# US 12,600,373 B2 · Positioning application for ADAS calibration target

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- Number: US 12,600,373 B2
- Document ID: US12600373B2
- Status: Granted
- Kind: B2
- Application number: 18/328,511
- Filed: 2023-06-02
- Published: 2024-10-17
- Granted: 2026-04-14
- Earliest claimed: 2023-04-13
- Assignees: Atieva, Inc.
- Applicants: Atieva, Inc.
- Inventors: Bobby James Marvin Sturgeon; Nikhil Arvind Bagul
- Web page: https://patentgrep.com/patent/US12600373B2

## CPC

- B60W 50/14: Means for informing the driver, warning the driver or prompting a driver intervention
- G01S 7/497: Means for monitoring or calibrating
- G01S 7/4972: {Alignment of sensor}
- G01S 17/86: Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
- G01S 17/89: for mapping or imaging
- G01S 17/894: Three-dimensional [3D] imaging with simultaneous measurement of time-of-flight at a two-dimensional [2D] array of receiver pixels, e.g. time-of-flight cameras or flash lidar
- G01S 17/931: of land vehicles
- G06T 5/50: using two or more images, e.g. averaging or subtraction
- G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51)
- G06T 7/80: Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
- G06T 17/00: Three-dimensional [3D] modelling for computer graphics
- G01S 13/862: {Combination of radar systems with sonar systems}
- G01S 13/865: {Combination of radar systems with lidar systems}
- G01S 13/867: {Combination of radar systems with cameras}
- G01S 13/931: of land vehicles
- G01S 15/931: of land vehicles
- G01S 2013/9323: {Alternative operation using light waves}
- G01S 2013/9324: {Alternative operation using ultrasonic waves}
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/20221: Image fusion; Image merging
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- 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

## Abstract

A method of calibrating an advanced driver assistance system (ADAS) for a vehicle comprises: receiving first information by a mobile device that has a camera and a light detection and ranging (LiDAR) device, the first information comprising a three-dimensional (3D) specification of physical dimensions for the vehicle having the ADAS; scanning, by the mobile device, the vehicle and a physical target for the ADAS, the scanning performed with the camera and the LiDAR device after receiving the first information, the scanning generating frame data; tracking, by the mobile device, the vehicle in the frame data using the first information; generating, by the mobile device, an output indicating whether the physical target is positioned at a target location for the ADAS; and initiating, after the output is generated, a calibration process of the ADAS that uses the physical target.

## Claims

### Claim 1

A method of calibrating an advanced driver assistance system (ADAS) for a vehicle, the method comprising:
receiving first information by a mobile device that has a camera and a light detection and ranging (LiDAR) device, the first information comprising a three-dimensional (3D) specification of physical dimensions for the vehicle having the ADAS;
scanning, by the mobile device, the vehicle and a physical target for the ADAS, the scanning performed with the camera and the LiDAR device after receiving the first information, the scanning generating frame data;
tracking, by the mobile device, the vehicle in the frame data using the first information;
generating, by the mobile device, an output indicating whether the physical target is positioned at a target location for the ADAS; and
initiating, after the output is generated, a calibration process of the ADAS that uses the physical target.

### Claim 2 (depends on 1)

The method of claim 1, wherein generating the output comprises presenting a camera feed on a display of the mobile device, and adding a virtual target in the camera feed, the virtual target positioned according to a calibration specification for the ADAS.

### Claim 3 (depends on 2)

The method of claim 2, wherein multiple virtual targets are specified by the calibration specification, the method further comprising selecting the virtual target from among the multiple virtual targets for adding the virtual target to the camera feed.

### Claim 4 (depends on 2)

The method of claim 2, further comprising rendering a virtual overlay on the vehicle in the camera feed, the virtual overlay positioned based on tracking the vehicle in the frame data.

### Claim 5 (depends on 2)

The method of claim 2, further comprising:
receiving, by the mobile device, second information comprising a 3D specification for the physical target; and
tracking, by the mobile device, also the physical target in the frame data, the physical target tracked using the second information, wherein the output indicates whether the physical target is positioned at the target location for the ADAS based on the tracking of the physical target.

### Claim 6 (depends on 5)

The method of claim 5, further comprising:
performing a determination, by the mobile device and based on the tracking of the physical target, that a position of the physical target substantially coincides with the target location for the ADAS, wherein the output is generated based on the determination.

### Claim 7 (depends on 2)

The method of claim 2, wherein the mobile device is a handheld device or a wearable device.

### Claim 8 (depends on 7)

The method of claim 7, wherein the method is a one-person process performed on the mobile device, and wherein a person carries the mobile device and observes, on the display, (i) the camera feed including the physical target and (ii) the virtual target while maneuvering the physical target.

### Claim 9 (depends on 1)

The method of claim 1, wherein the first information includes a computer aided design model of the vehicle.

### Claim 10 (depends on 5)

The method of claim 5, wherein the second information includes a computer aided design model of the physical target.

### Claim 11 (depends on 5)

The method of claim 5, wherein performing the tracking of the physical target comprises tracking a three-by-three pattern of alternating dark and light areas having a same size included in the physical target.

### Claim 12 (depends on 5)

The method of claim 5, wherein performing the tracking of the vehicle and the tracking of the physical target comprises performing sequential tracking.

### Claim 13 (depends on 5)

The method of claim 5, wherein performing the sequential tracking comprises running another thread in an object tracker.

### Claim 14 (depends on 1)

The method of claim 1, wherein performing the scanning comprises generating camera frame data using the camera and generating a depth map using the LiDAR, the method further comprising performing a conversion using the camera frame data and the depth map to generate the frame data.

### Claim 15 (depends on 14)

The method of claim 14, wherein performing the conversion comprises performing at least one of a pixel format conversion or a flattening.

### Claim 16 (depends on 15)

The method of claim 15, wherein performing the conversion comprises combining color data of the camera frame data with the depth data.

### Claim 17 (depends on 1)

The method of claim 1, wherein receiving the first information comprises obtaining the first information from a trained object database.

### Claim 18 (depends on 1)

The method of claim 1, wherein performing the tracking of the vehicle in the frame data comprises using an object recognition library.

### Claim 19 (depends on 1)

The method of claim 1, wherein performing the tracking of the vehicle in the frame data comprises comparing the frame data with an object definition in the first information.