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Machines
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Connected and Automated Vehicles (CAVs): Technologies and Applications
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Connected and Automated Vehicles (CAVs): Technologies and Applications

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 15488

Special Issue Editors

Dr. Yanjun Shi

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian, China
Interests: connected and automated vehicles; V2X; industrial IoT; digital twins; big data; intelligent machines; cooperative connected technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Zihui Zhang

E-Mail Website
Guest Editor
Institute of Automation, Qilu University of Technology(Shandong Academy of Sciences), Jinan, China
Interests: connected and automated vehicles; decision-making optimization model; real-time motion planning; autonomous vehicle control systems; reinforcement learning

Special Issue Information

Dear Colleagues,

Connected and automated vehicles (CAVs) (connected and autonomous vehicles or self-driving cars) for the automotive industry are of great potential for reducing traffic accidents, enhancing the quality of life, and improving the efficiency of transportation systems. In the era of cooperative connected and automated mobility (CCAM), CAVs are evolving toward a vision where cars or vehicles are becoming more and more automated and wirelessly connected to cooperate for safer and more efficient driving.

Many challenges could impede the deployment of CAVs today. These include deploying advanced V2X (vehicle-to-everything) technologies, leveraging data and algorithms for CAVs, cybersecurity threats, trust issues, and other technology needs. Thus, the objective of this Special Issue is to accumulate various innovative strategies that make CAVs and other connected technologies more effective, safe, efficient, and adaptive concerning prevailing task situations and uncertainties.

The main aim of this Special Issue is to seek high-quality submissions that highlight recent breakthroughs in CAV technologies and applications, including autonomous vehicle engineering, cooperative, connected technologies, etc.

Potential topics include but are not limited to the following categories:

  1. Autonomous vehicle engineering
  • Motion planning and control
  • Multisensor information fusion
  • Navigation system
  • Applications of machine learning
  • Development, test, and validation of automated vehicles
  1. Cooperative connected technologies
  • CCAM technologies and applications
  • Cooperative control systems
  • Edge computing in mobility
  • Platoons of connected and automated vehicles
  • Networked optimal control
  • Cooperative resource allocation and scheduling

Dr. Yanjun Shi
Dr. Zihui Zhang
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. Machines is an international peer-reviewed open access monthly 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.

Published Papers (5 papers)

