IEEE Transactions on Intelligent Transportation Systems最新文献_第9页

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3527805

引用次数: 0

Digital Twin-Based Task-Driven Resource Management in Intelligent UAV Swarms

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3531120

Tianyang Li;Supeng Leng;Xiwen Liao;Yan Zhang

{"title":"Digital Twin-Based Task-Driven Resource Management in Intelligent UAV Swarms","authors":"Tianyang Li;Supeng Leng;Xiwen Liao;Yan Zhang","doi":"10.1109/TITS.2025.3531120","DOIUrl":"https://doi.org/10.1109/TITS.2025.3531120","url":null,"abstract":"UAV swarms offer substantial opportunities for Search and Rescue (SAR) applications. Confronted with numerous concurrent sensing tasks in complicated environment, resource-scarce UAV networks need a dynamic, task-driven deployment and resource configuration strategy for multi-UAV swarm coordination to ensure the efficient execution of sensing tasks. This paper introduces a Digital Twin (DT)-based collaboration architecture for resource management in UAV swarms, connecting realistic task crowdsourcing and virtual traffic flow scheduling to achieve a complementary multi-UAV swarm allocation. We propose an intelligent dynamic task crowdsourcing scheme that manages the swarm scale and membership configuration of multiple UAV swarms based on theoretical evaluation results. The architecture constructs DTs of UAV swarms and shifts the scheduling of traffic flow paths to the virtual world, thereby sidestepping the overhead of routing configuration and network reorganisation. With the aid of a traffic flow allocation algorithm based on Stochastic Network Calculus (SNC), the virtual swarm pre-schedules traffic flows and assesses end-to-end delay theoretically, so as to achieve a collaborative deployment of sensing, computational, and communication resources within the swarm. The simulation results substantiate that our architecture can uphold a 90% achievement ratio for task requirements while keeping UAV costs comparable to other algorithms.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"5467-5480"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725111","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

A Sparse Cross Attention-Based Graph Convolution Network With Auxiliary Information Awareness for Traffic Flow Prediction

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3533560

Lingqiang Chen;Qinglin Zhao;Guanghui Li;Mengchu Zhou;Chenglong Dai;Yiming Feng;Xiaowei Liu;Jinjiang Li

{"title":"A Sparse Cross Attention-Based Graph Convolution Network With Auxiliary Information Awareness for Traffic Flow Prediction","authors":"Lingqiang Chen;Qinglin Zhao;Guanghui Li;Mengchu Zhou;Chenglong Dai;Yiming Feng;Xiaowei Liu;Jinjiang Li","doi":"10.1109/TITS.2025.3533560","DOIUrl":"https://doi.org/10.1109/TITS.2025.3533560","url":null,"abstract":"Deep graph convolutional networks (GCNs) have shown promising performance in traffic prediction tasks, but their practical deployment on resource-constrained devices faces challenges. First, few models consider the potential influence of historical and future auxiliary information, such as weather and holidays, on complex traffic patterns. Second, the computational complexity of dynamic graph convolution operations grows quadratically with the number of traffic nodes, limiting model scalability. To address these challenges, this study proposes a deep encoder-decoder model named AIMSAN, which comprises an auxiliary information-aware module (AIM) and a sparse cross-attention-based graph convolutional network (SAN). From historical or future perspectives, AIM prunes multi-attribute auxiliary data into diverse time frames, and embeds them into one tensor. SAN employs a cross-attention mechanism to merge traffic data with historical embedded data in each encoder layer, forming dynamic adjacency matrices. Subsequently, it applies diffusion GCN to capture rich spatial-temporal dynamics from the traffic data. Additionally, AIMSAN utilizes the spatial sparsity of traffic nodes as a mask to mitigate the quadratic computational complexity of SAN, thereby improving overall computational efficiency. In the decoder layer, future embedded data are fused with feed-forward traffic data to generate prediction results. Experimental evaluations on three public traffic datasets demonstrate that AIMSAN achieves competitive performance compared to state-of-the-art algorithms, while reducing GPU memory consumption by 41.24%, training time by 62.09%, and validation time by 65.17% on average.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3210-3222"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535523","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

