Pub Date : 2025-01-17DOI: 10.1109/OJVT.2025.3531652
Gaël Pongnot;Anatole Desreveaux;Clément Mayet;Denis Labrousse;Francis Roy
Electric Vehicles (EVs) based on Cascaded H Bridge (CHB) promise reduced consumption and improved modularity, repairability, resilience, and versatility. This study focuses on evaluating the efficiency of CHB inverters utilizing low-voltage Si MOSFETs to improve EV performance and range. Through a comprehensive system-level approach and modeling, a simulation of the CHB-based powertrain is developed and experimentally validated. Electrical and mechanical simulations are conducted separately and finally combined to streamline computation times. Subsequently, CHB-based EV is compared with standard two-level inverters (2LI) across different driving cycles, considering multiple sources of losses from the battery to the road. Despite increased battery losses, CHB proves reduction of consumption during urban driving cycles, making it a compelling choice for sustainable commuter vehicles.
{"title":"Comparative Analysis of a Low-Voltage CHB Inverter Without PWM and Two-Level IGBT/SiC Inverters for Electric Vehicles on Driving Cycles","authors":"Gaël Pongnot;Anatole Desreveaux;Clément Mayet;Denis Labrousse;Francis Roy","doi":"10.1109/OJVT.2025.3531652","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3531652","url":null,"abstract":"Electric Vehicles (EVs) based on Cascaded H Bridge (CHB) promise reduced consumption and improved modularity, repairability, resilience, and versatility. This study focuses on evaluating the efficiency of CHB inverters utilizing low-voltage Si MOSFETs to improve EV performance and range. Through a comprehensive system-level approach and modeling, a simulation of the CHB-based powertrain is developed and experimentally validated. Electrical and mechanical simulations are conducted separately and finally combined to streamline computation times. Subsequently, CHB-based EV is compared with standard two-level inverters (2LI) across different driving cycles, considering multiple sources of losses from the battery to the road. Despite increased battery losses, CHB proves reduction of consumption during urban driving cycles, making it a compelling choice for sustainable commuter vehicles.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"542-553"},"PeriodicalIF":5.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1109/OJVT.2025.3529495
Mehmood Nawaz;Sheheryar Khan;Muhammad Daud;Muhammad Asim;Ghazanfar Ali Anwar;Ali Raza Shahid;Ho Pui Aaron HO;Tom Chan;Daniel Pak Kong;Wu Yuan
In autonomous vehicles (AV), sensor fusion methods have proven to be effective in merging data from multiple sensors and enhancing their perception capabilities. In the context of sensor fusion, the distinct strengths of multi-sensors, such as LiDAR, RGB, Thermal sensors, etc., can be leveraged to mitigate the impact of challenges imposed by extreme weather conditions. In this paper, we address multi-sensor fusion in AVs and present a comprehensive integration of a thermal sensor aimed at enhancing the cognitive robustness of AVs. Thermal sensors possess an impressive capability to detect objects and hazards that may be imperceptible to traditional visible light sensors. When integrated with RGB and LiDAR sensors, the thermal sensor becomes highly beneficial for detecting and locating objects in adverse weather conditions. The proposed deep learning-assisted multi-sensor fusion technique consists of two parts: (1) visual information fusion and (2) object detection using LiDAR, RGB, and Thermal sensors. The visual fusion framework employs a CNN (convolutional neural network) inspired by a domain image fusion algorithm. The object detection framework uses the modified version of the YoloV8 model, which exhibits high accuracy in real-time detection. In the YoloV8 model, we adjusted the network architecture to incorporate additional convolutional layers and altered the loss function to enhance detection accuracy in foggy and rainy conditions. The proposed technique is effective and adaptable in challenging conditions, such as night or dark mode, smoke, and heavy rain. The experimental results of the proposed method demonstrate enhanced efficiency and cognitive robustness compared to state-of-the-art fusion and detection techniques. This is evident from tests conducted on two public datasets (FLIR and TarDAL) and one private dataset (CUHK).
