Pub Date : 2025-09-05DOI: 10.1109/OJVT.2025.3606652
Saddam Hussain;Ali Tufail;Haji Awg Abdul Ghani Naim;Muhammad Asghar Khan;Gordana Barb
Named Data Networking (NDN) is considered a future architecture for content distribution in the Internet of Vehicles (IoV). The primary principles of NDN, which include naming and in-network caching, are perfectly aligned with the IoV requirements for time and location independence. Despite significant research efforts, full-scale deployment remains limited due to ongoing concerns regarding trust, safety, and security within the IoV network. Moreover, traditional security algorithms proposed for IoV are complex, with high computational demands that challenge the strict real-time constraints. To minimize the computational overhead of vehicles, we proposed an RSU-empowered proxy signature scheme for NDN-based IoV. The security of the proposed scheme is proven to be Existentially Unforgeable against Adaptive Chosen-Message Attacks (EU-ACMA) under the Random Oracle Model (ROM), considering the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HCDLP). A performance analysis, which considers both computation time and communication overhead, shows that the proposed scheme effectively minimizes these factors. Besides, we applied the Multi-Criteria Decision-Making (MCDM) technique, namely Evaluation based on Distance from Average Solution (EDAS), to meet the particular need to prioritize data in IoV. The findings show that the proposed scheme performs better than those in the related literature.
{"title":"A Lightweight Proxy Signature Scheme for Resource-Constrained NDN-Based Internet of Vehicles","authors":"Saddam Hussain;Ali Tufail;Haji Awg Abdul Ghani Naim;Muhammad Asghar Khan;Gordana Barb","doi":"10.1109/OJVT.2025.3606652","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3606652","url":null,"abstract":"Named Data Networking (NDN) is considered a future architecture for content distribution in the Internet of Vehicles (IoV). The primary principles of NDN, which include naming and in-network caching, are perfectly aligned with the IoV requirements for time and location independence. Despite significant research efforts, full-scale deployment remains limited due to ongoing concerns regarding trust, safety, and security within the IoV network. Moreover, traditional security algorithms proposed for IoV are complex, with high computational demands that challenge the strict real-time constraints. To minimize the computational overhead of vehicles, we proposed an RSU-empowered proxy signature scheme for NDN-based IoV. The security of the proposed scheme is proven to be Existentially Unforgeable against Adaptive Chosen-Message Attacks (EU-ACMA) under the Random Oracle Model (ROM), considering the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HCDLP). A performance analysis, which considers both computation time and communication overhead, shows that the proposed scheme effectively minimizes these factors. Besides, we applied the Multi-Criteria Decision-Making (MCDM) technique, namely Evaluation based on Distance from Average Solution (EDAS), to meet the particular need to prioritize data in IoV. The findings show that the proposed scheme performs better than those in the related literature.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2607-2626"},"PeriodicalIF":4.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210155","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 : 2025-09-04DOI: 10.1109/OJVT.2025.3606120
Reza Jafari;Pouria Sarhadi;Amin Paykani;Shady S. Refaat;Pedram Asef
This study presents an innovative solution for simultaneous energy optimization and dynamic yaw control of all-wheel-drive (AWD) electric vehicles (EVs) using deep reinforcement learning (DRL) techniques. To this end, three model-free DRL-based methods, based on deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and TD3 enhanced with curriculum learning (CL TD3), are developed for determining optimal yaw moment control and energy optimization online. The proposed DRL controllers are benchmarked against model-based controllers, i.e., linear quadratic regulator with the sequential quadratic programming (LSQP) and sliding mode control with SQP (SSQP). A tailored multi-term reward function is structured to penalize excessive yaw rate error, sideslip angle, tire slip deviations beyond peak grip regions, and power losses based on a realistic electric machine efficiency map. The learning environment is based on a nonlinear double-track vehicle model, incorporating tire-road interactions. To evaluate the generalizability of the algorithms, the agents are tested across various velocities, tire–road friction coefficients, and additional scenarios implemented in IPG CarMaker, a high-fidelity vehicle dynamics simulator. In addition to the deployment without requiring an explicit model of the plant, the simulation results demonstrate that the proposed solution modifies vehicle dynamics and maneuverability in most cases compared to the model-based conventional controller. Furthermore, the reduction in sideslip angle, excellent traction through minimizing tire slip ratio, avoiding oversteering and understeering, and maintaining an acceptable range of energy optimization are demonstrated for DRL controllers, especially for the TD3 and CL TD3 algorithms.
