In this paper, an enhanced model-free adaptive control algorithm considering time delay is proposed for the path tracking problem of autonomous vehicles. First, a path tracking mechanism based on the preview-deviation-yaw angle is proposed, which transforms the path tracking problem into a control problem of the preview-deviation-yaw angle. A novel partial form dynamic linearization (PFDL) technique is then employed to transform the vehicle dynamic models into a discrete-time data model with a time-varying pseudogradient (PG), and the proposed controller (PFDL-EMFAC) is designed based on this data model. Moreover, a compensation mechanism is designed for the system time delay by combining the Smith predictor and tracking differentiator (TD). Notably, implementing the controller does not involve any model information; it is a purely data-driven control method. Furthermore, the convergence of the proposed controller is proven via mathematical analysis. The validity of the proposed controller was validated through CarSim-MATLAB cosimulation, and its applicability was verified via the Ankai HFF6668GEV1 autonomous driving platform on a test road in Hefei, China.
{"title":"A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles","authors":"Shida Liu;Guang Lin;Honghai Ji;Shangtai Jin;Zhongsheng Hou","doi":"10.1109/TITS.2024.3487299","DOIUrl":"https://doi.org/10.1109/TITS.2024.3487299","url":null,"abstract":"In this paper, an enhanced model-free adaptive control algorithm considering time delay is proposed for the path tracking problem of autonomous vehicles. First, a path tracking mechanism based on the preview-deviation-yaw angle is proposed, which transforms the path tracking problem into a control problem of the preview-deviation-yaw angle. A novel partial form dynamic linearization (PFDL) technique is then employed to transform the vehicle dynamic models into a discrete-time data model with a time-varying pseudogradient (PG), and the proposed controller (PFDL-EMFAC) is designed based on this data model. Moreover, a compensation mechanism is designed for the system time delay by combining the Smith predictor and tracking differentiator (TD). Notably, implementing the controller does not involve any model information; it is a purely data-driven control method. Furthermore, the convergence of the proposed controller is proven via mathematical analysis. The validity of the proposed controller was validated through CarSim-MATLAB cosimulation, and its applicability was verified via the Ankai HFF6668GEV1 autonomous driving platform on a test road in Hefei, China.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"579-590"},"PeriodicalIF":7.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/TITS.2024.3485061
Xu Han;Junyu Gao;Chuang Yang;Yuan Yuan;Qi Wang
Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. Unlike the general scene, detecting text in transportation has extra demand, such as a fast inference speed, except for high accuracy. Most existing real-time text detection methods are based on the shrink mask, which loses some geometry semantic information and needs complex post-processing. In addition, the previous method usually focuses on correct output, which ignores feature correction and lacks guidance during the intermediate process. To this end, we propose an efficient multi-scene text detector that contains an effective text representation similar mask (SM) and a feature correction module (FCM). Unlike previous methods, the former aims to preserve the geometric information of the instances as much as possible. Its post-progressing saves 50% of the time, accurately and efficiently reconstructing text contours. The latter encourages false positive features to move away from the positive feature center, optimizing the predictions from the feature level. Some ablation studies demonstrate the efficiency of the SM and the effectiveness of the FCM. Moreover, the deficiency of existing traffic datasets (such as the low-quality annotation or closed source data unavailability) motivated us to collect and annotate a traffic text dataset, which introduces motion blur. In addition, to validate the scene robustness of the SM-Net, we conduct experiments on traffic, industrial, and natural scene datasets. Extensive experiments verify it achieves (SOTA) performance on several benchmarks. The code and dataset are available at: https://github.com/fengmulin/SMNet.
