Intel Corporation

Ranganath Krishnan

Ignacio Javier Alvarez

Javier Felip Leon

David Gomez Gutierrez

Ibrahima J Ndiour

Nilesh Amar Ahuja

Satish Chandra Jha

Kathiravetpillai Sivanesan

Omesh Tickoo

David Israel Gonzalez Aguirre

S M Iftekharul Alam

Sridhar G Sharma

Mahesh M Subedar

Richard Dorrance

Deepak Dasalukunte

Tanay Karnik

Rafael De La Guardia Gonzalez

Indranil Chakraborty

Srivatsa Rangachar Srinivasa

Jainaveen Sundaram Priya

Dileep Kurian

Methods, apparatus, systems and articles of manufacture are disclosed to facilitate knowledge sharing among neural networks. An example apparatus includes a trainer to train, at a first computing system, a first Bayesian Neural Network (BNN) on a first subset of training data to generate a first weight distribution, and train, at a second computing system, a second BNN on a second subset of the training data to generate a second weight distribution, the second subset of the training data different from the first subset of training data. The example apparatus includes a knowledge sharing controller to generate a third BNN based on the first weight distribution and the second weight distribution.

Methods and apparatus to facilitate efficient knowledge sharing among neural networks

Methods, apparatus, systems, and articles of manufacture providing a Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same are disclosed. An example apparatus includes a number generator to generate a sequence of numbers; a multiplier to generate a plurality of products by multiplying respective numbers of the sequence of the numbers by a variance value; and an adder to generate a plurality of weights by adding a mean value to the plurality of products, the plurality of weights corresponding to a single probability distribution.

Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same

Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

Robust multimodal sensor fusion for autonomous driving vehicles

A communication device for a vehicle to communicate features about the vehicle's environment includes one or more processors configured to receive a communication from another device, wherein the communication includes a global reference coordinate system for an area covered by the other device and a number of allowed transmissions to be sent from the vehicle; transform stored data about the vehicle's environment based on the global reference coordinate system; divide the transformed stored data into a plurality of subsets of data; and select one or more subsets of data from the plurality of subsets for transmission according to the number of allowed transmissions.

Devices and methods for updating maps in autonomous driving systems in bandwidth constrained networks

Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Devices and methods for accurately identifying objects in a vehicle's environment

Vehicle navigation control systems in autonomous driving rely on the accuracy of maps which include features about a vehicle's environment so that a vehicle may safely navigate through its surrounding area. Accordingly, this disclosure provides methods and devices which implement mechanisms for communicating features observed about a vehicle's environment for use in updating maps so as to provide vehicles with accurate and 'real-time' features of its surroundings while taking network resources, such as available frequency-time resources, into consideration.

A safety system for a vehicle may include one or more processors configured to determine uncertainty data indicating uncertainty in one or more predictions from a driving model during operation of a vehicle; change or update one or more of the driving model parameters to one or more changed or updated driving model parameters based on the determined uncertainty data; and provide the one or more changed or updated driving model parameters to a control system of the vehicle for controlling the vehicle to operate in accordance with the driving model including the one or more changed or updated driving model parameters.

Safety system for a vehicle

According to various embodiments, devices, methods, and computer-readable media for reconstructing a 3D scene are described. A server device, sensor devices, and client devices may interoperate to reconstruct a 3D scene sensed by the sensor devices. The server device may generate one or more models for objects in the scene, including the identification of dynamic and/or static objects. The sensor devices may, provide model data updates based on these generated models, such that only delta changes in the scene may be provided, in addition to raw sensor data. Models may utilize semantic knowledge, such as knowledge of the venue or identity of one or more persons in the scene, to further facilitate model generation and updating. Other embodiments may be described and/or claimed.

Growing community of inventors

Hillsboro, OR, United States of America

Ranganath Krishnan