Pub Date : 2024-11-27DOI: 10.1109/TITS.2024.3500004
Sanjay Bhardwaj;Da-Hye Kim;Dong-Seong Kim
In federated learning (FL), devices contribute to global training by uploading only the local model gradients (outcomes), providing connected devices with the ability to learn while preserving privacy. FL-based resource allocation for V2X communications is proposed, referred to as FL-RA-V2X, which optimizes and maximizes the throughput of all vehicle users within the constraints of maximum power and the signal-to-interference-plus-noise ratio (SINR). It ensures fairness in resource allocation, meeting the minimum SINR requirements for cellular users and outage probability constraints for vehicle users. An approximate expression for vehicle users’ throughput is derived, eliminating the non-convexity associated with the SINR expression through iterative calculations of auxiliary variables. The resource allocation is designed to allow each vehicle user to share uplink resources with cellular users, maximizing the number of vehicle users while utilizing their maximum power transmission capability. Simulation results demonstrate the fairness and enhanced throughput efficiency of the proposed approach compared to contemporary algorithms, considering vehicle outage ratio, computational complexity, computing time, maximum transmitting power, cumulative distribution function of achievable sum rates, and convergence metrics. Furthermore, the proposed approach addresses critical aspects, including high mobility and distributed V2V communications, asynchronous training issues in cellular V2X networks, and the convergence analysis under different conditions such as varied vehicle densities and mobility patterns. These considerations broaden the applicability and robustness of the FL-RA-V2X method across diverse scenarios. The analysis also explores the impact of vehicle speed, auxiliary variables, interference effects of cellular users, and the dependence of throughput on FL iterations.
{"title":"Federated Learning-Based Resource Allocation for V2X Communications","authors":"Sanjay Bhardwaj;Da-Hye Kim;Dong-Seong Kim","doi":"10.1109/TITS.2024.3500004","DOIUrl":"https://doi.org/10.1109/TITS.2024.3500004","url":null,"abstract":"In federated learning (FL), devices contribute to global training by uploading only the local model gradients (outcomes), providing connected devices with the ability to learn while preserving privacy. FL-based resource allocation for V2X communications is proposed, referred to as FL-RA-V2X, which optimizes and maximizes the throughput of all vehicle users within the constraints of maximum power and the signal-to-interference-plus-noise ratio (SINR). It ensures fairness in resource allocation, meeting the minimum SINR requirements for cellular users and outage probability constraints for vehicle users. An approximate expression for vehicle users’ throughput is derived, eliminating the non-convexity associated with the SINR expression through iterative calculations of auxiliary variables. The resource allocation is designed to allow each vehicle user to share uplink resources with cellular users, maximizing the number of vehicle users while utilizing their maximum power transmission capability. Simulation results demonstrate the fairness and enhanced throughput efficiency of the proposed approach compared to contemporary algorithms, considering vehicle outage ratio, computational complexity, computing time, maximum transmitting power, cumulative distribution function of achievable sum rates, and convergence metrics. Furthermore, the proposed approach addresses critical aspects, including high mobility and distributed V2V communications, asynchronous training issues in cellular V2X networks, and the convergence analysis under different conditions such as varied vehicle densities and mobility patterns. These considerations broaden the applicability and robustness of the FL-RA-V2X method across diverse scenarios. The analysis also explores the impact of vehicle speed, auxiliary variables, interference effects of cellular users, and the dependence of throughput on FL iterations.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"382-396"},"PeriodicalIF":7.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976096","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 : 2024-11-27DOI: 10.1109/TITS.2024.3491472
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.2024.3491472","DOIUrl":"https://doi.org/10.1109/TITS.2024.3491472","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 12","pages":"19123-19155"},"PeriodicalIF":7.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736392","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 : 2024-11-27DOI: 10.1109/TITS.2024.3492872
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2024.3492872","DOIUrl":"https://doi.org/10.1109/TITS.2024.3492872","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"C3-C3"},"PeriodicalIF":7.9,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10769783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736561","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 : 2024-11-26DOI: 10.1109/TITS.2024.3494251
Yifan Zhu;Yisheng Lv;Shu Lin;Jungang Xu
In addressing the complex challenge of Traffic Signal Control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are sensitive to minor input changes, which complicates accurate predictions. This ambiguity hampers algorithm convergence, speed, and overall performance. Additionally, existing DRL methods for TSC employ high-dimensional state spaces, escalating computational complexity. This study addresses these challenges by introducing an innovative approach, SLFMLight, that integrates a stochastic traffic flow model with DRL algorithm for TSC. Our method employs an innovative network update algorithm that integrates traffic flow prediction in Q-value learning process to enhance interpretability and accelerate algorithm convergence. Utilizing mode-based multi-actor networks to handle diverse traffic conditions, SLFMLight excels in decision-making towards complex traffic scenarios, especially in congested ones. Concise state definition improves computational efficiency. SLFMLight contributes to the advancement of intelligent traffic management by providing an effective DRL solution that improves interpretability, efficiency, and adaptability in TSC.
