Pub Date : 2025-06-30DOI: 10.1109/TITS.2025.3575814
Anjum Mohd Aslam;Rajat Chaudhary;Aditya Bhardwaj
The convergence of intelligent transportation systems and urban informatics has given rise to the deployment of connected and autonomous vehicles (CAVs) which offers the potential to enhance the safety and efficiency. However, the increasing volume of automobiles on highways causes frequent and often mismanaged multi-lane changing (MLC), coupled with inadequate trajectory planning. This results in traffic congestion and accidents, which leads to substantial societal losses. Additionally, these issues raise substantial concerns about environmental sustainability, safety, and traffic efficiency, necessitating innovative solutions. To address these challenges, we leverage the transformative capabilities of Artificial Intelligence of Things (AIoT) and introduce a deep reinforcement learning (DRL)-based non-cooperative game approach, named Nash-SAC (Soft Actor-Critic), enabled by digital twin technology, to facilitate optimized decision-making in CAVs. We consider various driving behaviors and social interaction characteristics that influence driving safety, ride comfort, and travel efficiency. The efficacy of the proposed framework is validated through simulations using the Python-based Highway-env simulator and Matlab/Simulink. The simulation analysis reveals that the proposed algorithm attains 22.48%, 40.32%, and 52.02% reductions in average delay, and achieves 39.50%, 58%, and 64.46% lesser computational time compared to the Twin-Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN) algorithms, respectively.
{"title":"An AIoT-Enabled Digital Twin CAVs With a DRL-Based Framework for Trajectory Planning","authors":"Anjum Mohd Aslam;Rajat Chaudhary;Aditya Bhardwaj","doi":"10.1109/TITS.2025.3575814","DOIUrl":"https://doi.org/10.1109/TITS.2025.3575814","url":null,"abstract":"The convergence of intelligent transportation systems and urban informatics has given rise to the deployment of connected and autonomous vehicles (CAVs) which offers the potential to enhance the safety and efficiency. However, the increasing volume of automobiles on highways causes frequent and often mismanaged multi-lane changing (MLC), coupled with inadequate trajectory planning. This results in traffic congestion and accidents, which leads to substantial societal losses. Additionally, these issues raise substantial concerns about environmental sustainability, safety, and traffic efficiency, necessitating innovative solutions. To address these challenges, we leverage the transformative capabilities of Artificial Intelligence of Things (AIoT) and introduce a deep reinforcement learning (DRL)-based non-cooperative game approach, named Nash-SAC (Soft Actor-Critic), enabled by digital twin technology, to facilitate optimized decision-making in CAVs. We consider various driving behaviors and social interaction characteristics that influence driving safety, ride comfort, and travel efficiency. The efficacy of the proposed framework is validated through simulations using the Python-based Highway-env simulator and Matlab/Simulink. The simulation analysis reveals that the proposed algorithm attains 22.48%, 40.32%, and 52.02% reductions in average delay, and achieves 39.50%, 58%, and 64.46% lesser computational time compared to the Twin-Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN) algorithms, respectively.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18101-18115"},"PeriodicalIF":8.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405307","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 widespread application of Transportation Cyber Physical Systems (T-CPS), increasingly intelligent and interconnected vehicles are conducting extensive transportation activities. Compared with traditional transportation equipment, they integrate advanced information functions such as data collection, terminal communication, real-time computing, and remote coordination, which can generate and collect a large amount of real traffic data. The enormous value of these traffic data can be released through market-oriented transactions. Blockchain technology can support the transmission and collaborative control of information T-CPS, while protecting the privacy and data security of intelligent connected vehicles. This article proposes a blockchain based data trading system aimed at simplifying the transaction flow of traffic data for intelligent connected vehicle owners, while maintaining fairness, privacy, and sustainable market development. Our work introduces two key innovations: a two-stage availability verification process that reduces transaction costs while enhancing data reliability, and an efficient encryption confirmation mechanism that ensures privacy and security for data providers and buyers throughout the entire transaction lifecycle. Finally, we demonstrate the feasibility and overall performance of our system through comprehensive analysis including security and reliability assessment, market behavior analysis, and computational complexity modeling, as well as practical experiments based on the Ethereum blockchain network. The evaluation results indicate that this scheme can provide privacy and security data transaction services at lower transaction costs.
