# US 2022/0044359 A1 · Super-resolution radar for autonomous vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044359 A1 - Document ID: US20220044359A1 - Status: Published application - Kind: A1 - Application number: 16/984,972 - Filed: 2020-08-04 - Published: 2022-02-10 - Earliest claimed: 2020-08-04 - Assignees: Metawave Corporation - Applicants: Metawave Corporation - Inventors: Matthew Paul HARRISON - Web page: https://patentgrep.com/patent/US20220044359A1 ## CPC - G01S 7/40: Means for monitoring or calibrating - G06T 3/4061: by injecting details from different spectral ranges - G01S 13/865: {Combination of radar systems with lidar systems} - G01S 13/89: for mapping or imaging - G01S 13/931: of land vehicles - G01S 17/89: for mapping or imaging - G01S 17/931: of land vehicles - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06T 3/4038: Image mosaicing, e.g. composing plane images from plane sub-images - G06T 3/4046: using neural networks - G06T 3/60: Rotation of whole images or parts thereof - G06T 5/50: using two or more images, e.g. averaging or subtraction - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G01S 7/417: {involving the use of neural networks} - G01S 2013/9316: {combined with communication equipment with other vehicles or with base stations} - G01S 2013/9322: {using additional data, e.g. driver condition, road state or weather data} - G06T 2207/10044: Radar image - G06T 2207/20016: Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/20224: Image subtraction - G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## 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. ## Claims ### Claim 1 A method for a radar system, comprising: receiving radar data at a first time having a first resolution; receiving sensor data at the first time having a second resolution different from the first resolution; forming a training dataset from the radar data and the sensor data; and mapping the radar data having the first resolution to radar data having the second resolution using the training dataset. ### Claim 2 (depends on 1) The method of claim 1, wherein the first resolution is a coarse-resolution compared to the second resolution. ### Claim 3 (depends on 2) The method of claim 2, wherein the sensor data is densified data. ### Claim 4 (depends on 3) The method of claim 3, wherein the densified data is lidar data. ### Claim 5 (depends on 3) The method of claim 3, further comprising: densifying the sensor data in the training dataset, wherein the radar data has a dense arrangement and the sensor data has a sparse point cloud arrangement; mapping the densified data onto a three-dimensional perspective representation based on a field of view, the field of view subdivided into regularly spaced voxels of infinite height; applying a random rotation to the training dataset to augment the training dataset; and processing features of coarse-resolution radar data to output data having features substantially corresponding to features of fine-resolution sensor data. ### Claim 6 (depends on 5) The method of claim 5, wherein a value of each regularly spaced voxel corresponds to a difference in height between high and low points in voxel bounds. ### Claim 7 (depends on 6) The method of claim 6, further comprising: training the machine learning network with the training dataset. ### Claim 8 (depends on 7) The method of claim 7, wherein the sensor is a lidar sensor. ### Claim 9 (depends on 8) The method of claim 8, wherein processing features of coarse-resolution radar data comprising providing radar data as input to the machine learning network. ### Claim 10 (depends on 9) The method of claim 9, wherein the machine learning network is a neural network type architecture. ### Claim 11 (depends on 7) The method of claim 7, further comprising: after training on the training data set, the machine learning network receiving radar data at a coarse resolution and mapping to less coarse resolution. ### Claim 12 (depends on 1) The method of claim 1, further comprising: providing the radar having the second resolution to a target detection module. ### Claim 13 A radar system, comprising: signal generation module; radar antennas coupled to the signal generation module; radar processing unit coupled to the radar antennas to receive radar data at a first resolution; a sensor module; and resolution network receiving sensor data from the sensor module at a second resolution different from the first resolution, the resolution network trained on a training dataset from the radar data and the sensor data, the resolution network comprising: a training module; and a mapping element to map the received radar data at the first resolution to mapped radar data at the second resolution; and a target identification module receiving the mapped radar data. ### Claim 14 (depends on 13) The radar system of claim 13, wherein the sensor module is a lidar module. ### Claim 15 (depends on 13) The radar system of claim 13, the resolution network is a machine learning network. ### Claim 16 (depends on 15) The radar system of claim 15, wherein the machine learning network is a neural network type network. ### Claim 17 (depends on 15) The radar system of claim 15, wherein the target identification module comprises: a Doppler module to detect a velocity of a target using the mapped radar data; a range module to detect a range of the target using the mapped radar data; and a decision module to determine a next action based on the range and velocity of the target. ### Claim 18 (depends on 13) The radar system of claim 13, further comprising: an occupancy detection module to identify targets in a field of view. ### Claim 19 The radar system of claim, 13, wherein the sensor data forms a space point cloud of data. ### Claim 20 (depends on 19) The radar system of claim 19, further comprising: an antenna controller adapted to subdivide a field of view of a vehicle; and wherein the resolution network associates each subdivision with a voxel of space.