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

Steering Motion Quality Oriented Return-to-Center Control Strategy for Vehicles 面向转向运动质量的车辆回归中心控制策略

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-03 DOI: 10.1109/TITS.2025.3573780

Yimeng Song;Xin Guan;Yuning Zhang;Pingping Lu;Chunguang Duan;Jun Zhan

{"title":"Steering Motion Quality Oriented Return-to-Center Control Strategy for Vehicles","authors":"Yimeng Song;Xin Guan;Yuning Zhang;Pingping Lu;Chunguang Duan;Jun Zhan","doi":"10.1109/TITS.2025.3573780","DOIUrl":"https://doi.org/10.1109/TITS.2025.3573780","url":null,"abstract":"Most of existing steering system control strategies pay little attention to return time and speed. Additionally, most algorithms do not consider the impact of the applied torque of the driver on the steering wheel when determining the optimal return speed while the wheel is being held. This highlights an ongoing need to improve the return-to-center quality in vehicles. To address this challenge, this study proposes a steering motion quality control method based on a motion-closed loop. Here, we delineate the context wherein, by establishing a desired returnability, the return-to-center speed during release or holding phases is achieved via direct control through a motion closed-loop. This approach harmonizes the steering feel with the return-to-center performance, thereby enhancing the stability of the vehicle during straight-line driving. Subsequently, we validate the efficacy and robustness of the proposed steering motion quality-oriented vehicle return-to-center control strategy in managing returnability, as evidenced by low-speed and high-speed return-to-center simulation tests using a driving simulator.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9962-9978"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536426","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

Semantic-MoSeg: Semantics-Assisted Moving-Obstacle Segmentation in Bird-Eye-View for Autonomous Driving 语义- moseg：自动驾驶鸟瞰图中语义辅助移动障碍物分割

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-03 DOI: 10.1109/TITS.2025.3570058

Shiyu Meng;Yuxiang Sun

{"title":"Semantic-MoSeg: Semantics-Assisted Moving-Obstacle Segmentation in Bird-Eye-View for Autonomous Driving","authors":"Shiyu Meng;Yuxiang Sun","doi":"10.1109/TITS.2025.3570058","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570058","url":null,"abstract":"Bird-eye-view (BEV) perception for autonomous driving has become popular in recent years. Among various BEV perception tasks, moving-obstacle segmentation is very important, since it can provide necessary information for downstream tasks, such as motion planning and decision making, in dynamic traffic environments. Many existing methods segment moving obstacles with LiDAR point clouds. The point-wise segmentation results can be easily projected into BEV since point clouds are 3-D data. However, these methods could not produce dense 2-D BEV segmentation maps, because LiDAR point clouds are usually sparse. Moreover, 3-D LiDARs are still expensive to vehicles. To provide a solution to these issues, this paper proposes a semantics-assisted moving-obstacle segmentation network using only low-cost visual cameras to produce segmentation results in dense 2-D BEV maps. Our network takes as input visual images from six surrounding cameras as well as the corresponding semantic segmentation maps at the current and previous moments, and directly outputs the BEV map for the current moment. We also propose a movable-obstacle segmentation auxiliary task to provide semantic information to further benefit moving-obstacle segmentation. Extensive experimental results on the public nuScenes and Lyft datasets demonstrate the effectiveness and superiority of our network.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9251-9262"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536604","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

Hierarchical Attention-Enhanced Correlation Refinement for Robust Visual Tracking 鲁棒视觉跟踪的分层注意增强相关细化

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-03 DOI: 10.1109/TITS.2025.3570076

