Method and system for determining a driving maneuver

A method for determining a driving maneuver includes obtaining vehicle parameters (P) from a driving maneuver planning module (22) of the vehicle (10). At least one possible driving maneuver (M) is determined via the driving maneuver planning module (22) based on the vehicle parameters (P) received by means of at least one decision-making submodule of the driving maneuver planning module (22), At least one possible driving maneuver (M) is transmitted to a motion planning module (24) of the vehicle (10). An evaluation variable (B) is obtained from the motion planning module (24) via the driving maneuver planning module (22). The at least one decision-making submodule is adapted based on the evaluation variable (B) obtained. Furthermore, a system for determining a driving maneuver of a vehicle, a vehicle for executing the driving maneuver, a control device for a vehicle and a computer program for executing the method are shown.

RELATED APPLICATIONS

This application claims priority from German Application No. 10 2019 104 974.1 filed Feb. 27, 2019, the subject matter of which is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

The invention relates to a method and a system for determining a driving maneuver of a vehicle, a vehicle for executing a driving maneuver, a control device for a vehicle for controlling a vehicle and a computer program for performing the method.

One of the main challenges for the autonomous or semi-autonomous control of a vehicle is determining a driving maneuver for the vehicle. Possible driving maneuvers are typically determined based on the data collected from the vehicle such as the number of trafficable lanes of a road, the movement trajectories of adjacent vehicles, and/or the navigation data in order to reach a desired destination.

Systems which use a route planning module (also known as a “mission planning framework”) for planning the route of the vehicle are known from prior art. The systems use the current location and the destination for this purpose. Furthermore, the systems have a driving maneuver planning module (also known as a “behavioral planning framework”), which plans a driving maneuver based on sensor data. The driving maneuver generated from the route planning module and the driving maneuver planning module is transmitted to a motion planning module (also known as the “motion planning framework”) that executes the driving maneuver.

One disadvantage of these systems, however, is that the selection of the driving maneuver is based on theoretical considerations only.

SUMMARY OF THE INVENTION

It is therefore the object of the invention to provide a method for determining a driving maneuver that takes practical feedback into account when selecting driving maneuvers.

The object according to the invention is achieved by a method for determining a driving maneuver of a vehicle. First, a driving maneuver planning module of the vehicle receives vehicle parameters. Based on the vehicle parameters received, the driving maneuver planning module determines at least one possible driving maneuver by means of at least one decision-making submodule of the driving maneuver planning module. Then, a driving maneuver is selected and transmitted to a motion planning module of the vehicle. The driving maneuver planning module receives an evaluation variable from the motion planning module. Finally, the at least one decision-making submodule is adjusted on the evaluation variable received.

According to the invention, the motion planning module and the driving maneuver planning module are connected to one another, with the driving maneuver planning module determining at least one possible driving maneuver and transferring at least one driving maneuver to the motion planning module. The driving maneuver planning module receives an evaluation variable for the transmitted driving maneuver, which contains practical feedback about the transmitted driving maneuver. Based on the practical feedback, the at least one decision-making submodule of the driving maneuver planning module is then adapted so that practical bases can be used in addition to theoretical bases for selecting the driving maneuver.

The motion planning module can be cloud-based or server-based or implemented locally in the vehicle. In other words, the motion planning module does not have to be executed inside the vehicle.

A corresponding IT infrastructure may be provided in a vehicle, for example, so that the motion planning module is provided by the IT infrastructure inside the vehicle.

Alternatively, the motion planning module can also be executed on a central server, with the server receiving the vehicle parameters from the vehicle. In this case, the vehicle parameters are transmitted via a network, for example the

In general, it is also conceivable that the IT infrastructures of adjacent vehicles, i.e. vehicles that are within a certain maximum distance from each other, join together and the motion planning module is provided on the IT infrastructure of the vehicles.

The same applies to the driving maneuver planning module as well.

The vehicle parameters that the driving maneuver planning module receives may be position data, i.e. information about the current geographic location of the vehicle, based on GNSS data, acceleration and/or speed data of the vehicle, optical distance and speed measurements, provided by a LIDAR sensor (light detection and ranging), for example, and/or a radar sensor, camera data, the number of trafficable lanes, the trajectories of adjacent cars and/or navigation data.

This data can be recorded by sensors of the vehicle, originate from adjacent vehicles and/or can also be transmitted to the vehicle from a central server via a wireless interface. In one embodiment of the invention, a driving maneuver to be executed is selected from the at least one driving maneuver, with the motion planning module controlling the vehicle in such a way that the vehicle executes the driving maneuver to be executed. The motion planning module can thus directly evaluate the driving maneuver that has been executed.

