Abstract: Examples disclosed herein relate to a sensor fusion scanning system for wireless network planning.

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: Systems, methods, and apparatus for hybrid radar and camera edge sensors are disclosed. Examples disclosed herein relate to a hybrid sensor system having a plurality of edge sensors positioned on the perimeter of a vehicle. The hybrid sensor system processes signals received from the various sensors to identify a target located within the vicinity of the vehicle.

Abstract: A perception engine incorporating place information for a vehicular sensor. The system uses place information to trigger responses in a perception engine. In some examples the system implements a mirroring process in response to other vehicle actions.

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 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 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 ego 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 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 an ego vehicle. The autonomous driving system includes a radar system configured 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 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 ego 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 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.