Autonomous and semi-autonomous vehicles require accurate perception of their surrounding environment to ensure safe operation, yet onboard sensors frequently encounter occlusion challenges that result in incomplete dynamic environmental maps. Infrastructure-to-vehicle cooperative perception addresses this by deploying infrastructure nodes that monitor scenes and share reliable environmental maps with nearby vehicles via technologies like C-V2X. However, existing infrastructure perspective datasets lack diverse multi-modal data and aerial footage, which are crucial to determine effectively the necessary sensors for safety-critical infrastructure node applications. This paper introduces G-MIND, a multimodal infrastructure node dataset supporting research into sensor suitability for infrastructure-assisted safety-critical applications. G-MIND is the first dataset to incorporate this comprehensive range of sensing modalities for infrastructure-based perception: RGB, FIR, and neuromorphic cameras, LiDARs, RADAR, and aerial drone footage. With 91,500 annotated frames, G-MIND offers a larger scale than existing infrastructure perception datasets such as Ko-PER (10 k frames), CoopScenes (40 K frames), and DAIR-V2X (71 k frames), enabling more comprehensive training and evaluation. The dataset captures day and night scenarios featuring cars, pedestrians, and cyclists across diverse traffic scenarios. Beyond standard perception benchmarking, G-MIND includes specialized collections designed to test perception system boundaries: maximum detection distance scenarios, far and occluded object scenarios, and pedestrian action prediction scenarios that challenge current algorithms. Additionally, this paper analyzes what constitutes effective ITS infrastructure node sensors from a practical perspective, comparing modalities against technical criteria (field of view, spatial resolution, low light performance, adverse weather resilience) and pragmatic criteria (cost, durability).
{"title":"G-MIND: Galway Multimodal Infrastructure Node Dataset for Intelligent Transportation Systems","authors":"Dara Molloy;Roshan George;Tim Brophy;Brian Deegan;Darragh Mullins;Enda Ward;Jonathan Horgan;Ciaran Eising;Patrick Denny;Edward Jones;Martin Glavin","doi":"10.1109/OJVT.2025.3648251","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3648251","url":null,"abstract":"Autonomous and semi-autonomous vehicles require accurate perception of their surrounding environment to ensure safe operation, yet onboard sensors frequently encounter occlusion challenges that result in incomplete dynamic environmental maps. Infrastructure-to-vehicle cooperative perception addresses this by deploying infrastructure nodes that monitor scenes and share reliable environmental maps with nearby vehicles via technologies like C-V2X. However, existing infrastructure perspective datasets lack diverse multi-modal data and aerial footage, which are crucial to determine effectively the necessary sensors for safety-critical infrastructure node applications. This paper introduces G-MIND, a multimodal infrastructure node dataset supporting research into sensor suitability for infrastructure-assisted safety-critical applications. G-MIND is the first dataset to incorporate this comprehensive range of sensing modalities for infrastructure-based perception: RGB, FIR, and neuromorphic cameras, LiDARs, RADAR, and aerial drone footage. With 91,500 annotated frames, G-MIND offers a larger scale than existing infrastructure perception datasets such as Ko-PER (10 k frames), CoopScenes (40 K frames), and DAIR-V2X (71 k frames), enabling more comprehensive training and evaluation. The dataset captures day and night scenarios featuring cars, pedestrians, and cyclists across diverse traffic scenarios. Beyond standard perception benchmarking, G-MIND includes specialized collections designed to test perception system boundaries: maximum detection distance scenarios, far and occluded object scenarios, and pedestrian action prediction scenarios that challenge current algorithms. Additionally, this paper analyzes what constitutes effective ITS infrastructure node sensors from a practical perspective, comparing modalities against technical criteria (field of view, spatial resolution, low light performance, adverse weather resilience) and pragmatic criteria (cost, durability).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"491-509"},"PeriodicalIF":4.8,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11316264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082040","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}
This article investigates the performance of non-orthogonal multiple access (NOMA) integrated with automatic repeat request (ARQ) for point-to-point Internet of Things (IoT) wireless backhaul links. The proposed framework is generalized accommodate an arbitrary number of users, incorporates round–robin (RR) scheduling, and use iterative interference cancellation (IIC) with chase combining (CC) to enhance fair power allocation while reducing the packet drop rate (PDR). The results reveal that NOMA-ARQ with IIC significantly outperforms conventional orthogonal multiple access (OMA) in various scenarios. The signal-to-noise (SNR) gain in PDR performance is particularly evident in moderate to high SNR conditions, ranging from 13 to 35 dB, and for scenarios involving three users with multiple transmission attempts. The proposed approach demonstrates up to a threefold increase in system throughput compared to OMA, highlighting its potential for enhancing backhaul performance in dense IoT deployments. These findings highlight the potential of IIC-assisted ARQ-NOMA to address the requirements for spectrum efficiency and data reliability effectively.
