Method and device for cooperation in the coordination of driving maneuvers

Abstract

The disclosure relates to a method for cooperatively coordinating driving maneuvers of a vehicle with maneuvers of a third-party vehicle, wherein a set of trajectories is generated that includes several pre-planned ego trajectories with a reference trajectory. Tuples are then formed from an ego trajectory and a third-party trajectory or an additional third-party trajectory, the tuple is evaluated with a tuple effort value, and collision-free tuples are determined. If the ego vehicle has priority, an egoistic and an altruistic trajectory are selected.Based on the difference between the tuple effort values ​​of the tuple comprising the altruistic trajectory and the tuple comprising the egoistic trajectory, the egoistic trajectory is selected if the difference is greater than a threshold. Otherwise, the altruistic trajectory is selected, and the egoistic or altruistic trajectory is sent to the remote vehicle as the reference trajectory, with the threshold increasing as the cooperation duration continues. Furthermore, the disclosure relates to a corresponding device and a corresponding computer program product.

Description

[TECHNICAL FIELD]The invention relates to a method and a device for cooperatively tuning driving maneuvers between a vehicle and at least one other vehicle.[BACKGROUND ART]There are different possibilities for vehicles to enter into a cooperation for solving a specific situation in the traffic situation and also to carry out this situation. Current solutions frequently rely on explicit cooperation in which particular cooperation partners are selected via state machines and then a bilateral tuning is performed. In contrast, there are implicit matching mechanisms which are realized in principle via the sending of trajectories relating to the own driving intentions. The respective receiver must interpret itself whether or not it adjusts its own schedule to meet a desire of another vehicle.The published patent application WO 2017 / 076 593 A1 discloses a method for decentrally tuning driving maneuvers of motor vehicles, wherein a plan trajectory and the desired trajectory of a first motor vehicle are transmitted to another motor vehicle in the environment. The second motor vehicle checks the received plan and desired trajectory to determine whether they collide with the own plan trajectory, wherein the plan trajectories of the first and of the second motor vehicle are collision-free with respect to one another in this case. If an adaptation criterion is fulfilled, the plan trajectory of the second motor vehicle is adapted to an adapted plan trajectory, wherein the adaptation criterion is that the received desired trajectory of the first motor vehicle collides with the plan trajectory of the second motor vehicle and an overall cost function is optimized by the adaptation, wherein the overall cost function comprises at least cost functions of the first and of the second motor vehicle.The laid-open specification WO 2019 / 206 377 A1 describes a method for cooperatively matching future driving maneuvers of a vehicle with external maneuvers of at least one external vehicle, the method having the following steps: evaluating a trajectory group of pre-planned trajectories for the vehicle with a respective effort value using at least one evaluation criterion; receiving an external data packet from the external vehicle, wherein the external data packet contains an external trajectory group with different pre-planned external trajectories for the external vehicle and an external effort value for each external trajectory; combining a trajectory and an external trajectory into tuple and combining the respective effort value with the respective external effort value into a tuple effort value of the tuple; selecting collision-free tuple, wherein tuple values are selected in which the trajectory and the external trajectory within a collision horizon are collision-free; selecting the collision-free tuple having the lowest tuple expenditure value and classifying the trajectory and the associated expenditure value of this tuple as a reference trajectory and a reference expenditure value; selecting trajectories having a lower expenditure value than the reference expenditure value and classifying these trajectories and associated expenditure values as required trajectories and required expenditure values, wherein a required trajectory is a trajectory indicating a desire of the vehicle, with which trajectory a desired destination can be reached more favorably than with the reference trajectory; selecting trajectories having a higher cost than the reference cost and classifying these trajectories and associated cost as alternative trajectories and alternative cost values, wherein an alternative trajectory is a trajectory indicating a cooperation offer, which the vehicle would be ready to drive if necessary; and transmitting a data packet to the other vehicle, wherein the data packet contains a trajectory group from the reference trajectory and the associated reference cost value and at least one trajectory from a group comprising the required trajectories and the alternative trajectories and the corresponding cost values.In the usual systems, the approach is followed that tuple that contains a demand trajectory of a foreign vehicle receives a loss of outlay in the outlay evaluation by the vehicle, whereby such tuple is preferred in the subsequent outlay-based selection by the vehicle. In this way, a drive for cooperation is created for the vehicle in particular, in order to accept a separate additional outlay. As soon as this is communicated to the other vehicle, it will no longer transmit a required trajectory, but rather now a reference trajectory. In the next step, the cost mitigation is thus no longer active in the vehicle and the