Driver adaptive collision warning system

• US 7,206,697 B2
• Filed: 10/14/2003
• Issued: 04/17/2007
• Est. Priority Date: 10/14/2003
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Delphi Technologies, Inc. (BorgWarner Incorporated)
• Inventors: Olney, Ross D., Manorotkul, Suradech
• Primary Examiner(s): Camby; Richard M.
• Application Number:
  US10/684,757
• Publication Number:
  US 20050080565A1
• Time in Patent Office: 1,281 Days
• Field of Search: 701/200, 701/213, 701/93, 701/98, 701/300, 701/301, 701/23, 701/24, 701/27, 340/903, 340/436, 342/29, 342/41, 342/454, 342/455, 180167-170
• US Class Current: 701/301
• CPC Class Codes: B60K 31/0008 including means for detecting potential obstacles in vehicle path
• CPC Class Codes: B60T 2220/02 Driver type; Driving style; Driver adaptive features
• CPC Class Codes: B60W 2050/0025 Transfer function weighting factor
• CPC Class Codes: B60W 2050/0029 Mathematical model of the driver
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2540/30 Driving style
• CPC Class Codes: B60W 50/16 Tactile feedback to the driver, e.g. vibration or force feedback to the driver on the steering wheel or the accelerator pedal

Abstract

The present invention involves methods and systems for issuing a collision warning to a driver at timing based on the driver'"'"'s attitude level. The methods involve the steps of determining the driver attitude level based on the driver'"'"'s actions in a plurality of driving conditions, determining timing for issuing warning based on the driver attitude level, and issuing warning based on the determined timing. The systems include a first device for collecting data associated with driver attitude, and a second device connected to the first device for determining a driver attitude level. The second device includes a processor for processing the data by applying pre-determined algorithm to determine the driver attitude level. The systems may further include a third device connected to the second device for determining timing for issuing warning corresponding to the driver attitude level, and issuing a warning according to the determined timing. Alternatively or additionally, the third device may include a software for enabling a determination of the rate of change in terms of acceleration and deceleration, and a control for affecting the rate of change. Further, the third device may include a software for enabling a determination of a safety distance and a distance control.

Claims

1. A method for forewarning a driver of a potential collision comprising the steps of:

(a) determining a driving style of a driver;

(b) determining timing for issuing a warning based on the determined driving style; and

(c) issuing the warning based on the determined timing, wherein the step of (a) comprises the steps of;

(i) collecting data corresponding to a plurality of parametric models (PMs) relating to driver attitude level of an individual driver;

(j) determining parametric model (PM) value distribution for each PM from the collected data;

(k) determining an average value for each PM value distribution; and

(l) determining a parametric-specific attitude level for each PM by mapping the average value for each PM value distribution to a pre-determined scale specific to each PM.

2. The method of claim 1, wherein the step of (a) comprises the steps of:

(g) collecting data relating to a plurality of drivers'"'"' actions in a plurality of driving conditions; and

(h) determining the driving style of a driver based on the collected data.

3. The method of claim 1, wherein the step of (c) involves issuing at least one perceptible signal.

4. The method of claim 3, wherein the perceptible signal includes at least one of an audio signal, a visual signal, and a tactile signal.

5. The method of claim 1, wherein the step of (i) is performed by at least one sensor of an automobile.

6. The method of claim 1, wherein the step of (j) is performed by applying pre-determined algorithm corresponding to each of the PMs.

7. The method of claim 1, wherein the step of (j) includes selecting the collected data based on pre-determined criteria specific to each of the PMs.

8. The method of claim 1, wherein the step of (j) includes calculating percentile values representing the PM value distribution.

9. The method of claim 1 further comprising the step of:

(m) averaging weighted values of parametric-specific attitude level for all PMs, wherein each of the weighted values of parametric-specific attitude level is derived by multiplying the parametric-specific attitude level with a pre-determined weight corresponding to relevance of the PM to the driver attitude level.