{"title":"Generalized NOMA-ARQ With Round-Robin IIC for IoT Systems","authors":"Meshari Alshehri;Emad Alsusa;Mahmoud Alaaeldin;Arafat Al-Dweik","doi":"10.1109/OJVT.2025.3648320","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3648320","url":null,"abstract":"This article investigates the performance of non-orthogonal multiple access (NOMA) integrated with automatic repeat request (ARQ) for point-to-point Internet of Things (IoT) wireless backhaul links. The proposed framework is generalized accommodate an arbitrary number of users, incorporates round–robin (RR) scheduling, and use iterative interference cancellation (IIC) with chase combining (CC) to enhance fair power allocation while reducing the packet drop rate (PDR). The results reveal that NOMA-ARQ with IIC significantly outperforms conventional orthogonal multiple access (OMA) in various scenarios. The signal-to-noise (SNR) gain in PDR performance is particularly evident in moderate to high SNR conditions, ranging from 13 to 35 dB, and for scenarios involving three users with multiple transmission attempts. The proposed approach demonstrates up to a threefold increase in system throughput compared to OMA, highlighting its potential for enhancing backhaul performance in dense IoT deployments. These findings highlight the potential of IIC-assisted ARQ-NOMA to address the requirements for spectrum efficiency and data reliability effectively.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"306-324"},"PeriodicalIF":4.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11314727","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929654","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}
Experiments on dynamic wireless power transfer (DWPT) for electric vehicles (EVs) typically require vast testing grounds and significant costs, making them difficult to implement. As a solution, experimental systems utilizing rotational motion for DWPT have been proposed. However, few such systems have been developed and their design methods and fundamental characteristics remain unclear. This paper presents the construction of a rotation-type experimental system for DWPT and discusses its design methodology and basic characteristics. In developing the prototype, mechanical stability was verified through simulations analyzing stress and critical rotational speed of the rotating components. A sector-shaped transmitter coil, suitable for this system, was designed and analyzed using electromagnetic field simulations, confirming similar characteristics to conventional linear-rail-based systems. Furthermore, the effects of misalignment between transmitter and receiver coils and the electrical characteristics of slip rings and brushes were evaluated. Finally, power transfer characteristics were validated through experiments at a linear velocity equivalent of 40 km/h and 3.3 kW power transfer.