cooperation is therefore less beneficial for the vehicle itself and is therefore possibly interrupted. The other vehicle will thus again send demand trajectories which receive cost-penalty for the vehicle, as a result of which the cooperation appears to be useful again and causes the vehicle to cooperate again. Traffic situations can also change within a very short time, as a result of which changes in the cost values can likewise occur during successive evaluation cycles, with the result that the cooperation is aborted and a cooperation has to be initialized again. There is thus an oscillatory or unstable cooperation behavior.Document DE 10 2018 204 185 A1 discloses a driver assistance system for assisting a driver of a motor vehicle in a driving situation in which the motor vehicle interacts with a further occupant.Documents DE 10 2018 109 883 A1 and DE 10 2018 109 885 A1 each disclose a method for cooperatively matching future driving maneuvers of a vehicle with external maneuvers of at least one external vehicle.The document DE 10 2018 002 675 A1 discloses a method for tuning driving maneuvers between at least two motor vehicles.The document DE 10 2023 200 569 A1 discloses a method for cooperation of road users.[OBJECT OF THE INVENTION]The object of the invention is to improve the stability of cooperative driving maneuver planning and to avoid oscillating cooperation behavior.This object is achieved by the subject matters of the independent claims. Advantageous embodiments can be found, for example, in the dependent claims.Embodiments of the present invention may advantageously enable future driving maneuvers of different vehicles to be matched with one another in a mutually different agreement, such that none of the vehicles has to carry out, for example, an disproportionately large steering intervention, braking intervention and / or acceleration intervention in order to enable moving traffic. A method used for this can be carried out using a device, such as a control unit. For carrying out the method, signals, measured values or the like can be used and / or analyzed, which are provided, for example, by sensors, measuring devices or the like. The method can be carried out in a fully automated or partially automated manner. While human intervention does not need to be ruled out, it does not necessarily need to be necessary.The following are used to define terms used in describing the present invention:A trajectory describes a state profile (e.g. position, orientation, speed and acceleration vectors, etc.) over time, on which a vehicle can be controlled using driving maneuvers. A trajectory is generally multidimensional, in particular two-dimensional or three-dimensional, and can extend along a driving surface to be traveled by the vehicle, for example a road. The trajectory thus describes where the vehicle is located at a given point in time, has been or will be located. The trajectory is planned ahead at least up to a prediction horizon. For example, the prediction horizon can be determined by a sensor range of the vehicle. The prediction horizon can be speed dependent. Trajectories are not understood exclusively as trajectories in Euclidean space, but rather can also be trajectories in other possible spaces. An example of this is the Frenet space along the track centers, in which a trajectory can consist of the specification of the respective track and of the sections on the track center over time. In this way, the trajectory bundle can be efficiently transmitted in a structured traffic space (=the freeway with lane markings, for example). A collision horizon can be less than or equal to the prediction horizon and can likewise be speed-dependent or also situation-dependent, for example depending on the traffic density.The freedom from collision of trajectories is given when trajectories run from the vehicle to the collision horizon such that the vehicle and the other vehicle each have at least a minimum distance from one another.The reference trajectory can be the trajectory selected from the trajectory family in order to be actually traveled by the vehicle, or is the trajectory that the vehicle currently follows and that is the setpoint variable for the travel controller / driver. Reference trajectories should in principle be conflict-free; if conflicts occur, they can be solved according to the road traffic regulation. A conflict can be resolved by that vehicle which is subordinate, that is to say the preferred vehicle must continue to transmit its conflict-afflicted trajectory.An alternative trajectory is a trajectory that is more expensive than the reference trajectory but that the vehicle would nevertheless be ready to drive. "Possibly" in this context means "subject to reservation", i.e. e.g. approval of the driver can be queried before an alternative trajectory can become the reference trajectory. An evaluation of the resulting overall situation can also take place, wherein a check is made as to whether the own additional costs are opposed by a sufficiently sufficient benefit (according to local visibility) in the other vehicles. Alternative trajectories may be planned to be conflict-free.A demand trajectory describes a desired maneuver that the vehicle would like to drive due to lower "costs" or lower effort, but cannot currently drive, e.g. because the required maneuver space is occupied by other vehicles. A required trajectory may be a trajectory that better satisfies the desired driving target, thus is more favorable than the reference trajectory. The required trajectory is conflicted with other trajectories. The demand trajectory can optionally be communicated.A cost value can be transmitted for each trajectory. This makes it possible to set each trajectory with respect to the other trajectories and to derive their meaning and priority therefrom. The transmission of the cost value for each trajectory