公开(公告)号：US11815897B2

公开(公告)日：2023-11-14

申请号：US16871711

申请日：2020-05-11

Applicant: Amirhosein Nabatchian ,  Ehsan Taghavi

Inventor： Amirhosein Nabatchian ,  Ehsan Taghavi

IPC: G05D1/02 ,  B60W60/00 ,  G06T7/20 ,  G06V20/56 ,  G06F18/25 ,  G06F18/214 ,  G06V10/82 ,  G01S17/42 ,  G01S17/931

CPC classification number: G05D1/0214 ,  B60W60/001 ,  G05D1/0221 ,  G05D1/0278 ,  G06F18/214 ,  G06F18/25 ,  G06T7/20 ,  G06V10/82 ,  G06V20/56 ,  B60W2556/50 ,  G01S17/42 ,  G01S17/931 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30241 ,  G06T2207/30252

Abstract: A system and method for generating an importance occupancy grid map (OGM) for a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud; receiving a binary map, the binary map associated with a set of GPS coordinates of the vehicle; receiving information representative of a planned path for the vehicle; and generating an importance OGM based on the 3D point cloud, the binary map, and the planned path for the vehicle using a map generation module.

公开(公告)号：US11527084B2

公开(公告)日：2022-12-13

申请号：US16926096

申请日：2020-07-10

Applicant: Ehsan Taghavi ,  Amirhosein Nabatchian ,  Bingbing Liu

Inventor： Ehsan Taghavi ,  Amirhosein Nabatchian ,  Bingbing Liu

IPC: G06T7/00 ,  G06V20/64 ,  G06T3/60 ,  G06K9/62 ,  G06T7/20 ,  G06V20/56

Abstract: A system and method for generating a bounding box for an object in proximity to a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud representative of an environment; receiving a two-dimensional (2D) image of the environment; processing the 3D point cloud to identify an object cluster of 3D data points for a 3D object in the 3D point cloud; processing the 2D image to detect a 2D object in the 2D image and generate information regarding the 2D object from the 2D image; and when the 3D object and the 2D object correspond to the same object in the environment: generating a bird's eye view (BEV) bounding box for the object based on the object cluster of 3D data points and the information from the 2D image.

公开(公告)号：US20190050693A1

公开(公告)日：2019-02-14

申请号：US15676682

申请日：2017-08-14

Applicant: Ehsan Taghavi

Inventor： Ehsan Taghavi

IPC: G06K9/66 ,  G06K9/00

Abstract: Methods and systems for generating an annotated dataset for training a deep tracking neural network, and training of the neural network using the annotated dataset. For each object in each frame of a dataset, one or more likelihood functions are calculated to correlate feature score of the object with respective feature scores each associated with one or more previously assigned target identifiers (IDs) in a selected range of frames. A target ID is assigned to the object by assigning a previously assigned target ID associated with a calculated highest likelihood or assigning a new target ID. Track management is performed according to a predefined track management scheme to assign a track type to the object. This is performed for all objects in all frames of the dataset. The resulting annotated dataset contains target IDs and track types assigned to all objects in all frames.

公开(公告)号：US11676005B2

公开(公告)日：2023-06-13

申请号：US16191011

申请日：2018-11-14

Applicant: Ehsan Nezhadarya ,  Ehsan Taghavi ,  Bingbing Liu

Inventor： Ehsan Nezhadarya ,  Ehsan Taghavi ,  Bingbing Liu

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods and systems for deep neural networks using dynamically selected feature-relevant points from a point cloud are described. A plurality of multidimensional feature vectors arranged in a point-feature matrix are received. Each row of the point-feature matrix corresponds to a respective one of the multidimensional feature vectors, and each column of the point-feature matrix corresponds to a respective feature. Each multidimensional feature vector represents a respective unordered point from a point cloud and each multidimensional feature vector includes a respective plurality of feature-correlated values, each feature-correlated value represents a correlation extent of the respective feature. A reduced-max matrix having a selected plurality of feature-relevant vectors is generated. The feature-relevant vectors are selected by, for each respective feature, identifying a respective multidimensional feature vector in the point-feature matrix having a maximum feature-correlated value associated with the respective feature. The reduced-max matrix is output to at least one neural network layer.

公开(公告)号：US11410388B1

公开(公告)日：2022-08-09

申请号：US17203407

申请日：2021-03-16

Applicant: Ehsan Taghavi ,  Yuan Ren ,  Bingbing Liu

Inventor： Ehsan Taghavi ,  Yuan Ren ,  Bingbing Liu

IPC: G06T17/20 ,  G06N20/00 ,  G06K9/62 ,  G06T19/20

Abstract: Devices, systems, methods, and media are described for adaptive scene augmentation of a point cloud frame for inclusion in a labeled point cloud dataset used for training a machine learned model for a prediction task for point cloud frames, such as object detection or segmentation. A formal method is described for generating new point cloud frames based on pre-existing annotated large-scale labeled point cloud frames included in a point cloud dataset to generate new, augmented point cloud frames. A policy is generated for large-scale data augmentation using detailed quantitative metrics such as confusion matrices. The policy is a detailed and stepwise set of rules, procedures, and/or conditions that may be used to generate augmented data specifically targeted to mitigate the existing inaccuracies in the trained model. The augmented point cloud frames may then be used to further train the model to improve the prediction accuracy of the model.

公开(公告)号：US20200151557A1

公开(公告)日：2020-05-14

申请号：US16191011

申请日：2018-11-14

Applicant: Ehsan Nezhadarya ,  Ehsan Taghavi ,  Bingbing Liu

Inventor： Ehsan Nezhadarya ,  Ehsan Taghavi ,  Bingbing Liu

IPC: G06N3/08

Abstract: Methods and systems for deep neural networks using dynamically selected feature-relevant points from a point cloud are described. A plurality of multidimensional feature vectors arranged in a point-feature matrix are received. Each row of the point-feature matrix corresponds to a respective one of the multidimensional feature vectors, and each column of the point-feature matrix corresponds to a respective feature. Each multidimensional feature vector represents a respective unordered point from a point cloud and each multidimensional feature vector includes a respective plurality of feature-correlated values, each feature-correlated value represents a correlation extent of the respective feature. A reduced-max matrix having a selected plurality of feature-relevant vectors is generated. The feature-relevant vectors are selected by, for each respective feature, identifying a respective multidimensional feature vector in the point-feature matrix having a maximum feature-correlated value associated with the respective feature. The reduced-max matrix is output to at least one neural network layer.

公开(公告)号：US10592786B2

公开(公告)日：2020-03-17

申请号：US15676682

申请日：2017-08-14

Applicant: Ehsan Taghavi

Inventor： Ehsan Taghavi

IPC: G06K9/66 ,  G06K9/00 ,  G06T7/246

Abstract: Methods and systems for generating an annotated dataset for training a deep tracking neural network, and training of the neural network using the annotated dataset. For each object in each frame of a dataset, one or more likelihood functions are calculated to correlate feature score of the object with respective feature scores each associated with one or more previously assigned target identifiers (IDs) in a selected range of frames. A target ID is assigned to the object by assigning a previously assigned target ID associated with a calculated highest likelihood or assigning a new target ID. Track management is performed according to a predefined track management scheme to assign a track type to the object. This is performed for all objects in all frames of the dataset. The resulting annotated dataset contains target IDs and track types assigned to all objects in all frames.