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Vehicle-Road Collaboration and Connected Automated Driving
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Vehicle-Road Collaboration and Connected Automated Driving

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 1 December 2024 | Viewed by 8610

Special Issue Editors

Prof. Dr. Wuhong Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: intelligent connected vehicle and automated driving; green mobility behaviour and traffic safety; human factor and man–machine interaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lisheng Jin

E-Mail Website
Guest Editor
School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China
Interests: intelligent vehicle perception, decision and control; human–vehicle–road collaboration and vehicle networking technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Connected automated vehicle (CAV) technologies have the potential to change transportation on a global scale. These technologies may work at various automation levels of the vehicle and at the infrastructure level or both, which will improve safety, significantly alter transportation costs, and change traffic congestion. Thus, connected automated driving means that the vehicle senses the surrounding environment by using onboard sensors, makes control decisions based on the information collected and fused, and makes vehicle control decisions, including both longitudinal control and lateral control. Since CAV communicates and shares different levels of information among vehicles between vehicles and pedestrians and between vehicles and infrastructures according to different connectivities, it enables different levels of vehicle automation and accounts for different levels of integration among vehicles and infrastructure.

The objective of this Special Issue is to propose a systematic vision of vehicle-road collaboration and connected automated driving. The topics of interest include but are not limited to the following:

  • Human–vehicle interface;
  • Vehicle-road collaboration;
  • Design and operation of CAVs for safety and efficiency;
  • Smart road infrastructure;
  • Carbon neutrality for green transportations demand;
  • I2X and V2X Communication Technology;
  • Modelling and simulation of CAVs operations;
  • Emission control and energy saving for green mobility behaviour;
  • Coordinated control for smart transportation systems.

Prof. Dr. Wuhong Wang
Prof. Dr. Lisheng Jin
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. World Electric Vehicle Journal 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 1400 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

  • connected automated vehicle
  • human vehicle interface
  • vehicle-road collaboration
  • green mobility behaviour and safety

Published Papers (5 papers)