allows other road users to determine a local approximation of the cost function of the transmitting vehicle. This is particularly successful if the cost values and the associated trajectories are evaluated collected over time.The effort value describes the travel effort required for traversing one of the trajectories. The driving effort may be different for different vehicles. For example, the driving effort for an agile, light vehicle may be lower than for a large, cumbersome vehicle on the same trajectory. Constant driving without speed change and direction change can be evaluated with a low driving effort or no driving effort. A strong braking or acceleration can be evaluated with a greater driving effort than a weak braking or acceleration, and a narrow curve radius can also be evaluated with a greater driving effort than a wide curve radius. The cost value combines the travel costs required along the respective trajectory into a numerical value. A trajectory with extreme driving maneuvers is thereby evaluated with a higher outlay value than a trajectory with weakly pronounced driving maneuvers. An evaluation criterion can assign numerical values for the driving effort to the individual driving maneuvers. The evaluation criterion may be influenced by a driver of the vehicle.A group of possible trajectories is combined, which intersect at a common point or originate from a common point. The common point may be a current position of the vehicle at the current time. The trajectories of the trajectory family are all at least slightly different, and at least one driving maneuver is different for all trajectories of the trajectory family. Different trajectories of the trajectory family may cross. In this case, a future position can be reached at the same future point in time via different trajectories.A (trajectory) trajectory consists of at least two trajectories. The trajectories of a tuple are part of different trajectory groups of different vehicles. The trajectories of the tuple thus start from different points. The trajectories of the tuple may intersect. If the different vehicles reach the intersection point of the trajectories at different times, the tuple is evaluated as collision-free if the vehicles are at all times spaced apart from one another at least by a minimum distance.The minimum distance can be greater in the direction of the trajectories than transversely to the trajectories. The minimum distance may be speed dependent.A driving maneuver of a vehicle can be understood to mean steering, braking and / or accelerating the vehicle. Driving maneuvers may be speed-dependent, for example a current speed of the vehicle determines a currently minimally drivable curve radius.The vehicle may also be referred to as a self-vehicle or ego vehicle, and includes a device that executes a method according to at least one of the embodiments described herein. The vehicle can be controlled at least partially by a driver, and the driving maneuvers can also be controlled at least partially by a control unit. The vehicle may also be fully autonomous or semi-autonomous controlled by the controller.Another vehicle is another vehicle. The prefix "foreign" is also used in the text for distinguishability in other terms assigned to the foreign vehicle. The other vehicle is controlled by an other driver or a different control device. The method presented here can likewise be carried out on a foreign device of the foreign vehicle. A foreign maneuver can be understood to mean a driving maneuver of the foreign vehicle. The approach presented here can also be carried out with interchanged roles, from the perspective of the other vehicle, wherein the terms vehicle and other vehicle and the associated features are interchanged in this case.The concept of priority in a traffic situation means that other vehicles are subject to waiting over the vehicle with priority. Priority is given on the basis of traffic rules; for example, priority can be given by traffic signs, traffic lights or else by special rights (emergency vehicles with blue light and siren).A data packet may be a self-contained message and may be referred to as a maneuver tuning message. The data packet can be transmitted from the vehicle to other vehicles via a communication interface. Conversely, the other vehicles can provide foreign data packets to the vehicle via the communication interface. The data packet or the third-party data packet can contain individual trajectory information of an individual trajectory pre-planned for the vehicle. In particular, trajectory information of different trajectories of a trajectory group planned ahead for the vehicle is contained in the data packet. The trajectories can be mapped as a sequence of location coordinates at defined distances from one another. The distances can be spatial or temporal. At spatial distances, the location coordinates are provided with time stamps. The trajectories can also be mapped in a parameterized manner. The trajectory can be mathematically described as a curve. The trajectory can be described in sections. Sensor data of the vehicle may also be contained in a data packet. A current situation can be estimated in an improved manner by the sensor data, since sensor data are available from a different angle of view than its own. The collision horizon may be adjusted using the sensor data. By a combination of sensor data of a plurality of vehicles, obstacles can be easily detected, for example.In a preferred embodiment, a method for cooperatively matching driving maneuvers of an ego vehicle with external maneuvers of at least one external vehicle comprises the steps:generating a trajectory set comprising pre-planned ego trajectories for the ego vehicle, wherein the ego trajectories comprise a reference trajectory;receiving one or more third-party data packets from the at least one third-party vehicle, wherein