Pub Date : 2025-06-13DOI: 10.1109/TITS.2025.3570005
Rao Fu;Pengda Mao;Yangqi Lei;Kai-Yuan Cai;Quan Quan
With the rapid development of uncrewed aerial vehicle (UAV) technology in recent years, research on large-scale low-altitude UAV air traffic management (ATM) has gained attention. Unlike the traditional ATM, the number of small UAVs in the airspace may be in the millions, making air traffic management challenging. In an ATM, airspace is composed of airways, intersections, and nodes. In this paper, a three-dimensional (3-D) roundabout model is utilized as an airspace structure for air traffic intersections of known traffic network models, which is decomposed into a central island, several ramps, and buffer zones. In this paper, for simplicity, the distributed coordination of the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass through a 3-D roundabout is focused on, which is formulated as a 3-D roundabout passing-through problem. The corresponding control objectives include inter-agent conflict-free, keeping within the 3-D curved virtual tube, and avoiding local minima. Lyapunov-like functions are designed elaborately, and formal analysis is made to show that all UAVs can pass through the 3-D roundabout without getting trapped. Taking the kinematic model of VTOL UAVs into consideration, the horizontal control and attitude control channels are decoupled, which is more reasonable for practical applications. Numerical simulation and real experiment are given to show the effectiveness of the proposed method.
{"title":"Practical Distributed Control for Cooperative VTOL UAVs Within a 3-D Roundabout","authors":"Rao Fu;Pengda Mao;Yangqi Lei;Kai-Yuan Cai;Quan Quan","doi":"10.1109/TITS.2025.3570005","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570005","url":null,"abstract":"With the rapid development of uncrewed aerial vehicle (UAV) technology in recent years, research on large-scale low-altitude UAV air traffic management (ATM) has gained attention. Unlike the traditional ATM, the number of small UAVs in the airspace may be in the millions, making air traffic management challenging. In an ATM, airspace is composed of airways, intersections, and nodes. In this paper, a three-dimensional (3-D) roundabout model is utilized as an airspace structure for air traffic intersections of known traffic network models, which is decomposed into a central island, several ramps, and buffer zones. In this paper, for simplicity, the distributed coordination of the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass through a 3-D roundabout is focused on, which is formulated as a 3-D roundabout passing-through problem. The corresponding control objectives include inter-agent conflict-free, keeping within the 3-D curved virtual tube, and avoiding local minima. Lyapunov-like functions are designed elaborately, and formal analysis is made to show that all UAVs can pass through the 3-D roundabout without getting trapped. Taking the kinematic model of VTOL UAVs into consideration, the horizontal control and attitude control channels are decoupled, which is more reasonable for practical applications. Numerical simulation and real experiment are given to show the effectiveness of the proposed method.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9341-9357"},"PeriodicalIF":7.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536447","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}
Pavement cracks have a highly complex spatial structure, a low contrasting background and a weak spatial continuity, posing a significant challenge to an effective crack detection method. To precisely localize crack from an image, it is critical to effectively extract and aggregate multi-granularity context, including the fine-grained local context around the cracks (in spatial-level) and the coarse-grained semantics (in semantic-level). In this paper, we apply the dilated convolution as the backbone feature extractor to model local context, then we build a context guidance module to leverage semantic context to guide local feature extraction at multiple stages. To handle label alignment between stages, we apply the Multiple Instance Learning (MIL) strategy to align the feature between two stages. In addition, to our best knowledge, we have released the largest, most complex and most challenging Bitumen Pavement Crack (BPC) dataset. The experimental results on the three crack datasets demonstrate that the proposed method performs well and outperforms the current state-of-the-art methods. On BPC, the proposed model achieved AP 88.32% with the 16.89 M parameters under the 45.36 GFlops runing speed. Datset and code are publicly available at: https://github.com/pangjunbiao/BPC-Crack-Dataset.
