IEEE Transactions on Intelligent Vehicles最新文献_第3页

Enhancement Technology for Perception in Smart Mining Vehicles: 4D Millimeter-Wave Radar and Multi-Sensor Fusion 智能采矿车辆感知增强技术：4D 毫米波雷达和多传感器融合

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3427718

Jianjian Yang;Tianmu Gui;Yuyuan Zhang;Shirong Ge;Qiankun Huang;Guanghui Zhao

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IEEE Transactions on Intelligent Vehicles Publication Information 电气和电子工程师学会智能车辆论文集》出版信息

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3430209

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TechRxiv: Share Your Preprint Research with the World! TechRxiv：与世界分享您的预印本研究成果！

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3437221

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The Transactions on Intelligent Vehicles Information 智能车辆信息论文集

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3435289

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Imaginative Intelligence for Intelligent Vehicles: Sora Inspired New Directions for New Mobility and Vehicle Intelligence 智能汽车的想象力智能：Sora 为新交通和车辆智能带来的新灵感

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-29 DOI: 10.1109/TIV.2024.3393638

Fei-Yue Wang

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Sora for Smart Mining: Towards Sustainability With Imaginative Intelligence and Parallel Intelligence 用于智能采矿的 Sora：利用想象力智能和并行智能实现可持续发展

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-29 DOI: 10.1109/TIV.2024.3394520

Yuting Xie;Cong Wang;Kunhua Liu;Zhe Xuanyuan;Yuhang He;Hui Cheng;Andreas Nüchter;Lingxi Li;Rouxing Huai;Shuming Tang;Siji Ma;Long Chen

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TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras TUMTraf 事件：校准和融合形成路边基于事件和 RGB 摄像机的数据集

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-25 DOI: 10.1109/TIV.2024.3393749

Christian Creß;Walter Zimmer;Nils Purschke;Bach Ngoc Doan;Sven Kirchner;Venkatnarayanan Lakshminarasimhan;Leah Strand;Alois C. Knoll

{"title":"TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras","authors":"Christian Creß;Walter Zimmer;Nils Purschke;Bach Ngoc Doan;Sven Kirchner;Venkatnarayanan Lakshminarasimhan;Leah Strand;Alois C. Knoll","doi":"10.1109/TIV.2024.3393749","DOIUrl":"https://doi.org/10.1109/TIV.2024.3393749","url":null,"abstract":"Event-based cameras are predestined for Intelligent Transportation Systems (ITS). They provide very high temporal resolution and dynamic range, which can eliminate motion blur and improve detection performance at night. However, event-based images lack color and texture compared to images from a conventional RGB camera. Considering that, data fusion between event-based and conventional cameras can combine the strengths of both modalities. For this purpose, extrinsic calibration is necessary. To the best of our knowledge, no targetless calibration between event-based and RGB cameras can handle multiple moving objects, nor does data fusion optimized for the domain of roadside ITS exist. Furthermore, synchronized event-based and RGB camera datasets considering roadside perspective are not yet published. To fill these research gaps, based on our previous work, we extended our targetless calibration approach with clustering methods to handle multiple moving objects. Furthermore, we developed an Early Fusion, Simple Late Fusion, and a novel Spatiotemporal Late Fusion method. Lastly, we published the TUMTraf Event Dataset, which contains more than 4,111 synchronized event-based and RGB images with 50,496 labeled 2D boxes. During our extensive experiments, we verified the effectiveness of our calibration method with multiple moving objects. Furthermore, compared to a single RGB camera, we increased the detection performance of up to +9% mAP in the day and up to +13% mAP during the challenging night with our presented event-based sensor fusion methods.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5186-5203"},"PeriodicalIF":14.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508494","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320476","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

Perception and Planning of Intelligent Vehicles Based on BEV in Extreme Off-Road Scenarios 基于 BEV 的智能车辆在极端越野场景中的感知与规划

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-23 DOI: 10.1109/TIV.2024.3392753

Jingjing Fan;Lili Fan;Qinghua Ni;Junhao Wang;Yi Liu;Ren Li;Yutong Wang;Sanjin Wang

引用次数: 0

RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing RoadFormer：用于 RGB-Normal 道路场景语义解析的双工变换器

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-17 DOI: 10.1109/TIV.2024.3388726

Jiahang Li;Yikang Zhang;Peng Yun;Guangliang Zhou;Qijun Chen;Rui Fan

{"title":"RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing","authors":"Jiahang Li;Yikang Zhang;Peng Yun;Guangliang Zhou;Qijun Chen;Rui Fan","doi":"10.1109/TIV.2024.3388726","DOIUrl":"https://doi.org/10.1109/TIV.2024.3388726","url":null,"abstract":"The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this article, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5163-5172"},"PeriodicalIF":14.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320486","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

eVTOL Performance Analysis: A Review From Control Perspectives eVTOL 性能分析：从控制角度回顾

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-11 DOI: 10.1109/TIV.2024.3387405

Jiangcheng Su;Hailong Huang;Hong Zhang;Yutong Wang;Fei-Yue Wang

引用次数: 0