BEHAVIOR-BASED LEARNING OF VISUAL CHARACTERISTICS FROM REAL-WORLD TRAFFIC SCENES FOR DRIVER ASSISTANCE SYSTEMS

• US 20110095879A1
• Filed: 10/26/2010
• Published: 04/28/2011
• Est. Priority Date: 10/28/2009
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: DITTES, Benjamin, HERACLES, Martin
• Granted Patent: US 8,593,272 B2
• Time in Patent Office: Days
• US Class Current: 340/441
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/06 Improving the dynamic response of the control system, e.g. improving the speed of regulation or avoiding hunting or overshoot
• CPC Class Codes: B60W 50/10 Interpretation of driver requests or demands

Abstract

A computer-implemented method for setting-up and operating a driver assistance system,

the method comprising the steps of:

• • visually sensing the environment of a vehicle,
  • sensing at least one rate of change of a parameter representing a status of the car, which parameter can be influenced by a driver manipulating a man/machine interface of the vehicle,
  • structuring information gathered by the visual sensing by classifying the at least one sensed rate of change in different categories of e, in order to find visual features associated to a certain category of sensed rate of change and thus associated with a certain behavior of the driver.

Claims

1. A computer-implemented method for setting-up and operating a driver assistance system,

the method comprising the steps of:

visually sensing the environment of a vehicle,

sensing at least one rate of change of a parameter representing a status of the car, which parameter can be influenced by a driver manipulating a man/machine interface of the vehicle,

structuring information gathered by the visual sensing by classifying the at least one sensed rate of change in different categories, in order to find visual features associated to a certain category of sensed rate of change and thus associated with a certain behavior of the driver.

2. The method according to claim 1,

wherein, as a function of the structured information, a visual or acoustical signal is generated, or an effector of the vehicle, such as e.g. a steering wheel, an accelerator or a brake is operated.

3. The method according to claim 1,

wherein the step of structuring comprises:

splitting the visually sensed data stream in at least two categories of the at least one sensed rate of change, such as “

turning left”

, “

turning right”

or “

accelerating”

, preferably only in case the sensed rate of change exceed a predefined threshold value.

4. The method according to claim 1,

wherein one additional category is provided for portions of the visually sensed data stream in time period during which no sensed rate of change was present or did not exceeded a predefined threshold value.

5. The method according to claim 3,

comprising the step of preparing a histogram indicating how frequent a visual feature out of a set of different visual features occurred while the sensed rate of change indicated the occurrence of a category of a driver'"'"'s behavior.

6. The method according to claim 5,

wherein the set of visual features is gained by comparing the split classified data streams for the categories exceeding the threshold value with the data stream portion of the time periods during which the threshold values were not reached.

7. A computing unit, designed to carry out a method according to claim 1.

8. A driver assistance system, equipped with a computing unit according to claim 7,

furthermore comprising visual or acoustical indicating means, and/or an interface for supplying control commands to at least one effector of the vehicle.

9. A computer software program product, performing a method according to claim 1 when run on a computing device.

10. A land, sea or air vehicle, comprising a driver assistance system according to claim 8.

11. An autonomous robot, comprising a driver assistance system according to claim 8.