{"title":"Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle","authors":"Reza Jafari;Pouria Sarhadi;Amin Paykani;Shady S. Refaat;Pedram Asef","doi":"10.1109/OJVT.2025.3606120","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3606120","url":null,"abstract":"This study presents an innovative solution for simultaneous energy optimization and dynamic yaw control of all-wheel-drive (AWD) electric vehicles (EVs) using deep reinforcement learning (DRL) techniques. To this end, three model-free DRL-based methods, based on deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and TD3 enhanced with curriculum learning (CL TD3), are developed for determining optimal yaw moment control and energy optimization online. The proposed DRL controllers are benchmarked against model-based controllers, i.e., linear quadratic regulator with the sequential quadratic programming (LSQP) and sliding mode control with SQP (SSQP). A tailored multi-term reward function is structured to penalize excessive yaw rate error, sideslip angle, tire slip deviations beyond peak grip regions, and power losses based on a realistic electric machine efficiency map. The learning environment is based on a nonlinear double-track vehicle model, incorporating tire-road interactions. To evaluate the generalizability of the algorithms, the agents are tested across various velocities, tire–road friction coefficients, and additional scenarios implemented in IPG CarMaker, a high-fidelity vehicle dynamics simulator. In addition to the deployment without requiring an explicit model of the plant, the simulation results demonstrate that the proposed solution modifies vehicle dynamics and maneuverability in most cases compared to the model-based conventional controller. Furthermore, the reduction in sideslip angle, excellent traction through minimizing tire slip ratio, avoiding oversteering and understeering, and maintaining an acceptable range of energy optimization are demonstrated for DRL controllers, especially for the TD3 and CL TD3 algorithms.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2583-2606"},"PeriodicalIF":4.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210188","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 : 2025-09-04DOI: 10.1109/OJVT.2025.3606229
Siran Xu;Xiaomin Chen;Qiang Sun;Jiayi Zhang
In practical cell-free (CF) massive multiple-input multiple-output (mMIMO) networks, asynchronous reception occurs due to distributed and low-cost access points (APs), where the signals arrive at each AP at different time. In this paper, we investigate uplink (UL) spectral efficiency (SE) of asynchronous CF mMIMO with spatially correlated Rician fading channel. On the basis of the availability of prior information at APs, we derive the phase-aware minimum mean square error (MMSE) and non-perceptual linear MMSE (LMMSE) estimators. To mitigate the inter-user interference, we consider a two-layer decoding method in UL transmission. For the first-layer decoding, maximum ratio (MR) precoding is employed, while the large-scale fading decoding (LSFD) method is utilized in the second-layer decoding. Meanwhile, we consider the scenario in CF mMIMO where there is a large number of user equipment (UE), resulting in high computational complexity. To address this challenge, scalable CF mMIMO (SCF-mMIMO) architecture is proposed. On the basis of MMSE and LMMSE estimators, the novel low complexity partial MMSE (P-MMSE) detector and partial LMMSE (P-LMMSE) detector are proposed for centralized combining. For distributed combining, we also proposed the novel local partial MMSE (LP-MMSE) detector and local partial LMMSE (LP-LMMSE) detector. Numerical results demonstrate that LSFD method can enhance UL SE in CF mMIMO. Furthermore, the impact of performance loss resulting from the absence of phase information is contingent upon the length of pilot. It is minimal when pilot contamination is low. Finally, the simulation results demonstrate that the SE of the proposed detectors closely approximate the optimal combining technique for both distributed and centralized combing. It is important to note that the proposed detectors preserve performance while significantly lowering complexity.
