MULTI-MODAL SPEED LIMIT ASSISTANT

• US 20100188288A1
• Filed: 10/07/2008
• Published: 07/29/2010
• Est. Priority Date: 10/09/2007
• Status: Active Grant

• Current Assignee: Siemens AG
• Original Assignee: Siemens Corporate Research Incorporated (Siemens AG)
• Inventors: Pellkofer, Martin, Baratoff, Gregory, Giebel, Jan, Bahlmann, Claus
• Granted Patent: US 8,725,394 B2
• Time in Patent Office: Days
• US Class Current: 342/357.230
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/42 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/146 Speed limiting
• CPC Class Codes: G01S 19/14 specially adapted for specific applications
• CPC Class Codes: G06V 20/582 of traffic signs
• CPC Class Codes: G08G 1/0962 having an indicator mounted inside the vehicle, e.g. giving voice messages

Abstract

A speed limit assistant (SLA) system includes a camera based SLA, a map based SLA, and a fusion unit. The camera based SLA is configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs. The map based SLA is configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits. The fusion unit is configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

Claims

1. A method of determining a speed limit, the method comprising;

receiving a first probability distribution from a camera based speed limit assistant (SLA) system, wherein the first probability distribution includes probabilities that an image processed by the camera based SLA corresponds to a discrete set of speed limits;

receiving a second probability distribution from a global positioning system (GPS), wherein the second probability distribution includes probabilities that a coordinate processed by the GPS corresponds to the discrete set of speed limits; and

determining a final speed limit from the discrete list of speed limits based on the first and second probability distributions.

2. The method of claim 1, wherein determining the final speed limit comprises performing a Bayesian fusion on the first and second probability distributions.

3. The method of claim 1, wherein the second probability distribution corresponds to one of a discrete set of street types.

4. The method of claim 3, wherein the street type has upper and lower boundary speed limits, and the probabilities of the second probability distribution that are outside the boundaries are lower than the other probabilities of the second probability distribution.

5. The method of claim 3, wherein the street type has a recommended speed limit, and the probabilities of the second probability distribution that are not equal to the recommended speed limit are lower than the other probabilities of the second probability distribution.

6. The method of claim 2, wherein the performing the Bayesian fusion comprises:

multiplying probabilities of the first and second probability distributions for each of the corresponding speed limits to generate product values;

determining a highest one of the product values; and

setting the final speed limit to the speed limit corresponding to the highest determined product value.

7. The method of claim 6, wherein the product values are normalized before the highest one is determined.

8. A method of determining a speed limit, the method comprising:

determining a type of a street that corresponds to a global positioning system (GPS) coordinate from a discrete list of street types;

determining a discrete set of speed limits that correspond to the determined street type;

determining a first set of probabilities for each tuple comprising the determined street type and a corresponding one of the speed limits;

receiving a second set of probabilities from a camera based speed limit assistant (SLA) system based on an image of a speed limit sign, wherein the second set of probabilities correspond to each of the speed limits; and

determining a final speed limit from the discrete set of speed limits based on the first and second set of probabilities.

9. The method of claim 8, wherein determining the final speed limit comprises performing a Bayesian fusion on the first and second sets of probabilities.

10. The method of claim 8, wherein the street type has a corresponding maximum and minimum speed limit values, and the probabilities of the tuples including speed limits outside the minimum and maximum are set to zero.

11. The method of claim 8, wherein performing the Bayesian fusion comprises:

multiplying the first and second probabilities for each of the corresponding speed limits to generate product values;

determining a highest one of the product values; and

setting the final speed limit to the speed limit corresponding to the highest determined product value.

12. The method of claim 11, wherein the product values are normalized before the highest one is determined.

13. A speed limit assistant (SLA) system comprising:

a camera based SLA configured to determine a first set of probabilities for an input image, wherein the probabilities indicate how likely the image includes a discrete set of speed limit signs;

a map based SLA configured to determine a second set of probabilities for an input coordinate, wherein the probabilities indicate how likely the coordinate is to correspond to one of a discrete set of speed limits; and

a fusion unit configured to perform a Bayesian fusion on the first and second set of probabilities to determine a final speed limit from the discrete set of speed limits.

14. The SLA system of claim 13, further comprising a global positioning system (GPS) to provide the coordinate.

15. The SLA system of claim 13, further comprising a database of road segment context tuples and a probability lookup table, wherein each tuple includes a type of the road segment and a recommended speed limit for that type, and the probability lookup table includes probabilities for each tuple for each of the discrete set of speed limits.

16. The SLA system of claim 15, wherein the probabilities of the lookup table that correspond to speed limits above the recommended speed limit are set to a first value, the probabilities of the lookup table that correspond to speed limits below the recommended speed limit are set to a second value, and the probabilities of the lookup table that correspond to speed limits equal to the recommended speed limit are set to a third value, wherein the third value exceeds the second value and the second value exceeds the first value.

17. The SLA system of claim 13, wherein the fusion unit determines the final speed limit by multiplying the first and second probabilities for each of the corresponding speed limits to generate product values and sets the final speed limit to the speed limit corresponding to the highest generated product value

18. A program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for determining a speed limit, the method steps comprising:

determining a recommended speed limit for a global positioning system (GPS) coordinate;

determining a first set of probabilities for a discrete set of speed limits at and below the recommended speed limit;

receiving a second set of probabilities from a camera based speed limit assistant (SLA) system based on an image of a speed limit sign, wherein the second set of probabilities correspond to each of the speed limits; and

determining a final speed limit from the discrete set of speed limits based on the first and second set of probabilities.

19. The program storage device of claim 18, wherein the determining of the recommended speed limit comprises:

determining a road segment from a table of road segments that corresponds to the coordinate;

determining a type of the road segment; and

determining the recommended speed limit from a table of recommended speed limits using the type.

20. The program storage device of claim 18, wherein determining the final speed limit comprises performing a Bayesian Fusion on the first and second sets of probabilities.