Calibration for autonomous vehicle operation

Patent metadata

Field	Value
Publication number	US-2017124781-A1
Application number	US-201514756996-A
Country	US
Kind code	A1
Filing date	Nov 4, 2015
Priority date	Nov 4, 2015
Publication date	May 4, 2017
Grant date	—

Abstract

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include receiving data associated with a sensor measurement of a perceived object, determining a label associated with the perceived object based on an initial calibration, retrieving log file data associated with the label, determining a calibration parameter associated with the sensor measurement based on the retrieved log file data, and storing the calibration parameter in association with a sensor associated with the sensor measurement. Sensors may be calibrated on the fly while the autonomous vehicle is in operation using one or more other sensors and/or fused data from multiple types of sensors.

First claim

1 . A method comprising:
receiving data at an autonomous vehicle system comprising a plurality of sensors, the data representing measurements made using the plurality of sensors; identifying an abnormal sensor measurement based at least in part on the data,
wherein a plurality of the measurements, excluding the abnormal sensor measurement, share a common characteristic;
identifying a sensor of the plurality of sensors corresponding to the abnormal sensor measurement; generating an initial calibration parameter, associated with the sensor, based at least in part on a probabilistic model and the abnormal sensor measurement; generating an expected sensor measurement, associated with the sensor, based on the data; and modifying the initial calibration parameter based at least in part on the expected sensor measurement.

2 . The method of claim 1 , wherein the plurality of sensors includes an array of LIDAR sensors arranged at one end of the autonomous vehicle system, and wherein the abnormal sensor measurement comprises a measurement associated with one LIDAR sensor of the array of LIDAR sensors.

3 . The method of claim 2 , further comprising:
determining an extrinsic calibration of the one LIDAR sensor based on a physical location of the one LIDAR sensor relative to the autonomous vehicle system.

4 . The method of claim 2 , further comprising:
determining an intrinsic calibration of the one LIDAR sensor based on data associated with at least one additional LIDAR sensor of the array disposed adjacent to the one LIDAR sensor.

5 . The method of claim 1 , further comprising:
retrieving one or more log files, including information indicative of past sensor measurements, based on the abnormal sensor measurement; and determining the expected sensor measurement based, in part, on the information indicative of past sensor measurements.

6 . The method of claim 1 , wherein the plurality of sensors includes at least one RADAR sensor and a multi-beam LIDAR sensor, the method further comprising:
determining a current physical location of the multi-beam LIDAR sensor, relative to the autonomous vehicle system, based at least in part on the at least one RADAR sensor; comparing the current physical location to a stored indication of an initial position of the multi-beam LIDAR sensor; and calibrating the multi-beam LIDAR sensor based on the comparing.

7 . The method of claim 1 , wherein the plurality of sensors includes an inertial measurement unit (IMU) sensor, an odometry sensor, and a multi-beam LIDAR sensor, the method further comprising:
determining a distance traveled by the autonomous vehicle system based on one or more sensor measurements of the odometry sensor; determining a velocity of the autonomous vehicle system based on one or more sensor measurements of the IMU sensor; and calibrating the multi-beam LIDAR sensor based at least in part on the distance traveled by the autonomous vehicle system and the velocity of the autonomous vehicle system.

8 . The method of claim 1 , wherein the expected sensor measurement is further based at least in part on a teleoperation interface selection made via a teleoperator system.

9 . The method of claim 1 , wherein the expected sensor measurement is further based at least in part on a map tile associated with a pose of the autonomous vehicle system at a time of the abnormal sensor measurement.

10 . The method of claim 1 , wherein the probabilistic model comprises a first probabilistic model, the method further comprising:
generating a second probabilistic model; and generating an additional expected sensor measurement, associated with the sensor, using the second probabilistic model, wherein the additional expected sensor measurement is generated based on a measurement made using at least one additional sensor of the plurality of sensors.

11 . A method comprising:
receiving, at an autonomous vehicle system comprising a plurality of sensors, a plurality of sensor measurements associated with an environmental feature; identifying an abnormal sensor measurement of the plurality of sensor measurements, wherein the abnormal sensor measurement is incongruent with one or more other measurements of the plurality of sensor measurements; identifying a sensor of the plurality of sensors corresponding to the abnormal sensor measurement; determining a plurality of calibration parameters associated with the sensor, wherein at least one parameter of the plurality of calibration parameters is determined based on a statistical computation associated with the environmental feature; determining an expectation model associated with the sensor, wherein the expectation model is configured to output one or more expected sensor measurements; and modifying the plurality of calibration parameters based on an output of the expectation model.

12 . The method of claim 11 , further comprising:
comparing the plurality of sensor measurements to a predetermined sensor measurement range; and identifying the abnormal sensor measurement based at least in part on the comparing.

13 . The method of claim 11 , wherein the abnormal sensor measurement is incongruent with the one or more other measurements of the plurality of sensor measurements based on the abnormal sensor measurement not matching the one or more other measurements of the plurality of sensor measurements.

14 . The method of claim 11 , wherein the statistical computation associated with the environmental feature comprises an average of the plurality of sensor measurements associated with the environmental feature.

15 . The method of claim 11 , wherein the statistical computation associated with the environmental feature comprises a median of the plurality of sensor measurements associated with the environmental feature.

16 . The method of claim 11 , wherein modifying the plurality of calibration parameters based on the expectation model comprises:
determining a heuristic rule to identify a subset of calibration parameters based on the expectation model and the plurality of calibration parameters; identifying the subset of calibration parameters using the heuristic rule; and replacing the plurality of calibration parameters with the subset of calibration parameters.

17 . A method comprising:
receiving, at an autonomous vehicle system comprising a plurality of sensors, data associated with a measurement made by a sensor of the plurality of sensors, wherein the measurement comprises detection of an object by the sensor; determining a labeled data point associated with the perceived object, wherein the labeled data point is determined based at least in part on an initial calibration of the plurality of sensors; retrieving log file data associated with the labeled data point; determining a calibration parameter associated with the sensor based at least in part on the retrieved log file data; and associating the calibration parameter with information, identifying the sensor, in a memory associated with the autonomous vehicle system.

18 . The method of claim 17 , wherein:
the calibration parameter is determined based at least in part on at least one generative probabilistic model; the log file data comprises one or more contradictory sensor measurements from one or more sensors, the measurement is made by the sensor at a particular time, and the one or more contradictory sensor measurements are made substantially simultaneously with the measurement of the perceived object.

19 . The method of claim 17 , wherein:
t

Assignees

• Zoox Inc

Inventors

• Douillard Bertrand Robert
• Levinson Jesse Sol
• Sibley Gabriel Thurston

Classifications

• B60Q1/28

  for indicating front of vehicle · CPC title

• G01S13/865

  Combination of radar systems with lidar systems · CPC title

• G08G1/005

  including pedestrian guidance indicator · CPC title

• G08G1/056

  with provision for distinguishing direction of travel · CPC title

• G01S17/875

  for determining attitude · CPC title