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Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control

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

Deadline for manuscript submissions: closed (12 July 2023) | Viewed by 34587

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Special Issue Editors

Dr. Yuanjian Zhang

E-Mail Website
Guest Editor

School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast BT7 1NN, UK
Interests: vehicle dynamic and control; electric vehicles; automatic and connected vehicles, intelligent manufacture
Special Issues, Collections and Topics in MDPI journals

Dr. Zhengyu Zhu

E-Mail Website
Guest Editor

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: Information theory and signal processing for wireless communications; Internet of Things; machine learning; massive multiple-input multiple-output; millimeter wave communication; UAV communication; physical layer security; wireless cooperative networks; convex optimization techniques; energy harvesting communication systems

Dr. Wanming Hao

E-Mail Website
Guest Editor

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: millimeter/terahertz wave; massive MIMO; vehicle edge computing; SWIPT; cloud radio access network; intelligent reflective surface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent advanced wireless communication technologies brings the application of V2X communication into a new stage, prospering the rapid development of Internet of Vehicles (IoVs). In addition, the ultra-wide bandwidth and ultra-high speed in IoVs promote the information sharing and exploitation in IoVs, and enable further investigation in Mobile Edge Computing (MEC), which all underpin the proposal of novel vehicle-environment co-operation solutions. With vehicle-environment cooperative control strategies, connected electric vehicles can attain better fuel economy because abundant environmental information from IoVs is incorporated to instruct the optimal decision making. Automatic vehicles, shared with states of surrounding vehicles and infrastructures via V2X communication, can make smart decisions in safe and eco autonomous driving. To guarantee optimal application effectiveness of vehicle-environment co-operation, key techniques, e.g., V2X communication technologies, advanced MEC frameworks, wireless resource allocation, etc., must be investigated in details. To fully exploit potential of V2X communication, more vehicle-environment cooperation control solutions are needed. This Special Issue will seek dramatic solutions among the high-quality submissions. The topics of interest cover V2X communication optimization, MEC application in connected and automatic vehicles, IoVs application and vehicle-environment cooperative control application in connected and automatic vehicles. The suggested topics include but not limit to:

Advanced resource ooptimization technologies in V2X communication.
MEC framework design, optimization and implementation towards connected and automatic vehicles.
IoVs application and optimization towards connected and automatic vehicles.
Vehicle-environment cooperative control methods for connected automatic vehicles with target of eco-driving.
Vehicle-environment cooperative control methods for connected automatic vehicles with target of safe and efficient autonomous driving.
Vehicle-environment cooperative control methods for connected automatic vehicles with target of smart energy system management.

Dr. Yuanjian Zhang
Dr. Zhengyu Zhu
Dr. Wanming Hao
Guest Editors

Manuscript Submission Information

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Keywords

V2X communication optimization
MEC application
IoVs application
vehicle-environment cooperative control application
connected and automatic vehicles

Published Papers (14 papers)

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Research

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16 pages, 2845 KiB

Open AccessArticle

An Interactive Framework of Cross-Lingual NLU for In-Vehicle Dialogue

by Xinlu Li, Liangkuan Fang, Lexuan Zhang and Pei Cao

Sensors 2023, 23(20), 8501; https://0-doi-org.brum.beds.ac.uk/10.3390/s23208501 - 16 Oct 2023

Viewed by 827

Abstract

As globalization accelerates, the linguistic diversity and semantic complexity of in-vehicle communication is increasing. In order to meet the needs of different language speakers, this paper proposes an interactive attention-based contrastive learning framework (IABCL) for the field of in-vehicle dialogue, aiming to effectively enhance cross-lingual natural language understanding (NLU). The proposed framework aims to address the challenges of cross-lingual interaction in in-vehicle dialogue systems and provide an effective solution. IABCL is based on a contrastive learning and attention mechanism. First, contrastive learning is applied in the encoder stage. Positive and negative samples are used to allow the model to learn different linguistic expressions of similar meanings. Its main role is to improve the cross-lingual learning ability of the model. Second, the attention mechanism is applied in the decoder stage. By articulating slots and intents with each other, it allows the model to learn the relationship between the two, thus improving the ability of natural language understanding in languages of the same language family. In addition, this paper constructed a multilingual in-vehicle dialogue (MIvD) dataset for experimental evaluation to demonstrate the effectiveness and accuracy of the IABCL framework in cross-lingual dialogue. With the framework studied in this paper, IABCL improves by 2.42% in intent, 1.43% in slot, and 2.67% in overall when compared with the latest model. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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15 pages, 2381 KiB

