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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 13913

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Special Issue Editor

Prof. Dr. Dimitri Konstantas

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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:

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

Prof. Dr. Dimitri Konstantas
Guest Editor

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Keywords

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

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Published Papers (5 papers)

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Research

19 pages, 7790 KiB

Open AccessArticle

The Impact of Automated Vehicles on Road and Intersection Capacity

by Quan Yu, Longsheng Wu, Haonan Zhang, Linlong Lei and Li Wang

Appl. Sci. 2023, 13(8), 5073; https://doi.org/10.3390/app13085073 - 18 Apr 2023

Cited by 1 | Viewed by 2597

Abstract

With the rapid development of autonomous driving technology, future road traffic must be composed of autonomous vehicles and artificial vehicles. Although autonomous vehicles have greatly improved road capacity, few studies have involved capacity at signal-controlled intersections, and most of the studies are based on experimental simulation. As such, there is a need to more scientifically analyze the impact of autonomous vehicles on road and intersection capacity. Based on three theories of flow-density relationships, traffic flow equilibrium analysis, and the following model, this paper firstly deduces the flow-density relationship of different vehicle types in a single environment. Secondly, flow-density relationships under different proportions of self-driving vehicles are derived. Through the derivation of these two models, the basic road saturation flow rates under different permeabilities of self-driving vehicles, can be obtained. Based on these results, a revised calculation model for the capacity of signalized intersections with different proportions of autonomous vehicles is proposed, which is essentially to revise the basic saturation flow rate under different permeabilities of autonomous vehicles. By using SUMO 1.15.0 traffic simulation software, the theoretical models are individually tested. The results show that the error rate between the theoretical calculation results and the SUMO simulation results, is less than 16%. This study can provide a basis for the calculation of basic capacity of roads and intersections in a future man-machine hybrid driving environment, and provide theoretical guidance for traffic management and control. Full article

(This article belongs to the Special Issue Autonomous Vehicles for Public Transportation Services)

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15 pages, 2598 KiB

Open AccessArticle

A Novel Deep-Learning Model for Remote Driver Monitoring in SDN-Based Internet of Autonomous Vehicles Using 5G Technologies

by Sherine Nagy Saleh and Cherine Fathy

Appl. Sci. 2023, 13(2), 875; https://doi.org/10.3390/app13020875 - 8 Jan 2023

Cited by 6 | Viewed by 2574

Abstract

The rapid advancement in the Internet of Things (IoT) and its integration with Artificial Intelligence (AI) techniques are expected to play a crucial role in future Intelligent Transportation Systems (ITS). Additionally, the continuous progress in the industry of autonomous vehicles will accelerate and increase their short adoption in smart cities to allow safe, sustainable and accessible trips for passengers in different public and private means of transportation. In this article, we investigate the adoption of 5G different technologies, mainly, the Software-Defined Networks (SDN) to support the communication requirements of delegation of control of level-2 autonomous vehicles to the Remote-Control Center (RCC) in terms of ultra-low delay and reliability. This delegation occurs upon the detection of a drowsy driver using our proposed deep-learning-based technique deployed at the edge to reduce the level of accidents and road congestion. The deep learning-based model was evaluated and produced higher accuracy, precision and recall when compared to other methods. The role of SDN is to implement network slicing to achieve the Quality of Service (QoS) level required in this emergency case. Decreasing the end-to-end delay required to provide feedback control signals back to the autonomous vehicle is the aim of deploying QoS support available in an SDN-based network. Feedback control signals are sent to remotely activate the stopping system or to switch the vehicle to direct teleoperation mode. The mininet-WiFi emulator is deployed to evaluate the performance of the proposed adaptive SDN framework, which is tailored to emulate radio access networks. Our simulation experiments conducted on realistic vehicular scenarios revealed significant improvement in terms of throughput and average Round-Trip Time (RTT). Full article

(This article belongs to the Special Issue Autonomous Vehicles for Public Transportation Services)

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16 pages, 6367 KiB

Open AccessArticle

Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning

by Songxiao Cao, Ye Jin, Toralf Trautmann and Kang Liu

Appl. Sci. 2023, 13(1), 317; https://doi.org/10.3390/app13010317 - 27 Dec 2022

Cited by 7 | Viewed by 2578

Abstract

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m. Full article

(This article belongs to the Special Issue Autonomous Vehicles for Public Transportation Services)

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21 pages, 766 KiB

Open AccessArticle

The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis

by Meriem Benyahya, Sotiria Kechagia, Anastasija Collen and Niels Alexander Nijdam

Appl. Sci. 2022, 12(9), 4413; https://doi.org/10.3390/app12094413 - 27 Apr 2022

Cited by 6 | Viewed by 2466

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 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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13 pages, 1732 KiB

Open AccessArticle

Detection of Logos of Moving Vehicles under Complex Lighting Conditions

by Qiang Zhao and Wenhao Guo

Appl. Sci. 2022, 12(8), 3835; https://doi.org/10.3390/app12083835 - 11 Apr 2022

Cited by 3 | Viewed by 1922

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 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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