IEEE Transactions on Intelligent Transportation Systems最新文献_第4页

Obstacle Avoidance for a Large-Scale High-Speed Underactuated AUV in Complex Environments 复杂环境下大型高速欠驱动自动潜航器的避障技术

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/tits.2024.3453769

Lin Yu, Lei Qiao, Chao Shen

引用次数: 0

3PFS: Protecting Pedestrian Privacy Through Face Swapping 3PFS：通过人脸交换保护行人隐私

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/TITS.2024.3421917

Zixian Zhao;Xingchen Zhang;Yiannis Demiris

{"title":"3PFS: Protecting Pedestrian Privacy Through Face Swapping","authors":"Zixian Zhao;Xingchen Zhang;Yiannis Demiris","doi":"10.1109/TITS.2024.3421917","DOIUrl":"10.1109/TITS.2024.3421917","url":null,"abstract":"In the era of artificial intelligence, privacy has become a paramount concern, especially within intelligent transportation systems (ITS) where pedestrians are frequently captured by vehicle-mounted cameras for deep learning model training. To address this, we introduce 3PFS, a novel method designed to protect pedestrian privacy via face swapping while preserving the utility of processed images. Our method consists of a pedestrian detector, a face detector, a pre-processing module, a source face selection algorithm, and a face swapping algorithm. After detecting pedestrians and their corresponding faces, the pre-processing module enhances image quality. Our unique source face selection algorithm then chooses an appropriate face from our source face library, which is subsequently swapped with the target face using a face swapping algorithm. Notably, with the combination of a pedestrian tracking algorithm, our 3PFS is well-suited for video anonymization. Additionally, we propose a comprehensive evaluation strategy to evaluate the performance of pedestrian anonymization methods. We validate the effectiveness of 3PFS through extensive experiments on a dataset we created based on the publicly available JAAD dataset and on videos captured using our robotic wheelchair.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"16845-16854"},"PeriodicalIF":7.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264261","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

Spatial-Temporal Correlation Learning for Traffic Demand Prediction 用于交通需求预测的时空相关性学习

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/TITS.2024.3443341

Yiling Wu;Yingping Zhao;Xinfeng Zhang;Yaowei Wang

{"title":"Spatial-Temporal Correlation Learning for Traffic Demand Prediction","authors":"Yiling Wu;Yingping Zhao;Xinfeng Zhang;Yaowei Wang","doi":"10.1109/TITS.2024.3443341","DOIUrl":"10.1109/TITS.2024.3443341","url":null,"abstract":"Traffic demand prediction has been drawing increasing research interest due to its critical role in intelligent transportation systems. However, conventional deep learning methods for traffic demand forecast ignore the correlations between the pick-up and drop-off demands, thus not fully exploring the patterns of demand evolution. In this work, the pick-up and drop-off demands are treated as two modalities, and an architecture is designed to explicitly model the interactions between the pick-up and drop-off demands both spatially and temporally. Specifically, the self-attention mechanism is adopted to automatically discover spatio-temporal patterns without manual designation for each demand. Then, the cross-attention mechanism is utilized to let the two demands attend to each other, resulting in information exchange between the two demands. The self-attention and cross-attention are combined to capture spatio-temporal correlations simultaneously. Finally, experiments are carried out on three real-world datasets, NYC Citi Bike, NYC Taxi, and BJ Subway, and the results show that this newly proposed method outperforms the state-of-the-art methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15745-15758"},"PeriodicalIF":7.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264262","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 Assignment Method for Multiple Extended Target Tracking With Azimuth Ambiguity Based on Pseudo Measurement Set 基于伪测量集的方位角模糊多扩展目标跟踪分配方法

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/TITS.2024.3452647

Ming Chen;Ratnasingham Tharmarasa;Thia Kirubarajan;Sunil Chomal

{"title":"An Assignment Method for Multiple Extended Target Tracking With Azimuth Ambiguity Based on Pseudo Measurement Set","authors":"Ming Chen;Ratnasingham Tharmarasa;Thia Kirubarajan;Sunil Chomal","doi":"10.1109/TITS.2024.3452647","DOIUrl":"10.1109/TITS.2024.3452647","url":null,"abstract":"Autonomous vehicle technology is rapidly becoming the driving force in the automobile industry. As such, the interest in high-resolution radio detection and ranging (radar) for autonomous vehicle applications is increasing due to its affordability and high angular resolution. However, for Advanced Driver Assistance Systems (ADAS), the challenge of azimuth ambiguity caused by a large physical distance between radar antennas is prevalent. This causes false measurements in a direction different from the target’s true angle due to grating lobes. This challenge increases when extended targets are considered. This paper proposes a Pseudo-3D Assignment (P3DA) method based on a Pseudo Measurement Set (PMS) to resolve azimuth ambiguity in multiple extended target tracking. The proposed method can resolve mono (single) and split (duplicated) azimuth ambiguities common in extended target tracking. The proposed solution uses Lagrangian Relaxation based on a Flexible Search (LR-FS) algorithm to solve the P3DA-PMS problem efficiently. The performance of the proposed algorithm in a typical traffic scenario simulated in Unreal Engine 4, with an ego vehicle mounted with both 2D (unambiguous) and 3D (ambiguous) radars, is evaluated. Simulation and experiment results suggest that the proposed P3DA-PMS-based tracking algorithm can perform better than conventional methods.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"15512-15531"},"PeriodicalIF":7.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264263","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