{"title":"Improving Autonomous Vehicle Cognitive Robustness in Extreme Weather With Deep Learning and Thermal Camera Fusion","authors":"Mehmood Nawaz;Sheheryar Khan;Muhammad Daud;Muhammad Asim;Ghazanfar Ali Anwar;Ali Raza Shahid;Ho Pui Aaron HO;Tom Chan;Daniel Pak Kong;Wu Yuan","doi":"10.1109/OJVT.2025.3529495","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3529495","url":null,"abstract":"In autonomous vehicles (AV), sensor fusion methods have proven to be effective in merging data from multiple sensors and enhancing their perception capabilities. In the context of sensor fusion, the distinct strengths of multi-sensors, such as LiDAR, RGB, Thermal sensors, etc., can be leveraged to mitigate the impact of challenges imposed by extreme weather conditions. In this paper, we address multi-sensor fusion in AVs and present a comprehensive integration of a thermal sensor aimed at enhancing the cognitive robustness of AVs. Thermal sensors possess an impressive capability to detect objects and hazards that may be imperceptible to traditional visible light sensors. When integrated with RGB and LiDAR sensors, the thermal sensor becomes highly beneficial for detecting and locating objects in adverse weather conditions. The proposed deep learning-assisted multi-sensor fusion technique consists of two parts: (1) visual information fusion and (2) object detection using LiDAR, RGB, and Thermal sensors. The visual fusion framework employs a CNN (convolutional neural network) inspired by a domain image fusion algorithm. The object detection framework uses the modified version of the YoloV8 model, which exhibits high accuracy in real-time detection. In the YoloV8 model, we adjusted the network architecture to incorporate additional convolutional layers and altered the loss function to enhance detection accuracy in foggy and rainy conditions. The proposed technique is effective and adaptable in challenging conditions, such as night or dark mode, smoke, and heavy rain. The experimental results of the proposed method demonstrate enhanced efficiency and cognitive robustness compared to state-of-the-art fusion and detection techniques. This is evident from tests conducted on two public datasets (FLIR and TarDAL) and one private dataset (CUHK).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"426-441"},"PeriodicalIF":5.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1109/OJVT.2025.3530008
Mamady Delamou;Ahmed Naeem;Hüseyin Arslan;El Mehdi Amhoud
Millimeter wave (mmWave)-based orthogonal frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission. To meet communication and sensing requirements, many works propose a static configuration where the wave's hyperparameters such as the number of symbols in a frame and the number of frames in a communication slot are already predefined. However, two facts oblige us to redefine the problem, 1) the environment is often dynamic and uncertain, and 2) mmWave is severely impacted by wireless environments. A striking example where this challenge is very prominent is autonomous vehicle (AV). Such a system leverages integrated sensing and communication (ISAC) using mmWave to manage data transmission and the dynamism of the environment. In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing. This enables the AV to achieve two primary objectives: establishing a stable communication link with other AVs and accurately estimating the velocities of surrounding objects with high resolution. The communication performance is therefore evaluated based on the queue state, the effective data rate, and the discarded packets rate. In contrast, the effectiveness of the sensing is assessed using the velocity resolution. In addition, we exploit adaptive OFDM techniques for dynamic modulation, and we suggest a reward function that leverages the age of updates to handle the communication buffer and improve sensing. The system is validated using advantage actor-critic (A2C) and proximal policy optimization (PPO). Furthermore, we compare our solution with the existing design and demonstrate its superior performance by computer simulations.
