SYSTEM AND METHOD FOR ESTIMATED DRIVER INTENTION FOR DRIVER ASSISTANCE SYSTEM CONTROL

• US 20100131148A1
• Filed: 11/26/2008
• Published: 05/27/2010
• Est. Priority Date: 11/26/2008
• Status: Abandoned Application

• Current Assignee: Audi AG (Volkswagen AG), Volkswagen AG
• Original Assignee: Audi AG (Volkswagen AG), Volkswagen AG
• Inventors: Camhi, Jaime, Langer, Dirk, Switkes, Joshua P., Stoschek, Arne
• Application Number:
  US12/323,890
• Publication Number:
  US 20100131148A1
• Time in Patent Office: Days
• US Class Current: 701/29
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/28 Wheel speed
• CPC Class Codes: B60W 40/09 Driving style or behaviour

Abstract

A system and method relate to estimating a driver intention for driver assistance systems control, including an analysis device that receives data from each of a vehicle environment sensor, a vehicle dynamics sensor, and a driver attributes sensor, such that the analysis device makes a prediction of the driver intention based on the received data. A control device controls a vehicle and a driver partially based on the predicted driver intention.

Claims

1. A system for estimating a driver intention for driver assistance system control of a vehicle, comprising:

an analysis device configured to receive data from each of at least one vehicle environment sensor, at least one vehicle dynamics sensor, and at least one driver attributes sensor, the analysis device configured to predict the driver intention based on the received data; and

a control device configured to control the vehicle based at least partially on the predicted driver intention.

2. The system according to claim 1, wherein the at least one driver attributes sensor includes at least one of:

(a) a vision sensor;

(b) a capacitive sensor;

(c) a resistive sensor;

(d) a micro bolometer;

(e) an infrared sensor;

(f) an ultrasound sensor; and

(g) a vehicle controls sensor.

3. The system according to claim 1, wherein the at least one driver attributes sensor is configured to measure at least one of:

(a) a head pose;

(b) a head orientation;

(c) an eye gaze;

(d) an eye open or closed state;

(e) a body position;

(f) a body movement;

(g) a hand position;

(h) a hand movement;

(i) a hand grasp;

(j) a usage of vehicle controls;

(k) a foot position; and

(l) a foot movement.

4. The system according to claim 3, wherein the vehicle controls include at least one of:

(a) a MMI knob;

(b) radio controls; and

(c) climate controls.

5. The system according to claim 1, wherein the vehicle environment sensor includes at least one of:

(a) a radar sensor;

(b) a vision sensor;

(c) LIDAR;

(d) a 3D time-of-flight sensor;

(e) an ultrasound sensor; and

(f) a GPS sensor.

6. The system according to claim 5, wherein the radar sensor includes at least one of (a) an adaptive cruise control radar sensor and (b) a short range wide angle radar sensor.

7. The system according to claim 1, wherein the analysis device is configured to recognize distinctive driver task patterns in the data received from the at least one driver attributes sensor.

8. The system according to claim 1, wherein the analysis device is configured to recognize driver intention patterns in the data received from each of the at least one vehicle environment sensor, the at least one vehicle dynamics sensor, and the at least one driver attributes sensor.

9. The system according to claim 8, wherein the analysis device is configured to determine a probability of the driver intention based on the recognized driver intention patterns.

10. The system according to claim 9, wherein the analysis device is configured to predict the driver intention if the probability meets or exceeds a threshold value.

11. The system according to claim 8, wherein the analysis device is configured to recognize the driver intention patterns using at least one of a computer learning model and a computer vision algorithm.

12. The system according to claim 11, wherein the computer learning model includes at least one of (a) a Hidden Markov Model and (b) a Sparse Bayesian Learning Model.

13. The system according to claim 1, wherein the driver intention includes at least one of:

(a) an intention to change lanes in highway driving for passing another vehicle;

(b) an intention to change lanes in highway driving to allow another vehicle to pass;

(c) an intention to change lanes on a highway for an evasive maneuver;

(d) an intention to change lanes in urban driving;

(e) an intention to speed up;

(f) an intention to slow down;

(g) an intention to stop;

(h) an intention to maintain a lane position;

(i) an intention to make a right-hand turn;

(j) an intention to make a left-hand turn;

(k) an intention to take a next exit;

(l) an intention to reverse; and

(m) an intention to be inattentive.

14. The system according to claim 1, wherein the control device is configured to output at least one of (a) an assistance and (b) countermeasure to at least one of (a) the vehicle and (b) the driver based at least partially on the predicted driver intention.

15. The system according to claim 14, wherein the at least one of (a) the assistance and (b) the countermeasure includes at least one of:

(a) an audible warning;

(b) a haptic warning;

(c) a visible warning;

(d) a recommended given maneuver; and

(e) an automatically executed maneuver.

16. The system according to claim 15, wherein the recommended given maneuver includes at least one of:

(a) brake;

(b) slow down;

(c) speed up;

(d) change lane; and

(e) maintain lane position.

17. The system according to claim 15, wherein the automatically executed maneuver includes at least one of:

(a) brake;

(b) slow down;

(c) speed up;

(d) change lane;

(e) keep lane; and

(f) steer to maintain lane position.

18. A method for estimating a driver intention for driver assistance system control of a vehicle, comprising:

receiving, by an analysis device, data from each of at least one vehicle environment sensor, at least one vehicle dynamics sensor, and at least one driver attributes sensor;

predicting, by the analysis device, the driver intention based on the received data; and

controlling, by a control device, the vehicle based at least partially on the predicted driver intention.

19. The method according to claim 18, further comprising recognizing, by the analysis device, distinctive driver task patterns in the data received from the at least one driver attributes sensor.

20. The method according to claim 18, further comprising recognizing, by the analysis device, driver intention patterns in the data received from each of the at least one vehicle environment sensor, the at least one vehicle dynamics sensor, and the at least one driver attributes sensor.

21. The method according to claim 20, further comprising determining, by the analysis device, a probability of the driver intention based on the recognized driver intention patterns.

22. The method according to claim 21, further comprising predicting, by the analysis device, the driver intention, if the probability meets or exceeds a threshold value.

23. The method according to claim 20, wherein the analysis device recognizes the driver intention patterns using at least one of (a) a computer learning model and (b) a computer vision algorithm.

24. The method according to claim 18, further comprising outputting, by the control device, at least one of (a) an assistance and (b) countermeasure to at least one of (a) the vehicle and (b) the driver based at least partially on the predicted driver intention.