Determining changes in a driving environment based on vehicle behavior

• US 9,633,564 B2
• Filed: 09/27/2012
• Issued: 04/25/2017
• Est. Priority Date: 09/27/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ferguson, David I.
• Primary Examiner(s): Melton, Todd
• Assistant Examiner(s): ROBERSON, JASON R
• Application Number:
  US13/628,905
• Publication Number:
  US 20140088855A1
• Time in Patent Office: 1,671 Days
• CPC Class Codes: G01C 21/32 Structuring or formatting of map data
• CPC Class Codes: G01C 21/3819 Road shape data, e.g. outline of a route
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A method and apparatus are provided for determining whether a driving environment has changed relative to previously stored information about the driving environment. The apparatus may include an autonomous driving computer system configured to detect one or more vehicles in the driving environment, and determine corresponding trajectories for those detected vehicles. The autonomous driving computer system may then compare the determined trajectories to an expected trajectory of a hypothetical vehicle in the driving environment. Based on the comparison, the autonomous driving computer system may determine whether the driving environment has changed and/or a probability that the driving environment has changed, relative to the previously stored information about the driving environment.

Claims

1. An apparatus comprising:

a laser-based sensing system configured to identify information about a first vehicle in a driving environment of a second vehicle;

a computer-readable memory that stores;

detailed map information for the driving environment, the detailed map information comprising information about a road on which the first vehicle travels; and

first state information for the first vehicle, the first state information identifying positions traveled for the first vehicle over time; and

one or more processors of the second vehicle in communication with the computer-readable memory and the sensor, the one or more processors being configured to;

use the detailed map information to control the second vehicle in an autonomous driving mode;

receive sensor information from the laser-based sensing system, the sensor information including information about the first vehicle in the driving environment for a predetermined period of time;

determine the first state information based on the received sensor information;

determine a first trajectory based on the first state information, the first trajectory corresponding to an actual path taken by the first vehicle for the predetermined period of time;

determine an expected trajectory for the first vehicle based on the detailed map information;

compare the determined expected trajectory with the determined first trajectory; and

stop using the detailed map information to control the second vehicle in the autonomous driving mode based on the comparison.

2. The apparatus of claim 1, wherein the one or more processors are further configured to:

determine a deviation metric value by comparing the determined expected trajectory with the determined first trajectory;

wherein the one or more processors are further configured to determine that the driving environment has changed from the detailed map information to the extent that the detailed map information is no longer accurate when the deviation metric value exceeds a deviation metric threshold.

3. The apparatus of claim 2, wherein the determined deviation metric value comprises a maximum deviation metric value representing a maximum difference between the determined first trajectory and the determined expected trajectory.

4. The apparatus of claim 2, wherein each of the determined first trajectory and the determined expected trajectory include a plurality of positions the determined deviation metric value comprises an average signed deviation metric value, the average signed deviation metric value representing a magnitude and direction of a difference between the plurality of positions of the determined first trajectory and the plurality of positions of the determined expected trajectory.

5. The apparatus of claim 1, wherein the determined first trajectory comprises an average trajectory, the average trajectory having been averaged over a predetermined time period.

6. The apparatus of claim 1, wherein the determined expected trajectory is based on a centerline rail of the road indicating the direction in which vehicles travel in a respective lane , the centerline rail being defined in the detailed map information.

7. The apparatus of claim 1, wherein the computer-readable memory further stores:

a probability model that defines a probability that the driving environment has changed relative to the detailed map information based on at least one deviation metric value determined from the comparison of the determined first trajectory with the determined expected trajectory;

a probability function that determines the probability that the driving environment has changed relative to the detailed map information based on the probability model; and

wherein the one or more processors are further configured to;

determine the probability that the driving environment has changed relative to the detailed map information based on the probability function; and

determine that the driving environment has changed from the detailed map information to the extent that the detailed map information is no longer accurate is further based on the determined probability.

8. The apparatus of claim 7, wherein the probability model is one of a plurality of probability models;

and

the one or more processors are further configured to select the probability model from the plurality of probability models based on a first geographic location.

