IEEE Transactions on Intelligent Transportation Systems最新文献

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

A Real-Time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-19 DOI: 10.1109/TITS.2024.3524394

Zongbo Liao;Xuanxuan Zhang;Tianxiang Zhang;Zhi Li;Zhenqi Zheng;Zhichao Wen;You Li

{"title":"A Real-Time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM","authors":"Zongbo Liao;Xuanxuan Zhang;Tianxiang Zhang;Zhi Li;Zhenqi Zheng;Zhichao Wen;You Li","doi":"10.1109/TITS.2024.3524394","DOIUrl":"https://doi.org/10.1109/TITS.2024.3524394","url":null,"abstract":"LiDAR is widely used in Simultaneous Localization and Mapping (SLAM) and autonomous driving. The LiDAR odometry is of great importance in multi-sensor fusion. However, in some unstructured environments, the point cloud registration cannot constrain the poses of the LiDAR due to its sparse geometric features, which leads to the degeneracy of multi-sensor fusion accuracy. To address this problem, we propose a novel real-time approach to sense and compensate for the degeneracy of LiDAR. Firstly, this paper introduces the degeneracy factor with clear meaning, which can measure the degeneracy of LiDAR. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method adaptively perceives the degeneracy with better environmental generalization. Finally, the degeneracy perception results are utilized to fuse LiDAR and IMU, thus effectively resisting degeneracy effects. Experiments on our dataset show the method’s high accuracy and robustness and validate our algorithm’s adaptability to different environments and LiDAR scanning modalities.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"4202-4213"},"PeriodicalIF":7.9,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535507","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

Electric Vehicle Routing Optimization for Postal Delivery and Waste Collection in Smart Cities

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-14 DOI: 10.1109/TITS.2025.3529181

Maria Asuncion del Cacho Estil-Les;Agostino Marcello Mangini;Michele Roccotelli;Maria Pia Fanti

引用次数: 0

A Guide to Image- and Video-Based Small Object Detection Using Deep Learning: Case Study of Maritime Surveillance

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-14 DOI: 10.1109/TITS.2025.3530678

Aref Miri Rekavandi;Lian Xu;Farid Boussaid;Abd-Krim Seghouane;Stephen Hoefs;Mohammed Bennamoun

{"title":"A Guide to Image- and Video-Based Small Object Detection Using Deep Learning: Case Study of Maritime Surveillance","authors":"Aref Miri Rekavandi;Lian Xu;Farid Boussaid;Abd-Krim Seghouane;Stephen Hoefs;Mohammed Bennamoun","doi":"10.1109/TITS.2025.3530678","DOIUrl":"https://doi.org/10.1109/TITS.2025.3530678","url":null,"abstract":"Detecting small objects in optical images and videos is a significant challenge in numerous intelligent transportation and autonomous systems. State-of-the-art generic object detection methods fail to accurately localize and identify such small objects (e.g., pedestrians, small vehicles, obstacles). Because small objects occupy only a small area in the input image (e.g., <inline-formula> <tex-math>$32 times 32$ </tex-math></inline-formula> pixels or less), the information extracted from such a small area is not always rich enough to support decision-making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of Small Object Detection (SOD). In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provides a taxonomy that illustrates the broad picture of current research. We further explore methods to boost the performance of small object detection in maritime settings, where enhanced performance is crucial for ensuring safety and managing traffic. Detecting small objects in the maritime environment requires additional considerations and the current survey aims to review the advanced techniques addressing those aspects. In addition, the popular SOD datasets for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided. The link to these datasets appears in <uri>https://github.com/arekavandi/Datasets_SOD</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"2851-2879"},"PeriodicalIF":7.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535525","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 Data-Driven Dynamics Simulation Model for Railway Vehicles Based on Lightweight 3DCNN With Physics-Informed Constraints

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-13 DOI: 10.1109/TITS.2025.3533614

Zhiwei Zheng;Cai Yi;Jianhui Lin

{"title":"A Data-Driven Dynamics Simulation Model for Railway Vehicles Based on Lightweight 3DCNN With Physics-Informed Constraints","authors":"Zhiwei Zheng;Cai Yi;Jianhui Lin","doi":"10.1109/TITS.2025.3533614","DOIUrl":"https://doi.org/10.1109/TITS.2025.3533614","url":null,"abstract":"The dynamics simulation of complex railway vehicles requires a dedicated vehicle model, such as multi-body dynamics model. However, the multi-body model is time-consuming in long-distance simulation due to its computational complexity. This issue can be alleviated by using a data-driven vehicle dynamics model due to its effective generalization and computational speed. Firstly, the construction of the physical model of the vehicle system is carried out to obtain the coupling relationship between the components. Secondly, the coupling relationship between the components is embedded into the loss function of the deep neural network as physics-informed constraints. Further, the network parameters satisfying certain physical laws are obtained by minimizing the loss function. Finally, the proposed lightweight 3D convolutional neural network is used to predict the vibration state of the vehicle system. The dynamic response resulting from both the data-driven simulation model and the multi-body simulation model are investigated and compared. The simulation results show that the data-driven dynamics simulation model can accurately predict the vibration state of the vehicle system. The data-driven simulation model has smaller size and faster operation speed, which can be applied to long-distance prediction research of vehicle systems.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3004-3015"},"PeriodicalIF":7.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535424","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

