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Progress and Challenges of Autonomous Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2759

Special Issue Editor


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Guest Editor
Department of Informatics, J Selye University, Komarno, Slovakia
Interests: information security; autonomous vehicle; mobile device; healthcare; awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The introduction of autonomous vehicles has been planned by manufacturers for some time, and the SAE levels have been increasing; however, it seems that full self-driving has not yet been achieved. There have been many promises of a launch date, but these have been postponed. We are seeing more attempts and experimentation around the world, but we are also seeing increasingly unplanned, unresolved traffic situations that vehicles cannot handle because they are not prepared for them. The aim of this Special Issue is to gather research results that will help to make full self-driving accessible and achievable, whether on the ground or in the air. All research areas of interest to us are those that are closer to being able to safely and reliably solve all traffic situations through vehicle decision-making, thus contributing to social acceptance and making transport safer.  People will only buy self-driving vehicles if they have full confidence that the decision-making is the best in any given case. We need to use science to show the benefits of using self-driving vehicles, in addition to safer driving, faster and better decision-making, and reduced accident rates. We welcome any studies that show the challenges researchers face in designing and developing self-driving vehicles, describing the problem and its possible solutions, and thus aiding further research and implementation in this direction. 

Topics of interest for submission include, but are not limited to, the following:

  • Challenges of self-driving;
  • Artificial intelligence research in self-driving;
  • Decision-making in self-driving;
  • Safety issues in self-driving;
  • Protection against hacking in self-driving;
  • Vehicle communication;
  • Smart city self-driving relationship
  • Smart roads in self-driving
  • Challenges of traffic situations in self-driving
  • Human factor in self-driving
  • Impact of pedestrians, cyclists, motorcyclists on self-driving
  • Challenges of aircraft in self-driving.

Dr. Gabor Kiss
Guest Editor

Manuscript Submission Information

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Keywords

  • self-driving
  • challenge
  • smart road
  • smart city
  • decision making
  • artificial intelligence
  • aircraft
  • take off
  • landing

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Published Papers (2 papers)

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Research

18 pages, 3301 KiB  
Article
Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving
by Gabor Kiss and Peter Bakucz
Appl. Sci. 2024, 14(6), 2261; https://doi.org/10.3390/app14062261 - 7 Mar 2024
Cited by 1 | Viewed by 861
Abstract
In this paper, we outline the analysis of a fully provable traffic system based on the Kolmogorov entropy. The completeness of the traffic node dynamics is realized in the form of a nonlinear dynamical model of the participating transport objects. The goal of [...] Read more.
In this paper, we outline the analysis of a fully provable traffic system based on the Kolmogorov entropy. The completeness of the traffic node dynamics is realized in the form of a nonlinear dynamical model of the participating transport objects. The goal of this study is to determine the completeness of transport nodes based on the Kolmogorov entropy of the traffic trajectories of a node with an unspecified number of actors, like cars and pedestrians. The completeness of a highly autonomous driving detection system describing a traffic node could be realized if the entropy-based error-doubling time of the trajectories of the Euler–Lagrange equation interpreted at the transport junction is less than 1.3. Full article
(This article belongs to the Special Issue Progress and Challenges of Autonomous Vehicles)
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21 pages, 2286 KiB  
Article
Deciphering Autonomous Vehicle Regulations with Machine Learning
by Raj Bridgelall and Denver Tolliver
Appl. Sci. 2024, 14(4), 1396; https://doi.org/10.3390/app14041396 - 8 Feb 2024
Cited by 1 | Viewed by 1328
Abstract
The emergence of autonomous vehicles (AVs) presents a transformative shift in transportation, promising enhanced safety and economic efficiency. However, a fragmented legislative landscape across the United States hampers AV deployment. This fragmentation creates significant challenges for AV manufacturers and stakeholders. This research contributes [...] Read more.
The emergence of autonomous vehicles (AVs) presents a transformative shift in transportation, promising enhanced safety and economic efficiency. However, a fragmented legislative landscape across the United States hampers AV deployment. This fragmentation creates significant challenges for AV manufacturers and stakeholders. This research contributes by employing advanced machine learning (ML) techniques to analyze state data, aiming to identify factors associated with the likelihood of passing AV-friendly legislation, particularly regarding the requirement for human backup drivers. The findings reveal a nuanced interplay of socio-economic, political, demographic, and safety-related factors influencing the nature of AV legislation. Key variables such as democratic electoral college votes per capita, port tons per capita, population density, road fatalities per capita, and transit agency needs significantly impact legislative outcomes. These insights suggest that a combination of political, economic, and safety considerations shape AV legislation, transcending traditional partisan divides. These findings offer a strategic perspective for developing a harmonized regulatory approach, potentially at the federal level, to foster a conducive environment for AV development and deployment. Full article
(This article belongs to the Special Issue Progress and Challenges of Autonomous Vehicles)
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