John Lord Kirkwood

US Patent:

20220335279, Oct 20, 2022

Filed:

Apr 14, 2021

Appl. No.:

17/230877

Inventors:

- St. Michael, BB
Yihang Zhang - Calabasas CA, US
John Kirkwood - Playa del Rey CA, US
Shan Zhang - Thousand Oaks CA, US
Sanling Song - Northport AL, US
Narbik Manukian - Los Angeles CA, US

International Classification:

G06N 3/063
G06N 20/00
G06N 3/08
G01S 13/931

Abstract:

This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

US Patent:

20210293927, Sep 23, 2021

Filed:

Mar 20, 2020

Appl. No.:

16/825186

Inventors:

- St. Michael, BB
John Kirkwood - Playa del Rey CA, US

International Classification:

G01S 7/41
G01S 13/931
G01S 13/72

Abstract:

Techniques and apparatuses are described that implement object classification using low-level radar data. In particular, a radar system extracts features of a detected object based on low-level data. The radar system analyzes these features using machine learning to determine an object class associated with the detected object. By relying on low-level data, the radar system is able to extract additional information regarding the distribution of energy across range, range rate, azimuth, or elevation, which is not available in detection-level data. With the use of machine learning, the object can be classified quickly (e.g., within a single frame or observation), thereby enabling sufficient time for the autonomous-driving logic to initiate an appropriate action based on the object's class. Furthermore, this classification can be performed without the use of information from other sensors.