Abstract: Method and system for processing a point cloud frame representing a real-world scene that includes one or more objects, including assigning data-element-level classification labels to data elements that each respectively represent one or more points included in the point cloud frame, estimating an approximate position of a first object instance represented in the point cloud frame, assigning an object-instance-level classification label to the first object instance, selecting, for the first object instance, a subgroup of the data elements based on the approximate position, selecting from the subgroup a first cluster of data elements that have assigned data-element-level classification labels that match the object-instance-level classification label assigned to the first object instance, and outputting an object instance list that indicates, for the first object instance, the first cluster of data elements, and the object-instance-level classification label assigned to the first object instance.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

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.

Abstract: Method and system for computing a total variation loss for use in backpropagation during training a neural network which individually classifies data points, comprising: predicting, using a neural network, a respective label for each data point in a set of input data points; determining a variation indicator that indicates a variance between: (i) smoothness of the predicted labels among neighboring data points and (ii) smoothness of the ground truth labels among the same neighboring data points; and computing the total variation loss based on the variation indicator.

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.

Abstract: Devices, systems, methods, and media are described for point cloud data augmentation using model injection, for the purpose of training machine learning models to perform point cloud segmentation and object detection. A library of surface models is generated from point cloud object instances in LIDAR-generated point cloud frames. The surface models can be used to inject new object instances into target point cloud frames at an arbitrary location within the target frame to generate new, augmented point cloud data. The augmented point cloud data may then be used as training data to improve the accuracy of a machine learned model trained using a machine learning algorithm to perform a segmentation and/or object detection task.

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.

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.

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.

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.

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.

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.