Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can identify a network request as being deferrable and embed data of the deferrable network request within a header of a suitable carrier network request for sending to a remote server. Embodiments of the present invention can receive a carrier network request with embedded data of a deferrable network request within a header of the carrier network request. Embodiments of the present invention can then parse the embedded data from the header into a separate request message for actioning at the server.

Abstract: Examples disclosed herein relate to a node in a fixed wireless network. A controller determines optimal paths between nodes through relational calculations. Phase shifts are made to signals generated from one node to another according to the optimal path direction.

Abstract: Examples disclosed herein relate to generating continuous visualizations of beam steering vehicle radar scans by acquiring data for a beam steering radar scan, generating a Range Doppler Map (“RDM”) corresponding to the acquired radar data, displaying a visualization of the RDM showing a plurality of identified objects, shifting each identified object by its velocity to generate a shifted RDM, and updating the visualization at a display rate that is higher than a radar scan rate to display continuous movement. The display may be part of an augmented reality system presented to a driver on a windshield or dashboard.

Abstract: Examples disclosed herein relate to a multi-sensor fusion platform for use in autonomous vehicles, the multi-sensor fusion platform including a camera perception engine having a camera neural network to detect and identify objects in camera data, a lidar perception engine having a lidar neural network to detect and identify objects in lidar data, and a radar perception engine having a radar neural network to detect and identify objects in radar data, such that training of the radar neural network is bootstrapped with the camera and lidar neural networks.

Abstract: Examples disclosed herein relate to a sensor fusion scanning system for wireless network planning. The system includes a sensor scanning mobile platform comprising a beam steering radar sensor and one or more auxiliary sensors, the sensor scanning mobile platform configured to scan a wireless environment, a reflectivity engine configured to generate a reflectivity representation of the wireless environment based on radar data from the beam steering radar sensor, a sensor fusion processing engine configured to generate a Three-Dimensional (“3D”) representation of the wireless environment based on the radar data and sensor data from the one or more auxiliary sensors, and a reflectarray planning engine configured to design a plurality of reflectarrays and determine locations for the plurality of reflectarrays in the wireless environment based on the reflectivity representation and the 3D representation.

Abstract: Examples disclosed herein relate to a radar system in an autonomous vehicle for object detection and classification. The radar system has a radar module having a dynamically controllable beam steering antenna and a perception module. The perception module includes a machine learning module trained on a first set of data and retrained on a second set of data to generate a set of object locations and classifications, and a classifier to use velocity information combined with the set of object locations and classifications to output a set of classified data.

Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in a vehicle, including a radar system with a reinforcement learning engine to control a beam steering antenna and identity targets in a path and a surrounding environment of the vehicle, and a sensor fusion module to receive information from the radar system on the identified targets and compare the information received from the radar system to information received from at least one sensor in the vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: Examples disclosed herein relate to a radar system in an autonomous vehicle for object detection and classification. The radar system has a radar module having a dynamically controllable beam steering antenna and a perception module. The perception module includes a machine learning module trained on a first set of data and retrained on a second set of data to generate a set of object locations and classifications, and a classifier to use velocity information combined with the set of object locations and classifications to output a set of classified data.

Abstract: Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system configured to detect and identify a target in a path and a surrounding environment of the ego vehicle. The autonomous driving system also includes a sensor fusion module configured to receive radar data on the identified target from the radar system and compare the identified target with one or more targets identified by a plurality of perception sensors that are geographically disparate from the radar system. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Abstract: Examples disclosed herein relate to a scanning system for determining enhanced placement of an antenna within an environment. The scanning system includes a sensor system configured to emit an optical signal pulse to a surrounding environment of the scanning system and receive one or more returning optical signal pulses reflected from one or more reflective objects in the surrounding environment. The sensor system obtains a plurality of sensor data slices along a first direction from the one or more returning optical signal pulses. Each of the sensor data slices corresponds to a different position of the scanning system along a second direction orthogonal to the first direction. The scanning system also includes a perception module communicably coupled to the sensor system and configured to generate mapping information of the identified one or more reflective objects in the scene with one or more trained neural networks in the perception module.

Abstract: Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system configured to detect and identify a target in a path and a surrounding environment of the ego vehicle. The autonomous driving system also includes a sensor fusion module configured to receive radar data on the identified target from the radar system and compare the identified target with one or more targets identified by a plurality of perception sensors that are geographically disparate from the radar system. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in an ego vehicle. The autonomous driving system includes a radar system to detect a target in a path and a surrounding environment of the ego vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a recurrent super-resolution network having recurrent encoder layers to receive the radar data with the first resolution and produce radar data with a second resolution using first neural networks. The recurrent encoder layers perform recurrence operations prior to a max pooling operation. The radar data with the second resolution may be produced from at least an output of the recurrent encoder layers. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the ego vehicle.

Abstract: Examples disclosed herein relate to an autonomous driving system in a vehicle, including a radar system with a reinforcement learning engine to control a beam steering antenna and identity targets in a path and a surrounding environment of the vehicle, and a sensor fusion module to receive information from the radar system on the identified targets and compare the information received from the radar system to information received from at least one sensor in the vehicle.

Abstract: Examples disclosed herein relate to an autoencoder assisted radar for target identification. The radar includes an Intelligent Metamaterial (“iMTM”) antenna module to radiate a transmission signal with an iMTM antenna structure and generate radar data capturing a surrounding environment, a data pre-processing module having an autoencoder to encode the radar data into an information-dense representation, and an iMTM perception module to detect and identify a target in the surrounding environment based on the information-dense representation and to control the iMTM antenna module. An autoencoder for assisting a radar system and a method for identifying a target with an autoencoder assisted radar in a surrounding environment are also disclosed herein.

Abstract: Examples disclosed herein relate to an autonomous driving system in an vehicle. The autonomous driving system includes a radar system configured to detect a target in a path and a surrounding environment of the vehicle and produce radar data with a first resolution that is gathered over a continuous field of view on the detected target. The system includes a super-resolution network configured to receive the radar data with the first resolution and produce radar data with a second resolution different from the first resolution using first neural networks. The system also includes a target identification module configured to receive the radar data with the second resolution and to identify the detected target from the radar data with the second resolution using second neural networks. Other examples disclosed herein include a method of operating the radar system in the autonomous driving system of the vehicle.

Abstract: Examples disclosed herein relate to a method for semi-supervised training of a radar system. The method includes training a first radar network of the radar system with a first set of radar object detection labels corresponding to a first set of radar data, training a generative adversarial network (GAN) with the trained first radar network, synthesizing a training data set for a second radar network of the radar system with the trained GAN, training a second radar network with the synthesized training data set, and generating a second set of radar object detection labels based on the training of the second radar network.

Abstract: Examples disclosed herein relate to generating continuous visualizations of beam steering vehicle radar scans by acquiring data for a beam steering radar scan, generating a Range Doppler Map (“RDM”) corresponding to the acquired radar data, displaying a visualization of the RDM showing a plurality of identified objects, shifting each identified object by its velocity to generate a shifted RDM, and updating the visualization at a display rate that is higher than a radar scan rate to display continuous movement. The display may be part of an augmented reality system presented to a driver on a windshield or dashboard.