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Advances in Intelligent Transportation Systems (ITS)
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Advances in Intelligent Transportation Systems (ITS)

A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Network Services and Applications".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 19886

Special Issue Editors

Dr. Hovannes Kulhandjian

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA
Interests: underwater communications; visible light communications; applied machine learning; wireless communications; wireless networking; digital signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Michel Kulhandjian

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
Interests: wireless communications; adaptive machine learning; adaptive signal processing; error correcting codes; optimization algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in information and communication technologies are facilitating substantial improvements in transportation, providing technologies and developments that are leading to safer and more efficient mobility. Novel technologies, such as the Internet of Things (IoT), artificial intelligence, machine learning, and advanced automation, are providing solutions which, when applied to the transport industry, are creating groundbreaking applications that are challenging contemporary ideas of transport.

Areas such as autonomous and connected vehicles, advanced driver assistance systems, traffic control, cybersecurity, and human factors in intelligent vehicles are examples of how intelligent transportation systems are fostering the development of novel solutions for the transport of the new connected and smart societies.

This Special Issue aims to provide advances in these topics, providing insight into the technologies that are transforming people’s lives. The topics covered include, but are not limited to, the following:

  • Interconnected vehicles and transportation systems;
  • Advanced driver assistance systems (ADASs);
  • Field trials, tests, and deployment;
  • Autonomous and connected vehicles;
  • Modeling, control, and simulation algorithms and techniques;
  • Multimodal transportation networks and systems;
  • Smart traffic control and management;
  • Sensors, detectors, and actuators;
  • Cybersecurity in vehicular communications;
  • Advanced driver assistance systems onboard vehicles;
  • Virtual sensor modeling using neural networks and/or deep learning;
  • Reliability and security in transport;
  • Data fusion;
  • Computer vision;
  • Smart mobility.

Our intention is to bring together the latest developments in the field within this single Special Issue, as a valuable resource for both new and experienced researchers in the field.

Dr. Hovannes Kulhandjian
Dr. Michel Kulhandjian
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. Journal of Sensor and Actuator Networks 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 2000 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

  • autonomous vehicles
  • advance control
  • deep learning
  • data fusion
  • vehicle cybersecurity

Published Papers (7 papers)

