MODIFYING BEHAVIOR OF AUTONOMOUS VEHICLES BASED ON SENSOR BLIND SPOTS AND LIMITATIONS

• US 20140214255A1
• Filed: 01/25/2013
• Published: 07/31/2014
• Est. Priority Date: 01/25/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Dolgov, Dmitri A., Urmson, Christopher Paul
• Granted Patent: US 9,367,065 B2
• Time in Patent Office: Days
• US Class Current: 701/23
• CPC Class Codes: B60W 2050/0095 Automatic control mode change
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 30/18154 Approaching an intersection
• CPC Class Codes: G05D 1/0248 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058

Abstract

Aspects of the present disclosure relate generally to modeling a vehicle'"'"'s view of its environment. This view need not include what objects or features the vehicle is actually seeing, but rather those areas that the vehicle is able to observe using its sensors if the sensors were completely un-occluded. For example, for each of a plurality of sensors of the object detection component, a computer may an individual 3D model of that sensor'"'"'s field of view. Weather information is received and used to adjust one or more of the models. After this adjusting, the models may be aggregated into a comprehensive 3D model. The comprehensive model may be combined with detailed map information indicating the probability of detecting objects at different locations. A model of the vehicle'"'"'s environment may be computed based on the combined comprehensive 3D model and detailed map information and may be used to maneuver the vehicle.

Claims

1. A method comprising:

generating, for each given sensor of a plurality of sensors for detecting objects in a vehicle'"'"'s environment, a 3D model of the given sensor'"'"'s field of view;

receiving weather information, the weather information including one or more of reports, radar information, forecasts and real-time measurements concerning actual or expected weather conditions in the vehicle'"'"'s environment;

adjusting one or more characteristics of the plurality of 3D models based on the received weather information to account for an impact of the actual or expected weather conditions on one or more of the plurality of sensors;

after the adjusting, aggregating, by a processor, the plurality of 3D models to generate a comprehensive 3D model;

combining the comprehensive 3D model with detailed map information; and

using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.

2. The method of claim 1, wherein the 3D model of each given sensor'"'"'s field of view is based on a pre-determined model of the given sensor'"'"'s unobstructed field of view.

3. The method of claim 1, wherein the 3D model for each given sensor'"'"'s field of view is based on the given sensor'"'"'s location and orientation relative to the vehicle.

4. The method of claim 1, wherein the weather information is received from a remote computer via a network.

5. The method of claim 1, wherein the weather information is received from one of the plurality of sensors.

6. The method of claim 1, wherein:

at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and

this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.

7. The method of claim 1, wherein:

the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and

this probability data is used when combining the comprehensive 3D model with detailed map information.

8. The method of claim 1, wherein combining the comprehensive 3D model with detailed map information results in a model of the vehicle'"'"'s environment annotated with information describing whether various portions of the environment are occupied, unoccupied, or unobserved.

9. A system comprising:

a processor configured to;

generate, for each given sensor of a plurality of sensors for detecting objects in a vehicle'"'"'s environment, a 3D model of the given sensor'"'"'s field of view;

receive weather information, the weather information including one or more of reports, radar information, forecasts and real-time measurements concerning actual or expected weather conditions in the vehicle'"'"'s environment;

adjust one or more characteristics of the plurality of 3D models based on the received weather information to account for an impact of the actual or expected weather conditions on one or more of the plurality of sensors;

after the adjusting, aggregate the plurality of 3D models to generate a comprehensive 3D model;

combine the comprehensive 3D model with detailed map information; and

use the combined comprehensive 3D model with detailed map information to maneuver the vehicle.

10. The system of claim 9, wherein the 3D model of each given sensor'"'"'s field of view is based on a pre-determined model of the given sensor'"'"'s unobstructed field of view.

11. The system of claim 9, wherein the 3D model for each given sensor'"'"'s field of view is based on the given sensor'"'"'s location and orientation relative to the vehicle.

12. The system of claim 9, wherein the weather information is received from a remote computer via a network.

13. The system of claim 9, wherein the weather information is received from one of the plurality of sensors.

14. The system of claim 9, wherein:

at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and

this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.

15. The system of claim 9, wherein:

the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and

this probability data is used when combining the comprehensive 3D model with detailed map information.

16. The system of claim 9, wherein combining the comprehensive 3D model with detailed map information results in a model of the vehicle'"'"'s environment annotated with information describing whether various portions of the environment are occupied, unoccupied, or unobserved.

17. A tangible, non-transitory computer-readable storage medium on which computer readable instructions of a program are stored, the instructions, when executed by a processor, cause the processor to perform a method, the method comprising:

generating, for each given sensor of a plurality of sensors for detecting objects in a vehicle'"'"'s environment, a 3D model of the given sensor'"'"'s field of view;

receiving weather information, the weather information including one or more of reports, radar information, forecasts and real-time measurements concerning actual or expected weather conditions in the vehicle'"'"'s environment;

adjusting one or more characteristics of the plurality of 3D models based on the received weather information to account for an impact of the actual or expected weather conditions on one or more of the plurality of sensors;

after the adjusting, aggregating the plurality of 3D models to generate a comprehensive 3D model;

combining the comprehensive 3D model with detailed map information; and

using the combined comprehensive 3D model with detailed map information to maneuver the vehicle.

18. The medium of claim 17, wherein the 3D model of each given sensor'"'"'s field of view is based on a pre-determined model of the given sensor'"'"'s unobstructed field of view.

19. The medium of claim 17, wherein:

at least one model of the plurality of 3D models includes probability data indicating a probability of detecting an object at a given location of the at least one model, and

this probability data is used when aggregating the plurality of 3D models to generate the comprehensive 3D model.

20. The medium of claim 17, wherein:

the detailed map information includes probability data indicating a probability of detecting an object at a given location of the map, and

this probability data is used when combining the comprehensive 3D model with detailed map information.