Method and device for cooperation in coordinating driving maneuvers

Abstract

Method and Device for Coordinating Driving Maneuvers: The present invention relates to a method for the cooperative coordination of driving maneuvers of a vehicle with maneuvers of a third vehicle, in which a family of trajectories is generated, comprising multiple pre-defined trajectories including a reference trajectory. Tuples are then formed from a pre-defined trajectory and a third-party trajectory or an additional third-party trajectory. The tuple is evaluated using a tuple effort value, and collision-free tuples are determined. If the pre-defined vehicle has priority, selfish and altruistic trajectories are selected.Based on the difference between the tuple effort values ​​of the tuple containing the altruistic trajectory and the tuple containing the selfish trajectory, the selfish trajectory is selected if the difference is greater than a limiting value, the altruistic trajectory is selected otherwise, and either the selfish or altruistic trajectory is sent as the reference trajectory to the third-party vehicle, the limiting value being increased as the duration of cooperation increases. The present invention further relates to a corresponding device and a corresponding computer program product. [Figure 1].

Description

Title of the invention: Method and device for cooperation in the coordination of driving maneuvers technical field

[0001] The invention relates to a method and device for the cooperative coordination of driving maneuvers between a vehicle and at least one third vehicle. Previous technique

[0002] Various options exist for vehicles to cooperate in finding a solution to a specific situation within the context of traffic events and to implement it. Such solutions often require explicit cooperation, in which specific cooperation partners are sought through state machines, and then bilateral coordination is implemented. Conversely, there are implicit coordination mechanisms, which are essentially implemented by sending trajectories corresponding to ego driving intentions. The respective receiver must decide whether or not to adapt its own driving intentions to the wishes of another vehicle.

[0003] Published application WO 2017 076 593 A1 discloses a method for decentralized coordination of driving maneuvers of motor vehicles, in which a planned trajectory and the desired trajectory of a first motor vehicle are transmitted to another nearby motor vehicle. The second motor vehicle checks whether the received planned trajectory and desired trajectory intersect the planned trajectory of the first and second motor vehicles, with the planned trajectories of the first and second motor vehicles not resulting in a collision in this case.If an adaptation criterion is satisfied, an adaptation of the intended trajectory of the second motor vehicle is performed to create an adapted intended trajectory, the adaptation criterion being that the desired trajectory received from the first motor vehicle meets the intended trajectory of the second motor vehicle and that an overall cost function is optimized by the adaptation, the overall cost function comprising at least cost functions of the first and second motor vehicles.

[0004] Published application WO 2019 206 377 A1 describes a method for the cooperative coordination of future driving maneuvers of a vehicle with third-party maneuvers of at least one third-party vehicle, the method comprising the following steps: evaluating, using at least one evaluation criterion, a family of trajectories composed of trajectories planned in advance for the vehicle, each having an effort value; receiving a third-party data packet from the third-party vehicle, the third-party data packet containing a third-party trajectory family comprising trajectories pre-defined third-party paths for the third-party vehicle and a third-party effort value for each third-party path; combine one path and one third-party path in each case to form tuples and combine the respective effort value with the respective third-party effort value to form a tuple effort value for the tuple; select collision-free tuples, the selected tuples being tuples in which the path and the third-party path do not result in a collision within a collision horizon; select the collision-free tuple with the lowest tuple effort value and classify the path and associated effort value of that tuple as the reference path and reference effort value;select trajectories with an effort value lower than the reference effort value and classify these trajectories and associated effort values ​​as desired trajectories and desired effort values, a desired trajectory being a trajectory indicating a wish of the vehicle, with which a desired driving destination can be reached more economically than with the reference trajectory; select trajectories with an effort value higher than the reference effort value and classify these trajectories and associated effort values ​​as possible trajectories and possible effort values, a possible trajectory being a trajectory indicating an offer of cooperation, which the vehicle would be willing to follow;and send a data packet to the third-party vehicle, the data packet containing a family of trajectories composed of the reference trajectory and the associated reference force value, as well as at least one trajectory from a group comprising the desired trajectories and the possible trajectories with the corresponding force values.

