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1. 发明申请

US20230136871A1 CAMERA CALIBRATION 有权
公开(公告)号：US20230136871A1
公开(公告)日：2023-05-04
申请号：US17515855
申请日：2021-11-01
申请人： Ford Global Technologies, LLC
发明人： Punarjay Chakravarty , Shubham Shrivastava , Bhushan Ghadge , Mostafa Parchami , Gaurav Pandey
IPC分类号： G05D1/00 , G06T7/80 , G06T7/70
摘要： A first plurality of center points of first two-dimensional bounding boxes corresponding to a vehicle occurring in a first plurality of images acquired by a first camera can be determined. A second plurality of center points of second two-dimensional bounding boxes corresponding to the vehicle occurring in a second plurality of images acquired by a second camera can also be determined. A plurality of non-linear equations based on the locations of the first and second pluralities of center points and first and second camera parameters corresponding to the first and second cameras can be determined. The plurality of non-linear equations can be solved simultaneously for the locations of the vehicle with respect to the first and second cameras and the six degree of freedom pose of the second camera with respect to the first camera. Real-world coordinates of the six degree of freedom pose of the second camera can be determined based on real-world coordinates of a six degree of freedom pose of the first camera.

2. 发明授权

US11620475B2 Domain translation network for performing image translation 有权
公开(公告)号：US11620475B2
公开(公告)日：2023-04-04
申请号：US16829114
申请日：2020-03-25
申请人： Ford Global Technologies, LLC
发明人： Praveen Narayanan , Nikita Jaipuria , Punarjay Chakravarty , Vidya Nariyambut murali
IPC分类号： G06K9/62 , G06N3/04 , G06N3/08 , G06N3/049
摘要： The present disclosure discloses a system and a method that includes receiving, at a decoder, a latent representation of an image having a first domain, and generating a reconstructed image having a second domain, wherein the reconstructed image is generated based on the latent representation.

3. 发明授权

US11574463B2 Neural network for localization and object detection 有权
公开(公告)号：US11574463B2
公开(公告)日：2023-02-07
申请号：US16799210
申请日：2020-02-24
申请人： Ford Global Technologies, LLC
发明人： Gaurav Pandey , Nikita Jaipuria , Praveen Narayanan , Punarjay Chakravarty
IPC分类号： G06V10/774 , G06K9/62 , G06N3/08 , B60W60/00 , G06V20/58
摘要： The present disclosure discloses a system and a method. In an example implementation, the system and the method generate, at a first encoder neural network, an encoded representation of image features of an image received from a vehicle sensor of a vehicle. The system and method can also generate, at a second encoder neural network, an encoded representation of map tile features and generate, at the decoder neural network, a semantically segmented map tile based on the encoded representation of image features, the encoded representation of map tile features, and Global Positioning System (GPS) coordinates of the vehicle. The semantically segmented map tile includes a location of the vehicle and detected objects depicted within the image with respect to the vehicle.

4. 发明申请

US20220180106A1 VEHICLE NEURAL NETWORK LOCALIZATION 有权
公开(公告)号：US20220180106A1
公开(公告)日：2022-06-09
申请号：US17113171
申请日：2020-12-07
申请人： Ford Global Technologies, LLC
发明人： Mokshith Voodarla , Shubham Shrivastava , Punarjay Chakravarty
IPC分类号： G06K9/00 , G06K9/62 , G06K9/32 , G06N3/08 , H04W4/46
摘要： A plurality of temporally successive vehicle sensor images are received as input to a variational autoencoder neural network that outputs an averaged semantic birds-eye view image that includes respective pixels determined by averaging semantic class values of corresponding pixels in respective images in the plurality of temporally successive vehicle sensor images. From a plurality of topological nodes that each specify respective real-world locations, a topological node closest to the vehicle, and a three degree-of-freedom pose for the vehicle relative to the topological node closest to the vehicle, is determined based on the averaged semantic birds-eye view image. A real-world three degree-of-freedom pose for the vehicle is determined by combining the three degree-of-freedom pose for the vehicle relative to the topological node and the real-world location of the topological node closest to the vehicle.

5. 发明授权

US11189049B1 Vehicle neural network perception and localization 有权
公开(公告)号：US11189049B1
公开(公告)日：2021-11-30
申请号：US17072334
申请日：2020-10-16
申请人： Ford Global Technologies, LLC
发明人： Punarjay Chakravarty , Shubham Shrivastava
IPC分类号： G06K9/00 , G06T7/73
摘要： A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine a plurality of topological nodes wherein each topological node includes a location in real-world coordinates and a three-dimensional point cloud image of the environment at the location of the topological node and process an image acquired by a sensor included in a vehicle using a variational auto-encoder neural network trained to output a semantic point cloud image, wherein the semantic point cloud image includes regions labeled by region type and region distance relative to the vehicle. The instructions include further instructions to determine a topological node closest to the vehicle and a six degree-of-freedom pose for the vehicle relative to the topological node closest to the vehicle based on the semantic point cloud data, determine a real-world six degree-of-freedom pose for the vehicle by combining the six degree-of-freedom for the vehicle relative to the topological node and the location in real-world coordinates of the topological node closest to the vehicle and determine a location and size of a three-dimensional object in the semantic point cloud image based on three-dimensional background subtraction using the three-dimensional point cloud image included in the topological node closest to the vehicle. The instructions include further instructions to improve the three-dimensional point cloud image included in the topological node based on the semantic point cloud image and the real-world six degree-of-freedom pose for the vehicle.