in a third-party data packet a third-party trajectory group comprising different pre-planned third-party trajectories for the respective third-party vehicle;generating additional third-party trajectories that correspond to further possible driving maneuvers of a third-party vehicle;forming tuple from an ego trajectory and a third-party trajectory or an additional third-party trajectory, respectively, and evaluating the tuple with a tuple cost value;determining collision-free tuple, wherein those tuple are selected in which the ego trajectory and the third trajectory are collision-free within a collision horizon;determining whether the ego vehicle has priority and if the ego vehicle has priority:selecting a trajectory from the tuple having the lowest tuple cost value as the egoistic trajectory from those collision-free tuple that includes both pre-scheduled third party trajectories and additional third party trajectories;selecting a trajectory from the tuple having the lowest tuple cost value as an altruistic trajectory from all collision-free tuple that only comprise pre-scheduled third party trajectories of third party vehicles;determining a difference between a tuple cost value of the tuple comprising the altruistic trajectory and a tuple cost value of the tuple comprising the egoistic trajectory;selecting a trajectory, wherein the egoistic trajectory is selected if the magnitude of the determined difference is greater than a tuple cost limit value or the altruistic trajectory is selected if the magnitude of the determined difference is less than the tuple cost limit value or equal to the tuple cost limit value;transmitting a data packet to the other vehicle, wherein the data packet comprises the egoistic trajectory or the altruistic trajectory as a reference trajectory as a function of the result of the comparison,wherein the tuple cost limit is increased with a continuous cooperation duration.In this specific embodiment, the ego vehicle may select the combination of ego and extraneous trajectories that leads to a sensible cooperation between the vehicles, based on information that includes the own reference and demand trajectories as well as received and (self-)generated extraneous trajectories. In this case, a tuple cost limit value specifies whether an egoistic or altruistic solution is preferred within the scope of the cooperation. If the difference between the egoistic or altruistic solution is small (less than the tuple cost limit), then the altruistic solution is taken.In general, such a cooperation is initiated by virtue of the fact that the messages exchanged by the vehicles contain required trajectories, and it is thereby clear that there is not yet any ongoing cooperation, because no required trajectories but reference trajectories are transmitted in the case of an existing cooperation.As described, one or more-in particular correspondingly calculated-additional trajectory(s) are generated by the vehicle. This can be effected in particular when there is no external reference trajectory of the external vehicle. These one or more additional trajectories represent trajectories that the other vehicle could probably likewise follow in the respective situation. These can be used as a foreign reference trajectory. For example, this may be provided if no external reference trajectory has been received by the external vehicle, in particular if it is not capable of V2X communication.Furthermore, a tuple cost limit value is used here, which must be used by the difference between the tuple cost values of the collision-free tuple, so that a cooperation with a further vehicle is generally accepted at all or an existing cooperation is continued. If the tuple cost limit value is reached or exceeded, then the cooperation is aborted according to at least one embodiment and another tuple can be selected.In other words, for the cost of a maneuver of the vehicle and other vehicle, a threshold is defined below which a cooperative solution must come in order to be accepted. This will initially usually be above the cost of an egoistic solution without considering the further vehicle. The tuple cost limit value thus represents a defined measure of a general readiness for cooperation with the acceptance of its own additional costs. Since traffic situations change permanently and planned maneuvers cannot usually always be converted as scheduled over time, deviations in the evaluation and the cost calculation will possibly occur during later calculation cycles. The tuple effort limit value is therefore raised with a continuous cooperation duration according to at least one embodiment, irrespective of the fact that the difference of the tuple effort value may likewise possibly become greater as a result compared to the optimum maneuver planning solution actually present at the respective point in time. The adaptation of the tuple expenditure limit value can thus be seen as the increasing motifation, and a cooperative maneuver that has been agreed once also wishes to lead to a successful completion. The stability of cooperative driving maneuver planning is thus improved.In a further embodiment, a maximum value is provided for the tuple cost limit value, from which no further increase of the tuple cost limit value takes place with a continuous cooperation duration. The extra expenditure of its own is thus limited to a tolerated extent.In a further embodiment, incrementing of a counter takes place when and as long as the arithmetic trajectory is selected, wherein the cooperation duration results from the counter. Even in the case that no explicit cooperation is provided, for example, according to a maneuver planning protocol used, it is nevertheless possible in this way to determine whether or not there is a cooperation with a further road user. The counter can be reset in particular when an egoistic trajectory is selected. According