In order to take into account uncertainties in the various vehicle parameters and to assess the feasibility of the selected driving maneuver, the motion planning module can perform a reachability analysis.

The evaluation variable can be determined by the motion planning module based on the executed driving maneuver and/or based on the vehicle parameters during the execution of the driving maneuver. The evaluation variable is thus directly dependent on the executed driving maneuver so that the decision-making submodule can be adapted directly to the execution of the driving maneuver.

The evaluation variable can, for example, be the smallest distance to at least one adjacent vehicle during the driving maneuver, the highest lateral acceleration of the vehicle during the driving maneuver, the highest longitudinal acceleration of the vehicle and/or a cost value of the driving maneuver.

In general, it is also conceivable that the evaluation variable has multiple values. The evaluation variable may be a vector, for example, that contains one or more of the aforementioned values.

Furthermore, the evaluation variable may be a multidimensional vector and comprise, for example, one or more of the previously mentioned value or values for each time step of the driving maneuver.

The time step can be based on the time resolution of one or more sensors of the vehicle or can be a freely selected value, for example10ms.

Knowledge data may be transmitted to other vehicles in order to provide them with information about the executed driving maneuver. The knowledge data includes at least the vehicle parameters, the driving maneuver to be executed, the driving maneuver that was executed and/or the evaluation variable.

A decision-making submodule of the further vehicle may be adapted on the basis of the transmitted knowledge data.

This means that several vehicles that have a corresponding decision-making submodule can use the knowledge data from a driving maneuver that has been executed and adapt their decision-making submodule based on a driving maneuver that has been executed. Accordingly, the vehicles no longer have to execute the driving maneuver themselves and at least one decision-making submodule can learn faster to provide better driving maneuvers.

In one embodiment of the invention, the server determines the required adaptation of the decision-making submodule of the further vehicles based on the driving maneuver executed by a given vehicle.

Alternatively, it is also conceivable for the IT infrastructure of the further vehicles to determine the necessary adaptations of the respective, at least one decision-making submodule, based on the driving maneuver executed.

In general, it is also conceivable that the server processes and/or transmits the vehicle parameters, the selected driving maneuvers, the executed driving maneuver and/or the evaluation variable, that is to say in general the knowledge data.

In one embodiment of the invention , the at least one decision-making submodule is able to determine the possible driving maneuver on the basis of a state machine.

The possible driving maneuver is accordingly determined by a model that describes the behavior of the vehicle with states, state transitions and actions. Driving on the freeway, parking in the parking lot, waiting at the traffic lights, driving through a restricted traffic zone, turning off at an intersection, etc. can be considered a state, and the actions would then be the corresponding driving maneuvers that the vehicle can carry out in the corresponding states. The performance of a driving maneuver can then lead to a state transition.

It is also conceivable that the at least one decision-making submodule determines the possible driving maneuvers by using a cost function.

A cost function is generally understood to be a function that assigns a value to an action or a route, which indicates the cost of the corresponding action or the corresponding route. The best driving maneuver can be determined by comparing several values, namely the action or the route that has the lowest value and thus the lowest cost.

Each lane is parameterized with a cost function, for example, so that the at least one decision-making submodule can decide by comparing the cost functions of the different lanes whether the vehicle is in the best-possible lane and if not can determine a corresponding lane change as a driving maneuver.

In one embodiment of the invention, the at least one decision-making submodule identifies a possible driving maneuver on the basis of a vehicle-following model. Vehicle-following models are generally models that determine driving maneuvers on the basis of the driving maneuvers of other vehicles.

The at least one decision-making submodule may be a distance-dependent vehicle-following model so that possible driving maneuvers are determined on the basis of the distance to adjacent vehicles.

According to Wiedemann, in one embodiment of the invention, the vehicle-following model is a psychophysical following model in which four driving modes are used, namely normal driving, approaching, following and braking. Depending on the speed of and the distance from an adjacent vehicle, a distinction is made between a range allowing for free driving behavior, a following range, an approaching range, a braking range and a collision range so that possible driving maneuvers, which on the driving modes are determined based on the range in which the vehicle is located.

For example, the at least one decision-making submodule determines the possible driving maneuver with the help of a decision tree, i.e., with the help of decision rules.

In general, a combination of the decision-making submodules mentioned is possible as well. The at least one decision-making submodule can, for example, determine the possible driving maneuver based on a cost function, a decision tree, a vehicle-following model, in particular a distance-dependent vehicle-following model and/or a psychophysical vehicle-following model according to Wiedemann, and/or a state machine.