[0005] In classical systems, the approach taken is that tuples containing a desired trajectory for a third vehicle receive effort compensation in the vehicle's effort evaluation, making such tuples preferable by the vehicle in the subsequent effort-based selection. Thus, in particular, an incentive to cooperate is created for the vehicle so that it accepts additional ego effort. As soon as this is communicated to the third vehicle, it will no longer send a desired trajectory, but only a reference trajectory. Consequently, in the next step, the effort compensation will no longer be active in the vehicle, and cooperation will become less advantageous for the vehicle in question, and it may therefore cease.The third vehicle will then send desired trajectories again, incorporating effort compensation in the vehicle. Cooperation will thus appear advantageous again, encouraging the vehicle to resume cooperation. Traffic conditions can also change very quickly, and changes in effort values ​​can therefore occur in successive evaluation cycles, resulting in the cessation of cooperation. and that a new form of cooperation must be established. Therefore, there is a variable or unstable cooperative behavior.

[0006] [OBJECT OF THE INVENTION]

[0007] The object of the invention is to improve the stability of cooperative planning of driving maneuvers and to avoid variable cooperative behavior.

[0008] This object is achieved by means of the content of the independent claims. Advantageous embodiments can be derived from the dependent claims, for example.

[0009] Embodiments of the present invention may advantageously allow for the mutually coordinated future driving maneuvers of various vehicles, such that none of the vehicles has to perform, for example, excessive steering, braking, and / or acceleration interventions in order to allow for smooth traffic flow. A method used for this purpose may be carried out by means of a device, such as a control unit. Signals, measured values, or the like, originating, for example, from sensors, measuring devices, or the like, may be used and / or analyzed to carry out the method. The method may be carried out in a fully automated or semi-automated manner. In this case, human intervention is not necessarily excluded, but is not necessarily required.

[0010] Terms used in the context of the description of the present invention are defined below.

[0011] A trajectory describes a change in state (e.g., position, orientation, velocity, and acceleration vectors, etc.) over time, relative to which a vehicle can be controlled by means of driving maneuvers. A trajectory is generally multidimensional, in particular two-dimensional or three-dimensional, and may extend along a driving surface intended to be traversed by the vehicle, for example, a road. The trajectory therefore describes where the vehicle is, where it was, or where it will be at any given time. The trajectory is predicted in advance, at least with respect to a prediction horizon. For example, the prediction horizon may be determined by the range of a sensor on the vehicle. The prediction horizon may depend on the velocity. Trajectories are not understood to be exclusively trajectories in Euclidean space; they may also be trajectories in other possible spaces.An example of this is the Frenet frame along lane centers, in which a trajectory can be defined by specifying the respective lane and the sections on the lane center over time. In this way, the set of trajectories can be efficiently transferred to a structured traffic space (e.g., a highway with lane markings). A collision horizon can be lower. or equal to the forecast horizon, and may also depend on speed or situation, for example, depend on traffic density.

[0012] The absence of collisions on trajectories is established if the trajectories extend from the vehicle to the collision horizon in such a way that the vehicle and the third vehicle are located at least at a minimum distance from each other.

[0013] The trajectory selected from the family of trajectories to be actually followed by the vehicle, or the trajectory currently followed by the vehicle and which is the setting value for the driving control device / driver, can be designated as the reference trajectory. Reference trajectories must be essentially free of conflict; if conflicts arise, they can be resolved according to road regulations. A conflict can be resolved by the subordinate vehicle, and the preferred vehicle can thus continue to transmit its conflicting trajectory.

[0014] A possible trajectory is a trajectory that is more costly than the reference trajectory, but which the vehicle would nevertheless be willing to follow. In this context, "it is possible / possibly" means "under certain conditions," i.e., for example, that the driver's consent may be required before a possible trajectory can become the reference trajectory. An assessment of the resulting overall situation may also be carried out, consisting of verifying whether the additional ego costs result in a sufficient benefit (from a local perspective) for other vehicles. The possible trajectories may be designed so as to be free of conflicts.

[0015] A desired trajectory describes a preferred maneuver that the vehicle would readily perform due to lower costs or effort, but cannot currently perform, for example, because the required maneuvering space is occupied by other vehicles. A desired trajectory may be one that more closely matches the desired driving objective and is therefore more advantageous than the reference trajectory. The desired trajectory may conflict with other trajectories. The desired trajectory may optionally be communicated.

[0016] A cost value can be transmitted for each trajectory. This makes it possible to establish a relationship between each trajectory and the other trajectories and to deduce their importance and priority level. Transmitting the cost value for each trajectory allows other road users to determine a local approximation of the transmitting vehicle's cost function. This works particularly well if the cost values ​​and associated trajectories are evaluated and collected over time.

