Systems and Methods for Determining Driver Fatigue Level from Lane Variability and Geographic Location

• US 20150045986A1
• Filed: 08/11/2014
• Published: 02/12/2015
• Est. Priority Date: 08/09/2013
• Status: Abandoned Application

• Current Assignee: Pulsar Informatics, Inc.
• Original Assignee: Pulsar Informatics, Inc.
• Inventors: Kan, Kevin Gar Wah, Mott, Christopher Grey, Mollicone, Daniel Joseph
• Application Number:
  US14/456,951
• Publication Number:
  US 20150045986A1
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: B60W 2040/0827 due to sleepiness
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 40/08 related to drivers or passengers

Abstract

Systems and methods are disclosed for determining a fatigue level of a human operator of a motor vehicle based upon lane variability data and geographic position data of the vehicle, used either alone or in combination with other data such as (without limitation) vehicle operational data, vehicle environment data, road segments, and/or the like.

Claims

1. A method, using a processor, for determining a fatigue level of a driver of a vehicle based at least in part upon lane variability data and geographic location data for the vehicle, the method comprising:

receiving, at the processor, lane variability data, the lane variability data corresponding to a change of position of a vehicle in a driving lane;

receiving, at the processor, geographical position data for the vehicle;

identifying, with the processor, a road segment of interest based at least in part on the received geographical data;

selecting, with the processor, a subset of the received lane variability data corresponding to the vehicle travelling over the identified road segment of interest; and

determining, with the processor, a fatigue level, F, of a driver of the vehicle based at least in part upon the selected subset of the received lane variability data.

2. The method of claim 1 wherein the determined fatigue level, F, of a driver of the vehicle is of the form:

F=α

LV

where LV represents the lane variability of the vehicle as it travels the road segment of interest and is determined from the selected subset of the received lane variability data, and where a represents a proportionality constant.

3. The method of claim 1 wherein the determined fatigue level, F, of a driver of the vehicle comprises a nonlinear function of the selected subset of the received lane variability data.

4. A method according to claim 1 wherein receiving lane variability data comprises:

receiving, at the processor, lane position data from a lane position sensor affixed to the vehicle; and

deriving, at the processor, lane variability data based upon the received lane position data.

5. A method according to claim 1 wherein receiving lane variability data comprises:

receiving, at the processor, steering position data from a steering sensor affixed to a steering apparatus of the vehicle; and

deriving, at the processor, lane variability data based upon the received steering position data.

6. A method according to claim 5 wherein deriving lane variability data based upon the received steering position data comprises:

deriving, with the processor, steering variability data based upon the received steering position data; and

applying a transfer function, with the processor, to the derived steering variability data to determine lane variability data.

7. A method according to claim 1 wherein receiving the geographical position data for the vehicle comprises receiving GPS data from a GPS device associated with the vehicle.

8. A method according to claim 1 wherein identifying a road segment of interest comprises at least in part determining road geometry by analyzing at least the received geographical location data.

9. A method according to claim 8 wherein determining road geometry comprises identifying a road classification type comprising one or one or more of:

a straight road portion, a curved road portion with a constant radius of curvature, a curved road portion with a variable radius of curvature, a curved road portion with both a positive and a negative radius of curvature, an uphill road segment, a downhill road segment, and a road segment with both uphill and downhill portions.

10. A method according to claim 1 wherein identifying a road segment of interest comprises:

receiving one or more waypoint locations, each waypoint location representing the location of point of travel for the vehicle; and

wherein identifying, with the processor, a road segment of interest is further based on comparing the received one or more waypoint locations to the received geographical position data.

11. A method according to claim 10 wherein the received one or more waypoint locations correspond to one of a start road segment waypoint, and an end road segment waypoint.

12. A method according to claim 1 wherein identifying a road segment of interest comprises at least in part:

identifying, with the processor, characteristics of the road based at least in part on map data corresponding to the received geographical position data.

13. A method according to claim 9 wherein determining a fatigue level of the driver further comprises determining a fatigue level of the driver based at least in part upon the road type classification.

14. A method according to claim 13 wherein the determined fatigue level, F, of a driver of the vehicle is of the form:

F=α

_RCTLV

where LV represents the lane variability of the vehicle as it travels the road segment of interest and is determined from the selected subset of the received lane variability data, and α

_RCT represents a proportionality constant determined at least in part upon the road classification type.

15. A method according to claim 1 further comprising:

receiving, at the processor, vehicle operational data corresponding to one or more operational states of the vehicle, and

wherein determining, with the processor, a fatigue level, F, of a driver of the vehicle is further based at least in part upon the received vehicle operational data.

