METHOD AND DEVICE FOR DETERMINING A DRIVING BEHAVIOR

Abstract

A method for ascertaining a driving behavior of a driver of a vehicle includes: acquiring a three-dimensional signal of an acceleration sensor, the three-dimensional signal including a respective acceleration value in each of independent spatial directions; calculating a characteristic variable of the three-dimensional signal; and outputting the driving behavior based on the characteristic variable, via an output device, the characteristic variable being a measure of an aggressiveness of a driving behavior, and the characteristic variable including a fractal dimension of an embedding of the three-dimensional signal and/or a Kolmogorov entropy of the three-dimensional signal.

Claims

1-10. (canceled)

11. A method comprising: acquiring a three-dimensional signal of an acceleration sensor, the three-dimensional signal including a respective acceleration value in each of a plurality of spatial directions; calculating a characteristic variable of the three-dimensional signal, wherein the characteristic variable: includes a fractal dimension of an embedding of the three-dimensional signal and/or a Kolmogorov entropy of the three-dimensional signal; and is a measure of an aggressiveness of a driving behavior of a driver of a vehicle; determining the driving behavior based on the characteristic value; and outputting the determined driving behavior.

12. The method of claim 11, wherein: the characteristic variable includes the Kolmogorov entropy of the three-dimensional signal; different probability distributions are predefined for the Kolmogorov entropy of the three-dimensional signal; and a respective predefined driving behavior is assigned to each probability distribution.

13. The method of claim 11, wherein the characteristic variable includes the Kolmogorov entropy of the three-dimensional signal, and the Kolmogorov entropy is such that the greater is a value of the Kolmogorov entropy of the three-dimensional signal, the greater the aggressiveness that is indicated by the value of the Kolmogorov entropy.

14. The method of claim 11, wherein: the characteristic variable includes the fractal dimension of an embedding of the three-dimensional signal; and the embedding takes place through a nonlinear transformation of the three-dimensional signal of the acceleration sensor that separates an acceleration/braking portion of the three-dimensional signal from a curved travel portion of the three-dimensional signal.

15. The method of claim 14, further comprising: ascertaining a first fractal dimension for the acceleration/braking portion and a second fractal dimension for the curved travel portion, wherein a higher of the first and second fractal dimensions is used as the characteristic variable.

16. The method of claim 11, wherein: the characteristic variable includes the fractal dimension of an embedding of the three-dimensional signal; and a plurality of intervals of fractal dimensions are predefined, with a different driving behavior being assigned to each of the intervals.

17. The method of claim 11, wherein the characteristic variable includes the fractal dimension of an embedding of the three-dimensional signal, and the fractal dimension is such that the greater is a value of the fractal dimension, the greater the aggressiveness that is indicated by the value of the fractal dimension.

18. The method of claim 11, wherein the characteristic variable is ascertained from an unfiltered and/or unprocessed three-dimensional signal of the acceleration sensor.

19. A non-transitory computer-readable medium on which are stored instructions that are executable by a processor and that, when executed by the processor, cause the processor to perform a method, the method comprising: acquiring a three-dimensional signal of an acceleration sensor, the three-dimensional signal including a respective acceleration value in each of a plurality of spatial directions; calculating a characteristic variable of the three-dimensional signal, wherein the characteristic variable: includes a fractal dimension of an embedding of the three-dimensional signal and/or a Kolmogorov entropy of the three-dimensional signal; and is a measure of an aggressiveness of a driving behavior of a driver of a vehicle; determining the driving behavior based on the characteristic value; and outputting the determined driving behavior

20. A device comprising: an acceleration sensor configured to acquire acceleration values in each of three spatial directions; an output; and a control device that is connected to the acceleration sensor and to the output; wherein the control device is configured to: acquire a three-dimensional signal of the acceleration sensor, the three-dimensional signal including a respective acceleration value in each of the three spatial directions; calculate a characteristic variable of the three-dimensional signal, wherein the characteristic variable: includes a fractal dimension of an embedding of the three-dimensional signal and/or a Kolmogorov entropy of the three-dimensional signal; and is a measure of an aggressiveness of a driving behavior of a driver of a vehicle; determine the driving behavior based on the characteristic value; and output the determined driving behavior via the output.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] FIG. 1 is a schematic flowchart of a method according to an example embodiment of the present invention.