[0017] The effort value describes the driving effort required to drive along one of the trajectories. The driving effort may differ for different vehicles. By For example, the driving effort for a light and agile vehicle may be less than that for a large, heavy vehicle on the same trajectory. Constant driving without changing speed or direction can be considered to represent low or no driving effort. High-intensity braking or acceleration can be considered to represent greater driving effort than low-intensity braking or acceleration, and a tighter curve radius can also be considered to represent greater driving effort than a wider curve radius. The effort value combines the driving efforts required along the respective trajectory to form a numerical value. Therefore, a trajectory involving extreme driving maneuvers will be considered to be associated with a higher effort value than a trajectory involving less intense driving maneuvers.An evaluation criterion can assign numerical values ​​for driving effort to individual driving maneuvers. The evaluation criterion can be influenced by a vehicle driver.

[0018] A family of trajectories combines a group of possible trajectories that intersect at a common point or originate from a common point. The common point can be a current location of the vehicle at the present time. The trajectories in the family of trajectories are all at least slightly different; at least one driving maneuver is different in all the trajectories of the family of trajectories. Different trajectories in the family of trajectories can intersect. A random location can be reached at the same time in the future by following different trajectories.

[0019] A tuple (of trajectories) consists of at least two trajectories. The trajectories of a tuple belong to different families of trajectories of different vehicles. The trajectories of the tuple therefore originate from different points. The trajectories of the tuple may intersect. If the different vehicles reach the intersection of the trajectories at different times, the tuple is considered collision-free if the vehicles are at all times separated from each other by a minimum distance. The minimum distance may be greater in the direction of the trajectories than transversely to the trajectories. The minimum distance may depend on the speed.

[0020] A vehicle driving maneuver can be understood as steering, braking, and / or accelerating the vehicle. Driving maneuvers may depend on speed; for example, the vehicle's current speed determines a minimum current radius of curvature that can be traversed.

[0021] The vehicle can also be called an "ego vehicle" and includes a device that performs a process according to at least one of the embodiments described here. The vehicle can be at least partially controlled by a driver; driving maneuvers can also be at least partially performed. by a control unit. The vehicle can also be controlled fully or semi-autonomously by the control unit.

[0022] A third vehicle is a different vehicle. The qualifier "third" is also used in the text with other terms associated with the third vehicle in order to distinguish them. The third vehicle is controlled by a third driver or a third control unit. The method presented here can also be performed on a third device of the third vehicle. A third maneuver can be understood as a driving maneuver of the third vehicle. The approach presented here can also be implemented by reversing the roles, from the point of view of the third vehicle, the terms "vehicle" and "third vehicle" and their associated characteristics being, in this case, reversed.

[0023] The concept of priority in a traffic situation means that other vehicles must give way to the vehicle with priority. Priority is given according to traffic rules; for example, priority may be given by traffic signs, traffic lights, or also by special rights (e.g., vehicles with emergency lights and sirens).

[0024] A data packet can be a self-contained message, referred to as a "maneuver coordination message." The data packet can be transmitted via a vehicle communication interface to third-party vehicles. Conversely, third-party vehicles can provide third-party data packets to the vehicle via the communication interface. The data packet or third-party data packet may contain a single trajectory information item concerning a single trajectory predetermined for the vehicle. In particular, the data packet may contain trajectory information item concerning different trajectories within a family of trajectories predetermined for the vehicle. The trajectories can be represented as a sequence of location coordinates separated by defined intervals. The intervals, in this case, can be spatial or temporal.In the case of spatial intervals, location coordinates are assigned timestamps. Trajectories can also be represented parametrically. In this case, the trajectory can be mathematically described as a curve. The trajectory can be described in sections. Furthermore, a data package can contain sensor data from the vehicle. A current situation can be better assessed using sensor data, since sensor data from a viewing angle different from the ego's viewing angle is available. The collision horizon can be defined using sensor data. Obstacles can be efficiently identified, for example, using a combination of sensor data from multiple vehicles.

[0025] In a preferred embodiment, a method for cooperatively coordinating driving maneuvers of an ego vehicle with third-party maneuvers of at least one third vehicle comprises the following steps:

[0026] generate a family of trajectories which includes ego trajectories planned in advance for the ego vehicle, the ego trajectories including a reference trajectory;

[0027] receive one or more third-party data packets from at least one third-party vehicle, a third-party data packet containing a third-party trajectory family that includes different third-party pre-planned trajectories for the respective third-party vehicle;

[0028] generate additional third trajectories that correspond to other possible driving maneuvers of a third vehicle;

[0029] form tuples composed, in each case, of an ego trajectory and a third trajectory or an initial third trajectory and evaluate the tuple using a tuple effort value;

[0030] determine collision-free tuples, the selected tuples being tuples in which the ego trajectory and the third trajectory do not give rise to a collision in a collision horizon;

