# US 12,181,562 B2 · Methods for classifying objects in automotive-grade radar signals

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- Number: US 12,181,562 B2
- Document ID: US12181562B2
- Status: Granted
- Kind: B2
- Application number: 17/684,022
- Filed: 2022-03-01
- Published: 2022-09-08
- Granted: 2024-12-31
- Earliest claimed: 2021-03-02
- Assignees: Indie Semiconductor, Inc.
- Applicants: Indie Semiconductor, Inc.
- Inventors: Tao Yu; Atulya Yellepeddi; Michael Price
- Web page: https://patentgrep.com/patent/US12181562B2

## CPC

- G01S 13/42: Simultaneous measurement of distance and other co-ordinates (indirect measurement G01S13/46)
- G01S 13/583: {using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets}
- G01S 7/35: Details of non-pulse systems
- G01S 7/41: using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S 7/417: {involving the use of neural networks}
- G01S 13/584: {adapted for simultaneous range and velocity measurements}
- G01S 13/931: of land vehicles
- G06N 3/02: Neural networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/0495: Quantised networks; Sparse networks; Compressed networks
- G06N 3/045: Combinations of networks
- G06N 3/08: Learning methods
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method includes an operation to collect radar signals reflected from objects in a field of view. A three-dimensional range-angle-velocity cube is formed from the radar signals. The three-dimensional range-angle-velocity cube includes individual bins with radar intensity values characterizing angle and range for a specific velocity. Point-pillar sub-cubes are selected from the three-dimensional range-angle-velocity cube. Each point-pillar sub-cube includes a predefined range surrounding a high energy peak in the range-angle dimensions and an entire range in a velocity vector. The point-pillar sub-cubes are processed to compress, decompress, detect, classify or track objects in the field of view.

## Claims

### Claim 1

A method, comprising:
collecting radar signals reflected from objects in a field of view;
forming a three-dimensional range-angle-velocity cube from the radar signals, the three-dimensional range-angle-velocity cube including individual bins with radar intensity values characterizing angle and range for a specific velocity;
selecting point-pillar sub-cubes from the three-dimensional range-angle-velocity cube, each point-pillar sub-cube including a region surrounding a high energy peak in range-angle dimensions and an entire range in a velocity vector; and
processing the point-pillar sub-cubes to compress, decompress, detect, classify or track objects in the field of view including:
compressing velocity data in the point-pillar sub-cubes using one or more of a neural network or a pointwise operator before applying a classifier; and
reconstructing uncompressed velocity data in the point-pillar sub-cubes.

### Claim 2 (depends on 1)

The method of claim 1, wherein the region is defined by having values within a specified percentage of the high energy peak.

### Claim 3 (depends on 1)

The method of claim 1, wherein the region is defined by being within a threshold distance of the high energy peak.

### Claim 4 (depends on 1)

The method of claim 1, wherein the compressing the velocity data uses the pointwise operator.

### Claim 5 (depends on 1)

The method of claim 1, wherein the compressing the velocity data uses the neural network.

### Claim 6 (depends on 1)

The method of claim 1, wherein the reconstructing uncompressed velocity data occurs after classification in a processing pipeline.

### Claim 7 (depends on 1)

The method of claim 1 wherein the neural network is used to detect, classify or track objects in the field of view.

### Claim 8 (depends on 1)

The method of claim 1, wherein a compressed representation of the point-pillar sub-cubes are transmitted to another processing facility where classification is performed.

### Claim 9

A radar system, comprising:
a transmitter configured to transmit radar signals;
a receiver configured to receive radar signals reflected from objects in a field of view;
a signal processor configured to:
form a three-dimensional range-angle-velocity cube from the received radar signals, the three-dimensional range-angle-velocity cube including individual bins with radar intensity values characterizing angle and range for a specific velocity;
select point-pillar sub-cubes from the three-dimensional range-angle-velocity cube, each point-pillar sub-cube including a region surrounding a high energy peak in range-angle dimensions and an entire range in a velocity vector; and
process the point-pillar sub-cubes to compress, decompress, detect, classify or track objects in the field of view including:
compressing velocity data in the point-pillar sub-cubes using one or more of a neural network or a pointwise operator before applying a classifier; and
reconstructing uncompressed velocity data in the point-pillar sub-cubes.

### Claim 10 (depends on 9)

The radar system of claim 9, wherein the region is defined by having values within a specified percentage of the high energy peak.

### Claim 11 (depends on 9)

The radar system of claim 9, wherein the region is defined by being within a threshold distance of the high energy peak.

### Claim 12 (depends on 9)

The radar system of claim 9, wherein the compressing the velocity data uses the pointwise operator.

### Claim 13 (depends on 9)

The radar system of claim 9, wherein the compressing the velocity data uses the neural network.

### Claim 14 (depends on 9)

The radar system of claim 9, wherein the signal processor utilizes the neural network to detect, classify or track objects in the field of view.

### Claim 15 (depends on 9)

The radar system of claim 9, wherein the transmitter is configured to transmit frequency modulated continuous wave (FMCW) signals.

### Claim 16 (depends on 9)

The radar system of claim 9, further comprising a duplexer configured to isolate the transmitted and received radar signals.

### Claim 17 (depends on 9)

The radar system of claim 9, wherein the signal processor is configured to trigger transmission of a compressed representation of the point-pillar sub-cubes to another processing facility where classification is performed.