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Electric Vehicle Autonomous Driving Based on Image Recognition
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Electric Vehicle Autonomous Driving Based on Image Recognition

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 7451

Special Issue Editors

Dr. Yuan-Wei Tseng

E-Mail Website
Guest Editor
Department of Electrical Engineering, I-Shou University, Kaohsiung City 840, Taiwan
Interests: electric vehicles; sliding mode control; optimal control; nonlinear control; variable structure control; computer vision; embedded system; feedback control
Prof. Dr. En-Chih Chang

E-Mail Website1 Website2
Guest Editor
Department of Electrical Engineering, I-Shou University, Kaohsiung City 84001, Taiwan
Interests: sliding mode control; intelligent control; grey theory; power electronic converters
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chun-An Cheng

E-Mail Website
Guest Editor
Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
Interests: power electronics; power conversion; electronic lighting; energy-saving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the dynamic interaction between electric vehicle (EV) autonomous driving and image recognition by exploring cutting-edge advancements in this rapidly evolving field. This collection of potential articles delves into the application of image recognition techniques to enhance the perception and decision-making capabilities of autonomous electric vehicles. From leveraging artificial intelligent object detection and lane tracking to the real-time recognition of traffic signs and pedestrians, these potential contributions illuminate the pivotal role of computer vision in creating safe and efficient EV autonomous systems. This Special Issue serves as a platform for researchers, engineers, and practitioners to share innovative methodologies, case studies, and insights, fostering the development of intelligent, sustainable, and future-ready autonomous electric transportation.

Dr. Yuan-Wei Tseng
Prof. Dr. En-Chih Chang
Prof. Dr. Chun-An Cheng
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

  • electric vehicle (EV)
  • autonomous driving
  • Semantic segmentation
  • intelligent driving
  • image recognition
  • computer vision
  • convolutional neural networks (CNNs)
  • deep learning
  • neural network architectures
  • machine learning
  • artificial intelligence
  • lane detection
  • pedestrian detection
  • object detection
  • traffic sign recognition
  • sensor fusion
  • LiDAR camera fusion
  • environmental perception
  • real-time processing and decision making
  • vehicle localization
  • path planning
  • safety and regulation
  • human–machine interaction
  • simulation and testing
  • automotive lighting applications
  • automotive inverter applications

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Published Papers (4 papers)

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Research

21 pages, 5549 KiB  
Article
YOLO-ADual: A Lightweight Traffic Sign Detection Model for a Mobile Driving System
by Simin Fang, Chengming Chen, Zhijian Li, Meng Zhou and Renjie Wei
World Electr. Veh. J. 2024, 15(7), 323; https://doi.org/10.3390/wevj15070323 - 21 Jul 2024
Viewed by 1222
Abstract
Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop [...] Read more.
Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy. Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
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19 pages, 2999 KiB  
Article
Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4
by Ahmed Gomaa and Ahmad Abdalrazik
World Electr. Veh. J. 2024, 15(6), 255; https://doi.org/10.3390/wevj15060255 - 12 Jun 2024
Cited by 4 | Viewed by 1222
Abstract
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with [...] Read more.
Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets. Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
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19 pages, 11234 KiB  
Article
A Study on the Performance Improvement of a Conical Bucket Detection Algorithm Based on YOLOv8s
by Xu Li, Gang Li and Zhe Zhang
World Electr. Veh. J. 2024, 15(6), 238; https://doi.org/10.3390/wevj15060238 - 29 May 2024
Cited by 1 | Viewed by 737
Abstract
In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order [...] Read more.
In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order to cope with these problems, the thesis is based on YOLOv8s to improve the cone bucket detection algorithm. Firstly, a P2 detection layer for detecting tiny objects is added on top of YOLOv8s to detect small targets with 160 × 160 pixels, which improves the detection of small conical buckets in the distant view. At the same time, to reduce the network’s complexity to achieve lightweightness, the original 20 × 20 pixel detection header is deleted. Second, the head of the original YOLOv8 is replaced with a multi-scale fusion Dynamic Head, designed to improve the head’s ability in scale, space, and task perception to enhance the detection performance of the model in complex scenes. Again, a novel loss function, MPDIoU, is introduced, which has advantages in simplifying the bounding box similarity comparison, and it can adapt to the overlapping or non-overlapping situation of the bounding box more effectively. It reduces the phenomenon of missed detection caused by overlapping conical buckets. Finally, the LAMP pruning method is used to trim the model to make the model lightweight. By adding and modifying the above modules, the improved algorithm improves the detection accuracy from 92.2% to 95.2%, the recall rate from 84.2% to 91.8%, and the average accuracy from 91.3% to 96%, while the number of parameters is reduced from 28.7 M to 26.6 M. The detection speed still meets the real-time requirement in real-vehicle testing compared to the original algorithm. In the real car test, compared with the original algorithm, the improved algorithm shows apparent advantages in reducing the missed detection of cones and barrels, which meets the demand for high accuracy of cones and barrel detection in the complex race environment and also meets the conditions for deployment on small devices with limited resources. Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
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13 pages, 4468 KiB  
Article
TF-YOLO: A Transformer–Fusion-Based YOLO Detector for Multimodal Pedestrian Detection in Autonomous Driving Scenes
by Yunfan Chen, Jinxing Ye and Xiangkui Wan
World Electr. Veh. J. 2023, 14(12), 352; https://doi.org/10.3390/wevj14120352 - 18 Dec 2023
Cited by 2 | Viewed by 3516
Abstract
Recent research demonstrates that the fusion of multimodal images can improve the performance of pedestrian detectors under low-illumination environments. However, existing multimodal pedestrian detectors cannot adapt to the variability of environmental illumination. When the lighting conditions of the application environment do not match [...] Read more.
Recent research demonstrates that the fusion of multimodal images can improve the performance of pedestrian detectors under low-illumination environments. However, existing multimodal pedestrian detectors cannot adapt to the variability of environmental illumination. When the lighting conditions of the application environment do not match the experimental data illumination conditions, the detection performance is likely to be stuck significantly. To resolve this problem, we propose a novel transformer–fusion-based YOLO detector to detect pedestrians under various illumination environments, such as nighttime, smog, and heavy rain. Specifically, we develop a novel transformer–fusion module embedded in a two-stream backbone network to robustly integrate the latent interactions between multimodal images (visible and infrared images). This enables the multimodal pedestrian detector to adapt to changing illumination conditions. Experimental results on two well-known datasets demonstrate that the proposed approach exhibits superior performance. The proposed TF-YOLO drastically improves the average precision of the state-of-the-art approach by 3.3% and reduces the miss rate of the state-of-the-art approach by about 6% on the challenging multi-scenario multi-modality dataset. Full article
(This article belongs to the Special Issue Electric Vehicle Autonomous Driving Based on Image Recognition)
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