{"title":"Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates","authors":"Mamady Delamou;Ahmed Naeem;Hüseyin Arslan;El Mehdi Amhoud","doi":"10.1109/OJVT.2025.3530008","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3530008","url":null,"abstract":"Millimeter wave (mmWave)-based orthogonal frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission. To meet communication and sensing requirements, many works propose a static configuration where the wave's hyperparameters such as the number of symbols in a frame and the number of frames in a communication slot are already predefined. However, two facts oblige us to redefine the problem, 1) the environment is often dynamic and uncertain, and 2) mmWave is severely impacted by wireless environments. A striking example where this challenge is very prominent is autonomous vehicle (AV). Such a system leverages integrated sensing and communication (ISAC) using mmWave to manage data transmission and the dynamism of the environment. In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing. This enables the AV to achieve two primary objectives: establishing a stable communication link with other AVs and accurately estimating the velocities of surrounding objects with high resolution. The communication performance is therefore evaluated based on the queue state, the effective data rate, and the discarded packets rate. In contrast, the effectiveness of the sensing is assessed using the velocity resolution. In addition, we exploit adaptive OFDM techniques for dynamic modulation, and we suggest a reward function that leverages the age of updates to handle the communication buffer and improve sensing. The system is validated using advantage actor-critic (A2C) and proximal policy optimization (PPO). Furthermore, we compare our solution with the existing design and demonstrate its superior performance by computer simulations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"455-470"},"PeriodicalIF":5.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10842044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-07DOI: 10.1109/OJVT.2025.3526847
Pengyu Cong;Chenyang Yang;Shengqian Han;Shuangfeng Han;Xiaoyun Wang
Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. While DNNs have demonstrated the ability to reduce the time complexity of inference, their training often incurs a high computational cost. Since practical wireless systems require retraining due to operating in open and dynamic environments, it is crucial to analyze the factors affecting the training complexity, which can guide the DNN architecture selection and the hyper-parameter tuning for efficient policy learning. As a metric of time complexity, the number of floating-point operations (FLOPs) for inference has been analyzed in the literature. However, the time complexity of training DNNs for learning wireless communication policies has only been evaluated in terms of runtime. In this paper, we introduce the number of serial FLOPs (se-FLOPs) as a new metric of time complexity, accounting for the ability of parallel computing. The se-FLOPs metric is consistent with actual runtime, making it suitable for measuring the time complexity of training DNNs. Since graph neural networks (GNNs) can learn a multitude of wireless communication policies efficiently and their architectures depend on specific policies, no universal GNN architecture is available for analyzing complexities across different policies. Thus, we first use precoder learning as an example to demonstrate the derivation of the numbers of se-FLOPs required to train several DNNs. Then, we compare the results with the se-FLOPs for inference of the DNNs and for executing a popular numerical algorithm, and provide the scaling laws of these complexities with respect to the numbers of antennas and users. Finally, we extend the analyses to the learning of general wireless communication policies. We use simulations to validate the analyses and compare the time complexity of each DNN trained for achieving the best learning performance and achieving an expected performance.
{"title":"Time Complexity of Training DNNs With Parallel Computing for Wireless Communications","authors":"Pengyu Cong;Chenyang Yang;Shengqian Han;Shuangfeng Han;Xiaoyun Wang","doi":"10.1109/OJVT.2025.3526847","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3526847","url":null,"abstract":"Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. While DNNs have demonstrated the ability to reduce the time complexity of inference, their training often incurs a high computational cost. Since practical wireless systems require retraining due to operating in open and dynamic environments, it is crucial to analyze the factors affecting the training complexity, which can guide the DNN architecture selection and the hyper-parameter tuning for efficient policy learning. As a metric of time complexity, the number of floating-point operations (FLOPs) for inference has been analyzed in the literature. However, the time complexity of training DNNs for learning wireless communication policies has only been evaluated in terms of runtime. In this paper, we introduce the number of serial FLOPs (se-FLOPs) as a new metric of time complexity, accounting for the ability of parallel computing. The se-FLOPs metric is consistent with actual runtime, making it suitable for measuring the time complexity of training DNNs. Since graph neural networks (GNNs) can learn a multitude of wireless communication policies efficiently and their architectures depend on specific policies, no universal GNN architecture is available for analyzing complexities across different policies. Thus, we first use precoder learning as an example to demonstrate the derivation of the numbers of se-FLOPs required to train several DNNs. Then, we compare the results with the se-FLOPs for inference of the DNNs and for executing a popular numerical algorithm, and provide the scaling laws of these complexities with respect to the numbers of antennas and users. Finally, we extend the analyses to the learning of general wireless communication policies. We use simulations to validate the analyses and compare the time complexity of each DNN trained for achieving the best learning performance and achieving an expected performance.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"359-384"},"PeriodicalIF":5.3,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/OJVT.2025.3526133
Ionela-Cristina Voicu;Filip Rosu
Radar is an essential technology for Advanced Driving Assistance Systems (ADAS), used to accurately localize objects even in unfavorable weather conditions. Most radar systems that are now being produced for ADAS provide either 3D or 4D point clouds, containing range, Doppler, azimuth, and elevation information for every detected point target. Out of all dimensions, the azimuth and elevation are estimated using more advanced algorithms than the ones generally used for range and Doppler. This is due to the restricted size of the aperture that can be safely mounted on a vehicle, hence the resolution must be enhanced digitally. When using advanced algorithms challenges such as precise antenna manufacturing are of significant importance, to avoid phase and gain mismatch between the antenna elements along with their inherent coupling. These negative effects lead to a significant degradation in the Direction of Arrival estimation. Super-resolution techniques such as MUSIC and CAPON are widely referenced, however their performance throughout prior work is evaluated in ideal environments and generally with multiple available data acquisition snapshots. In this paper we address the issues faced when applying such algorithms in a radar application and offer a solution based on linear prediction and spatial smoothing to enhance the performance of such algorithms.