{"title":"Real-Time Text Detection With Similar Mask in Traffic, Industrial, and Natural Scenes","authors":"Xu Han;Junyu Gao;Chuang Yang;Yuan Yuan;Qi Wang","doi":"10.1109/TITS.2024.3485061","DOIUrl":"https://doi.org/10.1109/TITS.2024.3485061","url":null,"abstract":"Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. Unlike the general scene, detecting text in transportation has extra demand, such as a fast inference speed, except for high accuracy. Most existing real-time text detection methods are based on the shrink mask, which loses some geometry semantic information and needs complex post-processing. In addition, the previous method usually focuses on correct output, which ignores feature correction and lacks guidance during the intermediate process. To this end, we propose an efficient multi-scene text detector that contains an effective text representation similar mask (SM) and a feature correction module (FCM). Unlike previous methods, the former aims to preserve the geometric information of the instances as much as possible. Its post-progressing saves 50% of the time, accurately and efficiently reconstructing text contours. The latter encourages false positive features to move away from the positive feature center, optimizing the predictions from the feature level. Some ablation studies demonstrate the efficiency of the SM and the effectiveness of the FCM. Moreover, the deficiency of existing traffic datasets (such as the low-quality annotation or closed source data unavailability) motivated us to collect and annotate a traffic text dataset, which introduces motion blur. In addition, to validate the scene robustness of the SM-Net, we conduct experiments on traffic, industrial, and natural scene datasets. Extensive experiments verify it achieves (SOTA) performance on several benchmarks. The code and dataset are available at: <uri>https://github.com/fengmulin/SMNet</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"865-877"},"PeriodicalIF":7.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/TITS.2024.3480817
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2024.3480817","DOIUrl":"https://doi.org/10.1109/TITS.2024.3480817","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"C3-C3"},"PeriodicalIF":7.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1109/TITS.2024.3480168
Simona Sacone
Summary form only: Abstracts of articles presented in this issue of the publication.
仅为摘要形式：在本期刊物上发表的文章摘要。
{"title":"Scanning the Issue","authors":"Simona Sacone","doi":"10.1109/TITS.2024.3480168","DOIUrl":"https://doi.org/10.1109/TITS.2024.3480168","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15136-15190"},"PeriodicalIF":7.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1109/TITS.2024.3453268
Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar
This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.
{"title":"New Spatial Analysis and Hybrid Heuristics Enhance Truck Freight Tonnage Estimation Based on Weigh-in-Motion Data","authors":"Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar","doi":"10.1109/TITS.2024.3453268","DOIUrl":"https://doi.org/10.1109/TITS.2024.3453268","url":null,"abstract":"This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19581-19591"},"PeriodicalIF":7.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01DOI: 10.1109/TITS.2024.3443261
Qihui Zhu;Shenwen Chen;Tong Guo;Yisheng Lv;Wenbo Du
Accurate flight delay prediction is crucial for the secure and effective operation of the air traffic system. Recent advances in modeling inter-airport relationships present a promising approach for investigating flight delay prediction from the multi-airport scenario. However, the previous prediction works only accounted for the simplistic relationships such as traffic flow or geographical distance, overlooking the intricate interactions among airports and thus proving inadequate. In this paper, we leverage casual inference to precisely model inter-airport relationships and propose a self-corrective spatio-temporal graph neural network (named CausalNet) for flight delay prediction. Specifically, Granger causality inference coupled with a self-correction module is designed to construct causality graphs among airports and dynamically modify them based on the current airport’s delays. Additionally, the features of the causality graphs are adaptively extracted and utilized to address the heterogeneity of airports. Extensive experiments are conducted on the real data of top-74 busiest airports in China. The results show that CausalNet is superior to baselines. Ablation studies emphasize the power of the proposed self-correction causality graph and the graph feature extraction module. All of these prove the effectiveness of the proposed methodology.
