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Electronics
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IoT for Intelligent Transportation Systems
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IoT for Intelligent Transportation Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 10941

Special Issue Editors

Dr. Paweł Skruch

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Guest Editor
Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland
Interests: autonomous systems; control engineering; artificial intelligence; mathematical modeling; computer simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Joanna Jaworek-Korjakowska

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Guest Editor
Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering , AGH University of Science and Technology, 30-059 Cracow, Poland
Interests: deep neural networks; machine learning; computer vision; AI; pattern recognition; medical imaging; dermoscopy; dermatology
Special Issues, Collections and Topics in MDPI journals
Dr. Saleh Mobayen

E-Mail Website
Guest Editor
Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 640301, Taiwan
Interests: fault diagnosis; robust control; variable structure control; robotics; wind turbines
Special Issues, Collections and Topics in MDPI journals
Dr. Damian Grzechca

E-Mail Website
Guest Editor
Department of Electronics, Electrical Engineering and Microelectronics, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 16 Akademicka Street, 44-100 Gliwice, Poland
Interests: predictive maintenance of electronic sensors/systems; failure analysis; ADAS methodology; signal processing and data analysis; positioning and localization systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue. The Internet of Things (IoT) is transforming the automotive industry. It allows vehicles to connect with the Internet, and to communicate with one another and with infrastructure. This technological step can be considered a key milestone towards achieving higher levels of driving automation. The connection of hundreds millions of vehicles requires intelligent strategies for data logging, storage, processing, analysis and sharing, as well as efficient and effective data-management systems.

This Special Issue aims to collect current developments of the IoT technology that facilitates the creation of safer, more connected and intelligent transportation systems. Contributions in sensing and identification, network construction, information processing, and integrated applications are welcome. Novel results in cloud and edge computing as complementary technologies powering IoT are also within the scope of this Special Issue.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: sensing (fixed sensors, mobile sensing), data processing (clustering, decision support, classification, event detection, feature extraction, rules formulation and management), data fusion (on multiple levels), simulation and modeling, visualization, and applications.

We look forward to receiving your contributions.

Dr. Paweł Skruch
Dr. Joanna Jaworek-Korjakowska
Dr. Saleh Mobayen
Dr. Damian Grzechca
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. Electronics 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 2400 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

  • Internet of Things
  • cloud and edge computing
  • vehicle-to-vehicle
  • vehicle-to-infrastructure
  • automated and autonomous vehicles
  • sensing
  • data fusion
  • communication networks
  • traffic control and management

Related Special Issue

Published Papers (7 papers)

