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Electronics
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Advancement on Smart Vehicles and Smart Travel
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Advancement on Smart Vehicles and Smart Travel

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 4172

Special Issue Editors

Dr. Marco Del-Coco

E-Mail Website
Guest Editor
National Research Council (CNR), Institute of Applied Science and Intelligent Systems, Lecce 73100, Italy
Interests: neural network; image processing; graph neural network
Dr. Sergio Trilles Oliver

E-Mail Website
Guest Editor
Institute of New Imaging Technologies (INIT), Universitat Jaume I, Av. Vicente Sos Baynat s/n, 12071 Castelló de la Plana, Spain
Interests: Internet of Things; sensor web; interoperability; GIS; computer science
Special Issues, Collections and Topics in MDPI journals
Dr. Pierluigi Carcagni

E-Mail Website
Guest Editor
National Research Council (CNR), Institute of Applied Science and Intelligent Systems, Lecce 73100, Italy
Interests: neural network; image processing; medical imaging
Dr. Ditsuhi Iskandaryan

E-Mail Website
Guest Editor
Oncology Data Analytics Program, Catalan Institute of Oncology (ICO) & Colorectal Cancer Group, ONCOBELL Program, Institut de Recerca Biomedica de Bellvitge (IDIBELL), Avinguda de la Gran via de l’Hospitalet, 199, 08908 Barcelona, Spain
Interests: machine learning; data mining; geoinformatics (GIS); air quality prediction, spatio-temporal analysis
Dr. Xin Wang

E-Mail Website
Guest Editor
University at Albany, State University of New York, Buffalo, New York, NY 14260, USA
Interests: artificial intelligence; artificial intelligence generated content (AIGC); deep learning; precision medicine

Special Issue Information

Dear Colleagues,

In the last 100 years, the attitude of people towards travelling has changed dramatically. The distances that can be covered in a given time has increased exponentially, making trips previously requiring days, or even months, affordable in a few hours.

Vehicle ownership has become affordable for everyone, leading to an increase in their density, especially in urban areas. Moreover, long-range travel has become affordable to a wider range of people, making air, train, and car traffic denser. Even e-commerce, which changed the buying paradigm, requires an upward effort in delivering systems. Last but not least, autonomous driving is becoming a reality, introducing additional issues to transportation networks.

Such changes have raised a set of new problems including increasing environmental pollution, managing of traffic, street security and surveillance and the debate on autonomous driving reliability.

On the other hand, the recent astonishing development of electronics has made available high-resolution sensors and impressive computing capabilities that have enabled the use of high-performance analysis, prediction and control techniques.

In this complex setting, research is needed to provide answers and solutions exploiting frontier technology, both in terms of hardware and algorithms, in order to mitigate and, where possible, solve the above-mentioned issues.

This Special Issue has the scope to collect high-profile original research articles and reviews dealing with all the discussed points. 

More precisely, areas may include (but are not limited to) the following:

  • Vehicle detection, classification and tracking;
  • Pedestrian detection and tracking;
  • Vehicle/pedestrian behavior;
  • Traffic jam detection;
  • Car/pedestrian accidents;
  • Activity monitoring Systems;
  • Scene understanding;
  • Environment city/street monitoring;
  • Visual attention and visual saliency;
  • Matching vehicles across cameras;
  • Smart environments;
  • Safety and security ;
  • Technology for cognition;
  • Navigation systems;
  • Sensory substitution;
  • Datasets and evaluation procedures;
  • Systems and control engineering for traffic monitoring;
  • Ethics in autonomous driving;
  • Graph-based environmental analysis.

We look forward to receiving your contributions.

Dr. Marco Del-Coco
Dr. Sergio Trilles Oliver
Dr. Pierluigi Carcagni
Dr. Ditsuhi Ditsuhi Iskandaryan
Dr. Xin Wang
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

  • artificial intelligence
  • machine learning
  • video analytics
  • data mining
  • smart environments
  • Internet of Things
  • geoinformatics (GIS)
  • spatio-temporal analysis
  • air quality prediction

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Published Papers (4 papers)