Si Chen;Rui Xu;Yan Yan;Yang Hua;Da-Han Wang;Shunzhi Zhu

{"title":"Hierarchical Attention-Enhanced Correlation Refinement for Robust Visual Tracking","authors":"Si Chen;Rui Xu;Yan Yan;Yang Hua;Da-Han Wang;Shunzhi Zhu","doi":"10.1109/TITS.2025.3570076","DOIUrl":"https://doi.org/10.1109/TITS.2025.3570076","url":null,"abstract":"In recent years, visual tracking has witnessed remarkable advancements with the exploration of feature extraction and correlation modeling techniques. However, inadequate robustness of either the backbone network or the correlation operation continues to plague existing trackers, leading to frustrating drift when confronted with similar distractors or cluttered backgrounds. To address this problem, we propose a hierarchical attention-enhanced correlation refinement network (HarNet) for achieving robust visual tracking. Specifically, a gated dual-view attention (GDA) module is first designed to aggregate the intra-layer attention and the inter-layer self-attention based on a fusion gate, so as to enhance hierarchical feature representations of the template. Meanwhile, a target-aware attention (TA) module introduces the template information to the inter-layer self-attention, which can highlight the target information in the search region. Moreover, a graph guided correlation (GGC) module leverages the pixel-to-local and pixel-to-global correlations to fully exploit both local- and global-spatial information between the template and the search region, and then uses the graph convolutional network (GCN) to further learn the node relationships of the correlation map for more finegrained correlations. Thus, with the above three elaborately designed modules, the HarNet is beneficial for the enhancement of feature representation and the precise localization of the target. Extensive experiments on popular visual tracking datasets (including OTB100, VOT2016, VOT2018, VOT2019, UAV123, UAV20L, GOT-10k, and LaSOT) demonstrate the superiority of our proposed method against several state-of-the-art tracking methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9370-9386"},"PeriodicalIF":7.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536600","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

Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing 自动驾驶汽车自然驾驶环境分布一致性仿真研究

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3571966

Xintao Yan;Shuo Feng;Haowei Sun;Henry X. Liu

{"title":"Distributionally Consistent Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing","authors":"Xintao Yan;Shuo Feng;Haowei Sun;Henry X. Liu","doi":"10.1109/TITS.2025.3571966","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571966","url":null,"abstract":"Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs’ safety performance unbiasedly, the probability distributions of environment statistics in the simulated naturalistic driving environment (NDE) need to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without considering the distributional consistency of driving behaviors, which could cause significant evaluation biasedness for AV testing. To fill this research gap, a distributionally consistent NDE modeling framework is proposed in this paper. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions. To address the error accumulation problem during the simulation, an optimization-based method is further designed to refine the empirical behavior models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. The framework is evaluated in the case study of a multi-lane highway driving simulation, where the distributional accuracy of the generated NDE is validated and the safety performance of an AV model is effectively evaluated.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9187-9200"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536442","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

Road Side Unit Location Optimization Considering Communication Channel Competition and 6G Technology 考虑通信信道竞争和6G技术的路边单元位置优化

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3572183

Yining Ren;Yinhai Wang;Zhizhou Wu;Constantinos Antoniou;Yunyi Liang

{"title":"Road Side Unit Location Optimization Considering Communication Channel Competition and 6G Technology","authors":"Yining Ren;Yinhai Wang;Zhizhou Wu;Constantinos Antoniou;Yunyi Liang","doi":"10.1109/TITS.2025.3572183","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572183","url":null,"abstract":"This study investigates the problem of road side unit (RSU) location optimization considering vehicle-to-RSU (V2R) communication channel competition. To hedge against the uncertainty of vehicle density, the problem is formulated as a stochastic mixed-integer nonlinear program with equilibrium constraints. This program aims to minimize the expectation of weighted sum of V2R communication delay, packet loss rate and packet collision rate and age of information in V2R communication over all scenarios given RSU location budget limit. Decision variables are RSU locations and the number of connected autonomous vehicles (CAVs) communicating with each located RSU. Equilibrium constraints in the program model V2R communication channel competition among CAVs and ensures the choice of CAVs on RSUs to satisfy user equilibrium principle. The V2R communication is calculated under 6G technology. The program is linearized by using piecewise linearization method. To enhance the solution efficiency, a progressive hedging algorithm is developed to decompose the relaxed linearized model into several subproblems. The optimal solution to the relaxed linearized model is found by iteratively formulating and the solving subproblems. A branch and bound algorithm is introduced to obtain the optimal integer solution to the linearized model. The numerical results show that the proposed model can achieve 20.55% lower total communication delay than the state-of-the-art model only optimizing total V2R information propagation delay, when CAVs choose RSUs for communication in a competitive manner.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9867-9881"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536689","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 Ieee智能交通系统学会

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3571552

引用次数: 0

MFV3DL: Monocular Vision Method for Future Vehicle 3D Localization MFV3DL：未来车辆三维定位的单目视觉方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3572742