{"title":"Intelligent Connected Vehicle Data Privacy and Security Transaction Sharing System Based on Blockchain","authors":"Jiwei Zhang;Yufei Tu;Ziang Sun;Tianqi Song;Shaozhang Niu","doi":"10.1109/TITS.2025.3578015","DOIUrl":"https://doi.org/10.1109/TITS.2025.3578015","url":null,"abstract":"With the widespread application of Transportation Cyber Physical Systems (T-CPS), increasingly intelligent and interconnected vehicles are conducting extensive transportation activities. Compared with traditional transportation equipment, they integrate advanced information functions such as data collection, terminal communication, real-time computing, and remote coordination, which can generate and collect a large amount of real traffic data. The enormous value of these traffic data can be released through market-oriented transactions. Blockchain technology can support the transmission and collaborative control of information T-CPS, while protecting the privacy and data security of intelligent connected vehicles. This article proposes a blockchain based data trading system aimed at simplifying the transaction flow of traffic data for intelligent connected vehicle owners, while maintaining fairness, privacy, and sustainable market development. Our work introduces two key innovations: a two-stage availability verification process that reduces transaction costs while enhancing data reliability, and an efficient encryption confirmation mechanism that ensures privacy and security for data providers and buyers throughout the entire transaction lifecycle. Finally, we demonstrate the feasibility and overall performance of our system through comprehensive analysis including security and reliability assessment, market behavior analysis, and computational complexity modeling, as well as practical experiments based on the Ethereum blockchain network. The evaluation results indicate that this scheme can provide privacy and security data transaction services at lower transaction costs.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"14192-14204"},"PeriodicalIF":8.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128532","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}
Autonomous vehicles (AVs) offer a promising glimpse into a future where transportation is smarter, safer, and more streamlined. Nevertheless, as AVs continue to interact with conventional vehicles (CVs), the potential for increased complexities and challenges cannot be overlooked, such as the frozen robot problem. This study proposes a regret-based model for motion planning responsibilities, encompassing self-respect and courtesy for conflicting personal interests. By incorporating these reciprocal responsibilities, socially compatible driving behaviors are promoted, and uncertainties in behavior are also reduced. A Self-Respect-Courtesy (SR-C) plane is further introduced, illustrating the interaction intensity and tendency. To navigate the trade-offs of responsibilities in varying situations, the concept of environmental niche is provided. Niches help to characterize the outcomes of specific actions with the resulting conditions to fulfill responsibilities. Finally, a hierarchical multi-agent inverse reinforcement learning algorithm is designed to calibrate the proposed model with NGSIM highway lane-changing cases. We found that the proposed model can significantly improve the calibration results and reduce the predictions error of mandatory lane changes by up to 20%. Moreover, the cross-entropy error also significantly decreases in a stable stage, indicating that responsible actions can safely reduce the behavior uncertainties of interactions. Our research revealed that drivers prioritize courtesy responsibility in discretionary lane changes with more consistency, whereas their self-respect preferences are stronger but show more variability in mandatory lane changes. These findings provide valuable insights into the underlying mechanism of interactions.