{"title":"Uplink Performance Analysis of Asynchronous Cell-Free mMIMO With Two-Layer Decoding","authors":"Siran Xu;Xiaomin Chen;Qiang Sun;Jiayi Zhang","doi":"10.1109/OJVT.2025.3606229","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3606229","url":null,"abstract":"In practical cell-free (CF) massive multiple-input multiple-output (mMIMO) networks, asynchronous reception occurs due to distributed and low-cost access points (APs), where the signals arrive at each AP at different time. In this paper, we investigate uplink (UL) spectral efficiency (SE) of asynchronous CF mMIMO with spatially correlated Rician fading channel. On the basis of the availability of prior information at APs, we derive the phase-aware minimum mean square error (MMSE) and non-perceptual linear MMSE (LMMSE) estimators. To mitigate the inter-user interference, we consider a two-layer decoding method in UL transmission. For the first-layer decoding, maximum ratio (MR) precoding is employed, while the large-scale fading decoding (LSFD) method is utilized in the second-layer decoding. Meanwhile, we consider the scenario in CF mMIMO where there is a large number of user equipment (UE), resulting in high computational complexity. To address this challenge, scalable CF mMIMO (SCF-mMIMO) architecture is proposed. On the basis of MMSE and LMMSE estimators, the novel low complexity partial MMSE (P-MMSE) detector and partial LMMSE (P-LMMSE) detector are proposed for centralized combining. For distributed combining, we also proposed the novel local partial MMSE (LP-MMSE) detector and local partial LMMSE (LP-LMMSE) detector. Numerical results demonstrate that LSFD method can enhance UL SE in CF mMIMO. Furthermore, the impact of performance loss resulting from the absence of phase information is contingent upon the length of pilot. It is minimal when pilot contamination is low. Finally, the simulation results demonstrate that the SE of the proposed detectors closely approximate the optimal combining technique for both distributed and centralized combing. It is important to note that the proposed detectors preserve performance while significantly lowering complexity.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2494-2508"},"PeriodicalIF":4.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150737","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141680","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 : 2025-09-01DOI: 10.1109/OJVT.2025.3604823
Rajendramayavan Sathyam;Yueqi Li
Foundation models are revolutionizing autonomous driving perception, transitioning the field from narrow, task-specific deep learning models to versatile, general-purpose architectures trained on vast, diverse datasets. This survey examines how these models address critical challenges in autonomous perception, including limitations in generalization, scalability, and robustness to distributional shifts. The survey introduces a novel taxonomy structured around four essential capabilities for robust performance in dynamic driving environments: generalized knowledge, spatial understanding, multi-sensor robustness, and temporal reasoning. For each capability, the survey elucidates its significance and comprehensively reviews cutting-edge approaches. Diverging from traditional method-centric surveys, our unique framework prioritizes conceptual design principles, providing a capability-driven guide for model development and clearer insights into foundational aspects. We conclude by discussing key challenges, particularly those associated with the integration of these capabilities into real-time, scalable systems, and broader deployment challenges related to computational demands and ensuring model reliability against issues like hallucinations and out-of-distribution failures. The survey also outlines crucial future research directions to enable the safe and effective deployment of foundation models in autonomous driving systems.
{"title":"Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities","authors":"Rajendramayavan Sathyam;Yueqi Li","doi":"10.1109/OJVT.2025.3604823","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3604823","url":null,"abstract":"Foundation models are revolutionizing autonomous driving perception, transitioning the field from narrow, task-specific deep learning models to versatile, general-purpose architectures trained on vast, diverse datasets. This survey examines how these models address critical challenges in autonomous perception, including limitations in generalization, scalability, and robustness to distributional shifts. The survey introduces a novel taxonomy structured around four essential capabilities for robust performance in dynamic driving environments: generalized knowledge, spatial understanding, multi-sensor robustness, and temporal reasoning. For each capability, the survey elucidates its significance and comprehensively reviews cutting-edge approaches. Diverging from traditional method-centric surveys, our unique framework prioritizes conceptual design principles, providing a capability-driven guide for model development and clearer insights into foundational aspects. We conclude by discussing key challenges, particularly those associated with the integration of these capabilities into real-time, scalable systems, and broader deployment challenges related to computational demands and ensuring model reliability against issues like hallucinations and out-of-distribution failures. The survey also outlines crucial future research directions to enable the safe and effective deployment of foundation models in autonomous driving systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2554-2582"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11146457","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210033","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 : 2025-09-01DOI: 10.1109/OJVT.2025.3604561
Martin Zavrel;Pavel Drabek;Vladimir Kindl;Michal Frivaldsky
This article presents the design and development of a low-level control approach for a wireless charger intended for modern electro-mobility (e-mobility) applications. It outlines future trends in the e-mobility market and technical advancements in wireless power transfer (WPT) systems, aligning them with the proposed wireless charger design methodology. A key advantage of the proposed solution is its full competitiveness with conventional wired charging stations. The primary focus of this work is the control system design for the wireless charging station (WCS), which features active and optimal load impedance tracking. This tracking adapts to varying load parameters (such as battery characteristics) and misalignments in coupling elements, ensuring maximum power transfer efficiency and high-power transfer controlled by supply voltage. The system complies fully with the SAE J2954 standard for wireless charging in e-mobility. The developed test system achieves power transfer of up to 65 kW across an air gap of 15 to 25 cm, with an overall system efficiency exceeding 95.5%.
