IP Library › Granted Patent US 12,566,248
Granted Patent B2
US 12,566,248 · App. 18/458,393 · Granted Mar 3, 2026

Vehicular radar system with overlapping fields of sensing and azimuth misalignment correction

Inventors: Samuel Cerqueira Pinto (Medfield, MA); Ashesh Goswami (Somerville, MA)
Assignee: Magna Electronics Inc.
G01S7/4004G01S7/023G01S13/931
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,566,248
App. No.
18/458,393
Granted
Mar 3, 2026
Kind
B2
Abstract

A method for calibrating a vehicular radar sensing system includes providing a plurality of radar sensors at a vehicle that includes a first radar sensor and a second radar sensor, the first radar sensor having a field of sensing that at least partially overlaps a field of sensing of the second radar sensor. An object in the field of sensing of the first radar sensor is detected via processing sensor data captured by the first radar sensor. The object in the field of sensing of the second radar sensor is detected via processing sensor data captured by the second radar sensor. Based on detection of the object with the first radar sensor and with the second radar sensor, a detection error is determined. Calibration of the second radar sensor is adjusted based on the detection error.

Claims (42)

1 . A method for calibrating a vehicular radar sensing system, the method comprising:

providing at least two radar sensors at a vehicle, wherein the at least two radar sensors comprise at least a first radar sensor and a second radar sensor, and wherein a field of sensing of the first radar sensor at least partially overlaps a field of sensing of the second radar sensor;

processing, at a data processor, sensor data captured by the first radar sensor and sensor data captured by the second radar sensor;

detecting, via processing at the data processor of sensor data captured by the first radar sensor, an object in the field of sensing of the first radar sensor;

detecting, via processing at the data processor of sensor data captured by the second radar sensor, the object in the field of sensing of the second radar sensor;

determining, based on detection of the object via processing at the data processor of sensor data captured by the first radar sensor and via processing at the data processor of sensor data captured by the second radar sensor, a detection error of the second radar sensor relative to the first radar sensor, wherein determining the detection error comprises determining that a Euclidean distance between a first position of the detected object based on sensor data captured by the first radar sensor and a second position of the detected object based on sensor data captured by the second radar sensor is less than a threshold distance; and

adjusting calibration of the second radar sensor based on the detection error.

2 . The method of claim 1 , wherein the at least two radar sensors comprise a third radar sensor, and wherein the field of sensing of the third radar sensor at least partially overlaps the field of sensing of the second radar sensor, and wherein the method further comprises:

detecting, via processing at the data processor of sensor data captured by the second radar sensor, a second object in the field of sensing of the second radar sensor;

detecting, via processing at the data processor of sensor data captured by the third radar sensor, the second object in the field of sensing of the third radar sensor;

determining, based on detection of the second object via processing at the data processor of the sensor data captured by the second radar sensor and via processing at the data processor of the sensor data captured by the third radar sensor, a second detection error of the third radar sensor relative to the second radar sensor, and wherein determining the second detection error comprises determining that a Euclidean distance between a first position of the second detected object based on sensor data captured by the second radar sensor and a second position of the second detected object based on sensor data captured by the third radar sensor is less than the threshold distance; and

adjusting calibration of the third radar sensor based on the second detection error.

3 . The method of claim 1 , wherein determining the detection error further comprises determining a distance between the first position of the detected object determined via processing at the data processor of the sensor data captured by the first radar sensor and the second position of the detected object determined via processing at the data processor of the sensor data captured by the second radar sensor.

4 . The method of claim 1 , wherein determining the detection error comprises optimizing a cost function.

5 . The method of claim 1 , wherein the at least two radar sensors comprises at least a third radar sensor and a fourth radar sensor, and wherein the third radar sensor and the fourth radar sensor are disposed at respective corners of the vehicle.

6 . The method of claim 1 , wherein the at least two radar sensors comprise six radar sensors.

