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Electronics
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Emerging Communication, Computing and Control Technologies for Internet of Vehicles
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Emerging Communication, Computing and Control Technologies for Internet of Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 10425

Special Issue Editors

Prof. Dr. Changyan Yi

E-Mail Website
Guest Editor
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China
Interests: edge/fog computing; industrial IoT; vehicular/UAV systems; 5G and beyond communication networks
Prof. Dr. Jun Cai

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 1M8, Canada
Interests: 5G and beyond; edge/fog computing; vehicular and non-terrestrial communications; machine learning; network economics; mobile health
Prof. Dr. Xiaolong Li

E-Mail Website1 Website2
Guest Editor
College of Computer Science, Hunan University of Technology and Business, Changsha 410205, China
Interests: edge/fog computing; industrial IoT; Internet of Vehicle; M2M networks; wireless networks

Special Issue Information

Dear Colleagues,

Internet of Vehicles (IoV), as one of the utmost important parts of a smart city, is a promising paradigm that allows intelligent vehicles to interact with drivers, environments and road-side units. IoV is envisioned to improve the road safety and provide in-car entertainment and on-board Internet services, such as social networking, online media streaming, online navigation and real-time traffic report acquisition.

Relying on the emergence of 6G, which is characterized by higher bandwidth and lower latency, IoV will be allowed to provide more accurate, real-time, and customized services for vehicle networks in the future. However, with the explosively increasing number of IoV devices (e.g., smart vehicles, road-side units, sensors, ICT infrastructures), the operation and management efficiency of IoV systems face unprecedented challenges in such a new era. Moreover, autonomous driving, as a key enabling technology of IoV, is rapidly developing and is expected to achieve Level 4/5 very recently. These prompt the new evolution of IoV with significant demands on computing, communication and control infrastructures/resources, which are inherently limited, and thus a system-wide optimization becomes imperative and necessary. In addition, through integration with advanced Artificial Intelligence (AI), IoV could contribute to the establishment of intelligent transportation systems (ITS) by achieving full automation, data-driven decision making and ultra-high reliability. Motivated by these requirements, a variety of corresponding communication, computing and control technologies are recently developed.

The purpose of this Special Issue is to present emerging communication, computing and control technologies for IoV. The editorial board invites academic researchers and industrial experts to submit insightful and revolutionary contributions in the form of research and review articles focusing on, but not limited to, the following potential topics:

  1. IoV architecture and system design;
  2. IoV applications and smart transportations;
  3. Communication technologies and protocols for IoV;
  4. Resource optimization/scheduling and QoS guarantee for IoV;
  5. Cloud/edge/fog computing enabled IoV;
  6. Machine learning, deep learning and federated learning for IoV;
  7. IoV-based intelligent traffic signal control;
  8. IoV-based traffic management and congestion control;
  9. Autonomous driving techniques for IoV;
  10. Data-driven intelligent transportation systems (ITS);
  11. Signal sensing, processing, transmission techniques for IoV;
  12. Intelligent control techniques;
  13. Computer vision and pattern recognition for IoV;
  14. Camera/Radar/Lidar-based tracking and detection systems.

Prof. Dr. Changyan Yi
Prof. Dr. Jun Cai
Prof. Dr. Xiaolong Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Vehicles (IoV)
  • 5g/6g communication techniques
  • resource optimization/scheduling
  • cloud/edge/fog computing
  • artificial intelligence
  • autonomous driving
  • signal processing
  • vehicular electronics

Published Papers (6 papers)

