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Special Issue "Sensors Technologies for Intelligent Transportation Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Prof. Dr. Ignacio Parra Alonso
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Guest Editor
Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, 288805, Alcalá de Henares, Madrid, Spain
Interests: Vehicle localization; autonomous vehicles; driver assistance systems; imaging and image analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Noelia Hernández Parra
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Guest Editor
Assistant professor, Computer Engineering Department. INVETT Research Group. Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: Accurate Indoor and Outdoor Global Positioning; Vehicle Localization; Autonomous Vehicles; Driver Assistance Systems; Imaging and Image Analysis
Special Issues and Collections in MDPI journals
Prof. Dr. Iván García Daza
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Guest Editor
Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, 288805, Alcalá de Henares, Madrid, Spain
Interests: accurate mapping systems based on optimal optimization algorithms; advanced driver assistance systems; assistive intelligent vehicles; driver and road user state and intent recognition; dynamic and cinematic car models; intelligent localization systems based on LiDAR odometry; intelligent navigation and localization systems based on inertial navigation systems; intelligent-vehicle-related image, radar, and LiDAR signal processing; sensor fusion systems for driverless cars
Special Issues and Collections in MDPI journals
Dr. Augusto Luis Ballardini
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Guest Editor
Alcalá de Henares Automation Department, Polytechnic School, Universidad de Alcalá, 28871 Madrid, Spain
Interests: vehicle localization; lidar localization; digital maps; computer vision; deep learning
Prof. Dr. David Fernández-Llorca
Website
Guest Editor

Special Issue Information

Dear Colleagues,

Modern society is facing important challenges related to transportation systems, including but not limited to pollution, safety and congestion. More recently, additional health restrictions have imposed and additional burden to the traditional transportation models. It is now more imperative than ever to integrate and optimize all the information gathered from infrastructure and onboard sensors to achieve a fully operational and cooperative intelligent transportation system (ITS) environment. Current technologies rely on multimodal sensor systems, such as cameras, radar, LiDAR and so on, and are based on highly complex and sophisticated algorithms, including artificial intelligence. 5G will also have a crucial role on the next-generation cooperative ITS systems once it reaches enough penetration and coverage.

In this Special Issue, we want to explore how sensor technology can be integrated with transportation infrastructure to achieve a sustainable ITS and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS.

The aim of this Special Issue is to contribute to the state-of-the-art and to introduce current developments concerning sensor technologies for intelligent transportation systems. We encourage potential authors to submit contributions of original research, new developments and substantial experimental works concerning sensor technologies for intelligent transportation systems. Surveys are also welcomed.

Therefore, prospective authors are invited to submit original contributions or survey papers for review for publication in the Sensors open-access journal. Topics of interest include (but are not limited to) the following:

  • Sensor technologies for intelligent transportation systems
  • Connected vehicles and communication technologies
  • Sensor and information fusion
  • Distributed intelligent transportation systems technologies
  • Cooperative driving
  • Applications and services for real-time traffic management
  • Vehicle navigation and localization-based advanced sensor technologies
  • HD and accurate mapping systems
  • Peer-to-peer data sharing for fleet management and safety purposes
  • Traffic sensor management for routing and computing and traffic control
  • Communication protocols for seamless and optimized connectivity

Prof. Dr. Ignacio Parra Alonso
Prof. Dr. Noelia Hernández Parra
Prof. Dr. Iván García Daza
Dr. Augusto Luis Ballardini
Prof. Dr. David Fernández-Llorca
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 papers will be 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. Sensors 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 2200 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.

Published Papers (1 paper)

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Research

Open AccessArticle
A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios
Sensors 2021, 21(4), 1523; https://doi.org/10.3390/s21041523 - 22 Feb 2021
Abstract
Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect [...] Read more.
Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green. Full article
(This article belongs to the Special Issue Sensors Technologies for Intelligent Transportation Systems)
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