Target vehicle movement classification

• US 8,731,742 B2
• Filed: 04/05/2012
• Issued: 05/20/2014
• Est. Priority Date: 04/05/2012
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Zagorski, Chad T., Chatterjee, Aamrapali
• Primary Examiner(s): Cheung, Mary
• Assistant Examiner(s): BRUSHABER, FREDERICK M
• Application Number:
  US13/440,193
• Publication Number:
  US 20130268184A1
• Time in Patent Office: 775 Days
• Field of Search: 701/3
• US Class Current: 701/3
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/801 Lateral distance
• CPC Class Codes: B60W 2554/803 Relative lateral speed
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

Methods, program products, and vehicles are provided for classifying movement of target vehicles in proximity to a host vehicle and taking appropriate action based on the classification. An active safety system is coupled to a drive system, and is configured to provide an action during a drive cycle of the vehicle. The active safety system comprises a detection unit and a processor. The detection unit is configured to measure movement of a target vehicle in proximity to a host vehicle. The processor is coupled to the detection unit, and is configured to assess a pattern of the movement of the target vehicle relative to the host vehicle or a third vehicle, classify the movement of the target vehicle based on the pattern to generate a classification, the classification pertaining to a deviation from a typical vehicle movement, and take action based on the classification.

Claims

1. A method comprising:

measuring movement of a target vehicle in proximity to a host vehicle;

assessing a pattern of the movement of the target vehicle relative to the host vehicle or a third vehicle;

generating, via a processor, a classification by classifying the movement of the target vehicle as;

erratic if the pattern represents a first deviation from a typical vehicle movement; and

aggressive if the pattern represents a second deviation from the typical vehicle movement, the second deviation being greater than the first deviation; and

taking action based on the classification.

2. The method of claim 1, wherein:

the step of classifying the movement of the target vehicle as erratic comprises;

classifying the movement of the target vehicle as erratic toward the third vehicle if the pattern represents the first deviation from the typical vehicle movement in proximity to the third vehicle; and

classifying the movement of the target vehicle as erratic toward the host vehicle if the pattern represents the first deviation from the typical vehicle movement in proximity to the host vehicle; and

the step of classifying the movement of the target vehicle as aggressive comprises;

classifying the movement of the target vehicle as aggressive toward the third vehicle if the pattern represents the second deviation from the typical vehicle movement in proximity to the third vehicle; and

classifying the movement of the target vehicle as aggressive toward the host vehicle if the pattern represents the second deviation from the typical vehicle movement in proximity to the host vehicle.

3. The method of claim 2, wherein the step of taking action comprises:

taking a first action if the movement of the target vehicle is classified as erratic toward the third vehicle;

taking a second action if the movement of the target vehicle is classified as aggressive toward the third vehicle;

taking a third action if the movement of the target vehicle is classified as erratic toward the host vehicle; and

taking a fourth action if the movement of the target vehicle is classified as aggressive toward the host vehicle, wherein the first action, the second action, the third action, and the fourth action are each different from one another.

4. The method of claim 1, wherein:

the step of classifying the movement of the target vehicle comprises classifying the movement of the target vehicle as erratic versus aggressive based on a relative velocity of the target vehicle relative to the host vehicle or the third vehicle.

5. The method of claim 1, wherein:

the step of classifying the movement of the target vehicle comprises classifying the movement of the target as erratic versus aggressive vehicle based on a vector closing rate between the target vehicle and the target vehicle or the third vehicle.

6. The method of claim 1, wherein:

the step of classifying the movement of the target vehicle comprises classifying the movement of the target vehicle as erratic versus aggressive based on a lateral range between the target vehicle and the target vehicle or the third vehicle.

7. The method of claim 1, wherein:

the step of classifying the movement of the target vehicle comprises classifying the movement of the target vehicle as erratic versus aggressive based on a longitudinal range acceleration between the target vehicle and the target vehicle or the third vehicle.

8. The method of claim 1, wherein:

the step of classifying the movement of the target vehicle comprises classifying the movement of the target vehicle as erratic versus aggressive based on a longitudinal velocity of the target vehicle.