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Research

19 pages, 2082 KiB  
Article
Enhanced Traffic Sign Recognition with Ensemble Learning
by Xin Roy Lim, Chin Poo Lee, Kian Ming Lim and Thian Song Ong
J. Sens. Actuator Netw. 2023, 12(2), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan12020033 - 07 Apr 2023
Cited by 3 | Viewed by 2274
Abstract
With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ResNet50, DenseNet121, and VGG16. To enhance the accuracy and [...] Read more.
With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ResNet50, DenseNet121, and VGG16. To enhance the accuracy and robustness of the model, the authors implement an ensemble learning technique with majority voting, to combine the predictions of multiple CNNs. The proposed approach was evaluated on three different traffic sign datasets: the German Traffic Sign Recognition Benchmark (GTSRB), the Belgium Traffic Sign Dataset (BTSD), and the Chinese Traffic Sign Database (TSRD). The results demonstrate the efficacy of the ensemble approach, with recognition rates of 98.84% on the GTSRB dataset, 98.33% on the BTSD dataset, and 94.55% on the TSRD dataset. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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21 pages, 10485 KiB  
Article
Cognitive Risk-Assessment and Decision-Making Framework for Increasing in-Vehicle Intelligence
by George Dimitrakopoulos, Elena Politi, Konstantina Karathanasopoulou, Elias Panagiotopoulos and Theodore Zographos
J. Sens. Actuator Netw. 2022, 11(4), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11040072 - 31 Oct 2022
Viewed by 1727
Abstract
The key challenge for future automated driving systems is the need to imitate the intelligence and ability of human drivers, both in terms of driving agility, as well as in their intuitive understanding of the surroundings and dynamics of the vehicle. In this [...] Read more.
The key challenge for future automated driving systems is the need to imitate the intelligence and ability of human drivers, both in terms of driving agility, as well as in their intuitive understanding of the surroundings and dynamics of the vehicle. In this paper a model that utilizes data from different sources coming from vehicular sensor networks is presented. The data is processed in an intelligent manner while integrating knowledge and experience associated with potential and any decision. Moreover, the appropriate directives for the safety of the vehicle as well as alerts in case of upcoming emergencies are provided to the driver. The innovation lies in attributing human-like cognitive capabilities—non-causal reasoning, predictive decision-making, and learning—integrated into the processes for perception and decision-making in safety-critical autonomous use cases. The overall approach is described and formulated, while a heuristic function is proposed for assisting the driver in reaching the appropriate decisions. Comprehensive results from our experiments showcase its efficiency, simplicity, and scalability. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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15 pages, 3022 KiB  
Article
Photonic Sensor for Multiple Targets Detection under Adverse Weather Conditions in Autonomous Vehicles
by Abhishek Sharma, Sushank Chaudhary, Jyoteesh Malhotra, Sunita Khichar and Lunchakorn Wuttisittikulkij
J. Sens. Actuator Netw. 2022, 11(4), 60; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11040060 - 24 Sep 2022
Cited by 8 | Viewed by 2139
Abstract
Detection and tracing of multiple targets in a real-time scenario, particularly in the urban setup under adverse atmospheric conditions, has become a major challenge for autonomous vehicles (AVs). Photonic radars have emerged as promising candidates for Avs to realize via the recognition of [...] Read more.
Detection and tracing of multiple targets in a real-time scenario, particularly in the urban setup under adverse atmospheric conditions, has become a major challenge for autonomous vehicles (AVs). Photonic radars have emerged as promising candidates for Avs to realize via the recognition of traffic patterns, navigation, lane detection, self-parking, etc. In this work we developed a direct detection-based, frequency-modulated photonic radar to detect multiple stationary targets using four different transmission channels multiplexed over a single free space channel via wavelength division multiplexing (WDM). Additionally, the performance of the proposed photonic radar was examined under the impact of adverse weather conditions, such as rain and fog. The reported results in terms of received power and signal-to-noise ratio (SNR) showed successful detection of all the targets with bandwidths of 1 GHz and 4 GHz. The proposed system was also tested for range resolution of targets at 150 m and 6.75 cm resolution with 4 GHz bandwidth was reported, while resolution of 50 cm was reported with 1 GHz of bandwidth. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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22 pages, 764 KiB  
Article
Efficient and Privacy-Preserving Certificate Activation for V2X Pseudonym Certificate Revocation
by Jan Wantoro and Masahiro Mambo
J. Sens. Actuator Netw. 2022, 11(3), 51; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11030051 - 01 Sep 2022
Viewed by 1937
Abstract
Vehicle to everything (V2X) technology allows the broader development of driving safety, efficiency, and comfort. Because the vehicles can quickly send and receive frequent messages from other vehicles and nearby devices, e.g., cooperative awareness message applications on the intelligent transport system (ITS), V2X [...] Read more.
Vehicle to everything (V2X) technology allows the broader development of driving safety, efficiency, and comfort. Because the vehicles can quickly send and receive frequent messages from other vehicles and nearby devices, e.g., cooperative awareness message applications on the intelligent transport system (ITS), V2X requires a good security and privacy protection system to make the messages reliable for the ITS requirements. The existing standards developed in the US and Europe use many short valid period pseudonym certificates to meet the security and privacy requirements. However, this method has difficulty ensuring that revoked pseudonym certificates are treated as revoked by any vehicles because distributing revocation information on a wireless vehicular network with intermittent and rapidly changing topology is demanding. A promising approach to solving this problem is the periodic activation of released pseudonym certificates. Initially, it releases all required pseudonym certificates for a certain period to the vehicle, and pseudonym certificates can be used only after receiving an activation code. Such activation-code-based schemes have a common problem in the inefficient use of network resources between the road-side unit (RSU) and vehicles. This paper proposes an efficient and privacy-preserving activation code distribution strategy solving the problem. By adopting the unicast distribution model of modified activation code for pseudonym certificate (ACPC), our scheme can obtain benefits of efficient activation code distribution. The proposed scheme provides small communication resource usage in the V2X network with various channel options for delivering activation codes in a privacy preserved manner. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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11 pages, 3509 KiB  
