METHOD AND DEVICE FOR AUTOMATING A DRIVING FUNCTION

Abstract

A method for automating a driving function. The method includes an activation of a process controlling the driving function is triggered using an activation condition modelled by a trigger, and the process is temporally controlled by an activation manager during the controlling of the driving function.

Claims

1. A method for automating a driving function, comprising the following steps: triggering, using an activation condition modelled by a trigger, an activation of a process controlling the driving function; and temporally controlling, by an activation manager, the process, during the controlling of the driving function.

2. The method as recited in claim 1, further comprising at least one of the following: the triggering is carried out due to a time-out, or the triggering is carried out by an interprocess communication, or the triggering is carried out periodically.

3. The method as recited in claim 2, wherein the interprocess communication is carried out via middleware interfaces.

4. The method as recited in claim 3, wherein the activation condition is modelled using a predefined modelling language.

5. The method as recited in claim 4, wherein states of the interfaces are expressed in the modelling language by Boolean variables, and the modeling language includes Boolean functions of the variables and comparison predicates for time stamps and sequence numbers.

6. The method as recited in claim 5, wherein the functions are expressed in the modelling language in disjunctive normal form.

7. The method as recited in claim 1, wherein the trigger is transferred to the process in the case of an activation.

8. A non-transitory machine-readable memory medium on which is stored a computer program for automating a driving function, the computer program, when executed by a computer, causing the computer to perform the following steps: triggering, using an activation condition modelled by a trigger, an activation of a process controlling the driving function; and temporally controlling, by an activation manager, the process, during the controlling of the driving function.

9. A device for automating a driving function, the device configured to: trigger, using an activation condition modelled by a trigger, an activation of a process controlling the driving function; and temporally control, by an activation manager, the process, during the controlling of the driving function.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Exemplary embodiments of the present invention are shown in the figures and are explained in greater detail below.

[0018] FIG. 1 shows the data flowchart of a method according to a first specific embodiment of the present invention.

[0019] FIG. 2 schematically shows a control unit according to a second specific embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0020] FIG. 1 illustrates the basic features of method 10 according to an example embodiment of the present invention on the basis of an automotive operating system. This provides on the one hand periodic 19 triggers or stimuli 11 for activating 13 a process 14, as they are known from the related art. On the other hand, an activation 13as subsequently describedmay be carried out on the basis of predefined events 17, 18.

[0021] Middleware-centric stimuli from the IPC 18 are preferably considered as input variables for activation condition 12. These potentially include individual sample values (samples), which are transmitted via the middleware. Basically, all types of event-based stimuli 11 are considered which are encompassed by the model.

[0022] Moreover, an activation 13 is preferably provided in the absence of events over a certain period of time, like 100 ms. In this case, the information about the present time-out may be transferred to activating process 14.

[0023] A simple modelling language, which is based on Boolean functions according to the present invention, is defined for event-based stimuli 17, 18. According to the present invention, the Boolean functions are hereby expressed in disjunctive normal form (DNF), in particular as a disjunction of product terms. According to one preferred specific embodiment, the Boolean expression is further limited, for example, by excluding the negation (NOT) or contravalence (XOR).

[0024] A dot notation is preferably used to more precisely specify IPC stimuli 18. In the following, eventA designates such a stimulus 19. The modelling language defines the following Boolean functions: [0025] eventA.any_sample: delivers 1, if a buffered sample value is available or if a new sample value was received since the last activation 13, [0026] eventA.new_sample: delivers 1, if a new sample value was received since the last activation 13, [0027] eventA.any_newest_sample: delivers the last stored sample value, which is available, thus the most recently received sample value since the last activation 13, [0028] eventA.new_newest_sample: delivers the newest of the new sample values, which was received since the last activation 13.

[0029] This notation preferably represents both individual sample values and also sets of the same, so that, for example, eventA.any_sample may return a set of sample values. eventA.new_newest_sample, in contrast, always provides one single sample value, namely the newest.

[0030] IPC stimuli 18 indicated in this dot notation are then used as arguments of predicates, which they map onto logical values. IPC stimuli 18 may hereby preferably be specified on the basis of their sequence numbers or time stamps, in particular if these are known to the middleware and are transported according to them.

[0031] In addition to predicates for determining the equivalence of sequence numbers (for example: seq eq(eventA.any_sample, eventB.any_sample))optionally while specifying an offsetother predicates are preferably provided with a predefined interval for the comparison of sequence numbers and time stamps. For time stamps, such a predicate might correspond to the following form:

[0032] tstamp_is_inside_interval(eventA.new_sample, eventB.any sample, tearly, tlate)

[0033] As these examples already demonstrate, two-digit or multi-digit predicates are considered for the described modelling language without departing from the scope of the invention.

[0034] This method 10 may be implemented, for example, in software or hardware or in a mixed form of software and hardware, for example, in a control unit 20, as the schematic depiction of FIG. 2 illustrates.