to one aspect, the incrementing of the counter takes place only when there is a cooperation with the same other vehicle. According to a development, this can be done by following and comparing the egoistic trajectory with the altruistic trajectory, it being established whether the same other vehicle as before is to be made possible by the external requirement trajectory or external reference trajectory. As a result, an existing cooperation can be detected even in the case of rapidly changing cooperation partners. The sender can be identified in the trajectory, the sender is thus documented and can thus be easily determined.In a further embodiment, the trajectory and the associated cost value of the collision-free tuple having the smallest tuple cost value is selected as the reference trajectory and reference cost value, in which the tuple cost value is less than or equal to the tuple cost limit value.In a further embodiment, the ego trajectories comprise one or more required trajectories.In a further specific embodiment, the trajectory and the associated cost value of the collision-free tuple having the smallest tuple cost value, which includes an external reference trajectory and an external reference cost value of the external vehicle ( 100), is selected as reference trajectory ( 110) and reference cost value, in which the tuple cost value is less than or equal to the tuple cost limit value.In a further embodiment, a computer program product is configured to execute, implement and / or actuate the steps described in the preceding embodiments. The computer program product can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and can be used for carrying out, implementing and / or controlling the steps of the method according to one of the embodiments described above, in particular if the program product or program is executed on a computer or a device.In a further embodiment, a device comprises means which execute, implement and / or actuate the steps described in the preceding embodiments. The device can be an electrical device having at least one computing unit for processing signals or data, at least one memory unit for storing signals or data, and at least one interface and / or one communication interface for reading in or outputting data embedded in a communication protocol. The computing unit can be, for example, a signal processor, a so-called system ASIC, a microprocessor or a microcontroller for processing sensor signals of the own and / or other vehicles and outputting data signals depending on the sensor signals. The memory unit may be, for example, a flash memory, an EPROM, or a magnetic memory unit. The interface can be designed as a sensor interface for reading in the sensor signals from a sensor and / or as an actuator interface for outputting the data signals and / or control signals to an actuator. The communication interface can be designed to read in or output the data wirelessly and / or in a wired manner. The interfaces can also be software modules which are present, for example, on a microcontroller, in addition to other software modules.With regard to the tuple cost limit value discussed above, the tuple cost limit value can be adapted with a hysteresis, wherein the hysteresis becomes greater and greater with the cooperation duration. This takes into account dynamic changes of a journey during a cooperative maneuver, which can prevent repeated termination and reassembling of a cooperation and thus unclear conditions for the other vehicles around the maneuver space from arising. The prospects of success of a cooperation granted once increase. When the cooperation is aborted, the tuple cost limit value can then be reset. In a new cooperation, the tuple cost limit value can be set to zero, alternatively also to a predetermined initial value.It may be useful to establish the tuple cost limit value as a function of the vehicle. For example, the type of vehicle may be considered, wherein the type may include a passenger car (car), truck (truck), bus, or the like. Alternatively or additionally, the speed can also be taken into account when establishing the tuple outlay limit value, or else the priority / priority of the vehicle.The data packet may contain cost values for each trajectory. The trajectories are comparable by the cost values without having to understand a driving cost of the individual driving maneuvers for each trajectory. The individual cost values of the trajectories of a tuple can be added, for example, in order to obtain the tuple cost value of the tuple. Likewise, the cost values may be weighted differently to determine the tuple cost value. When weighting the cost values, a readiness for cooperation of the drivers of the vehicles under consideration can be taken into account.A function-specific component can also be incorporated into the tuple cost value. A function-specific component can be, for example, a comfort condition which leads to the fact that, in particular, tuple with non-critical vehicle distances are preferred.At least one further third-party data packet can be received by a further third-party vehicle. A further foreign trajectory can be added to the tuple. The tuple cost value can be supplemented with a further extraneous cost value. The tuple can be formed from three or more trajectories.As described, in the approach presented here, decentralized coordination of cooperative driving maneuvers is presented on the basis of optional trajectories. A method for cooperative maneuver matching is described, which is based on the exchange of trajectories. Here, information about the currently planned trajectory or reference trajectory and possible trajectories, for example for an avoidance maneuver (alternative trajectories) or for an intended maneuver (required trajectories), is transmitted with associated outlay evaluations. As a result, the vehicles involved can