10. The method of claim 1, wherein the parametric models (PMs) in the step of (i) includes at least one of:

host vehicle velocity (hostv) PM for capturing how fast a driver drives in an absence of a lead vehicle, acceleration (host-acc) PM for capturing acceleration profile as a function of hostv, and driver'"'"'s allowable response time (T_ares) PM for capturing how much time the driver is allowed to react to an extreme stop condition without colliding with a lead vehicle.

11. The method of claim 10, wherein the step of (i) includes collecting data corresponding to hostv parametric model based on criteria including:

no lead vehicle, hostv being greater than a pre-determined speed, and no lane change.

12. The method of claim 11 wherein the step of (j) includes calculating from the collected data, hostv PM value distribution for percentiles from 0 to 100, wherein the step of (k) includes obtaining an average value of hostv PM values.

13. The method of claim 10, wherein the step of (i) includes collecting data corresponding to host-acc based on criteria including:

host-acc being greater than a pre-determined rate, and no lane change.

14. The method of claim 13, wherein the step of (j) includes calculating from the collected data, host-acc PM value distribution for percentiles from 0 to 100, and wherein the step of (k) includes obtaining an average value of host-acc PM values.

15. The method of claim 10, wherein step (j) includes calculating T_ares values using equation I:

16. The method of claim 15, wherein a_max, d_max, and J_max are constants.

17. The method of claim 15, wherein the step of (j) further includes selecting T_ares values based on criteria comprising:

no lead vehicle, no lane change, lead vehicle velocity(leadv)-hostv being less than 5 mph for 1 second, and T_are<

T_ares+(2* std(T_ares)) , where T_ares is an approximate average and std(T_ares) is an approximate standard deviation, of all selected T_ares data.

18. The method of claim 17, wherein the step of (k) further includes calculating T_ares PM value distribution for percentiles from 0 to 100, and wherein the step of (1) includes obtaining an average value of T_ares PM values.

19. The method of claim 1, wherein the parametric models (PMs) in the step of (i) include host vehicle velocity (hostv) PM for capturing how fast a driver drives in an absence of a lead vehicle, acceleration (host–

acc) PM for capturing acceleration profile as a function of hostv, and drive'"'"'s allowable response time (T_ares) PM for capturing how much time the driver is allowed to react to an extreme stop condition without colliding with a lead vehicle;

wherein the step of (j) includes determining PM value distribution for hostv PM, host–

acc PM, and T_ares PM;

wherein the step of (k) includes determining an average value for hostv PM, host–

acc PM, and T_ares PM; and

wherein the step of (1) includes determining a parametric-specific attitude level for hostv PM, host–

acc PM, and T_ares PM.

20. The method of claim 10 further comprising the steps of:

calculating a weighted value of hostv PM by multiplying the corresponding parameter-specific driver attitude level with a pre-determined weight corresponding to hostv PM;

calculating a weighted value of host–

acc PM by multiplying the corresponding parameter-specific driver attitude level with a pre-determined weight corresponding to host–

acc PM;

calculating a weighted value of T_ares PM by multiplying the corresponding parameter-specific driver attitude level with a pre-determined weight corresponding to T_ares PM; and

averaging the weighted values of hostv PM, host–

acc PM, and T_ares PM to create an overall driver attitude level.

21. The method of claim 20 further comprising the step of:

(n) determining timing for issuing warning based on the overall driver attitude level.

22. A method for forewarning a driver of a potential collision comprising the steps of:

(a) collecting data corresponding to a plurality of parametric models (PMs) relating to a driver attitude level of an individual driver including at least one parametric model associated with said driver'"'"'s aggression level;

(b) applying a pre-determined algorithm to the data to determine the driver'"'"'s aggression level;

(c) determining timing for issuing a warning corresponding to the driver'"'"'s aggression level; and

(d) issuing a warning based on the determined timing, wherein the step (a) comprises the steps of;

determining parametric model (PM) value distribution for each PM from the collected data;

determining an average value for each PM value distribution; and

determining a parametric-specific attitude level for each PM by mapping the average value for each PM value distribution to a pre-determined scale specific to each PM.