Vehicle state prediction in real time risk assessments

• US 9,342,986 B2
• Filed: 02/26/2014
• Issued: 05/17/2016
• Est. Priority Date: 02/25/2013
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Dariush, Behzad
• Primary Examiner(s): Do, Truc M
• Application Number:
  US14/190,981
• Publication Number:
  US 20140244068A1
• Time in Patent Office: 811 Days
• Field of Search: 701/1, 701/36, 701/96, 701/70, 701/300, 701/450
• US Class Current: 1/1
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A driver assistance system takes as input a number of different types of vehicle environment inputs including positions of objects in the vehicle'"'"'s environment. The system identifies possible outcomes that may occur as a result of the positions of the objects in the environment. The possible outcomes include predicted positions for the objects involved in each outcome. The system uses the inputs to determine a likelihood of occurrence of each of the possible outcomes. The system also uses the inputs to determine a current risk value for objects as well as predicted risk values for objects for the possible outcomes. A total risk value can be determined by aggregating the current and predicted risk values of an object weighted by the likelihood of occurrence. Total risk values for objects can be used to determine how the driver assistance system responds to the inputs.

Claims

1. A computer based method comprising:

receiving a plurality of vehicle environment inputs, the inputs comprising a current position for each of a plurality of objects located around a vehicle;

determining, based on the inputs, a possible outcome involving the plurality of objects, the possible outcome comprising a predicted position for each of the involved objects;

determining a numerical likelihood of occurrence of the possible outcome based on the inputs;

determining, for each of the involved objects, a current risk value for the object based on the current position of the object, and a predicted risk value for the object based on the predicted position of the object;

determining, for each of the involved objects, a total risk value based on the current risk value and based on the predicted risk value weighted by the numerical likelihood of occurrence; and

controlling a driver assistance system of the vehicle based on the total risk values of the involved objects;

wherein determining the numerical likelihood of occurrence of the possible outcome comprises;

determining a plurality of memberships by the inputs in a plurality of input membership functions;

combining the memberships into a plurality of permutations of the input membership functions;

mapping the permutations to a plurality of outcome membership functions;

aggregating the outcome membership functions;

determining the numerical likelihood of occurrence based on the aggregation.

2. The computer based method of claim 1 wherein the inputs comprise a time to collision between two of the plurality of objects, and wherein the numerical likelihood of occurrence is based on the time to collision.

3. The computer based method of claim 2 wherein the inputs comprise a change in the time to collision between the two objects, and wherein the numerical likelihood of occurrence is based on the change in the time to collision.

4. The computer based method of claim 1 wherein the possible outcome is stored in a database, and determining the possible outcome comprises matching a set of criteria to the inputs.

5. The computer based method of claim 4 comprising determining the predicted positions wherein the possible outcome is stored in a database, and is accessed based on the inputs matching a set of criteria for the possible outcome.

6. The computer based method of claim 1 comprising:

determining, based on the inputs, a plurality of possible outcomes involving the plurality of objects, the possible outcomes each comprising a predicted position for each of the involved objects;

determining a numerical likelihood of occurrence for each of the possible outcomes based on the inputs.

7. The computer based method of claim 6 comprising:

determining, for each of the involved objects of each of the possible outcomes, a current risk value for the object based on the current position of the object, and a predicted risk value for the object based on the predicted position of the object for the corresponding possible outcome; and

determining, for each of the involved objects, a total risk value based on the current risk value and based on the predicted risk value for each possible outcome weighted by the numerical likelihood of occurrence of that possible outcome.

8. A non-transitory computer readable storage medium including instructions that, when executed by a processor, cause the processor to:

receive a plurality of vehicle environment inputs, the inputs comprising a current position for each of a plurality of objects located around a vehicle;

determine, based on the inputs, a possible outcome involving the plurality of objects, the possible outcome comprising a predicted position for each of the involved objects;

determine a numerical likelihood of occurrence of the possible outcome based on the inputs;

determine, for each of the involved objects, a current risk value for the object based on the current position of the object, and a predicted risk value for the object based on the predicted position of the object;

determine, for each of the involved objects, a total risk value based on the current risk value and based on the predicted risk value weighted by the numerical likelihood of occurrence; and

control a driver assistance system of the vehicle based on the total risk values of the objects involved in the possible outcome;

wherein determining the numerical likelihood of occurrence of the possible outcome comprises;

determine a plurality of memberships by the inputs in a plurality of input membership functions;

combine the memberships into a plurality of permutations of the input membership functions;

map the permutations to a plurality of outcome membership functions;

aggregate the outcome membership functions;

determine the numerical likelihood of occurrence based on the aggregation.

9. The non-transitory computer readable storage medium of claim 8 wherein the inputs comprise a time to collision between two of the plurality of objects, and wherein the numerical likelihood of occurrence is based on the time to collision.

10. The non-transitory computer readable storage medium of claim 9 wherein the inputs comprise a change in the time to collision between the two objects, and wherein the numerical likelihood of occurrence is based on the change in the time to collision.

11. The non-transitory computer readable storage medium of claim 8 wherein the possible outcome is stored in a database, and determining the possible outcome comprises matching a set of criteria to the inputs.

12. The non-transitory computer readable storage medium of claim 11 further comprising determining the predicted positions wherein the possible outcome is stored in a database, and is accessed based on the inputs matching a set of criteria for the possible outcome.

13. The non-transitory computer readable storage medium of claim 8 further comprising instructions, that when executed by the processor cause the processor to:

determine, based on the inputs, a plurality of possible outcomes involving the plurality of objects, the possible outcomes each comprising a predicted position for each of the involved objects;

determine a numerical likelihood of occurrence for each of the possible outcomes based on the inputs.

14. The non-transitory computer readable storage medium of claim 13 further comprising instructions, that when executed by the processor cause the processor to:

determine, for each of the involved objects of each of the possible outcomes, a current risk value for the object based on the current position of the object, and a predicted risk value for the object based on the predicted position of the object for the corresponding possible outcome; and

determine, for each of the involved objects, a total risk value based on the current risk value and based on the predicted risk value for each possible outcome weighted by the numerical likelihood of occurrence of that possible outcome.