Open AccessArticle

LLM Adaptive PID Control for B5G Truck Platooning Systems

by I. de Zarzà, J. de Curtò, Gemma Roig and Carlos T. Calafate

Sensors 2023, 23(13), 5899; https://0-doi-org.brum.beds.ac.uk/10.3390/s23135899 - 25 Jun 2023

Cited by 4 | Viewed by 2379

Abstract

This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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19 pages, 7174 KiB

Open AccessArticle

Effect of Autonomous Vehicles on Fatigue Life of Orthotropic Steel Decks

by Shengquan Zou, Dayong Han, Wei Wang and Ran Cao

Sensors 2022, 22(23), 9353; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239353 - 01 Dec 2022

Cited by 1 | Viewed by 1067

Abstract

The fatigue life of orthotropic steel decks (OSDs) is significantly affected by vehicle loads, and the local stress response of OSDs is sensitive to the transverse position of vehicle loads. However, the presence of autonomous vehicles is likely to change the transverse distribution of vehicles within the lane, thereby affecting vehicle-induced fatigue damage to OSDs. Therefore, it is necessary to evaluate the potential effect of autonomous vehicles on the fatigue life of OSDs so that appropriate strategies can be implemented to control the transverse positions of autonomous vehicles passing the bridge deck. To this end, fatigue damages of several typical fatigue details in a conventional OSD (COSD) and a lightweight composite OSD (LWCD) induced by vehicle loads were calculated based on finite element analysis, and their fatigue lives were evaluated based on Miner’s Rule, in which different transverse distribution patterns of autonomous vehicles and their proportions in the mixed traffic flow were considered. The results indicate that fatigue lives of both the COSD and the LWCD can be negatively affected by autonomous vehicles traveling across the bridge without any constraints on the transverse distribution, especially when their proportion in the mixed traffic flow exceeds 30%. Compared to the scenario without autonomous vehicles, the fatigue damage of most fatigue details in OSDs may increase by 51% to 210% in the most unfavorable case due to the presence of autonomous vehicles. Nevertheless, it is feasible to extend the fatigue life of OSDs by optimizing the transverse distribution of autonomous vehicles. Specifically, the fatigue life of most fatigue details in the COSD could be extended by more than 86% in the most favorable case when a bimodal Gaussian distribution is adopted as the transverse distribution pattern of autonomous vehicles. Moreover, both the negative and positive effects of autonomous vehicles on the fatigue life of the COSD are more significant than those of the LWCD in most cases. The results can provide references for the maintenance of OSDs under the action of autonomous vehicles. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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18 pages, 7732 KiB

Open AccessArticle

Load Effect of Automated Truck Platooning on Highway Bridges and Loading Strategy

by Tianyang Ling, Lu Deng, Wei He, Haibing Wu and Jiayu Deng

Sensors 2022, 22(20), 7704; https://0-doi-org.brum.beds.ac.uk/10.3390/s22207704 - 11 Oct 2022

Cited by 4 | Viewed by 1602

Abstract

Automated truck platooning (ATP) has gained growing attention due to its advantage in reducing fuel consumption and carbon emissions. However, it poses serious challenges to highway bridges due to the load effect of multiple closely spaced heavy-duty trucks on the bridge. In China, ATP also has great application prospects in the massive and ever-increasing highway freight market. Therefore, the load effects of ATP on bridges need to be thoroughly investigated. In this study, typical Chinese highway bridges and trucks were adopted. ATP load models were designed according to the current Chinese road traffic regulations. The load effects of ATP on highway bridges were calculated using the influence line method and evaluated based on the Chinese bridge design specifications. Results show that the load effect of ATP on bridges increases with the increase in the gross vehicle mass and the truck platooning size but decreases with the increasing inter-truck spacing and the critical wheelbase. The Grade-I (best quality standard) highway bridges are generally capable of withstanding the ATP loads, while caution should be exercised for other bridges. Strategies for preventing serious adverse impacts of ATP load on highway bridges are proposed. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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19 pages, 6134 KiB

Open AccessArticle

Research on Lane Changing Game and Behavioral Decision Making Based on Driving Styles and Micro-Interaction Behaviors

by Ming Ye, Pan Li, Zhou Yang and Yonggang Liu

Sensors 2022, 22(18), 6729; https://0-doi-org.brum.beds.ac.uk/10.3390/s22186729 - 06 Sep 2022

