DETERMINING CHANGES IN A DRIVING ENVIRONMENT BASED ON VEHICLE BEHAVIOR

• US 20140088855A1
• Filed: 09/27/2012
• Published: 03/27/2014
• 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.
• Granted Patent: US 9,633,564 B2
• Time in Patent Office: Days
• US Class Current: 701/117
• 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 sensor configured to detect a first vehicle in a driving environment;

a computer-readable memory that stores;

detailed map information for a 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 at least one of position, speed, or direction of travel for the first vehicle; and

a processor in communication with the computer-readable memory and the sensor, the processor being configured to;

receive sensor information from the sensor, the sensor information based on having detected the first vehicle in the driving environment;

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

determine a first trajectory based on the first state information;

determine an expected trajectory based on the detailed map information; and

determine that the driving environment has changed by comparing the determined expected trajectory with the determined first trajectory.

2. The apparatus of claim 1, wherein the processor is further configured to:

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

wherein the processor determines that the driving environment has changed 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 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 determined first trajectory and 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 of the road corresponding to 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 processor is further configured to;

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

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

and

the processor is 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 processor is 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 processor is 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

further determine the probability that the driving environment has changed relative to the detailed map information based on the determined consolidated trajectory quality value.

11. A method comprising:

detecting, with a sensor of an autonomous vehicle, a first vehicle in a driving environment;

receiving, with a processor in communication with the sensor, sensor information based on having detected the first vehicle in the driving environment;

determining, with the processor, the first state information based on the received sensor information, the first state information identifying at least one of position, speed, or direction of travel for the first vehicle;

determining, with the processor, a first trajectory based on the first state information;

determining, with the processor, an expected trajectory based on detailed map information, the detailed map information comprising information about the driving environment in which the first vehicle travels; and

determining, with the processor, that the driving environment has changed by comparing the determined expected trajectory with the determined first trajectory.

12. The method of claim 11, further comprising:

determining, with the processor, a deviation metric value by comparing the determined expected trajectory with the determined first trajectory; and

determining, with the processor, that the driving environment has changed 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 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 determined first trajectory and 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 of the road corresponding to the detailed map information.

17. The method of claim 11, further comprising:

determining, with the processor, 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.

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 processor, 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 processor, 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 processor, 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 processor, the probability that the driving environment has changed relative to the detailed map information based on the determined consolidated trajectory quality value.