Article
High Resolution-Based Coherent Photonic Radar Sensor for Multiple Target Detections
by Sushank Chaudhary, Abhishek Sharma, Sunita Khichar, Xuan Tang, Xian Wei and Lunchakorn Wuttisittikulkij
J. Sens. Actuator Netw. 2022, 11(3), 49; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11030049 - 28 Aug 2022
Cited by 8 | Viewed by 2203
Abstract
The last decade witnessed remarkable growth in the number of global road accidents. To minimize road accidents, transportation systems need to become more intelligent. Multiple detection of target vehicles under adverse weather conditions is one of the primary challenges of autonomous vehicles. Photonic [...] Read more.
The last decade witnessed remarkable growth in the number of global road accidents. To minimize road accidents, transportation systems need to become more intelligent. Multiple detection of target vehicles under adverse weather conditions is one of the primary challenges of autonomous vehicles. Photonic radar sensors may become the promising technology to detect multiple targets to realize autonomous vehicles. In this work, high-speed photonic radar is designed to detect multiple targets by incorporating a cost-effective wavelength division multiplexing (WDM) scheme. Numerical simulations of the proposed WDM-based photonic radar is demonstrated in terms of received power and signal to noise (SNR) ratio. The performance of the proposed photonic radar is also investigated under diverse weather conditions, particularly low, medium, and thick fog. The proposed photonic radar demonstrated a significant range resolution of 7 cm when the target was placed at 80 m distance from the photonic radar sensor-equipped vehicle. In addition to this, traditional microwave radar is demonstrated to prove the effectiveness of the proposed photonic radar. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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16 pages, 1097 KiB  
Article
Utilizing B-Spline Curves and Neural Networks for Vehicle Trajectory Prediction in an Inverse Reinforcement Learning Framework
by Mohammad Sadegh Jazayeri and Arash Jahangiri
J. Sens. Actuator Netw. 2022, 11(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11010014 - 10 Feb 2022
Cited by 6 | Viewed by 3583
Abstract
The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems that aim to identify critical events such as near-crash situations and traffic violations. In a connected environment, important information about these critical events can be communicated to road users or [...] Read more.
The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety systems that aim to identify critical events such as near-crash situations and traffic violations. In a connected environment, important information about these critical events can be communicated to road users or the infrastructure to avoid or mitigate potential crashes. Intersections require special attention in this context because they are hotspots for crashes and involve numerous and complex interactions between road users. In this work, we developed an advanced machine learning method for trajectory prediction using B-spline curve representations of vehicle trajectories and inverse reinforcement learning (IRL). B-spline curves were used to represent vehicle trajectories; a neural network model was trained to predict the coefficients of these curves. A conditional variational autoencoder (CVAE) was used to generate candidate trajectories from these predicted coefficients. These candidate trajectories were then ranked according to a reward function that was obtained by training an IRL model on the (spline smoothed) vehicle trajectories and the surroundings of the vehicles. In our experiments we found that the neural network model outperformed a Kalman filter baseline and the addition of the IRL ranking module further improved the performance of the overall model. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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20 pages, 730 KiB  
Article
Towards a Lightweight Intrusion Detection Framework for In-Vehicle Networks
by Dheeraj Basavaraj and Shahab Tayeb
J. Sens. Actuator Netw. 2022, 11(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/jsan11010006 - 10 Jan 2022
Cited by 17 | Viewed by 4387
Abstract
With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has [...] Read more.
With the emergence of networked devices, from the Internet of Things (IoT) nodes and cellular phones to vehicles connected to the Internet, there has been an ever-growing expansion of attack surfaces in the Internet of Vehicles (IoV). In the past decade, there has been a rapid growth in the automotive industry as network-enabled and electronic devices are now integral parts of vehicular ecosystems. These include the development of automobile technologies, namely, Connected and Autonomous Vehicles (CAV) and electric vehicles. Attacks on IoV may lead to malfunctioning of Electronic Control Unit (ECU), brakes, control steering issues, and door lock issues that can be fatal in CAV. To mitigate these risks, there is need for a lightweight model to identify attacks on vehicular systems. In this article, an efficient model of an Intrusion Detection System (IDS) is developed to detect anomalies in the vehicular system. The dataset used in this study is an In-Vehicle Network (IVN) communication protocol, i.e., Control Area Network (CAN) dataset generated in a real-time environment. The model classifies different types of attacks on vehicles into reconnaissance, Denial of Service (DoS), and fuzzing attacks. Experimentation with performance metrics of accuracy, precision, recall, and F-1 score are compared across a variety of classification models. The results demonstrate that the proposed model outperforms other classification models. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))
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