{"title":"Design of a 65-kW Wireless Charging Station Characterized by Optimal Load Impedance Tracking Control","authors":"Martin Zavrel;Pavel Drabek;Vladimir Kindl;Michal Frivaldsky","doi":"10.1109/OJVT.2025.3604561","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3604561","url":null,"abstract":"This article presents the design and development of a low-level control approach for a wireless charger intended for modern electro-mobility (e-mobility) applications. It outlines future trends in the e-mobility market and technical advancements in wireless power transfer (WPT) systems, aligning them with the proposed wireless charger design methodology. A key advantage of the proposed solution is its full competitiveness with conventional wired charging stations. The primary focus of this work is the control system design for the wireless charging station (WCS), which features active and optimal load impedance tracking. This tracking adapts to varying load parameters (such as battery characteristics) and misalignments in coupling elements, ensuring maximum power transfer efficiency and high-power transfer controlled by supply voltage. The system complies fully with the SAE J2954 standard for wireless charging in e-mobility. The developed test system achieves power transfer of up to 65 kW across an air gap of 15 to 25 cm, with an overall system efficiency exceeding 95.5%.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2462-2478"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11145950","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110277","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 : 2025-08-28DOI: 10.1109/OJVT.2025.3603690
Yi Zhao;Mohammed El-Hajjar;Lie-Liang Yang
Future wireless communications are expected to support massive connectivity in various applications, such as massive Machine-Type Communications (mMTC) and different types of IoT networks, where many applications have the data traffic of sporadicnature. To support these kinds of applications, grant free multiple-access (GFMA) has been recognized to be more efficient thanthe conventional granted multiple access (GMA). However, due to sporadic transmission, GFMA faces the main challenges of User Activity Detection (UAD) and Channel Estimation (CE). To meet these challenges, in this paper, a multicarrier GFMA (MC-GFMA) system is introduced for supporting massive connectivity. A block-sparse signal model is derived, where the Expectation Maximization assisted Block Sparse Bayesian Learning (EM-BSBL) algorithm is employed to solve the joint UAD and CE problem. Furthermore, to augment the performance of EM-BSBL algorithm in GFMA systems, the statistical properties of the activity weights generated by EM-BSBL algorithm are investigated, showing that the activity weights follow closely the Gamma distribution. Then, using the Gamma modelling of the activity weights, the Neyman-Pearson (NP) method is considered for optimizing the threshold used for decision making in the EM-BSBL algorithm. Finally, the performance of GFMA systems is comprehensively studied by numerical simulations. Our results and analysis demonstrate that MC-GFMA is a feasible signalling scheme for supporting a massive number of users transmitting sporadic information. With the aid of the EM-BSBL algorithm enhanced by the NP-assisted threshold optimization, MC-GFMA is robust for operation in the communications environments where active users are random and the number of them is highly dynamic.
