Modifying behavior of autonomous vehicles based on sensor blind spots and limitations

• US 9,367,065 B2
• Filed: 01/25/2013
• Issued: 06/14/2016
• 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
• Primary Examiner(s): Algahaim, Helal A
• Assistant Examiner(s): WALLACE, DONALD JOSEPH
• Application Number:
  US13/749,793
• Publication Number:
  US 20140214255A1
• Time in Patent Office: 1,236 Days
• Field of Search: 701/23, 701/28
• US Class Current: 1/1
• 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

Models can be generated of a vehicle'"'"'s view of its environment and used to maneuver the vehicle. 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 generate 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. The model of the vehicle'"'"'s environment may be computed based on the combined comprehensive 3D model and detailed map information.

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 a range of the field of view for one or more of the plurality of sensors;

after the adjusting, aggregating, by one or more processors, the plurality of 3D models to generate a comprehensive 3D model, wherein the comprehensive 3D model indicates an extent of an aggregated field of view for the plurality of sensors;

combining the comprehensive 3D model with detailed map information using probability data of the detailed map information indicating a probability of detecting objects at various locations in the detailed map information from various possible locations of the vehicle to produce a combined model annotated with information identifying a first portion of the environment as occupied by an object, a second portion of the environment as unoccupied by an object, and a third portion of the environment as unobserved by any of the plurality of sensors; and

using the combined model 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 6, wherein the probability data includes information about the likelihood of detecting an object at a given location based on a size or shape of the object.

8. The method of claim 6, wherein aggregating the plurality of 3D models comprises:

comparing the probability data of the plurality of 3D models; and

keeping the highest probability of detecting an object at a particular location.

9. The method of claim 6, wherein aggregating the plurality of 3D models comprises creating confidence regions or thresholds.

10. The method of claim 1, wherein adjusting one or more characteristics of the plurality of 3D models includes building at least one parameterized model for a set of weather conditions.

11. The method of claim 1, wherein adjusting one or more characteristics of the plurality of 3D models includes at least one of reducing the effective range for a given sensor and incorporating a blind spot into the field of view for a given sensor.

12. The method of claim 1, wherein using the combined model to maneuver the vehicle includes reducing the speed of the vehicle and repositioning the vehicle.

13. A system comprising:

non-transitory storage medium storing detailed map information including probability data indicating a probability of detecting objects at various locations in the detailed map information from various possible locations of a vehicle; and

one or more processors 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 a range of the field of view for one or more of the plurality of sensors;

after the adjusting, aggregate the plurality of 3D models to generate a comprehensive 3D model, wherein the comprehensive 3D model indicates an extent of an aggregated field of view for the plurality of sensors;

combine the comprehensive 3D model with the detailed map information using the probability data to produce a combined model annotated with information identifying a first portion of the environment as occupied by an object, a second portion of the environment as unoccupied by an object, and a third portion of the environment as unobserved by any of the plurality of sensors; and

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

14. The system of claim 13, 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.

15. The system of claim 13, 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.

16. The system of claim 13, wherein the one or more processors are configured to adjust the one or more characteristics of the plurality of 3D models by building at least one parameterized model for a set of weather conditions.

17. The system of claim 13, wherein the one or more processors are configured to adjust the one or more characteristics of the plurality of 3D models by reducing the effective range for a given sensor.

18. The system of claim 13, further comprising the vehicle.

19. A tangible, non-transitory computer-readable storage medium on which computer readable instructions of a program are stored, the instructions, when executed by one or more processors, cause the one or more processors 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 a range of the field of view for one or more of the plurality of sensors;

after the adjusting, aggregating the plurality of 3D models to generate a comprehensive 3D model, wherein the comprehensive 3D model indicates an extent of an aggregated field of view for the plurality of sensors;

combining the comprehensive 3D model with detailed map information using probability data of the detailed map information indicating a probability of detecting objects at various locations in the detailed map information from various possible locations of the vehicle to produce a combined model annotated with information identifying a first portion of the environment as occupied by an object, a second portion of the environment as unoccupied by an object, and a third portion of the environment as unobserved by any of the plurality of sensors; and

using the combined model to maneuver the vehicle.

20. The medium of claim 19, 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.