Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path—without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.

Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.

Abstract: Apparatuses, systems, and techniques to identify features within one or more images. Features are identified in one or more images using one or more neural networks containing convolutional layers with multiple filters that may be executed by one or more parallel processing unit.

Abstract: An apparatus for classifying an input includes a classifier and a feature extractor. The feature extractor is configured to generate a feature vector based on the input. The feature vector is also configured to set a number of elements of the feature vector to zero to produce a sparse feature vector. The sparse feature vector has the same dimensions as the feature vector generated by the feature extractor. However, the sparse feature vector includes fewer non-zero elements than the feature vector generated by the feature extractor. The feature vector is further configured to forward the sparse feature vector to the classifier to classify the input.

Abstract: A method of managing memory usage of a stored training set for classification includes calculating one or both of a first similarity metric and a second similarity metric. The first similarity metric is associated with a new training sample and existing training samples of a same class as the new training sample. The second similarity metric is associated with the new training sample and existing training samples of a different class than the new training sample. The method also includes selectively storing the new training sample in memory based on the first similarity metric, and/or the second similarity metric.

Abstract: An apparatus for learning a rank of an artificial neural network is configured to decompose a weight tensor into a first weight tensor and a second weight tensor. A set of rank selection parameters are applied to the first weight tensor and the second weight tensor to truncate the rank of the first weight tensor and the second weight tensor. The set of rank selection parameters are updated simultaneously with the weight tensors by averaging updates calculated for each rank selection parameter of the set of rank selection parameters.

Abstract: A method of training a neural network model includes determining a specificity of multiple filters after a predetermined number of training iterations. The method also includes determining whether to continue training each filter of the multiple filters based at least in part on the specificity. The method further includes classifying an input based on features obtained by convolving the input with the trained filters.

Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.

Abstract: A method for classifying an object includes applying multiple confidence values to multiple objects. The method also includes determining a metric based on the multiple confidence values. The method further includes determining a classification of a first object from the multiple objects based on a knowledge-graph when the metric is above a threshold.

Abstract: An apparatus for learning a rank of an artificial neural network is configured to decompose a weight tensor into a first weight tensor and a second weight tensor. A set of rank selection parameters are applied to the first weight tensor and the second weight tensor to truncate the rank of the first weight tensor and the second weight tensor. The set of rank selection parameters are updated simultaneously with the weight tensors by averaging updates calculated for each rank selection parameter of the set of rank selection parameters.

Abstract: Context-based priors are utilized in machine learning networks (e.g., neural networks) for detecting objects in images. The likely locations of objects are estimated based on context labels. A machine learning network identifies a context label of an entire image. Based on the context label, the network selects a set of likely regions for detecting objects of interest in the image.

Abstract: A method for guiding a robot equipped with a camera to facilitate three-dimensional (3D) reconstruction through sampling based planning includes recognizing and localizing an object in a two-dimensional (2D) image. The method also includes computing 3D depth maps for the localized object. A 3D object map is constructed from the depth maps. A sampling based structure is grown around the 3D object map and a cost is assigned to each edge of the sampling based structure. The sampling based structure may be searched to determine a lowest cost sequence of edges that may, in turn be used to guide the robot.

Abstract: A method performed by an electronic device is described. The method includes obtaining an auxiliary product request or an auxiliary service request in conjunction with a vehicle request. The method also includes obtaining image data from an interior of at least one vehicle. The method further includes performing computer vision analysis on the image data, including processing the image data to classify one or more objects within a scene of the interior of the at least one vehicle. The method additionally includes determining, based on the classification, whether the one or more objects satisfy the auxiliary product request or the auxiliary service request within the interior of the at least one vehicle.

Abstract: In general, techniques are described for ensuring occupant awareness in vehicles. A device comprising a processor may be configured to perform the techniques. The processor may be configured to determine an event which will change an operating characteristic of the vehicle, and determine an extent of time remaining until an estimated occurrence of the expected event. The processor may also be configured to determine a process by which to inform the occupant of an operating context of the vehicle based on the determined extent of time remaining until the estimated occurrence the expected event, and perform the process by which to inform the occupant of the operating context of the vehicle. The device may also include a memory configured to store the process.

Abstract: A method of biasing a deep neural network includes determining whether an element has an increased probability of being present in an input to the network. The method also includes adjusting a bias of activation functions of neurons in the network to increase sensitivity to the element. In one configuration, the bias is adjusted without adjusting weights of the network. The method further includes adjusting an output of the network based on the biasing.

Abstract: A method of motion planning includes observing an object from a first pose of an agent having a controllable camera. The method also includes determining one or more subsequent control inputs to move the agent and the camera to observe the object from at least one subsequent pose. The subsequent control input(s) are determined so as to minimize an expected enclosing measure of the object based on visual data collected from the camera. The method further includes controlling the agent and the camera based on the subsequent control input(s).

Abstract: In general, techniques are described by which a first vehicle may provide support services for a second vehicle. A first vehicle, including a memory and a processor, may be configured to perform the techniques. The memory may be configured to store vehicle information relating to a second vehicle, the second vehicle configured to be operated by an operator. The processor may be configured to autonomously control positioning, based on the vehicle information, the first vehicle at a location relative to the second vehicle, and perform, after reaching the location, at least one service for the second vehicle.

Abstract: In general, techniques are described for ensuring occupant awareness in vehicles. A device comprising a processor may be configured to perform the techniques. The processor may be configured to determine an event which will change an operating characteristic of the vehicle, and determine an extent of time remaining until an estimated occurrence of the expected event. The processor may also be configured to determine a process by which to inform the occupant of an operating context of the vehicle based on the determined extent of time remaining until the estimated occurrence the expected event, and perform the process by which to inform the occupant of the operating context of the vehicle. The device may also include a memory configured to store the process.

Abstract: A method performed by an electronic device is described. The method includes obtaining an auxiliary product request or an auxiliary service request in conjunction with a vehicle request. The method also includes obtaining image data from an interior of at least one vehicle. The method further includes performing computer vision analysis on the image data, including processing the image data to classify one or more objects within a scene of the interior of the at least one vehicle. The method additionally includes determining, based on the classification, whether the one or more objects satisfy the auxiliary product request or the auxiliary service request within the interior of the at least one vehicle.

Abstract: An intelligent camera network cooperatively acquires images over a wireless network. The network automatically captures images based on a trigger. The trigger may include messages from other image capturing devices. A first image capture devices is triggered to acquire an image based on a message from at least one other image capture device.