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Electronics
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Emerging Technologies in Autonomous Vehicles
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Emerging Technologies in Autonomous Vehicles

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 2390

Special Issue Editors

Dr. George Dimitrakopoulos

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 17676 Kallithea, Greece
Interests: intelligent transportation systems; wireless communications; cognitive networks; autonomous systems
Special Issues, Collections and Topics in MDPI journals
Dr. Morten Larsen

E-Mail Website
Guest Editor
AnyWi Technologies BV, 2311 NR Leiden, The Netherlands
Interests: robustness and reliability through functional redundancy; human factors

Special Issue Information

Dear Colleagues,

The concept of creating machines (systems) that can work autonomously dates back to classical times. Today, autonomous systems are a rapidly growing branch of engineering that involves the conception, design, and operation of systems that overlap with principles of computer science, electronics, nanotechnology, Artificial Intelligence (AI), and bioengineering. The current maturity of autonomous systems is allowing them to leave the research lab and come to the consumer market in large numbers. This is especially valid in the Fifth Industrial Revolution era, the hallmark of which is bringing human cognition and AI-enabled autonomous systems together, working collaboratively to the benefit of both.

In the particular area of transport, Autonomous Transport Systems (ATS) include the full range of transport vehicles, including trucks, buses, trains, metros, ships, drones, helicopters, and airplanes. Automation in this area has the potential to make land, air, and marine operations safer, faster, more efficient, and greener, contributing to both the adapted Vision Zero, including work-related deaths as a new central element, and to the European Green Deal initiative. The European Green Deal defines four key elements for a sustainable mobility and automotive industry, namely: climate neutrality, a zero-pollution Europe, sustainable transport, and the transition to a circular economy.

The greatest challenge for the success of ATS is safety and the resulting trust in autonomy, which in turn represents the prominent driving force to reach a high user acceptance. Without addressing these issues across the entire Electronic Components and Systems (ECS) value chain, we will not be able to fully use the technological and commercial potentials of autonomous systems in general.

Aligned with the above, the goal of this Special Issue is to present key findings and challenges related to ECS and architectures for future mass market ATS.

Key technical topics can include (but are not limited to):

  • Fusion and perception of digital platforms for efficient and federated computing;
  • Efficient propulsion and energy modules;
  • Advanced connectivity for cooperative mobility applications;
  • Vehicle/edge/cloud computing integration concepts;
  • Intelligent components based on trustworthy AI techniques and methods;
  • Perception enhanced with Operational Design Domains (ODD) attributes;
  • ODD-aware decision making and planning for cross-domain applications;
  • Monitoring and control solutions for Electric, Connected, Autonomous and Shared (ECAS) vehicles;
  • ATS user acceptance.

Dr. George Dimitrakopoulos
Dr. Morten Larsen
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 transport systems
  • highly automated vehicles
  • electronic components and systems
  • ODD
  • AI
  • acceptance

Published Papers (2 papers)

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Research

25 pages, 3242 KiB  
Article
GPU-Accelerated Interaction-Aware Motion Prediction
by Juan Luis Hortelano, Vinicius Trentin, Antonio Artuñedo and Jorge Villagra
Electronics 2023, 12(18), 3751; https://doi.org/10.3390/electronics12183751 - 05 Sep 2023
Cited by 1 | Viewed by 908
Abstract
Before their massive deployment, autonomous vehicles need to prove in complex scenarios such that they can reach human driving proficiency while guaranteeing higher safety levels. One of the most important human traits to negotiating traffic is the ability to predict the future behavior [...] Read more.
Before their massive deployment, autonomous vehicles need to prove in complex scenarios such that they can reach human driving proficiency while guaranteeing higher safety levels. One of the most important human traits to negotiating traffic is the ability to predict the future behavior of surrounding vehicles as a basis for agile and safe navigation. This capability is particularly challenging for an autonomous system in highly interactive driving situations, such as intersections or roundabouts. In this paper, a set of techniques to bring a computationally expensive state-of-the-art motion prediction algorithm to real-time execution are presented with the goal of meeting a standard motion-planning algorithm execution frequency of 5 Hz, which is the primary consumer of motion predictions. This is achieved by applying novel and existing parallelization algorithms that take advantage of graphic processing units (GPUs) through the compute unified device architecture (CUDA) programming language and managing to produce an average 5× speedup over raw C++ in the cases studied. The optimizations are then evaluated in public datasets and a real vehicle on a test track. Full article
(This article belongs to the Special Issue Emerging Technologies in Autonomous Vehicles)
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16 pages, 20078 KiB  
Article
Cooperative Environmental Perception Task Offloading for Connected and Autonomous Vehicles
by Pin Lv, Jie Huang and Heng Liu
Electronics 2023, 12(17), 3714; https://doi.org/10.3390/electronics12173714 - 02 Sep 2023
Viewed by 872
Abstract
Cooperative environmental perception is an effective way to provide connected and autonomous vehicles (CAVs) with the necessary environmental information. The research goal of this paper is to achieve efficient sharing of cooperative environmental perception information. Hence, a novel vehicular edge computing scheme is [...] Read more.
Cooperative environmental perception is an effective way to provide connected and autonomous vehicles (CAVs) with the necessary environmental information. The research goal of this paper is to achieve efficient sharing of cooperative environmental perception information. Hence, a novel vehicular edge computing scheme is proposed. In this scheme, the environmental perception tasks are selected to be offloaded based on their shareability, and the edge server directly delivers the task results to the CAVs who need the perception information. The experimental results show that the proposed task offloading scheme can decrease the perception information delivery latency up to 20%. Therefore, it is an effective way to improve cooperative environmental perception efficiency by taking the shareability of the perception information into consideration. Full article
(This article belongs to the Special Issue Emerging Technologies in Autonomous Vehicles)
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