tune an optimum joint maneuver (with the lowest total outlay). In this case, only exactly one desired trajectory is not always transmitted, which increases the scope of action in complex scenarios and increases the probability of a cooperation which yields all pages. The decision as to whether a demand trajectory is enabled is not made on the basis of a global cost function, which makes assumptions about the costs of other vehicles. For optimum decision making, therefore, no cost function that is the same among all vehicle manufacturers is required or an error-prone assumption can be dispensed with. The own costs are calculated internally in each vehicle and can be transmitted to the other vehicles standardized to the interval [-1, 1].In the approach presented here, no active request is required for cooperation. Here, the cooperation can be offered by means of alternative trajectories by a vehicle which already identifies the future cooperation requirement by another vehicle in advance, because it has, for example, a much more comprehensive environment model thanks to its better sensor system.Furthermore, negotiation or optimizing tuning of maneuvers is possible. For example, the first vehicle could send a demand trajectory that the second vehicle needs to reduce the speed by 20 km / h if the second vehicle wants to cooperate. In the approach presented here, the second vehicle can notify, for example by sending a corresponding alternative trajectory, that a reduction of the speed by 10 km / h would be acceptable and then it would be ready for cooperation. In this case, a specific change of the reference trajectory is not required, as a result of which an immediate intervention in the vehicle control can be avoided. Thus, non-consecutive negotiation / optimization is achieved.In the approach presented here, there is an unconnectionable sending of trajectories. Decentralized cooperative maneuver matching has two parts. Firstly, a protocol with a set of rules is presented which allows vehicles to communicate. On the other hand, methods are presented for carrying out different cooperative maneuver adjustments with the aid of this protocol. The basic principle is that vehicles exchange trajectories. The vehicle can thus notify preventively / proactively trajectories that it would be ready to drive, since the disadvantages are acceptable within the scope of its own cost function. There is the possibility of negotiation about cooperation before influence is exerted on the respective vehicle. This results in the possibility of efficient optimization of the maneuvers of the vehicles involved in a cooperation. Furthermore, the possibility of extreme reduction of the computing requirement results, since preferences of all cooperation partners are explicitly encoded and communicated via outlay values or costs and therefore a complicated cost analysis for foreign vehicles can be dispensed with. The chance of a successful cooperation increases due to the transferred cost value, since the cost of the cooperation partner can be better estimated; if necessary, a local estimation of extraneous costs can also be dispensed with completely. The costs for one of the received external trajectories can also be estimated locally in the vehicle. If necessary, the received cost values of all extraneous costs can be scaled. This allows a rough comparison of the own and external costs to be made possible with a low computing effort. Introducing categories results in better assistance from maneuver planning algorithms.In order to represent a stable cooperative maneuver, a threshold is defined for the costs of a maneuver, below which a cooperative solution must come in order to be accepted. This will initially be a factor or offset above the cost of the egoistic solution. It represents a measure of a general readiness for cooperation with the acceptance of its own additional costs. Since situations are now easily changed permanently and plans can never be perfectly converted, deviations in the evaluation and the cost calculation will occur during later calculation cycles. In particular, the costs can again pass above the cooperation threshold. Therefore, this cooperation threshold is raised with continuous cooperation duration according to at least one embodiment, even if the difference from the best result is also made larger thereby. The raising in this case represents the increasing motifation of leading to a granted cooperation also to a successful completion. At the same time, this can be expediently provided to be restricted in that the own requirement is not completely lost from the eyes. One possibility could also be that the threshold is equipped with a hysteresis, wherein the hysteresis becomes increasingly greater with the cooperation duration. This allows dynamic changes of a journey during a cooperative maneuver to be taken into account. A continuous termination and reassembling of a cooperation and thus unclear conditions for the other vehicles in the region of the maneuver space can thereby be avoided. The prospects of success of a cooperation granted once increase.[BRIEF DESCRIPTION OF THE FIGURES]FIG. 1 shows a representation of a vehicle having a device according to an exemplary embodiment, and a different vehicle.