{"title":"Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection","authors":"Junbiao Pang;Baocheng Xiong;Jiaqi Wu;Qingming Huang","doi":"10.1109/TITS.2024.3438883","DOIUrl":"https://doi.org/10.1109/TITS.2024.3438883","url":null,"abstract":"Pavement cracks have a highly complex spatial structure, a low contrasting background and a weak spatial continuity, posing a significant challenge to an effective crack detection method. To precisely localize crack from an image, it is critical to effectively extract and aggregate multi-granularity context, including the fine-grained local context around the cracks (in spatial-level) and the coarse-grained semantics (in semantic-level). In this paper, we apply the dilated convolution as the backbone feature extractor to model local context, then we build a context guidance module to leverage semantic context to guide local feature extraction at multiple stages. To handle label alignment between stages, we apply the Multiple Instance Learning (MIL) strategy to align the feature between two stages. In addition, to our best knowledge, we have released the largest, most complex and most challenging Bitumen Pavement Crack (BPC) dataset. The experimental results on the three crack datasets demonstrate that the proposed method performs well and outperforms the current state-of-the-art methods. On BPC, the proposed model achieved AP 88.32% with the 16.89 M parameters under the 45.36 GFlops runing speed. Datset and code are publicly available at: <uri>https://github.com/pangjunbiao/BPC-Crack-Dataset</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9165-9174"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536408","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 : 2025-06-12DOI: 10.1109/TITS.2025.3574837
Yi Rong;Yingchi Mao;Yinqiu Liu;Ling Chen;Xiaoming He;Guojian Zou;Shahid Mumtaz;Dusit Niyato
Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, called Interpretable Causal Spatio-Temporal Diffusion Network (ICST-DNET). Specifically, ICST-DNET consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road’s historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results on two real-world traffic datasets prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.
{"title":"ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction","authors":"Yi Rong;Yingchi Mao;Yinqiu Liu;Ling Chen;Xiaoming He;Guojian Zou;Shahid Mumtaz;Dusit Niyato","doi":"10.1109/TITS.2025.3574837","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574837","url":null,"abstract":"Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, called Interpretable Causal Spatio-Temporal Diffusion Network (ICST-DNET). Specifically, ICST-DNET consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road’s historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results on two real-world traffic datasets prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9781-9798"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536695","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 : 2025-06-12DOI: 10.1109/TITS.2025.3576220
Marzieh Jalal Abadi;Sara Khalifa;Mahbub Hassan;Salil Kanhere;Mohamed Ali Kaafar
Vibration energy harvesting (VEH) has emerged as a viable option for mobile devices that serves the dual purpose of generating power and sensing ambient vibrations. This paper highlights the location privacy leakage resulting from unrestricted access to seemingly innocuous VEH data on mobile devices. We present VEH-Attack, a side-channel attack that exploits an inference model and VEH data patterns generated from train vibrations, enabling precise tracking of train passengers. VEH-Attack achieves an accuracy of 97% and 83.13% for VEH derived data and actual VEH data, respectively, for trip length of 6 stations with the accuracy reaching 100% for longer trip lengths.
{"title":"VEH-Attack: Stealthy Tracking of Train Passengers With Side-Channel Attack on Vibration Energy Harvesting Wearables","authors":"Marzieh Jalal Abadi;Sara Khalifa;Mahbub Hassan;Salil Kanhere;Mohamed Ali Kaafar","doi":"10.1109/TITS.2025.3576220","DOIUrl":"https://doi.org/10.1109/TITS.2025.3576220","url":null,"abstract":"Vibration energy harvesting (VEH) has emerged as a viable option for mobile devices that serves the dual purpose of generating power and sensing ambient vibrations. This paper highlights the location privacy leakage resulting from unrestricted access to seemingly innocuous VEH data on mobile devices. We present VEH-Attack, a side-channel attack that exploits an inference model and VEH data patterns generated from train vibrations, enabling precise tracking of train passengers. VEH-Attack achieves an accuracy of 97% and 83.13% for VEH derived data and actual VEH data, respectively, for trip length of 6 stations with the accuracy reaching 100% for longer trip lengths.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9669-9681"},"PeriodicalIF":7.9,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536440","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 : 2025-06-11DOI: 10.1109/TITS.2025.3574789
Ruxin Wang;Lei Nie;Yuyan Tan
In the process of railway operation planning, it is essential to take into account both railway capacity and origin to destination (OD) passenger demand. Stop plan plays a vital role in generating a train timetable with maximum railway capacity and ensuring high-quality service to transport passengers. Therefore, we are addressing the challenge of optimizing both the stop plan and timetable for a group of trains on a railway line, focusing on railway capacity estimation and passenger demand satisfaction. To provide realistic and precise passenger distribution, the preferences of different categories of passengers are given due regard. A classic time-space network describes the integrated problem, based on which a mathematical model is formulated to minimize train occupancy time on the high-speed railway line and maximize passenger kilometers at the same time. A decomposition approach based on Lagrangian relaxation (LR) is suggested to address the problem, which decomposes the integrated scheduling problem into two sub-problems: a train timetabling sub-problem, and a stop planning and passenger distributing sub-problem by dualizing constraints linking the two. A heuristic approach based on genetic algorithms is designed to obtain feasible solutions. The proposed model and approach are shown to generate good solutions efficiently. A series of real-world instances are conducted on the Beijing-Shanghai high-speed railway line in China, and the experimental outcomes show the benefits of optimizing the stop plan. Other related analyses are discussed by comparing results with different total number of stops, heterogeneous and homogeneous cases.