{"title":"Enabling Super-Resolution for Automotive Imaging Radars in the Presence of Antenna Calibration Errors","authors":"Ionela-Cristina Voicu;Filip Rosu","doi":"10.1109/OJVT.2025.3526133","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3526133","url":null,"abstract":"Radar is an essential technology for Advanced Driving Assistance Systems (ADAS), used to accurately localize objects even in unfavorable weather conditions. Most radar systems that are now being produced for ADAS provide either 3D or 4D point clouds, containing range, Doppler, azimuth, and elevation information for every detected point target. Out of all dimensions, the azimuth and elevation are estimated using more advanced algorithms than the ones generally used for range and Doppler. This is due to the restricted size of the aperture that can be safely mounted on a vehicle, hence the resolution must be enhanced digitally. When using advanced algorithms challenges such as precise antenna manufacturing are of significant importance, to avoid phase and gain mismatch between the antenna elements along with their inherent coupling. These negative effects lead to a significant degradation in the Direction of Arrival estimation. Super-resolution techniques such as MUSIC and CAPON are widely referenced, however their performance throughout prior work is evaluated in ideal environments and generally with multiple available data acquisition snapshots. In this paper we address the issues faced when applying such algorithms in a radar application and offer a solution based on linear prediction and spatial smoothing to enhance the performance of such algorithms.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"385-395"},"PeriodicalIF":5.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/OJVT.2025.3525781
Asim Ul Haq;Seyed Salar Sefati;Syed Junaid Nawaz;Albena Mihovska;Michail J. Beliatis
Recent revolutionary advancements in the services as observed with the use cases of Industry 5.0, consumer electronics 2.0/smart devices 2.0, digital healthcare ecosystem, Internet-of-Things (IoT), advanced digital finance/currency, and Non-Terrestrial Network (NTN) expansion, to name a few, have resulted in a spectacular growth in the number of wireless-connected devices. Subsequently, this has drastically increased the demands for network capacity, channel capacity, reliability, privacy, and security provisions. Despite that, the 5th Generation (5G) of wireless communication networks has introduced various innovative services such as Ultra-Reliable Low Latency Communication (URLLCs), Massive Machine Type Communication (mMTCs), and Enhanced Mobile Broadband (eMBB). These services only support isolated operations and the requisite reliable service delivery remains a challenge. The Beyond 5G (B5G)/6th Generation (6G) wireless networks aim at simultaneously providing multiple integrated services through intelligent network operations with ultra-high speed and reliability supporting integrated NTN and terrestrial networks. However, the prospect of such an extensively connected decentralized 3D wireless network also foresees security concerns, underscoring the necessity for seamless and infrastructure-free (decentralized) security solutions. The conventional security mechanisms are considered inadequate to ensure the security provisions of such extensive, decentralized, and heterogeneous networks. Physical Layer Security (PLS) is a promising technique to extend seamless and infrastructure-less security solutions, ensuring the availability, confidentiality, and integrity of legitimate transmissions. This paper provides a comprehensive overview with tutorials and presents the state-of-the-art of PLS, focusing mainly on NTN wireless communications. Furthermore, current research challenges, open issues, and future research directions are also thoroughly discussed in an amalgamation of various emerging 6G technologies. Finally, we provide an overview of implementation challenges in NTN and potential solutions to support the standardization progression of NTN in upcoming releases of 3rd Generation Partnership Project (3GPP).
