Method and apparatus for providing road surface friction data for a response action

• US 9,815,476 B2
• Filed: 12/22/2014
• Issued: 11/14/2017
• Est. Priority Date: 12/22/2014
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Lynch, James D.
• Primary Examiner(s): PATEL, SHARDUL D
• Application Number:
  US14/578,956
• Publication Number:
  US 20160176408A1
• Time in Patent Office: 1,058 Days
• Field of Search: 701 23, 701 72, 701 80, 701400, 701410
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 40/064 Degree of grip
• CPC Class Codes: B60W 40/068 Road friction coefficient
• CPC Class Codes: B60W 50/082 Selecting or switching between different modes of propelling
• CPC Class Codes: B60W 60/0053 from vehicle to occupant

Abstract

An approach is provided for determining road surface friction data for at least one travel segment via sensor data and/or guideline friction map to cause at least one response action. The approach involves processing and/or facilitating a processing of sensor data to determine at least one road-vehicle friction change associated with at least one travel segment. The approach also involves causing, at least in part, a comparison of the at least one road-vehicle friction change to at least one guideline friction map. The approach further involves determining at least one response action to the at least one road-vehicle friction change based, at least in part, on the comparison.

Claims

1. A method for effectuating a mode of operation in an autonomous vehicle, comprising:

processing sensor data associated with the autonomous vehicle to determine at least one sensed road-vehicle friction change associated with at least one travel segment, wherein the autonomous vehicle is operating in an autonomous mode;

calculating at least one expected friction change from friction data of at least one guideline friction map for the at least one travel segment, wherein the friction data of the at least one guideline friction map is aggregated from a plurality of vehicles that previously traveled on the at least one travel segment;

comparing the at least one sensed road-vehicle friction change to the at least one expected friction change; and

determining at least one response action to change the mode of operation of the autonomous vehicle from the autonomous mode to a manual mode based on the comparison.

2. The method of claim 1, further comprising:

determining at least one expected extent of the at least one expected friction change value for the at least one travel segment from the at least one guideline friction map.

3. The method of claim 1, further comprising:

determining that the at least one sensed road-vehicle friction change is similar to the at least one expected road friction change and that the at least one expected extent is less than at least one distance threshold; and

designating a continuation of the autonomous mode of operation as the at least one response action.

4. The method of claim 1, further comprising:

determining that the at least one sensed road-vehicle friction change is similar to the at least one expected road friction change and that the at least one expected extent is greater than at least one distance threshold; and

designating a change to the manual mode of operation as the at least one response action.

5. The method of claim 1, further comprising:

determining that the at least one sensed road-vehicle friction change is similar to the at least one expected road friction change and that the at least one sensed road-vehicle friction change is expected to be constant for the at least one travel segment; and

designating a change to at least one manual mode of operation as the at least one response action.

6. The method of claim 1, further comprising:

receiving at least one incident report regarding at least one friction related event associated with the at least one travel segment; and

determining whether to ignore the at least one incident report based on the at least one guideline friction map.

7. The method of claim 1, wherein the friction data is collected under standard conditions for the at least one travel segment, the one or more other travel segments, or a combination thereof.

8. A method of claim 1, wherein the friction data is collected over a period of time, the method further comprising:

processing the friction data to cause a removal of one or more time-varying features from the friction data prior to the generation of the at least one guideline friction map.

9. The method of claim 1, further comprising:

determining the at least one road-vehicle friction change based on a difference in a first friction value at a first point and a second friction value measured with respect to a same vehicle traveling between the first point and the second point.

10. The method of claim 1, wherein the at least one guideline friction map maps the friction data based on road surface conditions for a road network including the at least one travel segment.

11. The method of claim 1, further comprising:

presenting a notification to the user in the autonomous vehicle for effectuating the change in the mode of operation of the autonomous vehicle.

12. The method of claim 11, further comprising:

requesting a user interaction for authenticating the change in the mode of operation of the autonomous vehicle.

13. The method of claim 1, wherein the aggregation of the friction data of the at least one guideline friction map comprises:

calculating a friction profile for each of the plurality of vehicles used in generating the at least one guideline friction map, wherein the friction profile represents absolute friction values along the at least one travel segment for said each of the plurality of vehicles;

normalizing the absolute friction values based on a slope of the friction profile for said each of the plurality of vehicles,

wherein the friction data of the at least one guideline friction map is aggregated from the normalized absolute friction values.

14. The method of claim 13, further comprising:

calculating a friction bias based on the friction profile for said each of the plurality of vehicles; and

normalizing the absolute friction values based on the friction bias for said each of the plurality of vehicles.

15. 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,

process sensor data associated with an autonomous vehicle to determine at least one sensed road-vehicle friction change associated with at least one travel segment, wherein the autonomous vehicle is operating in an autonomous mode;

calculate at least one expected friction change from friction data of at least one guideline friction map for the at least one travel segment, wherein the friction data of the at least one guideline friction map is aggregated from a plurality of vehicles that previously traveled on the at least one travel segment;

compare the at least one sensed road-vehicle friction change to the at least one expected friction change; and

determine at least one response action to change a mode of operation of the autonomous vehicle from the autonomous mode to a manual mode based on the comparison.

16. The apparatus of claim 15, wherein the apparatus is further caused to:

determine at least one expected extent of the at least one expected friction change value for the at least one travel segment from the at least one guideline friction map.

17. The apparatus of claim 15, wherein the apparatus is further caused to:

determine that the at least one sensed road-vehicle friction change is similar to the at least one expected road friction change and that the at least one expected extent is less than at least one distance threshold; and

designate a continuation of the autonomous mode of operation as the at least one response action.

18. The apparatus of claim 15, wherein the apparatus is further caused to:

determine that the at least one sensed road-vehicle friction change is similar to the at least one expected road friction change and that the at least one expected extent is greater than at least one distance threshold; and

designate a change to at least one manual mode of operation as the at least one response action.

19. The apparatus of claim 15, wherein the apparatus is further caused to:

determine that the at least one sensed road-vehicle friction change is similar to the at least one expected road friction change and that the at least one sensed road-vehicle friction change is expected to be constant for the at least one travel segment; and

designate a change to at least one manual mode of operation as the at least one response action.

20. The apparatus of claim 15, wherein the apparatus is further caused to:

receive at least one incident report regarding at least one friction related event associated with the at least one travel segment; and

determine whether to ignore the at least one incident report based on the at least one guideline friction map.

21. A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

process sensor data associated with the autonomous vehicle to determine at least one sensed road-vehicle friction change associated with at least one travel segment, wherein the autonomous vehicle is operating in an autonomous mode;

calculate at least one expected friction change from friction data of at least one guideline friction map for the at least one travel segment, wherein the friction data of the at least one guideline friction map is aggregated from a plurality of vehicles that previously traveled on the at least one travel segment

compare the at least one sensed road-vehicle friction change to the at least one expected friction change guideline friction map; and

determine at least one response action to change a mode of operation of the autonomous vehicle from the autonomous mode to a manual mode based on the comparison.

22. The non-transitory computer-readable storage medium of claim 21, wherein the apparatus is further caused to perform:

determine at least one expected extent of the at least one expected friction change value for the at least one travel segment from the at least one guideline friction map.