BRAKE PREDICTION AND ENGAGEMENT

• US 20190023208A1
• Filed: 07/19/2017
• Published: 01/24/2019
• Est. Priority Date: 07/19/2017
• Status: Abandoned Application

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Boston, Daniel Lewis, Rhodes, Kevin James, Ahmed, Nayaz Khalid
• Application Number:
  US15/653,649
• Publication Number:
  US 20190023208A1
• Time in Patent Office: Days
• CPC Class Codes: B60R 2021/01259 Brakes
• CPC Class Codes: B60R 21/013 including means for detecting collisions, impending collisions or roll-over
• CPC Class Codes: B60R 21/0134 responsive to imminent contact with an obstacle , e.g. using radar systems
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G06V 40/175 Static expression
• CPC Class Codes: G06V 40/176 Dynamic expression
• CPC Class Codes: G06V 40/179 metadata assisted face recognition

Abstract

A computing device in a vehicle, programmed to predict a collision risk by comparing an acquired occupant facial expression to a plurality of stored occupant facial expressions, and, brake a vehicle based on the collision risk. The computing device can be programmed to predict a collision risk by determining a number of seconds until a negative event, including a collision, a near-miss, or vehicle miss-direction, is predicted to occur at a current vehicle trajectory.

Claims

1. A method, comprising:

predicting a collision risk by comparing an acquired occupant facial expression to a plurality of previously acquired occupant facial expressions; and

braking a vehicle based on the collision risk.

2. The method of claim 1, further comprising predicting the collision risk by determining a number of seconds until a negative traffic event that includes a collision, a near-miss, or vehicle miss-direction is predicted to occur at a current vehicle trajectory.

3. The method of claim 2, wherein the current vehicle trajectory includes a speed, a direction, and steering torque.

4. The method of claim 3, further comprising acquiring the occupant facial expression by acquiring video data including an occupant'"'"'s face and extracting features from the video data that represent the occupant facial expression.

5. The method of claim 4, further comprising extracting features from the video data including determining an occupant'"'"'s gaze direction and comparing the occupant'"'"'s gaze direction with a direction to the negative traffic event.

6. The method of claim 5, wherein comparing the occupant facial expression to the previously acquired occupant facial expressions includes processing the occupant facial expression with a machine learning program.

7. The method of claim 6, wherein the previously acquired facial expressions are associated with negative traffic events including their proximity in time to negative traffic events.

8. The method of claim 7, further comprising predicting the collision risk by comparing facial expressions with previously acquired facial expressions associated with their proximity in time to negative events.

9. The method of claim 1, further comprising braking the vehicle by pre-charging brakes or regenerative braking.

10. The method of claim 9, wherein braking the vehicle includes pre-charging brakes or regenerative braking and then applying braking torque.

11. A computer, programmed to:

predict a collision risk by comparing an acquired occupant facial expression to a plurality of previously acquired occupant facial expressions; and

brake a vehicle based on the collision risk.

12. The computer of claim 11, further programmed to predict the collision risk by determining a number of seconds until a negative traffic event that includes a collision, a near-miss, or vehicle miss-direction, is predicted to occur at a current vehicle trajectory.

13. The computer of claim 12, wherein the current vehicle trajectory includes a speed, a direction and steering torque.

14. The computer of claim 13, further programmed to acquire the occupant facial expression by acquiring video data including an occupant'"'"'s face and extracting features from the video data that represent the occupant facial expression.

15. The computer of claim 14, further programmed to extract features from the video data including by determining an occupant'"'"'s gaze direction and comparing the occupant'"'"'s gaze direction with a direction to the negative traffic event.

16. The computer of claim 15, wherein comparing the occupant facial expression to the previously acquired occupant facial expressions includes processing the occupant facial expression with a machine learning program.

17. The computer of claim 16, programmed to associate the previously acquired facial expressions with negative traffic events based on their proximity in time to negative traffic events.

18. The computer of claim 17, further programmed to predict the collision risk by comparing facial expressions with previously acquired extracted features associated with their proximity in time to negative events.

19. The computer of claim 11, further programmed to brake the vehicle by pre-charging brakes or increasing brake regeneration.

20. The computer of claim 19, wherein brake the vehicle includes pre-charging brakes or increasing brake regeneration and then applying braking torque.