IEEE Transactions on Intelligent Transportation Systems最新文献_第3页

Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances 基于强化学习的安全生态驾驶控制，适用于有干扰的混合交通流

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1109/TITS.2025.3544812

Ke Lu;Dongjun Li;Qun Wang;Kaidi Yang;Lin Zhao;Ziyou Song

{"title":"Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances","authors":"Ke Lu;Dongjun Li;Qun Wang;Kaidi Yang;Lin Zhao;Ziyou Song","doi":"10.1109/TITS.2025.3544812","DOIUrl":"https://doi.org/10.1109/TITS.2025.3544812","url":null,"abstract":"This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing system constraints during real-system operations. Even though reinforcement learning (RL) is capable of optimizing energy efficiency in intricate environments, it is challenged by safety requirements during both the training and deployment stages. The lack of safety guarantees impedes the application of RL to real-world problems. Compared with RL, model predicted control (MPC) can handle constrained dynamics systems, ensuring safe driving. However, the major challenges lie in complicated eco-driving tasks and the presence of disturbances, which pose difficulties for MPC design and constraint satisfaction. To address these limitations, the proposed framework incorporates the tube-based enhanced MPC (RMPC) to ensure the safe execution of the RL policy under disturbances, thereby improving the control robustness. RL not only optimizes the energy efficiency of the connected and automated vehicle in mixed traffic but also handles more uncertain scenarios, in which the energy consumption of the human-driven vehicle and its diverse and stochastic driving behaviors are considered in the optimization framework. Simulation results demonstrate that the proposed algorithm achieves an average improvement of 10.88% in holistic energy efficiency compared to the RMPC technique, while effectively preventing inter-vehicle collisions when compared to the RL algorithm.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4948-4959"},"PeriodicalIF":7.9,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740342","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}

引用次数: 0

Scanning the Issue

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-03 DOI: 10.1109/TITS.2025.3540181

Simona Sacone

引用次数: 0

IEEE Intelligent Transportation Systems Society Information

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-03 DOI: 10.1109/TITS.2025.3542211

引用次数: 0

Time-Aware and Direction-Constrained Collective Spatial Keyword Query

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-03 DOI: 10.1109/TITS.2024.3523406

Zhe Feng;Guohui Li;Jianjun Li;Changlong Jin;Xiaokun Du

{"title":"Time-Aware and Direction-Constrained Collective Spatial Keyword Query","authors":"Zhe Feng;Guohui Li;Jianjun Li;Changlong Jin;Xiaokun Du","doi":"10.1109/TITS.2024.3523406","DOIUrl":"https://doi.org/10.1109/TITS.2024.3523406","url":null,"abstract":"Collective spatial keyword query (CoSKQ) is an important variant of spatial keyword queries and has become a research hotspot. In real life, user behavior usually has a certain directionality, so they may want to obtain the result object that conforms to a specific direction, which is what the direction-constrained query studies. In addition, query time information also plays an important role in location-based query processing. To this end, this paper takes the lead in studying the Time-aware and Direction-constrained Collective Spatial Keyword Query (TDCoSKQ). To facilitate direction-related operations, space objects are organized using the polar coordinate system. Firstly, an efficient space partition method is designed, and on this basis, a new hybrid index structure KRPQT is designed. Based on KRPQT, several pruning strategies are proposed to prune irrelevant regions and objects from the perspective of keyword, time, and direction, and the basic algorithm KRPQB is proposed. To further improve the efficiency of query processing, the possible areas of the result objects are analyzed and shrunk to greatly reduce the number of candidate objects, and three optimization algorithms KRPSW, KRPSW+LFO, and KRPSW+KRPQB are proposed. Then, we discuss how to extend the proposed methods to deal with TDCoSKQ queries with other distance functions and TDCoSKQ queries with weight objects. Finally, the efficiency of the proposed algorithm is verified by simulation experiments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3039-3055"},"PeriodicalIF":7.9,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535583","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}

