DRIVER BEHAVIOR FROM PROBE DATA FOR AUGMENTING A DATA MODEL

• US 20140244125A1
• Filed: 06/03/2013
• Published: 08/28/2014
• Est. Priority Date: 02/27/2013
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: Navteq B.V. (HERE Holding Corporation)
• Inventors: Dorum, Ole Henry, MacFarlane, Jane
• Granted Patent: US 9,045,041 B2
• Time in Patent Office: Days
• US Class Current: 701/70
• CPC Class Codes: B60K 31/00 Vehicle fittings, acting on a single sub-unit only, for automatically controlling vehicle speed, i.e. preventing speed from exceeding an arbitrarily established velocity or maintaining speed at a particular velocity, as selected by the vehicle operator fittings acting on two or more sub-units B60W30/14; speedometers G01P; systems or devices for controlling speed in general G05D13/00 ; in traffic anti-collision system for road vehicles G08G1/16
• CPC Class Codes: B60K 31/0066 responsive to vehicle path curvature
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 40/10 related to vehicle motion
• CPC Class Codes: B60W 40/109 Lateral acceleration
• CPC Class Codes: B60W 40/12 related to parameters of the vehicle itself , e.g. tyre models
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G01C 21/3844 Data obtained from position sensors only, e.g. from inertial navigation
• CPC Class Codes: G06F 7/00 Methods or arrangements for processing data by operating upon the order or content of the data handled logic circuits H03K19/00
• CPC Class Codes: G08B 23/00 Alarms responsive to unspecified undesired or abnormal conditions
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]

Abstract

In one implementation, sets of probe data are collected by probe vehicles. The probe data describes the driving characteristics of the probe vehicles. The sets of probe data are sent to a server or a mobile device for analysis. A polycurve, including a piecewise function of map data, is modified based on the probe data. The polycurve may be a spline curve for an advanced driver assistance system. The modified polycurve may be used in the advanced driver assistance system for a vehicle traveling along the same path previously traversed by the probe vehicles. Based on the polycurve modified by the probe data, a driver assistance feature is provided to the vehicle.

Claims

1. A method comprising:

receiving a plurality of sets of probe data collected by probe vehicles;

modifying a polycurve based on the plurality of sets of probe data, wherein the polycurve is a piecewise function of map data; and

generating data for a driver assistance feature based the polycurve modified based on the plurality of sets of probe data.

2. The method of claim 1, wherein the piecewise function is a series of polynomial functions defined by a plurality of knots and a plurality of control points.

3. The method of claim 1, wherein the plurality of sets of probe data are historical data describing speed, acceleration, deceleration, or braking patterns of the probe vehicles.

4. The method of claim 1, wherein the plurality of sets of probe data are collected by one or more position sensors.

5. The method of claim 1, wherein the plurality of sets of probe data are collected by at least one of an inertial sensor, a magnetometer, or an on-board diagnostic system.

6. The method of claim 1, wherein the piecewise function is derived from a spline model of geographic data.

7. The method of claim 1, further comprising:

sorting the plurality of sets of probe data according to traffic levels, weather condition, classifications of the probe vehicles, or time of day.

8. The method of claim 1, further comprising:

providing the data for the driver assistance feature according to a geographic position of a mobile device.

9. A method comprising:

receiving data indicative of a position of a vehicle;

comparing movement of the vehicle to data indicative of a typical driver behavior in a dimension of a polycurve, wherein the typical driver behavior is defined by a plurality of probe vehicles; and

generating a driving assistance message when the movement of the vehicle exceeds the typical driver behavior dimension of the polycurve.

10. The method of claim 9, wherein the driving assistance message is a warning to a driver to maintain a slower driving speed.

11. The method of claim 9, wherein the driving assistance message is a command to automatically slow down or brake the vehicle.

12. The method of claim 9, wherein the data indicative of the typical driver behavior includes a typical velocity associated with the position of the vehicle.

13. The method of claim 9, wherein the data indicative of the typical driver behavior includes a typical lateral acceleration associated with the position of the vehicle.

14. The method of claim 13, wherein the movement of the vehicle includes a current lateral acceleration of the vehicle calculated from a curvature of a path times the square of the speed of the vehicle.

15. The method of claim 14, wherein the dimension of the polycurve including the data indicative of the typical driver behavior is a first dimension of the polycurve, and the curvature of the path is stored in or derived from a second dimension of the polycurve.

16. An apparatus comprising:

a memory configured to store previously recorded probe data collected at a plurality of probe vehicles; and

a controller configured to modify a spline curve by adding an additional dimension of data to the spline curve based on the previously recorded probe data,

wherein the additional dimension of data of the spline curve provides a driving assistance feature to a user.

17. The apparatus of claim 16, wherein the controller is configured to analyze the previously recorded probe data to generate a driving threshold for a location where the previously recorded probe data was collected.

18. The apparatus of claim 17, wherein the driving threshold is based on an average of the previously recorded probe data and at least one standard deviation of the previously recorded probe data.

19. The apparatus of claim 16, wherein the controller is configured to analyze the previously recorded probe data to generate a comfort threshold at a first number of standard deviations from an average of the previously recorded probe data and a safety threshold at a second number of standard deviations from the average of the previously recoded probe data.

20. The apparatus of claim 16, wherein the controller is configured to analyze the previously recorded probe data to a first position where, on average, drivers of the plurality of probe vehicles begin to brake for a curve and a second position where, on average, drivers of the plurality of probe vehicles stop braking for the curve.

21. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following;

receiving data indicative of a position of a vehicle;

determining a speed of the vehicle or an acceleration of the vehicle;

comparing the speed of the vehicle or the acceleration of the vehicle to a typical driver behavior dimension of a data model; and

generating a warning when the speed or the acceleration indicates that the typical driver behavior dimension has been exceeded.

22. The apparatus of claim 21, wherein the acceleration of the vehicle is a current lateral acceleration of the vehicle calculated based on a speed of the vehicle and a current curvature of a path of travel of the vehicle.

23. The apparatus of claim 21, wherein the typical driver behavior dimension is a comfort level or a safety level.

24. The apparatus of claim 21, wherein the typical driver behavior dimension is based on probe data for a plurality of probe vehicles.

25. The apparatus of claim 24, wherein the probe data describes historical speed, acceleration, deceleration, or braking patterns of the plurality of probe vehicles.

26. The apparatus of claim 21, wherein the data model is an advanced driver assistance system spline curve.