Special Issue "Autonomous Vehicles for Public Transportation Services"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 February 2023 | Viewed by 1779

Special Issue Editor

Prof. Dr. Dimitri Konstantas
E-Mail Website
Guest Editor
Geneva School of Economics and Management/Information Science Institute, Universite de Geneve, 1205 Geneve, Switzerland
Interests: autonomous vehicles; information security; mobile applications

Special Issue Information

Dear Colleagues,

The dream of autonomous driving is as old as the invention of cars. In the last few years, this dream has begun to slowly become a reality, with companies investing massively in automated and autonomous driving all around the world. Although the much-advertised large investment developments target private or taxi-like cars, it seems that the most probable large-scale deployment will come from public transportation autonomous vehicle services. The reasons for this are many, including the by definition geo-fenced service areas, zero cost to citizens, great potentials for green deployments, great potential for large cost savings, and relatively low-speed operation.

Autonomous vehicles in public transportation have the potential to revolutionize the way that citizens commute in urban and suburban environments, making fixed itineraries and predefined bus stops obsolete. Autonomous vehicles for public transportation will be deployed to their full potential, where they will operate in a highly personalized mode, picking up passengers in front of their house, like a taxi, but with the price and service of good old public transportation models, offering on-demand, door-to-door shared public transportation services.

However, passengers who are familiar with today’s public transportations have high service expectations, ranging from predictable timing of commuting to simplicity in ticketing and boarding, and from services for special needs passengers to safety onboard. We thus need to develop not just vehicles, but also a complete ecosystem of passenger and public transport operator services that improves the passenger experience.

The target of this Special Issue is to identify what the technologies and services are that can make autonomous vehicles the prime choice for public transportation services in urban and suburban environments.

The topics for this call include but are not limited to:

  1. Technology issues (state of AI, state of AV driver, and road behavior);
  2. Technologies for regulatory compliance;
  3. Services and requirements;
  4. Autonomous vehicles’ IT security;
  5. Safety of AVs;
  6. Integration in urban public transport: ticketing, MaaS, smart city needs, PTO IT integration.

Prof. Dr. Dimitri Konstantas
Guest Editor

Manuscript Submission Information

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Keywords

  • public transportation
  • services
  • on-demand door-to-door commuting

Published Papers (2 papers)

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Research

Article
The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis
Appl. Sci. 2022, 12(9), 4413; https://doi.org/10.3390/app12094413 - 27 Apr 2022
Viewed by 469
Abstract
The fast evolution and prevalence of driverless technologies has facilitated the testing and deployment of automated city shuttles (ACSs) as a means of public transportation in smart cities. For their efficient functioning, ACSs require a real-time data compilation and exchange of information with [...] Read more.
The fast evolution and prevalence of driverless technologies has facilitated the testing and deployment of automated city shuttles (ACSs) as a means of public transportation in smart cities. For their efficient functioning, ACSs require a real-time data compilation and exchange of information with their internal components and external environment. However, that nexus of data exchange comes with privacy concerns and data protection challenges. In particular, the technical realization of stringent data protection laws on data collection and processing are key issues to be tackled within the ACSs ecosystem. Our work provides an in-depth analysis of the GDPR requirements that should be considered by the ACSs’ stakeholders during the collection, storage, use, and transmission of data to and from the vehicles. First, an analysis is performed on the data processing principles, the rights of data subjects, and the subsequent obligations for the data controllers where we highlight the mixed roles that can be assigned to the ACSs stakeholders. Secondly, the compatibility of privacy laws with security technologies focusing on the gap between the legal definitions and the technological implementation of privacy-preserving techniques are discussed. In face of the GDPR pitfalls, our work recommends a further strengthening of the data protection law. The interdisciplinary approach will ensure that the overlapping stakeholder roles and the blurring implementation of data privacy-preserving techniques within the ACSs landscape are efficiently addressed. Full article
(This article belongs to the Special Issue Autonomous Vehicles for Public Transportation Services)
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Article
Detection of Logos of Moving Vehicles under Complex Lighting Conditions
Appl. Sci. 2022, 12(8), 3835; https://doi.org/10.3390/app12083835 - 11 Apr 2022
Viewed by 411
Abstract
This study proposes a method for vehicle logo detection and recognition to detect missing and inaccurate vehicle marks under complex lighting conditions. For images acquired in complex light conditions, adaptive image enhancement is used to improve the accuracy of car sign detection by [...] Read more.
This study proposes a method for vehicle logo detection and recognition to detect missing and inaccurate vehicle marks under complex lighting conditions. For images acquired in complex light conditions, adaptive image enhancement is used to improve the accuracy of car sign detection by more than 2%; for the problems of multi-scale and detection speed of vehicle logo recognition in different images, the paper improves the target detection algorithm to improve the detection accuracy by more than 3%. The adaptive image enhancement algorithm and improved You Only Look One-level Feature (YOLOF) detection algorithm proposed in this study can effectively improve the correct identification rate under complex lighting conditions. Full article
(This article belongs to the Special Issue Autonomous Vehicles for Public Transportation Services)
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