{"title":"Train Timetabling With Stop Planning and Passenger Distributing Integration Orientated by Railway Capacity and Passenger Service","authors":"Ruxin Wang;Lei Nie;Yuyan Tan","doi":"10.1109/TITS.2025.3574789","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574789","url":null,"abstract":"In the process of railway operation planning, it is essential to take into account both railway capacity and origin to destination (OD) passenger demand. Stop plan plays a vital role in generating a train timetable with maximum railway capacity and ensuring high-quality service to transport passengers. Therefore, we are addressing the challenge of optimizing both the stop plan and timetable for a group of trains on a railway line, focusing on railway capacity estimation and passenger demand satisfaction. To provide realistic and precise passenger distribution, the preferences of different categories of passengers are given due regard. A classic time-space network describes the integrated problem, based on which a mathematical model is formulated to minimize train occupancy time on the high-speed railway line and maximize passenger kilometers at the same time. A decomposition approach based on Lagrangian relaxation (LR) is suggested to address the problem, which decomposes the integrated scheduling problem into two sub-problems: a train timetabling sub-problem, and a stop planning and passenger distributing sub-problem by dualizing constraints linking the two. A heuristic approach based on genetic algorithms is designed to obtain feasible solutions. The proposed model and approach are shown to generate good solutions efficiently. A series of real-world instances are conducted on the Beijing-Shanghai high-speed railway line in China, and the experimental outcomes show the benefits of optimizing the stop plan. Other related analyses are discussed by comparing results with different total number of stops, heterogeneous and homogeneous cases.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9445-9460"},"PeriodicalIF":7.9,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536509","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 : 2025-06-10DOI: 10.1109/TITS.2025.3573718
Lei Liu;Zitong Zhao;Jie Feng;Feng Xu;Yue Zhang;Qingqi Pei;Ming Xiao
Benefiting from the outstanding advantages in speeding up task processing and saving energy consumption, vehicular edge computing has entered a period of rapid development. Given the sharp increase in application services, it is vital to fully utilize all available computation resources to guarantee personalized requirements from different users. Specially, a lot of idle vehicle resources can be exploited for task execution to improve the service experience. On the other hand, most works focus on the system performance and fail to guarantee diversified user demands. To this end, we propose a novel distributed collaborative computing scheme for task completion rate maximization (TCRM) in vehicular networks by taking into account both vertical and horizontal collaboration. The novelty of horizontal collaboration lies in the full use of available one-hop vehicle resources for task computing. In order to simultaneously guarantee the system-level performance and the user-level performance, TCRM aims to maximize the task completion rate while minimizing the energy consumption by intelligent resource optimization and task allocation. A TD3-based algorithm combined with the Dirichlet distribution is proposed to obtain the optimization decisions. Extensive simulations demonstrate that TCRM significantly improves performance compared to baseline algorithms.
