IEEE Transactions on Intelligent Transportation Systems最新文献

{"title":"Controllable Multimodal Motion Behavior Generation for Autonomous Driving","authors":"Wenxing Lan;Jialin Liu;Bo Yuan;Xin Yao","doi":"10.1109/TITS.2025.3645248","DOIUrl":"https://doi.org/10.1109/TITS.2025.3645248","url":null,"abstract":"The generation of motion behaviors plays a pivotal role in constructing effective simulated scenarios for testing autonomous driving systems (ADSs). The controllability (i.e., the ability to synthesize specific motion patterns) and multimodality (i.e., the capacity to represent multiple motion intentions) of generated motion behaviors are essential for the purposeful and comprehensive evaluation of ADS. Although recent studies have made progress in either multimodal or controllable motion behavior generation, it remains a major challenge to simultaneously generate multimodal motion behaviors in a controllable manner. In this work, we propose a unified framework, CoMoGen, to generate multimodal motion behaviors in a controllable manner under open-loop evaluation assumption. The proposed framework consists of three core components: i) a learning-based vehicle placer, responsible for positioning generated vehicles in non-conflicting initial locations; ii) a robust model-based trajectory candidate generator, capable of synthesizing controllable and multimodal trajectory candidates. iii) a learning-based trajectory selector, developed to evaluate and select multimodal trajectories for the placed vehicles. Experiments on the INTERACTION dataset demonstrate strong controllability and multimodality of CoMoGen. Further experiments on three additional real-world datasets, that are unseen during training, as well as on diverse synthesized high-definition maps, validate the remarkable generalization capability of CoMoGen.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 4","pages":"4896-4911"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665402","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}

{"title":"Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching","authors":"Nur Uddin Javed;Yuvraj Singh;Qadeer Ahmed","doi":"10.1109/TITS.2025.3648744","DOIUrl":"https://doi.org/10.1109/TITS.2025.3648744","url":null,"abstract":"Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model’s prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 4","pages":"4924-4929"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665366","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}

{"title":"An Adaptive Forwarding With Path Optimization Method for Vehicular Named Data Networking","authors":"Sihan Xiong;Rui Hou;Wei Li;Yuanai Xie;Wanneng Shu;Mianxiong Dong;Kaoru Ota;Deze Zeng","doi":"10.1109/TITS.2025.3650220","DOIUrl":"https://doi.org/10.1109/TITS.2025.3650220","url":null,"abstract":"Vehicular named data networking (VNDN), which integrates the principles of named data networks with vehicular ad hoc networks, represents a promising paradigm for future intelligent transportation systems. Nevertheless, VNDN faces significant hurdles, including broadcast storms from excessive interest packet flooding and reverse-path disruptions due to high vehicular mobility. To address these challenges, we introduce an adaptive forwarding with path optimization method. First, a dynamic caching algorithm is designed to optimize roadside unit storage efficiency and maximize cache hit rates. Second, a gated recurrent unit-based adaptive data forwarding mechanism is introduced to dynamically select optimal forwarders and preserve reverse paths via decentralized heartbeat detection and interface remapping, improving link reliability. Simulation outcomes demonstrate that the proposed approach significantly lowers data retrieval delays while curbing overall communication overhead.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 4","pages":"4912-4923"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665348","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}

{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2026.3677519","DOIUrl":"https://doi.org/10.1109/TITS.2026.3677519","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 4","pages":"C3-C3"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11480689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665370","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}

{"title":"IEEE Intelligent Transportation Systems Society Information","authors":"","doi":"10.1109/TITS.2026.3667019","DOIUrl":"https://doi.org/10.1109/TITS.2026.3667019","url":null,"abstract":"","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"C3-C3"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11435251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558007","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}