{"title":"A Spatio-Temporal Approach With Self-Corrective Causal Inference for Flight Delay Prediction","authors":"Qihui Zhu;Shenwen Chen;Tong Guo;Yisheng Lv;Wenbo Du","doi":"10.1109/TITS.2024.3443261","DOIUrl":"https://doi.org/10.1109/TITS.2024.3443261","url":null,"abstract":"Accurate flight delay prediction is crucial for the secure and effective operation of the air traffic system. Recent advances in modeling inter-airport relationships present a promising approach for investigating flight delay prediction from the multi-airport scenario. However, the previous prediction works only accounted for the simplistic relationships such as traffic flow or geographical distance, overlooking the intricate interactions among airports and thus proving inadequate. In this paper, we leverage casual inference to precisely model inter-airport relationships and propose a self-corrective spatio-temporal graph neural network (named CausalNet) for flight delay prediction. Specifically, Granger causality inference coupled with a self-correction module is designed to construct causality graphs among airports and dynamically modify them based on the current airport’s delays. Additionally, the features of the causality graphs are adaptively extracted and utilized to address the heterogeneity of airports. Extensive experiments are conducted on the real data of top-74 busiest airports in China. The results show that CausalNet is superior to baselines. Ablation studies emphasize the power of the proposed self-correction causality graph and the graph feature extraction module. All of these prove the effectiveness of the proposed methodology.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"20820-20831"},"PeriodicalIF":7.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1109/TITS.2024.3484004
Alessandro Colombo;Matteo Depaola;Francesco Ferrise;Nicolò Dozio;Gabriel Rodrigues de Campos
This paper presents a cognitive model designed to reproduce human drivers’ errors in predicting the motion of nearby vulnerable road users. We aim to define a computational model that, given both the trajectory of the eye gaze of a human driver and the trajectory of a bicycle, can compute the probability distribution of where the human driver believes the bicycle will be in the near future. For the design and validation of the proposed cognitive model, we tested 51 subjects in immersive virtual reality scenarios. The results indicate that the proposed model can generate probability distributions of the human drivers’ beliefs about the future bicycle position that are very similar, though not statistically equivalent, to those obtained experimentally. Such models could easily be generalized to describe how drivers misjudge the motion of other road users. This may enable ADAS to evaluate and improve drivers’ situational awareness. In the future, these models could also be used by autonomous cars to evaluate situational awareness of nearby humans, enabling a safer coexistence of autonomous vehicles and vulnerable road users.
{"title":"Predicting Mispredictions: A Model of Human Misjudgment About Vulnerable Road Users’ Trajectories","authors":"Alessandro Colombo;Matteo Depaola;Francesco Ferrise;Nicolò Dozio;Gabriel Rodrigues de Campos","doi":"10.1109/TITS.2024.3484004","DOIUrl":"https://doi.org/10.1109/TITS.2024.3484004","url":null,"abstract":"This paper presents a cognitive model designed to reproduce human drivers’ errors in predicting the motion of nearby vulnerable road users. We aim to define a computational model that, given both the trajectory of the eye gaze of a human driver and the trajectory of a bicycle, can compute the probability distribution of where the human driver believes the bicycle will be in the near future. For the design and validation of the proposed cognitive model, we tested 51 subjects in immersive virtual reality scenarios. The results indicate that the proposed model can generate probability distributions of the human drivers’ beliefs about the future bicycle position that are very similar, though not statistically equivalent, to those obtained experimentally. Such models could easily be generalized to describe how drivers misjudge the motion of other road users. This may enable ADAS to evaluate and improve drivers’ situational awareness. In the future, these models could also be used by autonomous cars to evaluate situational awareness of nearby humans, enabling a safer coexistence of autonomous vehicles and vulnerable road users.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"157-168"},"PeriodicalIF":7.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The recovery of coarsely sampled trajectories considering the road network topology characteristics is a crucial task for many downstream applications in intelligent transportation systems. Existing approaches in this domain primarily focus on extracting spatio-temporal correlations for the observed trajectory points but neglect the critical role of road network topology characteristics in making the recovery results more accurate and realistic. In addition, too many road segments in cities undermine the model inference performance. To address these challenges, we propose a novel Map-informed Adaptive Spatio-Temporal Autoencoder, which follows an encoder-decoder architecture for trajectory recovery. Specifically, we utilize a pre-trained attributed network embedding module to incorporate the road segment characteristics into the input data to make it easier for the model to extract the spatio-temporal dependencies from coarse trajectories. Furthermore, we construct a novel adaptive mask inference module that contains a distance-based mask matrix and a learnable adaptive mask matrix to assist the model in making segment inferences by weighting each candidate segment adaptively in the recovery process. To evaluate the performance of the proposed model, we conduct a series of comprehensive case studies on two representative real-world trajectory datasets. The experimental results demonstrate that the proposed model consistently outperforms state-of-the-art approaches.