Wen Zhang;Zhexuan Sun;Shengrong Lv;Konghao Mei;Guangkun Chen;Zhenya Yang

{"title":"MFV3DL: Monocular Vision Method for Future Vehicle 3D Localization","authors":"Wen Zhang;Zhexuan Sun;Shengrong Lv;Konghao Mei;Guangkun Chen;Zhenya Yang","doi":"10.1109/TITS.2025.3572742","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572742","url":null,"abstract":"Vision-based future vehicle localization provides intuitive trajectory prediction, serving as a critical foundation for Advanced Driving Assistance Systems (ADAS) to formulate collision avoidance decisions. Among existing approaches, ego-view trajectory prediction has proven effective for driver monitoring and intervention in vision-based localization. This method aligns closely with human perceptual processing, making it essential for the Driver-in-the-Loop (DIL) development stage in modern ADAS. However, most existing ego-view trajectory prediction approaches rely on two-dimensional image-based predictions, creating a gap with human three-dimensional perception. This disparity negatively impacts the accuracy and timeliness of driver decision-making and intervention. In this paper, we propose MFV3DL (Monocular Vision Method for Future Vehicle 3D Localization), a dual-stream framework integrating 2D image trajectory prediction and depth prediction to achieve future vehicle 3D localization. To enhance accuracy, we leverage Multi-Object Tracking and Segmentation (MOTS) results and depth estimation as inputs for the dual-stream architecture. Additionally, we introduce a Related Information Fusion (RIF) unit to enable cross-modal interaction between the two streams. For depth stream predictions, we propose a ConvLSTM-based depth prediction method. Experimental results on the KITTI dataset demonstrate that MFV3DL outperforms state-of-the-art methods. In diverse driving scenarios, MFV3DL achieves superior 3D visualization results compared to 2D trajectory-based predictions. Baseline comparisons and ablation studies further validate that the proposed ConvLSTM-based depth prediction enhances the dual-stream architecture and RIF unit for 3D localization tasks.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9277-9292"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536280","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

Self-Supervised Multi-Camera Collaborative Depth Prediction With Latent Diffusion Models 基于潜扩散模型的自监督多相机协同深度预测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3571027

Jialei Xu;Xianming Liu;Yuanchao Bai;Junjun Jiang;Xiangyang Ji

{"title":"Self-Supervised Multi-Camera Collaborative Depth Prediction With Latent Diffusion Models","authors":"Jialei Xu;Xianming Liu;Yuanchao Bai;Junjun Jiang;Xiangyang Ji","doi":"10.1109/TITS.2025.3571027","DOIUrl":"https://doi.org/10.1109/TITS.2025.3571027","url":null,"abstract":"Depth map estimation from images is a crucial task in self-driving applications. Existing methods can be categorized into two groups: multi-view stereo and monocular depth estimation. The former requires cameras to have large overlapping areas and a sufficient baseline between them, while the latter that processes each image independently can hardly guarantee the structure consistency between cameras. In this paper, we propose a novel self-supervised multi-camera collaborative depth prediction method with latent diffusion models, which does not require large overlapping areas while maintaining structure consistency between cameras. Specifically, we introduce MCDP, a new generative foundation model for estimating depth attributes for multi-cameras. We formulate the depth estimation as a weighted combination of depth bases, in which the weights are updated iteratively by the recurrent refinement strategy. During the iterative update, the results of depth estimation are compared across cameras, and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation in diffusion process. We integrate the GRU-based Weight Net into the diffusion process, allowing the refined hidden state to serve as a conditional input to accurately control the next iterative denoising step. Furthermore, by incorporating the proposed depth consistency loss, we ensure structural consistency across cameras, even in regions with minimal overlap. Experimental results on DDAD, NuScenes, Cityscapes, and Waymo Open Datasets demonstrate the superior performance of our method, and show great help for the downstream task.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9609-9624"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536605","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 扫描问题

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3567793

Simona Sacone

引用次数: 0

Safety-Quantifiable Line Feature-Based Monocular Visual Localization With 3D Prior Map 基于安全可量化线特征的三维先验地图单目视觉定位

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-06-02 DOI: 10.1109/TITS.2025.3572620

Xi Zheng;Weisong Wen;Li-Ta Hsu

{"title":"Safety-Quantifiable Line Feature-Based Monocular Visual Localization With 3D Prior Map","authors":"Xi Zheng;Weisong Wen;Li-Ta Hsu","doi":"10.1109/TITS.2025.3572620","DOIUrl":"https://doi.org/10.1109/TITS.2025.3572620","url":null,"abstract":"Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as Autonomous ground vehicles (AGVs) and autonomous aerial vehicles (AAVs). The visual odometry-based method can provide accurate positioning in a short period but is subject to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation, which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite system (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected by AAV and AGV in indoor and outdoor environments, respectively. The open-source code is available at <uri>https://github.com/ZHENGXi-git/SafetyQuantifiable-PLVINS</uri>","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 7","pages":"9226-9240"},"PeriodicalIF":7.9,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536551","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