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The Weather and Pollution Sensing for Intelligent Transport Systems

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 6784

Special Issue Editors


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Guest Editor
Escola Superior de Tecnologia e Gestão de Águeda, Instituto de Telecomunicações, Universidade de Aveiro, 3010-193 Aveiro, Portugal
Interests: vehicular communications; dependable systems; real-time communications; cybersecurity; cooperative, connected and automated mobility; embedded systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
UVSQ Paris-Saclay University, France
Interests: cellular networks; C-V2X: security and routing; cloud networking; wireless multimedia sensor networks

Special Issue Information

Dear Colleagues,

Adverse road weather and pollution are challenging for human drivers, urban populations, and for automated vehicles. To reach the potential safety, comfort, and efficiency benefits of cooperative, connected, and automated mobility (CCAM), vehicles need to sense road conditions and see beyond the fog and/or rain wall. In addition, existing solutions for road weather services are limited in their scope, mostly by: i) scalability, ii) offline processing, and iii) high latencies. Therefore, there is a need for integrated solutions that can extract the most benefits from a real-time analysis of the data gathered from road weather sensing technologies and provide an on-time appropriate reaction to the end-user and/or to the automated vehicles. This objective requires a higher level of intelligence to be integrated into the i) sensing, ii) communication infrastructures, and iii) cloud/MEC storage and processing platforms. In doing so, decentralized aggregation for robust and timely decisions can be easily achieved.

The emergence of C-ITS standards, based on a common ITS station communication architecture, is an opportunity to break up the historical separation in non-interoperable silos. Therefore, exploiting and further extending C-ITS standards has the potential to expand the local data collection mechanisms from traditional road weather data sources to completely innovative ones. A new generation of solutions is now possible, taking advantage of the integration of roadside units and road weather/pollution stations, vehicle data, road sensors, and ultimately the mobile device data from each handheld device from road users.

This Special Issue aims to highlight recent breakthroughs in road weather and air pollution sensing in the scope of CCAM technologies and applications; therefore, we seek high-quality submissions featuring both robust theoretical advances and practical engineering aspects. The topics of interest include but are not limited to:

  • Weather-aware cooperative perception;
  • Standards to accommodate weather/pollution data;
  • Field trials;
  • Road weather or pollution models;
  • Impact of adverse road weather conditions in automated driving;
  • Road sensors;
  • Onboard pollution sensors;
  • Cooperative sensing;
  • Security and privacy issues;
  • Massive machine type communications for CCAM;
  • Software agents for cooperative sensing;
  • Ad-hoc and mobile networks for the collection layer;
  • High-definition dynamic maps with weather and pollution data;
  • Weather and pollution sensing technology;
  • Big data and AI for weather or pollution prediction;
  • Personal handheld devices as sensing peripherals;
  • Vehicular communications;
  • Case studies;
  • etc.

Prof. Dr. Joaquim Ferreira
Prof. Dr. Nadjib Aitsaadi
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.

Keywords

  • Cooperative intelligent transportation systems
  • Weather sensing
  • Pollution sensing
  • High-resolution weather and pollution maps
  • Cooperative connected and automated mobility
  • Big data
  • Deep learning
  • Distributed embedded systems
  • V2X communications
  • Internet of Things
  • Security

Published Papers (2 papers)

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14 pages, 2141 KiB  
Article
Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model
by Toon Bogaerts, Sylvain Watelet, Niko De Bruyne, Chris Thoen, Tom Coopman, Joris Van den Bergh, Maarten Reyniers, Dirck Seynaeve, Wim Casteels, Steven Latré and Peter Hellinckx
Sensors 2022, 22(7), 2732; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072732 - 02 Apr 2022
Cited by 4 | Viewed by 2386
Abstract
Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based [...] Read more.
Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach. Full article
(This article belongs to the Special Issue The Weather and Pollution Sensing for Intelligent Transport Systems)
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14 pages, 3989 KiB  
Letter
Analysis of Impact of Rain Conditions on ADAS
by Chang-Gyun Roh, Jisoo Kim and I-Jeong Im
Sensors 2020, 20(23), 6720; https://0-doi-org.brum.beds.ac.uk/10.3390/s20236720 - 24 Nov 2020
Cited by 18 | Viewed by 3391
Abstract
Various technologies are being developed to support safe driving. Among them, ADAS, including LDWS, is becoming increasingly common. This driver assistance system aims to create a safe road environment while compensating for the driver’s carelessness. The driver is affected by external environmental factors [...] Read more.
Various technologies are being developed to support safe driving. Among them, ADAS, including LDWS, is becoming increasingly common. This driver assistance system aims to create a safe road environment while compensating for the driver’s carelessness. The driver is affected by external environmental factors such as rainfall, snowfall, and bad weather conditions. ADAS is designed to recognize the surrounding situation and enable safe driving by using sensors, but it does not operate normally in bad weather conditions. In this study, we quantitatively measured the effect of bad weather conditions on the actual ADAS function. Additionally, we conducted a vehicle-based driving experiment to suggest an improvement plan for safer driving. In the driving experiment, when the vehicle driving speed was changed in four stages of rainfall, it was confirmed that it affected the View Range value, where the primary variable is the visibility of ADAS. As a result of the analysis, we demonstrated that when the rainfall exceeded a precipitation of 20 mm, the ADAS sensor did not operate, regardless of the vehicle speed. This means that a problem affecting safe driving may occur due to functionality in bad weather situations in which the driver requires ADAS assistance. Therefore, it is necessary to develop a technology that can maintain the minimum ADAS functionality under rainfall conditions and other bad weather conditions. Full article
(This article belongs to the Special Issue The Weather and Pollution Sensing for Intelligent Transport Systems)
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