{"title":"Need of UAVs and Physical Layer Security in Next-Generation Non-Terrestrial Wireless Networks: Potential Challenges and Open Issues","authors":"Asim Ul Haq;Seyed Salar Sefati;Syed Junaid Nawaz;Albena Mihovska;Michail J. Beliatis","doi":"10.1109/OJVT.2025.3525781","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3525781","url":null,"abstract":"Recent revolutionary advancements in the services as observed with the use cases of Industry 5.0, consumer electronics 2.0/smart devices 2.0, digital healthcare ecosystem, Internet-of-Things (IoT), advanced digital finance/currency, and Non-Terrestrial Network (NTN) expansion, to name a few, have resulted in a spectacular growth in the number of wireless-connected devices. Subsequently, this has drastically increased the demands for network capacity, channel capacity, reliability, privacy, and security provisions. Despite that, the 5th Generation (5G) of wireless communication networks has introduced various innovative services such as Ultra-Reliable Low Latency Communication (URLLCs), Massive Machine Type Communication (mMTCs), and Enhanced Mobile Broadband (eMBB). These services only support isolated operations and the requisite reliable service delivery remains a challenge. The Beyond 5G (B5G)/6th Generation (6G) wireless networks aim at simultaneously providing multiple integrated services through intelligent network operations with ultra-high speed and reliability supporting integrated NTN and terrestrial networks. However, the prospect of such an extensively connected decentralized 3D wireless network also foresees security concerns, underscoring the necessity for seamless and infrastructure-free (decentralized) security solutions. The conventional security mechanisms are considered inadequate to ensure the security provisions of such extensive, decentralized, and heterogeneous networks. Physical Layer Security (PLS) is a promising technique to extend seamless and infrastructure-less security solutions, ensuring the availability, confidentiality, and integrity of legitimate transmissions. This paper provides a comprehensive overview with tutorials and presents the state-of-the-art of PLS, focusing mainly on NTN wireless communications. Furthermore, current research challenges, open issues, and future research directions are also thoroughly discussed in an amalgamation of various emerging 6G technologies. Finally, we provide an overview of implementation challenges in NTN and potential solutions to support the standardization progression of NTN in upcoming releases of 3rd Generation Partnership Project (3GPP).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"554-595"},"PeriodicalIF":5.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824882","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1109/OJVT.2025.3525853
Tim Brophy;Darragh Mullins;Robert Cormican;Enda Ward;Martin Glavin;Edward Jones;Brian Deegan
As automated vehicles progress toward increasing levels of autonomy, the need for thorough testing of such systems in all relevant environments increases. These safety-critical systems often rely on visible-spectrum cameras to perceive the environment. Therefore, these systems must perform reliably under a range of adverse weather conditions. This study investigates the impact of rain on the quality of images taken in an experimental setting designed to vary rain intensity in a controlled manner. This study analyzes the impact of rain using low-level metrics such as contrast and spatial frequency response. In addition, overall image quality was evaluated using a range of full-reference image quality metrics. The results show a 45% reduction in SNR at 40 m and 38 mm/h, a 70% maximum decrease in Weber contrast at 30 m and 38 mm/h, and a 42% increase in color error as a result of rain in the environment. Consequently, degradation in image quality is likely to affect subsequent downstream computer vision performance. The results of this study highlight the need for robust testing and optimization of camera systems.