引用次数: 0

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-03-03 DOI: 10.1109/TITS.2025.3542310

引用次数: 0

Intelligent Reflective Surfaces Assisted Vehicular Networks: A Computer Vision-Based Framework

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-28 DOI: 10.1109/TITS.2025.3543870

Sohail Ahmad;Faisal Naeem;Muhammad Tariq

{"title":"Intelligent Reflective Surfaces Assisted Vehicular Networks: A Computer Vision-Based Framework","authors":"Sohail Ahmad;Faisal Naeem;Muhammad Tariq","doi":"10.1109/TITS.2025.3543870","DOIUrl":"https://doi.org/10.1109/TITS.2025.3543870","url":null,"abstract":"This paper addresses the challenges faced by 6G-enabled vehicular networks (V-Nets), including increasing road traffic, ultra-reliable and low latency communication, high data rates, and energy efficiency. The intelligent reflecting surface (IRS) is proposed as a solution to configure the propagation channel in a smart radio environment by adjusting phase shifts. However, designing IRS-assisted V-Nets that achieve ultra-reliability in dynamic and noisy communication is challenging due to the passive nature of the IRS and the limitations of deep reinforcement learning (DRL) methods. To overcome these challenges, this paper presents a computer vision (CV) enabled IRS framework for V-Nets, which combines a convolutional neural network and CV techniques. The framework utilizes real-time visual information to estimate and configure optimal beamforming for IRS-assisted V-Nets. Adapting to real-time network dynamics and intelligently guiding signals, the CV-IRS framework improves prediction accuracy to 95%, an achievable maximum rate of 11.2 bps/Hz with 100 IRS elements, and resource allocation efficiency of 88% with 10 vehicles. The simulation results demonstrate the superiority of the CV-IRS framework over benchmark schemes, making it a promising approach for the efficient configuration of IRS-assisted 6G V-Nets.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4481-4490"},"PeriodicalIF":7.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735340","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}

引用次数: 0

Advanced Optimization in Caching AAVs-Assisted Wireless Networks With Energy Constraint

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-28 DOI: 10.1109/TITS.2025.3543252

Jinming Huang;Jun Zhang;Wenchao Xia;Yi Wu;Chau Yuen

引用次数: 0

Heterogeneous Pedestrian Simulation in Commercial Complexes: When Attractive Potential Meets Social Force 商业综合体中的异质行人模拟：当吸引力与社会力量相遇

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-28 DOI: 10.1109/TITS.2025.3543613

Jingxuan Peng;Zhonghua Wei;Yanyan Chen;Shaofan Wang;Yongxing Li;Shihao Wang;Fujiyama Taku

{"title":"Heterogeneous Pedestrian Simulation in Commercial Complexes: When Attractive Potential Meets Social Force","authors":"Jingxuan Peng;Zhonghua Wei;Yanyan Chen;Shaofan Wang;Yongxing Li;Shihao Wang;Fujiyama Taku","doi":"10.1109/TITS.2025.3543613","DOIUrl":"https://doi.org/10.1109/TITS.2025.3543613","url":null,"abstract":"Commercial complex is a compositional scenario that involves pedestrians with different travel purposes such as commuting, shopping and business. Simulation of pedestrian behavior in a commercial complex is difficult due to the heterogeneity of pedestrians and different attractions from various kinds of shops. Inspired from the law of universal gravitation and the pedestrian dynamics theory, we propose an attractive potential based social force framework for pedestrian simulation in commercial complexes. Our framework consists of an attractive potential model and an attractive potential based social force model. The former model evaluates the attractive force of different types of shops towards heterogeneous pedestrians, by associating the attributes of pedestrians with the types of shops. The latter model effectively evaluates the travel direction of pedestrians by incorporating the attractive force with the social force model via a synthesis force criterion. We conduct the simulation experiment based on 600 groups of pedestrian tracking data collected in the China World Mall, Beijing, a typical commercial complex. The simulation results show that the model can effectively simulate not only the trajectory of real pedestrians, but also the behavior of entering shops. Our research significantly improves the authenticity of pedestrian simulation and provides support for travel behavior modeling of heterogeneous pedestrians in the commercial complex.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5102-5119"},"PeriodicalIF":7.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740340","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}

