Predicting a Behavior of a Road User

Abstract

A device and method predict a behavior of a road user. The device is configured to provide at least one hypothesis for the behavior of the road user, to provide, for each hypothesis, a hidden Markov model, the hidden Markov model including, for the particular hypothesis, two hidden states, with one of these hidden states representing the road user following the hypothesis and the other of these states representing the road user not following the hypothesis, and possible observations of the hidden Markov model characterizing, for the particular hypothesis, at least one feature of the road user, and to predict the behavior of the road user depending on the hidden states of the hidden Markov model for the at least one hypothesis.

Claims

1.-9. (canceled)

10. A device, comprising: a computer-implemented device that predicts a behavior of a road user, the device being operatively configured to: provide at least one hypothesis for the behavior of the road user, provide a hidden Markov model for each hypothesis, the hidden Markov model for a respective hypothesis comprising two hidden states, one of said two hidden states representing compliance with the hypothesis by the road user, and the other one of said two hidden states representing non-compliance with the hypothesis by the road user, and comprising possible observations of the hidden Markov model for the respective hypothesis characterizing at least one feature of the road user, and predict the behavior of the road user as a function of the two hidden states of the hidden Markov model for the at least one hypothesis.

11. The device according to claim 10, wherein the at least one feature of the road user is a quantifiable feature of the road user.

12. The device according to claim 10, wherein the possible observations of the hidden Markov model for the respective hypothesis characterize at least two mutually independent feature groups.

13. The device according to claim 10, wherein the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, a distance of the road user from a center of a traffic lane in which the road user is located.

14. The device according to claim 10, wherein the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, a deviation of an orientation of the road user relative to an orientation of a traffic lane in which the road user is located.

15. The device according to claim 10, wherein the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, an activation of a travel-direction indicator of the road user.

16. The device according to claim 10, wherein the possible observations of the hidden Markov model for the respective hypothesis include a feature that is characteristic of a yielding behavior of the road user.

17. The device according to claim 10, wherein the device is further operatively configured to: ascertain a traffic situation in which the road user is located, and ascertain the at least one hypothesis for the behavior of the road user as a function of this traffic situation.

18. A method for predicting a behavior of a road user, the method comprising the steps of: providing at least one hypothesis for the behavior of the road user; providing a hidden Markov model for each hypothesis, the hidden Markov model for the respective hypothesis comprising two hidden states, one of said two hidden states representing compliance with the hypothesis by the road user, and the other one of said two hidden states representing non-compliance with the hypothesis by the road user, and comprising possible observations of the hidden Markov model for the respective hypothesis characterizing at least one feature of the road user; and predicting the behavior of the road user as a function of the hidden states of the hidden Markov model for the at least one hypothesis.

19. The method according to claim 18, wherein the at least one feature of the road user is a quantifiable feature of the road user.

20. The method according to claim 18, wherein the possible observations of the hidden Markov model for the respective hypothesis characterize at least two mutually independent feature groups.

21. The method according to claim 18, wherein the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, a distance of the road user from a center of a traffic lane in which the road user is located.

22. The method according to claim 18, wherein the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, a deviation of an orientation of the road user relative to an orientation of a traffic lane in which the road user is located.

23. The method according to claim 18, wherein the possible observations of the hidden Markov model for the respective hypothesis include, by way of a feature, an activation of a travel-direction indicator of the road user.

24. The method according to claim 18, wherein the possible observations of the hidden Markov model for the respective hypothesis include a feature that is characteristic of a yielding behavior of the road user.

25. The method according to claim 18, wherein the method further comprises the steps of: ascertaining a traffic situation in which the road user is located, and ascertaining the at least one hypothesis for the behavior of the road user as a function of this traffic situation.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG. 1 shows an exemplary traffic situation;

[0044] FIG. 2 shows an exemplary embodiment of the device according to the invention; and

[0045] FIG. 3 shows a further exemplary embodiment of the device according to the invention.

DETAILED DESCRIPTION OF THE DRAWINGS

[0046] FIG. 1 shows an exemplary traffic situation. In this traffic situation, a road user VT is moving toward an intersection at which it has three different behavior options. It can turn off to the left, drive straight through the intersection, or turn off to the right.

[0047] A first hypothesis h1 for the behavior of the road user VT is that the road user VT will turn off to the left.

[0048] A second hypothesis h2 for the behavior of the road user VT is that the road user VT will drive straight through the intersection.

[0049] A third hypothesis h3 for the behavior of the road user VT is that the road user VT will turn off to the right.

[0050] FIG. 2 shows an exemplary embodiment of the device PV according to the invention for predicting a behavior of a road user VT.