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Research

20 pages, 995 KiB  
Article
Container-Based Electronic Control Unit Virtualisation: A Paradigm Shift Towards a Centralised Automotive E/E Architecture
by Nicholas Ayres, Lipika Deka and Daniel Paluszczyszyn
Electronics 2024, 13(21), 4283; https://doi.org/10.3390/electronics13214283 - 31 Oct 2024
Viewed by 785
Abstract
The past 40 years have seen automotive Electronic Control Units (ECUs) move from being purely mechanical controlled to being primarily digital controlled. While the safety of passengers and efficiency of vehicles has seen significant improvements, rising ECU numbers have resulted in increased vehicle [...] Read more.
The past 40 years have seen automotive Electronic Control Units (ECUs) move from being purely mechanical controlled to being primarily digital controlled. While the safety of passengers and efficiency of vehicles has seen significant improvements, rising ECU numbers have resulted in increased vehicle weight, greater demands placed on power, more complex hardware and software, ad hoc methods for updating software, and subsequent increases in costs for both vehicle manufacturers and consumers. To address these issues, the research presented in this paper proposes that virtualisation technologies be applied within automotive electrical/electronic (E/E) architecture. The proposed approach is evaluated by comprehensively studying the CPU and memory resource requirements to support container-based ECU automotive functions. This comprehensive performance evaluation reveals that lightweight container virtualisation has the potential to welcome a paradigm shift in E/E architecture, promoting consolidation and enhancing the architecture by facilitating power, weight, and cost savings. Container-based virtualisation will also enable efficient and robust online dynamic software updates throughout a vehicle’s lifetime. Full article
(This article belongs to the Special Issue Advancement on Smart Vehicles and Smart Travel)
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18 pages, 8219 KiB  
Article
Evolution of the “4-D Approach” to Dynamic Vision for Vehicles
by Ernst Dieter Dickmanns
Electronics 2024, 13(20), 4133; https://doi.org/10.3390/electronics13204133 - 21 Oct 2024
Viewed by 555
Abstract
Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its [...] Read more.
Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on. Full article
(This article belongs to the Special Issue Advancement on Smart Vehicles and Smart Travel)
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22 pages, 97889 KiB  
Article
Processing and Integration of Multimodal Image Data Supporting the Detection of Behaviors Related to Reduced Concentration Level of Motor Vehicle Users
by Anton Smoliński, Paweł Forczmański and Adam Nowosielski
Electronics 2024, 13(13), 2457; https://doi.org/10.3390/electronics13132457 - 23 Jun 2024
Cited by 1 | Viewed by 815
Abstract
This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify [...] Read more.
This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness and distraction signs. Our novel contribution includes utilizing state-of-the-art convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks for effective feature extraction and classification across diverse distraction scenarios. Additionally, we explore various data fusion techniques, demonstrating their impact on improving detection accuracy. The significance of this work lies in its potential to enhance road safety by providing more reliable and efficient tools for the real-time monitoring of driver attentiveness, thereby reducing the risk of accidents caused by distraction and fatigue. The proposed methods are thoroughly evaluated using a multimodal benchmark dataset, with results showing their substantial capabilities leading to the development of safety-enhancing technologies for vehicular environments. The primary challenge addressed in this study is the detection of driver states not relying on the lighting conditions. Our solution employs multimodal data integration, encompassing RGB, thermal, and depth images, to ensure robust and accurate monitoring regardless of external lighting variations Full article
(This article belongs to the Special Issue Advancement on Smart Vehicles and Smart Travel)
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22 pages, 11584 KiB  
Article
V2G Carbon Accounting and Revenue Allocation: Balancing EV Contributions in Distribution Systems
by Bingxuan Yu, Xiang Lei, Ziyun Shao and Linni Jian
Electronics 2024, 13(6), 1063; https://doi.org/10.3390/electronics13061063 - 13 Mar 2024
Cited by 1 | Viewed by 996
Abstract
Accurate carbon emission accounting for electric vehicles (EVs) is particularly important, especially for those participating in the carbon market. However, the participation of numerous EVs in vehicle-to-grid (V2G) scheduling complicates the precise accounting of individual EV emissions. This paper presents a novel approach [...] Read more.
Accurate carbon emission accounting for electric vehicles (EVs) is particularly important, especially for those participating in the carbon market. However, the participation of numerous EVs in vehicle-to-grid (V2G) scheduling complicates the precise accounting of individual EV emissions. This paper presents a novel approach to carbon accounting and benefits distribution for EVs. It includes a low-carbon dispatch model for a distribution system (DS), aimed at reducing total emissions through strategic EV charging scheduling. Further, an improved carbon emission flow accounting model is proposed to calculate the carbon reduction of EVs before and after low-carbon dispatch. It enables real-time carbon flow tracking during EV charging and discharging, then accurately quantifies the carbon reduction amount. Additionally, it employs the Shapley value method to ensure equitable distribution of carbon revenue, balancing low-carbon operation costs and carbon reduction contributions. A case study based on a 31-node campus distribution network demonstrated that effective scheduling of 1296 EVs can significantly reduce system carbon emissions. This method can accurately account for the carbon emissions of EVs under different charging states, and provides a balanced analysis of EV carbon reduction contributions and costs, advocating for fair revenue allocation. Full article
(This article belongs to the Special Issue Advancement on Smart Vehicles and Smart Travel)
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