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公开(公告)号：US11796670B2
公开(公告)日：2023-10-24
申请号：US17325762
申请日：2021-05-20
Inventor： Jin Fang , Dingfu Zhou , Xibin Song , Liangjun Zhang
Abstract: A radar point cloud data processing method and device, an apparatus, and storage medium are provided, which are related to technical fields of radar point cloud, automatic driving, and deep learning. An implementation includes: determining a target location area where a target object is located by utilizing a target detection box in the radar point cloud data; removing each point of the target object in the target location area from the radar point cloud data; and adding an object model to the target location area. By applying embodiments of the present disclosure, richer radar point cloud data may be obtained by removing the target object from the radar point cloud data and adding the needed three-dimensional model to the target location area in the radar point cloud data.
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公开(公告)号：US12104350B2
公开(公告)日：2024-10-01
申请号：US17594519
申请日：2020-11-26
Inventor： Lingfeng Qian , Pinxin Long , Liangjun Zhang
CPC classification number: E02F3/435 , G05D1/0223
Abstract: A speed determination method, an electronic device and a computer storage medium are provided, relates to the field of computer technology, and may be applied to the field of artificial intelligence, especially the field of automated driving. The method includes: determining an expected speed direction of a controlled point of a first controlled target according to an actual location of the controlled point of the first controlled target and a preset trajectory of the controlled point of the first controlled target, wherein the first controlled target is one of a plurality of controlled targets having a kinematic relationship; and determining a target speed of at least one controlled target of the plurality of controlled targets according to the expected speed direction of the controlled point of the first controlled target and the kinematic relationship.
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公开(公告)号：US20210270958A1
公开(公告)日：2021-09-02
申请号：US17325762
申请日：2021-05-20
Inventor： Jin Fang , Dingfu Zhou , Xibin Song , Liangjun Zhang
Abstract: A radar point cloud data processing method and device, an apparatus, and storage medium are provided, which are related to technical fields of radar point cloud, automatic driving, and deep learning. An implementation includes: determining a target location area where a target object is located by utilizing a target detection box in the radar point cloud data; removing each point of the target object in the target location area from the radar point cloud data; and adding an object model to the target location area. By applying embodiments of the present disclosure, richer radar point cloud data may be obtained by removing the target object from the radar point cloud data and adding the needed three-dimensional model to the target location area in the radar point cloud data.
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公开(公告)号：US11841921B2
公开(公告)日：2023-12-12
申请号：US17112247
申请日：2020-12-04
Inventor： Xibin Song , Dingfu Zhou , Jin Fang , Liangjun Zhang
IPC: G06T7/50 , G06N20/00 , G06F18/214 , G06V10/42 , G06T3/40
CPC classification number: G06F18/214 , G06N20/00 , G06T3/40 , G06T7/50 , G06V10/42 , G06T2207/10028 , G06T2207/20081 , G06T2207/20084
Abstract: The present application provides a model training method and apparatus, and a prediction method and apparatus, and it relates to fields of artificial intelligence, deep learning, image processing, and autonomous driving. The model training method includes: inputting a first sample image of sample images into a depth information prediction model, and acquiring depth information of the first sample image; acquiring inter-image posture information based on a second sample image of the sample images and the first sample image; acquiring a projection image corresponding to the first sample image, at least according to the inter-image posture information and the depth information; and acquiring a loss function by determining a function for calculating a similarity between the second sample image and the projection image, and training the depth information prediction model using the loss function.
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