16. A method according to claim 15 wherein the determined fatigue level, F, of a driver of the vehicle is of the form:

F=α

LV+β

VOD

where LV represents the lane variability of the vehicle and is determined from the selected subset of the received lane variability data, VOD represents the vehicle operational data, and α and

β

represent proportionality constants.

17. A method according to claim 15 wherein the determined fatigue level, F, of a driver of the vehicle comprises a nonlinear combination of the selected subset of the received lane variability data and the received vehicle operational data.

18. A method according to claim 15 wherein the received vehicle operational data comprises one or more of:

steering data, variability of steering data, braking data, variability of braking data, speed data, variability of speed data, acceleration data, variability of acceleration data, turn signal data, variability of turn signal data, vehicle load, gas consumption, variability of gas consumption, gas mileage, and variability of gas mileage.

19. A method according to claim 15 wherein identifying a road segment of interest further comprises identifying a road segment of interest based upon the received vehicle operational data.

20. A method according to claim 15 wherein determining a fatigue level, F, of a driver of the vehicle is further based at least in part upon the received vehicle operational data.

21. A method according to claim 1 further comprising:

receiving, at the processor, vehicle environmental data corresponding to one or more environmental states of the vehicle, and

wherein determining, with the processor, a fatigue level, F, of a driver of the vehicle is further based at least in part upon the received vehicle environmental data.

22. A method according to claim 21 wherein the determined fatigue level, F, of a driver of the vehicle is of the form:

F=α

LV+γ

VED

where LV represents the lane variability of the vehicle and is determined from the selected subset of the received lane variability data, VED represents the received vehicle environmental data, and α and

γ

represent proportionality constants.

23. A method according to claim 21 wherein the received vehicle environmental data comprises one or more of:

weather data, traffic data, road condition data, and wind speed.

24. A method according to claim 21 wherein identifying a road segment of interest further comprises identifying a road segment of interest based upon the received vehicle environmental data.

25. A method according to claim 21 wherein determining a fatigue level of a driver of the vehicle is further based at least in part upon the received vehicle environmental data.

26. A method according to claim 1 further comprising:

repeating two or more times the steps of;

identifying, with the processor, a road segment of interest based at least in part on the received geographical data; and

selecting, with the processor, a subset of the received lane variability data corresponding only to lane position data corresponding to the identified road segment of interest; and

wherein determining a fatigue level, F, of a driver of the vehicle is based at least in part upon two or more of the selected subsets of the received lane variability data

27. A method according to claim 26 wherein determining a fatigue level, F, of a driver of the vehicle comprises:

determining, with the processor, a segment fatigue level, F_i, of a driver of the vehicle for each of the selected subsets of the received lane variability data,

determining, the fatigue level, F, based at least in part on a function of each of the determined segment fatigue levels, F_i.

28. A method according to claim 27 wherein the function of each of the determined segment fatigue levels, F_i, is a weighted average function, such that

29. A method according to claim 26 further comprising:

receiving, at the processor, vehicle operational data corresponding to one or more operational states of the vehicle; and

wherein for at least one repetition of the steps of identifying a road segment and selecting a subset of the lane variability data, the determined fatigue level, F, of a driver of the vehicle is of the form;

F=α

LV+β

VOD

where LV represents the lane variability of the vehicle and is determined from the selected subset of the received lane variability data, VOD represents the vehicle operational data, and α and

β

represent proportionality constants.

30. A method according to claim 26 further comprising:

receiving, at the processor, vehicle environmental data corresponding to one or more environmental states of the vehicle; and

wherein for at least one repetition of the steps of identifying a road segment and selecting a subset of the lane variability data, the determined fatigue level, F, of a driver of the vehicle is of the form;

F=α

LV+γ

VED

where LV represents the lane variability of the vehicle and is determined from the selected subset of the received lane variability data, VED represents the received vehicle environmental data, and α and

γ

represent proportionality constants.

31. A computer program product embodied in a non-transitory medium and comprising computer-readable instructions that when executed by a suitable computer cause the computer to perform a method for determining a fatigue level of a driver of a vehicle based at least in part upon lane variability data and geographic location data for the vehicle, the method comprising:

receiving, at the processor, lane variability data, the lane variability data corresponding to a change of position of a vehicle in a driving lane;

receiving, at the processor, geographical position data for the vehicle;

identifying, with the processor, a road segment of interest based at least in part on the received geographical data;

selecting, with the processor, a subset of the received lane variability data corresponding to the vehicle travelling over the identified road segment of interest; and

determining, with the processor, a fatigue level, F, of a driver of the vehicle based at least in part upon the selected subset of the received lane variability data.