Special Issue "Emerging Technologies in Future Intelligent Electrified Vehicles"

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

Deadline for manuscript submissions: 30 June 2022 | Viewed by 2899

Special Issue Editors

Prof. Shiho Kim
E-Mail Website
Guest Editor
School of Integrated Technology, College of Engineering, Yonsei University, 85 Songdo-kwahak-ro, Yeonsu-gu, Incheon 406-840, Korea
Interests: development of software and hardware technologies for autonomous vehicles; UAV; AI and reinforcement learning for intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals
Dr. Rakesh Shrestha
E-Mail Website
Guest Editor
Yonsei Institute of Convergence Technology (YICT), Yonsei University, Incheon 406-840, Korea
Interests: wireless communications; intelligent transportation systems; blockchain; IoT; AI; 5G; wireless security
Dr. Rojeena Bajracharya
E-Mail Website
Guest Editor
Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea
Interests: 5GB; 6G wireless communications; LTE-unlicensed; heterogeneous networks; new radio
Dr. Jaekwang Cha
E-Mail Website
Guest Editor
School of Integrated Technology (SIT), Yonsei University, Incheon 406-840, Korea
Interests: vision technology for autonomous vehicles; facial expression recognition (FER); VR/AR interface technology

Special Issue Information

Dear Colleagues,

Recently, the mass deployment of automated, electric, and hybrid electric vehicles (EV) are maturing rapidly. The future intelligent electrified vehicle (FIEV) has developed itself as an important cornerstone of today's intelligent transportation innovations including aerial vehicles. The integration of advanced sensing technology, data mining, machine learning (ML), E/E architecture, and vehicle control technologies allows FIEV to function independently in dynamic and unpredictable real-world environments. For the safety of the passengers, ML techniques and UI/UX systems are required for passenger interaction and user behavior/intention recognition. In addition, it is necessary to detect irregular situations in-cabin and around the vehicles for the safety of the humans and vehicles as well in FIEV. Similarly, efficient E/E architecture and new communication technologies are essential for integrated solutions of FIEV. The next-generation network (NGN) is considered a crucial factor for the success of FIEV by allowing different entities to exchange data in a fast and reliable manner. FIEV would revolutionize the way people experience transportation and mobility, hence making passengers free from conventional travel practices. There will soon be new classes of delivery vehicles like EV shuttle buses, air-taxis, and autonomous delivery vehicles that save passengers time and make travel fun. Moreover, simulation technology such as traffic and scenario-based simulation might be required to validate the operations of FIEV before being deployed in the real world.

The goal of this Special Issue is to attract and publish high-quality peer-reviewed articles in the field of FIEV. The guest editorial team invites researchers and industry experts to submit insightful and revolutionary contributions in the form of research and review articles focusing on, but not limited to, state-of-the-art and emerging trends covering the following potential topics:

Machine Learning and UI/UX in FIEV

  • UI system architecture of intelligent EV
  • Passenger interaction applications
  • AI and deep learning for user intention recognition in intelligent EV
  • Safety & security of intelligent EV UI/UX

Connectivity and big data in FIEV

  • Next-generation wireless communication for FIEV (5G/6G, Wi-Fi, VLC, etc.)
  • In-vehicle connectivity
  • Mobility link between EV shuttle and UAV/PAV
  • Big data analytics in IoV
  • Edge-cloud computing for V2X communication

Irregular Situations in FIEV

  • Irregular situations in in-cabin of FIEV
  • Irregular situations around the vehicles of FIEV
  • Irregular situations in UAVs/PAVs

Electrical/Electronic (E/E) architecture for FIEV

  • E/E architecture for electric and autonomous vehicles
  • E/E architecture for UAVs/PAVs
  • E/E architecture for autonomous underwater vehicles

Simulation Technology for FIEV

  • Traffic based simulation
  • Scenario-based simulation
  • Simulation tools and testbed and for autonomous and EV
  • Simultaneous localization and 3D mapping
  • Simultaneous control localization and mapping (SCLAM)

New Classes of Delivery Vehicles

  • Electric shuttle bus and air taxis
  • Autonomous delivery vehicles
  • Smart mobility

Prof. Shiho Kim
Dr. Rakesh Shrestha
Dr. Rojeena Bajracharya
Dr. Jaekwang Cha
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 2000 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.


  • Next-generation communication technology
  • simulation technology
  • irregular situations
  • UI system architecture
  • E/E architecture

Published Papers (1 paper)

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Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
Electronics 2021, 10(13), 1549; https://doi.org/10.3390/electronics10131549 - 26 Jun 2021
Cited by 5 | Viewed by 1624
The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve [...] Read more.
The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers. Full article
(This article belongs to the Special Issue Emerging Technologies in Future Intelligent Electrified Vehicles)
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