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Fault-Tolerant Sensing Paradigms for Autonomous Vehicles

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 19774

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Special Issue Editors

Dr. Imran Ashraf

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea
Interests: indoor positioning and localization; indoor user navigation; location based services; data mining; sentiment analysis; sensors for autonomous vehicles (LIDAR); accident analysis and prevention; wireless positioning; magnetic field based positioning
Special Issues, Collections and Topics in MDPI journals

Dr. Yousaf Bin Zikria

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: IoT; 5G; wireless networks; cognitive radio networks; information and network security
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Dr. Rashid Ali

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Guest Editor

Department of Information and Communication Technologies (DTIC), Universitat Pompeu Fabra, 08002 Barcelona, Spain
Interests: reinforcement learning; federated reinforcement learning; wireless networks; network performance analysis; applied mathematics for wireless networks; IP routing; IoTs; tactile internet; 5G; URLLC
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yongwan Park

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Guest Editor

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
Interests: 5G systems in communication; OFDM; PAPR reduction; indoor location-based services in wireless communication; smart sensors (LIDAR) for smart cars
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles (AVs) are envisioned to provide driverless transportation with a complex heterogeneous network of proprioceptive and exteroceptive sensors and actuators. AVs will be driving in complex road conditions in the presence of non-autonomous vehicles to provide on-demand services with safety, reliability, and deficiency. Road conditions are highly volatile, complex, and unstructured, which complicates the process of autonomous driving. AVs rely on sensors and actuators which provide the data for environment sensing, route planning, trajectory estimation, etc. In this regard, the quality of the data is a crucial part of all intelligent decision making. The quality of data ultimately depends on the health of deployed sensors and fault-free sensing frameworks. Sensors serve as the pivotal devices on which the decision-making paradigms function to carry out the functions of AVs. Since all sensing procedures are autonomous, monitoring the sensors’ health and sensing paradigms should be autonomous as well. In this regard, sensing paradigms should be fault-tolerant, where any faults in the operational functionality of the sensors should be monitored and incorporated accordingly. For this purpose, we need both sensor monitoring procedures that can monitor and report sensor malfunction and sensing paradigms which can compensate for such anomalies. This is a complicated task, especially when the heterogeneity of sensors such as RADAR, inertial sensors, cameras, LiDAR, etc. is considered and the nature of data such as 2D, 3D audio, video, etc. is taken into account. From this perspective, the characteristics, capability, and performance of sensors, sensing approaches, sensors’ health-monitoring schemes, and fusion frameworks require in-depth evaluation.

This Special Issue aims to publish both favorable and unfavorable characteristics of sensors used for autonomous vehicles by employing data-based, simulation-based, and field-test-based solutions. Specifically, the most recent advancements in the research area that consider sensing paradigms, fault-tolerant schemes for AV sensors, sensor health monitoring frameworks, and multisensor fusion frameworks are given special focus. This also includes emerging technologies, algorithms, models, data analytics, system design, and sensor analysis approaches that point out the limitations and capability of sensors and sensing models and approaches. Topics of interest include but are not limited to the following:

Sensors’ pros and cons within the perspective of autonomous vehicles;
Challenges for proprioceptive and exteroceptive sensors;
Sensors’ health monitoring frameworks;
Fault-tolerant sensing approaches;
Fusion frameworks for multimodal data to compensate for sensor faults;
Deep learning algorithms based on DNNs, CNNs, LSTMS, etc.;
Emerging deep learning approaches for multisensor data-based intelligent decision making;
Machine learning architectures;
Sensing paradigms for the camera, LiDAR, RADAR, etc.

Dr. Rashid Ali
Dr. Yousaf Bin Zikria
Dr. Imran Ashraf
Prof. Dr. Yongwan Park
Guest Editors

Manuscript Submission Information

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Keywords

fault-tolerant sensing
sensor health monitoring
autonomous vehicles
LiDAR
RADAR
camera
multimodal fusion
fusion frameworks
sensing paradigm

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Published Papers (7 papers)

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Research

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26 pages, 1060 KiB

Open AccessArticle

Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model

by Fiaz Majeed, Umair Shafique, Mejdl Safran, Sultan Alfarhood and Imran Ashraf

Sensors 2023, 23(21), 8741; https://doi.org/10.3390/s23218741 - 26 Oct 2023

Cited by 11 | Viewed by 3937

Abstract

Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the ‘yawning’ and ‘no_yawning’ classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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17 pages, 61863 KiB

Open AccessArticle

A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks

by Sonia Shahzadi, Nauman Riaz Chaudhry and Muddesar Iqbal

Sensors 2023, 23(17), 7366; https://doi.org/10.3390/s23177366 - 23 Aug 2023

Viewed by 1623

Abstract

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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20 pages, 541 KiB