{"title":"A Stochastic Traffic Flow Model-Based Reinforcement Learning Framework For Advanced Traffic Signal Control","authors":"Yifan Zhu;Yisheng Lv;Shu Lin;Jungang Xu","doi":"10.1109/TITS.2024.3494251","DOIUrl":"https://doi.org/10.1109/TITS.2024.3494251","url":null,"abstract":"In addressing the complex challenge of Traffic Signal Control (TSC), Deep Reinforcement Learning (DRL) has emerged as a popular solution. In traditional DRL methods applied to TSC problems, deep neural networks are sensitive to minor input changes, which complicates accurate predictions. This ambiguity hampers algorithm convergence, speed, and overall performance. Additionally, existing DRL methods for TSC employ high-dimensional state spaces, escalating computational complexity. This study addresses these challenges by introducing an innovative approach, SLFMLight, that integrates a stochastic traffic flow model with DRL algorithm for TSC. Our method employs an innovative network update algorithm that integrates traffic flow prediction in Q-value learning process to enhance interpretability and accelerate algorithm convergence. Utilizing mode-based multi-actor networks to handle diverse traffic conditions, SLFMLight excels in decision-making towards complex traffic scenarios, especially in congested ones. Concise state definition improves computational efficiency. SLFMLight contributes to the advancement of intelligent traffic management by providing an effective DRL solution that improves interpretability, efficiency, and adaptability in TSC.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"714-723"},"PeriodicalIF":7.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976137","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 : 2024-11-26DOI: 10.1109/TITS.2024.3498701
Guoqi Ma;Prabhakar R. Pagilla;Swaroop Darbha
In this paper, we investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, we consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the selection of the time headway in predecessor-follower type vehicle platooning with a Constant Time Headway Policy (CTHP). Employing a CTHP based control law for each vehicle that utilizes onboard sensors for measurement of position and velocity of the predecessor vehicle and wireless communication network for obtaining the acceleration of the predecessor vehicle, we investigate how the implementable time headway is affected by communicated signal noise. We derive constraints on the CTHP controller gains for predecessor acceleration, velocity error and spacing error and a lower bound on the time headway which will ensure robust string stability of the platoon against signal noise. We perform comparative numerical simulations on an example to illustrate the main results.
{"title":"Selection of Time Headway in Connected and Autonomous Vehicle Platoons Under Noisy V2V Communication","authors":"Guoqi Ma;Prabhakar R. Pagilla;Swaroop Darbha","doi":"10.1109/TITS.2024.3498701","DOIUrl":"https://doi.org/10.1109/TITS.2024.3498701","url":null,"abstract":"In this paper, we investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, we consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the selection of the time headway in predecessor-follower type vehicle platooning with a Constant Time Headway Policy (CTHP). Employing a CTHP based control law for each vehicle that utilizes onboard sensors for measurement of position and velocity of the predecessor vehicle and wireless communication network for obtaining the acceleration of the predecessor vehicle, we investigate how the implementable time headway is affected by communicated signal noise. We derive constraints on the CTHP controller gains for predecessor acceleration, velocity error and spacing error and a lower bound on the time headway which will ensure robust string stability of the platoon against signal noise. We perform comparative numerical simulations on an example to illustrate the main results.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"1029-1038"},"PeriodicalIF":7.9,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976053","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}
Periodically monitoring the pavement cracks is of great importance to many transportation infrastructures. This paper proposed an unsupervised deep-learning-based method to match the cracks in multi-temporal unmanned aerial vehicle (UAV) images and identify the changes of pavement cracks over time. A regional focus module was specially designed to enforce the network to focus on regions where cracks were located and enhance its capacity for small-crack identification. Moreover, a data augmentation method which combined Poisson blending and random projective transformations was introduced for generating images with crack variations for model training. The superiority of the method was validated using actual image collected from real pavements. The experimental results showed that the proposed method outperformed the feature-based method and existing unsupervised deep learning-based UAV image registration method.