{"title":"Responsibility-Based Socially Compatible Driving Behavior Modeling Verified by Hierarchical Multi-Agent Inverse Reinforcement Learning","authors":"Tingjun Li;Nan Xu;Shuo Feng;Hassan Askari;Bruno Henrique Groenner Barbosa;Konghui Guo","doi":"10.1109/TITS.2025.3577660","DOIUrl":"https://doi.org/10.1109/TITS.2025.3577660","url":null,"abstract":"Autonomous vehicles (AVs) offer a promising glimpse into a future where transportation is smarter, safer, and more streamlined. Nevertheless, as AVs continue to interact with conventional vehicles (CVs), the potential for increased complexities and challenges cannot be overlooked, such as the frozen robot problem. This study proposes a regret-based model for motion planning responsibilities, encompassing self-respect and courtesy for conflicting personal interests. By incorporating these reciprocal responsibilities, socially compatible driving behaviors are promoted, and uncertainties in behavior are also reduced. A Self-Respect-Courtesy (SR-C) plane is further introduced, illustrating the interaction intensity and tendency. To navigate the trade-offs of responsibilities in varying situations, the concept of environmental niche is provided. Niches help to characterize the outcomes of specific actions with the resulting conditions to fulfill responsibilities. Finally, a hierarchical multi-agent inverse reinforcement learning algorithm is designed to calibrate the proposed model with NGSIM highway lane-changing cases. We found that the proposed model can significantly improve the calibration results and reduce the predictions error of mandatory lane changes by up to 20%. Moreover, the cross-entropy error also significantly decreases in a stable stage, indicating that responsible actions can safely reduce the behavior uncertainties of interactions. Our research revealed that drivers prioritize courtesy responsibility in discretionary lane changes with more consistency, whereas their self-respect preferences are stronger but show more variability in mandatory lane changes. These findings provide valuable insights into the underlying mechanism of interactions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"14353-14370"},"PeriodicalIF":8.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128517","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}
The rapid expansion of intelligent vehicles in 6G networks has intensified the demand for real-time task processing. However, traditional cloud-edge collaboration models for large-scale vehicle task offloading are increasingly inadequate to address the growing complexity and demands. To address this challenge, we propose a unified cloud-edge-end collaborative vehicle task offloading multiobjective optimization model for large-scale vehicle task offloading, which simultaneously considers four optimization objectives: latency, energy consumption, load balancing and quality of service (QoS). To solve the large-scale multiobjective optimization problem, we propose a large-scale multiobjective evolutionary algorithm based on problem transformation and bidirectional vectors (LSMOEA-PTBV). Experiments in a simulated 6G vehicular network demonstrate that LSMOEA-PTBV outperforms state-of-the-art methods. Our work enhances the end-user experience, meets the increasingly complex demands of modern applications, and advances the development of integrated sensing and computing systems and intelligent transportation systems in the 6G era.
{"title":"Large-Scale Multiobjective Vehicle Task Offloading Optimization Based on Cloud-Edge-End Collaboration for 6G Enabled Transport Systems","authors":"Xin Liu;Wenzhuo Li;Bin Cao;Shuqiang Wang;Zhihan Lyu","doi":"10.1109/TITS.2025.3579164","DOIUrl":"https://doi.org/10.1109/TITS.2025.3579164","url":null,"abstract":"The rapid expansion of intelligent vehicles in 6G networks has intensified the demand for real-time task processing. However, traditional cloud-edge collaboration models for large-scale vehicle task offloading are increasingly inadequate to address the growing complexity and demands. To address this challenge, we propose a unified cloud-edge-end collaborative vehicle task offloading multiobjective optimization model for large-scale vehicle task offloading, which simultaneously considers four optimization objectives: latency, energy consumption, load balancing and quality of service (QoS). To solve the large-scale multiobjective optimization problem, we propose a large-scale multiobjective evolutionary algorithm based on problem transformation and bidirectional vectors (LSMOEA-PTBV). Experiments in a simulated 6G vehicular network demonstrate that LSMOEA-PTBV outperforms state-of-the-art methods. Our work enhances the end-user experience, meets the increasingly complex demands of modern applications, and advances the development of integrated sensing and computing systems and intelligent transportation systems in the 6G era.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18170-18179"},"PeriodicalIF":8.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384609","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 : 2025-06-17DOI: 10.1109/TITS.2025.3578464
Weijun Hu;Xianlong Ma
Task assignment and path planning are crucial links in the task execution of uncrewed aerial vehicle (UAV) cluster, especially in high-dimensional complex scenarios, the calculation difficulty increases significantly. To solve this problem, swarm intelligence as an efficient strategy emerged. In order to solve the challenge of incomplete information in the task assignment of UAVs and the problems of intra-group cooperation and competition, we propose an innovative multi-agent near-end strategy optimization algorithm (MAPPO algorithm). The algorithm is designed for the task assignment of UAV in incomplete information environment. By constructing a practical algorithm model and combining the incomplete information game theory, the original algorithm is optimized to better deal with the cooperation and competition mechanism within the UAVs. Secondly, the global search capability is poor and local optimization is easy to occur. The dynamic cluster particle swarm optimization (DCPSO) algorithm is proposed to model the task scenario of UAVs path planning problem by using artificial potential field method and rolling time domain control principle. Tent chaos mapping and dynamic cluster mechanism are introduced to further improve the global search capability and search accuracy. Finally, DCPSO algorithm is used to optimize the objective function of the model, and the selection of UAV trajectory points is obtained. Simulation results under different combinations of single-peak/multi-peak, low-dimensional/high-dimensional benchmark test functions show that DCPSO algorithm has better optimization ability, mean value and variance compared with PSO, pigeon inspired optimization (PIO), Sparrow search algorithm (SSA) and chaotic disturbed pigeon flock optimization (CDPIO) algorithms. Better search accuracy and stability.