These models are tried and tested methods for determining appropriate driving maneuvers in different driving situations.

At least one of the at least one decision-making submodules can perform the possible driving maneuver by means of a machine learning decision process, in particular a reinforcing machine learning decision process.

The at least one decision-making submodule thus generates artificial experience from the possible driving maneuvers, the driving maneuver to be executed and/or the driving maneuver that was executed. The at least one decision-making submodule is thus trained by the driving maneuvers.

In reinforcing machine learning decision processes, the process independently learns a strategy to maximize received rewards. The machine learning decision process receives a reward, for example, if specified distances from adjacent vehicles are maintained during a driving maneuver.

The evaluation variable can be used as a reward for driving maneuvers which are executed in accordance with road traffic regulations to which at least one decision-making submodule is transmitted. The decision-making submodule can thus be modified so that driving maneuvers are executed in accordance with road traffic regulations.

In one embodiment, the machine learning decision process is a monitored or partially monitored machine learning decision process, according to which the possible driving maneuvers are monitored by a driver.

The possible driving maneuver determined by the machine learning decision process is, for example, compared with a driving maneuver executed by a driver of the vehicle.

It is conceivable that the machine learning decision process is realized at least by a neural network, is based on game theory, is a Markow decision process and/or a partially observable Markow decision process. Some of the observable decision processes are also known in English as “partially observable Markov decision processes”. In Markow decision processes, the vehicle is, similar to state machines, described with states, actions and state transitions. A reward function is provided as well, for example the evaluation variable that assigns a reward to a particular state transition and a policy that assigns the best-possible action to every state. A discount factor with which all rewards still achievable after an action are reduced is provided as well. The decision-making submodule can independently determine driving maneuvers by means of the Markow decision processes.

In terms of game theory, the machine learning decision process in one embodiment of the invention can determine the possible driving maneuvers of adjacent vehicles and assign them probabilities so that at least one possible driving maneuver is determined based on the possible driving maneuvers of adjacent vehicles.

In one embodiment of the invention, the evaluation variable is used to train a machine learning decision process of the at least one decision-making submodule. The decision-making submodule can thus be trained directly by the evaluation variable.

In other words, a person who trains the machine learning decision process is not necessarily necessary, but the machine learning decision process is trained based on the evaluation variable. This makes it possible to reduce the required teaching resources.

In order to use the strengths of the individual methods for determining a driving maneuver, several decision-making submodules can be provided, each of which determines at least one possible driving maneuver. In this case, a decision maker selects one of the determined possible driving maneuvers to be executed, which is then transmitted to the motion planning module.

In one embodiment of the invention, the decision maker is a driver of the vehicle, a selection module or a combination thereof.

The selection module selects the driving maneuver to be executed, for example based on the weighting of the credibility of the corresponding decision-making submodule and/or the frequency of occurrence of a possible driving maneuver.

In general, it is conceivable that the weighting of the credibility depends on the number of already determined driving maneuvers and/or on the driving situation.

A possible driving maneuver from a certain decision-making submodule may therefore be preferred in a certain driving situation, for example when turning or overtaking another vehicle.

The credibility weighting may also be adapted to the evaluation variable so that the driving maneuver of the corresponding decision-making submodule receives a higher weighting in further selection processes of driving maneuvers or the corresponding decision-making submodule itself.

In one embodiment of the invention, the selection module may comprise an artificial neural network and be trained accordingly to select a driving maneuver in certain driving situations.

The object according to the invention is further achieved by a control device for a vehicle for controlling the vehicle, the control device being designed to execute the method described above. Regarding the advantages and features, reference is made to the above explanations provided for the method, which apply equally to the control unit.

In addition, the object according to the invention is achieved by a vehicle with a previously described control unit and a sensor for detecting at least one vehicle parameter with the vehicle being configured to perform the method described above. In this regard as well, reference is made to the above explanations provided for the method in terms of the advantages and features, which apply to the vehicle as well.

The object according to the invention is further achieved by a computer program with program code means for the steps of the method described above if the computer program is executed on a computing unit, in particular a computing unit of a control device described above. With regard to the advantages and features, reference is also made here to the above explanations, which apply equally to the computer program with program code means.

The term “program code means” refers here and below to computer-executable instructions in the form of program code and/or program code modules in compiled and/or in uncompiled form, which may be present in any programming language and/or in machine language.

Furthermore, the object according to the invention is achieved by a system for determining a driving maneuver with at least one vehicle as described above. With regard to the advantages and features, reference is also made here to the above explanations, which apply equally to the system.