[0031] determine if the ego vehicle has priority and, if the ego vehicle has priority:

[0032] select a tuple trajectory having the lowest tuple effort value as a selfish trajectory among collision-free tuples that include both third-party pre-planned trajectories and additional third-party trajectories;

[0033] select a tuple trajectory having the lowest tuple effort value as an altruistic trajectory from among all collision-free tuples that include only third-party pre-planned trajectories of third-party vehicles;

[0034] determine a difference between a tuple effort value of the tuple including the altruistic trajectory and a tuple effort value of the tuple including the selfish trajectory;

[0035] select a trajectory, the selfish trajectory being selected if the absolute value of the determined difference is greater than a tuple effort limitation value or the altruistic trajectory being selected if the absolute value of the determined difference is less than the tuple effort limitation value or is equal to the tuple effort limitation value;

[0036] send a data packet to the third-party vehicle, the data packet comprising, depending on the result of the comparison, the selfish trajectory or the altruistic trajectory as the reference trajectory,

[0037] the tuple effort limitation value being increased as the duration of cooperation increases.

[0038] In this embodiment, the ego vehicle can select, based on information including the reference and desired ego trajectories and received and (self-)generated third-party trajectories, the combination of ego and third-party trajectories resulting in reasonable cooperation between the vehicles. A tuple effort limitation value specifies, in this case, whether a selfish or altruistic solution is preferred in the context of the cooperation. If the difference between the selfish and altruistic solutions is small (less than the tuple effort limitation value), the altruistic solution is retained.

[0039] In general, the introduction of such cooperation is manifested by the presence of desired trajectories in the messages exchanged by the vehicles, so that it is evident that there is no ongoing cooperation yet since, within the framework of existing cooperation, the trajectories transmitted are not desired trajectories but rather reference trajectories.

[0040] As described, one or more additional trajectories—in particular, correspondingly calculated—are generated by the vehicle. This can occur, in particular, in the absence of a third-party reference trajectory from the third-party vehicle. This additional trajectory or trajectories represent trajectories that the third-party vehicle could probably also follow in the respective situation. These can be used as a third-party reference trajectory. For example, this may be the case if no third-party reference trajectory has been received from the third-party vehicle, in particular if it is not designed for Vehicle-to-Everything communication.

[0041] Furthermore, a tuple effort limitation value is used here; the difference in tuple effort values ​​of the collision-free tuple must be less than this value for cooperation with another vehicle to be fundamentally accepted or for existing cooperation to continue. If the tuple effort limitation value is reached or exceeded, cooperation is terminated according to at least one embodiment, and another tuple may be selected.

[0042] In other words, for the costs of a maneuver by the vehicle and the third vehicle, a threshold is defined, and a cooperative solution must be below this threshold to be accepted. This threshold will usually initially be above the costs of a selfish solution that does not take the other vehicle into account. The tuple effort limitation value thus represents a defined measure of a general willingness to cooperate by accepting additional selfish costs. Since traffic situations are constantly changing and planned maneuvers cannot generally always be implemented as planned over time, discrepancies may appear in the evaluation and calculation of costs in subsequent calculation cycles. Therefore, the tuple effort limitation value increases as the duration of cooperation increases according to at least one mode of implementation, regardless of the fact that the difference in the tuple effort value may also become greater as a result compared to the optimal maneuver planning solution actually available at the respective time. The adjustment of the tuple effort limitation value can therefore be considered a representation of the increasing motivation to carry out a previously agreed-upon cooperative maneuver. The stability of the cooperative driving maneuver planning is thus improved.

[0043] In another embodiment, a maximum value is established for the tuple effort limitation value, the tuple effort limitation value, which increases as the duration of cooperation increases, not increasing beyond this maximum value. The additional ego effort is, therefore, limited to a tolerable level.

[0044] In another embodiment, the value of a counter is increased when and as long as the altruistic trajectory is selected, the duration of cooperation being provided by the counter. Thus, even in the case where, for example, no explicit cooperation is implemented according to a maneuver planning protocol used, it is still possible to determine whether cooperation with another road user exists. The counter can, in particular, be reset to zero when a selfish trajectory is selected. According to one aspect, the value of the counter is increased only when cooperation with the same third-party vehicle exists. According to an improvement, this can be implemented by monitoring and comparing the selfish trajectory with the altruistic trajectory, establishing whether the third-party vehicle that would apply the desired third-party trajectory or the third-party reference trajectory is the same as before.Existing cooperation can thus be detected even when cooperation partners change rapidly. The transmitter can be identified in the trajectory, the sender is thus recorded and can therefore be easily determined.

