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Environmental Perception, Information Security, and Expected Functional Safety in Intelligent...
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Environmental Perception, Information Security, and Expected Functional Safety in Intelligent Vehicles

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 7057

Special Issue Editor

Dr. Teng Cheng

E-Mail Website
Guest Editor
School of Automotive and Traffic Engineering, Hefei University of Technology, Hefei, China
Interests: environmental perception and information security in intelligent vehicles

Special Issue Information

Dear Colleagues,

At present, communication security technology is an extremely important part of much technical research carried out in the technological field of the Internet of Vehicles, in which data interactions of vehicles, RSUs (road-side units), cloud servers, pedestrians and other traffic participants are the basis. These interactive data always contain important information, such as user privacy and location, thus, communication and data security during the data interaction process are of particular importance. While encrypting user data, we must also guard against potential attacks. In the field of the Internet of Vehicles communication security, research should be carried out in communication system architectures, anonymous identity authentication, information encryption, postquantum cryptography, etc.

This Special Issue aims to deliver contributions towards the realization of the Internet of Vehicles communication security.

Topics include:

  1. Design of the Internet of Vehicles communication system;
  2. Research on anonymous authentication technology for the Internet of Vehicles;
  3. Data transmission efficiency of the Internet of Vehicles;
  4. Communication security model building and security analysis;
  5. Communication data encryption algorithm.

At present, environment perception is a very important part of technological research in the field of autonomous driving. Accurate and real-time target detection is an important function for autonomous vehicles for accurately perceiving the surrounding complex environment. A classic problem of autonomous driving target detection is how to accurately and robustly judge the information of surrounding objects, such as the category, size, distance, position and attitude in harsh and changeable scenes. Therefore, research should be carried out in the fields of 3D target detection, multimodal fusion and depth estimation.

The Special Issue mainly hopes to contribute to improving environmental awareness in autonomous driving.

Topics include:

  1. Object detection;
  2. Target tracking;
  3. Multimodality fusion.

Dr. Teng Cheng
Guest Editor

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

  • communication security
  • privacy protection
  • authentication
  • postquantum cryptography

Published Papers (4 papers)