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Research

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25 pages, 2238 KiB  
Article
Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System
by Jia Gao, Xiaojun Zheng, Feng Gao, Xiaoying Tong and Qiaomei Han
Machines 2022, 10(3), 197; https://doi.org/10.3390/machines10030197 - 09 Mar 2022
Cited by 14 | Viewed by 2617
Abstract
In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based [...] Read more.
In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based on the analysis of AGV body structure and motion state, transport distance and energy consumption are selected as two optimization objectives. According to the characteristics and application context of the problem, this paper designs a hybrid genetic algorithm with large neighborhood search (GA-LNS) considering the farthest insertion heuristic. GA-LNS is improved by increasing the local search ability of genetic algorithm to enhance the solution optimal quality. Extensive computational experiments which are generated from Solomon’s benchmark instances and a real case of FMS are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results reveal that compared with using the traditional homogeneous fleet, the heterogeneous multitype AGV fleet transportation mode has a huge energy-saving potential in workshop intralogistics. Full article
(This article belongs to the Special Issue Connected and Automated Vehicles (CAVs): Technologies and Applications)
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18 pages, 4478 KiB  
Article
Modeling Lane-Changing Behavior Based on a Joint Neural Network
by Changyin Dong, Yunjie Liu, Hao Wang, Daiheng Ni and Ye Li
Machines 2022, 10(2), 109; https://doi.org/10.3390/machines10020109 - 31 Jan 2022
Cited by 2 | Viewed by 2179
Abstract
This paper proposes a joint neural network model to imitate lane-changing behaviors. Specifically, lane-changing decision-making process is captured by probabilistic neural network (PNN) and lane-changing decision-making process is learned by back-propagation neural network (BPNN). The link between the two neural networks is the [...] Read more.
This paper proposes a joint neural network model to imitate lane-changing behaviors. Specifically, lane-changing decision-making process is captured by probabilistic neural network (PNN) and lane-changing decision-making process is learned by back-propagation neural network (BPNN). The link between the two neural networks is the target gap for lane-changing. After testing and calibrating the joint neural network model, simulation experiments are designed to study heterogeneous traffic flow at an off-ramp bottleneck. Numerical simulations are conducted in various traffic scenarios with different market penetration rates (MPRs) of intelligent vehicles (IVs) and proportions of exit vehicles. Finally, the performance of heterogeneous flows is evaluated from the perspectives of average speed, road capacity, and safety. The results show that joint neural network can accurately predict the gap types chosen for lane changes and vehicle trajectory during lane-changing. For the traffic system, road capacity obtains the least value when the MPR of IVs is 50%. Moreover, frequent lane-changing movements upstream the off-ramp bottleneck determine the areas at greatest risk. However, when MPR of IVs is over 80% or proportion of exit vehicles is below 15%, both traffic efficiency and safety can be significantly improved. This work provides some insights into the application of machine learning algorithms to traffic flow modeling, and conducts quantitative analysis on the impact of key parameters on traffic systems. Findings of this work can support management and operation of automated highway systems in the future. Full article
(This article belongs to the Special Issue Connected and Automated Vehicles (CAVs): Technologies and Applications)
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13 pages, 2545 KiB  
Article
A Graph-Based Optimal On-Ramp Merging of Connected Vehicles on the Highway
by Yanjun Shi, Zhiheng Yuan, Hao Yu, Yijia Guo and Yuhan Qi
Machines 2021, 9(11), 290; https://doi.org/10.3390/machines9110290 - 16 Nov 2021
Cited by 4 | Viewed by 2232
Abstract
Connected and automated vehicles (CAVs) are a very promising alternative for reducing fuel consumption and improving traffic efficiency when vehicles merge at on-ramps. In this study, we propose a graph-based method to coordinate CAVs to merge at the highway ramp. First, the optimized [...] Read more.
Connected and automated vehicles (CAVs) are a very promising alternative for reducing fuel consumption and improving traffic efficiency when vehicles merge at on-ramps. In this study, we propose a graph-based method to coordinate CAVs to merge at the highway ramp. First, the optimized vehicles were divided into groups to pass the merging point. Then we built a directed graph model for each group of vehicles, where each path of the graph corresponds to one of all possible merging sequences. The improved shortest path algorithm is proposed to find the optimal merging sequence for minimizing total fuel consumption. The results of the simulation showed that the proposed graph-based method reduced fuel consumption and ensured high traffic efficiency; moreover, the vehicles can form a platoon after passing the merge point. Full article
(This article belongs to the Special Issue Connected and Automated Vehicles (CAVs): Technologies and Applications)
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17 pages, 6493 KiB  
Article
A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control
by Yanjun Shi, Yuhan Qi, Lingling Lv and Donglin Liang
Machines 2021, 9(11), 280; https://doi.org/10.3390/machines9110280 - 10 Nov 2021
Cited by 4 | Viewed by 1952
Abstract
Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal [...] Read more.
Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%. Full article
(This article belongs to the Special Issue Connected and Automated Vehicles (CAVs): Technologies and Applications)
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Review

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16 pages, 3741 KiB  
Review
Traffic Scenarios for Automated Vehicle Testing: A Review of Description Languages and Systems
by Jing Ma, Xiaobo Che, Yanqiang Li and Edmund M.-K. Lai
Machines 2021, 9(12), 342; https://doi.org/10.3390/machines9120342 - 08 Dec 2021
Cited by 7 | Viewed by 5331
Abstract
Testing and validation of the functionalities and safety of automated vehicles shifted from a distance-based to a scenario-based method in the past decade. A number of domain-specific languages and systems were developed to support scenario-based testing. The aim of this paper is to [...] Read more.
Testing and validation of the functionalities and safety of automated vehicles shifted from a distance-based to a scenario-based method in the past decade. A number of domain-specific languages and systems were developed to support scenario-based testing. The aim of this paper is to review and compare the features and characteristics of the major scenario description languages and systems (SDLS). Each of them is designed for different purposes and with different goals; therefore, they have their strengths and weaknesses. Their characteristics are highlighted with an example nontrivial traffic scenario that we designed. We also discuss some directions for further development and research of these SDLS. Full article
(This article belongs to the Special Issue Connected and Automated Vehicles (CAVs): Technologies and Applications)
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