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Research

17 pages, 1615 KiB  
Article
Divergent Selection Task Offloading Strategy for Connected Vehicles Based on Incentive Mechanism
by Senyu Yu, Yan Guo, Ning Li, Duan Xue and Hao Yuan
Electronics 2023, 12(9), 2143; https://doi.org/10.3390/electronics12092143 - 8 May 2023
Viewed by 1279
Abstract
With the improvements in the intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside the vehicle, which place extremely high demands on the computing power of onboard systems (OBSs). However, the arithmetic power of [...] Read more.
With the improvements in the intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside the vehicle, which place extremely high demands on the computing power of onboard systems (OBSs). However, the arithmetic power of a single CV often cannot meet the diverse service demands of the in-vehicle system. As a new computing paradigm, task offloading based on vehicular edge computing has significant advantages in remedying the shortcomings of single-CV computing power and balancing the allocation of computing resources. This paper studied the computational task offloading of high-speed connected vehicles without the help of roadside edge servers in certain geographic areas. User vehicles (UVs) with insufficient computing power offload some of their computational tasks to nearby CVs with abundant resources. We explored the high-speed driving model and task classification model of CVs to refine the task offloading process. Additionally, inspired by game theory, we designed a divergent selection task offloading strategy based on an incentive mechanism (DSIM), in which we balanced the interests of both the user vehicle and service vehicles. CVs that contribute resources are rewarded to motivate more CVs to join. A DSIM algorithm based on a divergent greedy algorithm was introduced to maximize the total rewards of all volunteer vehicles while respecting the will of both the user vehicle and service vehicles. The experimental simulation results showed that, compared with several existing studies, our approach can always obtain the highest reward for service vehicles and lowest latency for user vehicles. Full article
(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)
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12 pages, 547 KiB  
Article
A Dynamic Obstacle Avoidance Method for AGV Based on Improved Speed Barriers
by Yong Yuan, You Shi, Song Yue, Shanliang Xue, Changyan Yi and Bing Chen
Electronics 2022, 11(24), 4175; https://doi.org/10.3390/electronics11244175 - 14 Dec 2022
Cited by 1 | Viewed by 1657
Abstract
The complexity and difficulty of dynamic obstacle avoidance for AGVs are increased by the uncertainty in a dynamic environment. The adaptive speed obstacle method allows the size of the collision cone to be dynamically changed to solve this problem, but this method may [...] Read more.
The complexity and difficulty of dynamic obstacle avoidance for AGVs are increased by the uncertainty in a dynamic environment. The adaptive speed obstacle method allows the size of the collision cone to be dynamically changed to solve this problem, but this method may cause the AGV to turn too much when it is close to obstacles, as the collision cone expands too fast, which may lead to unstable operations or even collision. In order to address these problems, we propose an improved speed obstacle algorithm. The proposed algorithm uses Kalman filtering to estimate the positions of dynamic obstacles and adopts the idea of forward simulation to build a speed obstacle buffer according to the estimated positions of obstacles, such that the AGV can use the predicted positions of obstacles in the next moment, instead of the current positions, to build a speed obstacle model. Finally, an objective function that balances efficiency and safety was established to score all the candidate speeds, such that the highest-rated speed could be selected as the candidate speed for the next moment. Full article
(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)
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17 pages, 757 KiB  
Article
Virtual CANBUS and Ethernet Switching in Future Smart Cars Using Hybrid Architecture
by Olugbenga Olumuyiwa and Yuhua Chen
Electronics 2022, 11(21), 3428; https://doi.org/10.3390/electronics11213428 - 23 Oct 2022
Cited by 2 | Viewed by 1708
Abstract
Smart cars have gained much attention in recent years due to the introduction of several safety and convenience features. In this paper, we propose a virtual CANBUS architecture that will improve the safety and data processing in future smart cars with the hybrid [...] Read more.
Smart cars have gained much attention in recent years due to the introduction of several safety and convenience features. In this paper, we propose a virtual CANBUS architecture that will improve the safety and data processing in future smart cars with the hybrid use of Ethernet technology deployed in conjunction with a CANBUS system to take advantage of the virtualization, speed, and quality of data processing. Data will be routed intelligently across the dual data paths of the traditional CANBUS and the Ethernet. The virtualized nature, with the help of a series of smart nodes and network traffic analyzers, will allocate the needed resources at the right time during the execution of different processes. This enables the possibility of routing data traffic over both Ethernet and CANBUS connections. The architecture is backward compatible with older vehicles and therefore takes advantage of the existing CANBUS system. The proposed architecture ensures that different segments are isolated from each other so that a breakdown in a segment does not bring down the entire system. The experimental results demonstrate the benefits of the proposed solution, which is to switch between two data pathways depending on the traffic loads. While the CANBUS is sufficient with low-bandwidth data, the Ethernet will create a better performance with high-bandwidth processes. The virtualized environment creates virtual topologies among communicating nodes, greatly simplifying the network management and enhancing the data traffic performance as the bandwidth requirement and the number of processors in future smart cars continue to scale. Full article
