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The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

Since the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).

An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.

The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.

There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

This research paper aims to investigate the effects of gradual deployment of market penetration rates (MPR) of connected vehicles (MPR of CVs) on delay time and fuel consumption.

A real-world origin-destination demand matrix survey was conducted in Boston, MA to identify the number of peak hour passing vehicles in the case study.

The results showed that as the number of CVs (MPR) in the network increases, the total delay time decreases by an average of 14% and the fuel consumption decreases by an average of 56%, respectively, from scenarios 3 to 15 compared to scenario 2.

The first limitation of this study was considering a small network. The considered network shows a small part of the case study.

This study can be a milestone for future research regarding gradual deployment of CVs’ effects on transport networks. Efficient policy(s) may define based on the results of this network for Brockton transport network.

In order to meet the different quality of service (QoS) requirements of vehicle-to-infrastructure (V2I) and multiple vehicle-to-vehicle (V2V) links in vehicle networks, an efficient V2V spectrum access mechanism is proposed in this paper.

A long-short-term-memory-based multi-agent hybrid proximal policy optimization (LSTM-H-PPO) algorithm is proposed, through which the distributed spectrum access and continuous power control of V2V link are realized.

Simulation results show that compared with the baseline algorithm, the proposed algorithm has significant advantages in terms of total system capacity, payload delivery success rate of V2V link and convergence speed.

The LSTM layer uses the time sequence information to estimate the accurate system state, which ensures the choice of V2V spectrum access based on local observation effective. The hybrid PPO framework shares training parameters among agents which speeds up the entire training process. The proposed algorithm adopts the mode of centralized training and distributed execution, so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

The rapid development of Intelligent and Connected Vehicles (ICVs) has boomed a new round of global technological and industrial revolution in recent decades. The Technology Roadmap of Intelligent and Connected Vehicles (2020) comprehensively analyzes the technical architecture, research status and future trends of ICVs. The methodology that supports the roadmap should get studied.

This paper interprets the roadmap from the aspects of strategic significance, technical content and characteristics of the roadmap, and evaluates the impact of the roadmap on researchers, industries and international strategies.

The technical architecture of ICVs as the “three rows and two columns” structure is studied, the methodology that supported the roadmap is explained with a case study and the influence of key technologies with proposed development routes is analyzed.

This paper could help researchers understand both thoughts and methodologies behind the technology roadmap of ICVs.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The transportation system in any city is complex and evolving, shaped by various driving forces and uncertainties in the social, economic, technological, political and environmental situations. Its development and demands upon it cannot be projected by simply extrapolating past and current trends. This paper aims to present a foresight study examining the future of urban mobility, focusing on the dense Asian city-state of Singapore. The objective is to develop scenarios for the future of urban mobility, to facilitate future policy implementation by highlighting long term challenges and opportunities for transportation planning in cities.

To create future scenarios, the authors first sought to identify key drivers of change through environmental scanning, expert interviews, focus group discussions and technology scanning. These drivers of change were subsequently used in a scenario planning workshop, organized to co-create alternative future visions for urban mobility 2040 with experts and local stakeholders.

Two scenarios emerged, called the Shared World and the Virtual World. For each scenario, the authors described the key features in terms of dominant transport modes for the movements of passengers and freight. Subsequently, the authors discussed possible implications of each scenario to the individual, society, industry and government.

As cities grow and develop, city and transport planners should not only address daily operational issues but also develop a well-informed, long-term understanding of the evolving mobility system to address challenges that lie beyond the five- or even ten-year horizon. By using scenario planning approach, the authors hope to prepare stakeholders for the uncertain futures that are continuously shaped by the decisions today.

5G systems will enable an improved transmission performance and the delivery of advanced communication services. To meet the expected requirements, operators will need to invest in network modernisation, with the radio access network being the most expensive network component. One possible way for operators to reduce this investment would be via sharing of resources by means of a multi-tenancy concept. This implies that a mobile service provider may use the common infrastructure of one or various infrastructure providers, whereby it provides services to multiple tenants. This paper aims to study the expected cost savings in terms of capital expenditures (CAPEX) and operational expenditures (OPEX) that can be achieved when using a cloudified 5G multi-tenant network.

A cost model was used. The study period is 2020-2030 and the study area consists of three local districts in central London, UK.

This paper describes that the total cost reduction achieved when using multi-tenancy for a 5G broadband network in comparison with the case where operators make the investment independently ranges from 5.2% to 15.5%.

Further research is needed to assess the cost implications of network sharing for 5G on a regional or nationwide basis.

Very little quantitative research about the cost implications of network sharing under 5G networks has been published so far. This paper sheds light on the economic benefits of multi-tenancy in a 5G broadband network.