DE102021102956A1 - Process for the continuous determination of control signals for controlling actuators of a vehicle - Google Patents

Abstract

The invention relates to a method for continuously determining control signals for controlling actuators of a vehicle in order to provide optimal path following control. Furthermore, the invention relates to a system for carrying out the method according to the invention.

Description

The invention relates to a method for the continuous determination of control signals for controlling actuators of a vehicle, and a system for carrying out such a method.

It is known from the prior art that vehicle fleets for the transport of goods or people, be they road vehicles such as buses or watercraft such as ferries, are coordinated by a higher-level body, i.e. the route planning is carried out at a higher level. As is known, fixed timetables with routes and arrival and departure times can be set, which are followed regardless of external circumstances. These systems are inflexible and cannot, or only with a delay, take actual circumstances into account.

The individual vehicles, which are part of the vehicle fleet, are assigned their respective routes and times. It is also hardly possible to react to external circumstances that would affect the route and/or the times. For example, the number of passengers could exceed the capacity of the vehicle, any obstacles could block the route, or weather conditions could increase the duration of the journey, meaning that the times and/or the route could not be adhered to.

It is accordingly the object of the present invention to provide a method and a corresponding system by means of which the disadvantages of the prior art mentioned can be eliminated.

This object is achieved by a method having the features of patent claim 1 and a system according to patent claim 11.

1. a. Bestimmen und Setzen von Zielkoordinaten und einem Ankunftszeitraum für das Fahrzeug und Senden der Zielkoordinaten und des Ankunftszeitraums an eine Pfadplanungseinheit;
2. b. Berechnen von Pfadfolgedaten durch die Pfadplanungseinheit basierend auf den Zielkoordinaten, dem Ankunftszeitraum und einem aktuellen Fahrzeugzustand und Senden der Pfadfolgedaten an eine Optimierungseinheit;
3. c. Bestimmen zumindest eines ersten Wertes und eines zweiten Wertes zumindest eines Stellsignals durch die Optimierungseinheit und Senden des zumindest ersten und zweiten Wertes des zumindest einen Stellsignals an eine Modellrecheneinheit;
4. d. Berechnen von zumindest einem ersten prädizierten Fahrzeugzustand und einem zweiten prädizierten Fahrzeugzustand über einen Prädiktionszeitraum durch die Modellrecheneinheit mittels einer Fahrzeugmodellsimulationsgleichung, wobei der erste prädizierte Fahrzeugzustand basierend auf dem ersten Wert des zumindest einen Stellsignals und dem aktuellen Fahrzeugzustand und der zweite prädizierte Fahrzeugzustand basierend auf dem ersten und dem zweiten Wert des zumindest einen Stellsignals und dem aktuellen Fahrzeugzustand berechnet wird und Senden des zumindest ersten und zweiten prädizierten Fahrzeugzustands an die Optimierungseinheit;
5. e. Berechnen zumindest eines optimalen ersten Wertes und eines optimalen zweiten Wertes des zumindest einen Stellsignals durch die Optimierungseinheit basierend zumindest auf den Pfadfolgedaten, dem zumindest ersten und zweiten prädizierten Fahrzeugzustand und dem aktuellen Fahrzeugzustand, wobei der optimale Wert des zumindest einen Stellsignals derart berechnet wird, dass eine Kostenfunktion minimiert wird;
6. f. Senden des optimalen ersten Wertes des zumindest einen Stellsignals an das Stellglied des Fahrzeugs und eine Vergleichseinheit; und
7. g. Übernehmen des optimalen ersten Wertes des zumindest einen Stellsignals durch das zumindest eine Stellglied des Fahrzeugs.

The core idea of the invention is a method for the continuous determination of control signals for controlling actuators of a vehicle, comprising the steps:

1. a. determining and setting destination coordinates and an arrival period for the vehicle and sending the destination coordinates and the arrival period to a path planning unit;
2. b. calculating path following data by the path planning unit based on the destination coordinates, the arrival period and a current vehicle condition and sending the path following data to an optimization unit;
3. c. determining at least a first value and a second value of at least one control signal by the optimization unit and sending the at least first and second value of the at least one control signal to a model computing unit;
4. i.e. Calculation of at least a first predicted vehicle state and a second predicted vehicle state over a prediction period by the model computing unit using a vehicle model simulation equation, the first predicted vehicle state based on the first value of the at least one control signal and the current vehicle state and the second predicted vehicle state based on the first and the second value of the at least one control signal and the current vehicle state is calculated and sending the at least first and second predicted vehicle state to the optimization unit;
5. e. Calculation of at least one optimal first value and an optimal second value of the at least one control signal by the optimization unit based at least on the path sequence data, the at least first and second predicted vehicle status and the current vehicle condition, wherein the optimal value of the at least one control signal is calculated in such a way that a cost function is minimized;
6. f. Sending the optimal first value of the at least one control signal to the actuator of the vehicle and a comparison unit; and
7. G. Adoption of the optimal first value of the at least one control signal by the at least one actuator of the vehicle.

The method according to the invention calculates the value of an actuating signal, which is then accepted by an actuator of a vehicle. The value of the actuating signal is calculated taking into account several factors, which are explained in more detail below. The method is intended to ensure an optimal route or path sequence of the vehicle on a target route or a target path (expressed by the path sequence data) to the target coordinates by using sensor-related data and data or Information for the calculation of an optimal value of a control signal is taken into account. By taking over the optimum value of the control signal from at least one actuator of the vehicle, an optimum route sequence or path sequence of the vehicle can in turn be achieved. Consequently, the optimal value of the control signal for controlling the actuator of the vehicle is calculated in such a way that the target path/distance or the target route to the target coordinates can be optimally followed depending on the situation. Flow in the optimization unit for calculating the optimal value of the actuating signal in step e. further data or information coming from the comparison unit and the model arithmetic unit, as a result of which the calculation of the optimal value of the control signal is improved or becomes more precise. The optimization unit thus solves an optimal control problem on the prediction period (moving horizon), which is a model predictive control that implements an optimization-based path following control.

Put simply, in step c. a trivial (or also non-trivial, "better") solution (value) for the actuating signal is assumed in a first run of the method, e.g. "full throttle" or rudder angle = 0° for the entire prediction period. In this case, it is assumed that the current vehicle state is assumed to be known or is estimated. In connection with this particular input, the vehicle model simulation equation in step d. solved/integrated with the current vehicle state and an associated, predicted vehicle state is obtained for the prediction period or preferably for each prediction time interval into which the prediction period is subdivided. The predicted vehicle states calculated in this way (so-called “initial guess”) are transferred to the optimization unit, which then iteratively determines the optimal solution (optimal value of the control signal) for the prediction period or preferably for each prediction time interval into which the prediction period is divided. This input (optimal first value of the actuating signal) is implemented for the sampling time (first time interval) on the vehicle's actuator, with the entire solution (of all time intervals) of the optimization problem being noted/saved and the optimal first value being discarded, so to speak, since this "now" is past. These optimal values of the actuating signal (apart from the optimal first value) are now used as the new "initial guess" (determined in step c.) for the next iteration in step d. (Calculation of the predicted vehicle states), preferably used together with the calculated estimated vehicle state from the comparison unit, and the method runs again.

