Signal Processing and AI Applications for Vehicles, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 662

Special Issue Editors

Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: wireless communications; vehicular communications; signal processing for communications; ITS
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electronic and AI System Engineering, Kangwon National University, Samcheok, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence; CRN; 5G beyond and 6G; wireless networks; wireless security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Financial Information Security, Kookmin University, Seoul 02707, Republic of Korea
Interests: communication network design; intrusion detection; data mining; machine learning; security
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Convergence Science, Kongju National University, Gongju 32588, Republic of Korea
Interests: AI; webometrics; open data; data security; SNS security; SNS analysis; knowledge management; digital convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an era where technological advancements are reshaping the automotive industry, machine learning, and artificial intelligence have emerged as pivotal catalysts for transformation. Integrating these cutting-edge technologies into vehicles can revolutionize how we perceive, interact with, and utilize our automobiles.

Advancements in machine learning and AI have enabled vehicles to become more than just modes of transportation. They are evolving into intelligent systems capable of autonomous navigation, predictive maintenance, adaptive driving, and personalized services. The potential impact of these technologies spans a wide array of domains, including driver-assistance systems, autonomous driving, vehicle-to-everything (V2X) communication, energy optimization, vehicle diagnostics, and connected car ecosystems.

This Special Issue aims to provide a comprehensive platform for researchers, practitioners, and enthusiasts to delve into the diverse realms of machine learning and artificial intelligence within the context of vehicles. Our focus is on exploring the latest breakthroughs, methodologies, and applications that leverage machine learning algorithms, AI models, and data-driven insights to enhance vehicle performance, safety, efficiency, and user experience.

Topics of interest include, but are not limited to, the following:

  • Autonomous vehicle technologies;
  • Advanced driver-assistance systems (ADAS);
  • Predictive maintenance and vehicle health monitoring;
  • Intelligent traffic management and control;
  • Human–machine interfaces for enhanced user experience;
  • Energy-efficient vehicle systems and optimization;
  • Vehicle-to-everything (V2X) communication;
  • Sensor technologies for vehicle perception and control;
  • Data analytics and machine learning for vehicle diagnostics;
  • Cybersecurity and privacy in connected vehicles.

Dr. Woong Cho
Dr. Gyanendra Prasad Joshi
Dr. Eunmok Yang
Dr. Srijana Acharya
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. Electronics is an international peer-reviewed open access semimonthly 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.

Keywords

  • machine learning
  • artificial intelligence
  • deep learning
  • automotive industry
  • intelligent transportation systems
  • autonomous vehicles
  • predictive maintenance
  • driver-assistance systems
  • traffic management
  • vehicle dynamics
  • intelligent control
  • vehicular communication
  • edge computing
  • safety and security
  • energy efficiency
  • fleet management
  • simulation and modeling
  • perception and localization vehicle

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 2957 KB  
Article
DVIOR: Dynamic Vertical and Low-Intensity Outlier Removal for Efficient Snow Noise Removal from LiDAR Point Clouds in Adverse Weather
by Guanqiang Ruan, Fanhao Kong, Chenglin Ding, Kuo Yang, Tao Hu and Rong Yan
Electronics 2025, 14(18), 3662; https://doi.org/10.3390/electronics14183662 - 16 Sep 2025
Viewed by 489
Abstract
With the advancement of autonomous driving technology, the performance of LiDAR in adverse weather conditions has garnered increasing attention. Traditional denoising algorithms, including intensity-based methods like LIOR (a representative intensity-based filter that relies solely on signal intensity), have limited effectiveness in handling snow [...] Read more.
With the advancement of autonomous driving technology, the performance of LiDAR in adverse weather conditions has garnered increasing attention. Traditional denoising algorithms, including intensity-based methods like LIOR (a representative intensity-based filter that relies solely on signal intensity), have limited effectiveness in handling snow noise, especially in removing dynamic noise points and distinguishing them from environmental features. This paper proposes a Dynamic Vertical and Low-Intensity Outlier Removal (DVIOR) algorithm, specifically designed to optimize LiDAR point cloud data under snowy conditions. The DVIOR algorithm, as an extension of intensity-based filtering augmented with vertical height information, dynamically adjusts filter parameters by combining the height and intensity information of the point cloud, effectively filtering out snow noise while preserving environmental features. In our experiments, the DVIOR algorithm was evaluated on several publicly available adverse weather datasets, including the Winter Adverse Driving Scenarios (WADS), the Canadian Adverse Driving Conditions (CADC), and the Radar Dataset for Autonomous Driving in Adverse weather conditions (RADIATE) datasets. Compared with both the mainstream dynamic distance–intensity hybrid algorithm in recent years, Dynamic Distance–Intensity Outlier Removal (DDIOR), and the representative intensity-based filter LIOR, DVIOR achieved notable improvements: it gained a 10.2-point higher F1-score than DDIOR and an 11.8-point higher F1-score than LIOR (79.00) on the WADS dataset. Additionally, DVIOR performed excellently on the CADC and RADIATE datasets, achieving F1-scores of 87.35 and 86.68, respectively—representing an improvement of 19.82 and 36.9 points over DDIOR and 4.67 and 17.95 points over LIOR (82.68 and 68.73). These results demonstrate that the DVIOR algorithm outperforms existing methods, including both distance–intensity hybrid approaches and intensity-based filters like LIOR, in snow noise removal, particularly in complex snowy environments. Full article
(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles, 2nd Edition)
Show Figures

Figure 1

Back to TopTop