Multi-Agent DRL-Controlled Connected and Automated Vehicles in Mixed Traffic With Time Delays 有时间延迟的混合交通中由 DRL 控制的多代理互联和自动驾驶车辆

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/TITS.2024.3435036

Zhuwei Wang;Yi Xue;Lihan Liu;Haijun Zhang;Chunhui Qu;Chao Fang

{"title":"Multi-Agent DRL-Controlled Connected and Automated Vehicles in Mixed Traffic With Time Delays","authors":"Zhuwei Wang;Yi Xue;Lihan Liu;Haijun Zhang;Chunhui Qu;Chao Fang","doi":"10.1109/TITS.2024.3435036","DOIUrl":"10.1109/TITS.2024.3435036","url":null,"abstract":"The development of intelligent transportation systems (ITS) has attracted significant attention to connected and autonomous vehicles (CAVs). It is urgent to investigate multi-CAV intelligent cruise control solutions in mixed traffic environments. In addition, the impact of platoon dynamics and time delays, induced by shared wireless communications, data processing, and actuation cannot be ignored. This article investigates the development of a multi-agent deep reinforcement learning (MADRL) controller tailored for CAVs operating within mixed and dynamic traffic scenarios that involve time delays. Firstly, the error dynamics in the discrete-time domain for each subplatoon is derived by considering the time-varying delays and leading vehicle states, and then the optimal CAV cruise control problem is formulated. Subsequently, the partially observable Markov game (POMG) is used to construct the multi-agent environment, and then a centralized training decentralized execution (CTDE) algorithm framework is proposed based on the multi-agent deep deterministic policy gradient (MADDPG) method. Finally, the computational complexity and the influence of delay are analyzed. The simulation results illustrate the effectiveness of the proposed intelligent algorithm.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"17676-17688"},"PeriodicalIF":7.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264175","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

DSFormer-LRTC: Dynamic Spatial Transformer for Traffic Forecasting With Low-Rank Tensor Compression DSFormer-LRTC：利用低张量压缩进行交通预测的动态空间变换器

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/TITS.2024.3436523

Jianli Zhao;Futong Zhuo;Qiuxia Sun;Qing Li;Yiran Hua;Jianye Zhao

{"title":"DSFormer-LRTC: Dynamic Spatial Transformer for Traffic Forecasting With Low-Rank Tensor Compression","authors":"Jianli Zhao;Futong Zhuo;Qiuxia Sun;Qing Li;Yiran Hua;Jianye Zhao","doi":"10.1109/TITS.2024.3436523","DOIUrl":"10.1109/TITS.2024.3436523","url":null,"abstract":"Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic patterns. Previous works captured spatial dependencies based on graph neural networks and used fixed graph construction methods to characterize spatial relationships, which limits the ability of models to capture dynamic and long-range spatial dependencies. Meanwhile, prior studies did not consider the issue of a large number of redundant parameters in traffic prediction models, which not only increases the storage cost of the model but also reduces its generalization ability. To address the above challenges, we propose a Dynamic Spatial Transformer for Traffic Forecasting with Low-Rank Tensor Compression (DSFormer-LRTC). Specifically, we constructed a global spatial Transformer to capture remote spatial dependencies, and a distance-based mask matrix is used in local spatial Transformer to enhance the adjacent spatial influence. To reduce the complexity of the model, the model adopts a design that separates temporal and spatial. Meanwhile, we introduce low-rank tensor decomposition to reconstruct the parameter matrix in Transformer module to compress the proposed model. Experimental results show that DSFormer-LRTC achieves state-of-the-art performance on four real-world datasets. The experimental analysis of attention matrix also proves that the model can learn dynamic and distant spatial features. Finally, the compressed model parameters reduce the original parameter size by two-thirds, while significantly outperforming the baseline model in terms of computational efficiency.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 11","pages":"16323-16335"},"PeriodicalIF":7.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264172","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

MSF-SLAM: Multi-Sensor-Fusion-Based Simultaneous Localization and Mapping for Complex Dynamic Environments MSF-SLAM：基于多传感器融合的复杂动态环境同步定位与绘图

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/tits.2024.3451996

Xudong Lv, Zhiwei He, Yuxiang Yang, Jiahao Nie, Zhekang Dong, Shuo Wang, Mingyu Gao

引用次数: 0

Ensemble SARSA and LSTM for User-Centric Handover Decisions in 5G Vehicular Networks 针对 5G 车载网络中以用户为中心的切换决策的集合 SARSA 和 LSTM

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/tits.2024.3447357

Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande

引用次数: 0

The Application of 6G Technology and Back Propagation Neural Network in the Smart Public Transport System 6G 技术和反向传播神经网络在智能公共交通系统中的应用

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/tits.2024.3449336

Miao Yu, Hao Zhang, Jiaqi Li, Shuling Kang, Mingyuan Gao, Jin Zhu, Jian Fu

引用次数: 0

Quantitative Estimation of Driver Cognitive Workload: A Dual-Stage Learning Approach 驾驶员认知工作量的定量估算：双阶段学习法

IF 8.5 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2024-09-18 DOI: 10.1109/tits.2024.3451144

Jieyu Zhu, Chen Lv, Yanli Ma, Haohan Yang, Yaping Zhang

引用次数: 0