{"title":"M-SIALNS for Air–Ground Collaborative Inspection: Spatio-Temporal Conflict Mitigation in Complex Bi-Layer Networks","authors":"Miaohan Zhang;Yuanhao Xu;Xuewei Yu;Chunyan Zhang;Jianlei Zhang","doi":"10.1109/TITS.2025.3647141","DOIUrl":"https://doi.org/10.1109/TITS.2025.3647141","url":null,"abstract":"Multi-vehicle–UAV collaborative inspection systems face critical challenges in complex environments, where spatiotemporal node conflicts, coupled scheduling, and congestion severely affect operational efficiency. To address these issues, we define the Multi-UAV–Multi-Vehicle Collaborative Inspection Vehicle Routing Problem in a Bi-Layer Road Network (MUMV-CIVRP-BLRN). The model captures realistic inspection scenarios through (i) spatiotemporal node-conflict constraints that prevent simultaneous vehicle access to occupied nodes and (ii) flexible UAV operations, enabling chained multi-task sorties and cross-vehicle recovery. To solve this NP-hard problem, we propose a Multi-Strategy Improved Adaptive Large Neighborhood Search (M-SIALNS) algorithm. Beyond standard ALNS frameworks, M-SIALNS incorporates a cluster-based initialization method, task-chain destroy–repair operators, and air–ground coordination strategies specifically tailored for the bi-layer structure. These strategies enhance global search, solution feasibility, and robustness. Comprehensive experiments on benchmark datasets and a power-grid case study demonstrate the advantages of M-SIALNS. Compared with state-of-the-art algorithms, it reduces inspection duration by 1.6%–11.0%, consistently delivering statistically significant improvements. Ablation and sensitivity analyses confirm the contribution of tailored operators and provide managerial insights into optimal fleet configurations and resource allocation thresholds. This work advances both the theoretical modeling of bi-layer vehicle–UAV routing and its practical deployment in large-scale inspection missions.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 3","pages":"3530-3545"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147558022","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}

{"title":"A Constant-Gain Equation-Error Framework for Airliner Aerodynamic Monitoring Using QAR Data","authors":"Ruiying Wen;Yuntao Dai;Hongyong Wang","doi":"10.1109/TITS.2026.3651385","DOIUrl":"https://doi.org/10.1109/TITS.2026.3651385","url":null,"abstract":"Monitoring in-service aerodynamic performance of airliners is critical for operational efficiency and safety, yet presents significant challenges when using operational Quick Access Recorder (QAR) data due to sensor noise, low excitation, and the absence of key model parameters like moments of inertia. These constraints render conventional state-propagation and recursive estimation methods unsuitable. To address these challenges, this paper proposes and validates the Constant-Gain Equation-Error Method (CG-EEM), a robust framework tailored for QAR data analysis. The CG-EEM employs a custom constant-gain estimator that avoids both the infeasibility of state-propagation filters and the premature convergence or instability issues of standard recursive algorithms. Extensive validation on a multi-fleet dataset of over 200 flights demonstrates that the framework produces highly consistent and physically meaningful aerodynamic parameters. Crucially, follow-up work has verified that this approach successfully resolves the fundamental thrust-drag ambiguity problem, ensuring the estimates are not just plausible, but physically unique and correct. This demonstrates that CG-EEM is a scalable and computationally efficient tool for reliable fleet-wide performance monitoring and early detection of airframe degradation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6040-6049"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828334","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}

{"title":"MonoRange: Monocular 3-D Object Detection Based on Object-Centric Range Map in Adverse Weather Conditions","authors":"Jae Hyun Yoon;Jong Won Jung;Seok Bong Yoo","doi":"10.1109/TITS.2026.3652840","DOIUrl":"https://doi.org/10.1109/TITS.2026.3652840","url":null,"abstract":"Monocular 3D object detection has been studied as a promising task for diverse applications, such as autonomous driving, due to its lower cost and more straightforward configuration than multiple sensors. However, existing studies have focused on clear weather without considering diverse weather conditions with varying intensity, such as rain, snow, and fog, affecting detection performance. In this paper, we propose MonoRange, a monocular 3D object detection method that uses object-centric images and range maps in adverse weather conditions. Leveraging the 2D detection results, MonoRange generates range maps from images via an object-centric range map reconstruction. Furthermore, MonoRange flexibly removes adverse weather noise in images via weather intensity adaptive image restoration with a weight modulation transformer. Then, MonoRange fuses the range map and restored image and predicts 3D bounding boxes via the range map aligned detector. Introducing the projected box consistency loss between 2D and 3D boxes also enables to consistent and accurate 3D object detection. Experimental results on diverse weather datasets demonstrate that MonoRange surpasses existing monocular 3D object detection approaches. The source code is available at <uri>https://github.com/jhyoon964/MonoRange</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6121-6133"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828345","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}