Binocular-Separated Modeling for Efficient Binocular Stereo Matching

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3531115

Yeping Peng;Jianrui Xu;Guangzhong Cao;Runhao Zeng

{"title":"Binocular-Separated Modeling for Efficient Binocular Stereo Matching","authors":"Yeping Peng;Jianrui Xu;Guangzhong Cao;Runhao Zeng","doi":"10.1109/TITS.2025.3531115","DOIUrl":"https://doi.org/10.1109/TITS.2025.3531115","url":null,"abstract":"Binocular stereo matching is a crucial task in autonomous driving for accurately estimating the depth information of objects and scenes. This task, however, is challenging due to various ill-posed regions within binocular image pairs, such as repeated textures and weak textures which present complex correspondences between the points. Existing methods extract features from binocular input images mainly by relying on deep convolutional neural networks with a substantial number of convolutional layers, which may incur high memory and computation costs, thus making it hard to deploy in real-world applications. Additionally, previous methods do not consider the correlation between view unary features during the construction of the cost volume, thus leading to inferior results. To address these issues, a novel lightweight binocular-separated feature extraction module is proposed that includes a view-shared multi-dilation fusion module and a view-specific feature extractor. Our method leverages a shallow neural network with a multi-dilation modeling module to provide similar receptive fields as deep neural networks but with fewer parameters and better computational efficiency. Furthermore, we propose incorporating the correlations of view-shared features to dynamically select view-specific features during the construction of the cost volume. Extensive experiments conducted on two public benchmark datasets show that our proposed method outperforms the deep model-based baseline method (i.e., 13.6% improvement on Scene Flow and 2.0% on KITTI 2015) while using 29.7% fewer parameters. Ablation experiments show that our method achieves superior matching performance in weak texture and edge regions. The source code will be made publicly available.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3028-3038"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535550","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

Optimal Sequential Merging Strategy Based on Adaptive Threshold for Ramp Traffic Involving Platoons

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3534717

Yun Meng;Shilong Liao;Ping Wang;Yinli Jin;Panpan Yang

{"title":"Optimal Sequential Merging Strategy Based on Adaptive Threshold for Ramp Traffic Involving Platoons","authors":"Yun Meng;Shilong Liao;Ping Wang;Yinli Jin;Panpan Yang","doi":"10.1109/TITS.2025.3534717","DOIUrl":"https://doi.org/10.1109/TITS.2025.3534717","url":null,"abstract":"The platoon technology is gradually applied in the intelligent transportation systems to improve the safety, efficiency, energy saving, and emission reduction of vehicles during driving. However, in contrast to the traditional ramp merging scenario that consists of individual vehicles, the existing platoons from the upstream bring new challenges to the merging problem on ramps. Without an effective coordination strategy, the unnecessary congestion and disintegration of the existing platoons will lead to an increase in driving costs and safety risk. Therefore, this paper optimizes the merging strategy of vehicle sequences involving platoons on highway ramps. A merging strategy, based on an adaptive headway threshold, is first proposed, where the comprehensive economic benefit is considered as its optimization objective while taking into account the decrements of the time, fuel, and carbon emission costs. The vehicle sequence merging is then modeled as a Markov decision process to derive the optimal threshold, where the action set includes three actions: accelerating, decelerating, and maintaining the constant cruising speed. Afterwards, the constraints are brought to the decision-making to ensure the safety of the merging process. The calculation of the action value, combining the derived optimal threshold and constraint, is then detailed. Finally, the results of the simulation are evaluated, and a comparison between the proposed strategy and other existing methods is conducted, which demonstrates that the proposed approach improves the comprehensive economic benefits.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4661-4675"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726352","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

Enhancing Cyclist Safety Through Driver Gaze Analysis at Intersections With Cycle Lanes

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3530872

Jibran A. Abbasi;Ashkan Parsi;Nicolas Ringelstein;Patrice Reilhac;Edward Jones;Martin Glavin

{"title":"Enhancing Cyclist Safety Through Driver Gaze Analysis at Intersections With Cycle Lanes","authors":"Jibran A. Abbasi;Ashkan Parsi;Nicolas Ringelstein;Patrice Reilhac;Edward Jones;Martin Glavin","doi":"10.1109/TITS.2025.3530872","DOIUrl":"https://doi.org/10.1109/TITS.2025.3530872","url":null,"abstract":"In urban areas, roads with dedicated cycle lanes play a vital role in cyclist safety. However, accidents can still occur when vehicles cross the cycle lane at intersections. Accidents mostly occur due to failure of the driver to see a cyclist on the cycle lane, particularly when the cyclist is going straight through the intersection, and the vehicle is turning. For safe driving, it is critical that the drivers visually scan the area in the vicinity of the junction and the car, particularly using the wing-mirror, prior to making turns. This paper describes results from a set of test drives using in-vehicle non-invasive eye-tracking and in-vehicle CAN bus sensors to determine driver behaviour. In total, 20 drivers were monitored through 5 different intersections with cycle lanes. The study found that approximately 83% of drivers did not check their wing mirror prior to, or during their turning manoeuvre, potentially putting pedestrian, cyclists, scooter and hoverboard users in danger. An algorithm was developed to analyse driver gaze during the turning manoeuvre to identify cases where they failed to look at the wing mirror. The gaze pattern and gaze concentration on the mirror helps to identify safe and unsafe driving behaviour. This information can then be used to improve Advanced Driver-Assistance Systems (ADAS) to create a safer environment for all road users.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3175-3184"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10871193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535568","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}

引用次数: 0

V2VDisCS: Vehicle to Vehicle Distributed Charge Sharing in Intelligent Transportation Systems