[0045] In another embodiment, the trajectory and associated force value of the collision-free tuple having the lowest tuple force value is selected as the reference trajectory and the selected reference force value is a value for which the tuple force value is less than or equal to the tuple force limitation value.

[0046] In another embodiment, the ego trajectories include one or more desired trajectories.

[0047] In another embodiment, the trajectory and associated force value of the collision-free tuple having the lowest tuple force value that includes a third reference trajectory and a third reference force value of the third vehicle (100) are selected as the reference trajectory (110) and force value of reference for which the tuple effort value is less than or equal to the tuple effort limitation value.

[0048] In another embodiment, a computer program product is designed to execute, implement, and / or trigger the steps described in the preceding embodiments. The computer program product may be stored on a machine-readable medium or a storage medium such as semiconductor memory, hard disk memory, or optical memory and may be used to execute, implement, and / or trigger the steps of the process according to any of the embodiments described above, in particular when the program product or program is executed on a computer or device.

[0049] In another embodiment, a device includes means that execute, implement, and / or trigger the steps described in the preceding embodiments. The device may be an electrical device comprising at least one processing unit for signal or data processing, at least one recording unit for recording signals or data, and at least one communication interface for inputting or transmitting data, which are integrated into a communication protocol. The processing unit may be, for example, a signal processor, a system application-specific integrated circuit, a microprocessor, or a microcontroller for processing sensor signals from the vehicle and / or third parties and producing data signals based on the sensor signals.The recording unit can be, for example, flash memory, reprogrammable read-only memory, or a magnetic recording unit. The interface can be designed as a sensor interface for inputting sensor signals from a sensor and / or an actuator interface for transmitting data signals and / or control signals to an actuator. The communication interface can be designed for wireless and / or wired data input or transmission. Interfaces can also be software modules present, for example, on a microcontroller in addition to other software modules.

[0050] With regard to the aforementioned tuple effort limitation value, the adjustment of the tuple effort limitation value can occur with hysteresis, the hysteresis increasing as the duration of cooperation increases. Dynamic changes in a path during a cooperative maneuver are, therefore, taken into account, thus avoiding repeated interruptions and resumptions of cooperation and therefore confusion for other vehicles near the maneuvering space. The chances of success of a once-permitted cooperation increase. If the cooperation is terminated, the effort limitation value The tuple can then be reset to its initial state. When a new cooperation begins, the tuple effort limitation value can be set to zero, or alternatively to a predetermined initial value.

[0051] It may be reasonable to define the tuple force limitation value based on the vehicle. For example, the type of vehicle may be taken into account, which could include a passenger car, a truck, a bus, or the like. Alternatively, or in addition, speed may also be taken into account when defining the tuple force limitation value, or even whether or not the vehicle has priority.

[0052] The data package can contain effort values ​​for each trajectory. The trajectories are comparable using the effort values ​​without requiring an understanding of the driving effort of the individual driving maneuvers for each trajectory. The individual effort values ​​of the trajectories of a tuple can be added together, for example, to obtain the tuple effort value. The effort values ​​can also be weighted differently to determine the tuple effort value. In weighting the effort values, a willingness to cooperate on the part of the drivers of the observed vehicles can be taken into account.

[0053] A function-specific component can also be incorporated into the tuple effort value. A function-specific component can be, for example, a comfort condition which results in tuples with non-critical inter-vehicle distances being preferred.

[0054] At least one other third-party data packet can be received from another third-party vehicle. Another third-party trajectory can be added to the tuple. Another third-party effort value can be added as a supplement to the tuple's effort value. The tuple can consist of three or more trajectories.

[0055] As described, the approach presented here presents a decentralized coordination of cooperative driving maneuvers based on optional trajectories. A method for cooperative maneuver coordination based on the exchange of trajectories is described. Information concerning the current reference or planned trajectory and possible trajectories, for example, for an evasive maneuver (possible trajectories) or for a desired maneuver (desired trajectories), is transmitted here along with associated effort assessments. The participating vehicles can thus coordinate an optimal joint maneuver (having the lowest total effort). In this case, it is not always just one desired trajectory that is transmitted, which creates more room for negotiation in complex scenarios and increases the probability of mutually beneficial cooperation.The decision to apply or not a desired trajectory is not made based on an overall cost function, which relates to . Assumptions for the costs of other vehicles. For optimal decision-making, it is not necessary for a cost function to be identical for all vehicle manufacturers, or a potentially erroneous assumption can be omitted. Intrinsic costs are calculated internally within each vehicle and can be passed on to other vehicles proportionally within the interval [-1, 1].