{"title":"Optimization Algorithm of UAVs Task Assignment and Path Planning Based on Dynamic Cluster Particle Swarm Optimization","authors":"Weijun Hu;Xianlong Ma","doi":"10.1109/TITS.2025.3578464","DOIUrl":"https://doi.org/10.1109/TITS.2025.3578464","url":null,"abstract":"Task assignment and path planning are crucial links in the task execution of uncrewed aerial vehicle (UAV) cluster, especially in high-dimensional complex scenarios, the calculation difficulty increases significantly. To solve this problem, swarm intelligence as an efficient strategy emerged. In order to solve the challenge of incomplete information in the task assignment of UAVs and the problems of intra-group cooperation and competition, we propose an innovative multi-agent near-end strategy optimization algorithm (MAPPO algorithm). The algorithm is designed for the task assignment of UAV in incomplete information environment. By constructing a practical algorithm model and combining the incomplete information game theory, the original algorithm is optimized to better deal with the cooperation and competition mechanism within the UAVs. Secondly, the global search capability is poor and local optimization is easy to occur. The dynamic cluster particle swarm optimization (DCPSO) algorithm is proposed to model the task scenario of UAVs path planning problem by using artificial potential field method and rolling time domain control principle. Tent chaos mapping and dynamic cluster mechanism are introduced to further improve the global search capability and search accuracy. Finally, DCPSO algorithm is used to optimize the objective function of the model, and the selection of UAV trajectory points is obtained. Simulation results under different combinations of single-peak/multi-peak, low-dimensional/high-dimensional benchmark test functions show that DCPSO algorithm has better optimization ability, mean value and variance compared with PSO, pigeon inspired optimization (PIO), Sparrow search algorithm (SSA) and chaotic disturbed pigeon flock optimization (CDPIO) algorithms. Better search accuracy and stability.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18157-18169"},"PeriodicalIF":8.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384607","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 proposes an integrated framework for multi-ship trajectory prediction and motion planning based on joint learning, which significantly enhances the overall performance in multi-ship interaction scenarios by combining the global information from trajectory prediction with the local optimization capabilities of motion planning. In the trajectory prediction task, the proposed MTSGCN model achieves an average performance improvement of 26.3% compared to the Rain model, while the joint multi-task learning strategy yields a 26.9% performance gain over single-task learning, demonstrating the effectiveness of the multi-ship interaction adjacency matrix extraction module. In the motion planning task, the MTSGCN model outperforms the TFT model with an average performance improvement of 13.2%, whereas the MTSGCN-T model without joint learning experiences an 11.2% performance degradation. Furthermore, the study reveals that ship speed and heading decisions are influenced by multiple factors, with the inertial effect of historical parameters being the most significant. Additionally, the feature distributions of different datasets have a substantial impact on model performance.