{"title":"Analysis, Design, and Demonstration for Rotation-Type Experimental System of Dynamic Wireless Power Transfer","authors":"Takachika Hatano;Ryosuke Ota;Daiki Satou;Hiroyasu Kobayashi","doi":"10.1109/OJVT.2025.3647943","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3647943","url":null,"abstract":"Experiments on dynamic wireless power transfer (DWPT) for electric vehicles (EVs) typically require vast testing grounds and significant costs, making them difficult to implement. As a solution, experimental systems utilizing rotational motion for DWPT have been proposed. However, few such systems have been developed and their design methods and fundamental characteristics remain unclear. This paper presents the construction of a rotation-type experimental system for DWPT and discusses its design methodology and basic characteristics. In developing the prototype, mechanical stability was verified through simulations analyzing stress and critical rotational speed of the rotating components. A sector-shaped transmitter coil, suitable for this system, was designed and analyzed using electromagnetic field simulations, confirming similar characteristics to conventional linear-rail-based systems. Furthermore, the effects of misalignment between transmitter and receiver coils and the electrical characteristics of slip rings and brushes were evaluated. Finally, power transfer characteristics were validated through experiments at a linear velocity equivalent of 40 km/h and 3.3 kW power transfer.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"325-334"},"PeriodicalIF":4.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11314628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982238","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 : 2025-12-24DOI: 10.1109/OJVT.2025.3647818
Feiyan Guo;Xiaoqing Luo;Xingshu Liu
With the rapid advancement of autonomous driving technologies, Vehicle-to-Everything (V2X) networks are recognized as pivotal in enhancing the efficiency and safety of intelligent transportation systems. However, the highly dynamic and mobile nature of V2X environments poses considerable challenges for task offloading, particularly in achieving low delay, high energy efficiency and task success rate maximization. To address these issues, this study introduces the Dynamic Task Offloading Framework (DTOF) and the Hybrid Integrated Offloading Algorithm (HIOA). The DTOF incorporates dynamic task segmentation with cross-layer resource allocation, thereby improving adaptability under high mobility conditions, with vehicle speeds ranging from 20 to 120 km/h. The HIOA integrates deep reinforcement learning (DRL) with game-theoretic methods to achieve near-optimal multi-objective optimization, encompassing delay minimization and energy efficiency improvement across vehicle densities of 30 to 80 vehicles/km$^{2}$. Specifically designed to satisfy the delay requirement for Level 4 and beyond autonomous driving, the HIOA ensures both low delay and energy efficiency. Hybrid simulations combining SUMO traffic modeling and a 5 G New Radio (NR) channel model demonstrate that the HIOA achieves superior performance compared to existing approaches. Under typical operating conditions, it reduces service access delay by 25%,lowers energy consumption by 18% and elevates task success rate by 30%. Moreover, the HIOA maintains robust performance under peak traffic scenarios (80 vehicles/km$^{2}$) and amid infrastructure impairments (30% RSU failure rate). This work substantially augments the efficiency and adaptability of V2X, establishing a solid groundwork for further development of autonomous driving technologies. Future research will investigate federated learning for cross-domain collaboration and refine the integration of game-theoretic and DRL mechanisms to further reduce computational complexity.
{"title":"Hybrid Cloud-Edge-Vehicle Collaborative Task Offloading in VEC Based on Reinforcement Learning and Game Theory","authors":"Feiyan Guo;Xiaoqing Luo;Xingshu Liu","doi":"10.1109/OJVT.2025.3647818","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3647818","url":null,"abstract":"With the rapid advancement of autonomous driving technologies, Vehicle-to-Everything (V2X) networks are recognized as pivotal in enhancing the efficiency and safety of intelligent transportation systems. However, the highly dynamic and mobile nature of V2X environments poses considerable challenges for task offloading, particularly in achieving low delay, high energy efficiency and task success rate maximization. To address these issues, this study introduces the Dynamic Task Offloading Framework (DTOF) and the Hybrid Integrated Offloading Algorithm (HIOA). The DTOF incorporates dynamic task segmentation with cross-layer resource allocation, thereby improving adaptability under high mobility conditions, with vehicle speeds ranging from 20 to 120 km/h. The HIOA integrates deep reinforcement learning (DRL) with game-theoretic methods to achieve near-optimal multi-objective optimization, encompassing delay minimization and energy efficiency improvement across vehicle densities of 30 to 80 vehicles/km<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>. Specifically designed to satisfy the delay requirement for Level 4 and beyond autonomous driving, the HIOA ensures both low delay and energy efficiency. Hybrid simulations combining SUMO traffic modeling and a 5 G New Radio (NR) channel model demonstrate that the HIOA achieves superior performance compared to existing approaches. Under typical operating conditions, it reduces service access delay by 25%,lowers energy consumption by 18% and elevates task success rate by 30%. Moreover, the HIOA maintains robust performance under peak traffic scenarios (80 vehicles/km<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>) and amid infrastructure impairments (30% RSU failure rate). This work substantially augments the efficiency and adaptability of V2X, establishing a solid groundwork for further development of autonomous driving technologies. Future research will investigate federated learning for cross-domain collaboration and refine the integration of game-theoretic and DRL mechanisms to further reduce computational complexity.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"290-305"},"PeriodicalIF":4.8,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11314623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929681","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 : 2025-12-22DOI: 10.1109/OJVT.2025.3646814