[0027] FIG. 2 is a schematic view of a device according to an example embodiment of the present invention.

[0028] FIG. 3 is a schematic diagram of the course of a determination of a Kolmogorov entropy, according to an example embodiment of the present invention.

[0029] FIG. 4 is a schematic diagram of an assignment of different driving behaviors to different values of the Kolgomorov entropy, according to an example embodiment of the present invention.

[0030] FIG. 5 is a schematic diagram of a course of an embedding through nonlinear transformation, according to an example embodiment of the present invention.

[0031] FIG. 6 is a schematic diagram of a first three-dimensional signal of an acceleration sensor after the embedding, according to an example embodiment of the present invention.

[0032] FIG. 7 is a schematic diagram of a second three-dimensional signal of an acceleration sensor after the embedding, according to an example embodiment of the present invention.

[0033] FIG. 8 is a schematic diagram of a third three-dimensional signal of an acceleration sensor after the embedding, according to an example embodiment of the present invention.

DETAILED DESCRIPTION

[0034] FIG. 1 schematically shows a sequence plan of a method according to an example embodiment of the present invention. FIG. 2 shows a device 1 according to an example embodiment of the present invention. It is provided that device 1 can be attached to a vehicle in order to ascertain a driving behavior of the driver of the vehicle based on the method.

[0035] Device 1 includes an acceleration sensor 2, an output device 3, and a control device 4. Control device 4 is connected to acceleration sensor 2 and to output device 3 for signal transmission. In addition, control device 4 is preferably set up to carry out the method shown in FIG. 1.

[0036] The method includes the following steps: first, there is an acquisition 100 of a three-dimensional signal of acceleration sensor 1. For this purpose, acceleration sensor 1 can acquire an acceleration in three independent spatial directions x, y, and z. Thus, the three-dimensional signal indicates an acceleration value for each spatial direction. However, no information can be derived from the three-dimensional signal about concrete accelerations of the vehicle, because it is not known which of the spatial directions have which orientations in the vehicle. Because a calibration of acceleration sensor 2 inside the vehicle is complicated and often imprecise, the present invention dispenses with the requirement of such a calibration.

[0037] There subsequently follows a calculation 200 of a characteristic variable of the three-dimensional signal. The calculation 200 can in particular be done in two different ways. In both cases, it is advantageous that a driving behavior can be ascertained without the orientations of the spatial axes x, y, z having to be known.

[0038] One possibility for carrying out calculation 200 of the characteristic variable includes an embedding 210 of the three-dimensional signal and a subsequent determination 220 of a fractal dimension of the signal. This possibility is described below with reference to FIGS. 5-8. Alternatively, a determination 230 of a Kolmogorov entropy of the three-dimensional signals can be carried out. This is described below with reference to FIGS. 3 and 4. Thus, the characteristic variable is either the fractal dimension or the Kolmogorov entropy. A combination of these is also possible.

[0039] The calculated characteristic variable is in particular a measure of the driving behavior. Thus, there takes place a step of outputting 300 of the driving behavior via an output device 3, based on the characteristic variable. Output device 3 is advantageously a transmit station, so that the driving behavior can be sent to a receiver. In this way, the driving behavior of different drivers can be stored by a central unit and further processed. A local storing of the ascertained driving behavior in the respective devices 1 is also possible.

[0040] The three-dimensional signal of acceleration sensor 2 includes in particular an acceleration/braking portion, a curved travel portion, and a noise portion. All these portions are superposed to form the three dimensional signal. If the characteristic variable is calculated through the embedding 210 and determination 200 of the fractal dimension, the signal is partitioned, at least with regard to the acceleration/braking portion and the curved travel portion. In contrast, in the determination of the Kolmogorov entropy such a partitioning is not required.