{"title":"Joint User Activity Detection and Channel Estimation in MC-GFMA Systems by Block Sparse Bayesian Learning With Threshold Optimization","authors":"Yi Zhao;Mohammed El-Hajjar;Lie-Liang Yang","doi":"10.1109/OJVT.2025.3603690","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3603690","url":null,"abstract":"Future wireless communications are expected to support massive connectivity in various applications, such as massive Machine-Type Communications (mMTC) and different types of IoT networks, where many applications have the data traffic of sporadicnature. To support these kinds of applications, grant free multiple-access (GFMA) has been recognized to be more efficient thanthe conventional granted multiple access (GMA). However, due to sporadic transmission, GFMA faces the main challenges of User Activity Detection (UAD) and Channel Estimation (CE). To meet these challenges, in this paper, a multicarrier GFMA (MC-GFMA) system is introduced for supporting massive connectivity. A block-sparse signal model is derived, where the Expectation Maximization assisted Block Sparse Bayesian Learning (EM-BSBL) algorithm is employed to solve the joint UAD and CE problem. Furthermore, to augment the performance of EM-BSBL algorithm in GFMA systems, the statistical properties of the activity weights generated by EM-BSBL algorithm are investigated, showing that the activity weights follow closely the Gamma distribution. Then, using the Gamma modelling of the activity weights, the Neyman-Pearson (NP) method is considered for optimizing the threshold used for decision making in the EM-BSBL algorithm. Finally, the performance of GFMA systems is comprehensively studied by numerical simulations. Our results and analysis demonstrate that MC-GFMA is a feasible signalling scheme for supporting a massive number of users transmitting sporadic information. With the aid of the EM-BSBL algorithm enhanced by the NP-assisted threshold optimization, MC-GFMA is robust for operation in the communications environments where active users are random and the number of them is highly dynamic.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2441-2458"},"PeriodicalIF":4.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11143207","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060960","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 : 2025-08-27DOI: 10.1109/OJVT.2025.3603417
An-Toan Nguyen;Binh-Minh Nguyen;João Pedro F. Trovão;Minh C. Ta
Multi-motor electric vehicles (MMEVs) present complex challenges for control and optimization due to the distribution of control actions and state variables across multiple subsystems and hierarchical levels. Although electric vehicle (EV) modeling has been widely studied, accurately capturing and optimizing the longitudinal energy efficiency and dynamic performance of MMEVs remains a significant challenge. This complexity is further increased by the presence of different motor types, such as induction motors (IMs) and permanent magnet synchronous motors (PMSMs), and various mechanical configurations in all-wheel drive systems. To address these issues, this paper proposes a global-local modeling framework that extends the Energetic Macroscopic Representation (EMR) methodology. The framework integrates detailed models of the electrical drive system with comprehensive mechanical subsystem modeling, including gearbox, differential, half-shafts, wheels, and tires. A global input power model links local control actions and state variables to overall energy flow, supporting a unified approach to longitudinal motion control and energy optimization. In contrast to conventional EMR-based models, the proposed framework explicitly incorporates driveline and tire dynamics, which significantly affect energy consumption due to drivetrain losses and tire slip. The model is evaluated through two scenarios that assess the effects of drivetrain modeling and force distribution strategies. The results show improved control system performance and enhanced energy efficiency, supporting future advancements in longitudinal dynamics modeling for MMEV.
{"title":"An Integrated Modeling Framework for Motion Control and Energy Management in Multi-Motor Electric Vehicles","authors":"An-Toan Nguyen;Binh-Minh Nguyen;João Pedro F. Trovão;Minh C. Ta","doi":"10.1109/OJVT.2025.3603417","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3603417","url":null,"abstract":"Multi-motor electric vehicles (MMEVs) present complex challenges for control and optimization due to the distribution of control actions and state variables across multiple subsystems and hierarchical levels. Although electric vehicle (EV) modeling has been widely studied, accurately capturing and optimizing the longitudinal energy efficiency and dynamic performance of MMEVs remains a significant challenge. This complexity is further increased by the presence of different motor types, such as induction motors (IMs) and permanent magnet synchronous motors (PMSMs), and various mechanical configurations in all-wheel drive systems. To address these issues, this paper proposes a global-local modeling framework that extends the Energetic Macroscopic Representation (EMR) methodology. The framework integrates detailed models of the electrical drive system with comprehensive mechanical subsystem modeling, including gearbox, differential, half-shafts, wheels, and tires. A global input power model links local control actions and state variables to overall energy flow, supporting a unified approach to longitudinal motion control and energy optimization. In contrast to conventional EMR-based models, the proposed framework explicitly incorporates driveline and tire dynamics, which significantly affect energy consumption due to drivetrain losses and tire slip. The model is evaluated through two scenarios that assess the effects of drivetrain modeling and force distribution strategies. The results show improved control system performance and enhanced energy efficiency, supporting future advancements in longitudinal dynamics modeling for MMEV.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2479-2493"},"PeriodicalIF":4.8,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141696","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}