Ayoub Aroua;Walter Lhomme;Clément Depature;Philippe Adam;Matthieu Renault
This paper investigates the fuel-saving potential of a retrofitted diesel hybrid dual-mode train based on energy management optimization and different railway operation scenarios. Dual-mode trains offer high operational flexibility by operating on both electrified and non-electrified tracks. The presented investigation begins with experimental campaigns on real trains currently operating in France. The results revealed a 22% to 25% reduction in fuel consumption for the retrofitted hybrid train compared to its conventional counterpart. These outcomes, accomplished using a rules-based energy management strategy following a charge-sustaining concept, reflect a suboptimal utilization of the hybridization potential. To fully exploit this potential, the energy management is herein optimized through the dynamic programming method, following a charge-depleting concept. This permits leveraging different factors, such as the large size of batteries afforded by the retrofitting procedure, the recharging opportunities provided by the rail electrical infrastructure, the knowledge of the track characteristics, and the daily operational missions. This approach is analogous to the operational logic of on-road plug-in hybrid electric vehicles. The optimization is conducted on daily rail operational schedules under three real-world scenarios, which vary based on their authorization of intermediate recharging using the catenary as well as the online transition from catenary-free to catenary-powered operation. On average, the optimization results, over the daily schedules and the defined scenarios, reveal that the fuel saving ranges from 4% to 37% relative to the charge-sustaining. When compared with the conventional version of the train, this translates into a fuel-saving of 25% to 51%.
{"title":"Fuel-Saving Potential of a Retrofitted Diesel Hybrid Dual-Mode Train","authors":"Ayoub Aroua;Walter Lhomme;Clément Depature;Philippe Adam;Matthieu Renault","doi":"10.1109/OJVT.2025.3646814","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3646814","url":null,"abstract":"This paper investigates the fuel-saving potential of a retrofitted diesel hybrid dual-mode train based on energy management optimization and different railway operation scenarios. Dual-mode trains offer high operational flexibility by operating on both electrified and non-electrified tracks. The presented investigation begins with experimental campaigns on real trains currently operating in France. The results revealed a 22% to 25% reduction in fuel consumption for the retrofitted hybrid train compared to its conventional counterpart. These outcomes, accomplished using a rules-based energy management strategy following a charge-sustaining concept, reflect a suboptimal utilization of the hybridization potential. To fully exploit this potential, the energy management is herein optimized through the dynamic programming method, following a charge-depleting concept. This permits leveraging different factors, such as the large size of batteries afforded by the retrofitting procedure, the recharging opportunities provided by the rail electrical infrastructure, the knowledge of the track characteristics, and the daily operational missions. This approach is analogous to the operational logic of on-road plug-in hybrid electric vehicles. The optimization is conducted on daily rail operational schedules under three real-world scenarios, which vary based on their authorization of intermediate recharging using the catenary as well as the online transition from catenary-free to catenary-powered operation. On average, the optimization results, over the daily schedules and the defined scenarios, reveal that the fuel saving ranges from 4% to 37% relative to the charge-sustaining. When compared with the conventional version of the train, this translates into a fuel-saving of 25% to 51%.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"402-417"},"PeriodicalIF":4.8,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11308122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026359","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 : 2025-12-10DOI: 10.1109/OJVT.2025.3642724
Bingkui Li;Lei Zhuang;Sijin Yang;Jianhui Zhang
Within In-Vehicle Time-Sensitive Networking (IVTSN), periodic Time-Triggered (TT) and sporadic Event-Triggered (ET) data streams coexist in a shared communication infrastructure. TT streams require deterministic transmission, whereas ET streams frequently carry critical data that demands low-latency delivery. Conventional Time-Aware Shaper (TAS) mechanisms, while effective in maintaining determinism for TT traffic, are inadequate for handling the bursty and time-sensitive characteristics of ET streams, resulting in degraded real-time performance and compromised safety and reliability in IVTSN systems. To overcome these challenges, this study introduces a novel hybrid scheduling strategy based on the Time-Slot Offset Difference (TOD) between adjacent transmission links. The proposed approach integrates a global TOD-based hybrid scheduling algorithm for static time-slot allocation with a local dynamic adaptation mechanism that adjusts ET transmission at runtime. This combination ensures deterministic delivery of TT streams while significantly reducing the end-to-end delay of ET streams. Simulation results on the OMNeT++ platform demonstrate that the proposed strategy outperforms the baseline TAS and eTAS methods, effectively addressing the coexistence challenges of TT and ET streams and enhancing the overall transmission performance of IVTSN systems.