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Research

16 pages, 4049 KiB  
Article
Research on Cone Bucket Detection Algorithm Based on Improved YOLOv5s
by Jiyue Zhuo, Gang Li and Yang He
World Electr. Veh. J. 2023, 14(10), 269; https://doi.org/10.3390/wevj14100269 - 27 Sep 2023
Viewed by 1089
Abstract
In order to address the problems associated with low detection accuracy, weak detection ability of small targets, insufficiently obvious differentiation of colors, and inability to accurately locate the actual position of the target object in the Formula Student Autonomous China, the YOLOv5s algorithm [...] Read more.
In order to address the problems associated with low detection accuracy, weak detection ability of small targets, insufficiently obvious differentiation of colors, and inability to accurately locate the actual position of the target object in the Formula Student Autonomous China, the YOLOv5s algorithm is improved by adding coordinate attention, modifying the color space transformation module, and adding a normalized Gaussian Wasserstein distance module and a monocular camera distance measurement module. Finally, it is experimentally verified that by adding and modifying the above modules, the YOLOv5s algorithm’s precision is improved by 6.9%, recall by 4.4%, and mean average precision by 4.9%; although the detection frame rate decreases, it still meets the requirement. Monocular camera distance measurement has a maximum error of 5.64% within 20 m in the Z-direction and 5.33% in the X-direction. Full article
(This article belongs to the Special Issue Environmental Perception, Information Security, and Expected Functional Safety in Intelligent Vehicles)
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14 pages, 5164 KiB  
Article
Gradient-Based Metrics for the Evaluation of Image Defogging
by Gerard deMas-Giménez, Pablo García-Gómez, Josep R. Casas and Santiago Royo
World Electr. Veh. J. 2023, 14(9), 254; https://doi.org/10.3390/wevj14090254 - 09 Sep 2023
Viewed by 1223
Abstract
Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as [...] Read more.
Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository. Full article
(This article belongs to the Special Issue Environmental Perception, Information Security, and Expected Functional Safety in Intelligent Vehicles)
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14 pages, 7651 KiB  
Article
Research on a Lightweight Panoramic Perception Algorithm for Electric Autonomous Mini-Buses
by Yulin Liu, Gang Li, Liguo Hao, Qiang Yang and Dong Zhang
World Electr. Veh. J. 2023, 14(7), 179; https://doi.org/10.3390/wevj14070179 - 08 Jul 2023
Cited by 1 | Viewed by 1089
Abstract
Autonomous mini-buses are low-cost passenger vehicles that travel along designated routes in industrial parks. In order to achieve this goal, it is necessary to implement functionalities such as lane-keeping and obstacle avoidance. To address the challenge of deploying deep learning algorithms to detect [...] Read more.
Autonomous mini-buses are low-cost passenger vehicles that travel along designated routes in industrial parks. In order to achieve this goal, it is necessary to implement functionalities such as lane-keeping and obstacle avoidance. To address the challenge of deploying deep learning algorithms to detect environmental information on low-performance computing units, which leads to difficulties in model deployment and the inability to meet real-time requirements, a lightweight algorithm called YOLOP-E based on the YOLOP algorithm is proposed. (The letter ‘E’ stands for EfficientNetV2, and YOLOP-E represents the optimization of the entire algorithm by replacing the backbone of the original model with EfficientNetV2.) The algorithm has been optimized and improved in terms of the following three aspects: Firstly, the YOLOP backbone network is reconstructed using the lightweight backbone network EfficientNet-V2, and depth-wise separable convolutions are used instead of regular convolutions. Secondly, a hybrid attention mechanism called CABM is employed to enhance the model’s feature-representation capability. Finally, the Focal EIoU and Smoothed Cross-Entropy loss functions are utilized to improve detection accuracy. YOLOP-E is the final result after the aforementioned optimizations are completed. Experimental results demonstrate that on the BDD100K dataset, the optimized algorithm achieves a 3.5% increase in mAP50 and a 4.1% increase in mIoU. During real-world vehicle testing, the detection rate reaches 41.6 FPS, achieving the visual perception requirements of the autonomous shuttle bus while maintaining a lightweight design and improving detection accuracy. Full article
(This article belongs to the Special Issue Environmental Perception, Information Security, and Expected Functional Safety in Intelligent Vehicles)
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14 pages, 374 KiB  
Article
Perceptions of Autonomous Vehicles: A Case Study of Jordan
by Dana Abudayyeh, Malek Almomani, Omar Almomani, Hadeel Alsoud and Farah Alsalman
World Electr. Veh. J. 2023, 14(5), 133; https://doi.org/10.3390/wevj14050133 - 22 May 2023
Cited by 4 | Viewed by 3085
Abstract
Technologies for automated driving have advanced rapidly in recent years. Autonomous Vehicles (AVs) are one example of these recent technologies that deploy elements such as sensors or processing units to assist the driver. The effective integration of these vehicles into public roads depends [...] Read more.
Technologies for automated driving have advanced rapidly in recent years. Autonomous Vehicles (AVs) are one example of these recent technologies that deploy elements such as sensors or processing units to assist the driver. The effective integration of these vehicles into public roads depends on the drivers’ acceptance and how they adjust to this new generation of vehicles. This study investigated the acceptance and willingness of Jordanians to purchase AVs in Jordan. The ordinal logit model was deployed to determine the factors attributed to individual acceptance of AVs, such as the cost, security, privacy, along with the environmental impact, among others. The findings of a national survey conducted on 582 Jordanians to assess their perception about AVs revealed that Jordanians were generally interested in using AVs. However, their decisions about purchasing AVs are influenced by several factors. The results indicated that the cost of AVs greatly influences purchasing decisions, though if the cost is affordable, respondents were more interested in using AVs. The findings also revealed that there is a substantial relationship between the level of security and the likelihood of buying a self-driving car, as respondents are concerned about the level of security and privacy. Furthermore, the results revealed that environmentally friendly AVs are more likely to be owned compared to conventional vehicles. This study helps to enhance the current understanding by highlighting road user perceptions, with practical implications for practitioners. Full article
(This article belongs to the Special Issue Environmental Perception, Information Security, and Expected Functional Safety in Intelligent Vehicles)
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