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Advanced Sensing Technology for Intelligent Transportation Systems
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Advanced Sensing Technology for Intelligent Transportation Systems

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

Deadline for manuscript submissions: 25 May 2024 | Viewed by 8264

Special Issue Editors

Dr. Ashish Khanna

E-Mail Website
Guest Editor
Computer Science & Engineering, Maharaja Agrasen Institute of Technology, New Delhi 110086, India
Interests: distributed systems; fault tolerance; distributed computing; VANETs; sensor networks; cloud computing
Prof. Dr. Isabel De La Torre Díez

E-Mail Website
Guest Editor
Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
Interests: artificial intelligence; medical images, systems and its variants; fault tolerance; distributed computing; Ad hoc Networks
Special Issues, Collections and Topics in MDPI journals
Dr. Jameel Ahamed

E-Mail Website
Guest Editor
Department of Computer Science and Information Technology, Maulana Azad National Urdu University, Gachibowli, Hyderabad 500032, Telangana, India
Interests: Internet of Things; computer networks; machine learning; network security; artificial intelligence

Special Issue Information

Dear Colleagues,

Intelligent Transport Systems (ITS) aim to improve transport by using contemporary computers and communications in a smarter, faster, safer, and more convenient way. The contemporary uses of ITS are:

There are several benefits from intelligent traffic control (ITC) systems as they minimize the amount of time we have to wait on the motorway or at red lights when an accident happens. Vehicles pass through toll booths more rapidly with automatic toll collection, which lessens traffic and emissions. Another application of ITS is that one can make better decisions while travelling due to the assistance of traveller information systems, which provides with up-to-date, multimodal information on travel circumstances. The safety will be increased by in-vehicle navigation systems that provide in-vehicle maps, direct us to our destination, and automatically alert emergency services when a significant accident occurs and where it is located. Modern transit systems make it easier and more appealing to use public transportation by assisting transit agencies in running more efficiently and giving passengers real-time information. Commercial vehicle operators will benefit from the use of intelligent commercial vehicle systems to process the necessary documentation for carrying products. By checking the vehicles that need it the most, these devices will also assist government organizations in enhancing safety.

Vehicular Ad hoc Networks (VANETs) are an inherent part of the intelligent transportation system (ITS) framework. It is believed that they have developed into a larger "Internet of Vehicles", which will eventually develop into an "Internet of Autonomous Vehicles." An ad hoc network is another paradigm that connects Unmanned Aerial Vehicles (UAVs) is surveyed, called Flying Ad hoc Networks (FANETs). These mechanisms are the futuristic technologies of ITS.

All the above-mentioned applications and usages can be realized by the employment of different type of Sensors located at perception layer to collect the effective data for accurate decisions and intelligent operations with the help of Artificial Intelligence and telemetry. The applications and sub areas of Intelligent Transport System can be classified as:

  • Sensors for Intelligent Traffic Management System for VANETs;
  • Sensors for Intelligent Traffic Management System for FANETs;
  • Advanced Traveler Information System using Sensors;
  • Advanced Vehicle Control system using Sensors;
  • Advanced Public Transportation System using Sensors;
  • Advanced Rural Transportation Systems using Sensors;
  • Advanced Commercial Vehicles Operations system using Sensors;
  • Sensors for Toll Booths.

Dr. Ashish Khanna
Prof. Dr. Isabel De La Torre Díez
Dr. Jameel Ahamed
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

  • intelligent transport system
  • sensors
  • Internet of Things
  • decision making
  • artificial intelligence
  • VANETs

Published Papers (6 papers)