A vehicle according to the invention is preferably a motor vehicle, in which case a land vehicle, water vehicle and/or aircraft would be suitable. A vehicle is particularly preferably understood to mean a ship, in particular a ferry, which is designed to transport passengers and/or goods. The vehicle according to the invention has at least one actuator, an actuator preferably being understood as meaning an actuator which can be influenced or set in a targeted manner by means of an actuating signal. The actuator is preferably provided and designed to influence or change the vehicle state (see below). An actuator within the meaning of the invention is preferably drive units or corresponding parts of the drive unit, steering units or corresponding parts of the steering unit or the like, with the actuators not being limited to these examples.

A model computing unit according to the invention is provided and designed to provide at least one mathematical vehicle model, by means of which, among other things, vehicle states can be predicted based on a control signal value. The model computing unit preferably represents a digital twin of the real vehicle. The model computing unit is particularly preferably to be understood in such a way that it represents or makes available a mathematical vehicle model which the optimization unit and the comparison unit can access or use the vehicle model for any calculations be able. The model computing unit preferably makes the mathematical vehicle model available to the optimization unit and the comparison unit for any calculations. It is conceivable that the functionality of the model arithmetic unit is at least partly taken over by the optimization unit and/or the comparison unit and the model arithmetic unit is not designed as a separate unit, but rather the functionality is at least partly taken over by the optimization unit and/or the comparison unit. It would be conceivable for the optimization unit itself to calculate the predicted vehicle states using the vehicle model simulation equation and/or for the comparison unit itself to carry out calculations using the vehicle model simulation equation and the sensor model simulation equation. In this case, different vehicle models can preferably be selected for the calculation depending on the situation, it being conceivable for the different vehicle models to have a different number of degrees of freedom. For example, for a ferry, a maneuvering model with more degrees of freedom, which is used as a basis for the calculation when docking and casting off or the like, i.e. maneuvers with a lot of movement or with increased ship dynamics, and an autopilot model with fewer degrees of freedom, with essentially free travel, be provided, between which can be selected depending on the situation of the model computing unit. The model computing unit can preferably select from different vehicle models or vehicle model simulation equations depending on the current vehicle status, the path sequence data and/or the estimated vehicle status.

The predicted vehicle states are calculated based on the value of the control signal and the current vehicle state or the estimated vehicle state by the vehicle model simulation equation according to the invention over the prediction period. The vehicle model simulation equation describes the rate of change of the vehicle state as a function of the value of the actuating signal and the current vehicle state/the estimated vehicle state. This is preferably a vehicle-specific differential equation, which includes, among other things, vehicle parameters such as mass, inertia and/or damping terms, in order to mathematically map the relationships between the vehicle state and the control signal. The vehicle model simulation equation preferably represents the model arithmetic unit. The underlying differential equation can differ depending on the vehicle model used. The predicted vehicle states are preferably calculated by (numerical) integration of the vehicle model simulation equation (differential equation), integration being carried out over the prediction period. The predicted vehicle states reflect the course of the vehicle states, so to speak, and can also be referred to as the dynamic behavior of the vehicle.

The first predicted vehicle state is calculated based on the first value of the at least one control signal and the current vehicle state, and the second predicted vehicle state is calculated based on the first and second values of the at least one control signal and the current vehicle state. These values of the actuating signal are determined by the optimization unit, with at least two values being determined, preferably three values, more preferably five values and particularly preferably a plurality of values. A value for the actuating signal is preferably determined for the prediction time interval into which the prediction time period is/is subdivided, the number of prediction time intervals for the optimization unit being assumed to be known or could also be predetermined by the optimization unit.

The model computing unit thus uses the vehicle model simulation equation to predict the vehicle states which are to be expected for the prediction period given the specific value for the control signal.

The vehicle status within the meaning of the invention preferably includes a position (e.g. in a coordinate system such as NED), a direction of movement (for ships such as ferries, preferably called heading) and/or a speed (translational and/or rotational) of the associated vehicle, the vehicle condition shall not be limited thereto. The stated vehicle state is particularly preferably relevant for the sequence of routes or the sequence of paths and can particularly preferably be influenced by the control signal or the execution of the control signal by an actuator of the vehicle. The control signal can have a specific value, which causes a specific setting of the actuator of the vehicle. In the case of a ferry, for example, a certain rudder angle, e.g. 10°, could be specified by the actuating signal, which is then taken over by the actuator, in this case the rudder of the ferry, with the rudder angle influencing the vehicle status (particularly position and direction). The current vehicle status, which is determined in steps b., d. and e. is used, includes at least the current position and more preferably a direction of movement (heading), the speeds of the vehicle at the beginning of the method can preferably be assumed to be zero (at least in a first run of the method). The current vehicle status is preferably known to the optimization unit (the optimization unit is preferably designed as part of the vehicle and the current vehicle status can therefore be transmitted to the optimization unit in a simple manner, e.g. by means of a detected sensor variable) or a current estimated vehicle status is used.

An optimization unit according to the invention is provided and designed to calculate an optimal value of a control signal based on the available data or information for the prediction period (horizon) in order to optimally follow the target path specified by the path planning unit, i.e. to minimize the cost function . The optimization unit solves the optimal control problem using a mathematical function. The calculation is carried out in such a way that a cost function is minimized. In this case, the cost function represents, so to speak, a sum of individual cost values that arise as a result of a deviation from data or values on which the calculation is based. In the event of a discrepancy, the costs can preferably be specified in a data-specific or value-specific manner. It can thus be determined that a deviation from the path sequence data on which the calculation is based causes very high costs, as a result of which a route sequence or path sequence that is as accurate as possible is ensured. The optimum value of the actuating signal is preferably the value for which the cost function is minimal given the given restrictions (predicted vehicle states, estimated vehicle state, obstacle data, vehicle data and/or environmental data).

Sensor variables are measured variables that can be recorded directly using a sensor or by appropriate conversion of the sensor data can be obtained. At least one sensor is therefore arranged on and/or in the vehicle, it also being possible for a plurality of sensors to be provided which detect different sensor variables. The sensors can be, for example, an acceleration sensor, a displacement sensor, GPS sensor, radar, lidar, AIS, torque sensor, speed sensor, optical sensor or the like. The sensor variable is preferably always recorded at the current point in time, preferably corresponding to the first prediction time interval.

A calculated sensor variable is understood to mean a sensor variable that is calculated using a sensor variable model simulation equation based on an optimal predicted vehicle state and thus indirectly the associated optimal value of the at least one control signal that has been adopted. This is not a sensor variable that is detected by a sensor, but rather a sensor variable that is calculated or simulated using a model. The sensor variable model simulation equation, which represents a mathematical model, preferably calculates a sensor variable that is dependent on the vehicle state and the value of the at least one control signal or is influenced by them. The sensor variable model simulation equation, by which the model computing unit calculates the calculated sensor variable, is preferably adapted to the sensors or the detected sensor variables of the vehicle, so that the calculated sensor variable and the sensor variable detected by a sensor specify the same physical variable and are comparable.

