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Electronics
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Autonomous Vehicles Technological Trends, Volume II
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Autonomous Vehicles Technological Trends, Volume II

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2688

Special Issue Editors

Dr. Calin Iclodean

E-Mail Website
Guest Editor
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca Romania, 400114 Cluj-Napoca, Romania
Interests: electric vehicles; fuel cell vehicles; powertrain concept; electronic control unit; in-vehicle communication network; energy efficiency; autonomous vehicles; computer modeling and simulation in the automotive field
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bogdan Ovidiu Varga

E-Mail Website
Guest Editor
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400001 Cluj-Napoca, Romania
Interests: electric vehicles; hybrid vehicles; electric vehicle battery, fuel cell vehicles; autonomous vehicles; general powertrain efficiency; OBD diagnostics protocols; powertrain simulation; virtual vehicle testing
Special Issues, Collections and Topics in MDPI journals
Dr. Felix Pfister

E-Mail Website
Guest Editor
IPG Automotive GmbH, Bannwaldallee 60, 76185 Karlsruhe, Germany
Interests: vehicle simulation platforms; ADAS; HIL; ECU control and simulation; vehicle dynamics; virtual vehicle testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The automotive industry has always gone hand in hand with research and innovation; however, currently, the industry is under pressure from the agendas of researchers in the field. Both the hardware and the software exist, and the only question remaining is: “who is going to deliver”? To answer this question, we welcome scientists, researchers, academics, and industry specialists to share their vision of the autonomous vehicle. What will the platform look like? What kind of hardware and software is most suitable? Who will make the link and connection between these two interdependent environments and how? How will AI define the process? All these are the hot themes of the current moment, and following the success of Volume I of this Special Issue, in Volume II we continue to assist all those interested in the topic to promote their vision and ideas. Since the automotive field does not belong to a classic scientific field but has become an independent self-made scientific branch, all those who feel that they can bring their contribution to this highly dynamic environment are requested to join in promoting their particular research or reviews in this Special Issue, which is coordinated from both academia and industry.

Dr. Calin Iclodean
Prof. Dr. Bogdan Ovidiu Varga
Dr. Felix Pfister
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

  • autonomous vehicle (AV)
  • autonomous driving systems (ADS)
  • advanced vehicle control
  • driver assistance systems
  • automotive computing platform
  • adaptive AUTOSAR for ADS
  • autonomous vehicular clouds and edges
  • Internet of Vehicles (IoV)
  • advanced vehicular networks
  • neural networks for ADS
  • V2X communications
  • big data for connected vehicles
  • human–vehicle interface
  • cyber security in autonomous driving
  • 5G/6G applications in autonomous driving
  • smart sensors for AV
  • multi-sensor data fusion
  • multi-sensor data processing

Published Papers (3 papers)

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Research

24 pages, 14353 KiB  
Article
Development of an Integrated Longitudinal Control Algorithm for Autonomous Mobility with EEG-Based Driver Status Classification and Safety Index
by Munjung Jang and Kwangseok Oh
Electronics 2024, 13(7), 1374; https://doi.org/10.3390/electronics13071374 - 05 Apr 2024
Viewed by 480
Abstract
During unexpected driving situations in autonomous vehicles, such as a system failure, the driver should take over control from the vehicles in SAE Level 3 to cope with unexpected situations. Therefore, it is necessary to develop reasonable takeover technologies to ensure safe driving. [...] Read more.
During unexpected driving situations in autonomous vehicles, such as a system failure, the driver should take over control from the vehicles in SAE Level 3 to cope with unexpected situations. Therefore, it is necessary to develop reasonable takeover technologies to ensure safe driving. In this study, an electroencephalogram (EEG)-based driver status classification model and a safety index-based integrated longitudinal control algorithm considering the takeover time and driving characteristics are proposed. The driver status is classified into two states: road monitoring and non-driving-related tasks. EEG data are acquired while the driver performs certain tasks. The driver status classification model is presented using the EEG data based on a machine learning method. It is designed such that the desired takeover time is determined based on the classified driver state. To design the integrated longitudinal control algorithm, a safety index is designed and calculated based on the vehicle state and driver’s driving characteristics. The desired clearances based on the desired takeover time and driver characteristics are calculated and selected based on the safety index. A sliding-mode control algorithm is adopted to allow the vehicle to track the desired clearance reasonably. The performance of the proposed control algorithm is evaluated using the MATLAB/Simulink R2019a (Mathworks, Natick, Massachusetts, U.S.A) and CarMaker software 8.1.1 (IPG Automotive, Karlsruhe, Germany). Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends, Volume II)
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19 pages, 10975 KiB  
Article
The Distributed HTAP Architecture for Real-Time Analysis and Updating of Point Cloud Data
by Juhyun Kim and Changjoo Moon
Electronics 2023, 12(18), 3959; https://doi.org/10.3390/electronics12183959 - 20 Sep 2023
Viewed by 926
Abstract
Updating the most recent set of point cloud data is critical in autonomous driving environments. However, existing systems for point cloud data management often fail to ensure real-time updates or encounter situations in which data cannot be effectively refreshed. To address these challenges, [...] Read more.
Updating the most recent set of point cloud data is critical in autonomous driving environments. However, existing systems for point cloud data management often fail to ensure real-time updates or encounter situations in which data cannot be effectively refreshed. To address these challenges, this study proposes a distributed hybrid transactional/analytical processing architecture designed for the efficient management and real-time processing of point cloud data. The proposed architecture leverages both columnar and row-based tables, enabling it to handle the substantial workloads associated with its hybrid architecture. The construction of this architecture as a distributed database cluster ensures real-time online analytical process query performance through query parallelization. A dissimilarity analysis algorithm for point cloud data, built by utilizing the capabilities of the spatial database, updates the point cloud data for the relevant area whenever the online analytical process query results indicate high dissimilarity. This research contributes to ensuring real-time hybrid transactional/analytical processing workload processing in dynamic road environments, helping autonomous vehicles generate safe, optimized routes. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends, Volume II)
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25 pages, 7089 KiB  
Article
Lane-Level Map Generation and Management Framework Using Connected Car Data
by Jungseok Kim, Jeongmin Moon and Changjoo Moon
Electronics 2023, 12(18), 3738; https://doi.org/10.3390/electronics12183738 - 05 Sep 2023
Viewed by 778
Abstract
This study proposes a lane-level map generation and management framework using connected sensor data to reduce the manpower and time required for producing and updating high-definition (HD) maps. Unlike previous studies that relied on the onboard processing capabilities of vehicles to collect map-constructing [...] Read more.
This study proposes a lane-level map generation and management framework using connected sensor data to reduce the manpower and time required for producing and updating high-definition (HD) maps. Unlike previous studies that relied on the onboard processing capabilities of vehicles to collect map-constructing elements, this study offloads computing for map generation to the cloud, assigning vehicles solely the role of transmitting sensor data. For efficient data collection, we divide the space into a grid format to define it as a partial map and establish the state of each map and its transition conditions. Lastly, tailored to the characteristics of the road elements composing the map, we propose an automated map generation technique and method for selectively collecting data. The map generation method was tested using data collected from actual vehicles. By transmitting images with an average size of 350 KB, implementation was feasible even with the current 5G upload bandwidth. By utilizing 12,545 elements, we were able to achieve a position accuracy and regression RMSE of less than 0.25 m, obtaining 651 map elements to construct the map. We anticipate that this study will help reduce the manpower and time needed for deploying and updating HD maps. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends, Volume II)
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