{"title":"Multi-Task Learning for Ship Trajectory Prediction and Motion Planning via Node Relationship Modeling","authors":"Yuegao Wu;Weiqiang Liao;Wanneng Yu;Guangmiao Zeng;Yifan Shang;Xin Dong","doi":"10.1109/TITS.2025.3573811","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573811","url":null,"abstract":"This study proposes an integrated framework for multi-ship trajectory prediction and motion planning based on joint learning, which significantly enhances the overall performance in multi-ship interaction scenarios by combining the global information from trajectory prediction with the local optimization capabilities of motion planning. In the trajectory prediction task, the proposed MTSGCN model achieves an average performance improvement of 26.3% compared to the Rain model, while the joint multi-task learning strategy yields a 26.9% performance gain over single-task learning, demonstrating the effectiveness of the multi-ship interaction adjacency matrix extraction module. In the motion planning task, the MTSGCN model outperforms the TFT model with an average performance improvement of 13.2%, whereas the MTSGCN-T model without joint learning experiences an 11.2% performance degradation. Furthermore, the study reveals that ship speed and heading decisions are influenced by multiple factors, with the inertial effect of historical parameters being the most significant. Additionally, the feature distributions of different datasets have a substantial impact on model performance.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"13051-13064"},"PeriodicalIF":8.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315524","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 : 2025-06-16DOI: 10.1109/TITS.2025.3577308
Can Tan;Peng Yu;Zhaowei Qu;Lixin Zhang;Wenjing Li;Xuesong Qiu;Shaoyong Guo
The rapid development of autonomous vehicles and smart city has led to an exponential increase in data generation within Intelligent Transportation Systems (ITS). However, comprehensive extraction and utilization of these data are severely hindered by communication and energy constraints, security and privacy concerns, vehicle mobility limitations, and spatial distribution challenges. Using 6G and Digital Twin (DT) technologies offers a promising solution to these problems. In this paper, we propose a DT-based model training architecture for vehicular networks and introduce Federated Learning (FL) to preserve data privacy. While distributed model training and parameter transmission introduce challenges in delay and energy consumption, which conflict with real-time service requirements in ITS. In addition, the quality of the data and the processing capability of each vehicle varies widely, which will affect the efficiency of data sharing and model accuracy. Therefore, it is vital to select appropriate training nodes and optimize resource allocation under the constraints of task delay and energy consumption. We formulate an optimization model to improve the selection of FL participating nodes and energy management strategies, aiming to maximize accuracy while minimizing energy consumption. We then develop a DT-assisted deep reinforcement learning (DRL) method. Experiments show that our scheme achieves higher training accuracy and energy efficiency compared to the benchmark.
{"title":"Energy-Efficient Federated Learning Training Optimization for Digital Twin Driven 6G Air-Ground Integrated Vehicular Networks","authors":"Can Tan;Peng Yu;Zhaowei Qu;Lixin Zhang;Wenjing Li;Xuesong Qiu;Shaoyong Guo","doi":"10.1109/TITS.2025.3577308","DOIUrl":"https://doi.org/10.1109/TITS.2025.3577308","url":null,"abstract":"The rapid development of autonomous vehicles and smart city has led to an exponential increase in data generation within Intelligent Transportation Systems (ITS). However, comprehensive extraction and utilization of these data are severely hindered by communication and energy constraints, security and privacy concerns, vehicle mobility limitations, and spatial distribution challenges. Using 6G and Digital Twin (DT) technologies offers a promising solution to these problems. In this paper, we propose a DT-based model training architecture for vehicular networks and introduce Federated Learning (FL) to preserve data privacy. While distributed model training and parameter transmission introduce challenges in delay and energy consumption, which conflict with real-time service requirements in ITS. In addition, the quality of the data and the processing capability of each vehicle varies widely, which will affect the efficiency of data sharing and model accuracy. Therefore, it is vital to select appropriate training nodes and optimize resource allocation under the constraints of task delay and energy consumption. We formulate an optimization model to improve the selection of FL participating nodes and energy management strategies, aiming to maximize accuracy while minimizing energy consumption. We then develop a DT-assisted deep reinforcement learning (DRL) method. Experiments show that our scheme achieves higher training accuracy and energy efficiency compared to the benchmark.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18116-18128"},"PeriodicalIF":8.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384608","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 : 2025-06-13DOI: 10.1109/TITS.2025.3572987
Jianlong Wang;Chuanwei Zhang;Zhi Yang;Meng Dang