A comparison unit according to the invention is designed as a so-called observer known from control engineering. The comparison unit is provided and designed to estimate a vehicle state (estimated vehicle state) based on the comparison of the at least one sensor variable and the at least one calculated sensor variable. The estimated vehicle state is advantageously calculated for vehicle states or information that the vehicle state includes that cannot itself be measured. A difference between the at least one sensor variable and the at least one calculated sensor variable is preferably determined by the comparison. The estimated vehicle state is calculated using the vehicle model simulation equation, which is also available on the model computing unit and is expanded by the correction term that can be weighted. The correction term results from the comparison of the sensor variables or the difference in the sensor variables determined by the comparison. The correction term can be weighted, i.e. either the detected sensor size or the calculated sensor size and thus the underlying sensor size model simulation equation can be estimated as more trustworthy or weighted, and the correction term estimates or calculates the estimated vehicle state accordingly in the direction of the detected sensor size or of the calculated sensor size.

The weighting can preferably be predetermined, with the weighting correspondingly determining the error source or the source for the difference in the sensor variables. It is conceivable that the correctness/trustworthiness of the vehicle model or the vehicle model simulation equation and thus the calculated sensor variable is classified as low, whereas the sensor variable detected by the sensor is assessed as very reliable. This weighting can also be reversed if, for example, a sensor variable is detected whose value is no longer within the designated detection range of the sensor. The estimated vehicle condition can preferably also be referred to as the estimated actual vehicle condition, since it is calculated using the currently executed optimal value of the actuating signal on the actuator and the comparison of the detected sensor variable and the calculated sensor variable, with the sensor variable being recorded at the current time and the calculated sensor size is calculated based on the associated optimal predicted vehicle state.

According to the invention, the estimated vehicle state is a state of the vehicle that is calculated or estimated by means of the comparison unit. An estimated vehicle condition, like the vehicle condition, is preferably understood to mean a position, a direction of movement (for ships such as ferries, preferably called heading) and/or a speed (translational and/or rotational) of the associated vehicle, with the Estimated vehicle condition should not be limited to this. The estimated vehicle status is used when the position, the direction of movement and/or a speed cannot be measured/detected by itself.

The path planning unit according to the invention is provided and designed to repeatedly calculate path sequence data, whereby current or new data can be included in the calculation, with the path sequence data being sent to the optimization unit and used there to calculate the values of the control signals. According to the present invention, path sequence data is understood to be data which relates to the route or the route or path following of the vehicle. In addition to data relating to the route itself (e.g. coordinates), this should also include data that how to follow the route (e.g. speed).

The destination coordinates and the arrival time can preferably be set or determined by a driver or a higher-level coordinating device. The arrival period is a period within which the vehicle is supposed to have reached the destination coordinates from the starting coordinates (preferably comprised by the vehicle state).

Instead of an arrival period, it can preferably also be an arrival time, for example a time. The current position of the vehicle can be detected using the at least one sensor or by a further sensor, e.g. in the form of GPS data, or can be calculated by the comparison unit, which is included in the estimated vehicle state. It would also be conceivable for the current position to be sent to the path planning unit together with the destination coordinates and the time of arrival, which could be entered by a driver or made available by an external coordinating device. The current position represents the start coordinates, so to speak. The current position is assumed to be known.

According to a preferred embodiment, steps c. to g. of the method is carried out continuously and repeatedly after a first time interval. In a first implementation of steps c. to g. the current vehicle status is a predefined vehicle status and the at least first and second values of the at least one actuating signal are each a predefined value. The predefined vehicle state is preferably known or is estimated. It is conceivable that the predefined vehicle state can be at least partially detected by a sensor (sensor variable) or that a last known vehicle state is stored and used the first time the method is carried out. The specified value is preferably an arbitrarily selected value, such as “full throttle”, “0 km/h or knots or rudder angle=0°, with the value only being used to be able to start the optimization. It would be conceivable that the specified value is available as a stored value and is used the first time the method is carried out. In step e. an optimal first predicted vehicle state from the optimal first value of the at least one control signal and an optimal second predicted vehicle state from the optimal first and the optimal second value of the at least one control signal. The optimal first predicted vehicle state is preferably sent to the model arithmetic unit. The repeated implementation of the method ensures that new, optimal values for the control signal are always calculated, current data always flowing in, so that an optimal path sequence is made possible, which is adapted to changing conditions. It is preferably assumed that the calculation of the optimal values for the actuating signal in step e. an associated optimum predicted vehicle state also results in each case. It is assumed that the method with the implementation of step a. begins again, i.e. a first execution starts.

• h. Berechnen von zumindest einer berechneten Sensorgröße durch die Modellrecheneinheit mittels zumindest einer Sensorgrößenmodellsimulationsgleichung basierend auf dem optimalen ersten prädizierten Fahrzeugzustand und Senden der zumindest einen berechneten Sensorgröße an die Vergleichseinheit;
• i. Erfassen von zumindest einer Sensorgröße durch zumindest einen Sensor des Fahrzeugs (1) und Senden der zumindest einen Sensorgröße an die Vergleichseinheit;
• j. Berechnen des Schätz-Fahrzeugzustands durch die Vergleichseinheit basierend auf dem optimalen ersten Wert des zumindest einen Stellsignals mittels der Fahrzeugmodellsimulationsgleichung, welche um einen gewichtbaren Korrekturterm erweitert ist, wobei der Korrekturterm basierend auf einem Vergleich der zumindest einen Sensorgröße und der zumindest einen berechneten Sensorgröße durch die Vergleichseinheit bestimmt wird, und Senden des Schätz-Fahrzeugzustands an die Optimierungseinheit, die Modellrecheneinheit und die Pfadplanungseinheit.

According to a preferred embodiment, if steps c. to g. the current vehicle condition an estimated vehicle condition. The estimated vehicle state is preferably used each time steps c are carried out repeatedly. to g. calculated as follows:

• H. Calculating at least one calculated sensor variable by the model computing unit using at least one sensor variable model simulation equation based on the optimal first predicted vehicle state and sending the at least one calculated sensor variable to the comparison unit;
• i. Detection of at least one sensor variable by at least one sensor of the vehicle (1) and sending the at least one sensor variable to the comparison unit;
• j. Calculation of the estimated vehicle state by the comparison unit based on the optimal first value of the at least one control signal using the vehicle model simulation equation, which is expanded to include a weightable correction term, the correction term being based on a comparison of the at least one sensor variable and the at least one calculated sensor variable by the comparison unit is determined, and sending the estimated vehicle state to the optimization unit, the model calculation unit and the path planning unit.

