Vehicle with environmental context analysis

• US 10,377,376 B2
• Filed: 01/27/2017
• Issued: 08/13/2019
• Est. Priority Date: 10/06/2016
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Diedrich, Jonathan, Klosek, Robert, Reed, Eric, Siddiqui, Adil Nizam, Gaboury, Scott Howard
• Primary Examiner(s): Mott, Adam R
• Application Number:
  US15/418,556
• Publication Number:
  US 20180099663A1
• Time in Patent Office: 928 Days
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0953 the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/18154 Approaching an intersection
• CPC Class Codes: B60W 40/072 Curvature of the road
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road

Abstract

A host vehicle may include: motor(s), brakes, sensors, processor(s) configured to: (a) predict a target path of a target vehicle based on lane boundaries of a virtual map; (b) compare the target path to a predicted host path of the host vehicle; (c) apply the brakes based on the comparison; (d) predict the target path and the host path as shapes, each having a two-dimensional surface area; (e) determine whether the shapes intersect, and based on the determination, (f) calculate a first timespan where the target vehicle reaches the intersection, and (g) a second timespan where the host vehicle reaches the intersection.

Claims

1. A host vehicle comprising:

motor(s), brakes, sensors, processor(s) configured to;

recognize a lane of a target vehicle;

determine a radius of curvature of the lane boundaries of the recognized lane;

predict a target path of the target vehicle based on lane boundaries of a virtual map and the radius of curvature of the lane boundaries of the recognized lane;

compare the target path to a predicted host path of the host vehicle; and

apply the brakes based on the comparison.

2. The host vehicle of claim 1, wherein the processor(s) are configured to build the virtual map based on (a) a received street map and (b) measurements received from the sensors.

3. The host vehicle of claim 2, wherein the sensors comprise a camera and the processor(s) are configured to apply lane boundaries to the virtual map based on images captured by the camera.

4. The host vehicle of claim 1, wherein the processor(s) are configured to:

determine (a) a first radius of curvature of a first lane boundary of the recognized lane, (b) a second radius of curvature of a second lane boundary of the recognized lane, and an intermediate;

calculate an intermediate radius of curvature based on (a) and (b);

predict the target path based on the calculated intermediate radius of curvature.

5. The host vehicle of claim 4, wherein the processor(s) are configured to, based on the target path, predict a range of positions of the target vehicle at a first future time and predict a range of positions of the target vehicle at a second future time.

6. The host vehicle of claim 5, wherein the first future time is a predetermined time interval multiplied by a first number, wherein the second future time is the predetermined time interval multiplied by a value, and wherein the value is the first number plus one.

7. The host vehicle of claim 6, wherein the processor(s) are configured such that a total area of the predicted range of positions of the target vehicle at the second future time exceeds a total area of the predicted range of positions of the target vehicle at the first future time.

8. The host vehicle of claim 7, wherein the processor(s) are configured such that a total area of the predicted range of positions of the target vehicle at the first future time exceeds a total area of the target vehicle.

9. The host vehicle of claim 1, wherein the processor(s) are configured to predict the target path and the host path as shapes, each having a two-dimensional surface area.

10. The host vehicle of claim 9, wherein the processor(s) are configured to:

determine whether the shapes intersect; and

in response to determining that the shapes intersect at an intersection, calculate a first timespan where the target vehicle reaches the intersection, and a second timespan where the host vehicle reaches the intersection.

11. The host vehicle of claim 10, wherein the processor(s) are configured to determine whether any portion of the first timespan overlaps any portion of the second timespan.

12. The host vehicle of claim 11, wherein the processor(s) are configured to apply the brakes based on a positive overlap determination.

13. A host vehicle comprising:

motor(s), steering, sensors, processor(s) configured to;

recognize a lane of a target vehicle;

determine a radius of curvature of the land boundaries of the recognized lane;

predict a target path of the target vehicle based on lane boundaries of a virtual map and the radius of curvature of the lane boundaries of the recognized lane;

compare the target path to a predicted host path of the host vehicle; and

actuate the steering based on the comparison.

14. The host vehicle of claim 13, wherein the processor(s) are configured to predict the target path and the host path as shapes, each having a two-dimensional surface area.

15. The host vehicle of claim 14, wherein the processor(s) are configured to determine whether the shapes intersect, and based on the determination, calculate a first timespan where the target vehicle reaches the intersection, and a second timespan where the host vehicle reaches the intersection.

16. The host vehicle of claim 15, wherein the processor(s) are configured to determine whether any portion of the first timespan overlaps any portion of the second timespan.

17. The host vehicle of claim 16, wherein the processor(s) are configured to actuate the steering based on a positive overlap determination.

18. The host vehicle of claim 17, wherein the processor(s) are configured to build the virtual map based on (a) a received street map and (b) measurements received from the sensors.

19. A host vehicle comprising:

motor(s), a warning light, sensors, processor(s) configured to;

recognize a lane of a target vehicle;

determine a radius of curvature of the lane boundaries of the recognized lane;

predict a target path of the target vehicle based on lane boundaries of a virtual map and the radius of curvature of the lane boundaries of the recognized lane;

compare the target path to a predicted host path of the host vehicle; and

activate the warning light based on the comparison.