{"title":"Hybrid Scheduling of Time-Triggered and Event-Triggered Streams for In-Vehicle Time-Sensitive Networking","authors":"Bingkui Li;Lei Zhuang;Sijin Yang;Jianhui Zhang","doi":"10.1109/OJVT.2025.3642724","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3642724","url":null,"abstract":"Within In-Vehicle Time-Sensitive Networking (IVTSN), periodic Time-Triggered (TT) and sporadic Event-Triggered (ET) data streams coexist in a shared communication infrastructure. TT streams require deterministic transmission, whereas ET streams frequently carry critical data that demands low-latency delivery. Conventional Time-Aware Shaper (TAS) mechanisms, while effective in maintaining determinism for TT traffic, are inadequate for handling the bursty and time-sensitive characteristics of ET streams, resulting in degraded real-time performance and compromised safety and reliability in IVTSN systems. To overcome these challenges, this study introduces a novel hybrid scheduling strategy based on the Time-Slot Offset Difference (TOD) between adjacent transmission links. The proposed approach integrates a global TOD-based hybrid scheduling algorithm for static time-slot allocation with a local dynamic adaptation mechanism that adjusts ET transmission at runtime. This combination ensures deterministic delivery of TT streams while significantly reducing the end-to-end delay of ET streams. Simulation results on the OMNeT++ platform demonstrate that the proposed strategy outperforms the baseline TAS and eTAS methods, effectively addressing the coexistence challenges of TT and ET streams and enhancing the overall transmission performance of IVTSN systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"249-259"},"PeriodicalIF":4.8,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293791","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886645","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 : 2025-12-10DOI: 10.1109/OJVT.2025.3642721
A. Mirzaee-Sisan;M. Alilou;A. M. Shotorbani;B. Mohammadi-Ivatloo;A. Asadi-rad
Electric vehicles (EVs) have the potential to revolutionize the energy and transportation sectors, yet widespread adoption faces challenges, notably the complexity of managing energy demand. While prior studies have modeled EV impacts in developed economies, little work has analyzed such impacts under the constraints of fossil fuel-dominated, subsidy-heavy systems like Iran. So, this paper investigates the impacts of EV integration on Iran’s energy and transportation infrastructure, advocating that EVs are instrumental in decarbonizing and grid balancing. Our focus turns to Iran’s energy landscape as a compelling case study for a fossil-fuel-rich country, due to its specific geographical aspects and unique energy sector challenges. The study extensively analyses historical peak demand data and national statistics, underscoring the urgent need for more sustainable energy management practices and the modernization of transportation systems. The analysis emphasizes the critical challenge posed by surging peak power demand in Iran while highlighting the pivotal role that EVs could play in reshaping Iran’s transportation and energy sectors. Numerical analysis reveals that managing EV energy through V1G and V2G can help alleviate the peak demands, providing a flexible alternative to traditional network upgrades. Moreover, the calculated estimates of peak power demand for unconstrained charging versus the impact of V1G and V2G can assist decision-makers in assessing future energy flexibility requirements, identifying strategies to overcome potential barriers to EV adoption, and exploring different scenarios.
