Huawei Technologies Co., Limited

Renu, Inc.

Ehsan Taghavi

Bingbing Liu

Sanjiv Sirpal

Paul Edward Reeves

Brian Joseph Reeves

Richard Zane DeLoach

Sean Burke

Amirhosein Nabatchian

Taylor Michael Keep

Mohammad Aliakbari Miyanmahaleh

Yuan Ren

Ehsan Nezhadarya

Ryan Razani

Devices, systems, methods, and media are disclosed for performing an object detection task comprising: obtaining a semantic segmentation map representing a real-world space, the semantic segmentation map including an array of elements that each represent a respective location in the real-world space and are assigned a respective element classification label; clustering groups of the elements based on the assigned respective element classification labels to identify at least a first cluster of elements that have each been assigned the same respective element classification label; generating, based on a location of the first cluster within the semantic segmentation map, at least one anchor that defines a respective probable object location of a first dynamic object; and generating, based on the semantic segmentation map and the at least one anchor, a respective bounding box and object instance classification label for the first dynamic object.

Systems and methods for enhancement of 3D object detection using point cloud semantic segmentation and attentive anchor generation

A networked HVAC system is implemented as part of a multi-unit dwelling. At least one HVAC unit is installed within each unit of the multi-unit dwelling. Each individual HVAC unit includes a localized HVAC unit controller, which is connected to an external network. The HVAC unit controller also can be connected to any other controllable HVAC devices in the unit, such as an exhaust fan. The HVAC system addresses the open-loop issue by monitoring temperature, air flow, humidity, air pressure, occupancy, window open/close state, and HVAC units of all units in a multi-unit dwelling, as well as common areas, and optimizing operating parameters to minimize wide swings in operational states and managing the overall system of multiple units/common areas so that energy usage and temperature/ventilation control is optimized. The HVAC system also enables learning and predictive modeling for adapting to real-time and anticipated condition requirements within each zone.

HVAC system having learning and prediction modeling

Networked HVAC system having local and networked control

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.

Method and system for generating an importance occupancy grid map

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.

Method and system for deep neural networks using dynamically selected feature-relevant points from a point cloud

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.

Method and system for generating a bird's eye view bounding box associated with an object

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.

Devices, systems, methods, and media for adaptive augmentation for a point cloud dataset used for training

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.

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Toronto, Canada

Ehsan Taghavi