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Vehicle-to-Everything (V2X) Communication for Intelligent Transportation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 15526

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Special Issue Editors

Prof. Dr. Chen Chen

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State Key Laboratory of Integrated Services Networks, Xidian University, No. 2 Taibai Road, Xi'an 70071, China
Interests: Internet of Things; intelligent transportation systems; wireless communications
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Prof. Dr. Kai Liu

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College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: internet of vehicles; big data; pervasive computing
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Dr. Lei Liu

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State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: intelligent transportation systems; internet of vehicles; distributed computing
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Prof. Dr. Qingqi Pei

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Guest Editor

School of Communication Engineering, Xidian University, Xi’an 710071, China
Interests: trusted computing network; internet of things and edge computing security; wireless network physical layer security; blockchain technology; distributed collaborative attack and defense technology; data security and privacy protection
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Dr. Dapeng Lan

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Department of Informatics, University of Oslo, 0316 Oslo, Norway
Interests: mobile edge computing; artificial intelligence
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Special Issue Information

Dear Colleagues,

Over the last few years, as a result of advanced communication technologies, V2X (Vehicle-to-Everything) communication has applied to Intelligent Transportation Systems (ITS), such as road safety, cooperative autonomous driving, entertainment services, and many other use cases. The V2X-enabled ITS guarantees more efficient and reliable travel. Increasingly, the substantial development of wireless communication technology for V2X communication and networking enables the development of novel ITS services and applications:

New wireless communications and networking architecture for V2X and ITS;
Novel theory, technology, methodology, tools, and applications for V2X and ITS;
Modelling, simulation, and field evaluation for V2X and ITS;
Big data and data analytics for V2X and ITS;
Machine learning techniques for V2X and ITS;
Edge architecture, service, and applications for V2X and ITS;
New paradigms and management for smart mobility;
Vehicular networking, vehicular cloud, and Internet of Vehicles (IoV).

This Special Issue of the Sensors aims to discuss a novel design of V2X architecture, technique, networks, services, and applications for ITS and search for innovative solutions for meeting the expectation of V2X communication and ITS.

Prof. Dr. Chen Chen
Prof. Dr. Kai Liu
Dr. Lei Liu
Prof. Dr. Qingqi Pei
Dr. Dapeng Lan
Guest Editors

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Keywords

V2X (Vehicle-to-Everything)
Intelligent Transportation Systems (ITS)
wireless communication

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Related Special Issue

Vehicle-to-Everything (V2X) Communication for Intelligent Transportation: 2nd Edition in Sensors (3 articles)

Published Papers (8 papers)

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Research

31 pages, 8466 KiB

Open AccessArticle

Resource Cluster-Based Resource Search and Allocation Scheme for Vehicular Clouds in Vehicular Ad Hoc Networks

by Hyunseok Choi, Yoonhyeong Lee, Gayeong Kim, Euisin Lee and Youngju Nam

Sensors 2024, 24(7), 2175; https://doi.org/10.3390/s24072175 - 28 Mar 2024

Viewed by 856

Abstract

Vehicular clouds represent an appealing approach, leveraging vehicles’ resources to generate value-added services. Thus, efficiently searching for and allocating resources is a challenge for the successful construction of vehicular clouds. Many recent schemes have relied on hierarchical network architectures using clusters to address this challenge. These clusters are typically constructed based on vehicle proximity, such as being on the same road or within the same region. However, this approach struggles to rapidly search for and consistently allocate resources, especially considering the diverse resource types and varying mobility of vehicles. To address these limitations, we propose the Resource Cluster-based Resource Search and Allocation (RCSA) scheme. RCSA constructs resource clusters based on resource types rather than vehicle proximity. This allows for more efficient resource searching and allocation. Within these resource clusters, RCSA supports both intra-resource cluster search for the same resource type and inter-resource cluster search for different resource types. In RCSA, vehicles with longer connection times and larger resource capacities are allocated in vehicular clouds to minimize cloud breakdowns and communication traffic. To handle the reconstruction of resource clusters due to vehicle mobility, RCSA implements mechanisms for replacing Resource Cluster Heads (RCHs) and managing Resource Cluster Members (RCMs). Simulation results validate the effectiveness of RCSA, demonstrating its superiority over existing schemes in terms of resource utilization, allocation efficiency, and overall performance. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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18 pages, 389 KiB