{"title":"Distributed Collaborative Computing for Task Completion Rate Maximization in Vehicular Edge Computing","authors":"Lei Liu;Zitong Zhao;Jie Feng;Feng Xu;Yue Zhang;Qingqi Pei;Ming Xiao","doi":"10.1109/TITS.2025.3573718","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573718","url":null,"abstract":"Benefiting from the outstanding advantages in speeding up task processing and saving energy consumption, vehicular edge computing has entered a period of rapid development. Given the sharp increase in application services, it is vital to fully utilize all available computation resources to guarantee personalized requirements from different users. Specially, a lot of idle vehicle resources can be exploited for task execution to improve the service experience. On the other hand, most works focus on the system performance and fail to guarantee diversified user demands. To this end, we propose a novel distributed collaborative computing scheme for task completion rate maximization (TCRM) in vehicular networks by taking into account both vertical and horizontal collaboration. The novelty of horizontal collaboration lies in the full use of available one-hop vehicle resources for task computing. In order to simultaneously guarantee the system-level performance and the user-level performance, TCRM aims to maximize the task completion rate while minimizing the energy consumption by intelligent resource optimization and task allocation. A TD3-based algorithm combined with the Dirichlet distribution is proposed to obtain the optimization decisions. Extensive simulations demonstrate that TCRM significantly improves performance compared to baseline algorithms.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18070-18082"},"PeriodicalIF":8.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405259","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 : 2025-06-10DOI: 10.1109/TITS.2025.3574763
Yonghang Li;Chang Wang;Yifei Wang;Miao Ren;Jin Niu;Jikang Zhao;Kai Du
The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. Firstly, a Memory Fruit-fly-optimizer with Individual Differences (MFID) is innovatively proposed to drive Multi-threshold Otsu (MOtsu)-based multi-class segmentation of lanes, which is a high-dimensional optimization task with real-time and local optimal challenges, for capturing lane clues obscured in multi-intensity reflections, consequently reducing missed detections. Specifically, to solve the challenges inherent in the task, the MFID incorporates a novel memory mechanism to establish fast-converging initial conditions for real-time detection, while creatively considering individual differences to motivate multi-swarm optimization that mitigates local optima risks. After integration, the MFID-MOtsu is constructed for lane segmentation. Subsequently, a dynamic discretization mechanism is proposed to efficiently separate lane edges from interference edges, mitigating accuracy degradation caused by their entanglement. Finally, the false detection issue is greatly reduced through the implementation of adaptive geometric filters. The experimental results demonstrate that the proposed method achieves an average accuracy of 93.21% on rainy nights, indicating an average improvement of 12.7% over state-of-the-art methods. Additionally, without any parameter modifications, the proposed method is applicable to both normal and classic challenging scenes, such as nights, tunnels, rainy days, and shadows. The algorithm achieves an average accuracy of 96.2% and an average detection speed of 46 frames per second.
{"title":"Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms","authors":"Yonghang Li;Chang Wang;Yifei Wang;Miao Ren;Jin Niu;Jikang Zhao;Kai Du","doi":"10.1109/TITS.2025.3574763","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574763","url":null,"abstract":"The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. Firstly, a Memory Fruit-fly-optimizer with Individual Differences (MFID) is innovatively proposed to drive Multi-threshold Otsu (MOtsu)-based multi-class segmentation of lanes, which is a high-dimensional optimization task with real-time and local optimal challenges, for capturing lane clues obscured in multi-intensity reflections, consequently reducing missed detections. Specifically, to solve the challenges inherent in the task, the MFID incorporates a novel memory mechanism to establish fast-converging initial conditions for real-time detection, while creatively considering individual differences to motivate multi-swarm optimization that mitigates local optima risks. After integration, the MFID-MOtsu is constructed for lane segmentation. Subsequently, a dynamic discretization mechanism is proposed to efficiently separate lane edges from interference edges, mitigating accuracy degradation caused by their entanglement. Finally, the false detection issue is greatly reduced through the implementation of adaptive geometric filters. The experimental results demonstrate that the proposed method achieves an average accuracy of 93.21% on rainy nights, indicating an average improvement of 12.7% over state-of-the-art methods. Additionally, without any parameter modifications, the proposed method is applicable to both normal and classic challenging scenes, such as nights, tunnels, rainy days, and shadows. The algorithm achieves an average accuracy of 96.2% and an average detection speed of 46 frames per second.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9529-9541"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536407","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 : 2025-06-10DOI: 10.1109/TITS.2025.3575540