{"title":"Impacts of Electric Vehicle Integration on Transportation and Energy Systems: Case Study in Iran","authors":"A. Mirzaee-Sisan;M. Alilou;A. M. Shotorbani;B. Mohammadi-Ivatloo;A. Asadi-rad","doi":"10.1109/OJVT.2025.3642721","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3642721","url":null,"abstract":"Electric vehicles (EVs) have the potential to revolutionize the energy and transportation sectors, yet widespread adoption faces challenges, notably the complexity of managing energy demand. While prior studies have modeled EV impacts in developed economies, little work has analyzed such impacts under the constraints of fossil fuel-dominated, subsidy-heavy systems like Iran. So, this paper investigates the impacts of EV integration on Iran’s energy and transportation infrastructure, advocating that EVs are instrumental in decarbonizing and grid balancing. Our focus turns to Iran’s energy landscape as a compelling case study for a fossil-fuel-rich country, due to its specific geographical aspects and unique energy sector challenges. The study extensively analyses historical peak demand data and national statistics, underscoring the urgent need for more sustainable energy management practices and the modernization of transportation systems. The analysis emphasizes the critical challenge posed by surging peak power demand in Iran while highlighting the pivotal role that EVs could play in reshaping Iran’s transportation and energy sectors. Numerical analysis reveals that managing EV energy through V1G and V2G can help alleviate the peak demands, providing a flexible alternative to traditional network upgrades. Moreover, the calculated estimates of peak power demand for unconstrained charging versus the impact of V1G and V2G can assist decision-makers in assessing future energy flexibility requirements, identifying strategies to overcome potential barriers to EV adoption, and exploring different scenarios.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"335-353"},"PeriodicalIF":4.8,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11296851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982282","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 : 2025-12-10DOI: 10.1109/OJVT.2025.3642719
Jianhong Zhou;Yong Wang;Qian Xie;Zixia Shang;Yinliang Jiang;Qiuyu DU
Most state-of-the-art (SOTA) uncrewed aerial vehicle (UAV) path planning approaches depend on global environmental knowledge. While algorithms like adaptive soft actor-critic (ASAC) have improved training efficiency, their obstacle avoidance in partially observable environments remains limited. To address this, we propose a depth-based collision risk prediction (DCRP) algorithm that integrates into the ASAC framework. DCRP processes depth images alongside UAV pose and velocity to calculate a dense collision risk signal, enriching the reward function for more effective avoidance learning. Furthermore, we enhance the policy network with a novel skip connection that directly injects critical state information into the final action output. This innovation mitigates gradient vanishing and accelerates policy learning. Additionally, a generalized transfer learning (GTL) strategy accelerates convergence in complex environments by leveraging policies pre-trained in simpler ones. Extensive evaluation in high-fidelity AirSim environments demonstrates the superiority of our method. It outperforms several SOTA baselines, achieving an approximately 20% higher task success rate and 39% faster training efficiency on average, while maintaining a real-time inference time of around 15 ms.
