CONFIDENCE ESTIMATION FOR PREDICTIVE DRIVER ASSISTANCE SYSTEMS BASED ON PLAUSIBILITY RULES

• US 20150112570A1
• Filed: 10/22/2014
• Published: 04/23/2015
• Est. Priority Date: 10/22/2013
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: SCHMDDERICH, Jens
• Granted Patent: US 9,889,858 B2
• Time in Patent Office: Days
• US Class Current: 701/93
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2556/20 Data confidence level
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/18163 Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/9316 combined with communication equipment with other vehicles or with base stations
• CPC Class Codes: G01S 2013/9322 using additional data, e.g. driver condition, road state or weather data
• CPC Class Codes: G01S 7/411 Identification of targets based on measurements of radar reflectivity G01S7/415 takes precedence
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

The invention relates to a driving assistance system including a prediction subsystem in a vehicle. According to a method aspect of the invention, the method comprises the steps of accepting an environment representation; calculating a confidence estimate related to the environment representation based on applying plausibility rules on the environment representation, and providing the confidence estimate as input for an evaluation of a prediction based on the environment representation.

Claims

1. A method for a prediction subsystem in a driving assistance system of a vehicle,

the method comprising the following steps:

accepting an environment representation representing at least one entity detected by one or more sensors in an environment of the vehicle;

calculating a confidence estimate related to the environment representation based on applying one or more plausibility rules on the environment representation, wherein each plausibility rule comprises a plausible relation between the at least one entity and at least one of another entity and a previous detection of the at least one entity; and

providing the confidence estimate as input for an evaluation of a prediction based on the environment representation.

2. The method according to claim 1, wherein a plausibility rule comprises a condition which returns a binary value representing either true or false when applied to the environment representation.

3. The method according to claim 1, wherein a plausibility rule comprises a numerical expression which returns a numerical value when applied to the environment representation.

4. The method according to claim 1, wherein the confidence estimate is assigned a numerical value representing a confidence of the associated environment representation.

5. The method according to claim 4, wherein the confidence is expressed as a probability value.

6. The method according to claim 1, wherein applying one of the plausibility rules comprises computing a confidence estimate as one of:

setting a predefined value (c_start) for the confidence estimate indicating a plausible detection of the environment representation,

setting a predefined value (c_low) for the confidence estimate indicating an implausible detection of the environment representation, and

increasing or decreasing a value of the confidence estimate by a predefined amount (Δ

c).

7. The method according to claim 1, wherein calculating the confidence estimate comprises calculating a combination of multiple confidence estimates, resulting from applying multiple plausibility rules, based on at least one of a summation, weighted summation, product, weighted product, and selecting a minimum or maximum.

8. The method according to claim 1, wherein the evaluation comprises deciding on whether or not the environment representation is used for a prediction by comparing a numerical value of the confidence estimate with a numerical value of at least one of a predefined threshold value and numerical values of one or more other confidence estimates.

9. The method according to claim 1, wherein the driving assistance system is adapted to perform a cruise control functionality.

10. The method according to claim 1, wherein the driving assistance system is adapted to perform at least one of an active control and passive control based on the prediction.

11. A computer program product comprising program code portions for performing the method according to claim 1 when the computer program product is executed on a computing device.

12. A driving assistance system for a vehicle, the driving assistance system including a prediction subsystem and comprising:

a component adapted to accept an environment representation representing at least one entity detected by one or more sensors in an environment of the vehicle;

a component adapted to calculate a confidence estimate related to the environment representation based on applying one or more plausibility rules on the environment representation, wherein each plausibility rule comprises a plausible relation between the at least one entity and at least one of another entity and a previous detection of the at least one entity; and

a component adapted to provide the confidence estimate as input for an evaluation of a prediction based on the environment representation.

13. The system according to claim 12, wherein the prediction subsystem is adapted to perform a context based prediction and a physical prediction.

14. A vehicle comprising a system according to claim 12.