[DETAILED DESCRIPTION OF THE FIGURES]Embodiments of the invention are described below with reference to the attached drawings, wherein neither the drawings nor the description should be interpreted as restricting the invention. The figures are schematic only and not to scale. Identical reference numerals designate identical or identically acting features in the figures.When maneuvers between vehicles are tuned to increase comfort, efficiency, and safety, among other things, this may be referred to as cooperative driving. This is favored by the possibility of direct vehicle-to-vehicle communication (V2V) and by the increasing automation of vehicles. The adjustment of the maneuver can also take place in general situations in road traffic. Vehicles can, if required, transmit information on their current driving behavior and their intended driving behavior in the form of a trajectory. A further vehicle checks whether it can enable the intended maneuver of the transmitting vehicle and optionally acknowledges it explicitly or implicitly by adapting its reference trajectory, so that the vehicle which has sent its intended driving maneuver can carry it out.FIG. 1 shows a representation of a vehicle 100 which comprises a device (not shown) for cooperatively tuning driving maneuvers with at least one other vehicle 110. The vehicle 100 travels on a road in the area of an entrance with acceleration lanes. The other vehicle 110 is located on the acceleration strip and intended to drive onto the road. The other vehicle 110 may also be referred to as a foreign vehicle. Sensors included in the vehicle 100 record the current traffic situation in the environment of the vehicle 100, alternatively or additionally, the vehicle 100 can receive information about the traffic situation via wireless communication paths, for example in the form of V2X messages.Depending on the traffic situation, currently possible trajectories for the vehicle 100 are planned and combined in a trajectory group 101. As described in detail above, a trajectory describes the predicted movement of the vehicle and thus indicates the location at which the vehicle 100 will be future if it followed this trajectory by driving maneuvers such as steering, braking or acceleration.For reasons of clarity, the trajectory group 101 comprises only two trajectories, namely the trajectories 101 aand 101 b. It is understood that the number of trajectories is not limited to two. The trajectories have the same origin, this origin being located at the current position of the vehicle 100. Depending on how the vehicle 100 is accelerated, the trajectories are of different lengths and end at different locations. In this case, trajectory 101 ais a simple trajectory that consists essentially only of a forward movement. Trajectory 101 bincludes a lane change and describes an operation in which vehicle 100 makes room for vehicle 110 so that it can change from the acceleration strip to the road without having to accelerate or brake.The vehicle 110 travels on the acceleration strip, the planned trajectory 111 of the vehicle 110 describes the acceleration of the vehicle 110 together with the lane change from the acceleration strip to the road on which the vehicle 100 is located. The vehicle 110 sends a data packet with the planned trajectory 111 to the vehicle 100. It is understood that the vehicle 110 may also have more than one planned trajectory, for the sake of simplicity it is assumed that in this case only a single trajectory is planned.The vehicle 100 receives the data packet from vehicle 110 for clarity, the data packet is not shown in FIG. 1. The external trajectories of the vehicle 110 can be taken from the data packet. In a next step, the vehicle 101 generates one or more additional external trajectories, each describing a possible driving maneuver of the vehicle 110. In this case, trajectory 112 is generated, this trajectory describing a simple straight-ahead travel with braking to the end of the acceleration lane. Thus, the vehicle 110 would brake on the acceleration strip such that the vehicle 100 may pass and the vehicle 110 may ride on the road when the vehicle 100 has passed the area of the acceleration strip. As already described above, these generated (n) additional external trajectory(s) are driving maneuvers which, although not planned by the vehicle 110, may be relevant for the maneuver planning of the vehicle 100 and are therefore assumed by the latter as hypothetical or conceivable driving maneuvers.Based on the own planned trajectories 101 aand 101 band on the received trajectory 111 and on the generated trajectory 112, the vehicle 100 forms trajectory tuple which each comprise an ego trajectory and a foreign trajectory. Thus, in the present example, 4 tuple can be generated, namely tuple A: (101a 111), B: (101b 111), C: (101a 112) and D: (101b 112) as shown in the bottom portion of FIG. 1. A tuple cost value is determined for each tuple.Tuple A corresponds to the situation that the vehicle 100 continues to travel straight while the vehicle 110 is undergoing a lane change. Here, the outlay value is low, because no steering movement and possibly a low acceleration / deceleration are necessary for the vehicle 100.Tuple B corresponds to the situation that the vehicle 100 carries out a lane change and that the other vehicle also carries out a lane change. Here, the cost value is likewise low, but somewhat greater than for tuple A, because a steering movement and possibly a low acceleration / deceleration are necessary for vehicle 100.Tuple C corresponds to the situation that the vehicle 100 continues to travel straight and that the other vehicle travels straight on the acceleration lane and brakes to the end of the acceleration lane. Here, the cost value is also low because no steering movement is necessary for the vehicle 100 and no acceleration / deceleration is to be expected. In comparison with tuple A, the cost value of tuple C is lower because smaller distances occur between the vehicles in tuple A than in tuple C.Tuple D corresponds to the situation that the vehicle 100 makes a lane change and that the other vehicle is traveling straight on the acceleration lane and braking to the end of the acceleration lane. Here, the cost value is comparable to that of tuple B.For purposes of illustration, but not by way of limitation, the following tuple cost values are assumed: A: 0.2 B: 0.25 C: 0.16 D: 0.25.The check for collision freedom reveals that all tuple is collision-free. A priority