{"title":"The Impact of Rain on Image Quality From Sensors on Connected and Autonomous Vehicles","authors":"Tim Brophy;Darragh Mullins;Robert Cormican;Enda Ward;Martin Glavin;Edward Jones;Brian Deegan","doi":"10.1109/OJVT.2025.3525853","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3525853","url":null,"abstract":"As automated vehicles progress toward increasing levels of autonomy, the need for thorough testing of such systems in all relevant environments increases. These safety-critical systems often rely on visible-spectrum cameras to perceive the environment. Therefore, these systems must perform reliably under a range of adverse weather conditions. This study investigates the impact of rain on the quality of images taken in an experimental setting designed to vary rain intensity in a controlled manner. This study analyzes the impact of rain using low-level metrics such as contrast and spatial frequency response. In addition, overall image quality was evaluated using a range of full-reference image quality metrics. The results show a 45% reduction in SNR at 40 m and 38 mm/h, a 70% maximum decrease in Weber contrast at 30 m and 38 mm/h, and a 42% increase in color error as a result of rain in the environment. Consequently, degradation in image quality is likely to affect subsequent downstream computer vision performance. The results of this study highlight the need for robust testing and optimization of camera systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"632-646"},"PeriodicalIF":5.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824872","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-31DOI: 10.1109/OJVT.2024.3523247
Muhammad Fahad Ijaz;Umar Rashid;Omer Waqar
With the emergence of real-time applications, modern wireless networks have witnessed the use of Age of Information (AoI) as a critical metric for evaluating the timeliness of data delivery. This paper considers multi-user scheduling, extending beyond traditional single-user scheduling, to exploit the potential of Massive Multiple-Input Multiple-Output (mMIMO) systems for concurrent data delivery over imperfectly known channel state information (CSI). We propose a novel transmission scheduling framework that leverages the spatial multiplexing capabilities of mMIMO to minimize the AoI across multiple users. This results in a joint optimization of multi-user scheduling and power allocation problem for optimum data freshness in a wireless broadcast network. We handle the non-convexity of the resulting problem by utilizing successive convex approximation to specifically reformulate the binary/integer and non-convex constraints of the problem. Extensive simulations demonstrate superior performance of the proposed framework and its solution in terms of AoI compared to existing benchmarks.
{"title":"Beyond Single-User Scheduling: Exploiting Massive MIMO for Concurrent Data Delivery With Minimum Age of Information","authors":"Muhammad Fahad Ijaz;Umar Rashid;Omer Waqar","doi":"10.1109/OJVT.2024.3523247","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3523247","url":null,"abstract":"With the emergence of real-time applications, modern wireless networks have witnessed the use of Age of Information (AoI) as a critical metric for evaluating the timeliness of data delivery. This paper considers multi-user scheduling, extending beyond traditional single-user scheduling, to exploit the potential of Massive Multiple-Input Multiple-Output (mMIMO) systems for concurrent data delivery over imperfectly known channel state information (CSI). We propose a novel transmission scheduling framework that leverages the spatial multiplexing capabilities of mMIMO to minimize the AoI across multiple users. This results in a joint optimization of multi-user scheduling and power allocation problem for optimum data freshness in a wireless broadcast network. We handle the non-convexity of the resulting problem by utilizing successive convex approximation to specifically reformulate the binary/integer and non-convex constraints of the problem. Extensive simulations demonstrate superior performance of the proposed framework and its solution in terms of AoI compared to existing benchmarks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"301-314"},"PeriodicalIF":5.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/OJVT.2024.3524271
Felip Riera-Palou;Miquel Duran;Guillem Femenias
Cell-free networks are expected to be a forthcoming (r)evolutionary step in the coming generation of mobile networks, the so called 6 G. While mobile infrastructure is often assumed to be deployed outdoor by the operators, reality is that most of the traffic has at least one of the communication ends located indoors. This paper introduces the problem of providing wireless service to a heterogeneous population made of indoor and outdoor users using an outdoor cell-free massive MIMO (CF-mMIMO) infrastructure. It is shown how the pervasive max-min criterion (in cell-free setups) that results in near-uniform quality-of-service to all users may lead to catastrophic consequences when some of the users happen to be indoor. This problem is analyzed in both communication directions, uplink and downlink, exposing the similarities and differences of these two scenarios. Direction-specific solutions are then provided that involve improving the channel estimation and connectivity of indoor users and modifying the power allocation so as to somehow compensate for the wall propagation indoor users have to endure. All the techniques introduced satisfy the scalability requirements thus making our proposal realistically implementable.
