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公开(公告)号：US20240257384A1
公开(公告)日：2024-08-01
申请号：US18424785
申请日：2024-01-27
发明人： Moritz Luszek , Simon Roesler
IPC分类号： G06T7/70 , G06T7/62 , G06V10/764
CPC分类号： G06T7/70 , G06T7/62 , G06V10/764 , G06V2201/07
摘要： A computer-implemented method for training a birds-eye-view (BEV) object detection model includes inputting a training sample into the model. The training sample includes a BEV image with multiple pixels, and multiple target confidence values. Each pixel of the pixels is associated with a target confidence value of the target confidence values. The method includes receiving as output from the model multiple predicted confidence values. Each predicted confidence value is associated with a pixel of the pixels. The method includes adjusting a parameter set of the model according to a loss. The loss is based on the predicted confidence values and the target confidence values.
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公开(公告)号：US20240265631A1
公开(公告)日：2024-08-08
申请号：US18432809
申请日：2024-02-05
CPC分类号： G06T17/05 , G06T7/11 , G06V20/56 , G06T2207/20081 , G06T2207/20112
摘要： Disclosed is a computer-implemented method for creating a data sample for training semantic segmentation models usable in a vehicle assistance system. The method includes obtaining a first point cloud representing a surrounding of a vehicle at a first point in time and a second point cloud representing the surrounding of the vehicle at a second point in time. The method includes joining the first and second point cloud to obtain a global point cloud representing the surrounding of the vehicle over a duration of the first point in time and the second point in time. The method includes creating a representation of the surrounding based on the global point cloud. The method includes extracting from the representation a semantic map and one or more elevation maps. The method includes providing the semantic map and the one or more elevation maps as the data sample.
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公开(公告)号：US12118797B2
公开(公告)日：2024-10-15
申请号：US17457339
申请日：2021-12-02
发明人： Marco Braun , Moritz Luszek , Jan Siegemund
IPC分类号： G06V20/56 , B60W40/10 , G06F18/2415 , G06N3/02
CPC分类号： G06V20/56 , B60W40/10 , G06F18/24155 , G06N3/02 , B60W2420/403 , B60W2420/408
摘要： A method is provided for semantic segmentation of an environment of a vehicle. Via a processing device, a grid of cells is defined dividing the environment of the vehicle. A radar point cloud is received from a plurality of radar sensors, and at least one feature of the radar point cloud is assigned to each grid cell. By using a neural network including deterministic weights, high-level features are extracted for each grid cell. Several classes are defined for the grid cells. For layers of a Bayesian neural network, various sets of weights are determined probabilistically. Via the Bayesian neural network, confidence values are determined for each class and for each grid cell based on the high-level features and based on the various sets of weights in order to determine a predicted class and an extent of uncertainty for the predicted class for each grid cell.
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公开(公告)号：US20240176008A1
公开(公告)日：2024-05-30
申请号：US18524595
申请日：2023-11-30
发明人： Moritz Luszek
IPC分类号： G01S13/72 , G01S13/931
CPC分类号： G01S13/723 , G01S13/931 , G01S2013/9323
摘要： Disclosed is a computer-implemented method for tracking an object. The method includes obtaining a motion of the object within a time frame based on data received from a sensing system. The method includes splitting the time frame into multiple sub-intervals. The method includes determining, using a tracking system, a position of the object in a next time frame based on sub-motions of the object within the sub-intervals.
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