[0056] In the approach presented here, cooperation does not require an active request. Cooperation can be offered here by a vehicle that already recognizes in advance a future need for cooperation from another, for example, because it possesses a much more complete model of the surroundings thanks to a better sensor system, using possible trajectories.

[0057] Furthermore, optimized negotiation or coordination of maneuvers is possible. For example, the first vehicle could send a desired trajectory that requires the second vehicle to reduce its speed by 20 km / h if the second vehicle wishes to cooperate. In the approach presented here, the second vehicle can communicate, for example, by sending a corresponding possible trajectory, that a speed reduction of 10 km / h would be acceptable and that it would then be willing to cooperate. A specific change to the reference trajectory is not necessary in this case, so it is possible to avoid immediate intervention in the vehicle's control. This results in a non-binding, progressive negotiation / optimization.

[0058] In the approach presented here, trajectories are sent in a non-binding manner. The decentralized cooperative coordination of maneuvers comprises two parts. First, a protocol including a set of rules enabling vehicles to communicate is presented. Second, methods for executing different cooperative coordinations of maneuvers using this protocol are presented. The fundamental principle is that vehicles exchange trajectories. The vehicle can thus communicate preventively / proactively the trajectories it would be willing to follow since the drawbacks are acceptable within the framework of its own cost function. There is the possibility of negotiating cooperation before exerting influence on the respective vehicle. Efficient optimization of the maneuvers of the vehicles participating in a cooperation is thus made possible.Furthermore, it becomes possible to drastically reduce the need for calculations, since preferences for all cooperation partners are coded and explicitly communicated using effort or cost values, and a more complex cost analysis for third-party vehicles can therefore be omitted. The probability of successful cooperation increases due to the transmitted cost value, as the cooperation partner's costs can be more accurately estimated, and a local estimation of third-party costs can even potentially be omitted altogether. The costs for any of the received third-party trajectories can also be... Estimated costs are calculated locally within the vehicle. If necessary, the cost values ​​received from all third-party sources can be adjusted proportionally. Consequently, an approximate comparability of internal and third-party costs can be achieved with minimal computational effort. This results in improved assistance for maneuver planning algorithms due to the introduction of categories.

[0059] To represent a stable cooperative maneuver, a threshold for the costs of a maneuver is defined, a cooperative solution having to be below this threshold to be accepted. This threshold will initially be higher by a certain factor or with a certain lag than the costs of the selfish solution. It represents a measure of a general willingness to cooperate by accepting additional selfish costs. Since situations can now constantly change slightly and plans can never be implemented perfectly, discrepancies will appear in the evaluation and calculation of costs in subsequent calculation cycles. In particular, the costs may again exceed the cooperation threshold. Therefore, this cooperation threshold is increased as the duration of cooperation increases according to at least one embodiment, even if the difference relative to the best outcome also increases as a result.In this case, the increase represents the growing motivation to successfully complete an agreed-upon cooperation. At the same time, it can advantageously be stipulated that this is only implemented to the extent that the ego's desire is not completely disregarded. It is also possible to consider the possibility that the threshold includes hysteresis, with the hysteresis increasing as the duration of cooperation increases. Thus, dynamic changes in a trajectory during a cooperative maneuver can be taken into account. This makes it possible to avoid continuous interruptions and resumptions of cooperation, and therefore confusion for other vehicles near the maneuvering area. The chances of success for a once-permitted cooperation increase.

[0060] [BRIEF DESCRIPTION OF FIGURES]

[0061] Fig. 1 is a representation of a vehicle comprising a device according to an example of an embodiment, and also of a third vehicle.

[0062] [DETAILED DESCRIPTION OF FIGURES]

[0063] Embodiments of the invention are described below with reference to the accompanying drawings, neither the drawings nor the description being construed as a limitation of the invention. The figures are schematic only and are not to scale. In the figures, identical reference numerals indicate identical elements or elements acting in the same manner.

[0064] The coordination of maneuvers between vehicles in order to achieve an improvement in, among other things, comfort, efficiency and safety, can be called "Cooperative driving." This is facilitated by the possibility of direct vehicle-to-vehicle communication and the increasing automation of vehicles. Coordination of maneuvers can also be implemented in general road traffic situations. Vehicles can, if necessary, transmit information about their current and desired driving behavior in the form of a trajectory. Another vehicle checks whether it can execute the desired maneuver from the transmitting vehicle and may provide explicit or implicit confirmation by adjusting its reference trajectory so that the vehicle that sent its desired driving maneuver can perform it.