6. 发明申请

US20210303926A1 DOMAIN TRANSLATION NETWORK FOR PERFORMING IMAGE TRANSLATION 有权
公开(公告)号：US20210303926A1
公开(公告)日：2021-09-30
申请号：US16829114
申请日：2020-03-25
申请人： Ford Global Technologies, LLC
发明人： Praveen Narayanan , Nikita Jaipuria , Punarjay Chakravarty , Vidya Nariyambut murali
IPC分类号： G06K9/62 , G06N3/08 , G06N3/04
摘要： The present disclosure discloses a system and a method that includes receiving, at a decoder, a latent representation of an image having a first domain, and generating a reconstructed image having a second domain, wherein the reconstructed image is generated based on the latent representation.

7. 发明授权

US11410667B2 Hierarchical encoder for speech conversion system 有权
公开(公告)号：US11410667B2
公开(公告)日：2022-08-09
申请号：US16457150
申请日：2019-06-28
申请人： Ford Global Technologies, LLC
发明人： Punarjay Chakravarty , Lisa Scaria , Ryan Burke , Francois Charette , Praveen Narayanan
IPC分类号： G10L19/02 , G10L19/00 , G10L19/16
摘要： A speech conversion system is described that includes a hierarchical encoder and a decoder. The system may comprise a processor and memory storing instructions executable by the processor. The instructions may comprise to: using a second recurrent neural network (RNN) (GRU1) and a first set of encoder vectors derived from a spectrogram as input to the second RNN, determine a second concatenated sequence; determine a second set of encoder vectors by doubling a stack height and halving a length of the second concatenated sequence; using the second set of encoder vectors, determine a third set of encoder vectors; and decode the third set of encoder vectors using an attention block.

8. 发明申请

US20220219708A1 MULTI-DEGREE-OF-FREEDOM POSE FOR VEHICLE NAVIGATION 有权
公开(公告)号：US20220219708A1
公开(公告)日：2022-07-14
申请号：US17148994
申请日：2021-01-14
申请人： Ford Global Technologies, LLC
发明人： Punarjay Chakravarty , Shubham Shrivastava
IPC分类号： B60W40/12 , G06N3/04 , G06T7/521 , G06T17/10
摘要： A computer, including a processor and a memory, the memory including instructions to be executed by the processor to capture, from a camera, one or more images, wherein the one or more images include at least a portion of a vehicle, receive a plurality of keypoints corresponding to markers on the vehicle and instantiate a virtual vehicle corresponding to the vehicle. The instructions include further instructions to determine rotational and translation parameters of the vehicle by matching a plurality of virtual keypoints to the plurality of keypoints and determine a multi-degree of freedom (MDF) pose of the vehicle based on the rotational and translation parameters.

9. 发明申请

US20220164582A1 VEHICLE NEURAL NETWORK 有权
公开(公告)号：US20220164582A1
公开(公告)日：2022-05-26
申请号：US17102557
申请日：2020-11-24
申请人： Ford Global Technologies, LLC
发明人： Nithin Raghavan , Shubham Shrivastava , Punarjay Chakravarty
IPC分类号： G06K9/00 , G06N3/08 , G06N3/063 , G06K9/62
摘要： A computer, including a processor and a memory, the memory including instructions to be executed by the processor to receive a monocular image and provide the image to a variational autoencoder neural network (VAE), wherein the VAE has been trained in a twin configuration that includes a first encoder-decoder pair that receives as input unlabeled real images and outputs reconstructed real images, and a second encoder-decoder pair that receives as input synthetic images and outputs reconstructed synthetic images and wherein the VAE includes third and fourth decoders that are trained using labeled synthetic images, segmentation ground truth and depth ground truth. The instructions can include further instructions to output from the VAE a segmentation map and a depth map based on inputting the monocular image.

10. 发明授权

US11299169B2 Vehicle neural network training 有权
公开(公告)号：US11299169B2
公开(公告)日：2022-04-12
申请号：US16751842
申请日：2020-01-24
申请人： Ford Global Technologies, LLC
发明人： Punarjay Chakravarty , Ashley Elizabeth Micks
IPC分类号： B60W50/00 , G06T7/70 , G06K9/62 , G06K9/00 , B60W10/04 , B60W10/20 , B60W10/18 , G06N3/08 , G06V20/56 , G05D1/00 , B60W60/00 , H04W4/44
摘要： A computer, including a processor and a memory, the memory including instructions to be executed by the processor to determine six degree of freedom (DoF) data for a first object in a first video image and generate a synthetic video image corresponding to the first video image including a synthetic object and a synthetic object label based on the six DoF data. The instructions can include further instructions to train a generative adversarial network (GAN) based on a paired first video image and a synthetic video image to generate a modified synthetic image and train a deep neural network to locate the synthetic object in the modified synthetic video image based on the synthetic object. The instructions can include further instructions to download the trained deep neural network to a computing device in a vehicle.

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