In particular, two of the vehicles described above may be provided for the system. In one embodiment of the invention, the system has a server which is connected to the at least one vehicle for data connection, with the motion planning module controlling the vehicle in such a way that the vehicle executes a driving maneuver, wherein the server receives and/or provides knowledge data from one of the vehicles, and the server transmits the knowledge data to the at least one further vehicle, and/or the server determines the adaptation of the decision-making submodule of the at least one other vehicle on the basis of the knowledge data and transmits said adaptation information to the at least one further vehicle. By means of this system, the vehicles can access the knowledge data of the other vehicles so that the decision-making submodules can be adapted faster and better.

The decision-making submodule of a vehicle can therefore be adapted, in particular, based on driving maneuvers executed by other vehicles.

Here too, the knowledge data includes at least the knowledge data of the one vehicle that is executing the driving maneuver, i.e., the vehicle parameters, the possible driving maneuvers, the driving maneuver to be executed, the driving maneuver that was executed and/or the evaluation variable.

The server can be reached by the vehicles, for example via a network such as the Internet.

DESCRIPTION

FIG.1shows a schematic illustration of a vehicle10. The vehicle10comprises several sensors12, one control unit14, one driver assistance system16and a plurality of control devices18for controlling the vehicle10.

The vehicle10is, for example, a motor vehicle for road traffic such as a truck or a car.

The sensors12are arranged on the front, rear and/or on the sides of the vehicle10and are designed to detect the surroundings of the vehicle10and/or data related to the vehicle10. The sensors12generate the corresponding vehicle parameters P and transmit them to the control unit14, as shown by the arrows inFIG.1.

The sensors12include a position sensor26(FIG.3), for example a satellite-based position sensor, which determines the current geographic position of the vehicle10. The position sensor26accordingly generates position data as the vehicle parameters P.

A camera sensor28(FIG.3) is provided as well, which records images and/or videos of the surroundings of the vehicle10by means of a camera, which is not shown. The camera sensor28correspondingly generates camera data as vehicle parameters P. Using the camera sensor28, adjacent vehicles can be detected, the lane boundaries recognized and/or striking objects captured.

“Striking objects” are understood to mean, for example, traffic signs and/or sights in the surrounding area.

In addition, an acceleration and/or speed sensor30(FIG.3) is provided, which records the acceleration and/or speed data of the vehicle10the vehicle parameters P.

Furthermore, an optical sensor32(FIG.3), for example a LIDAR and/or radar sensor, is provided, which generates the distances from adjacent objects and the speeds of the adjacent objects, for example adjacent vehicles, as vehicle parameters P.

For illustration purposes, a separate sensor12was described for each of these vehicle parameters P. Of course, a separate sensor12is not necessary for each of these vehicle parameters P.

The control device14comprises a data carrier19and a computing unit20with a computer program being stored on the data carrier19, which is executed in the computing unit20and comprises the program code means for determining a driving maneuver according to the method described below.

The control unit14may also include the driver assistance system16. However, it is also conceivable that the driver assistance system16is configured as a separate system.

In general terms, the vehicle parameters P are transmitted to the control unit14, the control unit14processes the vehicle parameters P received from the sensors1and controls the vehicle10at least semi-automatically, in particular fully automatically, by using the driver assistance system16.

The vehicle parameters P can also be transmitted to the control unit14from adjacent vehicles and/or a central server via a network.

The driver assistance system16can at least control a transverse movement and/or a longitudinal movement of the vehicle10in a semi-automated manner, preferably in a fully automatic manner. InFIG.1, this is shown schematically by the arrows, which represent the signals G transmitted to the corresponding control devices18of the vehicle10.

FIG.2shows a block diagram of the control unit14. The control unit14has a vehicle parameter acquisition module21, a driving maneuver planning module22and a motion planning module24.

The vehicle parameter acquisition module21collects and/or generates the vehicle parameters P, for example with the sensors12, and transmits them to the driving maneuver planning module22and the motion planning module24.

The driving maneuver planning module22determines at least one possible driving maneuver M, said determination including the vehicle parameters P and transmits at least one of the possible driving maneuvers M and/or a driving maneuver MAZto the motion planning module24.

The motion planning module24evaluates the at least one possible driving maneuver M and/or the driving maneuver MAZ to be executed, i.e., it calculates an evaluation variable B and generates control commands S for controlling the vehicle10.

The control commands S are transmitted to the driver assistance system16, which in turn transmits the signals G (FIG.1) to the control devices18of the vehicle10.