Steps h. to j. before step d. carried out. By calculating an estimated vehicle state, the calculation preferably being carried out repeatedly with each new optimal predicted vehicle state and corresponding current sensor variables that are detected. There is always a current estimated vehicle condition that can be used as the current vehicle condition, with non-measurable data or information from the estimated vehicle condition also being included.

According to a preferred embodiment, if steps c. to g. the at least first and second Value of the at least one control signal in step c. corresponding to the optimal second value of the at least one control signal from the previous execution of step e. definitely. Thus, if it is carried out repeatedly, the first value will be equal to the optimal second value from the previously carried out step e. determined and the second value in a repeated implementation equal to the optimal second value from the previously performed step e. or the first value of step c. the repeated instantaneous execution determined. Thus, with each repeated implementation of the method, the values of the control signal in step c. be determined on the basis of the optimal second value of the at least one control signal, which in the previous run of the method in step e. was calculated, since this value represents the optimal starting point for the new calculation. The optimal first value of the at least one control signal from the previous implementation is preferably not used for this purpose, since this has already been taken over by the actuator of the vehicle and is therefore no longer a value that can be optimized in the future. The values in step c are preferred. when performed repeatedly, if more than two values are determined, determined as follows: when performed repeatedly, a first value becomes equal to an optimal second value from the previously performed step e. determined, a second value equal to an optimal third value from previously performed step e., etc., with a last value equal to an optimal last value from previously performed step e. or the penultimate value of step c. of repeated instantaneous execution.

According to a preferred embodiment, the prediction period extends from a current point in time to a specific future end point in time. The prediction period is preferably subdivided into at least a first and a second prediction time interval. The prediction time intervals preferably have the same duration. The first prediction time interval preferably extends from the current point in time to a future first point in time and the second prediction time interval from the first point in time to the end point in time. The first value of the at least one control signal for the first point in time and the second value of the at least one control signal for the end point in time are preferably determined. The first predicted vehicle state is preferably calculated for the first point in time and the second predicted vehicle state for the end point in time. The duration of the prediction time intervals preferably corresponds to a duration of the first time interval. It is possible to subdivide the prediction period into any number of prediction time intervals, with the more prediction intervals provided, the more accurate the predicted vehicle states will be (this also depends on the integration method), but this increases the computational effort, so that a careful selection of the number of prediction time intervals must take place. The future point in time of the prediction period can preferably be selected as desired, with the stability of the regulation being able to be influenced by the length of the prediction period, since the predicted vehicle states are calculated further into the future. The longer the prediction period, the more predicted vehicle states are calculated or the further into the future the predicted vehicle states are calculated, with the computing power and time increasing accordingly, so that an optimum can also be found here. If more than two prediction time intervals are provided, e.g. three prediction time intervals, the first prediction time interval extends from the current time to a future first time, the second prediction time interval from the first time to a future second time and the third prediction time interval from the second time to at the end time. The second prediction time interval preferably always follows the first prediction time interval, which begins with the current time, and the last prediction time interval extends to the end time in each case.

According to a preferred embodiment, step b. of the method is carried out continuously and repeatedly after a second time interval. In a first implementation of step b. the current vehicle status the specified vehicle status. It is further preferred if step b is carried out repeatedly. the current vehicle status, the estimated vehicle status and preferably that in step j performed last. calculated estimated vehicle state, i.e. the current estimated vehicle state. The second time interval is preferably longer than the first time interval. In this way, new path sequence data can always be taken into account for the calculation of the value of the control signal and the value can be optimally adapted to it.

According to a preferred embodiment, the path planning unit additionally calculates the path sequence data based on environmental data and/or obstacle data. The path planning data preferably includes path coordinates and speed parameters. Obstacle data selected by the path planning unit are preferably sent from the path planning unit to the optimization unit and used for the calculation in step e. used. The calculation in step e is preferred. additionally carried out based on vehicle data. The vehicle data preferably limit the calculable optimal value of the control signal. Preference set the path coordinates individual points/positions along the target path/way or route between the start and destination coordinates, which the vehicle should follow. The speed parameter is preferably an optimal (target) operating speed of the vehicle at which the target path/path or route is to be followed. The environmental data preferably includes traffic rules, no-driving zones, collision avoidance rules (KVR), weather data or the like, ie data that could affect the route or the path/path of the vehicle, with the environmental data not being limited to the examples given. More preferably, the obstacle data include, in particular sensor-related, data or information about other road users, such as AIS, GPS, radar, lidar, camera data or the like. Particularly preferably, the position and/or the path/way or the route of the other road users can be inferred from the obstacle data. It is assumed that the other road users mentioned are relevant to the path/path or route to be planned, ie come close to it in a way that influences the path/path or route of the vehicle. The path planning unit preferably selects obstacle data that is sent to the optimization unit, the obstacle data including in particular the positions, the planned paths or routes and the approximate dimensions (such as length, width, height, etc.) of the road users. The obstacle data is preferably selected by the path planning unit with regard to the spatial influence on the vehicle and the associated path planning (calculation of the path sequence data). By including environmental data and obstacle data in the calculation of the route data, significantly more relevant circumstances can be included that influence the path/path of the vehicle and path planning can therefore take place much better and more precisely. Furthermore, by receiving the obstacle data selected or determined by the path planning unit, the optimization unit can optimally coordinate or calculate the path/path to that effect. The obstacle data and the environmental data preferably represent restrictions for the cost function, which must be complied with or which can cause high costs. It is also conceivable that the obstacle data or the corresponding road users (obstacles) are taken into account or implemented by the optimization unit as circular or as polygonal. The obstacle data are included as so-called "soft constraints" in the calculation of the value of the control signal (optimization) and/or cost function. The vehicle data preferably limit the calculable value of the control signal or the cost function. In this way, vehicle-specific restrictions can be taken into account for the calculation, for example a maximum possible speed of the vehicle, a maximum possible acceleration or the like. Thus, values for the control signal that cannot be realized due to vehicle-specific restrictions can be avoided and excluded as a possible solution (value of the control signal) of the cost function.

According to a preferred embodiment, the comparison unit is designed as a Kalman filter, a variant of the Kalman filter or as a moving horizon estimator. A variant of the Kalman filter is particularly preferably an extended Kalman filter, an unscented Kalman filter or the like. The functioning of a Kalman filter is known from the prior art and in connection with the estimation of system variables that cannot be measured directly during path planning (use as an observer), which is why no more detailed explanations are given here. Because the duration of the first time interval corresponds to that of the first prediction time interval, a newly calculated optimal value for the control signal is available after each first prediction time interval.

According to a preferred embodiment, the optimal values of the at least one control signal are calculated in step e. carried out in such a way that a deviation from the path following data due to the at least one actuator of the vehicle adopting the calculated optimum first value of the at least one actuating signal in step g. is minimized. This ensures that the specified path (based on the path coordinates and operating speeds) is followed as best as possible. A deviation from the path sequence data causes very high costs (cost function) in relation to the calculation of the value of the control signal.