Panshuo Li;Xingyan Mao;Chao Huang;Pengxu Li
This study proposes a novel distributed model predictive control (DMPC) approach for mixed vehicle platoons (MVPs), which achieves driving safety, asymptotic stability, and traffic wave dissipation simultaneously. The longitudinal dynamics model and safety constraints are first established for each vehicle. The MVPs are divided into several sub-platoons according to the distribution of connected-and-automated vehicles (CAVs) and human-driven vehicles (HDVs). Then, a compound controller to ensure the head-to-tail string stability of MVPs is constructed as a reference controller for the subsequent design of the double-layer distributed model predictive controller (DL-DMPC). To describe the behavior of human drivers, a car-following model specific to HDVs is developed. On the basis of the designed compound controller and the description of HDVs, the DL-DMPC is proposed. The first layer improves tracking performance and satisfies the state constraints of each CAV under different communication topologies through an optimization problem. The second layer utilizes controller-matching technology to ensure the asymptotic stability of vehicle platoons and dissipate traffic waves. With the above double-layer structure, the DL-DMPC can simultaneously address multiple tasks and is applicable in various communication topologies. Simulations and analyses based on the NGSIM dataset are conducted in various scenarios to validate the effectiveness of the developed approach.
{"title":"Dissipating Traffic Waves in Mixed Vehicle Platoons: A Controller-Matching-Based Double-Layer Distributed Model Predictive Control Approach","authors":"Panshuo Li;Xingyan Mao;Chao Huang;Pengxu Li","doi":"10.1109/TITS.2025.3575540","DOIUrl":"https://doi.org/10.1109/TITS.2025.3575540","url":null,"abstract":"This study proposes a novel distributed model predictive control (DMPC) approach for mixed vehicle platoons (MVPs), which achieves driving safety, asymptotic stability, and traffic wave dissipation simultaneously. The longitudinal dynamics model and safety constraints are first established for each vehicle. The MVPs are divided into several sub-platoons according to the distribution of connected-and-automated vehicles (CAVs) and human-driven vehicles (HDVs). Then, a compound controller to ensure the head-to-tail string stability of MVPs is constructed as a reference controller for the subsequent design of the double-layer distributed model predictive controller (DL-DMPC). To describe the behavior of human drivers, a car-following model specific to HDVs is developed. On the basis of the designed compound controller and the description of HDVs, the DL-DMPC is proposed. The first layer improves tracking performance and satisfies the state constraints of each CAV under different communication topologies through an optimization problem. The second layer utilizes controller-matching technology to ensure the asymptotic stability of vehicle platoons and dissipate traffic waves. With the above double-layer structure, the DL-DMPC can simultaneously address multiple tasks and is applicable in various communication topologies. Simulations and analyses based on the NGSIM dataset are conducted in various scenarios to validate the effectiveness of the developed approach.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9326-9340"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536500","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 : 2025-06-10DOI: 10.1109/TITS.2025.3574552
Ran Wei;Alfredo Garcia;Anthony McDonald;Gustav Markkula;Johan Engstrom;Matthew O'Kelly
In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstrations. The model’s structure consists of (i) the agent’s internal representation of how the environment and associated observations evolve as a result of control actions and (ii) the agent’s preferences over observable outcomes. We consider a model’s structure specification consistent with active inference, a theory of human perception and behavior from cognitive science. According to active inference, the agent acts upon the world so as to minimize surprise defined as a measure of the extent to which an agent’s current sensory observations differ from its preferred sensory observations. We propose a bi-level optimization approach to estimation which relies on a structural assumption on prior distributions that parameterize the statistical accuracy of the human agent’s model of the environment. To illustrate the proposed methodology, we present the estimation of a model for car-following behavior based upon a naturalistic dataset. Overall, the results indicate that learning active inference models of human perception and control from data is a promising alternative to closed-box models of driving.
