Journal of Sensor and Actuator Networks

Research

29 pages, 17952 KiB

Open AccessArticle

Housekeeping System for Suborbital Vehicles: VIRIATO Mock-Up Vehicle Integration and Testing

by Geraldo Rodrigues, Beltran Arribas, Rui Melicio, Paulo Gordo, Duarte Valério, João Casaleiro and André Silva

J. Sens. Actuator Netw. 2024, 13(6), 74; https://doi.org/10.3390/jsan13060074 - 4 Nov 2024

Viewed by 2060

Abstract

The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal [...] Read more.

The work presented in this paper regards the improvement of a housekeeping system for data acquisition of a suborbital vehicle (VIRIATO rocket or launcher). The specifications regarding the vehicle are presented and hardware is chosen accordingly, considering commercial off-the-shelf components. Mechanical and thermal simulations are performed regarding the designed system and a physical prototype is manufactured, assembled and programmed. Functional and field test results resorting to unmanned aerial vehicles, as well as the system’s integration within VIRIATO project’s mock-up vehicle, are presented. These tests demonstrate the viability of this system as an independent data acquisition system, and simulation results show that commercial off-the-shelf components have the capability of surviving expected launch environments. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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21 pages, 14370 KiB

Open AccessArticle

AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

by Hovannes Kulhandjian, Jeremiah Barron, Megan Tamiyasu, Mateo Thompson and Michel Kulhandjian

J. Sens. Actuator Netw. 2024, 13(3), 34; https://doi.org/10.3390/jsan13030034 - 14 Jun 2024

Cited by 4 | Viewed by 1906

Abstract

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A [...] Read more.

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian’s range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger’s touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night. Full article

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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19 pages, 2082 KiB

Open AccessArticle

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://doi.org/10.3390/jsan12020033 - 7 Apr 2023

Cited by 8 | Viewed by 3518

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

Open AccessArticle

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://doi.org/10.3390/jsan11040072 - 31 Oct 2022

Viewed by 2429

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

Open AccessArticle

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://doi.org/10.3390/jsan11040060 - 24 Sep 2022

Cited by 10 | Viewed by 2912

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

Open AccessArticle

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://doi.org/10.3390/jsan11030051 - 1 Sep 2022

Viewed by 2641

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

Open AccessArticle

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://doi.org/10.3390/jsan11030049 - 28 Aug 2022

Cited by 9 | Viewed by 3009

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

Open AccessArticle

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://doi.org/10.3390/jsan11010014 - 10 Feb 2022

Cited by 6 | Viewed by 4562

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

Open AccessArticle

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://doi.org/10.3390/jsan11010006 - 10 Jan 2022

Cited by 31 | Viewed by 5354

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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