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WEVJ
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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: 30 June 2024 | Viewed by 2229

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

Published Papers (1 paper)

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Research

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 1 | Viewed by 1941
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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