9. A program product comprising:

a program configured to at least facilitate;

measuring movement of a target vehicle in proximity to a host vehicle;

assessing a pattern of the movement of the target vehicle relative to the host vehicle or a third vehicle;

generating a classification by classifying the movement of the target vehicle as;

erratic if the pattern represents a first deviation from a typical vehicle movement; and

aggressive if the pattern represents a second deviation from the typical vehicle movement, the second deviation being greater than the first deviation; and

taking action based on the classification; and

a non-transitory computer-readable storage medium storing the program.

10. The program product of claim 9, wherein the program is further configured to at least facilitate:

classifying the movement of the target vehicle as erratic toward the third vehicle if the pattern represents the first deviation from the typical vehicle movement in proximity to the third vehicle;

classifying the movement of the target vehicle as erratic toward the host vehicle if the pattern represents the first deviation from the typical vehicle movement in proximity to the host vehicle; and

classifying the movement of the target vehicle as aggressive toward the third vehicle if the pattern represents the second deviation from the typical vehicle movement in proximity to the third vehicle; and

classifying the movement of the target vehicle as aggressive toward the host vehicle if the pattern represents the second deviation from the typical vehicle movement in proximity to the host vehicle.

11. The program product of claim 10, wherein the program is further configured to at least facilitate:

taking a first action if the movement of the target vehicle is classified as erratic toward the third vehicle;

taking a second action if the movement of the target vehicle is classified as aggressive toward the third vehicle;

taking a third action if the movement of the target vehicle is classified as erratic toward the host vehicle; and

taking a fourth action if the movement of the target vehicle is classified as aggressive toward the host vehicle, wherein the first action, the second action, the third action, and the fourth action are each different from one another.

12. A vehicle comprising:

a drive system; and

an active safety system coupled to the drive system, the active safety system comprising;

a sensor unit configured to measure movement of a target vehicle in proximity to a host vehicle; and

a processor coupled to the sensor unit and configured to;

assess a pattern of the movement of the target vehicle relative to the host vehicle or a third vehicle;

generate a classification by classifying the movement of the target vehicle as;

erratic if the pattern represents a first deviation from a typical vehicle movement; and

aggressive if the pattern represents a second deviation from the typical vehicle movement, the second deviation being greater than the first deviation; and

take action based on the classification.

13. The vehicle of claim 12, wherein the processor is further configured to:

classify the movement of the target vehicle as erratic toward the third vehicle if the pattern represents the first deviation from the typical vehicle movement in proximity to the third vehicle; and

classify the movement of the target vehicle as erratic toward the host vehicle if the pattern represents the first deviation from the typical vehicle movement in proximity to the host vehicle;

classify the movement of the target vehicle as aggressive toward the third vehicle if the pattern represents the second deviation from the typical vehicle movement in proximity to the third vehicle; and

classifying the movement of the target vehicle as aggressive toward the host vehicle if the pattern represents the second deviation from the typical vehicle movement in proximity to the host vehicle.

14. The vehicle of claim 13, wherein the processor is further configured to:

take a first action if the movement of the target vehicle is classified as erratic toward the third vehicle;

take a second action if the movement of the target vehicle is classified as aggressive toward the third vehicle;

take a third action if the movement of the target vehicle is classified as erratic toward the host vehicle; and

take a fourth action if the movement of the target vehicle is classified as aggressive toward the host vehicle, wherein the first action, the second action, the third action, and the fourth action are each different from one another.

15. The vehicle of claim 12, wherein the processor is further configured to:

classify the movement of the target vehicle as erratic versus aggressive based on a relative velocity of the target vehicle relative to the host vehicle or the third vehicle.

16. The vehicle of claim 12, wherein the processor is further configured to classify the movement of the target vehicle as erratic versus aggressive based on a vector closing rate between the target vehicle and the target vehicle or the third vehicle.

17. The vehicle of claim 12, wherein the processor is further configured to classify the movement of the target vehicle as erratic versus aggressive based on a lateral range between the target vehicle and the target vehicle or the third vehicle.