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公开(公告)号：US20220101548A1
公开(公告)日：2022-03-31
申请号：US17485402
申请日：2021-09-25
申请人： Tsinghua University
发明人： Jun LI , Xinyu ZHANG , Zhiwei LI , Zhenhong ZOU , Yi HUANG
摘要： A point cloud intensity completion method and system based on semantic segmentation are provided. The point cloud intensity completion method includes: acquiring an RGB image and point cloud data of a road surface synchronously by a photographic camera and a lidar; performing spatial transformation on the point cloud data by using a conversion matrix to generate a two-dimensional reflection intensity projection map and a two-dimensional depth projection map; performing reflection intensity completion on the RGB image and the two-dimensional reflection intensity projection map to obtain a single-channel reflection intensity projection map; performing depth completion on the RGB image and the two-dimensional depth projection map to obtain a single-channel depth projection map; and performing coarse-grained completion on the RGB image, the single-channel reflection intensity projection map and the single-channel depth projection map to obtain a two-dimensional coarse-grained reflectance intensity projection map.
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公开(公告)号：US20220315055A1
公开(公告)日：2022-10-06
申请号：US17834411
申请日：2022-06-07
申请人： Tsinghua University
发明人： Hong WANG , Wenhao YU , Ziwen DUAN , Jun LI
摘要： Embodiments of the present application disclose a safety control method and a safety control system based on environmental risk assessment for an intelligent connected vehicle. The method includes: when a vehicle is in an automatic driving mode, acquiring environmental parameter information of the vehicle in a current driving environment; determining a target driving control parameter which meets a preset safe driving condition under the current environmental parameter; and managing a current automatic driving level of the vehicle by using the target driving control parameter.
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公开(公告)号：US20220315054A1
公开(公告)日：2022-10-06
申请号：US17711200
申请日：2022-04-01
申请人： Tsinghua University
发明人： Hong WANG , Wenhao YU , Ziwen DUAN , Jun LI
摘要： Embodiments of the present application disclose a safety control method and a safety control system based on environmental risk assessment for an intelligent connected vehicle. The method includes: when a vehicle is in an automatic driving mode, acquiring environmental parameter information of the vehicle in a current driving environment; determining a target driving control parameter which meets a preset safe driving condition under the current environmental parameter; and managing a current automatic driving level of the vehicle by using the target driving control parameter.
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公开(公告)号：US20220229448A1
公开(公告)日：2022-07-21
申请号：US17577044
申请日：2022-01-17
申请人： Tsinghua University
发明人： Xinyu ZHANG , Jun LI , Qifan TAN , Jianxi LUO , Huaping LIU , Kangyao HUANG , Xingang WU
摘要： A takeoff and landing control method of a multimodal air-ground amphibious vehicle includes: receiving dynamic parameters of the multimodal air-ground amphibious vehicle; processing the dynamic parameters by a coupled dynamic model of the multimodal air-ground amphibious vehicle to obtain dynamic control parameters of the multimodal air-ground amphibious vehicle, wherein the coupled dynamic model of the multimodal air-ground amphibious vehicle comprises a motion equation of the multimodal air-ground amphibious vehicle in a touchdown state; and the motion equation of the multimodal air-ground amphibious vehicle in a touchdown state is determined by a two-degree-of-freedom suspension dynamic equation and a six-degree-of-freedom motion equation of the multimodal air-ground amphibious vehicle in the touchdown state; and controlling takeoff and landing of the multimodal air-ground amphibious vehicle according to the dynamic control parameters of the multimodal air-ground amphibious vehicle. The method is used for takeoff and landing control of a multimodal air-ground amphibious vehicle.
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公开(公告)号：US20160018537A1
公开(公告)日：2016-01-21
申请号：US14800635
申请日：2015-07-15
发明人： Lan ZHANG , Yulan LI , Yuanjing LI , Jianqiang FU , Yingshuai DU , Wei ZHANG , Xuming MA , Jun LI
摘要： The present invention provides a method and apparatus for processing signals of a semiconductor detector, including: acquiring a relationship of a time difference between anode and cathode signals of the semiconductor detector with an anode signal amplitude; obtaining an optimal data screening interval according to the relationship of the time difference between anode and cathode signals of the semiconductor detector with the anode signal amplitude, wherein the optimal data screening interval is an interval where the time difference between the anode and cathode signals is greater than 50 ns; and screening and processing the collected data according to the optimal data screening interval when the semiconductor detector collects data. The present invention better overcomes the inherent crystal defects of the detector, reduces the effect of background noise, increases the energy resolution of the cadmium zinc telluride detector under room temperature, and improves the peak-to-compton ratio.
摘要翻译： 本发明提供了一种用于处理半导体检测器的信号的方法和装置，包括：用阳极信号振幅获取半导体检测器的阳极和阴极信号之间的时间差的关系; 根据半导体检测器的阳极和阴极信号的时间差与阳极信号幅度的关系获得最佳数据筛选间隔，其中最佳数据筛选间隔是阳极和阴极信号之间的时间差较大的间隔 超过50 ns; 并且当半导体检测器收集数据时，根据最佳数据筛选间隔筛选和处理收集的数据。 本发明更好地克服了检测器的固有晶体缺陷，降低了背景噪声的影响，提高了碲化锌碲化镉检测器在室温下的能量分辨率，提高了峰 - 峰比。
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公开(公告)号：US20220366681A1
公开(公告)日：2022-11-17
申请号：US17732540
申请日：2022-04-29
申请人： Tsinghua University
发明人： Xinyu ZHANG , Li WANG , Jun LI , Lijun ZHAO , Zhiwei LI , Shiyan ZHANG , Lei YANG , Xingang WU , Hanwen GAO , Lei ZHU , Tianlei ZHANG
摘要： A vision-LiDAR fusion method and system based on deep canonical correlation analysis are provided. The method comprises: collecting RGB images and point cloud data of a road surface synchronously; extracting features of the RGB images to obtain RGB features; performing coordinate system conversion and rasterization on the point cloud data in turn, and then extracting features to obtain point cloud features; inputting point cloud features and RGB features into a pre-established and well-trained fusion model at the same time, to output feature-enhanced fused point cloud features, wherein the fusion model fuses RGB features to point cloud features by using correlation analysis and in combination with a deep neural network; and inputting the fused point cloud features into a pre-established object detection network to achieve object detection. A similarity calculation matrix is utilized to fuse two different modal features.
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公开(公告)号：US20220207868A1
公开(公告)日：2022-06-30
申请号：US17543777
申请日：2021-12-07
申请人： Tsinghua University
发明人： Xinyu ZHANG , Jun LI , Zhiwei LI , Huaping LIU , Xingang WU
IPC分类号： G06V10/80 , G06V10/77 , G06V10/774 , G06V20/56 , G06V10/82 , G01S13/86 , G01S13/931 , G01S13/89 , G01S7/41
摘要： An all-weather target detection method based on a vision and millimeter wave fusion includes: simultaneously acquiring continuous image data and point cloud data using two types of sensors of a vehicle-mounted camera and a millimeter wave radar; pre-processing the image data and point cloud data; fusing the pre-processed image data and point cloud data by using a pre-established fusion model, and outputting a fused feature map; and inputting the fused feature map into a YOLOv5 detection network for detection, and outputting a target detection result by non-maximum suppression. The method fully fuses millimeter wave radar echo intensity and distance information with the vehicle-mounted camera images. It analyzes different features of a millimeter wave radar point cloud and fuses the features with image information by using different feature extraction structures and ways, so that the advantages of the two types of sensor data complement each other.
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