Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Sensors Technologies for Intelligent Transportation Systems

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

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

Deadline for manuscript submissions: closed (1 November 2021) | Viewed by 15616

Share This Special Issue

Special Issue Editors

Prof. Dr. Ignacio Parra Alonso

E-Mail Website
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, Collections and Topics in MDPI journals

Prof. Dr. Noelia Hernández Parra

E-Mail Website
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, Collections and Topics in MDPI journals

Dr. Iván García Daza

E-Mail Website
Guest Editor

Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, Alcalá de Henares, 288805 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, Collections and Topics in MDPI journals

Dr. Augusto Luis Ballardini

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. David Fernández-Llorca

E-Mail Website
Guest Editor

Computer Engineering Department, University of Alcalá, 28805 Madrid, Spain
Interests: automated and autonomous vehicles; predictive perception and planning; human-vehicle interaction; trustworthy artificial intelligence; traffic behaviour; assistive intelligent transportation systems; digital twins for ITS
Special Issues, Collections and Topics in MDPI journals

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 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. 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 2600 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 (4 papers)

Download All Papers

Research

17 pages, 827 KiB

Open AccessArticle

Speed Management Strategy: Designing an IoT-Based Electric Vehicle Speed Control Monitoring System

by Gatera Antoine, Chomora Mikeka, Gaurav Bajpai and Kayalvizhi Jayavel

Sensors 2021, 21(19), 6670; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196670 - 07 Oct 2021

Cited by 2 | Viewed by 3879

Abstract

Road accidents represent the greatest public health burden in the world. Road traffic accidents have been on the rise in Rwanda for several years. Speed has been identified as a core factor in these road accidents. Therefore, understanding road accidents caused by excessive speeding is critical for road safety planning. In this paper, input and out pulse width modulation (PWM) was used to command the metal–oxide–semiconductor field-effect transistor (MOSFET) controller which supplied voltage to the motor. A structural speed control and Internet of Things (IoT)-based online monitoring system was developed to monitor vehicle data in a continuous manner. Two modeling techniques, multiple linear regression (MLR) and random forest (RF) models, were evaluated to find the best model to estimate the required voltage to be supplied to the motors in a particular zone. The built models were evaluated based upon the coefficient of determination

R^{2}

. The RF performs better than the MLR as it reveals a higher

R^{2}

value and it is found to be 98.8%. Based on the results, the proposed method was proven to significantly reduce the supplied voltage to the motor and consequently increase safety. Full article

(This article belongs to the Special Issue Sensors Technologies for Intelligent Transportation Systems)

► Show Figures

Figure 1

13 pages, 35056 KiB

Open AccessArticle

Extracting Key Traffic Parameters from UAV Video with On-Board Vehicle Data Validation

by Donghui Shan, Tian Lei, Xiaohong Yin, Qin Luo and Lei Gong

Sensors 2021, 21(16), 5620; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165620 - 20 Aug 2021

Cited by 7 | Viewed by 2085

Abstract

The advantages of UAV video in flexibility, traceability, easy-operation, and abundant information make it a popular and powerful aerial tool applied in traffic monitoring in recent years. This paper proposed a systematic approach to detect and track vehicles based on the YOLO v3 model and the deep SORT algorithm for further extracting key traffic parameters. A field experiment was implemented to provide data for model training and validation to ensure the accuracy of the proposed approach. In the experiment, 5400 frame images and 1192 speed points were collected from two test vehicles equipped with high-precision GNSS-RTK and onboard OBD after completion of seven experimental groups with a different height (150 m to 500 m) and operating speed (40 km/h to 90 km/h). The results indicate that the proposed approach exhibits strong robustness and reliability, due to the 90.88% accuracy of object detection and 98.9% precision of tracking vehicle. Moreover, the absolute and relative error of extracted speed falls within ±3 km/h and 2%, respectively. The overall accuracy of the extracted parameters reaches up to 98%. Full article

(This article belongs to the Special Issue Sensors Technologies for Intelligent Transportation Systems)

► Show Figures

Figure 1

19 pages, 13453 KiB

Open AccessArticle

Vehicle Localization Using 3D Building Models and Point Cloud Matching

by Augusto Luis Ballardini, Simone Fontana, Daniele Cattaneo, Matteo Matteucci and Domenico Giorgio Sorrenti

Sensors 2021, 21(16), 5356; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165356 - 09 Aug 2021

Cited by 5 | Viewed by 2554

Abstract

Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline. Full article

(This article belongs to the Special Issue Sensors Technologies for Intelligent Transportation Systems)

► Show Figures

Figure 1

19 pages, 1181 KiB

Open AccessArticle

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

by Nikita Smirnov, Yuzhou Liu, Aso Validi, Walter Morales-Alvarez and Cristina Olaverri-Monreal

Sensors 2021, 21(4), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041523 - 22 Feb 2021

Cited by 30 | Viewed by 5717

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 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)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sensors Technologies for Intelligent Transportation Systems

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI