This study realizes firmware-agnostic line-of-sight (LOS) identification to extend the range of WiFi-sensing applications. We developed a beamforming feedback (BFF)-based LOS identification algorithm. BFF frames are transmitted for multiple-input multiple-output (MIMO) communications. They can be obtained by capturing frames without custom firmware or specific chipsets and contain a beamforming feedback matrix (BFM) and subcarrier-averaged stream gain (SSG). These provide partial channel state information (CSI), and there are two major calculation steps involved from the CSI to the BFF: unquantized BFF (UQBFF) calculation and quantization. Focusing on the relationship between singular value decomposition and principal component analysis, we numerically demonstrated that the first column vectors of the BFM reflect the LOS/NLOS conditions. Therefore, the proposed BFF-based method extracts features from the first-column vectors of the BFM. In addition, SSGs were leveraged to improve the accuracy. To demonstrate the feasibility of the proposed method, we conducted experiments using commodity off-the-shelf devices compliant with the IEEE 802.11ac standard. In the experimental evaluation, the proposed BFF-based method achieved an identification accuracy of 75.0%, whereas the CSI-based method achieved an accuracy of 81.2%. Accuracy comparisons revealed that the accuracy degradation of the BFF-based identification from the CSI-based identification was primarily caused by UQBFF calculations rather than quantization.
{"title":"Beamforming Feedback-Based Line-of-Sight Identification Toward Firmware-Agnostic WiFi Sensing","authors":"Hiroki Shimomura;Koji Yamamoto;Takayuki Nishio;Akihito Taya","doi":"10.1109/OJVT.2024.3440400","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3440400","url":null,"abstract":"This study realizes firmware-agnostic line-of-sight (LOS) identification to extend the range of WiFi-sensing applications. We developed a beamforming feedback (BFF)-based LOS identification algorithm. BFF frames are transmitted for multiple-input multiple-output (MIMO) communications. They can be obtained by capturing frames without custom firmware or specific chipsets and contain a beamforming feedback matrix (BFM) and subcarrier-averaged stream gain (SSG). These provide partial channel state information (CSI), and there are two major calculation steps involved from the CSI to the BFF: unquantized BFF (UQBFF) calculation and quantization. Focusing on the relationship between singular value decomposition and principal component analysis, we numerically demonstrated that the first column vectors of the BFM reflect the LOS/NLOS conditions. Therefore, the proposed BFF-based method extracts features from the first-column vectors of the BFM. In addition, SSGs were leveraged to improve the accuracy. To demonstrate the feasibility of the proposed method, we conducted experiments using commodity off-the-shelf devices compliant with the IEEE 802.11ac standard. In the experimental evaluation, the proposed BFF-based method achieved an identification accuracy of 75.0%, whereas the CSI-based method achieved an accuracy of 81.2%. Accuracy comparisons revealed that the accuracy degradation of the BFF-based identification from the CSI-based identification was primarily caused by UQBFF calculations rather than quantization.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10631655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013198","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 : 2024-08-02DOI: 10.1109/OJVT.2024.3437470
Praveen Sai Bere;Mohammed Zafar Ali Khan;Lajos Hanzo
V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a $mathscr {C}(n,k)$ code having a minimum distance ${d_{min }}$ , the proposed decoder incurs a worst-case complexity order of $2^{({d_{min }}-1)}-1$ . Notably, for codes having low ${d_{min }}$ , this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder.
{"title":"A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes","authors":"Praveen Sai Bere;Mohammed Zafar Ali Khan;Lajos Hanzo","doi":"10.1109/OJVT.2024.3437470","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3437470","url":null,"abstract":"V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a \u0000<inline-formula><tex-math>$mathscr {C}(n,k)$</tex-math></inline-formula>\u0000 code having a minimum distance \u0000<inline-formula><tex-math>${d_{min }}$</tex-math></inline-formula>\u0000, the proposed decoder incurs a worst-case complexity order of \u0000<inline-formula><tex-math>$2^{({d_{min }}-1)}-1$</tex-math></inline-formula>\u0000. Notably, for codes having low \u0000<inline-formula><tex-math>${d_{min }}$</tex-math></inline-formula>\u0000, this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10621450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452772","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 : 2024-08-02DOI: 10.1109/OJVT.2024.3436857
Hang Mi;Bo Ai;Ruisi He;Anuraag Bodi;Raied Caromi;Jian Wang;Jelena Senic;Camillo Gentile;Yang Miao
Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician $K$ factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.