[0065] Figure 1 is a representation of a vehicle 100 that includes a device (not shown) for the cooperative coordination of driving maneuvers with at least one other vehicle 110. The vehicle 100 is traveling on a road in the area of ​​an on-ramp with an acceleration lane. The other vehicle 110 is in the acceleration lane and wishes to drive on the road. The other vehicle 110 may also be referred to as the "third vehicle". Sensors incorporated in the vehicle 100 detect the current traffic situation in the vicinity of the vehicle 100; alternatively, or in addition, the vehicle 100 may receive information about the traffic situation via wireless communication channels, for example, in the form of Vehicle-to-Everything messages.

[0066] Currently conceivable trajectories are planned for the vehicle 100 according to the traffic situation and combined to form a family of trajectories 101. As described in detail above, a trajectory describes the precalculated movement of the vehicle and therefore specifies the future location of the vehicle 100 if it follows this trajectory through driving maneuvers such as steering, braking or acceleration.

[0067] For the sake of clarity, the family of trajectories 101 comprises only two trajectories, namely trajectories 101a and 101b. It is understood that the number of trajectories is not limited to two. The trajectories have the same origin, which is located at the current position of vehicle 100. Depending on the acceleration imparted to vehicle 100, the trajectories will have different lengths and will end at different locations. In this case, trajectory 101a is a simple trajectory that essentially consists only of forward movement. Trajectory 101b includes a lane change and describes a procedure in which vehicle 100 makes way for vehicle 110, so that it can move from the acceleration lane to the road without having to accelerate or brake.

[0068] Vehicle 110 is traveling on the acceleration lane; the planned trajectory 111 of vehicle 110 describes the acceleration of vehicle 110 with the lane change of lane acceleration to the road on which vehicle 100 is located. Vehicle 110 sends a data packet containing the planned trajectory 111 to vehicle 100. It is obvious that vehicle 110 may also have more than one planned trajectory; for the sake of simplicity, we assume in this case that only one trajectory is planned.

[0069] Vehicle 100 receives the data packet from vehicle 110. For clarity, the data packet is not shown in [Fig. 1]. Third-party trajectories of vehicle 110 can be extracted from the data packet. In a subsequent step, vehicle 101 generates one or more additional third-party trajectories, each describing a possible driving maneuver for vehicle 110. In this case, trajectory 112 is generated. This trajectory describes a simple straight-ahead movement with braking at the end of the acceleration lane. Vehicle 110 would therefore brake in the acceleration lane so that vehicle 100 can pass and vehicle 110 can rejoin the road once vehicle 100 has passed the acceleration lane area.As already described above, this or these additional third trajectories generated are driving maneuvers that are not foreseen by vehicle 110, but may be relevant for the planning of maneuvers of vehicle 100 and are, therefore, considered by the latter as hypothetical or conceivable driving maneuvers.

[0070] Based on the predicted trajectories ego 101a and 101b, the received trajectory 111, and the generated trajectory 112, the vehicle 100 forms trajectory tuples, each comprising one ego trajectory and one third trajectory. In the present example, four tuples can therefore be generated, namely tuples A: (101a 111), B: (101b 111), C: (101a 112), and D: (101b 112), as illustrated in the lower part of [Fig. 1]. For each tuple, a tuple effort value is determined.

[0071] Tuple A corresponds to the situation in which vehicle 100 continues to travel straight ahead, while vehicle 110 changes lanes. The force value is low here, since no steering is required and only minor acceleration / deceleration may be necessary for vehicle 100.

[0072] Tuple B corresponds to the situation in which vehicle 100 changes lanes, and the third vehicle also changes lanes. The effort value is also low here, but slightly higher than for tuple A, since a steering maneuver and possibly minor acceleration / deceleration are required by vehicle 100.

[0073] Tuple C corresponds to the situation in which vehicle 100 continues to travel straight ahead and the third vehicle travels straight ahead in the acceleration lane and brakes at the end of the acceleration lane. The effort value is also low here, since no steering is required by vehicle 100 and there should be no acceleration / deceleration either. Compared with tuple A, the The effort value of tuple C is lower, given that the distances that exist between the vehicles are smaller in tuple A than in tuple C.

[0074] Tuple D corresponds to the situation in which vehicle 100 changes lanes and the third vehicle is traveling straight ahead in the acceleration lane and brakes at the end of the acceleration lane. The force value here is comparable to that of tuple B.

[0075] For the purposes of illustration, but in no way as a limitation, the following tuple effort values ​​are assumed: A: 0.2 B: 0.25 C: 0.16 D: 0.25.