According to a preferred embodiment, in step j. additionally calculates at least one estimated disturbance variable by the comparison unit using a disturbance variable model simulation equation based on the comparison of the at least one sensor variable and the at least one calculated sensor variable. The at least one estimated disturbance variable is preferably sent to the optimization unit and the optimal values of the at least one control signal are calculated in step e. is also based on the estimated disturbance variable. Preferably, based on the difference between the at least one sensor variable and the at least one calculated sensor variable determined by comparing the sensor variables, the disturbance variable model simulation equation can be used to infer a disturbance variable that affects the vehicle. So it would be conceivable that a speed (detected Sen sensor variable) is less than the calculated speed (calculated sensor variable), in which case, for example, headwind could be inferred by means of the disturbance variable model simulation equation, which reduces the detected speed of the vehicle compared to the calculated one. The calculated estimated disturbance variable can then be used to calculate the value for the actuating signal in step e. be taken into account and a higher speed (as a corresponding value of the control signal) can be provided, for example, due to the headwind. A disturbance variable is preferably an external circumstance/influence that influences the detected sensor variable or the vehicle state and thus influences the path following.

The second time interval is preferably longer than the first time interval. The second time interval is preferably longer than the prediction period. In this way, current data is always taken into account for the calculation and the values for the actuating signal can be flexibly and promptly adapted to changing circumstances.

Preferably, the first time interval and the first prediction time interval can be varied, i.e. it is adjustable, with the first time interval and the first prediction time interval being selected to be shorter, i.e. many calculations of control value signals are carried out if the vehicle is further away from the target path (the Path coordinates) is located and vice versa, the first time interval and the first prediction time interval are chosen to be shorter when the vehicle is close to the target path (the path coordinates).

The optimal first value of the at least one control signal is preferably adopted by the at least one actuator of the vehicle for the first prediction time interval. The value of the control signal is set by the actuator in step g. So taken for the first prediction time interval, this also for each repetition of step g. applies. The first prediction time interval can also be referred to as the sampling time.

In step j. in addition, at least one estimated disturbance variable is calculated/estimated by the comparison unit using a disturbance variable model simulation equation based on the comparison of the at least one sensor variable and the at least one calculated sensor variable. The at least one estimated disturbance variable is preferably sent to the optimization unit and the optimal values of the at least one control signal are calculated in step e. is also based on the estimated disturbance variable. Preferably, based on the difference between the at least one sensor variable and the at least one calculated sensor variable determined by comparing the sensor variables, the disturbance variable model simulation equation can be used to infer a disturbance variable that affects the vehicle. It would be conceivable that a speed (detected sensor size) is lower than the calculated speed (calculated sensor size), in which case the disturbance model simulation equation could be used to infer headwind, for example, which reduces the detected speed of the vehicle compared to the calculated one. The calculated estimated disturbance variable can then be taken into account in the calculation of the value for the control signal and thus, for example, due to the headwind, a higher speed (as the corresponding value of the control signal) can be provided. A disturbance variable is preferably an external circumstance/influence that influences the detected sensor variable or the vehicle state and thus influences the path following.

A path parameter simulation equation, which preferably represents a degree of freedom, is preferably additionally taken into account by the optimization unit when calculating the value of the actuating signal by minimizing the cost function. The path sequence data, ie the path coordinates and speed parameters, are also included in this path parameter simulation equation. The degree of freedom preferably results from the fact that it is possible to deviate from the speed parameter (desired operating speed), with the speed parameter indicating how quickly the path coordinates or the desired path should be followed. The deviation from the speed parameter is preferably greater the greater the distance between the vehicle and the path coordinates, since an attempt is initially made to minimize the distance from the path coordinates.

• - ein Fahrzeug;
• - zumindest ein Stellglied des Fahrzeugs;
• - zumindest einen Sensor zum Erfassen zumindest einer Sensorgröße;
• - eine Modellrecheneinheit zur Berechnung von prädizierten Fahrzeugzuständen und zumindest einer berechneten Sensorgröße;
• - eine Vergleichseinheit zur Berechnung eines Schätz-Fahrzeugzustands;
• - eine Optimierungseinheit zur Berechnung optimaler Werte zumindest eines Stellsignals zum Übernehmen durch das zumindest eine Stellglied; und
• - eine Pfadplanungseinheit zur Berechnung von Pfadfolgedaten.

Furthermore, the object is achieved by a system for carrying out a method for continuously determining control signals for controlling actuators of a vehicle, comprising:

• - a vehicle;
• - at least one actuator of the vehicle;
• - At least one sensor for detecting at least one sensor variable;
• - a model computing unit for computing predicted vehicle states and at least one computed sensor variable;
• - a comparison unit for calculating an estimated vehicle state;
• - an optimization unit for calculating optimal values of at least one actuating signal for acceptance by the at least one actuator; and
• - a path planning unit for calculating path sequence data.

The system is preferably used to carry out the method according to the invention. The units and sensors of the system are also preferably connected in terms of signal technology in order to be able to receive and/or transmit signals or data.

The model computing unit, the comparison unit, the optimization unit and the path planning unit are preferably arranged on and/or in the vehicle. However, it would also be conceivable for individual units, in particular the path planning unit, to be arranged outside of the vehicle. The model arithmetic unit, the comparison unit, the optimization unit and the path planning unit are also preferably designed as parts of a common arithmetic unit and are at least partially connected in terms of signal technology. The model computing unit preferably represents the vehicle model simulation equation, with the optimization unit and the comparison unit being able to access this vehicle model simulation equation. The functionality of the model computing unit is preferably at least partially integrated in the optimization unit and/or the comparison unit.

The above explanations and features relating to the method according to the invention should also apply mutatis mutandis to the system according to the invention and vice versa. The terms "transmit" and "send" are used interchangeably.

The invention is not limited by an exemplary embodiment, but rather includes every feature and every combination of features.

• 1 ein Verfahren gemäß einer bevorzugten Ausführungsform der Erfindung;
• 2 ein System zum Durchführen des Verfahrens nach 1 gemäß einer bevorzugten Ausführungsform der Erfindung.

Further objects, advantages and advantages of the present invention can be seen from the following description in connection with the drawings. Here show:

• 1 a method according to a preferred embodiment of the invention;
• 2 a system for performing the method 1 according to a preferred embodiment of the invention.

In 1 a method 100 for the continuous determination of control signals for controlling actuators of a vehicle according to a preferred embodiment of the invention is shown.