{"title":"A Pavement Crack Registration and Change Identification Method Based on Unsupervised Deep Neural Network","authors":"Zhengfang Wang;Hongliang Zhu;Yujie Yang;Haonan Jiang;Wenhao Li;Bingrui Li;Peng Li;Lei Xu;Qingmei Sui;Jing Wang","doi":"10.1109/TITS.2024.3493055","DOIUrl":"https://doi.org/10.1109/TITS.2024.3493055","url":null,"abstract":"Periodically monitoring the pavement cracks is of great importance to many transportation infrastructures. This paper proposed an unsupervised deep-learning-based method to match the cracks in multi-temporal unmanned aerial vehicle (UAV) images and identify the changes of pavement cracks over time. A regional focus module was specially designed to enforce the network to focus on regions where cracks were located and enhance its capacity for small-crack identification. Moreover, a data augmentation method which combined Poisson blending and random projective transformations was introduced for generating images with crack variations for model training. The superiority of the method was validated using actual image collected from real pavements. The experimental results showed that the proposed method outperformed the feature-based method and existing unsupervised deep learning-based UAV image registration method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"757-769"},"PeriodicalIF":7.9,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976076","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 : 2024-11-21DOI: 10.1109/TITS.2024.3485668
HongSheng Qi
Microscopic traffic models serve as indispensable tools in tasks such as constructing test scenarios for autonomous vehicles (AVs), predicting trajectories, and analyzing traffic flow dynamics. However, a significant proportion of these models rely on assumptions of normal behaviors. Yet, the validity of these assumptions is dubious given the heterogeneous nature of traffic flow and existence of abnormal driving behaviors. These limitations impede the efficacy of conventional microscopic models in crucial tasks like constructing AV test scenarios with specified risk levels, analyzing abnormal behaviors, etc. To address these challenges, this study contributes by proposing a model tailored to accommodate two-dimensional abnormal driving behaviors in microscopic traffic framework. The proposed approach have the following innovations: 1) it incorporates assumptions concerning abnormal behaviors in both the longitudinal and lateral dimensions; 2) abnormality at each dimension is captured by a combination of certain terms; 3) stochastic control barrier method is applied to customize the risk levels of the resulting traffic flow dynamics. Additionally, we present a method for retrieving vehicular maneuver information, enabling the extraction of detailed vehicle body gestures and driver control inputs, which would benefit the analysis of abnormal behavior. Our findings demonstrate that the proposed model yields longitudinal and lateral dynamics consistent with empirical observations, and various abnormal behavior patterns can be simulated.