(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)
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14 pages, 1840 KiB  
Article
Trajectory Prediction with Correction Mechanism for Connected and Autonomous Vehicles
by Pin Lv, Hongbiao Liu, Jia Xu and Taoshen Li
Electronics 2022, 11(14), 2149; https://doi.org/10.3390/electronics11142149 - 9 Jul 2022
Cited by 4 | Viewed by 1857
Abstract
Trajectory prediction of surrounding vehicles is a critical task for connected and autonomous vehicles (CAVs), helping them to realize potential dangers in the traffic environment and make the most appropriate decisions. In a practical traffic environment, vehicles may affect each other, and the [...] Read more.
Trajectory prediction of surrounding vehicles is a critical task for connected and autonomous vehicles (CAVs), helping them to realize potential dangers in the traffic environment and make the most appropriate decisions. In a practical traffic environment, vehicles may affect each other, and the trajectories may have multi-modality and uncertainty, which makes accurate trajectory prediction a challenge. In this paper, we propose an interactive network model based on long short-term memory (LSTM) and a convolutional neural network (CNN) with a trajectory correction mechanism, using our newly proposed probability forcing method. The model learns the interactions between vehicles and corrects their trajectories during the prediction process. The output is a multimodal distribution of predicted trajectories. In the experimental evaluation of the US-101 and I-80 Next-Generation Simulation (NGSIM) real highway datasets, our proposed method outperforms other contrast methods. Full article
(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)
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11 pages, 2136 KiB  
Article
A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise
by Daxing Xu, Bao Wang, Lu Zhang and Zhiqiang Chen
Electronics 2022, 11(12), 1863; https://doi.org/10.3390/electronics11121863 - 13 Jun 2022
Cited by 8 | Viewed by 1532
Abstract
Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining the system state information is the premise of realizing precise control. However, the statistics of system process noise are often unknown due to the [...] Read more.
Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining the system state information is the premise of realizing precise control. However, the statistics of system process noise are often unknown due to the complex physical process. It is challenging to estimate the system state when the process noise statistics are unknown. This paper proposes a new adaptive high-degree unscented Kalman filter based on the improved Sage–Husa algorithm. First, the traditional Sage–Husa algorithm is improved using a high-degree unscented transform. A noise estimator suitable for the high-degree unscented Kalman filter is obtained to estimate the statistics of the unknown process noise. Then, an adaptive high-degree unscented Kalman filter is designed to improve the accuracy and stability of the state estimation system. Finally, the target tracking simulation results verify the proposed algorithm’s effectiveness. Full article
(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)
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14 pages, 1177 KiB  
Article
A Particle Shift Prior Information Fusion Localization Algorithm for the Autonomous Internet of Vehicles
by Qian Chen, Xin Tang, Zhaoyu Su, Xiaohuan Li and Duiwu Wang
Electronics 2022, 11(12), 1816; https://doi.org/10.3390/electronics11121816 - 8 Jun 2022
Viewed by 1170
Abstract
Ensuring that a vehicle can obtain its real location in a high-precision prebuilt map is one of the most important tasks of the Autonomous Internet of Vehicles (AIoV). In this work, we show that the resampling of the particle filter (PF) algorithm is [...] Read more.
Ensuring that a vehicle can obtain its real location in a high-precision prebuilt map is one of the most important tasks of the Autonomous Internet of Vehicles (AIoV). In this work, we show that the resampling of the particle filter (PF) algorithm is optimized by using the prior information of particles that shift real localizations to improve vehicle localization accuracy without changing the existing PF process, i.e., the particle shift filter (PSF). The number of particles is critical to their convergence efficiency. We perform quantitative and qualitative analyses of how to improve particle localization accuracy while ensuring timeliness, without increasing the number of particles. Moreover, the cumulative error of the particles increases with time, and the localization accuracy and robustness decrease. Our findings show that the initial particle density is 159 particles/m3, and the cumulative variance of the PSF particles is improved by 27%, 29%, and 82% at the x-, y-, and z-axes, respectively, under the same conditions as the PF algorithm, while the calculation time only increases by 10.6%. Moreover, the cumulative mean error is reduced by 0.74 m, 0.88 m, and 0.68 m at the x-, y-, and z-axes, respectively, indicating that the localization error of the PSF changes less with time. All experiments were performed using open-source software and datasets. Full article
(This article belongs to the Special Issue Emerging Communication, Computing and Control Technologies for Internet of Vehicles)
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