Focusing on the poor applicability of existing brake stability control methods for intelligent electric vehicles and the problem that the actual braking intention of the driver and the actual running condition of the vehicle are less considered, a layered brake stability control method for electric vehicles is proposed which considers the driver’s braking intention and vehicle state. Firstly, a GRU (Gated Recurrent Unit) neural network with SE (Squeeze Excitation) module mechanism is proposed to obtain the driver’s real braking intention, and a vehicle state recognition algorithm is designed to obtain the real-time longitudinal speed of the vehicle under complex working conditions, which form a closed-loop control structure for the braking system. Secondly, the layered control structure is used to distribute braking force, and the upper control strategy of the braking system with multi-attention mechanism is proposed to obtain the braking torque required for stable braking of the vehicle. Then, the lower level control strategy is used to coordinate the electro-hydraulic braking torque, and the dynamic coordination distribution method of motor braking and hydraulic braking is designed. Finally, the effectiveness and real-time performance of the layered braking stability control method considering driver’s braking intention and vehicle state are verified by joint simulation and real vehicle road experiments. The experiment results show that the slip rate of the proposed braking control method is about 1.5%, the SOC value of the battery increases by 0.14%~0.18%, and the stability coefficient is stable in the range of $0.02sim 0.04$ . The braking system control method can not only ensure the braking efficiency and stability of the vehicle, but also effectively recover the braking energy, which provides a new solution for the braking stability control of intelligent vehicles.
{"title":"A Layered EV Braking Stability Control Approach Considering the Driver’s Braking Intention and Vehicle Condition","authors":"Jianlong Wang;Chuanwei Zhang;Zhi Yang;Meng Dang","doi":"10.1109/TITS.2025.3572987","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572987","url":null,"abstract":"Focusing on the poor applicability of existing brake stability control methods for intelligent electric vehicles and the problem that the actual braking intention of the driver and the actual running condition of the vehicle are less considered, a layered brake stability control method for electric vehicles is proposed which considers the driver’s braking intention and vehicle state. Firstly, a GRU (Gated Recurrent Unit) neural network with SE (Squeeze Excitation) module mechanism is proposed to obtain the driver’s real braking intention, and a vehicle state recognition algorithm is designed to obtain the real-time longitudinal speed of the vehicle under complex working conditions, which form a closed-loop control structure for the braking system. Secondly, the layered control structure is used to distribute braking force, and the upper control strategy of the braking system with multi-attention mechanism is proposed to obtain the braking torque required for stable braking of the vehicle. Then, the lower level control strategy is used to coordinate the electro-hydraulic braking torque, and the dynamic coordination distribution method of motor braking and hydraulic braking is designed. Finally, the effectiveness and real-time performance of the layered braking stability control method considering driver’s braking intention and vehicle state are verified by joint simulation and real vehicle road experiments. The experiment results show that the slip rate of the proposed braking control method is about 1.5%, the SOC value of the battery increases by 0.14%~0.18%, and the stability coefficient is stable in the range of <inline-formula> <tex-math>$0.02sim 0.04$ </tex-math></inline-formula>. The braking system control method can not only ensure the braking efficiency and stability of the vehicle, but also effectively recover the braking energy, which provides a new solution for the braking stability control of intelligent vehicles.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18083-18100"},"PeriodicalIF":8.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405257","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 : 2025-06-13DOI: 10.1109/TITS.2025.3572623
Mohammad Ali Arman;Chris M. J. Tampère
Whereas on many motorways, traffic operations are permanently monitored, and long historical logs of such data exist, they are not directly usable for lane change studies, as they only register local passages and speeds. This study proposes a novel method to transform discrete vehicle passage records of individual vehicle data (IVD) into approximations of vehicle trajectories and inference of lane change maneuvers (LCMs), such that large-scale LCM dataset can be retrieved from existing infrastructures where IVD is recorded at sufficiently close spacings (~600 meters). The method’s core is a probabilistic re-identification of individual vehicles in successive, lane-specific loop detectors. Dubbed Traffic Flow Crystallization (TFC), the methodology enhances traffic monitoring by providing vast and diverse LCM datasets. It consists of two key re-identification (ReID) modules: a lane-restricted module that matches vehicles strictly within the same lane and a non-lane-restricted module that recursively identifies lane-changing vehicles using boundary conditions imposed by previously matched vehicles. This recursive process resembles crystal growth, inspiring the method’s name. The ReID methodology is based on a weighted likelihood function consisting of Bayesian probability estimators that integrate three similarity measures: vehicle length, passage time, and passage speed. A lane-change feasibility filter ensures that re-identified vehicles satisfy plausible spatiotemporal constraints. The final module resolves inconsistencies and infers LCMs. The proposed method is trained and validated using CCTV footage, where visually-identified vehicles serve as ground truth. Validation results demonstrate a vehicle ReID success rate exceeding 96% and an inferred LCM rate with only a 2% underestimation compared to ground truth.