Open AccessArticle

A Game-Based Computing Resource Allocation Scheme of Edge Server in Vehicular Edge Computing Networks Considering Diverse Task Offloading Modes

by Xiangyan Liu, Jianhong Zheng, Meng Zhang, Yang Li, Rui Wang and Yun He

Sensors 2024, 24(1), 69; https://doi.org/10.3390/s24010069 - 22 Dec 2023

Cited by 6 | Viewed by 1246

Abstract

Introducing partial task offloading into vehicle edge computing networks (VECNs) can ease the burden placed on the Internet of Vehicles (IoV) by emerging vehicle applications and services. In this circumstance, the task offloading ratio and the resource allocation of edge servers (ES) need to be addressed urgently. Based on this, we propose a best response-based centralized multi-TaV computation resource allocation algorithm (BR-CMCRA) by jointly considering service vehicle (SeV) selection, offloading strategy making, and computing resource allocation in a multiple task vehicle (TaV) system, and the utility function is related to the processing delay of all tasks, which ensures the TaVs’s quality of services (QoS). In the scheme, SeV is first selected from three candidate SeVs (CSVs) near the corresponding TaV based on the channel gain. Then, an exact potential game (EPG) is conducted to allocate computation resources, where the computing resources can be allocated step by step to achieve the maximum benefit. After the resource allocation, the task offloading ratio can be acquired accordingly. Simulation results show that the proposed algorithm has better performance than other basic algorithms. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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18 pages, 3412 KiB

Open AccessArticle

Ultra-Reliable Deep-Reinforcement-Learning-Based Intelligent Downlink Scheduling for 5G New Radio-Vehicle to Infrastructure Scenarios

by Jizhe Wang, Yuanbing Zheng, Jian Wang, Zhenghua Shen, Lei Tong, Yahao Jing, Yu Luo and Yong Liao

Sensors 2023, 23(20), 8454; https://doi.org/10.3390/s23208454 - 13 Oct 2023

Cited by 2 | Viewed by 1327

Abstract

Higher standards for reliability and efficiency apply to the connection between vehicle terminals and infrastructure by the fifth-generation mobile communication technology (5G). A vehicle-to-infrastructure system uses a communication system called NR-V2I (New Radio-Vehicle to Infrastructure), which uses Link Adaptation (LA) technology to communicate in constantly changing V2I to increase the efficacy and reliability of V2I information transmission. This paper proposes a Double Deep Q-learning (DDQL) LA scheduling algorithm for optimizing the modulation and coding scheme (MCS) of autonomous driving vehicles in V2I communication. The problem with the Doppler shift and complex fast time-varying channels reducing the reliability of information transmission in V2I scenarios is that they make it less likely that the information will be transmitted accurately. Schedules for autonomous vehicles using Space Division Multiplexing (SDM) and MCS are used in V2I communications. To address the issue of Deep Q-learning (DQL) overestimation in the Q-Network learning process, the approach integrates Deep Neural Network (DNN) and Double Q-Network (DDQN). The findings of this study demonstrate that the suggested algorithm can adapt to complex channel environments with varying vehicle speeds in V2I scenarios and by choosing the best scheduling scheme for V2I road information transmission using a combination of MCS. SDM not only increases the accuracy of the transmission of road safety information but also helps to foster cooperation and communication between vehicle terminals to realize cooperative driving. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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17 pages, 10332 KiB

Open AccessArticle

An RFID Tag Movement Trajectory Tracking Method Based on Multiple RF Characteristics for Electronic Vehicle Identification ITS Applications