[0076] The check to determine whether collisions could occur resulted in all the tuples being collision-free. A priority review indicated that vehicle 100 had priority over vehicle 110, and was therefore allowed to proceed first.

[0077] The selfish or altruistic trajectory is selected in the tuple with the lowest tuple effort value. The selfish trajectory can be selected in tuples A through D, since these include both pre-planned third-order trajectories and additional third-order trajectories. The altruistic trajectory can be selected in tuples A and B, since these include only pre-planned third-order trajectories and no additional third-order trajectories. Consequently, the selfish trajectory is selected in tuple C and the altruistic trajectory is selected in tuple A.

[0078] The difference in tuple effort values ​​is 0.04 in this example; the comparison with a tuple effort limitation value, which is assumed here to be 0.05, results in the altruistic trajectory being sent to the third vehicle 110. Since the tuple effort limitation value is increased progressively, it would have been possible for the selfish trajectory to be selected if the duration of the cooperation had been shorter, since the tuple effort limitation value would then have been lower, for example, by 0.03.

[0079] As long as the altruistic trajectory, and not the selfish trajectory, is sent and followed, cooperation exists between vehicles 100, 110.

[0080] Although there are only two vehicles represented in the example illustrated in [Fig.1], the concept presented here can be extended to three or more vehicles.

[0081] The described method can be implemented, for example, in software or hardware or mixed software and hardware form, for example, in a control unit.

[0082] It should be noted that terms such as "including", "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 interpreted as a limitation.

Claims

1. Demands Method for the cooperative coordination of driving maneuvers of an ego vehicle (100) with third-party maneuvers of at least one third-party vehicle (110), the method comprising the following steps: generating a family of trajectories (101), which includes multiple pre-planned ego trajectories (101a, 101b) for the ego vehicle (100), the ego trajectories including a reference trajectory; receiving one or more third-party data packets from at least one third-party vehicle (102), a third-party data packet containing a family of third-party trajectories which includes different third-party pre-planned trajectories (111) for the respective third-party vehicle (110); generating additional third-party trajectories (112) which correspond to other conceivable driving maneuvers of a third-party vehicle (110); form tuples composed, in each case, of an ego trajectory and a third trajectory or an initial third trajectory and evaluate the tuple using a tuple effort value; determine collision-free tuples, the selected tuples being tuples in which the ego trajectory and the third trajectory do not result in a collision within a collision horizon; determine if the ego vehicle (100) has priority and, if the ego vehicle (100) has priority: select a tuple trajectory with the lowest tuple effort value as the selfish trajectory from among collision-free tuples that include both third-party pre-planned trajectories (111) and additional third-party trajectories (112); select a tuple trajectory with the lowest tuple effort value as the altruistic trajectory from among all collision-free tuples that include only third-party pre-planned trajectories (111) of third-party vehicles; determine a difference between a tuple effort value of the tuple including the altruistic trajectory and a tuple effort value of the tuple including the selfish trajectory; select a trajectory, the selfish trajectory being selected if the absolute value of the determined difference is greater than a tuple effort limitation value or the altruistic trajectory being selected if the absolute value of the determined difference is less than the tuple effort limitation value or is equal to the tuple effort limitation value; and send a data packet to the third vehicle (110), the data packet including, depending on the result of the comparison, the selfish trajectory or the altruistic trajectory as the reference trajectory, the tuple effort limitation value being increased as the duration of cooperation increases.

2. A method according to claim 1, wherein a maximum value for the tuple effort limitation value is established, the tuple effort limitation value, which increases as the duration of cooperation increases, not increasing beyond this maximum value.

3. A method according to any one of claims 1 and 2, wherein the value of a counter is increased when and as long as the altruistic trajectory is selected, the duration of cooperation being provided by the counter.

4. A method according to at least one of the preceding claims, wherein the ego trajectories further comprise one or more desired trajectories.

5. A method according to at least one of the preceding claims, wherein the trajectory and associated force value of the collision-free tuple having the lowest tuple force value is selected as the reference trajectory and reference force value, where the tuple force value is less than or equal to the tuple force limiting value.

6. A method according to at least one of the preceding claims, wherein the trajectory and associated force value of the collision-free tuple having the lowest tuple force value, which includes a third reference trajectory and a third reference force value of the third vehicle (100), is selected as the reference trajectory and reference force value, where the tuple force value is less than or equal to the tuple force limiting value.

7. Computer program product designed to execute, implement and / or trigger the process according to any one of the preceding claims.

8. Device comprising means designed to carry out, implement and / or initiate a process according to any one of claims 1 to 6.

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