9. The apparatus of claim 1, wherein:

the determined first trajectory comprises a plurality of trajectories, each of the trajectories of the plurality of trajectories corresponding to a vehicle in the driving environment; and

the one or more processors are further configured to consolidate the plurality of trajectories as the determined first trajectory based on at least one consolidation factor.

10. The apparatus of claim 9, wherein the one or more processors are further configured to:

determine a consolidated trajectory quality value for the plurality of trajectories, the consolidated trajectory quality value representing a quality of the determined first trajectory; and

determine the determined probability further based on the determined consolidated trajectory quality value.

11. A method for controlling a first vehicle, the method comprising:

using, by one or more processors, detailed map information to control the first vehicle in an autonomous driving mode;

receiving, with the one or more processors, sensor information including information about a second vehicle in a driving environment of the first vehicle for a predetermined period of time;

determining, with the one or more processors of the first vehicle, first state information based on the received sensor information, the first state information identifying positions traveled for the second vehicle over time;

determining, with the one or more processors of the first vehicle, a first trajectory based on the first state information, the first trajectory corresponding to an actual path taken by the second vehicle for the predetermined period of time;

determining, with the one or more processors of the first vehicle, an expected trajectory for the second vehicle based on detailed map information, the detailed map information comprising information about the driving environment;

comparing the determined expected trajectory with the determined first trajectory; and

stopping, by the one or more processors, using the detailed map information to control the first vehicle in the autonomous driving mode based on the comparison.

12. The method of claim 11, further comprising:

determining, with the one or more processors of the first vehicle, a deviation metric value by comparing the determined expected trajectory with the determined first trajectory; and

determining, with the one or more processors of the first vehicle, that the driving environment has changed from the detailed map information to the extent that the detailed map information is no longer accurate when the deviation metric value exceeds a deviation metric threshold.

13. The method of claim 12, wherein the determined deviation metric value comprises a maximum deviation metric value representing a maximum difference between the determined first trajectory and the determined expected trajectory.

14. The method of claim 12, wherein each of the determined first trajectory and the determined expected trajectory include a plurality of positions and the determined deviation metric value comprises an average signed deviation metric value, the average signed deviation metric value representing a magnitude and direction of a difference between the plurality of positions of the determined first trajectory and the plurality of positions of the determined expected trajectory.

15. The method of claim 11, wherein the determined first trajectory comprises an average trajectory, the average trajectory having been averaged over a predetermined time period.

16. The method of claim 11, wherein the determined expected trajectory is based on a centerline rail of a road indicating the direction in which vehicles travel in a respective lane, the centerline rail being defined in the detailed map information.

17. The method of claim 11, further comprising:

determining, with the one or more processors of the first vehicle, a probability that the driving environment has changed relative to the detailed map information based on a probability function, wherein;

the probability function determines the probability that the driving environment has changed relative to the detailed map information based on a probability model; and

the probability model defines a probability that the driving environment has changed relative to the detailed map information based on at least one deviation metric value determined from the comparison of the determined first trajectory with the determined expected trajectory,

wherein determining that the driving environment has changed from the detailed map information to the extent that the detailed map information is no longer accurate is further based on the determined probability.

18. The method of claim 17, wherein:

the probability model is one of a plurality of probability models; and

the method further comprises;

selecting, with the one or more processors of the first vehicle, the probability model from the plurality of probability models based on a first geographic location.

19. The method of claim 11, wherein:

the determined first trajectory comprises a plurality of trajectories, each of the trajectories of the plurality of trajectories corresponding to a vehicle in the driving environment; and

the method further comprises;

consolidating, with the one or more processors of the first vehicle, the plurality of trajectories as the determined first trajectory based on at least one consolidation factor.

20. The method of claim 19, further comprising:

determining, with the one or more processors of the first vehicle, a consolidated trajectory quality value for the plurality of trajectories, the consolidated trajectory quality value representing a quality of the determined first trajectory; and

determining, with the one or more processors of the first vehicle, the probability that the driving environment has changed relative to the detailed map information based on the determined consolidated trajectory quality value.