Cited by 4 | Viewed by 1678

Abstract

Autonomous driving technology plays an essential role in reducing road traffic accidents and ensuring more convenience while driving, so it has been widely studied in industrial and academic communities. The lane-changing decision-making process is challenging but critical for ensuring autonomous vehicles’ (AVs) safe and smooth maneuvering. This paper presents a closed-loop lane-changing behavioral decision-making framework suitable for AVs in fully autonomous driving environments to achieve both safety and high efficiency. The framework is based on a complete information non-cooperative game theory. Moreover, we attempt to introduce human driver-specific driving styles (reflected by aggressiveness types) and micro-interaction behaviors for both sides of the game in this model, enabling users to understand, adapt, and utilize intelligent lane-changing techniques. Additionally, a model predictive control controller based on the host-vehicle (HV) driving risk field (DRF) is proposed. The controller’s optimizer is used to find the optimal path with the lowest driving risk by using its optimizer and simultaneously adjusting its control variables to track the path. The method can synchronize path planning and motion control and provide real-time vehicle state feedback to the decision-making module. Simulations in several typical traffic scenarios demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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19 pages, 2791 KiB

Open AccessArticle

Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF

by Wen Sun, Zhenyuan Wang, Junnian Wang, Xiangyu Wang and Lili Liu

Sensors 2022, 22(9), 3386; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093386 - 28 Apr 2022

Cited by 7 | Viewed by 1624

Abstract

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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14 pages, 2104 KiB

Open AccessArticle

Intelligent Transportation Logistics Optimal Warehouse Location Method Based on Internet of Things and Blockchain Technology

by Jun Chen, Shiyan Xu, Kaikai Liu, Shuqi Yao, Xiao Luo and Huan Wu

Sensors 2022, 22(4), 1544; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041544 - 17 Feb 2022

Cited by 7 | Viewed by 3216

Abstract

In order to cut down on costs to the greatest possible extent, enterprises hope to distribute goods to different customers at the lowest costs possible. Based on this, this paper proposes an optimal location method for an intelligent transportation logistics warehouse. This scheme combines a variety of complex mechanisms to allow IoT devices to provide input. The scheme makes full use of the irreducibility of a blockchain system to promote the development and design of blockchain logistics applications. This method is aimed at tracking the progress of transportation of products in the whole supply chain. Experimental results show that, compared with traditional methods, the optimal positioning method has the advantages of fewer calculations, a high positioning accuracy, and a low overall cost, and it obtains the best warehouse positioning results. Based on the Internet of Things and blockchain technology, the application of intelligent logistics systems enables enterprises to intuitively understand their current inventory and the transportation status of goods, thus better controlling changes in enterprise resources. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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21 pages, 26453 KiB

Open AccessArticle

TALBOT: A Track-Leg Transformable Robot

by Wenzhi Guo, Jiandu Qiu, Xinrui Xu and Juan Wu

Sensors 2022, 22(4), 1470; https://0-doi-org.brum.beds.ac.uk/10.3390/s22041470 - 14 Feb 2022

Cited by 11 | Viewed by 2526

Abstract

This article introduces a tracked-leg transformable robot, TALBOT. The mechanical and electrical design, control method, and environment perception based on LiDAR are discussed. The original tracked-leg transformable structure allows the robot to switch between the tracked and legged mode to achieve all-terrain adaptation. In the tracked mode, TALBOT is controlled by the method of differential speed between the two tracked feet. In the legged mode, TALBOT is controlled based on a bionic control strategy of the central pattern generator to realize the generation and conversion of gait. In addition, the robot is equipped with a LiDAR, through sensor preprocessing and optimization of the slam mapping algorithm, so that the robot achieves a better mapping effect. We tested the robot’s motion performance and the slam mapping effect, including going straight and turning in tracked and legged modes and building a map in an indoor environment. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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21 pages, 8439 KiB

Open AccessArticle

A Predictive Energy Management Strategy for Multi-Energy Source Vehicles Based on Full-Factor Trip Information

by Fenglai Yue, Qiao Liu, Yan Kong, Junhong Zhang and Nan Xu

Sensors 2022, 22(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010023 - 22 Dec 2021

Cited by 2 | Viewed by 2329

Abstract

To achieve the real-time application of a dynamic programming (DP) control strategy, we propose a predictive energy management strategy (PEMS) based on full-factor trip information, including vehicle speed, slip ratio and slope. Firstly, the prediction model of the full-factor trip information is proposed, which provides an information basis for global optimization energy management. To improve the prediction’s accuracy, the vehicle speed is predicted based on the state transition probability matrix generated in the same driving scene. The characteristic parameters are extracted by a feature selection method taken as the basis for the driving condition’s identification. Similar to speed prediction, regarding the uncertain route at an intersection, the slope prediction is modelled as a Markov model. On the basis of the predicted speed and the identified maximum adhesion coefficient, the slip ratio is predicted based on a neural network. Then, a predictive energy management strategy is developed based on the predictive full-factor trip information. According to the statistical rules of DP results under multiple standard driving cycles, the reference SOC trajectory is generated to ensure global sub-optimality, which determines the feasible state domain at each prediction horizon. Simulations are performed under different types of driving conditions (Urban Dynamometer Driving Schedule, UDDS and World Light Vehicle Test Cycle, WLTC) to verify the effectiveness of the proposed strategy. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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24 pages, 11182 KiB