[0051] The device PV has been set up to provide at least one hypothesis h1, h2, h3 for the behavior of the road user VT. For this purpose, the device PV has been set up, in particular, to ascertain a traffic situation in which the road user VT is located, and to ascertain the at least one hypothesis h1, h2, h3 for the behavior of the road user VT as a function of this traffic situation. If, for instance, the road user VT is moving toward an intersection, as shown in FIG. 1, the traffic lanes leading away from the intersection may serve as hypotheses h1, h2, h3.

[0052] In addition, the device PV has been set up to provide a hidden Markov model for each hypothesis h1, h2, h3.

[0053] The hidden Markov model comprises two hidden states s1,1-s3,2 for the respective hypothesis—that is to say, hidden states s1,1 and s1,2 for hypothesis h1, hidden states s2,1 and s2,2 for hypothesis h2, and hidden states s3,1 and s3,2 for hypothesis h3.

[0054] In each instance, one of these hidden states—namely, for instance, hidden states s1,1, s2,1 and s3,1—represents compliance with the respective hypothesis h1, h2, h3 by the road user VT.

[0055] The respective other one of these states—that is to say, states s1,2, s2,2 and s3,2—represents non-compliance with the respective hypothesis h1, h2, h3 by the road user VT.

[0056] Each hidden Markov model includes a predetermined probability of switching between its two states. Accordingly, the hidden Markov model for hypothesis h1, for instance, switches from state s1,1 to state s1,2 with probability ps12, and from state s1,2 to state s1,1 with probability psi 1. The hidden Markov model for hypothesis h2, for instance, switches from state s2,1 to state s2,2 with probability ps22, and from state s2,2 to state s2,1 with probability ps21. The hidden Markov model for hypothesis h3, for instance, switches from state s3,1 to state s3,2 with probability ps32, and from state s3,2 to state s3,1 with probability ps31.

[0057] These probabilities are each 50%, for instance.

[0058] The respective hidden Markov models include, in addition, possible observations b1-b6. The possible observations b1-b6 characterize at least one feature of the road user VT, in particular a quantifiable feature of the road user VT.

[0059] The possible observations b1-b6 of the hidden Markov models for hypotheses h1, h2, h3 characterize at least two feature groups m1, m2 modeled independently of one another to a limited extent.

[0060] For instance, the possible observations b1, b2 of the hidden Markov model for hypothesis h1 characterize a distance of the road user VT from a center of a traffic lane in which the road user VT is located. In addition, the possible observations b3, b4 of the hidden Markov model for hypothesis h2 characterize, for instance, a deviation of an orientation of the road user VT relative to an orientation of a traffic lane in which the road user VT is located.

[0061] For each hidden state s1,1-s3,2 there is a certain probability p11,1-p32,2 that the respective possible observation b1-b6 is actually observed.

[0062] The device PV has, in addition, been set up to predict the behavior of the road user VT as a function of the hidden states s1,1-s3,2 of the hidden Markov model for the at least one hypothesis h1, h2, h3.

[0063] FIG. 3 shows a further exemplary embodiment of the device PV according to the invention for predicting a behavior of a road user VT.

[0064] The device PV has been set up to provide at least one hypothesis h1 for the behavior of the road user VT, and to provide a hidden Markov model for this hypothesis h1.

[0065] The hidden Markov model comprises two hidden states s1,1; s1,2 for hypothesis h1, one of these hidden states s1,1 representing compliance with hypothesis h1 by the road user VT, and the other one of these states s1,2 representing non-compliance with hypothesis h1 by the road user VT.

[0066] The possible observations b1-b4 of the hidden Markov model for hypothesis h1 characterize at least one feature of the road user VT. In this case, the possible observations b1-b4 of the hidden Markov model for hypothesis h1 characterize two mutually independent feature groups m1, m2, feature group m1 comprising observations b1 and b2, and feature group m2 comprising observations b3 and b4.

[0067] For instance, possible observations b1 and b2 of the hidden Markov model for hypothesis h1 characterize an activation of a travel-direction indicator of the road user VT, and possible observations b3 and b4 of the hidden Markov model for hypothesis h1 characterize a feature that is characteristic of a yielding behavior of the road user VT.

[0068] Alternatively, b1 characterizes a distance from the center line, b2 characterizes a difference of orientation relative to the lane, b3 characterizes a turn-indicator state in the current time-step, and b4 characterizes a priority feature which is computed, for instance, from at least one traffic rule and an acceleration behavior or deceleration behavior.

[0069] For the sake of clarity, in FIG. 3—in comparison with FIG. 2—no probabilities have been represented for transition between the hidden states s1,1 and s1,2, and also no probabilities for the possible observations b1-b4 in the hidden states s1,1 and s1,2.

[0070] The device PV has been set up to predict the behavior of the road user VT as a function of the hidden states s1,1 and s1,2 of the hidden Markov model for hypothesis h1.