7 . The method of claim 1 , wherein determining the detection error of the second radar sensor relative to the first radar sensor occurs during operation of the vehicle by a driver.

8 . The method of claim 1 , wherein the detection error comprises an azimuth misalignment error.

9 . A method for calibrating a vehicular radar sensing system, the method comprising:

providing a radar sensor at a vehicle, the radar sensor having a field of sensing exterior of the vehicle;

detecting, via processing at a data processor of sensor data captured by the radar sensor, an object in the field of sensing of the radar sensor;

determining that the detected object has a velocity of zero relative to the ground;

determining a velocity of the radar sensor relative to the ground;

determining, using the velocity of the radar sensor relative to the ground, an expected Doppler value of the detected object;

determining, via processing at the data processor of sensor data captured by the radar sensor, an actual Doppler value of the detected object;

determining, based on a comparison of the expected Doppler value of the object and the actual Doppler value of the detected object, a misalignment value; and

adjusting calibration of the radar sensor based on the determined misalignment value.

10 . The method of claim 9 , wherein detecting the object is responsive to determining that the vehicle is traveling at a speed greater than a threshold speed.

11 . The method of claim 9 , wherein the method further comprises determining a Doppler error value based on the comparison of the expected Doppler value of the object and the actual Doppler value of the detected object.

12 . The method of claim 11 , wherein determining the misalignment value comprises determining, for each misalignment value of a plurality of misalignment values, a respective relative Doppler error.

13 . The method of claim 12 , wherein the determined misalignment value minimizes the Doppler error value.

14 . The method of claim 9 , further comprising providing a plurality of radar sensors at the vehicle and adjusting calibration of each radar sensor of the plurality of radar sensors based on the respective determined misalignment value.

15 . The method of claim 9 , wherein adjusting the calibration of the radar sensor comprises adjusting an azimuth calibration of the radar sensor.

16 . A method for calibrating a vehicular radar sensing system, the method comprising:

providing at least two radar sensors at a vehicle;

wherein the at least two radar sensors comprise at least a first radar sensor and a second radar sensor, and wherein a field of sensing of the first radar sensor at least partially overlaps a field of sensing of the second radar sensor;

processing, at a data processor, sensor data captured by the first radar sensor and sensor data captured by the second radar sensor;

detecting, via processing at the data processor of sensor data captured by the first radar sensor and of sensor data captured by the second radar sensor, an object in an overlapping region of the field of sensing of the first radar sensor and the field of sensing of the second radar sensor;

determining, based on detection of the object via processing at the data processor of sensor data captured by the first radar sensor and via processing at the data processor of sensor data captured by the second radar sensor, a detection error of the second radar sensor relative to the first radar sensor, and wherein the detection error is determined based on a distance between a first position of the detected object determined via processing at the data processor of the sensor data captured by the first radar sensor and a second position of the detected object determined via processing at the data processor of the sensor data captured by the second radar sensor, and wherein determining the detection error comprises determining that a Euclidean distance between the first position of the detected object based on sensor data captured by the first radar sensor and the second position of the detected object based on sensor data captured by the second radar sensor is less than a threshold distance; and

adjusting azimuth calibration of the second radar sensor based on the detection error.

17 . The method of claim 16 , wherein determining the detection error comprises optimizing a cost function.