{"title":"Traffic Flow Crystallization Method for Trajectory Approximation and Lane Change Inference","authors":"Mohammad Ali Arman;Chris M. J. Tampère","doi":"10.1109/TITS.2025.3572623","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572623","url":null,"abstract":"Whereas on many motorways, traffic operations are permanently monitored, and long historical logs of such data exist, they are not directly usable for lane change studies, as they only register local passages and speeds. This study proposes a novel method to transform discrete vehicle passage records of individual vehicle data (IVD) into approximations of vehicle trajectories and inference of lane change maneuvers (LCMs), such that large-scale LCM dataset can be retrieved from existing infrastructures where IVD is recorded at sufficiently close spacings (~600 meters). The method’s core is a probabilistic re-identification of individual vehicles in successive, lane-specific loop detectors. Dubbed Traffic Flow Crystallization (TFC), the methodology enhances traffic monitoring by providing vast and diverse LCM datasets. It consists of two key re-identification (ReID) modules: a lane-restricted module that matches vehicles strictly within the same lane and a non-lane-restricted module that recursively identifies lane-changing vehicles using boundary conditions imposed by previously matched vehicles. This recursive process resembles crystal growth, inspiring the method’s name. The ReID methodology is based on a weighted likelihood function consisting of Bayesian probability estimators that integrate three similarity measures: vehicle length, passage time, and passage speed. A lane-change feasibility filter ensures that re-identified vehicles satisfy plausible spatiotemporal constraints. The final module resolves inconsistencies and infers LCMs. The proposed method is trained and validated using CCTV footage, where visually-identified vehicles serve as ground truth. Validation results demonstrate a vehicle ReID success rate exceeding 96% and an inferred LCM rate with only a 2% underestimation compared to ground truth.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9305-9325"},"PeriodicalIF":7.9,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536404","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 : 2025-06-13DOI: 10.1109/TITS.2025.3575812
Jingang Zhao;Wei Sun;Wei Ding;Yadan Li;Pengxiang Sun;Peilun Sun
Cooperative positioning technology based on multi-vehicle information fusion is essential for advanced applications in intelligent transportation systems (ITS). The integration of global navigation satellite systems (GNSS), inertial navigation system (INS), and ultra-wideband (UWB) technology holds significant promise for enhancing the continuity and reliability of vehicle cooperative positioning. In tightly coupled GNSS/INS/UWB integration, the tolerance against measurement outliers and state model perturbations is pivotal for fulfilling the specific requirements of critical ITS applications. To optimize the comprehensive performance of vehicle cooperative positioning under uncertain sensor observation environments, this paper proposes a robust multiple fading factors unscented Kalman filtering (RMFUKF) algorithm based on adaptive cost function. The proposed solution incorporates Huber M-estimation with an adaptive tuning strategy to perform measurement-specific outliers processing. Furthermore, the improved multiple fading factors based on an exponential weighting method are implemented to mitigate the effects of dynamic model mismatches. Experimental results from vehicular field experiments demonstrate that the proposed RMFUKF scheme significantly improves the robustness and adaptive performance of vehicle cooperative positioning under unpredictable, real-world operating conditions.
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