The motion planning module24transmits the driving maneuver MAG that has been executed and/or an evaluation variable B of the driving maneuver to the driving maneuver planning module22, which, with the help of the executed driving maneuver MAG that was executed and/or the evaluation variable B, improves the decision process for selecting the driving maneuvers M, for example by adjusting a decision-making submodule38(FIG.4) of the driving maneuver planning module22.

FIGS.3to5show the vehicle parameter acquisition module21and the driving maneuver planning module22or the motion planning module24in detail in a block diagram.

AsFIG.3shows, the vehicle parameter acquisition module21also includes a route planning module34and a further processing module36in addition to the sensors12described above.

The route planning module34plans a route for the vehicle10to a desired destination. For this purpose, it has access to corresponding map material, which is stored, for example, on the data carrier19, and can use the position data of the position sensor26to navigate from the current geographical position to the destination. The route planning module34thus generates navigation data, which is also understood as vehicle parameters P in the context of this invention.

The further processing module36is designed to combine the data from the sensors12and/or the vehicle parameters P in order to obtain processed and/or more detailed vehicle parameters P.

FIG.3shows that the position data and the camera data is combined. The inclusion of the number of detected lanes, the detected road signs and/or the surrounding sights in this embodiment improves the determination of the current geographical position.

It is also conceivable to combine the data of the distance from adjacent vehicles and the camera data in order to obtain movement trajectories of adjacent vehicles and/or to predict the behavior of adjacent vehicles. The data generated by the further processing module36is also to be understood as the vehicle parameters P.

The vehicle parameter acquisition module21transmits the vehicle parameters P to the driving maneuver planning module22and the motion planning module24.

InFIG.4, the driving maneuver planning module22is shown in a schematic block diagram and comprises a processing module37, three decision-making submodules38, a decision maker40and a knowledge module42.

The processing module37receives the vehicle parameters P from the vehicle parameter acquisition module21and is designed to prepare the vehicle parameters P for further use.

The processing module37transforms the vehicle parameters P into a common coordinate system, for example. It is also conceivable that the processing module37generates a model for the behavior of adjacent vehicles.

The processing module37transfers the processed vehicle parameters P and/or the unprocessed vehicle parameters P to the decision-making submodules38.

The decision-making submodules38each determine at least one possible driving maneuver M based on the vehicle parameters P, with the various decision-making submodules38determining a possible driving maneuver M based on the vehicle parameters P, by using various methods or models. The determination is based, for example, on a cost function, a decision tree, a vehicle-following model, in particular a distance-dependent vehicle-following model and/or a psychophysical vehicle-following model according to Wiedemann, and/or a state machine.

In the exemplary embodiment shown, three different decision submodules38are provided, namely a first, a second and a third decision submodule38a,38band38c.

The first decision-making submodule38adetermines a possible driving maneuver Maas a function of all or a plurality of the vehicle parameters P by means of a state machine.

The second decision-making submodule38bis an occupant and/or the driver of the vehicle10, who determines a possible driving maneuver Mb.

In the embodiment shown, the third decision-making submodule38cdetermines a possible driving maneuver Mcby means of a reinforcing machine learning decision making process.

For example, the third decision-making submodule38chas an artificial neural network that executes the machine learning decision process. However, it is also conceivable that the machine learning decision process is a Markow decision process, a partially observable Markow decision process and/or based on game theory.

In general, it is conceivable that the decision-making submodules38determine more than one possible driving maneuver M and/or that different decision-making submodules38select the same driving maneuver M. For example, the possibility that driving maneuver M of the decision-making submodule38a,i.e., the driving maneuver Maand the possible driving maneuver M of the decision-making submodule38b,i.e., the driving maneuver Mb, are identical.

The decision-making submodules38a,38band38ctransmit the possible driving maneuvers Ma, Mband Mcto the decision maker40.

Each decision-making submodule38is a weighting factor a of the credibility assigned to the relevant decision-making submodule38, i.e., the weighting factor a indicates the probability that the possible driving maneuver M determined by the corresponding decision-making submodule38is the best or best-possible driving maneuver.

The weighting factors a are not the confidence values that are identified during the determination of the possible driving maneuver M by the respective decision-making submodule38or the decision process being used but can be based on these.

The weighting factors a can be adjusted over time.

The weighting of the credibility aaof the first decision-making submodule38aand the weighting of the credibility abof the second decision-making submodule38bis, for example, initially higher than the weighting of the credibility acof the third decision-making submodule38c,which determines the possible driving maneuver Mcby means of the reinforcing machine learning decision process.