Uncertainty Quantification for Safe and Reliable Autonomous Vehicles: A Review of Methods and Applications

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-10 DOI: 10.1109/TITS.2025.3532803

Ke Wang;Chongqiang Shen;Xingcan Li;Jianbo Lu

{"title":"Uncertainty Quantification for Safe and Reliable Autonomous Vehicles: A Review of Methods and Applications","authors":"Ke Wang;Chongqiang Shen;Xingcan Li;Jianbo Lu","doi":"10.1109/TITS.2025.3532803","DOIUrl":"https://doi.org/10.1109/TITS.2025.3532803","url":null,"abstract":"In the past decade, deep learning has been widely applied across various fields. However, its applicability in open-world scenarios is often limited due to the lack of quantifying uncertainty in both data and models. In recent years, a multitude of uncertainty quantification (UQ) approaches for neural networks have emerged and found applications in safety-critical domains such as autonomous vehicles and medical analysis. This paper aims to review the latest advancements in UQ methods and investigate their application specifically in the field of computer vision and autonomous vehicles. Initially, we identify several key qualifications, namely practicability, robustness, accuracy, scalability, and efficiency (referred to as PRASE), and employ them as evaluation criteria throughout this study. By considering these criteria as uniform measurements, we meticulously evaluate and compare the performance of different types of UQ methods, including Bayesian methods, ensemble methods, and single deterministic methods. Furthermore, we delve into the discussion of their application in diverse tasks within the autonomous vehicle domain, such as semantic segmentation, object detection, depth estimation, and end-to-end control. Through comprehensive analysis and comparison, we identify a range of challenges and propose future research directions in this field. Our findings shed light on the importance of addressing uncertainty quantification in deep learning models and provide insights into enhancing the reliability and performance of autonomous vehicles in real-world scenarios.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"2880-2896"},"PeriodicalIF":7.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535418","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

Framework of Adaptive Driving: Linking Situation Awareness, Driving Goals, and Driving Intentions Using Eye-Tracking and Vehicle Kinetic Data

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-10 DOI: 10.1109/TITS.2025.3530252

Hsueh-Yi Lai

{"title":"Framework of Adaptive Driving: Linking Situation Awareness, Driving Goals, and Driving Intentions Using Eye-Tracking and Vehicle Kinetic Data","authors":"Hsueh-Yi Lai","doi":"10.1109/TITS.2025.3530252","DOIUrl":"https://doi.org/10.1109/TITS.2025.3530252","url":null,"abstract":"Although current Artificial Intelligence (AI) can detect maneuvering intentions, it often overlooks the underlying driving goals that reveal drivers’ genuine requirements. To detect real-time driving goals using AI for providing effective decision aids, this research introduces the Framework of Adaptive Driving (FAD), which considers cognitive activities and action strategies. We have outlined five driving goals to elucidate the connections between Situation Awareness (SA), and intentions. The study involved 31 participants and 573 driving simulation events, during which we collected both eye-tracking and kinetic data. Exploratory Factor Analysis (EFA) identified 8 factors, categorized into SA and maneuver-related factors. Statistical and qualitative analysis follow up to specify the varying requirements among the driving foals defined. Generally, factor ‘Cognitive load’ can reflect cognitive activities, while ‘Saccade on the surroundings’ and ‘Saccade movement’ can indicate action strategies. For the goals where emerging risks are not a concern, ‘Active acceleration’ signifies drivers’ intention to enhance driving efficiency. However, the diverse features in ‘Saccade on the surroundings’ imply varying driving considerations. Goals for routine tasks focus on internal vehicle operations, while goals for driving benefits management highlight adjacent surroundings. Conversely, for goals addressing emerging risks, ‘Deceleration’ prevails. Furthermore, ‘Steering strategy’ implies a preference for steering when SA is adequate. In this context, SA-related factors like ‘Front observation,’ ‘Saccade movement,’ and ‘Cognitive load’ signify efforts to enhance SA under time constraints. However, for the driving goals under extreme urgency, the factor ‘Lateral movement’ replaces ‘Steering strategy’, implying severe steering without adequate SA.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 3","pages":"3295-3306"},"PeriodicalIF":7.9,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535573","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