1. a. Bestimmen und Setzen von Zielkoordinaten k_x und einem Ankunftszeitraum kt für das Fahrzeug 1 und Senden der Zielkoordinaten k_x und des Ankunftszeitraums kt an eine Pfadplanungseinheit 7;
2. b. Berechnen von Pfadfolgedaten a durch die Pfadplanungseinheit 7 basierend auf den Zielkoordinaten k_x, dem Ankunftszeitraum kt und einem aktuellen Fahrzeugzustand und Senden der Pfadfolgedaten a an eine Optimierungseinheit 4;
3. c. Bestimmen zumindest eines ersten Wertes und eines zweiten Wertes zumindest eines Stellsignals u durch die Optimierungseinheit 4 und Senden des zumindest ersten und zweiten Wertes des zumindest einen Stellsignals u an eine Modellrecheneinheit 3;
4. d. Berechnen von zumindest einem ersten prädizierten Fahrzeugzustand x'1 und einem zweiten prädizierten Fahrzeugzustand x'2 über einen Prädiktionszeitraum durch die Modellrecheneinheit 3 mittels einer Fahrzeugmodellsimulationsgleichung f1, wobei der erste prädizierte Fahrzeugzustand x'1 basierend auf dem ersten Wert des zumindest einen Stellsignals u und dem aktuellen Fahrzeugzustand und der zweite prädizierte Fahrzeugzustand x'2 basierend auf dem ersten und dem zweiten Wert des zumindest einen Stellsignals u und dem aktuellen Fahrzeugzustand berechnet wird und Senden des zumindest ersten x'1 und zweiten prädizierten Fahrzeugzustands x'2 an die Optimierungseinheit 4;
5. e. Berechnen zumindest eines optimalen ersten Wertes und eines optimalen zweiten Wertes des zumindest einen Stellsignals u durch die Optimierungseinheit 4 basierend zumindest auf den Pfadfolgedaten a, dem zumindest ersten x'1 und zweiten prädizierten Fahrzeugzustand x'2 und dem aktuellen Fahrzeugzustand, wobei der optimale Wert des zumindest einen Stellsignals u derart berechnet wird, dass eine Kostenfunktion minimiert wird;
6. f. Senden des optimalen ersten Wertes des zumindest einen Stellsignals u an das Stellglied 2 des Fahrzeugs 1 und eine Vergleichseinheit 7; und
7. g. Übernehmen des optimalen ersten Wertes des zumindest einen Stellsignals u durch das zumindest eine Stellglied 2 des Fahrzeugs 1.

The method 100 shown for the continuous determination of control signals for controlling actuators 2 of a vehicle 1 comprises the steps:

1. a. determining and setting destination coordinates k _x and an arrival period kt for the vehicle 1 and sending the destination coordinates k _x and the arrival period kt to a path planning unit 7;
2. b. Calculating path following data a by the path planning unit 7 based on the destination coordinates k _x , the arrival period kt and a current vehicle condition and sending the path following data a to an optimization unit 4;
3. c. Determining at least a first value and a second value of at least one control signal u by the optimization unit 4 and sending the at least first and second value of the at least one control signal u to a model computing unit 3;
4. i.e. Calculation of at least a first predicted vehicle state x'1 and a second predicted vehicle state x'2 over a prediction period by the model computing unit 3 using a vehicle model simulation equation f1, the first predicted vehicle state x'1 based on the first value of the at least one control signal u and the current vehicle state and the second predicted vehicle state x'2 is calculated based on the first and the second value of the at least one control signal u and the current vehicle state and sending the at least first x'1 and second predicted vehicle state x'2 to the optimization unit 4;
5. e. Calculation of at least one optimal first value and an optimal second value of the at least one control signal u by the optimization unit 4 based at least on the path sequence data a, the at least first x'1 and second predicted vehicle state x'2 and the current vehicle state, with the optimal value of the at least one control signal u is calculated in such a way that a cost function is minimized;
6. f. Sending the optimal first value of the at least one control signal u to the actuator 2 of the vehicle 1 and a comparison unit 7; and
7. G. Adoption of the optimal first value of the at least one control signal u by the at least one actuator 2 of the vehicle 1.

The arrows shown here indicate the course of the method, with the determination and setting of the destination coordinates k _x and the arrival period kt for the vehicle 1 and the sending to the path planning unit 7 being the starting point or start of the method. The steps c. to g. are preferably carried out repeatedly until the vehicle 1 has reached the target coordinates k _x . Some of the arrows shown are provided with reference numbers, these reference numbers reflect data or information which is sent between the respective units and sensors in order to improve clarity.

• h. Berechnen von zumindest einer berechneten Sensorgröße y_p durch die Modellrecheneinheit 3 mittels zumindest einer Sensorgrößenmodellsimulationsgleichung f2 basierend auf einem optimalen ersten prädizierten Fahrzeugzustand x'1o, wobei sich der optimale erste prädizierte Fahrzeugzustand x'1o in Schritt e. aus dem optimalen ersten Wert des zumindest einen Stellsignals u ergibt und von der Optimierungseinheit 4 an die Modellrecheneinheit 3 gesendet wird, und Senden der zumindest einen berechneten Sensorgröße y_p an die Vergleichseinheit 5;
• i. Erfassen von zumindest einer Sensorgröße y_e durch zumindest einen Sensor 6 des Fahrzeugs 1 und Senden der zumindest einen Sensorgröße y_e an die Vergleichseinheit 5;
• j. Berechnen des Schätz-Fahrzeugzustands xs durch die Vergleichseinheit 5 basierend auf dem optimalen ersten Wert des zumindest einen Stellsignals u mittels der Fahrzeugmodellsimulationsgleichung f1, welche um einen gewichtbaren Korrekturterm erweitert ist, wobei der Korrekturterm basierend auf einem Vergleich der zumindest einen Sensorgröße y_e und der zumindest einen berechneten Sensorgröße y_p durch die Vergleichseinheit 5 bestimmt wird, und Senden des Schätz-Fahrzeugzustands xs an die Optimierungseinheit 4, die Modellrecheneinheit 3 und die Pfadplanungseinheit 7, wobei bei einer wiederholten Durchführung der Schritte c. bis g. der aktuelle Fahrzeugzustand der Schätz-Fahrzeugzustand x_s ist.

It is also shown that an estimated vehicle state x _s is calculated by the comparison unit 5, with the estimated vehicle state x _s each time steps c. to g. is calculated as follows:

• H. Calculation of at least one calculated sensor variable y _p by the model computing unit 3 using at least one sensor variable model simulation equation f2 based on an optimal first predicted vehicle state x'1o, the optimal first predicted vehicle state x'1o in step e. results from the optimal first value of the at least one control signal u and is sent from the optimization unit 4 to the model computing unit 3, and sending the at least one calculated sensor variable y _p to the comparison unit 5;
• i. Detection of at least one sensor variable y _e by at least one sensor 6 of the vehicle 1 and sending the at least one sensor variable y _e to the comparison unit 5;
• j. Calculation of the estimated vehicle state xs by the comparison unit 5 based on the optimal first value of the at least one control signal u using the vehicle model simulation equation f1, which is expanded to include a weightable correction term, the correction term being based on a comparison of the at least one sensor variable y _e and the at least a calculated sensor variable y _p is determined by the comparison unit 5, and sending the estimated vehicle state xs to the optimization unit 4, the model calculation unit 3 and the path planning unit 7, with a repeated implementation of steps c. to g. the current vehicle state is the estimated vehicle state x _s .