{"title":"Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud","authors":"Hang Mi;Bo Ai;Ruisi He;Anuraag Bodi;Raied Caromi;Jian Wang;Jelena Senic;Camillo Gentile;Yang Miao","doi":"10.1109/OJVT.2024.3436857","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3436857","url":null,"abstract":"Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077608","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 : 2024-07-31DOI: 10.1109/OJVT.2024.3436065
Adel Gohari;Anuar Bin Ahmad;Lawali Rabiu;Ruzairi Bin Abdul Rahim;A.S.M. Supa'at;Nassrin Ibrahim Mohamed Elamin;Mohammed Salih Mohammed Gismalla;Suhail I. Al-Dharrab;Rozeha A. Rashid;Sophan Wahyudi Nawawi;Nazri Nasir;Mohd Adib Bin Sarijari;Norhadija B. Darwin;Ali H. Muqaibel
Unmanned aerial vehicles (UAVs) are emerging and have been globally incorporated in wide range of technologies for various purposes due to its advantages over conventional techniques. Nonetheless, the strength of its application areas varies globally. The aim of this paper is to systematically review the literature to provide pertinent information on UAVs’ applications among the association of southeast Asian nations (ASEAN) countries by reviewing 179 documents published from 2012 to the end of 2023. Besides, we also investigated the current state of the relevant policies and regulations among member states. The results of the research demonstrate the state of UAV adoption, application areas, popularity among member states, key aspects that are main drivers for the adoption of UAV technology in the region, and a comparison of UAV policy usage among member states. In particular, the reviewed documents highlighted 12 distinct application areas and 4 major aspects making UAV technology attractive to the region, including geographical, climatic and environmental, ecosystem conservation, and economic factors.
{"title":"A Systematic Review of the UAV Technology Usage in ASEAN","authors":"Adel Gohari;Anuar Bin Ahmad;Lawali Rabiu;Ruzairi Bin Abdul Rahim;A.S.M. Supa'at;Nassrin Ibrahim Mohamed Elamin;Mohammed Salih Mohammed Gismalla;Suhail I. Al-Dharrab;Rozeha A. Rashid;Sophan Wahyudi Nawawi;Nazri Nasir;Mohd Adib Bin Sarijari;Norhadija B. Darwin;Ali H. Muqaibel","doi":"10.1109/OJVT.2024.3436065","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3436065","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are emerging and have been globally incorporated in wide range of technologies for various purposes due to its advantages over conventional techniques. Nonetheless, the strength of its application areas varies globally. The aim of this paper is to systematically review the literature to provide pertinent information on UAVs’ applications among the association of southeast Asian nations (ASEAN) countries by reviewing 179 documents published from 2012 to the end of 2023. Besides, we also investigated the current state of the relevant policies and regulations among member states. The results of the research demonstrate the state of UAV adoption, application areas, popularity among member states, key aspects that are main drivers for the adoption of UAV technology in the region, and a comparison of UAV policy usage among member states. In particular, the reviewed documents highlighted 12 distinct application areas and 4 major aspects making UAV technology attractive to the region, including geographical, climatic and environmental, ecosystem conservation, and economic factors.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10616173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045114","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 : 2024-07-30DOI: 10.1109/OJVT.2024.3435489
Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso
Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.
{"title":"Comparison and Analysis of Algorithms for Coordinated EV Charging to Reduce Power Grid Impact","authors":"Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso","doi":"10.1109/OJVT.2024.3435489","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3435489","url":null,"abstract":"Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965057","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 : 2024-07-26DOI: 10.1109/OJVT.2024.3434486
Atefeh Hajijamali Arani;Peng Hu;Yeying Zhu
Recent technological advancements in space, air, and ground components have made possible a new network paradigm called “space-air-ground integrated network” (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.