{"title":"Exploring Temporal and Manufacturer-Specific Trends of Automated Vehicle Crashes Using Sequence of Events","authors":"Cesar Andriola;Madhav V. Chitturi;David A. Noyce","doi":"10.1109/TITS.2026.3659741","DOIUrl":"https://doi.org/10.1109/TITS.2026.3659741","url":null,"abstract":"The present study uses Crash Sequence Analysis to identify automated vehicles (AV) crash patterns and evaluate the temporal (2014 to 2023) and manufacturer-specific (Cruise or Waymo) trends in these patterns. This method builds upon the evaluation of crash scenarios by considering the sequential nature of crashes, incorporating crash progression and contributing factors. The results highlight the current challenge faced by manufacturers in designing systems that are safe yet perform in a manner expected by human drivers. The proportional reduction of crash patterns involving rear-end collisions during left or right turns suggests that actions were taken by manufacturers to address the aforementioned challenge. However, other crash patterns have shown a proportional increase in recent years, such as collisions with objects and on narrow roads, with specific variations by manufacturers. These findings provide key insights for shaping future AV development, guiding public sector decisions, and building public trust in automation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6160-6164"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828350","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}

{"title":"Environment-Aware Reinforcement Learning-Based Energy Consumption Prediction Model for Electric Vehicles","authors":"Ziran Peng;Xiaoyang Yang;Zhenyu He","doi":"10.1109/TITS.2026.3654673","DOIUrl":"https://doi.org/10.1109/TITS.2026.3654673","url":null,"abstract":"As electric vehicles (EVs) gain growing popularity worldwide, demands for real-time and precise energy-consuming prediction have increased correspondingly. Targeting at limitations of existing models in environmental perception and dynamic calibration, this research put forward a novel model for energy-consuming prediction. This model integrated environmental perception with reinforcement learning. Specifically, at first, a road-condition perceiving approach deeply coupled with reinforcement learning was designed, and a linear multi-scale attention encoder was constructed. The aim was to extract multi-granularity environmental features related to energy efficiency and thus enhance the model’s representational capabilities under complicated dynamic driving situations. Second, a real-time energy efficiency estimation model was developed under a Markov decision process, which was also mapped to the reinforcement learning framework. Based on temporal-difference learning, the data-driven Q function was iteratively updated, and constant calibration of energy estimation was realized. Finally, a prioritization mechanism for causal-structure-based Kullback–Leibler (KL) divergence scenarios was proposed to enhance the sampling efficiency in cases of critical incidents such as slope variations and abrupt speed-accelerating/decelerating, while strengthening the robustness and generalization of the model under complicated conditions. Results confirmed the superior stability and robustness of the proposed approach across multiple operating conditions and vehicle types. Specifically, the mean absolute error (MAE) was below 12%; the root mean-squared error (RMSE) exhibited a value under 1.8%; and the R² value exceeded 99.5%. All these demonstrated its significantly improved efficiency over Transformer, Informer, Mamba, and long short-term memory (LSTM) models. EVs’ actual energy consumption in the real world was also compared with that in speed profile (EVECS) dataset, presenting an MAE below 1.15% and a RMSE under 1.65%, which further verified its excellent generalization.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"27 5","pages":"6147-6159"},"PeriodicalIF":8.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828370","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}