The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.
{"title":"Spatio-Temporal EV Task Offloading, Energy, and Traffic Management for 6G Communication-Power-Transportation Coupling Network","authors":"Chao Pan;Ziming Li;Haoyu Ci;Haijun Liao;Zhenyu Zhou;Anwer Al-Dulaimi;Muhammad Tariq","doi":"10.1109/TITS.2025.3574402","DOIUrl":"https://doi.org/10.1109/TITS.2025.3574402","url":null,"abstract":"The integration among 6G communication networks, power grids, and transportation systems is emerging as a promising paradigm to achieve mutual benefits among autonomous-driving electric vehicle (EV) users, communication operators, and power grids. Task offloading strategies for autonomous driving and the traveling patterns of EVs can induce communication load fluctuation within 6G network, which subsequently influences energy flow in power grid. Conversely, electricity price from the power grid affects EV charging/discharging strategies, impacting traffic flow and autonomous driving task offloading within the 6G network. Based on the interdependencies among the three networks, this paper constructs a communication-power-transportation coupling network with 6G base stations (BSs) and fast charge stations (FCSs) acting as coupling hubs. Besides, a spatio-temporal electricity price model considering spatial traffic distribution and temporal load fluctuation is developed. Moreover, the optimization problem is formulated to jointly coordinate FCS selection, bidirectional charging/discharging power regulation, task offloading decisions, and route selection strategies to maximize demand response quality of experience (QoE), grid stability and balance under the constraint of autonomous driving quality of service (QoS). Then, a knowledge transfer collaboration-based spatio-temporal EV task offloading, energy, and traffic management joint optimization algorithm is proposed, which improves the optimization performance through knowledge transfer collaboration among EV. Finally, simulation results validate the performance improvement of the proposed algorithm in demand response QoE, grid stability and balance, and autonomous driving QoS.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 10","pages":"18044-18057"},"PeriodicalIF":8.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405261","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 : 2025-07-02DOI: 10.1109/TITS.2025.3579612
{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2025.3579612","DOIUrl":"https://doi.org/10.1109/TITS.2025.3579612","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"C3-C3"},"PeriodicalIF":7.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536496","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 : 2025-07-02DOI: 10.1109/TITS.2025.3580163
Simona Sacone
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
{"title":"Scanning the Issue","authors":"Simona Sacone","doi":"10.1109/TITS.2025.3580163","DOIUrl":"https://doi.org/10.1109/TITS.2025.3580163","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":"26 7","pages":"9138-9164"},"PeriodicalIF":7.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536503","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 : 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}
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