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Research

15 pages, 656 KiB  
Article
Naturalistic Driving Data-Based Anomalous Driving Behavior Detection Using Hypertuned Deep Autoencoders
by Shafqat Abbas, Muhammad Ozair Malik, Abdul Rehman Javed and Seng-Phil Hong
Electronics 2023, 12(9), 2072; https://doi.org/10.3390/electronics12092072 - 30 Apr 2023
Cited by 1 | Viewed by 1450
Abstract
Autonomous driving is predicted to play a large part in future transportation systems, providing benefits such as enhanced road usage and mobility schemes. However, self-driving cars must be perceived as safe drivers by other road users and contribute to traffic safety in addition [...] Read more.
Autonomous driving is predicted to play a large part in future transportation systems, providing benefits such as enhanced road usage and mobility schemes. However, self-driving cars must be perceived as safe drivers by other road users and contribute to traffic safety in addition to being operationally safe. Despite efforts to develop machine learning algorithms and solutions for the safety of automated vehicles, researchers have yet to agree upon a single approach to categorizing and accurately detecting safe and unsafe driving behaviors. This paper proposes a modified Z-score method-based autoencoder for anomalous behavior detection using multiple driving indicators. The experiments are performed on the benchmark Next Generation Simulation (NGSIM) vehicle trajectories and supporting datasets to discover anomalous driving behavior to assess our proposed approach’s performance. The experiments reveal that the proposed approach detected 81 anomalous driving behaviors out of 1031 naturalistic driving behavior instances (7.86%) with an accuracy of 96.31% without early stopping. With early stopping, our method successfully detected 147 anomalous driving behaviors (14.26%) with an accuracy of 95.25%. Overall, the proposed approach provides promising results for detecting anomalous driving behavior in automated vehicles using multiple driving indicators. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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21 pages, 4114 KiB  
Article
Congestion Avoidance in Intelligent Transport Networks Based on WSN-IoT through Controlling Data Rate of Zigbee Protocol by Learning Automata
by Zhou He, Lian Chen, Feng Li and Ge Jin
Electronics 2023, 12(9), 2070; https://doi.org/10.3390/electronics12092070 - 30 Apr 2023
Cited by 4 | Viewed by 1312
Abstract
Congestion control is one of the primary challenges in improving the performance of wireless sensor networks (WSNs). With the development of this network based on the Internet of Things (IoT), the importance of congestion control increases, and the need to provide more efficient [...] Read more.
Congestion control is one of the primary challenges in improving the performance of wireless sensor networks (WSNs). With the development of this network based on the Internet of Things (IoT), the importance of congestion control increases, and the need to provide more efficient strategies to deal with this problem is strongly felt. This problem is even more important in applications such as Intelligent Transport Systems (ITSs). This article introduces a new method for congestion control in ITSs based on WSN-IoT infrastructure, namely, the Congestion Avoidance by Reinforcement Learning algorithm (CARLA). The purpose of the research was to improve the performance of the Zigbee protocol in congestion control through more efficient routing and also the intelligent adjustment of the data rate of the nodes. For this purpose, a topology control and routing strategy based on the multiple Bloom filter (MBF) is proposed in this research. Further, learning automata (LA) was used as a reinforcement learning model to adjust the data rate of network nodes in a distributed manner. These strategies distinguish the current research from previous efforts and can be effective in reducing the probability of congestion in the network. The performance evaluation results of the proposed algorithm in a simulated ITS environment were compared with conventional Zigbee and state of the art methods. According to the results, CARLA can improve PDR by 4.64%, and at the same time, reduce energy consumption and end-to-end delay by 11.44% and 25.26%, respectively. The results confirm that by using CARLA, in addition to congestion control in the ITS, energy consumption and the end-to-end delay can also be reduced. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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15 pages, 2326 KiB  
Article
Multiagent Manuvering with the Use of Reinforcement Learning
by Mateusz Orłowski and Paweł Skruch
Electronics 2023, 12(8), 1894; https://doi.org/10.3390/electronics12081894 - 17 Apr 2023
Viewed by 832
Abstract
This paper presents an approach for defining, solving, and implementing dynamic cooperative maneuver problems in autonomous driving applications. The formulation of these problems considers a set of cooperating cars as part of a multiagent system. A reinforcement learning technique is applied to find [...] Read more.
This paper presents an approach for defining, solving, and implementing dynamic cooperative maneuver problems in autonomous driving applications. The formulation of these problems considers a set of cooperating cars as part of a multiagent system. A reinforcement learning technique is applied to find a suboptimal policy. The key role in the presented approach is a multiagent maneuvering environment that allows for the simulation of car-like agents within an obstacle-constrained space. Each of the agents is tasked with reaching an individual goal, defined as a specific location in space. The policy is determined during the reinforcement learning process to reach a predetermined goal position for each of the simulated cars. In the experiments, three road scenarios—zipper, bottleneck, and crossroads—were used. The trained policy has been successful in solving the cooperation problem in all scenarios and the positive effects of applying shared rewards between agents have been presented and studied. The results obtained in this work provide a window of opportunity for various automotive applications. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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20 pages, 625 KiB  
Article
High-Level Sensor Models for the Reinforcement Learning Driving Policy Training
by Wojciech Turlej
Electronics 2023, 12(1), 71; https://doi.org/10.3390/electronics12010071 - 25 Dec 2022
Viewed by 1157
Abstract
Performance limitations of automotive sensors and the resulting perception errors are one of the most critical limitations in the design of Advanced Driver Assistance Systems and Autonomous Driving Systems. Ability to efficiently recreate realistic error patterns in a traffic simulation setup not only [...] Read more.