The weighting factors a are known to the decision maker40and are, for example, stored in a memory of the decision maker40.

The decision maker40selects one of the possible driving maneuvers Ma, Mband Mcbased, among other things, on the weighting factors aa, ab, acand thus determines the driving maneuver MAZto be executed, which is transmitted to the motion planning module24.

In the embodiment shown, the decision maker40is a selection module44, that is to say a module that determines the driving maneuver MAZ to be executed by means of a computer-assisted selection process.

Preferably, the selection module44is an artificial neural network with at least the possible driving maneuvers Ma, Mb, Mc, the vehicle parameters P and/or the weighting factors aa, ab, acare the input variables of the artificial neural network, and the driving maneuver MAZto be executed is the at least one output variable of the artificial neural network.

In general, it is also conceivable that a decision-making submodule38, which determines several possible driving maneuvers M, defines a preference and thus assists the decision maker40in the selection of the driving maneuver MAZto be executed. The decision-making submodule38aselects, for example, a preferred possible driving maneuver M and/or defines a hierarchy among the selected driving maneuvers M.

Furthermore, it is conceivable that the selection module44checks whether a similar driving situation has already existed and selects the possible driving maneuver M of the decision-making submodule38that made the best decision in the similar driving situation.

It is also conceivable that the decision maker40is an occupant and/or the driver of the vehicle10, who has possible driving maneuvers M displayed on a screen and makes a decision by selecting and/or performing a driving maneuver M.

The driving maneuver MAZto be executed is then transmitted to the motion planning module24.

FIG.5shows a schematic block diagram of the motion planning module24, which, in the exemplary embodiment shown, comprises an analysis module46and an assessment module48.

As explained above, the motion planning module24receives the vehicle parameters P from the vehicle parameter acquisition module21and the driving maneuver MAZto be executed from the driving maneuver planning module22.

The analysis module46analyzes the driving maneuver MAZ to be executed in terms of its feasibility based, among other things, on the vehicle parameters P. In particular, it can estimate whether a collision of the vehicle10with an object, for example another vehicle, would take place during the execution of the driving maneuver.

The analysis module46uses the uncertainties in the trajectories of adjacent vehicles, the uncertainties in the speed of the vehicle10and the uncertainties in the course of the lane, for example, in order to be able to estimate the feasibility of the driving maneuver.

The analysis module46can also determine whether the end of the driving maneuver can be reached, for example, due to physical limits or comfort limits for longitudinal and lateral acceleration.

The driving maneuver MAG that the vehicle10executes is then determined on the basis of the feasibility analysis and/or the accessibility analysis performed by the analysis module46. For this purpose, the analysis module46provides the control commands S which are transmitted to the driver assistance system16.

The evaluation module48, on the other hand, generates the evaluation variable B for a driving maneuver.

The evaluation module48evaluates the possible driving maneuver M, the driving maneuver MAZto be executed and/or the driving maneuver MAGthat was executed. For this purpose, the evaluation module48uses, among other things, the vehicle parameters P.

For the evaluation of the driving maneuver MAZto be executed or the driving maneuver MAGthat was executed, the evaluation module48stores and/or analyzes certain vehicle parameters P determined during the execution of the driving maneuver MAZto be executed or the driving maneuver MAG that was executed and/or accesses the results of the feasibility analysis of the analysis module46.

For example, the evaluation variable B includes the smallest distance to adjacent vehicles, the highest lateral acceleration of the vehicle10during the driving maneuver and/or a value that indicates whether the maximum speed of the traffic regulations was observed.

Further values or parameters as part of the evaluation variable B are conceivable as well, of course.

The evaluation variable B thus contains a plurality of values. The evaluation variable B is, for example, a vector which contains the values mentioned.

Furthermore, the evaluation variable may be a multidimensional vector and comprise, for example, one or more of the previously mentioned value or values for each time step of the driving maneuver.

After the evaluation module48has created the evaluation variable B, the motion planning module24transmits the driving maneuver MAGand returns the evaluation variable B to the driving maneuver planning module22.

The evaluation variable B is then transmitted to the decision-making submodule38c,and the decision-making submodule38cis adapted by including the evaluation variable B. More specifically, the machine learning decision process, in particular the artificial neural network that has determined the possible driving maneuver Mcis trained by using the evaluation variable B.

The selection module44, in particular the artificial neural network of the selection module44, can also be trained and adapted by means of the evaluation variable B.

The weighting factors aa, ab, acof the credibility of the decision-making submodules38a,38band38ccan also be adapted by using the evaluation variable B, for example by the selection module44or the decision maker40.

