公开(公告)号：US10861160B2

公开(公告)日：2020-12-08

申请号：US16143741

申请日：2018-09-27

Applicant: Aptiv Technologies Limited

Inventor： Ido Freeman ,  Jan Siegemund

IPC: G06T7/00 ,  G06T7/13 ,  G06N7/00 ,  G06K9/46

Abstract: A device for assigning one of a plurality of predetermined classes to each pixel of an image, the device is configured to receive an image captured by a camera, the image comprising a plurality of pixels; use an encoder convolutional neural network to generate probability values for each pixel, each probability value indicating the probability that the respective pixel is associated with one of the plurality of predetermined classes; generate for each pixel a class prediction value from the probability values, the class prediction value predicting the class of the plurality of predetermined classes the respective pixel is associated with; use an edge detection algorithm to predict boundaries between objects shown in the image, the class prediction values of the pixels being used as input values of the edge detection algorithm; and assign a label of one of the plurality of predetermined classes to each pixel of the image.

公开(公告)号：US20230034973A1

公开(公告)日：2023-02-02

申请号：US17812125

申请日：2022-07-12

Applicant: Aptiv Technologies Limited

Inventor： Dominic Spata ,  Arne Grumpe ,  Ido Freeman

IPC: G06N3/04 ,  G06N3/08

Abstract: The disclosure includes a computer-implemented method for predicting trajectory data of an object including: acquiring radar data of the object; determining a parametrization of the trajectory data of the object based on the radar data; and determining a variance of the trajectory data of the object based on the radar data. The trajectory data of the object includes a position of the object and a direction of the object. The parametrization includes a plurality of parameters and a polynomial of a pre-determined degree. The parameters include a plurality of coefficients related to elements of a basis of the polynomial space of polynomials of the pre-determined degree.

公开(公告)号：US20220212660A1

公开(公告)日：2022-07-07

申请号：US17646634

申请日：2021-12-30

Applicant: Aptiv Technologies Limited

Inventor： Ido Freeman ,  Kun Zhao

IPC: B60W30/095 ,  G01S13/931 ,  G06N3/02 ,  B60W50/00

Abstract: A computer implemented method for determining weights for an attention based trajectory prediction comprises the following steps carried out by computer hardware components: receiving a sequence of a plurality of captures taken by a sensor; determining an unnormalized weight for a first capture of the sequence based on the first capture of the sequence; and determining a normalized weight for the first capture of the sequence based on the unnormalized weight for the first capture of the sequence and a normalized weight for a second capture of the sequence.

公开(公告)号：US20220245955A1

公开(公告)日：2022-08-04

申请号：US17579536

申请日：2022-01-19

Applicant: Aptiv Technologies Limited

Inventor： Ido Freeman ,  Pascal Colling

IPC: G06V20/70 ,  G06V10/82 ,  G06T7/12 ,  G06T7/13 ,  G06T7/62 ,  G06V10/774 ,  G06V10/764 ,  G06V10/26

Abstract: A method is provided for classifying pixels of an image. An image comprising a plurality of pixels is captured by a sensor device. A neural network is used for estimating probability values for each pixel, each probability value indicating the probability for the respective pixel being associated with one of a plurality of predetermined classes. One of the classes is assigned to each pixel of the image based on the respective probability values to create a predicted segmentation map. For training the neural network, a loss function is generated by relating the predicted segmentation map to ground truth labels. Furthermore, an edge detection algorithm is applied to at least one of the predicted segmentation maps and the ground truth labels, wherein the edge detection algorithm predicts boundaries between objects. Generating the loss function is based on a result of the edge detection algorithm.

公开(公告)号：US20220026556A1

公开(公告)日：2022-01-27

申请号：US17384449

申请日：2021-07-23

Applicant: Aptiv Technologies Limited

Inventor： Dominic Spata ,  Arne Grumpe ,  Mirko Meuter ,  Ido Freeman

IPC: G01S13/58 ,  G01S13/06 ,  G06N3/04 ,  G06N3/08

Abstract: A computer implemented method for predicting a trajectory of an object comprises the following steps carried out by computer hardware components: acquiring radar data of the object; determining first intermediate data based on the radar data based on a residual backbone using a recurrent component; determining second intermediate data based on the first intermediate data using a feature pyramid; and predicting the trajectory of the object based on the second intermediate data.

公开(公告)号：US10832097B2

公开(公告)日：2020-11-10

申请号：US16241091

申请日：2019-01-07

Applicant: Aptiv Technologies Limited

Inventor： Ido Freeman ,  Lutz Roese-Koerner ,  Christof Petig ,  Peet Cremer

IPC: G06K9/00 ,  G06E1/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: A device for image classification comprising a convolutional neural network configured to generate a plurality of probability values, each probability value being linked to a respective one of a plurality of predetermined classes and indicating the probability that the image or a pixel of the image is associated with the respective class, and the convolutional neural network comprises a plurality of convolutional blocks and each of the convolutional blocks comprises: a first convolutional layer configured to perform a pointwise convolution using a first kernel, a second convolutional layer configured to perform a depthwise convolution using a second kernel, wherein the second kernel has one of a single row and a single column, a third convolutional layer configured to perform a depthwise convolution using a third kernel, wherein the third kernel has a single column if the second kernel has a single row, and the third kernel has a single row if the second kernel has a single column, and a fourth convolutional layer configured to perform a convolution using a fourth kernel.

公开(公告)号：US20210192347A1

公开(公告)日：2021-06-24

申请号：US17125783

申请日：2020-12-17

Applicant: Aptiv Technologies Limited

Inventor： Kun Zhao ,  Ido Freeman

IPC: G06N3/08 ,  G06N3/04 ,  G05D1/02

Abstract: Computer-implemented method for determining continuous information on an expected trajectory of an object, the method comprising at least the following steps carried out by computer hardware components: determining data related to an expected trajectory of an object; and determining at least one parameter value for a continuous function on the basis of the data, wherein the continuous function and the at least one parameter value represent continuous information on the expected trajectory of the object.

公开(公告)号：US20190220709A1

公开(公告)日：2019-07-18

申请号：US16241091

申请日：2019-01-07

Applicant: Aptiv Technologies Limited

Inventor： Ido Freeman ,  Lutz Roese-Koerner ,  Christof Petig ,  Peet Cremer

IPC: G06K9/62 ,  G06N3/04 ,  G06N3/08

Abstract: A device for image classification comprising a convolutional neural network configured to generate a plurality of probability values, each probability value being linked to a respective one of a plurality of predetermined classes and indicating the probability that the image or a pixel of the image is associated with the respective class, and the convolutional neural network comprises a plurality of convolutional blocks and each of the convolutional blocks comprises: a first convolutional layer configured to perform a pointwise convolution using a first kernel, a second convolutional layer configured to perform a depthwise convolution using a second kernel, wherein the second kernel has one of a single row and a single column, a third convolutional layer configured to perform a depthwise convolution using a third kernel, wherein the third kernel has a single column if the second kernel has a single row, and the third kernel has a single row if the second kernel has a single column, and a fourth convolutional layer configured to perform a convolution using a fourth kernel.