{"title":"Microscopic Modeling of Abnormal Driving Behavior: A Two-Dimensional Stochastic Formulation with Customizable Safety Levels","authors":"HongSheng Qi","doi":"10.1109/TITS.2024.3485668","DOIUrl":"https://doi.org/10.1109/TITS.2024.3485668","url":null,"abstract":"Microscopic traffic models serve as indispensable tools in tasks such as constructing test scenarios for autonomous vehicles (AVs), predicting trajectories, and analyzing traffic flow dynamics. However, a significant proportion of these models rely on assumptions of normal behaviors. Yet, the validity of these assumptions is dubious given the heterogeneous nature of traffic flow and existence of abnormal driving behaviors. These limitations impede the efficacy of conventional microscopic models in crucial tasks like constructing AV test scenarios with specified risk levels, analyzing abnormal behaviors, etc. To address these challenges, this study contributes by proposing a model tailored to accommodate two-dimensional abnormal driving behaviors in microscopic traffic framework. The proposed approach have the following innovations: 1) it incorporates assumptions concerning abnormal behaviors in both the longitudinal and lateral dimensions; 2) abnormality at each dimension is captured by a combination of certain terms; 3) stochastic control barrier method is applied to customize the risk levels of the resulting traffic flow dynamics. Additionally, we present a method for retrieving vehicular maneuver information, enabling the extraction of detailed vehicle body gestures and driver control inputs, which would benefit the analysis of abnormal behavior. Our findings demonstrate that the proposed model yields longitudinal and lateral dynamics consistent with empirical observations, and various abnormal behavior patterns can be simulated.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"1163-1176"},"PeriodicalIF":7.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976063","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 : 2024-11-20DOI: 10.1109/TITS.2024.3495997
Daohui Zeng;Chengtao Cai;Yongchao Liu;Jie Zhao
This study addresses the tracking control issue of underactuated marine surface vehicles (UMSVs) with parameter and external uncertainties, input saturation, and unmeasurable velocity. An adaptive output feedback control scheme is developed without assuming the fore-aft symmetry of the hull. First, a state observer is developed to estimate the unmeasurable velocity. Next, the UMSV model is transformed into an integral cascade form using the hand position approach to overcome the design difficulties caused by the underactuated feature and asymmetric hull characteristics. Then, an adaptive auxiliary dynamic system is designed to solve the problem of input saturation caused by actuator constraints. In addition, the Lyapunov theory is applied to demonstrate the capability of the proposed control scheme to ensure the boundedness of the observation and tracking errors in the control system. Finally, the effectiveness of the developed control scheme is verified through simulation.
{"title":"Adaptive Output Feedback Control of Underactuated Marine Surface Vehicles Under Input Saturation","authors":"Daohui Zeng;Chengtao Cai;Yongchao Liu;Jie Zhao","doi":"10.1109/TITS.2024.3495997","DOIUrl":"https://doi.org/10.1109/TITS.2024.3495997","url":null,"abstract":"This study addresses the tracking control issue of underactuated marine surface vehicles (UMSVs) with parameter and external uncertainties, input saturation, and unmeasurable velocity. An adaptive output feedback control scheme is developed without assuming the fore-aft symmetry of the hull. First, a state observer is developed to estimate the unmeasurable velocity. Next, the UMSV model is transformed into an integral cascade form using the hand position approach to overcome the design difficulties caused by the underactuated feature and asymmetric hull characteristics. Then, an adaptive auxiliary dynamic system is designed to solve the problem of input saturation caused by actuator constraints. In addition, the Lyapunov theory is applied to demonstrate the capability of the proposed control scheme to ensure the boundedness of the observation and tracking errors in the control system. Finally, the effectiveness of the developed control scheme is verified through simulation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"1101-1112"},"PeriodicalIF":7.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975991","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 : 2024-11-20DOI: 10.1109/TITS.2024.3495028
Xiaoyu Sun;Yaohui Zhu;Hua Huang
RGB-Infrared object detection is an essential technology for the intelligent transportation system. Existing most works on RGB-Infrared object detection focus on how to fuse RGB and infrared features. However, these works overlook the inherent differences between RGB and infrared modalities, leading to insufficient modal feature fusion and limiting the performance of RGB-Infrared object detection. To address the above issues, a Specificity-guided Cross-modal Feature Reconstruction(SCFR) algorithm is proposed to establish modality-specific correlation for RGB-Infrared object detection. Specifically, the proposed SCFR involves the modality-specific cross-modal feature reconstruction network and two modality-specific losses. The modality-specific cross-modal feature reconstruction network performs cross-modal feature reconstruction on RGB and infrared modalities to establish modality-specific correlation. The modality-specific losses guide the direction of feature learning for reconstructing the expressive modality-specific features. These specific features can be used to achieve more efficient feature fusion, thus improving object detection performance. Comprehensive experimental results on three RGB-Infrared detection datasets demonstrate the effectiveness and the superiority of the proposed method. Our code will be available at https://github.com/SXYSUOSUO/SCFR.git.
