Signal Processing and AI Applications for Vehicles

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 2906

Special Issue Editors


E-Mail Website
Guest Editor
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok 25913, Republic of Korea
Interests: wireless communications; vehicular communications; signal processing for communications; ITS

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, 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
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. The focus is 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 (ADASs).
  • 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.

Prof. 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 vehicles

Published Papers (3 papers)

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

Research

43 pages, 32187 KiB  
Article
An Integrated DQN and RF Packet Routing Framework for the V2X Network
by Chin-En Yen, Yu-Siang Jhang, Yu-Hsuan Hsieh, Yu-Cheng Chen, Chunghui Kuo and Ing-Chau Chang
Electronics 2024, 13(11), 2099; https://doi.org/10.3390/electronics13112099 - 28 May 2024
Viewed by 390
Abstract
With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic [...] Read more.
With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively. Full article
(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles)
Show Figures

Figure 1

18 pages, 3968 KiB  
Article
EEG_DMNet: A Deep Multi-Scale Convolutional Neural Network for Electroencephalography-Based Driver Drowsiness Detection
by Hanan Bin Obaidan, Muhammad Hussain and Reham AlMajed
Electronics 2024, 13(11), 2084; https://doi.org/10.3390/electronics13112084 - 27 May 2024
Viewed by 282
Abstract
Drowsy driving is one of the major causes of traffic accidents, injuries, and deaths on roads worldwide. One of the best physiological signals that are useful in detecting a driver’s drowsiness is electroencephalography (EEG), a kind of brain signal that directly measures neurophysiological [...] Read more.
Drowsy driving is one of the major causes of traffic accidents, injuries, and deaths on roads worldwide. One of the best physiological signals that are useful in detecting a driver’s drowsiness is electroencephalography (EEG), a kind of brain signal that directly measures neurophysiological activities in the brain and is widely utilized for brain–computer interfaces (BCIs). However, designing a drowsiness detection method using EEG signals is still challenging because of their non-stationary nature. Deep learning, specifically convolutional neural networks (CNNs), has recently shown promising results in driver’s drowsiness. However, state-of-the-art CNN-based methods extract features sequentially and discard multi-scale spectral-temporal features, which are important in tackling the non-stationarity of EEG signals. This paper proposes a deep multi-scale convolutional neural network (EEG_DMNet) for driver’s drowsiness detection that learns spectral-temporal features. It consists of two main modules. First, the multi-scale spectral-temporal features are extracted from EEG trials using 1D temporal convolutions. Second, the spatial feature representation module calculates spatial patterns from the extracted multi-scale features using 1D spatial convolutions. The experimental results on the public domain benchmark SEED-VIG EEG dataset showed that it learns discriminative features, resulting in an average accuracy of 97.03%, outperforming the state-of-the-art methods that used the same dataset. The findings demonstrate that the proposed method effectively and efficiently detects drivers’ drowsiness based on EEG and can be helpful for safe driving. Full article
(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles)
Show Figures

Figure 1

18 pages, 5839 KiB  
Article
Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems
by Eunmok Yang and Okyeon Yi
Electronics 2024, 13(4), 708; https://doi.org/10.3390/electronics13040708 - 9 Feb 2024
Viewed by 1664
Abstract
Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures [...] Read more.
Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s. Full article
(This article belongs to the Special Issue Signal Processing and AI Applications for Vehicles)
Show Figures

Figure 1

Back to TopTop