REAL-TIME DRIVING DANGER LEVEL PREDICTION

• US 20090040054A1
• Filed: 12/05/2007
• Published: 02/12/2009
• Est. Priority Date: 04/11/2007
• Status: Active Grant

• Current Assignee: NEC Corporation
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Gong, Yihong, Xu, Wei, Wang, Jinjun
• Granted Patent: US 7,839,292 B2
• Time in Patent Office: Days
• US Class Current: 340/576
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/105 Longitudinal acceleration
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2540/221 Physiology, e.g. weight, heartbeat, health or special needs
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness

Abstract

Systems and methods are disclosed to predict driving danger by capturing vehicle dynamic parameter, driver physiological data and driver behavior feature; applying a learning algorithm to the features; and predicting driving danger.

Claims

1. A method to predict driving danger, comprising:

a. capturing vehicle dynamic parameter, driver physiological data and driver behavior feature;

b. applying a learning algorithm to the features; and

c. predicting driving danger.

2. The method of claim 1, wherein the learning algorithm includes one of:

Hidden Markov Model, Conditional Random Field and Reinforcement Learning

3. The method of claim 1, wherein the vehicle dynamic parameter includes one or more of:

driver'"'"'s lateral lane position, steering wheel angle, longitudinal acceleration, longitudinal velocity, distance between vehicles.

4. The method of claim 1, wherein the driver'"'"'s physiological data includes one or more of:

respiration, heart rate, blood volume, skin temperature, skin conductance.

5. The method of claim 1, wherein the driver behavior feature comprises a PERCLOSE feature.

6. The method of claim 1, wherein the driver behavior feature comprises fatigue, vision, distraction.

7. The method of claim 1, comprising training the learning algorithm.

8. The method of claim 1, comprising performing off line cross-validation.

9. The method of claim 7, comprising prediticting driving danger in real time.

10. The method of claim 1, comprising communicating a reason for a predicted driving danger to a user.

19. The system of claim 7, wherein the learning processor predicts driving danger in real time.

20. The system of claim 1, comprising a display to communicate a reason for a predicted driving danger to a user.

11. A system to predict driving danger, comprising:

a. a data acquisition unit to capture vehicle dynamic parameter, driver physiological data and driver behavior feature;

b. a learning processor coupled to the data acquisition unit to process the features; and

c. a user interface coupled to the learning processor to indicate driving danger.

12. The system of claim 11, wherein the learning processor includes one of:

Hidden Markov Model, Conditional Random Field and Reinforcement Learning

13. The system of claim 11, wherein the vehicle dynamic parameter includes one or more of:

driver'"'"'s lateral lane position, steering wheel angle, longitudinal acceleration, longitudinal velocity, distance between vehicles.

14. The system of claim 11, wherein the driver'"'"'s physiological data includes one or more of:

respiration, heart rate, blood volume, skin temperature, skin conductance.

15. The system of claim 11, wherein the driver behavior feature comprises a PERCLOSE feature.

16. The system of claim 11, wherein the driver behavior feature comprises fatigue, vision, distraction.

17. The system of claim 11, wherein the learning processor is trained using previously captured data.

18. The system of claim 11, wherein the learning processor cross-validates predicted driving dangers.