{"title":"UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms","authors":"Atefeh Hajijamali Arani;Peng Hu;Yeying Zhu","doi":"10.1109/OJVT.2024.3434486","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3434486","url":null,"abstract":"Recent technological advancements in space, air, and ground components have made possible a new network paradigm called “space-air-ground integrated network” (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10612249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998659","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 : 2024-07-19DOI: 10.1109/OJVT.2024.3431449
Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela
In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone.
{"title":"Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence","authors":"Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela","doi":"10.1109/OJVT.2024.3431449","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3431449","url":null,"abstract":"In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965056","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 : 2024-07-16DOI: 10.1109/OJVT.2024.3428976
A. Elaidy;R. Rayner;C. Kalyvas
Pedestrians are exceptionally vulnerable in road accidents, and despite the advancements in airbag technology for vehicle occupants, fatal injuries still occur due to contact between pedestrians and vehicle components. To address this issue, an innovative solution is introduced in this research: an external airbag system designed to safeguard pedestrians in cases of brake failure. The proposed system includes four airbag modules strategically positioned within the front bumper of the vehicle. These modules are specifically designed to deploy during a collision, providing protection for the pedestrian's head, legs, and body. Equipped with a highly sensitive sensor, the system triggers the airbag electronic controller unit (ECU) upon collision detection. The external airbag curtains deploy, shielding the pedestrian's head from striking the bonnet, while an additional airbag safeguards the pedestrian's legs from impact with the front bumper. With the introduction of this innovative external airbag system, the main goal is to significantly improve road safety for all individuals and prevent numerous fatalities. The introduction of the innovative external airbag system marks a significant advancement in pedestrian safety within the realm of road accidents. By strategically positioning four airbag modules within the vehicle's front bumper and equipping them with a highly sensitive sensor, this system effectively deploys during collisions to protect pedestrians' heads, legs, and bodies. The deployment of external airbag curtains shields pedestrians' heads from striking the bonnet, while an additional airbag safeguards their legs from impact with the front bumper. Through this research and implementation, the primary objective is to enhance road safety for all individuals and mitigate the occurrence of numerous fatalities resulting from pedestrian-vehicle collisions.
{"title":"Innovative Design of External Airbag System for Improved Automotive Safety","authors":"A. Elaidy;R. Rayner;C. Kalyvas","doi":"10.1109/OJVT.2024.3428976","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3428976","url":null,"abstract":"Pedestrians are exceptionally vulnerable in road accidents, and despite the advancements in airbag technology for vehicle occupants, fatal injuries still occur due to contact between pedestrians and vehicle components. To address this issue, an innovative solution is introduced in this research: an external airbag system designed to safeguard pedestrians in cases of brake failure. The proposed system includes four airbag modules strategically positioned within the front bumper of the vehicle. These modules are specifically designed to deploy during a collision, providing protection for the pedestrian's head, legs, and body. Equipped with a highly sensitive sensor, the system triggers the airbag electronic controller unit (ECU) upon collision detection. The external airbag curtains deploy, shielding the pedestrian's head from striking the bonnet, while an additional airbag safeguards the pedestrian's legs from impact with the front bumper. With the introduction of this innovative external airbag system, the main goal is to significantly improve road safety for all individuals and prevent numerous fatalities. The introduction of the innovative external airbag system marks a significant advancement in pedestrian safety within the realm of road accidents. By strategically positioning four airbag modules within the vehicle's front bumper and equipping them with a highly sensitive sensor, this system effectively deploys during collisions to protect pedestrians' heads, legs, and bodies. The deployment of external airbag curtains shields pedestrians' heads from striking the bonnet, while an additional airbag safeguards their legs from impact with the front bumper. Through this research and implementation, the primary objective is to enhance road safety for all individuals and mitigate the occurrence of numerous fatalities resulting from pedestrian-vehicle collisions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965943","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 : 2024-07-16DOI: 10.1109/OJVT.2024.3428645
Zhiqiang Zhang;Lei Zhang;Mingqiang Wang;Cong Wang;Zhenpo Wang
Comprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. Then a two-dimensional Gaussian distribution-based dynamic risk assessment with a soft update method is developed to spatially and temporally quantify the driving risk by constructing the occupancy map based on the multi-modal distribution of the predicted trajectories for the surrounding vehicles. The optimal motion of the host vehicle is determined by minimizing a multi-objective function of the alternative driving behaviors. The effectiveness of the proposed scheme is verified under typical driving scenarios extracted from the NGSIM dataset. The results show that the proposed method can comprehensively evaluate the potential risk and efficiently achieve motion planning while minimizing the driving risk.