As can be seen inFIG.4, the knowledge module42of the driving maneuver planning module22is configured to provide the knowledge data E. The knowledge data E includes, for example, the possible driving maneuvers Ma, Mb, Mc, the driving maneuver MAZto be executed, the vehicle parameters P, the driving maneuver MAGthat was executed and the evaluation variable B.

The knowledge module42stores, for example, the vehicle parameters P that were measured during the maneuver MAG that was executed and evaluates them so that only relevant data of the vehicle parameters P is passed on as knowledge data E.

It is also conceivable for the knowledge data E to have more precise data on the selection process of the decision maker40.

As indicated by the arrows, the knowledge data E is provided by the driving maneuver planning module22so that, for example, further vehicles can access the knowledge data E of the vehicle10.

In addition, the knowledge data E can be used to adapt one of the decision-making submodules38, for example to train the reinforcing machine learning decision process.

The method specified is then explained in more detail below with the help of a greatly simplified example of a lane change of the vehicle10on a freeway.

The sensors12of the vehicle10detect, for example, another vehicle in the lane in which the vehicle10is traveling. The other vehicle moves at a certain distance in front of the vehicle10and has a lower speed than the vehicle10. If the vehicle10were to follow the lane at a constant speed, there would be a collision between the vehicle10and the other vehicle.

Therefore, the speed of the other vehicle and the distance to the other vehicle are transmitted as vehicle parameters P to the driving maneuver planning module22. In addition, the sensors12also recognize that the adjacent lane, the fast lane, is free.

Based on these vehicle parameters P, the driving maneuver planning module22, in particular, the decision-making submodules38a,38b,38c,determines possible driving maneuvers Ma, Mband Mc. A possible driving maneuver Ma could be that the vehicle10greatly reduces its speed and adapts to the speed of the other vehicle. As a further possible driving maneuver Mb, it is determined that the vehicle10is changing lanes in order to pass the other vehicle.

The possible driving maneuvers Ma, Mband Mcare transmitted to the decision maker40, and the decision maker then selects one of the driving maneuvers Ma, Mb, Mc, for example the first possible driving maneuver Ma. In the following, this driving maneuver is the driving maneuver MAZto be executed.

The driving maneuver planning module22transmits at least the driving maneuvers MAZto be executed to the motion planning module24, which transmits corresponding control signals S to the driver assistance system16.

In the exemplary embodiment described, the driving maneuver planning module22transmits a very unspecific driving maneuver MAZto the motion planning module24, namely the driving maneuver for slowing down the speed, and the motion planning module24converts this instruction or these objectives into specific control commands S.

In general, precise instructions are conceivable as well, for example, that the vehicle10first has to let another vehicle in the adjacent lane pass then increase the speed by a certain value and switch to the adjacent lane at the same time.

Furthermore, it is also conceivable that the driving maneuver planning module22defines a corridor, for example a time corridor or a route corridor, in which the motion planning module24has to execute a driving maneuver.

The sensors12, for example, detect a stationary car in the lane and transmit the request to the motion planning module24that the vehicle10must change lanes or stop within the next5seconds and/or within the next 200 m.

Furthermore, it is also conceivable that the driving maneuver planning module22plans the possible driving maneuvers M in great detail and, for example, comprises corresponding control commands S and/or signals G for the control devices18of the vehicle10.

The motion planning module24determines the evaluation variable B, which is negative in the example. The motion planning module24punishes the selection of the driving maneuver Mahere since the driving maneuver Macauses the vehicle to take longer to reach the destination and since the brakes have to be applied.

The driving maneuver planning module22thus receives a negative evaluation variable B from the motion planning module24and adjusts the decision-making submodule38in such a way that the decision-making submodule38or the decision maker40, for example by adapting the weighting factors aa, ab, ac, would chose the maneuver Mbnext time.

In this way, practical experience is taken into account when selecting the driving maneuver.

Three embodiments of a system50for determining a driving maneuver are shown below with reference toFIGS.6to8.

The system50has a plurality of vehicles10, which are essentially configured as described above, so that only the differences are addressed below. Identical and functionally identical components and modules are provided with the same reference symbols.

FIG.6shows a first embodiment of the system50in a schematic block diagram. The system50inFIG.6has three vehicles10, namely a vehicle10athat performs the driving maneuver MAG and two vehicles10bthat receive the knowledge data E of the vehicle10a.

The vehicle10a,more precisely the control unit14of the vehicle10a, determines analogously to the embodiment of the control unit14, described inFIGS.1to5, at least one possible driving maneuver and/or one driving maneuver to be executed, executes a driving maneuver by means of the driving assistance system16and generates the corresponding knowledge data E.