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3534025

Punyasha Chatterjee;Pratham Majumder;Sajal K. Das

{"title":"V2VDisCS: Vehicle to Vehicle Distributed Charge Sharing in Intelligent Transportation Systems","authors":"Punyasha Chatterjee;Pratham Majumder;Sajal K. Das","doi":"10.1109/TITS.2025.3534025","DOIUrl":"https://doi.org/10.1109/TITS.2025.3534025","url":null,"abstract":"Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. In this paper, the problem of V2V matching for charge sharing is mapped to the classical stable matching problem in bipartite graphs. The problem is formulated using integer linear programming that considers flexible decision making for EVs based on multiple charging criteria and constraints. However, as EVs have limited communication ranges, an EV can’t possess knowledge about the entire vehicular network. So we propose two sets of distributed heuristics under the name of Vehicle to Vehicle Distributed Charge Sharing (V2VDisCS), which yield a sub-optimal solution with lower computational and message complexities compared to existing distributed solutions. We analyze the average case matching probabilities and prove the sub-optimality of our approach. Simulation studies show that our heuristics outperform the existing distributed approaches in terms of message overhead and matching percentage. They show a comparable result for matching preference with respect to the standard centralized stable matching algorithm.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4960-4974"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740255","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

Corrections to “Toward Infotainment Services in Vehicular Named Data Networking: A Comprehensive Framework Design and Its Realization”

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3527256

Huhnkuk Lim;Sajjad Ahmad Khan

引用次数: 0

Multi-Agent Reinforcement Learning for Cooperative Transit Signal Priority to Promote Headway Adherence

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3533603

Meng Long;Edward Chung

{"title":"Multi-Agent Reinforcement Learning for Cooperative Transit Signal Priority to Promote Headway Adherence","authors":"Meng Long;Edward Chung","doi":"10.1109/TITS.2025.3533603","DOIUrl":"https://doi.org/10.1109/TITS.2025.3533603","url":null,"abstract":"Headway regularity is an essential indicator of transit reliability, directly influencing passenger waiting time and transit service quality. In this paper, we employ multi-agent reinforcement learning (MARL) to develop a Cooperative Transit signal priority strategy with Variable phase for Headway adherence (CTVH) under a multi-intersection network. Each signalized intersection is controlled by an RL agent, which determines the next step’s signal, adapting to real-time traffic dynamics of transits and non-transits and promoting transit headway adherence. The proposed approach considers four critical aspects, i.e., complicated states with multiple conflicting bus requests, rational actions constrained by domain knowledge, comprehensive rewards balancing buses and cars, and a collaborative training scheme among agents. They are correspondingly addressed by proper state representation with estimated bus headway deviations, irrational actions masking, reward functions formulated by general traffic queue and transit headway deviation, and appropriate MARL approach with synchronous action processing. Our method also takes into account the phase transition loss by setting yellow and all-red time. Simulation results compared with the coordinated fixed-time signal (CFT) and bus holding (BH) strategy verify the merits of the proposed method in terms of improvements in transit headway adherence and influence on general traffic. Based on the results, we further discuss the BH method’s limitations due to bus bay length and various holding lines and the CTVH method’s benefits in the three-intersection environment and the entire-line network. The proposed method has a promising application in practice to improve transit reliability.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3588-3602"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564215","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

Game Theory-Based Harmonious Decision-Making for Autonomous Bus Lane Change

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3533577

Fan Guo;Xiao Han;Kang Song;Kaichen Jiang;Dezong Zhao;Jinbo Hao;Caimei Wang;Hui Xie

{"title":"Game Theory-Based Harmonious Decision-Making for Autonomous Bus Lane Change","authors":"Fan Guo;Xiao Han;Kang Song;Kaichen Jiang;Dezong Zhao;Jinbo Hao;Caimei Wang;Hui Xie","doi":"10.1109/TITS.2025.3533577","DOIUrl":"https://doi.org/10.1109/TITS.2025.3533577","url":null,"abstract":"Blended traffic, comprising autonomous buses (ABs) and human-driven vehicles (HDVs), is becoming increasingly common, yet the lane change decision-making for ABs remains challenging due to complex interactions with heterogeneous HDVs. To address the challenge above, this paper proposes a game theory-based harmonious decision-making (GTHD) algorithm considering nuance of driving styles of HDVs, achieving human-like performance in interactions with HDVs. Technically, a game theoretic model of the GTHD uses predictions of the opposing vehicle’s motion and the information from preplanned trajectories. Besides, a prior estimation for driving styles is obtained utilizing clustering of historical data, and refined in real time through Bayesian estimation. Then, the driving style estimation is utilized to modify the game theoretic model. The modified model provides a closer depiction of the opponent’s preferences, meanwhile adjusts self-preferences to adapt to the opponent. The efficacy of GTHD is validated using a hardware and human in loop simulator and datasets in MLC scenarios. It is shown that the GTHD achieves human-like performance with 91.50%-98.50% accuracy compared with human bus driver under different conditions, better than several lane change models based on data driven methods. The code is open source and available at <uri>https://github.com/guofan999/GTHD</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 4","pages":"4934-4947"},"PeriodicalIF":7.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740243","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