{"title":"Learning an Active Inference Model of Driver Perception and Control: Application to Vehicle Car-Following","authors":"Ran Wei;Alfredo Garcia;Anthony McDonald;Gustav Markkula;Johan Engstrom;Matthew O'Kelly","doi":"10.1109/TITS.2025.3574552","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574552","url":null,"abstract":"In this paper we introduce a general estimation methodology for learning a model of human perception and control in a sensorimotor control task based upon a finite set of demonstrations. The model’s structure consists of (i) the agent’s internal representation of how the environment and associated observations evolve as a result of control actions and (ii) the agent’s preferences over observable outcomes. We consider a model’s structure specification consistent with active inference, a theory of human perception and behavior from cognitive science. According to active inference, the agent acts upon the world so as to minimize surprise defined as a measure of the extent to which an agent’s current sensory observations differ from its preferred sensory observations. We propose a bi-level optimization approach to estimation which relies on a structural assumption on prior distributions that parameterize the statistical accuracy of the human agent’s model of the environment. To illustrate the proposed methodology, we present the estimation of a model for car-following behavior based upon a naturalistic dataset. Overall, the results indicate that learning active inference models of human perception and control from data is a promising alternative to closed-box models of driving.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9475-9490"},"PeriodicalIF":7.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536295","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}
This study investigates the problem of joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling. The problem is formulated as a bi-level mixed-integer bilinear program to capture passengers’ realistic path choice behavior. The upper-level model aims to minimize the weighted sum of the cost of bus route construction, bus route operation, bus station construction, travel time of passengers, the delay caused by failures in aboarding to the bus trips at the origin, the delay caused by failures in transfer between the bus trips, and the overflow delay when the bus trip operates at capacity. The lower-level model aims to minimize the travel time of passengers. The travel time of passengers is formulated as the sum of the waiting time for boarding, the transfer time, and the in-vehicle travel time. The passenger transfer time and the delay caused by failures in transfer between the bus trips are formulated with exact modeling of passenger transfer behavior. This bi-level mixed-integer bilinear program is transformed into an equivalent mixed-integer bilinear program with equilibrium constraints using Karush-Kuhn-Tucker conditions. To seek a solution of good quality to the proposed model while not requiring a large amount of computer memory, a Benders decomposition algorithm integrated with piecewise linearization is developed. A numerical application demonstrates that the proposed model is able to achieve 3.49% lower total cost than the baseline model assuming passenger transfer time to be half of the headway.
{"title":"Joint Optimization of Transit Network Design, Timetable, and Passenger Assignment With Exact Transfer Behavior Modeling","authors":"Yunyi Liang;Constantinos Antoniou;Mohammad Sadrani;Jinjun Tang","doi":"10.1109/TITS.2025.3573284","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573284","url":null,"abstract":"This study investigates the problem of joint optimization of transit network design, timetable, and passenger assignment with exact transfer behavior modeling. The problem is formulated as a bi-level mixed-integer bilinear program to capture passengers’ realistic path choice behavior. The upper-level model aims to minimize the weighted sum of the cost of bus route construction, bus route operation, bus station construction, travel time of passengers, the delay caused by failures in aboarding to the bus trips at the origin, the delay caused by failures in transfer between the bus trips, and the overflow delay when the bus trip operates at capacity. The lower-level model aims to minimize the travel time of passengers. The travel time of passengers is formulated as the sum of the waiting time for boarding, the transfer time, and the in-vehicle travel time. The passenger transfer time and the delay caused by failures in transfer between the bus trips are formulated with exact modeling of passenger transfer behavior. This bi-level mixed-integer bilinear program is transformed into an equivalent mixed-integer bilinear program with equilibrium constraints using Karush-Kuhn-Tucker conditions. To seek a solution of good quality to the proposed model while not requiring a large amount of computer memory, a Benders decomposition algorithm integrated with piecewise linearization is developed. A numerical application demonstrates that the proposed model is able to achieve 3.49% lower total cost than the baseline model assuming passenger transfer time to be half of the headway.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9263-9276"},"PeriodicalIF":7.9,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536552","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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