The knowledge data E is then transmitted to the driving maneuver planning modules22of the vehicles10bso that the driving maneuver planning modules22of the vehicles10bcan adapt their corresponding decision-making submodule38.

In other words, the decision-making submodules38of the vehicles10bare adapted based on a driving maneuver that the vehicle10ahas performed. The data exchange between the vehicles10, i.e. the transmission of the knowledge data, can take place via a wireless interface.

FIG.7describes a second embodiment of the system50for determining a driving maneuver in a schematic block diagram. The embodiment shown essentially corresponds to the embodiment shown inFIG.6so that only the differences are discussed below.

In the embodiment shown inFIG.7, only the vehicle parameter acquisition module21and the driver assistance system16are implemented in the vehicles10aand10b.

A separate, stationary server60is provided, on which the driving maneuver planning module22aof the vehicle10a,the driving maneuver planning module22bof the vehicle10band the shared motion planning module24are implemented.

The server60is in contact with the vehicles10, for example via a wireless connection such as the cellular network. Corresponding transmitters and receivers, which are not shown inFIG.7, are provided for the server60and the vehicles10aand10b.

Via the wireless connection, the driving maneuver planning module22areceives the vehicle parameters Paof the vehicle10a,and the driving maneuver planning module22breceives the vehicle parameters Pbof the vehicle10b.

As already described, the driving maneuver planning modules22aand22bprovide the driving maneuver MAZ,ato be executed for the vehicle10aand MAZ,bfor the vehicle10b.The motion planning module24determines the driving maneuvers to be executed.

In the embodiment shown inFIG.7, the motion planning module24determines the driving maneuver MAG,afor the vehicle10aand no driving maneuver for the vehicle10b.The motion planning module24transmits the corresponding control commands Sa to the driver assistance system16of the vehicle10a.

In addition, only one knowledge module42is provided, which is implemented on the server side. The knowledge module42generates the knowledge data E based on the driving maneuvers MAZ,aand MAZ,bto be executed, the vehicle parameters Paand Pb, the evaluation variable B and/or the driving maneuver MAG,athat was executed.

The knowledge data E is transmitted to an adaptation module62that provides the adaptation information A so that the driving maneuver planning module22aof the vehicle10ais adapted by the adaptation information Aa, and the driving maneuver planning module22bof the vehicle10bis adapted by the adaptation information Ab.

In other words, the server60adapts the decision submodules of the driving maneuver planning modules22aand22baccordingly.

FIG.8shows a third embodiment of the system50in a schematic block diagram.

In contrast to the embodiment shown inFIG.7, the driving maneuver planning modules22of the vehicles10are each implemented in the vehicles10and not on the server60.

Correspondingly, the server60transmits the adaptation information Aaand Abto the respective driving maneuver planning module22of the vehicles10so that with the adaptation information Aaat least one decision-making submodule of the driving maneuver planning module22of vehicle10aand with the adaptation information Ab, at least one decision-making submodule of driving maneuver planning module22of vehicle10bais adapted.

For simplification, the embodiments ofFIGS.6,7and8each only show two vehicles10. The vehicle10aperforms a driving maneuver, and the driving maneuver planning module22bof the vehicle10bis adapted by using the knowledge data E. In general, any number of vehicles10aand10bis conceivable.

System50enables “fleet learning”, that is, vehicles can adapt their driving maneuver planning modules22, in particular their decision-making submodule38, based on experience, i.e., planned and/or executed driving maneuvers in special traffic situations, by other vehicles without having to have been part of these traffic situations.

The representation ofFIGS.7and8is to be understood as an example. The server60is intended to illustrate that the motion planning module24and/or the driving maneuver planning module22of the system50can also be executed on the server60, which serves as the central control center for the traffic.

In general, only individual modules of the system50, such as the analysis module46, the route planning module34, and the knowledge module42and/or the evaluation module48can also be executed on the server60.

In other words, not all modules of the system50need to be executed within vehicle10.

For illustration purposes, the description of the embodiments differentiates between possible driving maneuvers M, the driving maneuver MAZto be executed and the driving maneuver MAGthat was executed. In general, these driving maneuvers do not have to be different, but may be identical.

In particular, it is possible that the analysis module46only determines whether a driving maneuver can be executed or not. If the driving maneuver cannot be executed, the motion planning module24and/or the analysis module46would instruct the driving maneuver planning module22to provide a new driving maneuver, which in turn is analyzed.