Performance limitations of automotive sensors and the resulting perception errors are one of the most critical limitations in the design of Advanced Driver Assistance Systems and Autonomous Driving Systems. Ability to efficiently recreate realistic error patterns in a traffic simulation setup not only helps to ensure that such systems operate correctly in presence of perception errors, but also fulfills a key role in the training of Machine-Learning-based algorithms often utilized in them. This paper proposes a set of efficient sensor models for detecting road users and static road features. Applicability of the models is presented on an example of Reinforcement-Learning-based driving policy training. Experimental results demonstrate a significant increase in the policy’s robustness to perception errors, alleviating issues caused by the differences between the virtual traffic environment used in the policy’s training and the realistic conditions. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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15 pages, 1794 KiB  
Article
Attributation Analysis of Reinforcement Learning-Based Highway Driver
by Nikodem Pankiewicz and Paweł Kowalczyk
Electronics 2022, 11(21), 3599; https://doi.org/10.3390/electronics11213599 - 3 Nov 2022
Cited by 1 | Viewed by 1304
Abstract
While machine learning models are powering more and more everyday devices, there is a growing need for explaining them. This especially applies to the use of deep reinforcement learning in solutions that require security, such as vehicle motion planning. In this paper, we [...] Read more.
While machine learning models are powering more and more everyday devices, there is a growing need for explaining them. This especially applies to the use of deep reinforcement learning in solutions that require security, such as vehicle motion planning. In this paper, we propose a method for understanding what the RL agent’s decision is based on. The method relies on conducting a statistical analysis on a massive set of state-decisions samples. It indicates which input features have an impact on the agent’s decision and the relationships between the decisions, the significance of the input features, and their values. The method allows us to determine whether the process of making a decision by the agent is coherent with human intuition and what contradicts it. We applied the proposed method to the RL motion planning agent which is supposed to drive a vehicle safely and efficiently on a highway. We find out that making such an analysis allows for a better understanding of the agent’s decisions, inspecting its behavior, debugging the ANN model, and verifying the correctness of the input values, which increases its credibility. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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15 pages, 35273 KiB  
Article
WeaveNet: Solution for Variable Input Sparsity Depth Completion
by Mariusz Karol Nowak
Electronics 2022, 11(14), 2222; https://doi.org/10.3390/electronics11142222 - 16 Jul 2022
Viewed by 1166
Abstract
LIDARs produce depth measurements, which are relatively sparse when compared with cameras. Current state-of-the-art solutions for increasing the density of LIDAR-derived depth maps rely on training the models for specific input measurement density. This assumption can easily be violated. The goal of this [...] Read more.
LIDARs produce depth measurements, which are relatively sparse when compared with cameras. Current state-of-the-art solutions for increasing the density of LIDAR-derived depth maps rely on training the models for specific input measurement density. This assumption can easily be violated. The goal of this work was to develop a solution capable of producing reasonably accurate depth predictions while using input with a very wide range of depth information densities. To that end, we defined a WeaveBlock capable of efficiently propagating depth information. To achieve this goal, WeaveBlocks utilize long and narrow horizontal and vertical convolution kernels together with MobileNet-inspired pointwise convolutions serving as computational kernels. In this paper, we present the WeaveNet architecture for guided (LIDAR and camera) and unguided (LIDAR only) depth completion as well as a non-standard network training procedure. We present the results of the network on the KITTI test and validation sets. We analyze the network performance at various levels of input sparsity by randomly removing between 0% and 99% of the LIDAR points from the network inputs, and in each case, we obtain reasonable quality output. Additionally, we show that our trained network weights can easily be reused with a different LIDAR sensor. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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26 pages, 3336 KiB  
Article
Evaluation Methodology for Object Detection and Tracking in Bounding Box Based Perception Modules
by Paweł Kowalczyk, Jacek Izydorczyk and Marcin Szelest
Electronics 2022, 11(8), 1182; https://doi.org/10.3390/electronics11081182 - 8 Apr 2022
Cited by 3 | Viewed by 2015
Abstract
The aim of this work is to formulate a new metric to be used in the automotive industry for the evaluation process of software used to detect vehicles on video data. To achieve this goal, we have formulated a new concept for measuring [...] Read more.
The aim of this work is to formulate a new metric to be used in the automotive industry for the evaluation process of software used to detect vehicles on video data. To achieve this goal, we have formulated a new concept for measuring the degree of matching between rectangles for industrial use. We propose new measure based on three sub-measures focused on the area of the rectangle, its shape, and distance. These sub-measures are merged into a General similarity measure to avoid problems with poor adaptability of the Jaccard index to practical issues of recognition. Additionally, we create method of calculation of detection quality in the sequence of video frames that summarizes the local quality and adds information about possible late detection. Experiments with real and artificial data have confirmed that we have created flexible tools that can reduce time needed to evaluate detection software efficiently, and provide more detailed information about the quality of detection than the Jaccard index. Their use can significantly speed up data analysis and capture the weaknesses and limitations of the detection system under consideration. Our detection quality assessment method can be of interest to all engineers involved in machine recognition of video data. Full article
(This article belongs to the Special Issue IoT for Intelligent Transportation Systems)
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