Open AccessArticle

Energy Management Strategy Based on a Novel Speed Prediction Method

by Jiaming Xing, Liang Chu, Zhuoran Hou, Wen Sun and Yuanjian Zhang

Sensors 2021, 21(24), 8273; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248273 - 10 Dec 2021

Cited by 6 | Viewed by 2174

Abstract

Vehicle speed prediction can obtain the future driving status of a vehicle in advance, which helps to make better decisions for energy management strategies. We propose a novel deep learning neural network architecture for vehicle speed prediction, called VSNet, by combining convolutional neural network (CNN) and long-short term memory network (LSTM). VSNet adopts a fake image composed of 15 vehicle signals in the past 15 s as model input to predict the vehicle speed in the next 5 s. Different from the traditional series or parallel structure, VSNet is structured with CNN and LSTM in series and then in parallel with two other CNNs of different convolutional kernel sizes. The unique architecture allows for better fitting of highly nonlinear relationships. The prediction performance of VSNet is first examined. The prediction results show a RMSE range of 0.519–2.681 and a R2 range of 0.997–0.929 for the future 5 s. Finally, an energy management strategy combined with VSNet and model predictive control (MPC) is simulated. The equivalent fuel consumption of the simulation increases by only 4.74% compared with DP-based energy management strategy and decreased by 2.82% compared with the speed prediction method with low accuracy. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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21 pages, 12102 KiB

Open AccessArticle

Dual-Input and Multi-Channel Convolutional Neural Network Model for Vehicle Speed Prediction

by Jiaming Xing, Liang Chu, Chong Guo, Shilin Pu and Zhuoran Hou

Sensors 2021, 21(22), 7767; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227767 - 22 Nov 2021

Cited by 8 | Viewed by 2240

Abstract

With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R² are obtained compared with a Markov chain combined with Monte Carlo (MCMC) simulation, a support vector machine (SVM) and a single input CNN (SICNN). Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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16 pages, 700 KiB

Open AccessArticle

5G and IoT Based Reporting and Accident Detection (RAD) System to Deliver First Aid Box Using Unmanned Aerial Vehicle

by Monagi H. Alkinani, Abdulwahab Ali Almazroi, NZ Jhanjhi and Navid Ali Khan

Sensors 2021, 21(20), 6905; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206905 - 18 Oct 2021

Cited by 32 | Viewed by 4927

Abstract

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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18 pages, 7481 KiB

Open AccessArticle

Energy Management Strategy of a Hybrid Power System Based on V2X Vehicle Speed Prediction

by Ming Ye, Jing Chen, Xu Li, Kai Ma and Yonggang Liu

Sensors 2021, 21(16), 5370; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165370 - 09 Aug 2021

Cited by 10 | Viewed by 2446

Abstract

Energy consumption in vehicle driving is greatly influenced by traffic scenarios, and the intelligent traffic system (ITS) has a key role in solving the real-time optimal control of hybrid vehicles. To this end, a new energy management control strategy based on vehicle-to-everything (V2X) communication for vehicle speed prediction was proposed to dynamically adjust the engine and motor power output according to the traffic conditions. This study is based on intelligent network connectivity technology to obtain forward traffic state data and use a deep learning algorithm to model vehicle speed prediction using the traffic state data. The energy economy function was modeled using the MATLAB/Sinumlink platform and validated with a plug-in hybrid vehicle model simulation. The results indicate that the proposed strategy improves the vehicle energy economy by 13.02% and reduces CO₂ emissions by 16.04% under real vehicle driving conditions, compared with the conventional logic threshold-based control strategy. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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Review

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24 pages, 6265 KiB

Open AccessReview

An Overview of Eco-Driving Theory, Capability Evaluation, and Training Applications

by Nan Xu, Xiaohan Li, Qiao Liu and Di Zhao

Sensors 2021, 21(19), 6547; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196547 - 30 Sep 2021

Cited by 15 | Viewed by 3940

Abstract

Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving. Full article

(This article belongs to the Special Issue Advanced Vehicle to Everything (V2X) Communication and Application in Vehicle-Environment Cooperative Control)

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