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Research

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14 pages, 2976 KiB  
Article
Off-Road Environment Semantic Segmentation for Autonomous Vehicles Based on Multi-Scale Feature Fusion
by Xiaojing Zhou, Yunjia Feng, Xu Li, Zijian Zhu and Yanzhong Hu
World Electr. Veh. J. 2023, 14(10), 291; https://doi.org/10.3390/wevj14100291 - 13 Oct 2023
Viewed by 2089
Abstract
For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. [...] Read more.
For autonomous vehicles driving in off-road environments, it is crucial to have a sensitive environmental perception ability. However, semantic segmentation in complex scenes remains a challenging task. Most current methods for off-road environments often have the problems of single scene and low accuracy. Therefore, this paper proposes a semantic segmentation network based on LiDAR called Multi-scale Augmentation Point-Cylinder Network (MAPC-Net). The network uses a multi-layer receptive field fusion module to extract features from objects of different scales in off-road environments. Gated feature fusion is used to fuse PointTensor and Cylinder for encoding and decoding. In addition, we use CARLA to build off-road environments for obtaining datasets, and employ linear interpolation to enhance the training data to solve the problem of sample imbalance. Finally, we design experiments to verify the excellent semantic segmentation ability of MAPC-Net in an off-road environment. We also demonstrate the effectiveness of the multi-layer receptive field fusion module and data augmentation. Full article
(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)
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19 pages, 1093 KiB  
Article
Toward Synthetic Data Generation to Enhance Skidding Detection in Winter Conditions
by Bryan McKenzie, Sousso Kelouwani and Marc-André Gaudreau
World Electr. Veh. J. 2022, 13(12), 231; https://doi.org/10.3390/wevj13120231 - 2 Dec 2022
Viewed by 1386
Abstract
In this paper, we propose the use of a neural network to identify lateral skidding events of road vehicles used during winter driving conditions. Firstly, data from a simulation model was used to identify the essential vehicle dynamics variables needed and to create [...] Read more.
In this paper, we propose the use of a neural network to identify lateral skidding events of road vehicles used during winter driving conditions. Firstly, data from a simulation model was used to identify the essential vehicle dynamics variables needed and to create the network structure. Then this network was retrained to classify real-world vehicle skidding events. The final network consists of a 3 layer network with 10, 5 and 1 output neurons 13 inputs, 4 outputs and a 5 step time delay. The retrained network was used on a limited set of real vehicle data and confirmed the effectiveness of the network classifying lateral skidding events. Full article
(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)
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15 pages, 5111 KiB  
Article
Research on Short-Term Driver Following Habits Based on GA-BP Neural Network
by Cheng Wu, Bo Li, Shaoyi Bei, Yunhai Zhu, Jing Tian, Hongzhen Hu and Haoran Tang
World Electr. Veh. J. 2022, 13(9), 171; https://doi.org/10.3390/wevj13090171 - 14 Sep 2022
Cited by 4 | Viewed by 1500
Abstract
The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged [...] Read more.
The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged for a short time, five important parameters affecting the following scene are selected through correlation analysis, and vehicle-following research is carried out. This paper adopts a driver-following model based on a Genetic Algorithm (GA)-optimized Back Propagation (BP) neural network. Based on the data of next-generation simulation (ngsim), this paper selects vehicle 32 (32 represents the ID of the vehicle in the ngsim project) as the main vehicle in order to study short-term driving habits. A BP neural network is built using MATLAB; 60% of the data of vehicles 32 and 29 is used for the training set, 20% is used for the verification set, and 20% for the test set. Because short-term prediction requires high timeliness, the genetic algorithm is used to optimize the initial weights of the neural network, which not only accelerates the convergence speed but also plays a role in avoiding the local optimal solution. The experimental results show that compared with the traditional stimulus-response vehicle-following model, this model has a following ability that is more in line with the driver’s driving habits in terms of ensuring following safety. Full article
(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)
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15 pages, 7493 KiB  
Article
Research on Automatic Driving Path Tracking Control of Open-Pit Mine Transportation Vehicles with Delay Compensation
by Zhiyong Lei, Xiaolong Ma, Xiwen Yuan and Chuan He
World Electr. Veh. J. 2022, 13(8), 134; https://doi.org/10.3390/wevj13080134 - 26 Jul 2022
Viewed by 1855
Abstract
The transportation environment of the open-pit mine is complex, the steering actuator of the mine vehicle has a large delay and poor response accuracy, and there are a lot of bumpy roads, large undulating ramps, and narrow-area curves in the mining area. These [...] Read more.
The transportation environment of the open-pit mine is complex, the steering actuator of the mine vehicle has a large delay and poor response accuracy, and there are a lot of bumpy roads, large undulating ramps, and narrow-area curves in the mining area. These road sections seriously reduce the tracking accuracy of the mine vehicle path. Tracking control presents great challenges. Therefore, this study first conducts a simulation comparison study on commonly used path tracking methods such as pure pursuit control, Stanley control, and model predictive control (MPC), and then designs a path tracking control strategy for automatic driving of open-pit mine transportation vehicles based on the MPC algorithm. Finally, the proposed control strategy was verified through actual mining vehicle tests. The results showed that the maximum lateral deviation obtained by the MPC-based path tracking control strategy was reduced from 0.55 m to 0.08 m under the C-shaped reference path compared with the traditional method. Under the S-shaped reference path, the lateral deviation is reduced from 0.4 m to 0.16 m. Full article
(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)
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20 pages, 4152 KiB  
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Path-Following Control of Unmanned Vehicles Based on Optimal Preview Time Model Predictive Control
by Xinyu Wang, Xiao Ye, Yipeng Zhou and Cong Li
World Electr. Veh. J. 2024, 15(6), 221; https://doi.org/10.3390/wevj15060221 - 21 May 2024
Viewed by 513
Abstract
In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the [...] Read more.
In order to reduce the lateral error of path-following control of unmanned vehicles under variable curvature paths, we propose a path-following control strategy for unmanned vehicles based on optimal preview time model predictive control (OP-MPC). The strategy includes the longitudinal speed limit, the optimal preview time surface, and the model predictive control (MPC)controller. The longitudinal speed limit controls speed to prevent vehicle rollover and sideslip. The optimal preview time surface adjusts the preview time according to the vehicle speed and path curvature. The preview point determined by the preview time is used as the reference waypoint of OP-MPC controller. Finally, the effectiveness of the strategy was verified through simulation and with the real unmanned vehicle. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.522 m to 0.145 m under the simulation compared with an MPC controller. The maximum lateral deviation obtained by the OP-MPC controller was reduced from 0.5185 m to 0.2298 m under the real unmanned vehicle compared with the MPC controller. Full article
(This article belongs to the Special Issue Vehicle-Road Collaboration and Connected Automated Driving)
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