Automotive cameras are cost effective among perception sensors and have been widely deployed on cars, however the measurements generated by cameras can be affected by various noise factors. Flare, also known as straylight, is a common noise factor especially during night. Automotive headlights can be dazzling for human drivers and might be also challenging for Assisted and Automated Driving (AAD) functions. To enable higher levels of driving automation, investigating and testing this noise factor can be key to achieve AAD in challenging lighting conditions. Therefore, accurate automotive camera flare models need to be thoroughly investigated and developed. However, current camera datasets lack accuracy in representing state-of-the-art automotive cameras. This paper develops, describes, and validates an automotive specific parametrised method for modelling flare induced by automotive headlights. The presented model is validated with real-data and can be fine tuned to fit different types of automotive cameras and headlights. Additionally, this paper introduces a method to seamlessly integrate the modelling results into images generated by simulation platforms, such as CARLA and IPG CarMaker. Using the newly proposed model, automotive datasets with and without the realistic headlight flare can be generated. Overall, the integration of modelled flare provides a framework for accelerating the simulation and testing of assisted and automated driving functions.
{"title":"Data-Driven Headlight Flare Model for Automotive Cameras","authors":"Boda Li;Hetian Wang;Yiting Wang;Pak Hung Chan;Darryl Perks;Valentina Donzella","doi":"10.1109/OJVT.2025.3601400","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3601400","url":null,"abstract":"Automotive cameras are cost effective among perception sensors and have been widely deployed on cars, however the measurements generated by cameras can be affected by various noise factors. Flare, also known as straylight, is a common noise factor especially during night. Automotive headlights can be dazzling for human drivers and might be also challenging for Assisted and Automated Driving (AAD) functions. To enable higher levels of driving automation, investigating and testing this noise factor can be key to achieve AAD in challenging lighting conditions. Therefore, accurate automotive camera flare models need to be thoroughly investigated and developed. However, current camera datasets lack accuracy in representing state-of-the-art automotive cameras. This paper develops, describes, and validates an automotive specific parametrised method for modelling flare induced by automotive headlights. The presented model is validated with real-data and can be fine tuned to fit different types of automotive cameras and headlights. Additionally, this paper introduces a method to seamlessly integrate the modelling results into images generated by simulation platforms, such as CARLA and IPG CarMaker. Using the newly proposed model, automotive datasets with and without the realistic headlight flare can be generated. Overall, the integration of modelled flare provides a framework for accelerating the simulation and testing of assisted and automated driving functions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2709-2720"},"PeriodicalIF":4.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255912","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 : 2025-08-21DOI: 10.1109/OJVT.2025.3601542
Dong-Hua Chen;Peifu Peng
In case the dual-functional base station (BS) is only equipped with half-duplex (HD) transceivers, integrated sensing and communications (ISAC) becomes a challenge task especially when the bidirectional communications for each HD user are involved. To address this situation, under the framework of a wireless network with two adjacent cells and with the aid of BSs cooperation, this paper presents two integrated cooperative sensing and bidirectional communication schemes that involve two and four transmission phases, respectively. Power minimization problems under the constrains of bidirectional communication rates and sensing signal to interference plus noise ratios (SINRs) are formulated for optimizing the downlink transmit beamforming vectors, uplink transmit power, and transmission time of each phase. Due to variables coupling, the problems are shown to be non-linear and non-convex. Relying on the successive convex approximation, iterative algorithms that are guaranteed to be convergent are derived to obtain these design variables. Simulations show that both of the proposed schemes well accomplish the bidirectional communications and cooperative target sensing in the considered situation. By contrast, the scheme with two transmission phases possesses lower implementation complexity while the scheme with four transmission phases owns the performance advantage. When uplink non-orthogonal multiple access is further used, the performance difference between the two schemes is reduced substantially.
