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

RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation RoMe：通过网格表示实现大规模路面重建

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-21 DOI: 10.1109/TIV.2024.3417512

Ruohong Mei;Wei Sui;Jiaxin Zhang;Xue Qin;Gang Wang;Tao Peng;Tao Chen;Cong Yang

{"title":"RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation","authors":"Ruohong Mei;Wei Sui;Jiaxin Zhang;Xue Qin;Gang Wang;Tao Peng;Tao Chen;Cong Yang","doi":"10.1109/TIV.2024.3417512","DOIUrl":"https://doi.org/10.1109/TIV.2024.3417512","url":null,"abstract":"In autonomous driving applications, accurate and efficient road surface reconstruction is paramount. This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces. Leveraging a unique mesh representation, RoMe ensures that the reconstructed road surfaces are accurate and seamlessly aligned with semantics. To address challenges in computational efficiency, we propose a waypoint sampling strategy, enabling RoMe to reconstruct vast environments by focusing on sub-areas and subsequently merging them. Furthermore, we incorporate an extrinsic optimization module to enhance the robustness against inaccuracies in extrinsic calibration. Our extensive evaluations of both public datasets and wild data underscore RoMe's superiority in terms of speed, accuracy, and robustness. For instance, it costs only 2 GPU hours to recover a road surface of \u0000<inline-formula><tex-math>$600times 600$</tex-math></inline-formula>\u0000 square meters from thousands of images. Notably, RoMe's capability extends beyond mere reconstruction, offering significant value for auto-labeling tasks in autonomous driving applications.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5173-5185"},"PeriodicalIF":14.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320504","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

From RAG/RAT to SAGE for Social Transportation: A CoT and New Perspective on Smart Logistics and Mobility 从社会交通的 RAG/RAT 到 SAGE：智能物流和移动性的协同效应和新视角

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3426992

Fei-Yue Wang

引用次数: 0

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.2024.3430211

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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