[0041] In the following, based on FIGS. 3 and 4, it is explained how the driving behavior can be ascertained using the Kolmogorov entropy as characteristic variable. For this purpose, the Kolmogorov entropy is determined in three dimensions (1, 2, 3) K=(K1, K2, K3), using correlation integrals, as follows:

[00001]$K=f\left(K.Math..Math.1,K.Math..Math.2,K.Math..Math.3\right)=\underset{i=l}{\overset{3}{.Math.}}.Math.\underset{m.fwdarw.}{\lim }.Math.\underset{r.fwdarw.0}{\lim }.Math.K_i^m\left(r\right).$

Here,

[0042] [00002]$K_i^m\left(r\right)=\frac{1}{k.Math..Math.t}.Math.\ln .Math.\frac{P^m\left(r\right)}{P^{m+k}\left(r\right)};$

l=1, 2, 3 represents the three dimensions of the signal of acceleration sensor 2; k is a constant, in particular an adequately small integer; m is the dimension of the embedding; and P.sup.m(r) is the spectrum of the signal of acceleration sensor 2, stored in particular in a buffer.

[0043] FIG. 3 shows as an example how the characteristic variable K of the signal is ascertained. This characteristic value is a measure of the driving behavior. Here, high values of K mean that the driving behavior is to be evaluated as aggressive.

[0044] In order to avoid fluctuations, the above-described equation

[00003]$K_i^m\left(r\right)=\frac{1}{k.Math..Math.t}.Math.\ln .Math.\frac{P^m\left(r\right)}{P^{m+k}\left(r\right)}$

is averaged over four different starting values of the buffer, while at the same time the functional dependence of the characteristic K(1,2,3) on m is approximated by the following function, using the method of least squares:

[00004]$K_{i=1,2,3}^m\left(r\right)=K_2+\frac{v}{4.Math.L.Math..Math..Math..Math.t}.Math.\underset{i=1}{\overset{L}{.Math.}}.Math.\frac{1}{l.Math.}.Math.\ln \left(\frac{m+2.Math.l}{m}\right)$

[0045] FIG. 4 schematically shows some probability distributions of the Kolmogorov entropy K of the three-dimensional signal. Here, a driving behavior is assigned to each probability distribution. Thus, the solid line in FIG. 4 for example indicates a normal driving behavior, the dotted line indicates a moderate driving behavior, and the dashed line indicates an aggressive driving behavior. As described above, increasing values of the Kolmogorov entropy K indicate an increasingly aggressive driving behavior. Thus, FIG. 4 shows that, given aggressive driving behavior, high values of the Kolmogorov entropy K are most probable, while in the case of moderate driving behavior medium values of the Kolmogorov entropy K are most probable. In the case of normal driving behavior, small values of the Kolmogorov entropy K are most probable.

[0046] Through the categorization shown in FIG. 4, a driving behavior of the driver can be ascertained easily and at low expense from the three-dimensional acceleration signal. For this purpose, only the frequency distribution of the occurring values of the Kolmogorov entropy K is to be ascertained. Based on the probability distributions, this number can then be unambiguously assigned to a driving behavior.

[0047] FIGS. 5-8 show an alternative possibility for calculating the characteristic variable. The idea behind this is that the acceleration portion/braking portion can be approximated optimally by a manifold having low dimension. Through projection onto the stated manifold, there takes place a separation of the acceleration/braking portion from the curved travel portion.

[0048] For example, the three-dimensional signal can be as follows: {s.sub.n} (n=1, . . . , N), where N is the number of measurement points.

[0049] This three-dimensional signal can be unfolded into a multidimensional effective phase space, the following delay coordinates being used: s.sub.n=s.sub.n(m1)t, . . . , s.sub.n, where m=1, . . . , M, and where M is a size of the attractor and t is a delay.

[0050] Regarded mathematically, the three-dimensional signal is a scalar measurement of a deterministic dynamic system. Even if a deterministic dynamic system is not assumed here, serial functional dependencies are nonetheless present in the three-dimensional signal that have the result that the delay vectors s.sub.n fill the available m-dimensional space in an inhomogenous manner.

[0051] In order to carry out the embedding 210, first there is a selection 211 of three parameters: [0052] the length of the embedded window; [0053] the dimension d of the local manifold onto which projection is to take place; and [0054] the diameter d.sub.n of the neighborhood used for the linear approximation.