The steps h. to j. are preferably carried out before step d if the method is repeated. carried out.

It is also shown that an estimated disturbance variable ds, which is calculated by the comparison unit 5 using a disturbance variable model simulation equation f3 based on the comparison of the at least one sensor variable y _e and the at least one calculated sensor variable y _p , is sent to the optimization unit 4. The estimated disturbance variable ds is used to calculate the optimal value of the at least one control signal u in step e. used. It is assumed that the disturbance variable model simulation equation f3 is present on the comparison unit 5 .

According to the 1 The method 100 shown is also sent obstacle data h from the path planning unit 7 to the optimization unit 4 in addition to the path sequence data a. The obstacle data h, which are sent to the optimization unit 4, are preferred for calculating the optimal values of the control signal u in step e. used.

• - ein Fahrzeug 1;
• - zumindest ein Stellglied 2 des Fahrzeugs 1;
• - zumindest einen Sensor 6 zum Erfassen zumindest einer Sensorgröße y_e;
• - eine Modellrecheneinheit 3 zur Berechnung von prädizierten Fahrzeugzuständen x'1, x'2 und zumindest einer berechneten Sensorgröße y_p;
• - eine Vergleichseinheit 5 zur Berechnung eines Schätz-Fahrzeugzustands xs;
• - eine Optimierungseinheit 4 zur Berechnung optimaler Werte zumindest eines Stellsignals u zum Übernehmen durch das zumindest eine Stellglied 2; und
• - eine Pfadplanungseinheit 7 zur Berechnung von Pfadfolgedaten a.

In 2 a system 1000 for performing a method 100 is shown, the system 1000 comprising:

• - a vehicle 1;
• - At least one actuator 2 of the vehicle 1;
• - At least one sensor 6 for detecting at least one sensor variable y _e ;
• - a model computing unit 3 for computing predicted vehicle states x'1, x'2 and at least one computed sensor variable y _p ;
• - a comparison unit 5 for calculating an estimated vehicle state xs;
• - an optimization unit 4 for calculating optimal values of at least one actuating signal u for acceptance by the at least one actuator 2; and
• - A path planning unit 7 for calculating path sequence data a.

The signaling connections of the units, actuator and sensor are shown by dashed lines.

According to the 2 system shown, the at least one actuator 2, the at least one sensor 6, the model computing unit 3, the optimization unit 4, the comparison unit 5 and the path planning unit 7 are arranged in and/or on the vehicle 1. It would also be conceivable for the model computing unit 3 or its functionality to be at least partially integrated into the optimization unit 4 and/or the comparison unit 5 .

In the following, the vehicle model simulation equation f1, the calculation of the predicted vehicle states x′1,x′2 using the vehicle model simulation equation f1 and the cost function are to be presented according to a preferred embodiment.

The vehicle model simulation equation f1 describes the rate of change of the vehicle states as a function of the value of the actuating signal u and the vehicle state x, the vehicle state x being the current vehicle state or the first predicted vehicle state x'1: $$\text{f1}\left(\text{x}\text{,u}\right)$$

mit
k=0,...,N-1;
t_i entspricht dem aktuellen Zeitpunkt;
x_k ist der prädizierte Fahrzeugzustand x' zum Zeitpunkt t_k mit t_k = kΔt_m und k = 0, ..., N; Δt_m ist die Abtastzeit, d.h. das Prädiktionszeitintervall bzw. das erste Zeitintervall.The predicted vehicle states are obtained by numerical integration of the vehicle model simulation equation f1 dxdt = f1 (x, u). Thus x(t) is formally integrated, where x(t) corresponds to the integral over the prediction period f1 (x,u)dt: $$x_{{}_{k+1}}=x_k+{\displaystyle {\int }_{t_i+k\Delta t_m}^{t_i+\left(k+1\right)\Delta t_m}ƒ1\left(x\left(t\right),\mathrm{and}\left(t\right)\right)\mathrm{i.e}t}$$

With
k=0,...,N-1;
t _i corresponds to the current time;
x _k is the predicted vehicle state x' at time t _k with t _k = kΔt _m and k = 0,...,N; Δt _m is the sampling time, ie the prediction time interval or the first time interval.

dabei entspricht N der Anzahl an Prädiktionszeitintervallen, in die der Prädiktionszeitraum thor unterteilt ist.The prediction period t _hor results as follows: $${\text{t}}_{\text{hey}}=\text{N}\Delta {\text{t}}_{\text{m}};$$

N corresponds to the number of prediction time intervals into which the prediction period thor is divided.

wobei folgende Bedingungen erfüllt sein müssen:

• - prädizierte Fahrzeugzustände aus Fahrzeugmodellsimulationsgleichung: x' = f1(x,u)
• - Pfadparameter aus Pfadparametermodellsimulationsgleichung („timing law“): θ' = q(θ,x)
• - aktuelle Zustand oder Schätz-Fahrzeugzustand xs
• - Hindernisdaten h: h(x) ≤ 0 (die Hindernisse können als Kreise oder Polygone eingehen)
• - Fahrzeugdaten (geben zulässigen Bereich für den Wert des Stellsignals an):
  • Umin ≤ u ≤ u̇_max; |u̇| ≤ u̇_max (Ratenschranke für den Wert des Stellsignals, die das Stellglied innerhalb der Abtastzeit realisieren kann)

The cost function that the optimization unit 4 uses can be represented as follows: $$\underset{}{\underset{\mathrm{and}}{\text{at least}}J\left(\mathrm{and}\right)={\displaystyle {\int }_{t_i}^{t_i+N\Delta t_m}e}\left(x,\theta \right)e\left(x,\theta \right)\mathrm{i.e}t;}$$

where the following conditions must be met:

• - predicted vehicle states from vehicle model simulation equation: x' = f1(x,u)
• - Path parameters from path parameter model simulation equation (“timing law”): θ' = q(θ,x)
• - current condition or estimated vehicle condition xs
• - Obstacle data h: h(x) ≤ 0 (obstacles can be entered as circles or polygons)
• - Vehicle data (specify permissible range for the value of the control signal):
  • Umin ≤ u ≤ u̇ _max; |u̇| ≤ u̇ _max (rate limit for the value of the control signal that the actuator can realize within the sampling time)

The various embodiments with all their features can be combined and exchanged as desired.

The applicant reserves the right to claim all features disclosed in the application documents as essential to the invention, provided they are new compared to the prior art, either individually or in combination. It is also pointed out that the individual figures also describe features which can be advantageous in and of themselves. The person skilled in the art recognizes immediately that a specific feature described in a figure can also be advantageous without adopting further features from this figure. Furthermore, the person skilled in the art recognizes that advantages can also result from a combination of several features shown in individual figures or in different figures.