引用次数: 0

Federated Transfer Learning for Privacy-Preserved Cross-City Traffic Flow Prediction

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-28 DOI: 10.1109/TITS.2025.3545445

Xiaoming Yuan;Zhenyu Luo;Ning Zhang;Ge Guo;Lin Wang;Changle Li;Dusit Niyato

{"title":"Federated Transfer Learning for Privacy-Preserved Cross-City Traffic Flow Prediction","authors":"Xiaoming Yuan;Zhenyu Luo;Ning Zhang;Ge Guo;Lin Wang;Changle Li;Dusit Niyato","doi":"10.1109/TITS.2025.3545445","DOIUrl":"https://doi.org/10.1109/TITS.2025.3545445","url":null,"abstract":"Accurate future traffic flow prediction is essential for decision-making in travel recommendations and route planning, aiming to reduce congestion and enhance traffic safety. Traditional traffic flow prediction models often face limitations in quality and structure, leading to increased training costs and inefficiencies, due to data scarcity and centralized training modes that compromise data privacy. To address these issues, we propose a model called 2MGTCN, which combines Multi-modal Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN) for Cross-city Traffic Flow Prediction (TFP). Our 2MGTCN model utilizes federated transfer learning (FTL) to transfer the model from the source to the target domain, mitigating data scarcity. It also incorporates GCN and TCN to capture both spatial and temporal information, enhancing cross-city adaptability. Additionally, Grey Relation Analysis (GRA) and Dynamic Time Warping (DTW) methods are applied to capture road relationships, and a Federated Parameter Aggregation based on Spatial Similarity (FPASS) algorithm is proposed for ensuring effective parameter aggregation by considering spatial similarity. Simulation results show that our 2MGTCN algorithm outperforms traditional TFP models in both centralized and distributed training modes, ensuring higher accuracy and better privacy protection.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4418-4431"},"PeriodicalIF":7.9,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735343","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}

引用次数: 0

An Attention Deep Learning Framework-Based Drowsiness Detection Model for Intelligent Transportation System

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-27 DOI: 10.1109/TITS.2025.3544138

Biswarup Ganguly;Debangshu Dey;Sugata Munshi

{"title":"An Attention Deep Learning Framework-Based Drowsiness Detection Model for Intelligent Transportation System","authors":"Biswarup Ganguly;Debangshu Dey;Sugata Munshi","doi":"10.1109/TITS.2025.3544138","DOIUrl":"https://doi.org/10.1109/TITS.2025.3544138","url":null,"abstract":"Drivers’ drowsiness has been considered one of the prime reasons for accidents and road fatalities. Drowsiness may be caused by sleep disorders resulting in unusual mental and health conditions that have detrimental effects on human lives. This article aims to present an attention deep learning (DL) framework for drivers’ drowsiness monitoring for an intelligent transportation system. The proposed imaging system, comprising an Infrared-Cut camera embedded in a microcomputer, has been employed for capturing both day and night mode images for automated detection of drivers’ drowsiness. The frames captured are preprocessed and fed to the proposed attention DL framework based on “you only look once” version 3 (YOLOv3) for eye region detection followed by eye state classification and interpretation. Feature extraction has been carried out via a convolutional neural network module, and multiscale fusion along with the non-maximum suppression method has been applied to detect and classify the eye region of the drivers for monitoring drowsiness. Moreover, the eye region has been interpreted via a classification activation map using the proposed attention module. Experimental evaluations reveal the efficacy of the proposed system on our acquired dataset and two benchmark datasets. The proposed drowsiness detection device and system can possess good potential by increasing safety in an advanced driver assistance system.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4517-4527"},"PeriodicalIF":7.9,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735350","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}

引用次数: 0