{"title":"An Uncertainty-Aware Lane Change Motion Planning Algorithm Based on Probabilistic Trajectory Prediction Distribution","authors":"Zhiqiang Zhang;Lei Zhang;Mingqiang Wang;Cong Wang;Zhenpo Wang","doi":"10.1109/OJVT.2024.3428645","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3428645","url":null,"abstract":"Comprehensive and accurate understanding of the interactive traffic environment facilitates reasonable motion planning for automated vehicles. This paper presents an overall risk assessment method for the host vehicle to achieve efficient motion planning considering uncertainties of the predicted driving behaviors of surrounding vehicles. A Social Temporal Convolutional Long Short-Term Memory network is constructed to capture the interactive characteristics among the host and surrounding vehicles and to predict the statistical distribution of the trajectory prediction uncertainty in the prediction horizon. Then a two-dimensional Gaussian distribution-based dynamic risk assessment with a soft update method is developed to spatially and temporally quantify the driving risk by constructing the occupancy map based on the multi-modal distribution of the predicted trajectories for the surrounding vehicles. The optimal motion of the host vehicle is determined by minimizing a multi-objective function of the alternative driving behaviors. The effectiveness of the proposed scheme is verified under typical driving scenarios extracted from the NGSIM dataset. The results show that the proposed method can comprehensively evaluate the potential risk and efficiently achieve motion planning while minimizing the driving risk.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442999","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 : 2024-07-15DOI: 10.1109/OJVT.2024.3427326
Takamasa Higuchi;Lei Zhong;Ryokichi Onishi
The increasing network traffic from connected vehicles is putting a strain on the limited bandwidth resources of cellular networks. Delay-tolerant networking (DTN) over vehicle-to-vehicle (V2V) communications has been considered as an effective means of offloading the cellular data traffic, while its quantitative performance in urban road traffic remains unclear in many aspects. In this paper, we unveil the benefits of data offloading over vehicular DTNs by city-scale network simulations in Nagoya, Japan. The simulation scenario embraces more than 8 million vehicle trips over five consecutive days. The vehicle routes are carefully calibrated against public statistics on the road traffic volume to enable realistic simulations of V2V communication opportunities between vehicles on the road. The results indicate the strong potential of vehicular DTNs in mixed urban road traffic, comprised of both public transport and privately owned vehicles – a large amount data traffic can be offloaded from cellular networks to V2V communication networks even with the limited ratio of vehicles participating the vehicular DTNs.
{"title":"Data Offloading Over Vehicular DTNs: City-Wide Feasibility Study in Nagoya","authors":"Takamasa Higuchi;Lei Zhong;Ryokichi Onishi","doi":"10.1109/OJVT.2024.3427326","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3427326","url":null,"abstract":"The increasing network traffic from connected vehicles is putting a strain on the limited bandwidth resources of cellular networks. Delay-tolerant networking (DTN) over vehicle-to-vehicle (V2V) communications has been considered as an effective means of offloading the cellular data traffic, while its quantitative performance in urban road traffic remains unclear in many aspects. In this paper, we unveil the benefits of data offloading over vehicular DTNs by city-scale network simulations in Nagoya, Japan. The simulation scenario embraces more than 8 million vehicle trips over five consecutive days. The vehicle routes are carefully calibrated against public statistics on the road traffic volume to enable realistic simulations of V2V communication opportunities between vehicles on the road. The results indicate the strong potential of vehicular DTNs in mixed urban road traffic, comprised of both public transport and privately owned vehicles – a large amount data traffic can be offloaded from cellular networks to V2V communication networks even with the limited ratio of vehicles participating the vehicular DTNs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965070","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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