Open AccessArticle

An Intelligent Framework for Cyber–Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection

by Nazik Alturki, Turki Aljrees, Muhammad Umer, Abid Ishaq, Shtwai Alsubai, Oumaima Saidani, Sirojiddin Djuraev and Imran Ashraf

Sensors 2023, 23(16), 7154; https://doi.org/10.3390/s23167154 - 14 Aug 2023

Cited by 10 | Viewed by 2778

Abstract

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber–physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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21 pages, 3483 KiB

Open AccessArticle

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data

by Furqan Rustam, Abid Ishaq, Muhammad Shadab Alam Hashmi, Hafeez Ur Rehman Siddiqui, Luis Alonso Dzul López, Juan Castanedo Galán and Imran Ashraf

Sensors 2023, 23(16), 7018; https://doi.org/10.3390/s23167018 - 8 Aug 2023

Cited by 7 | Viewed by 3052

Abstract

Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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21 pages, 3261 KiB

Open AccessArticle

DrunkChain: Blockchain-Based IoT System for Preventing Drunk Driving-Related Traffic Accidents

by Hamza Farooq, Ayesha Altaf, Faiza Iqbal, Juan Castanedo Galán, Daniel Gavilanes Aray and Imran Ashraf

Sensors 2023, 23(12), 5388; https://doi.org/10.3390/s23125388 - 7 Jun 2023

Cited by 5 | Viewed by 2994

Abstract

Traffic accidents present significant risks to human life, leading to a high number of fatalities and injuries. According to the World Health Organization’s 2022 worldwide status report on road safety, there were 27,582 deaths linked to traffic-related events, including 4448 fatalities at the collision scenes. Drunk driving is one of the leading causes contributing to the rising count of deadly accidents. Current methods to assess driver alcohol consumption are vulnerable to network risks, such as data corruption, identity theft, and man-in-the-middle attacks. In addition, these systems are subject to security restrictions that have been largely overlooked in earlier research focused on driver information. This study intends to develop a platform that combines the Internet of Things (IoT) with blockchain technology in order to address these concerns and improve the security of user data. In this work, we present a device- and blockchain-based dashboard solution for a centralized police monitoring account. The equipment is responsible for determining the driver’s impairment level by monitoring the driver’s blood alcohol concentration (BAC) and the stability of the vehicle. At predetermined times, integrated blockchain transactions are executed, transmitting data straight to the central police account. This eliminates the need for a central server, ensuring the immutability of data and the existence of blockchain transactions that are independent of any central authority. Our system delivers scalability, compatibility, and faster execution times by adopting this approach. Through comparative research, we have identified a significant increase in the need for security measures in relevant scenarios, highlighting the importance of our suggested model. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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21 pages, 3726 KiB

Open AccessArticle

Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band

by Mohamed S. El-Gendy, Mohamed Mamdouh M. Ali, Ernesto Bautista Thompson and Imran Ashraf

Sensors 2023, 23(9), 4475; https://doi.org/10.3390/s23094475 - 4 May 2023

Cited by 3 | Viewed by 1994

Abstract

In this paper, a novel ultra-wideband UWB antenna element with triple-band notches is proposed. The proposed UWB radiator element operates from 2.03 GHz up to 15.04 GHz with triple rejected bands at the WiMAX band (3.28–3.8 GHz), WLAN band (5.05–5.9 GHz), and X-band (7.78–8.51 GHz). In addition, the radiator supports the Bluetooth band (2.4–2.483 GHz). Three different techniques were utilized to obtain the triple-band notches. An alpha-shaped coupled line with a stub-loaded resonator (SLR) band stop filter was inserted along the main feeding line before the radiator to obtain a WiMAX band notch characteristic. Two identical U-shaped slots were etched on the proposed UWB radiator to achieve WLAN band notch characteristics with a very high degree of selectivity. Two identical metallic frames of an octagon-shaped electromagnetic band gap structure (EBG) were placed along the main feeding line to achieve the notch characteristic with X-band satellite communication with high sharpness edges. A novel UWB multiple-input multiple-output (MIMO) radiator is proposed. The proposed UWB-MIMO radiator was fabricated on FR-4 substrate material and measured. The isolation between every two adjacent ports was below −20 dB over the FCC-UWB spectrum and the Bluetooth band for the four MIMO antennas. The envelope correlation coefficient (ECC) between the proposed antennas in MIMO does not exceed 0.05. The diversity gains (DG) for all the radiators are greater than 9.98 dB. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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Review

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28 pages, 2619 KiB

Open AccessReview

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

by Arooj Khan, Imran Shafi, Sajid Gul Khawaja, Isabel de la Torre Díez, Miguel Angel López Flores, Juan Castañedo Galvlán and Imran Ashraf

Sensors 2023, 23(18), 7710; https://doi.org/10.3390/s23187710 - 6 Sep 2023

Cited by 6 | Viewed by 2580

Abstract

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO. Full article

(This article belongs to the Special Issue Fault-Tolerant Sensing Paradigms for Autonomous Vehicles)

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