[0055] Using these parameters, an embedded transformation 212 into the phase space is carried out. The embedding window can be used to select components, and the neighborhood is used to define a length scaling in the phase space. These parameters thus represent a description for expressing the differences between the acceleration/braking portion and the curved travel portion. Here, the acceleration/braking portion has a much larger amplitude than does the curved travel portion, and the spectrum of the acceleration/braking portion appears shorter than the spectrum of the curved travel portion.

[0056] The ascertaining of the driving behavior of the driver takes place based on the characteristic variable of the fractal dimension. The larger the fractal dimension is, the greater the aggressiveness of the driving behavior. For this purpose, T is used as the topological dimension, FD as the fractal dimension, and H as the Hurst exponent. For the embedding, FD>2, because there are two spatial dimensions, and an additional dimension is to be seen in the image density of the spectrum of the acceleration/braking portion as well as of the spectrum of the curved travel portion. The parameters H and FD can be estimated based on the following equation: E[.sup.2f]=c[.sup.Hd].sup.2, where E is an expectation operator, f is an intensity operator, d is a spatial distance, and c is a scaling constant.

[0057] If, in this equation, the substitutions E=3FD and =E(|f|) are made, there then results E(|f|)= d.sup.H.

[0058] Application of the logarithmic function to both sides of this equation yields log E(|f|)=log +H log d.

[0059] The Hurst exponent H can be ascertained through linear regression using the method of least squares in order to estimate a gray level difference relative to k in a doubled logarithmic scale. Here, k varies from 1 to a maximum value s, and the following holds:

[00005]$\mathrm{GD}\left(k\right)=\frac{\underset{i=1}{\overset{N}{.Math.}}.Math.\underset{j=1}{\overset{N-k-1}{.Math.}}.Math..Math.I\left(i,j\right)-I(i,j+k.Math.+\underset{i=1}{\overset{N-k-1}{.Math.}}.Math.\underset{j=1}{\overset{N}{.Math.}}.Math..Math.I\left(i,j\right)-I(i+k,j.Math.}{2.Math.N\left(N-k-1\right)}$

[0060] The fractal dimension FD can be obtained from the equation FD=3H. A small value of the fractal dimension FD implies a large Hurst exponent, representing fine textures, while a large fractal dimension FD implies a small Hurst exponent H, representing coarse textures.

[0061] FIGS. 6-8 show individual examples of a three-dimensional signal transformed into the phase space. In FIG. 6, only an acceleration dynamic 400 is shown, while FIG. 7 shows both an acceleration dynamic 400 and a curved travel dynamic 500. Finally, FIG. 8 shows a pure curved travel dynamic 500. Acceleration dynamic 400 thus represents the acceleration/braking portion, while curved travel dynamic 500 represents the curved travel portion.

[0062] In order to ascertain the driving behavior, intervals can be defined that are each assigned to a driving behavior. Thus, for example, it can be defined that a driving behavior is to be regarded as normal given a fractal dimension of less than 2.1. Between 2.1 and 2.4, the driving behavior is to be regarded as moderate. However, if the fractal dimension exceeds 2.4, then the driving behavior is to be rated as aggressive.

[0063] In FIG. 6, the fractal dimension of the acceleration dynamic 400 is greater than 2.4, so that an aggressive behavior is ascertained. In FIG. 7, the fractal dimension of the curved travel dynamic 500 is indeed less than 2.1, which would permit inference of a normal driving behavior, but the fractal dimension for the acceleration dynamic 400 continues to be greater than 2.4. Therefore, in FIG. 7 as well, the driving behavior is to be regarded as aggressive, because here the larger value of the characteristic variable, i.e., of the fractal dimension, is always decisive.

[0064] Finally, FIG. 8 shows that only curved travel dynamic 500 is present. Here, the fractal dimension is less than 2.1. Thus, the driving behavior is to be rated as normal.

[0065] As described above, through the present invention inferences about the driving behavior can be made without having to filter the three-dimensional signal of the acceleration sensor. Calibration of the acceleration sensor is also not required. Thus, the driving behavior can be ascertained easily and with a low outlay.