METHOD FOR OPERATING A MOTORIZED TWO-WHEELED VEHICLE, IN PARTICULAR A MOTORCYCLE, AND COMPUTER PROGRAM FOR CARRYING OUT THE METHOD

Abstract

A method and computer program are provided for operating a motorized two-wheeled vehicle, in particular a motorcycle that includes a sensor system for accident recognition that generates measuring signals. The sensor system is used for recognizing a rotation of a front wheel of the two-wheeled vehicle that deviates from a normal steering movement and allows an inference concerning a collision of the two-wheeled vehicle with another object. Moreover, the invention relates to a computer program for carrying out the method.

Claims

1. A method for a motorized two-wheeled vehicle that includes a sensor system, the method comprising: the sensor system generating a signal indicating a rotation of a front wheel of the two-wheeled vehicle that deviates from a normal steering movement; and based on the deviation indicated by the signal, determining occurrence of a collision of the two-wheeled vehicle with another object.

2. The method of claim 1, wherein the two-wheeled vehicle is a motorcycle.

3. The method of claim 1, wherein the sensor system includes an acceleration sensor that detects, for the generation of the signal, an acceleration of the front wheel in at least one spatial direction.

4. The method of claim 3, wherein the acceleration sensor is a multichannel acceleration sensor.

5. The method of claim 1, wherein the sensor system detects, for the generation of the signal, a yaw rate of the front wheel.

6. The method of claim 1, wherein the sensor system includes a rotation rate sensor that detects, for the generation of the signal, a yaw rate of the front wheel.

7. The method of claim 1, wherein the signal is evaluated in an evaluation unit of the sensor system.

8. The method of claim 1, wherein the signal is centrally evaluated in a control unit of the two-wheeled vehicle that is separate from the sensor system.

9. The method of claim 1, wherein the signal is processed as a raw signal for the determination of the occurrence.

10. The method of claim 1, further comprising preprocessing the signal to mask undesirable signal components, wherein the determination is made using the preprocessed signal.

11. The method of claim 10, wherein the preprocessing includes filtering.

12. The method of claim 10, wherein the preprocessing includes integrating.

13. The method of claim 1, further comprising performing a time correlation of the signal over time, and identifying a change in the signal based on the time correlation, wherein the determination is based on the identified change.

14. The method of claim 13, further comprising comparing the change to a predefined threshold value, wherein the determination is based on a result of the comparison.

15. The method of claim 14, wherein the threshold value differs for different two-wheeled vehicles.

16. The method of claim 1, further comprising comparing the signal to a predefined threshold value, wherein the determination is based on a result of the comparison.

17. The method of claim 1, further comprising detecting, using the sensor system, a deceleration of the front wheel in at least one spatial direction, wherein the determination is further based on the detected deceleration.

18. The method of claim 18, wherein the at least one spatial direction is a longitudinal direction of the vehicle.

19. The method of claim 1, further comprising detecting, using the sensor system, a speed of the front wheel, wherein the determination is further based on the detected deceleration.

20. The method of claim 1, further comprising, responsive to the determination of the occurrence of the collision, activating a protection device.

21. The method of claim 20, wherein the protection device is an airbag.

22. The method of claim 1, further comprising, responsive to the determination of the occurrence of the collision, activating an emergency measure.

23. The method of claim 22, wherein the emergency measure includes placing a call.

24. A non-transitory computer-readable medium on which are stored instructions (a) that are executable by a processor of a motorized two-wheeled vehicle that includes a sensor system and (b) that, when executed by the processor, cause the processor to perform a method comprising: obtaining from the sensor system a signal indicating a rotation of a front wheel of the two-wheeled vehicle that deviates from a normal steering movement; and based on the deviation indicated by the signal, determining occurrence of a collision of the two-wheeled vehicle with another object.

Description

BRIEF DESCRIPTION OF THE DRAWING

[0029] FIG. 1 shows a perspective illustration of a motorized two-wheeled vehicle for carrying out a method according to an example embodiment of the present invention.

[0030] FIG. 2 is a flowchart illustrating the method according to an example embodiment of the present invention.

[0031] FIG. 3 is a schematic illustration of characteristic signal-time curves according to an example embodiment of the present invention.

[0032] FIG. 4 is a diagram illustrating an algorithm for carrying out the method according to an example embodiment of the present invention.

DETAILED DESCRIPTION

[0033] FIG. 1 shows a motorized two-wheeled vehicle 1, in particular a motorcycle that is equipped with a sensor system 2 for accident recognition. Sensor system 2 is situated in the area of a front wheel 3 of the motorcycle and is connected to a control unit 4 via a data-transmitting link 5. The data transmission can take place wirelessly or via a data line. Two-wheeled vehicle 1 illustrated in FIG. 1 is suitable for carrying out a method according to an example embodiment of the present invention.

[0034] FIG. 1 also illustrates a coordinate system that is used to define the three mutually orthogonal spatial directions x, y, z. Spatial direction x denotes the longitudinal direction, spatial direction y denotes the transverse direction, and spatial direction z denotes the vertical direction. When the motorcycle is traveling forward, it experiences an acceleration in the longitudinal direction (spatial direction x). In the present case, the acceleration is detected by sensor system 2 situated at front wheel 3. The acceleration of front wheel 3 in the transverse direction and the vertical direction is detected at the same time. The function of sensor system 2 is to recognize a rotation of front wheel 3 that differs from a normal steering movement, which indicates a collision of two-wheeled vehicle 1 with another object. Since front wheel 3 is rotated quickly and abruptly during a collision, a corresponding process can be detected via measuring signals concerning the linear acceleration of front wheel 3 in at least one spatial direction x, y, z, preferably in the longitudinal and the transverse directions. These measuring signals are referred to below as signal Sx and signal Sy, depending on which spatial direction they relate to.

[0035] As is apparent in the block diagram of FIG. 2, signals Sx and Sy generated using sensor system 2 initially undergo a feature extraction 10, in particular using values that exceed and/or fall below a threshold value within preset time intervals. Feature extraction 10 is used to detect changes in signals that define individual events.

[0036] A time correlation 11 of all individual events is subsequently carried out, for example via a timer query with signal threshold value comparison.

[0037] Final assessment 12 is then made in a further method step, in particular by logical combination or use of a combinational logic system that is preferably stored in control unit 4. The result of the assessment can be the detection of a collision, for example.

[0038] A signal-time curve that is characteristic in the event of a collision, in particular separately for signals Sx and Sy, is apparent from FIG. 3 by way of example. Indication Tort marks the point in time of collision, which can possibly be subsequently ascertained. By comparing with predefined threshold values within preset time intervals, deviations can be recognized that indicate a wheel flip situation.

[0039] FIG. 4 illustrates by way of example one possible characteristic form of an algorithm for wheel flip recognition, in particular based on the values taken from FIG. 3.