18 . The method of claim 16 , wherein the at least two radar sensors comprise at least a third radar sensor and a fourth radar sensor, and wherein of the third radar sensor and the fourth radar sensor are disposed at respective corners of the vehicle.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 30, 2023
From: CERQUEIRA PINTO, SAMUEL; GOSWAMI, ASHESH
To: MAGNA ELECTRONICS INC.
Reel/Frame 064752/0908 →
Continuity (2)
Provisional Application 63374039 · Aug 31, 2022
Related Publication 20240077582A1 · Mar 7, 2024
References Cited (53)
US 6587186B2 · Bamji et al. · 2003 [cited by applicant]
US 6674895B2 · Rafii et al. · 2004 [cited by applicant]
US 6678039B2 · Charbon · 2004 [cited by applicant]
US 6690354B2 · Sze · 2004 [cited by applicant]
US 6710770B2 · Tomasi et al. · 2004 [cited by applicant]
US 6876775B2 · Torunoglu · 2005 [cited by applicant]
US 6906793B2 · Bamji et al. · 2005 [cited by applicant]
US 6919549B2 · Bamji et al. · 2005 [cited by applicant]
US 7053357B2 · Schwarte · 2006 [cited by applicant]
US 7157685B2 · Bamji et al. · 2007 [cited by applicant]
US 7176438B2 · Bamji et al. · 2007 [cited by applicant]
US 7203356B2 · Gokturk et al. · 2007 [cited by applicant]
US 7212663B2 · Tomasi · 2007 [cited by applicant]
US 7283213B2 · O'Connor et al. · 2007 [cited by applicant]
US 7304602B2 · Shinagawa · 2007 [cited by examiner]
US 7310431B2 · Gokturk et al. · 2007 [cited by applicant]
US 7321111B2 · Bamji et al. · 2008 [cited by applicant]
US 7340077B2 · Gokturk et al. · 2008 [cited by applicant]
US 7352454B2 · Bamji et al. · 2008 [cited by applicant]
US 7375803B1 · Bamji · 2008 [cited by applicant]
US 7379100B2 · Gokturk et al. · 2008 [cited by applicant]
US 7379163B2 · Rafii et al. · 2008 [cited by applicant]
US 7405812B1 · Bamji · 2008 [cited by applicant]
US 7408627B2 · Bamji et al. · 2008 [cited by applicant]
US 8013780B2 · Lynam · 2011 [cited by applicant]
US 8027029B2 · Lu et al. · 2011 [cited by applicant]
US 9036026B2 · Dellantoni et al. · 2015 [cited by applicant]
US 9126525B2 · Lynam et al. · 2015 [cited by applicant]
US 9146898B2 · Ihlenburg et al. · 2015 [cited by applicant]
US 9500742B2 · Poiger · 2016 [cited by examiner]
US 9575160B1 · Davis et al. · 2017 [cited by applicant]
US 9599702B1 · Bordes et al. · 2017 [cited by applicant]
US 9689967B1 · Stark et al. · 2017 [cited by applicant]
US 9753121B1 · Davis et al. · 2017 [cited by applicant]
US 9869762B1 · Alland et al. · 2018 [cited by applicant]
US 9954955B2 · Davis et al. · 2018 [cited by applicant]
US 10239446B2 · May et al. · 2019 [cited by applicant]
US 10534081B2 · Wodrich · 2020 [cited by applicant]
US 10866306B2 · Maher et al. · 2020 [cited by applicant]
US 20060132357A1 · Pozgay · 2006 [cited by examiner]
US 20060220951A1 · Thome · 2006 [cited by examiner]
US 20100245066A1 · Sarioglu et al. · 2010 [cited by applicant]
US 20160187466A1 · Kim · 2016 [cited by examiner]
US 20170222311A1 · Hess et al. · 2017 [cited by applicant]
US 20170254873A1 · Koravadi · 2017 [cited by applicant]
US 20170276788A1 · Wodrich · 2017 [cited by applicant]
US 20170315231A1 · Wodrich · 2017 [cited by applicant]
US 20170356994A1 · Wodrich et al. · 2017 [cited by applicant]
US 20180015875A1 · May et al. · 2018 [cited by applicant]
US 20180045812A1 · Hess · 2018 [cited by applicant]
US 20180231635A1 · Woehlte · 2018 [cited by applicant]
US 20190187250A1 · Ru · 2019 [cited by examiner]
US 20190339382A1 · Hess et al. · 2019 [cited by applicant]