{"title":"Scalable Cell-Free Massive MIMO With Indoor/Outdoor Users","authors":"Felip Riera-Palou;Miquel Duran;Guillem Femenias","doi":"10.1109/OJVT.2024.3524271","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3524271","url":null,"abstract":"Cell-free networks are expected to be a forthcoming (r)evolutionary step in the coming generation of mobile networks, the so called 6 G. While mobile infrastructure is often assumed to be deployed outdoor by the operators, reality is that most of the traffic has at least one of the communication ends located indoors. This paper introduces the problem of providing wireless service to a heterogeneous population made of indoor and outdoor users using an outdoor cell-free massive MIMO (CF-mMIMO) infrastructure. It is shown how the pervasive max-min criterion (in cell-free setups) that results in near-uniform quality-of-service to all users may lead to catastrophic consequences when some of the users happen to be indoor. This problem is analyzed in both communication directions, uplink and downlink, exposing the similarities and differences of these two scenarios. Direction-specific solutions are then provided that involve improving the channel estimation and connectivity of indoor users and modifying the power allocation so as to somehow compensate for the wall propagation indoor users have to endure. All the techniques introduced satisfy the scalability requirements thus making our proposal realistically implementable.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"332-347"},"PeriodicalIF":5.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/OJVT.2024.3524088
Marco De Vincenzi;John Moore;Bradley Smith;Sanjay E. Sarma;Ilaria Matteucci
The evolution of Connected Vehicles (CVs) has introduced significant advancements in both in-vehicle and vehicle-edge platforms, creating a highly connected ecosystem. These advancements, however, have heightened exposure to cybersecurity risks. This work reviews emerging security challenges in the CV ecosystem from a new perspective, focusing on the integration of in-vehicle platforms such as the infotainment system and vehicle-edge platforms. By analyzing case studies such as Android Automotive, Message Queuing Telemetry Transport (MQTT), and the Robot Operating System (ROS), we identify the primary security threats, including malware attacks, data manipulation, and Denial of Service (DoS) attacks. The discussion extends to privacy concerns and the lack of trust-building mechanisms in CVs, highlighting how these gaps can be exploited. To mitigate these risks, we retrieve solutions drawn from the broader field of Internet of Things (IoT) security research, including Multi-Factor Authentication (MFA) and trust-based systems. The proposed framework aims to increase the trustworthiness of devices within the CV ecosystem. Finally, we identify future research directions in adaptive mechanisms and cross-domain security.
{"title":"Security Risks and Designs in the Connected Vehicle Ecosystem: In-Vehicle and Edge Platforms","authors":"Marco De Vincenzi;John Moore;Bradley Smith;Sanjay E. Sarma;Ilaria Matteucci","doi":"10.1109/OJVT.2024.3524088","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3524088","url":null,"abstract":"The evolution of Connected Vehicles (CVs) has introduced significant advancements in both in-vehicle and vehicle-edge platforms, creating a highly connected ecosystem. These advancements, however, have heightened exposure to cybersecurity risks. This work reviews emerging security challenges in the CV ecosystem from a new perspective, focusing on the integration of in-vehicle platforms such as the infotainment system and vehicle-edge platforms. By analyzing case studies such as Android Automotive, Message Queuing Telemetry Transport (MQTT), and the Robot Operating System (ROS), we identify the primary security threats, including malware attacks, data manipulation, and Denial of Service (DoS) attacks. The discussion extends to privacy concerns and the lack of trust-building mechanisms in CVs, highlighting how these gaps can be exploited. To mitigate these risks, we retrieve solutions drawn from the broader field of Internet of Things (IoT) security research, including Multi-Factor Authentication (MFA) and trust-based systems. The proposed framework aims to increase the trustworthiness of devices within the CV ecosystem. Finally, we identify future research directions in adaptive mechanisms and cross-domain security.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"442-454"},"PeriodicalIF":5.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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