{"title":"A UAV Path Planning Method Based on Deep Reinforcement Learning With Dense Rewards","authors":"Jianhong Zhou;Yong Wang;Qian Xie;Zixia Shang;Yinliang Jiang;Qiuyu DU","doi":"10.1109/OJVT.2025.3642719","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3642719","url":null,"abstract":"Most state-of-the-art (SOTA) uncrewed aerial vehicle (UAV) path planning approaches depend on global environmental knowledge. While algorithms like adaptive soft actor-critic (ASAC) have improved training efficiency, their obstacle avoidance in partially observable environments remains limited. To address this, we propose a depth-based collision risk prediction (DCRP) algorithm that integrates into the ASAC framework. DCRP processes depth images alongside UAV pose and velocity to calculate a dense collision risk signal, enriching the reward function for more effective avoidance learning. Furthermore, we enhance the policy network with a novel skip connection that directly injects critical state information into the final action output. This innovation mitigates gradient vanishing and accelerates policy learning. Additionally, a generalized transfer learning (GTL) strategy accelerates convergence in complex environments by leveraging policies pre-trained in simpler ones. Extensive evaluation in high-fidelity AirSim environments demonstrates the superiority of our method. It outperforms several SOTA baselines, achieving an approximately 20% higher task success rate and 39% faster training efficiency on average, while maintaining a real-time inference time of around 15 ms.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"260-272"},"PeriodicalIF":4.8,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293776","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886646","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 : 2025-12-04DOI: 10.1109/OJVT.2025.3640084
Clint Powell;Benjamin A. Rolfe;Dries Neirynck;Jim Lansford
This paper provides an overview of impulse radio ultra-wideband (IR-UWB), focusing on the low data rate version standardized in IEEE Std 802.15.4. It reviews the current state of standards-based IR-UWB adoption, including use cases targeted by industry alliances. Next, the fundamentals of IR-UWB signaling and key characteristics enabling accurate localization are summarized. Recent enhancements to IEEE Std 802.15.4 related to ranging, sensing, and data communication with IR-UWB are highlighted. Emerging application scenarios in digital vehicle access, indoor navigation, and vital sign monitoring, among others, are presented as indicators for future UWB proliferation, followed by an outlook on ongoing IEEE standardization efforts. Finally, the ability of IR-UWB’s low transmit power levels to enable spectral coexistence is discussed in the context of creating new sharing paradigms for congested midband spectrum.
{"title":"IEEE 802.15.4 IR-UWB: A Technology Precisely Positioned for Adoption","authors":"Clint Powell;Benjamin A. Rolfe;Dries Neirynck;Jim Lansford","doi":"10.1109/OJVT.2025.3640084","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3640084","url":null,"abstract":"This paper provides an overview of impulse radio ultra-wideband (IR-UWB), focusing on the low data rate version standardized in IEEE Std 802.15.4. It reviews the current state of standards-based IR-UWB adoption, including use cases targeted by industry alliances. Next, the fundamentals of IR-UWB signaling and key characteristics enabling accurate localization are summarized. Recent enhancements to IEEE Std 802.15.4 related to ranging, sensing, and data communication with IR-UWB are highlighted. Emerging application scenarios in digital vehicle access, indoor navigation, and vital sign monitoring, among others, are presented as indicators for future UWB proliferation, followed by an outlook on ongoing IEEE standardization efforts. Finally, the ability of IR-UWB’s low transmit power levels to enable spectral coexistence is discussed in the context of creating new sharing paradigms for congested midband spectrum.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"237-248"},"PeriodicalIF":4.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11277364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830882","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 datasets are typically captured using a small number of cameras, with each camera fixed at a single focus setting. In practice, however, camera modules exhibit unit-to-unit variability in their effective focus due to manufacturing tolerances. Since perception models are usually trained on images captured at one nominal focus position, real-world deviations in focus can introduce a domain mismatch that degrades perception performance. We demonstrate this effect by simulating two different optical systems on synthetic and real images with fields of view of $100^circ$ and $150^circ$. For all simulations, we utilise the Python-based ray-tracing library KrakenOS, an open-source optical simulation tool. By assigning each optical system to a suitable dataset, we degrade the held-out test data of four public automotive datasets: KITTI, Virtual KITTI 2.0, Woodscape, and Parallel Domain Woodscape. We evaluate the impact of applying optical defocus on 2D Object Detection models with the popular OpenMMLab toolkit for MMDetection and the YOLOv11 architecture. For each optical system, we simulate 9 defocus settings on the test data, representative of the production tolerance range for camera defocus. The results show that object detection performance degrades as the magnitude of defocus increases. Align DETR, despite having the second fewest parameters, establishes the strongest baseline and remains robust under modest defocus ($|Delta z|leq 20,mu mathrm{m}$) across all datasets. However, at extreme defocus ($pm 100 ,mu mathrm{m}$), YOLOv11x surpasses Align DETR by 1.5%–12.2% mAP$_{50:95}$ across all datasets. Finally, we show that defocus-augmented training of Align DETR, recovers the performance drop caused by the defocus in the held-out test data.