{"title":"Specificity-Guided Cross-Modal Feature Reconstruction for RGB-Infrared Object Detection","authors":"Xiaoyu Sun;Yaohui Zhu;Hua Huang","doi":"10.1109/TITS.2024.3495028","DOIUrl":"https://doi.org/10.1109/TITS.2024.3495028","url":null,"abstract":"RGB-Infrared object detection is an essential technology for the intelligent transportation system. Existing most works on RGB-Infrared object detection focus on how to fuse RGB and infrared features. However, these works overlook the inherent differences between RGB and infrared modalities, leading to insufficient modal feature fusion and limiting the performance of RGB-Infrared object detection. To address the above issues, a Specificity-guided Cross-modal Feature Reconstruction(SCFR) algorithm is proposed to establish modality-specific correlation for RGB-Infrared object detection. Specifically, the proposed SCFR involves the modality-specific cross-modal feature reconstruction network and two modality-specific losses. The modality-specific cross-modal feature reconstruction network performs cross-modal feature reconstruction on RGB and infrared modalities to establish modality-specific correlation. The modality-specific losses guide the direction of feature learning for reconstructing the expressive modality-specific features. These specific features can be used to achieve more efficient feature fusion, thus improving object detection performance. Comprehensive experimental results on three RGB-Infrared detection datasets demonstrate the effectiveness and the superiority of the proposed method. Our code will be available at <uri>https://github.com/SXYSUOSUO/SCFR.git</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"950-961"},"PeriodicalIF":7.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976079","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}
With the development of intelligent vehicle and platoon technology, multi-platoon system will become a new solution to further improve traffic efficiency on highways. However, the existing research seldom consider the interference of human-driven vehicles on multi-platoon stability and the following strategy of multi-platoon leader in mixed traffic. In this paper, a robust distributed model predictive control method for multi-platoon leader in mixed traffic is proposed to reduce the impact of human-driven vehicles on multi-platoon control performance. The following control strategy of multi-platoon leader is proposed firstly, which flexibly determines the following control targets according to the states of leader and HDV to avoid unnecessary frequent acceleration and deceleration. Then, the robust model prediction controller of multi-platoon leader is designed, where the states of sub-platoon leader are added to the objective function in the nominal system optimization problem to reduce the states change of the following vehicles under the influence of HDV from both forward and backward traffic. Furthermore, the auxiliary control law is designed to eliminate the error between the actual states and the nominal states to achieve the suppression of HDV interference. The simulation results show that the multi-platoon leader following control strategy can effectively reduce the speed variation of the multi-platoon to suppress the impact of HDV motion uncertainty on multi-platoon. Moreover, compared with the robust model prediction method of single-platoon leader without considering the state of the rear vehicle, the proposed method can reduce the control errors and improve the stability of multi-platoon.
{"title":"Robust Distributed Model Predictive Control of Multi-Platoon Leader in Mixed Traffic","authors":"Weiwei Kong;Weizhen Zhu;Keqiang Li;Yuhao Zhang;Yugong Luo;Mingchang Xu","doi":"10.1109/TITS.2024.3482725","DOIUrl":"https://doi.org/10.1109/TITS.2024.3482725","url":null,"abstract":"With the development of intelligent vehicle and platoon technology, multi-platoon system will become a new solution to further improve traffic efficiency on highways. However, the existing research seldom consider the interference of human-driven vehicles on multi-platoon stability and the following strategy of multi-platoon leader in mixed traffic. In this paper, a robust distributed model predictive control method for multi-platoon leader in mixed traffic is proposed to reduce the impact of human-driven vehicles on multi-platoon control performance. The following control strategy of multi-platoon leader is proposed firstly, which flexibly determines the following control targets according to the states of leader and HDV to avoid unnecessary frequent acceleration and deceleration. Then, the robust model prediction controller of multi-platoon leader is designed, where the states of sub-platoon leader are added to the objective function in the nominal system optimization problem to reduce the states change of the following vehicles under the influence of HDV from both forward and backward traffic. Furthermore, the auxiliary control law is designed to eliminate the error between the actual states and the nominal states to achieve the suppression of HDV interference. The simulation results show that the multi-platoon leader following control strategy can effectively reduce the speed variation of the multi-platoon to suppress the impact of HDV motion uncertainty on multi-platoon. Moreover, compared with the robust model prediction method of single-platoon leader without considering the state of the rear vehicle, the proposed method can reduce the control errors and improve the stability of multi-platoon.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"169-181"},"PeriodicalIF":7.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992886","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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