consideration reveals that the vehicle 100 has priority over the vehicle 110, that is to say is authorized for priority.The egoistic or altruistic trajectory is selected from the tuple having the lowest tuple cost value. The selection of the egoistic trajectory can be made from the tuple A-D, since these comprise pre-planned third-party trajectories and additional third-party trajectories, and the selection of the altruistic trajectory can be made from the tuple A and B, since these comprise only pre-planned and no additional third-party trajectories. It is thus the result that the egoistic trajectory is selected from tuple C and the altruistic trajectory is selected from tuple A.The difference between the tuple cost values is 0.04 in this example, and the comparison with a tuple cost limit value, which is assumed here as 0.05, reveals that the arithmetic trajectory is sent to the other vehicle 110. Since the tuple cost limit value is continuously increased, the selection of the egoistic trajectory would possibly have occurred if the cooperation existed for a shorter time, since the tuple cost limit value would then have a lower value of, for example, 0.03.As long as the altruistic trajectory, rather than the egoistic trajectory, is sent and tracked, there is cooperation between the vehicles 100, 110.Although only two vehicles are shown in the example described in FIG. 1, the concept presented here can be extended to three or more vehicles.The method described can be implemented, for example, in software or hardware or in a mixed form of software and hardware, for example in a control device.It is noted that terms such as "having", "comprising", etc. do not exclude other elements or steps, and terms such as "a" or "an" do not exclude a plurality. Reference signs in the claims should not be regarded as limiting.

Claims

Method for cooperatively matching driving maneuvers of an ego vehicle (100) with external maneuvers of at least one other vehicle (110), the method comprising: generating a trajectory group (101) which comprises a plurality of pre-planned ego trajectories (101a, 101b) for the ego vehicle (100), wherein the ego trajectories comprise a reference trajectory; receiving one or more external data packets from the at least one other vehicle (102), wherein in an external data packet an external trajectory group which comprises different pre-planned external trajectories (111) for the respective other vehicle (110); generating additional external trajectories (112) which correspond to further possible driving maneuvers of an other vehicle (110); forming tuple from an ego trajectory and a third-party trajectory or an additional third-party trajectory in each case and evaluating the tuple with a tuple cost value; ascertaining collision-free tuple, wherein those tuple are selected in which the ego trajectory and the third-party trajectory are collision-free within a collision horizon; ascertaining whether the ego vehicle (100) has priority, and if the ego vehicle (100) has priority: selecting a trajectory from the tuple with the lowest tuple cost value as the ego trajectory from those collision-free tuple which comprise both pre-planned third-party trajectories (111) and additional third-party trajectories (112); selecting a trajectory from the tuple having the lowest tuple cost value as an altruistic trajectory from all collision-free tuple that comprises only pre-scheduled third-party trajectories (111) of third-party vehicles; ascertaining a difference between a tuple cost value of the tuple comprising the altruistic trajectory and a tuple cost value of the tuple comprising the egoistic trajectory; selecting a trajectory, wherein the egoistic trajectory is selected if the amount of the ascertained difference is greater than a tuple cost limit value or the altruistic trajectory is selected if the amount of the ascertained difference is less than the tuple cost limit value or equal to the tuple cost limit value; and transmitting a data packet to the other vehicle (110), wherein the data packet comprises the egoistic trajectory or the altruistic trajectory as a reference trajectory as a function of the result of the comparison, wherein the tuple cost limit value is increased with a continuous cooperation duration.Method according to Claim 1, wherein a maximum value is provided for the tuple expenditure limit value, from which no further increase of the tuple expenditure limit value takes place with a continuous cooperation duration.Method according to one of Claims 1 or 2, wherein incrementing of a counter takes place when and as long as the arithmetic trajectory is selected, the cooperation duration resulting from the counter.The method of at least one of the preceding claims, wherein the ego trajectories further comprise one or more required trajectories.Method according to at least one of the preceding claims, wherein the trajectory and the associated cost value of the collision-free tuple with the lowest tuple cost value is selected as reference trajectory and reference cost value, in which the tuple cost value is less than or equal to the tuple cost limit value.Method according to at least one of the preceding claims, wherein the trajectory and the associated effort value of the collision-free tuple with the lowest tuple effort value, which comprises a third-party reference trajectory and a third-party reference effort value of the third-party vehicle (100), is selected as reference trajectory and reference effort value, in which the tuple effort value is less than or equal to the tuple effort limit value.Computer program product configured to execute, implement and / or drive the method according to one of the preceding claims.Device comprising means which are configured to execute, implement and / or actuate a method according to one of Claims 1 to 6.

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