in aviation by introducing new types of aerial vehicles along with new means of transportation. Addressing a plethora of use cases, drones are gaining attention in the industry and increasingly appear in the sky. Emerging concepts of flying taxi enable passengers to be transported over several tens of kilometers. Therefore, unmanned traffic management systems are under development to cope with the complexity of future airspace, thereby resulting in unprecedented communication needs. Moreover, the long-term increase in the number of commercial airplanes pushes the limits of voice-oriented communications, and future options such as single-pilot operations demand robust connectivity. In this survey, we provide a comprehensive review and vision for enabling the connectivity applications of aerial vehicles utilizing current and future communication technologies. We begin by categorizing the connectivity use cases per aerial vehicle and analyzing their connectivity requirements. By reviewing more than 500 related studies, we aim for a comprehensive approach to cover wireless communication technologies, and provide an overview of recent findings from the literature toward the possibilities and challenges of employing the wireless communication standards. After analyzing the proposed network architectures, we list the open-source testbed platforms to facilitate future investigations by researchers. This study helped us observe that while numerous works focused on cellular technologies to enable connectivity for aerial platforms, a single wireless technology is not sufficient to meet the stringent connectivity demands of the aerial use cases, especially for the piloting operations. We identified the need of further investigations on multi-technology heterogeneous network architectures to enable robust and real-time connectivity in the sky. Future works should consider suitable technology combinations to develop unified aerial networks that can meet the diverse quality of service demands of the aerial use cases.