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Research

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23 pages, 12189 KiB  
Article
Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers
by Parth Kadav, Sachin Sharma, Johan Fanas Rojas, Pritesh Patil, Chieh (Ross) Wang, Ali Riza Ekti, Richard T. Meyer and Zachary D. Asher
Sensors 2024, 24(7), 2327; https://0-doi-org.brum.beds.ac.uk/10.3390/s24072327 - 05 Apr 2024
Viewed by 581
Abstract
Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For [...] Read more.
Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Intelligent Transportation Systems)
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17 pages, 1414 KiB  
Article
Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data
by Gerhard Navratil and Ioannis Giannopoulos
Sensors 2024, 24(3), 1042; https://0-doi-org.brum.beds.ac.uk/10.3390/s24031042 - 05 Feb 2024
Viewed by 603
Abstract
Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe [...] Read more.
Human behaviour detection is relevant in many fields. During navigational tasks it is an indicator for environmental conditions. Therefore, monitoring people while they move along the street network provides insights on the environment. This is especially true for motorcyclists, who have to observe aspects such as road surface conditions or traffic very careful. We thus performed an experiment to check whether IMU data is sufficient to classify motorcyclist behaviour as a data source for later spatial and temporal analysis. The classification was done using XGBoost and proved successful for four out of originally five different types of behaviour. A classification accuracy of approximately 80% was achieved. Only overtake manoeuvrers were not identified reliably. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Intelligent Transportation Systems)
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23 pages, 6087 KiB  
Article
Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks
by Rodrigo F. Daguano, Leopoldo R. Yoshioka, Marcio L. Netto, Claudio L. Marte, Cassiano A. Isler, Max Mauro Dias Santos and João F. Justo
Sensors 2023, 23(21), 8798; https://0-doi-org.brum.beds.ac.uk/10.3390/s23218798 - 29 Oct 2023
Viewed by 1412
Abstract
Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the [...] Read more.
Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the transport network and user behavior parameters must be calibrated to mirror real scenarios. In general, calibration is performed manually by traffic engineers who use their knowledge and experience to adjust the parameters of the simulator. Unfortunately, there is still no systematic and automatic process for calibrating traffic simulation networks, although some methods have been proposed in the literature. This study proposes a methodology that facilitates the calibration process, where an artificial neural network (ANN) is trained to learn the behavior of the transport network of interest. The ANN used is the Multi-Layer Perceptron (MLP), trained with back-propagation methods. Based on this learning procedure, the neural network can select the optimized values of the simulation parameters that best mimic the traffic conditions of interest. Experiments considered two microscopic models of traffic and two psychophysical models (Wiedemann 74 and Wiedemann 99). The microscopic traffic models are located in the metropolitan region of São Paulo, Brazil. Moreover, we tested the different configurations of the MLP (layers and numbers of neurons) as well as several variations of the backpropagation training method: Stochastic Gradient Descent (SGD), Adam, Adagrad, Adadelta, Adamax, and Nadam. The results of the experiments show that the proposed methodology is accurate and efficient, leading to calibration with a correlation coefficient greater than 0.8, when the calibrated parameters generate more visible effects on the road network, such as travel times, vehicle counts, and average speeds. For the psychophysical parameters, in the most simplified model (W74), the correlation coefficient was greater than 0.7. The advantage of using ANN for the automatic calibration of simulation parameters is that it allows traffic engineers to carry out comprehensive studies on a large number of future scenarios, such as at different times of the day, as well as on different days of the week and months of the year. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Intelligent Transportation Systems)
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21 pages, 7640 KiB  
Article
A Double-Layer Vehicle Speed Prediction Based on BPNN-LSTM for Off-Road Vehicles
by Jichao Liu, Yanyan Liang, Zheng Chen, Huaiyi Li, Weikang Zhang and Junling Sun
Sensors 2023, 23(14), 6385; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146385 - 13 Jul 2023
Cited by 1 | Viewed by 858
Abstract
The accurate prediction of vehicle speed is crucial for the energy management of vehicles. The existing vehicle speed prediction (VSP) methods mainly focus on road vehicles and rarely on off-road vehicles. In this paper, a double-layer VSP method based on backpropagation neural network [...] Read more.
The accurate prediction of vehicle speed is crucial for the energy management of vehicles. The existing vehicle speed prediction (VSP) methods mainly focus on road vehicles and rarely on off-road vehicles. In this paper, a double-layer VSP method based on backpropagation neural network (BPNN) and long short-term memory (LSTM) for off-road vehicles is proposed. First of all, considering the motion characteristics of off-road vehicles, the VSP problem is established and the relationship between the variables in the problem is carefully analyzed. Then, the double-layer VSP framework is presented, which consists of speed prediction and information update layers. The speed prediction layer established by using LSTM is to predict vehicle speed in the horizon, and the information update layer built by BPNN is to update the prediction information. Finally, with the help of mining truck and loader operation scenarios, the proposed VSP method is compared with the analytical method, BPNN prediction method, and recurrent neural network (RNN) prediction method in terms of speed prediction accuracy. The results show that, under the premise of ensuring the real-time prediction performance, the average prediction error of the proposed BPNN-LSTM prediction method under two operation scenarios reduces by 48.14%, 35.82% and 30.09% compared with the other three methods, respectively. The proposed speed prediction method provides a new solution for predicting the speed of off-road vehicles, effectively improving the speed prediction accuracy. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Intelligent Transportation Systems)
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22 pages, 1854 KiB  
Article
Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm
by Qiliang Xu, Guo Zhang and Jianping Wang
Sensors 2023, 23(10), 4682; https://0-doi-org.brum.beds.ac.uk/10.3390/s23104682 - 12 May 2023
Cited by 3 | Viewed by 1619
Abstract
In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires [...] Read more.
In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Intelligent Transportation Systems)
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Review

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24 pages, 9446 KiB  
Review
Advancements in Triboelectric Nanogenerators (TENGs) for Intelligent Transportation Infrastructure: Enhancing Bridges, Highways, and Tunnels
by Arash Rayegani, Ali Matin Nazar and Maria Rashidi
Sensors 2023, 23(14), 6634; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146634 - 24 Jul 2023
Cited by 1 | Viewed by 1899
Abstract
The development of triboelectric nanogenerators (TENGs) over time has resulted in considerable improvements to the efficiency, effectiveness, and sensitivity of self-powered sensing. Triboelectric nanogenerators have low restriction and high sensitivity while also having high efficiency. The vast majority of previous research has found [...] Read more.
The development of triboelectric nanogenerators (TENGs) over time has resulted in considerable improvements to the efficiency, effectiveness, and sensitivity of self-powered sensing. Triboelectric nanogenerators have low restriction and high sensitivity while also having high efficiency. The vast majority of previous research has found that accidents on the road can be attributed to road conditions. For instance, extreme weather conditions, such as heavy winds or rain, can reduce the safety of the roads, while excessive temperatures might make it unpleasant to be behind the wheel. Air pollution also has a negative impact on visibility while driving. As a result, sensing road surroundings is the most important technical system that is used to evaluate a vehicle and make decisions. This paper discusses both monitoring driving behavior and self-powered sensors influenced by triboelectric nanogenerators (TENGs). It also considers energy harvesting and sustainability in smart road environments such as bridges, tunnels, and highways. Furthermore, the information gathered in this study can help readers enhance their knowledge concerning the advantages of employing these technologies for innovative uses of their powers. Full article
(This article belongs to the Special Issue Advanced Sensing Technology for Intelligent Transportation Systems)
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