{"title":"Integrated Cooperative Sensing and Two-Way Communications With Half-Duplex Base Stations","authors":"Dong-Hua Chen;Peifu Peng","doi":"10.1109/OJVT.2025.3601542","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3601542","url":null,"abstract":"In case the dual-functional base station (BS) is only equipped with half-duplex (HD) transceivers, integrated sensing and communications (ISAC) becomes a challenge task especially when the bidirectional communications for each HD user are involved. To address this situation, under the framework of a wireless network with two adjacent cells and with the aid of BSs cooperation, this paper presents two integrated cooperative sensing and bidirectional communication schemes that involve two and four transmission phases, respectively. Power minimization problems under the constrains of bidirectional communication rates and sensing signal to interference plus noise ratios (SINRs) are formulated for optimizing the downlink transmit beamforming vectors, uplink transmit power, and transmission time of each phase. Due to variables coupling, the problems are shown to be non-linear and non-convex. Relying on the successive convex approximation, iterative algorithms that are guaranteed to be convergent are derived to obtain these design variables. Simulations show that both of the proposed schemes well accomplish the bidirectional communications and cooperative target sensing in the considered situation. By contrast, the scheme with two transmission phases possesses lower implementation complexity while the scheme with four transmission phases owns the performance advantage. When uplink non-orthogonal multiple access is further used, the performance difference between the two schemes is reduced substantially.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2392-2405"},"PeriodicalIF":4.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11134088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021312","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 : 2025-08-19DOI: 10.1109/OJVT.2025.3600512
Qiang Wang;Yongchong Xue;Shuchang Lyu;Guangliang Cheng;Shaoyan Yang;Xin Jin
Effective autonomous driving systems require a delicate balance of high precision, efficient design, and immediate response capabilities. This study presents MTFENet, a cutting-edge multi-task deep learning model that optimizes network architecture to harmonize speed and accuracy for critical tasks such as object detection, drivable area segmentation, and lane line segmentation. Our end-to-end, streamlined multi-task model incorporates an Adaptive Feature Fusion Module (AF$^{2}$M) to manage the diverse feature demands of different tasks. We also introduced a fusion transform module (FTM) to strengthen global feature extraction and a novel detection head to address target loss and confusion. To enhance computational efficiency, we refined the segmentation head design. Experiments on the BDD100k dataset reveal that MTFENet delivers exceptional performance, achieving an mAP50 of 81.5% in object detection, an mIoU of 93.8% in drivable area segmentation, and an IoU of 33.7% in lane line segmentation. Real-world scenario evaluations demonstrate that MTFENet substantially outperforms current state-of-the-art models across multiple tasks, highlighting its superior adaptability and swift response. These results underscore that MTFENet not only leads in precision and speed but also bolsters the reliability and adaptability of autonomous driving systems in navigating complex road conditions.
{"title":"MTFENet: A Multi-Task Autonomous Driving Network for Real-Time Target Perception","authors":"Qiang Wang;Yongchong Xue;Shuchang Lyu;Guangliang Cheng;Shaoyan Yang;Xin Jin","doi":"10.1109/OJVT.2025.3600512","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3600512","url":null,"abstract":"Effective autonomous driving systems require a delicate balance of high precision, efficient design, and immediate response capabilities. This study presents MTFENet, a cutting-edge multi-task deep learning model that optimizes network architecture to harmonize speed and accuracy for critical tasks such as object detection, drivable area segmentation, and lane line segmentation. Our end-to-end, streamlined multi-task model incorporates an Adaptive Feature Fusion Module (AF<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>M) to manage the diverse feature demands of different tasks. We also introduced a fusion transform module (FTM) to strengthen global feature extraction and a novel detection head to address target loss and confusion. To enhance computational efficiency, we refined the segmentation head design. Experiments on the BDD100k dataset reveal that MTFENet delivers exceptional performance, achieving an mAP50 of 81.5% in object detection, an mIoU of 93.8% in drivable area segmentation, and an IoU of 33.7% in lane line segmentation. Real-world scenario evaluations demonstrate that MTFENet substantially outperforms current state-of-the-art models across multiple tasks, highlighting its superior adaptability and swift response. These results underscore that MTFENet not only leads in precision and speed but also bolsters the reliability and adaptability of autonomous driving systems in navigating complex road conditions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2406-2423"},"PeriodicalIF":4.8,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11130405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027938","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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