by Ruoyu Pan, Zhao Han, Tuo Liu, Honggang Wang, Jinyue Huang and Wenfeng Wang

Sensors 2023, 23(15), 7001; https://doi.org/10.3390/s23157001 - 7 Aug 2023

Cited by 1 | Viewed by 2253

Abstract

Intelligent transportation systems (ITS) urgently need to realize vehicle identification, dynamic monitoring, and traffic flow monitoring under high-speed motion conditions. Vehicle tracking based on radio frequency identification (RFID) and electronic vehicle identification (EVI) can obtain continuous observation data for a long period of time, and the acquisition accuracy is relatively high, which is conducive to the discovery of rules. The data can provide key information for urban traffic decision-making research. In this paper, an RFID tag motion trajectory tracking method based on RF multiple features for ITS is proposed to analyze the movement trajectory of vehicles at important checkpoints. The method analyzes the accurate relationship between the RSSI, phase differences, and driving distances of the tag. It utilizes the information weight method to obtain the weights of multiple RF characteristics at different distances. Then, it calculates the center point of the common area where the vehicle may move under multi-antenna conditions, confirming the actual position of the vehicle. The experimental results show that the average positioning error of moving RFID tags based on dual-frequency signal phase differences and RSSI is less than 17 cm. This method can provide real-time, high-precision vehicle positioning and trajectory tracking solutions for ITS application scenarios such as parking guidance, unmanned vehicle route monitoring, and vehicle lane change detection. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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11 pages, 461 KiB

Open AccessArticle

Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels

by J. Alberto Del Puerto-Flores, Francisco R. Castillo-Soria, J. Vázquez-Castillo and R. R. Palacio Cinco

Sensors 2023, 23(15), 6728; https://doi.org/10.3390/s23156728 - 27 Jul 2023

Cited by 2 | Viewed by 1361

Abstract

This paper examines the performance of orthogonal frequency division multiplexing (OFDM) systems for vehicle-to-vehicle (V2V) communication channels. More specifically, a doubly selective channel under high intercarrier interference (ICI) is considered. Current solutions involve complex detection and/or reduced spectral efficiency receivers. This paper proposes the use of virtual carriers (VC) in an OFDM system with a low-complexity maximal ratio combining (MRC) detector to improve the bit error rate (BER) performance. The results show that VC provides diversity in received data, resulting in a ≥5 dB gain compared to previous OFDM systems with conventional linear/nonlinear detectors used as a reference. The detector presented in this paper has linear complexity, making it a suitable solution for real-time V2V communication systems. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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15 pages, 2621 KiB

Open AccessArticle

A Hybrid Power-Rate Management Strategy in Distributed Congestion Control for 5G-NR-V2X Sidelink Communications

by Jiawei Tian, SangHoon An, Azharul Islam and KyungHi Chang

Sensors 2023, 23(15), 6657; https://doi.org/10.3390/s23156657 - 25 Jul 2023

Cited by 1 | Viewed by 1367

Abstract

The accelerated growth of 5G technology has facilitated substantial progress in the realm of vehicle-to-everything (V2X) communications. Consequently, achieving optimal network performance and addressing congestion-related challenges have become paramount. This research proposes a unique hybrid power and rate control management strategy for distributed congestion control (HPR-DCC) focusing on 5G-NR-V2X sidelink communications. The primary objective of this strategy is to enhance network performance while simultaneously preventing congestion. By implementing the HPR-DCC strategy, a more fine-grained and adaptive control over the transmit power and transmission rate can be achieved. This enables efficient control by dynamically adjusting transmission parameters based on the network conditions. This study outlines the system model and methodology used to develop the HPR-DCC algorithm and investigates its characteristics of stability and convergence. Simulation results indicate that the proposed method effectively controls the maximum CBR value at 64% during high congestion scenarios, which leads to a 6% performance improvement over the conventional DCC approach. Furthermore, this approach enhances the signal reception range by 20 m, while maintaining the 90% packet reception ratio (PRR). The proposed HPR-DCC contributes to optimizing the quality and reliability of 5G-NR-V2X sidelink communication and holds great promise for advancing V2X applications in intelligent transportation systems. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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19 pages, 2612 KiB

Open AccessArticle

Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network

by Xinghui Zhang, Xiumei Fan, Shunyuan Yu, Axida Shan and Rui Men

Sensors 2023, 23(14), 6303; https://doi.org/10.3390/s23146303 - 11 Jul 2023

Cited by 2 | Viewed by 1817

Abstract

Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current scheme in this case. The comparisons and discussions demonstrate the effectiveness of the proposed method designed for improving the efficiency of signalized intersections. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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15 pages, 2580 KiB

Open AccessArticle

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

by Ruoyu Pan, Lihua Jie, Xinyu Zhao, Honggang Wang, Jingfeng Yang and Jiwei Song

Sensors 2023, 23(6), 3248; https://doi.org/10.3390/s23063248 - 19 Mar 2023

Cited by 9 | Viewed by 2654

Abstract

V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the “stop–go” state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle’s active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communication for Intelligent Transportation)

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