Reference List

100

procedure

1000

system

1

vehicle

2

actuator

3

model arithmetic unit

4

optimization unit

5

comparison unit

6

sensor

7

path planning unit

and

control signal; Value of the actuating signal

a

path following data

H

obstacle data

x',x'1,x'2

predicted vehicle condition

x'1o,x'2o

optimal predicted vehicle condition

xs

Estimate vehicle condition

ct

arrival period

kx

target coordinates

dS

estimate disturbance variable

ye

detected sensor size

yp

calculated sensor size

f1

vehicle model simulation equation

f2

Sensor size model simulation equation

f3

Disturbance Model Simulation Equation

Claims (11)

Method (100) for continuously determining control signals for controlling actuators (2) of a vehicle (1), comprising the steps: a. determining and setting destination coordinates (k _x ) and an arrival time (kt) for the vehicle (1) and sending the destination coordinates (k _x ) and the arrival time (kt) to a path planning unit (7); b. Calculating path following data (a) by the path planning unit (7) based on the destination coordinates (k _x ), the arrival period (k _t ) and a current vehicle state and sending the path following data (a) to an optimization unit (4); c. determining at least a first value and a second value of at least one control signal (u) by the optimization unit (4) and sending the at least first and second value of the at least one control signal (u) to a model computing unit (3); i.e. Calculation of at least a first predicted vehicle state (x'1) and a second predicted vehicle state (x'2) over a prediction period by the model computing unit (3) using a vehicle model simulation equation (f1), the first predicted vehicle state (x'1) based on the first value of the at least one control signal (u) and the current vehicle condition and the second predicted vehicle condition (x'2) is calculated based on the first and the second value of the at least one control signal (u) and the current vehicle condition and sending the at least first (x'1) and second predicted vehicle state (x'2) to the optimization unit (4); e. Calculation of at least one optimal first value and an optimal second value of the at least one actuating signal (u) by the optimization unit (4) based at least on the path sequence data (a), the at least first (x'1) and second predicted vehicle state (x'2) and the current vehicle condition, the optimum value of the at least one actuating signal (u) being calculated in such a way that a cost function is minimized; f. Sending the optimal first value of the at least one control signal (u) to the actuator (2) of the vehicle (1) and a comparison unit (7); and G. Adoption of the optimum first value of the at least one control signal (u) by the at least one actuator (2) of the vehicle (1). Method (100) according to claim 1 , characterized in that steps c. to g. of the method are carried out continuously and repeatedly after a first time interval, with a first execution of steps c. to g. the current vehicle status is a predefined vehicle status and the at least first and second value of the at least one actuating signal (u) are each a predefined value, with step e. an optimal first predicted vehicle condition (x'1o) from the optimal first value of the at least one control signal (u) and an optimal second predicted vehicle condition (x'2o) from the optimal first and the optimal second value of the at least one control signal (u) results, the optimal first predicted vehicle state (x'1o) being sent to the model arithmetic unit (3). Method (100) according to claim 1 or 2 , characterized in that if steps c. to g. the current vehicle condition is an estimated vehicle condition (x _s ), wherein the estimated vehicle condition (x _s ) each time steps c. to g. is calculated as follows: h. Calculating at least one calculated sensor variable (y _p ) by the model computing unit (3) using at least one sensor variable model simulation equation (f2) based on the optimal first predicted vehicle state (x'1o) and sending the at least one calculated sensor variable (y _p ) to the comparison unit ( 5); i. Detection of at least one sensor variable (y _e ) by at least one sensor (6) of the vehicle (1) and sending the at least one sensor variable (y _e ) to the comparison unit (5); j. Calculation of the estimated vehicle state (x _s ) by the comparison unit (5) based on the optimal first value of the at least one control signal (u) using the vehicle model simulation equation (f1), which is expanded by a weightable correction term, the correction term based on a comparison the at least one sensor variable (y _e ) and the at least one calculated sensor variable (y _p ) is determined by the comparison unit (5), and sending the estimated vehicle state (x _s ) to the optimization unit (4), the model arithmetic unit (3) and the path planning unit (7); where steps h. to j. before step d. be performed. Method (100) according to claim 2 or 3 , characterized in that if steps c. to g. the at least first and second value of the at least one control signal (u) in step c. corresponding to the optimal second value of the at least one actuating signal (u) from the previous execution of step e. to be determined. Method (100) according to one of the preceding claims, characterized in that the prediction period of a current Point in time extends to a certain future end point in time, the prediction period being divided at least into a first and a second prediction time interval, the prediction time intervals having the same length of time, the first prediction time interval extending from the current point in time to a future first point in time and the second prediction time interval from the first point in time to the end point in time, the first value of the at least one control signal (u) being determined for the first point in time and the second value of the at least one control signal (u) being determined for the end point in time, the first predicted vehicle state (x'1 ) is calculated for the first point in time and the second predicted vehicle state (x′2) for the end point in time, with the duration of the prediction time intervals corresponding to a duration of the first time interval. Method (100) according to one of the preceding claims, characterized in that step b. of the method is carried out continuously and repeatedly after a second time interval, with a first execution of step b. the current vehicle state is the specified vehicle state, with a repeated implementation of step b. the current vehicle state is the estimated vehicle state (x _s ), the second time interval being longer than the first time interval. Method (100) according to one of the preceding claims, characterized in that the path planning unit (7) calculates the path sequence data (a), which include path coordinates and speed parameters, additionally based on environmental data and/or obstacle data, with obstacle data selected by the path planning unit (7). (h) sent from the path planning unit (7) to the optimization unit (4) and additionally for the calculation in step e. are used, the calculation in step e. is additionally carried out based on vehicle data, the vehicle data restricting the calculable optimal value of the control signal (u). Method (100) according to one of the preceding claims, characterized in that the comparison unit (5) is designed as a Kalman filter, a variant of the Kalman filter or as a moving horizon estimator. Method (100) according to one of the preceding claims, characterized in that the calculation of the optimal values of the at least one control signal (u) in step e. is carried out in such a way that a deviation from the path following data (a) is compensated for by the at least one actuator (2) of the vehicle (1) adopting the calculated optimum first value of the at least one control signal (u) in step g. is minimized. Method (100) according to one of the preceding claims, characterized in that in step j. in addition, at least one estimated disturbance variable (ds) is calculated by the comparison unit (5) using a disturbance variable model simulation equation (f3) based on the comparison of the at least one sensor variable (y _e ) and the at least one calculated sensor variable (y _p ), the at least one Estimated disturbance variable (ds) is sent to the optimization unit (4) and the calculation of the optimal values of the at least one control signal (u) in step e. additionally based on the estimated disturbance variable (ds). System (1000) for carrying out a method (100) according to one of Claims 1 until 10 , comprising: - a vehicle (1); - At least one actuator (2) of the vehicle (1); - At least one sensor (6) for detecting at least one sensor variable (y _e ); - a model computing unit (3) for computing predicted vehicle states (x'1, x'2) and at least one computed sensor variable (y _p ); - A comparison unit (5) for calculating an estimated vehicle state (xs); - an optimization unit (4) for calculating optimal values of at least one actuating signal (u) for acceptance by the at least one actuator (2); and - a path planning unit (7) for calculating path sequence data (a).

Images