汽车数据集通常使用少量相机捕获，每个相机固定在一个单一的焦点设置。然而，在实践中，由于制造公差，相机模块在其有效焦点上表现出单位到单位的可变性。由于感知模型通常是在一个名义焦点位置捕获的图像上训练的，因此真实世界的焦点偏差可能会引入域不匹配，从而降低感知性能。我们通过模拟两种不同的光学系统对$100^circ$和$150^circ$视场的合成图像和真实图像的影响来证明这种效果。对于所有的模拟，我们利用基于python的光线追踪库KrakenOS，一个开源的光学模拟工具。通过将每个光学系统分配到合适的数据集，我们对四个公共汽车数据集（KITTI、Virtual KITTI 2.0、Woodscape和Parallel Domain Woodscape）的持续测试数据进行了降级。我们使用流行的OpenMMLab MMDetection工具包和YOLOv11架构来评估光学离焦对二维目标检测模型的影响。对于每个光学系统，我们在测试数据上模拟了9个离焦设置，代表了相机离焦的生产公差范围。结果表明，随着离焦大小的增大，目标检测性能下降。Align DETR尽管参数第二少，但在所有数据集上建立了最强的基线，并在适度散焦（$|Delta z|leq 20,mu mathrm{m}$）下保持稳健。然而，在极端散焦（$pm 100 ,mu mathrm{m}$）下，YOLOv11x比Align DETR高1.5倍%–12.2% mAP$_{50:95}$ across all datasets. Finally, we show that defocus-augmented training of Align DETR, recovers the performance drop caused by the defocus in the held-out test data.
{"title":"SOLAS 1.1: Automotive Optical Simulation in Computer Vision","authors":"Daniel Jakab;Joel Herrera Vázquez;Julian Barthel;Jan Honsbrok;Brian Deegan;Reenu Mohandas;Tim Brophy;Anthony Scanlan;Enda Ward;Fiachra Collins;Ciarán Eising;Alexander Braun","doi":"10.1109/OJVT.2025.3640419","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3640419","url":null,"abstract":"Automotive datasets are typically captured using a small number of cameras, with each camera fixed at a single focus setting. In practice, however, camera modules exhibit unit-to-unit variability in their effective focus due to manufacturing tolerances. Since perception models are usually trained on images captured at one nominal focus position, real-world deviations in focus can introduce a domain mismatch that degrades perception performance. We demonstrate this effect by simulating two different optical systems on synthetic and real images with fields of view of <inline-formula><tex-math>$100^circ$</tex-math></inline-formula> and <inline-formula><tex-math>$150^circ$</tex-math></inline-formula>. For all simulations, we utilise the Python-based ray-tracing library KrakenOS, an open-source optical simulation tool. By assigning each optical system to a suitable dataset, we degrade the held-out test data of four public automotive datasets: KITTI, Virtual KITTI 2.0, Woodscape, and Parallel Domain Woodscape. We evaluate the impact of applying optical defocus on 2D Object Detection models with the popular OpenMMLab toolkit for MMDetection and the YOLOv11 architecture. For each optical system, we simulate 9 defocus settings on the test data, representative of the production tolerance range for camera defocus. The results show that object detection performance degrades as the magnitude of defocus increases. Align DETR, despite having the second fewest parameters, establishes the strongest baseline and remains robust under modest defocus (<inline-formula><tex-math>$|Delta z|leq 20,mu mathrm{m}$</tex-math></inline-formula>) across all datasets. However, at extreme defocus (<inline-formula><tex-math>$pm 100 ,mu mathrm{m}$</tex-math></inline-formula>), YOLOv11x surpasses Align DETR by 1.5%–12.2% mAP<inline-formula><tex-math>$_{50:95}$</tex-math></inline-formula> across all datasets. Finally, we show that defocus-augmented training of Align DETR, recovers the performance drop caused by the defocus in the held-out test data.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"179-193"},"PeriodicalIF":4.8,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778469","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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