{"title":"Map-Informed Trajectory Recovery With Adaptive Spatio-Temporal Autoencoder","authors":"Yongchao Ye;Ao Wang;Adnan Zeb;Shiyao Zhang;James Jianqiao Yu","doi":"10.1109/TITS.2024.3483941","DOIUrl":"https://doi.org/10.1109/TITS.2024.3483941","url":null,"abstract":"The recovery of coarsely sampled trajectories considering the road network topology characteristics is a crucial task for many downstream applications in intelligent transportation systems. Existing approaches in this domain primarily focus on extracting spatio-temporal correlations for the observed trajectory points but neglect the critical role of road network topology characteristics in making the recovery results more accurate and realistic. In addition, too many road segments in cities undermine the model inference performance. To address these challenges, we propose a novel Map-informed Adaptive Spatio-Temporal Autoencoder, which follows an encoder-decoder architecture for trajectory recovery. Specifically, we utilize a pre-trained attributed network embedding module to incorporate the road segment characteristics into the input data to make it easier for the model to extract the spatio-temporal dependencies from coarse trajectories. Furthermore, we construct a novel adaptive mask inference module that contains a distance-based mask matrix and a learnable adaptive mask matrix to assist the model in making segment inferences by weighting each candidate segment adaptively in the recovery process. To evaluate the performance of the proposed model, we conduct a series of comprehensive case studies on two representative real-world trajectory datasets. The experimental results demonstrate that the proposed model consistently outperforms state-of-the-art approaches.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"102-115"},"PeriodicalIF":7.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1109/TITS.2024.3482106
Xuan Liu;Jinglong Chen;Jingsong Xie;Yuanhong Chang
Generative Adversarial Networks (GANs) for generating realistic data, have substantially improved fault diagnosis algorithms in various Internet of Things (IoT) systems. However, challenges such as training instability and dynamical inaccuracy limit their utility in high-speed rail (HSR) bogie fault diagnosis. To address these challenges, we introduce the Pulse Voltage-Guided Conditional Diffusion Model (VGCDM). Unlike traditional implicit GANs, VGCDM adopts a sequential U-Net architecture, facilitating multi-steps denoising diffusion for generation, which bolsters training stability and mitigate convergence issues. VGCDM also incorporates control pulse voltage by cross-attention mechanism to ensure the alignment of vibration with voltage signals, enhancing the Conditional Diffusion Model’s progressive controlablity. Consequently, solely straightforward sampling of control voltages, ensuring the efficient transformation from Gaussian Noise to vibration signals. This adaptability remains robust even in scenarios with time-varying speeds. To validate the effectiveness, we conducted two case studies using SQ dataset and high-simulation HSR bogie dataset. The results of our experiments unequivocally confirm that VGCDM outperforms other generative models, achieving the best RSME, PSNR, and FSCS, showing its superiority in conditional HSR bogie vibration signal generation. For access, our code is available at https://github.com/xuanliu2000/VGCDM.
{"title":"Generating HSR Bogie Vibration Signals via Pulse Voltage-Guided Conditional Diffusion Model","authors":"Xuan Liu;Jinglong Chen;Jingsong Xie;Yuanhong Chang","doi":"10.1109/TITS.2024.3482106","DOIUrl":"https://doi.org/10.1109/TITS.2024.3482106","url":null,"abstract":"Generative Adversarial Networks (GANs) for generating realistic data, have substantially improved fault diagnosis algorithms in various Internet of Things (IoT) systems. However, challenges such as training instability and dynamical inaccuracy limit their utility in high-speed rail (HSR) bogie fault diagnosis. To address these challenges, we introduce the Pulse Voltage-Guided Conditional Diffusion Model (VGCDM). Unlike traditional implicit GANs, VGCDM adopts a sequential U-Net architecture, facilitating multi-steps denoising diffusion for generation, which bolsters training stability and mitigate convergence issues. VGCDM also incorporates control pulse voltage by cross-attention mechanism to ensure the alignment of vibration with voltage signals, enhancing the Conditional Diffusion Model’s progressive controlablity. Consequently, solely straightforward sampling of control voltages, ensuring the efficient transformation from Gaussian Noise to vibration signals. This adaptability remains robust even in scenarios with time-varying speeds. To validate the effectiveness, we conducted two case studies using SQ dataset and high-simulation HSR bogie dataset. The results of our experiments unequivocally confirm that VGCDM outperforms other generative models, achieving the best RSME, PSNR, and FSCS, showing its superiority in conditional HSR bogie vibration signal generation. For access, our code is available at <uri>https://github.com/xuanliu2000/VGCDM</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"116-127"},"PeriodicalIF":7.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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