WO2021089150A1

WO2021089150A1 - Autonomous driving function of a motor vehicle, taking into consideration vehicles located in the surroundings of the ego vehicle

Info

Publication number: WO2021089150A1
Application number: PCT/EP2019/080515
Authority: WO
Inventors: Valerie Engel; Andreas Wendzel; Maik DREHER; Julian KING; Lara Ruth TURNER
Original assignee: Zf Friedrichshafen Ag
Priority date: 2019-11-07
Filing date: 2019-11-07
Publication date: 2021-05-14

Abstract

The invention relates to a processor unit (3) for executing an autonomous driving function for an ego vehicle (1), taking into consideration at least one vehicle (19) located in the surroundings of the ego vehicle (1), wherein the autonomous driving function is formed by an MPC algorithm (13) for model-based predictive control of an ego vehicle (1), the MPC algorithm (13) comprising a longitudinal dynamics model (14) of the ego vehicle (1), a cost function (15) to be minimized and a predicted trajectory (20) of at least one vehicle (19) located in the surroundings of the ego vehicle (1), the processor unit (3) being designed to execute the MPC algorithm (13) such that the ego vehicle (1) drives autonomously on the basis of the execution of the MPC algorithm (13), and the processor unit (3) being designed to determine an input variable for the model-based predictive control of the ego vehicle (1) by executing the MPC algorithm (13), taking into consideration the longitudinal dynamics model (14) of the ego vehicle (1) and the predicted trajectory (20) of the at least one vehicle (19) located in the surroundings of the ego vehicle (1), such that the cost function is minimized.

Description

Autonomous driving function of a motor vehicle taking into account vehicles in the environment of the ego vehicle

The invention relates to an autonomous driving function for an ego vehicle. In this context, particular claims are made of a process unit set up for this purpose, a method and a computer program product. Another claim is directed to a motor vehicle with the aforementioned processor unit.

Autonomous driving strategies use environmental data, map data and vehicle data to determine optimal vehicle behavior. Vehicles in front are also taken into account, to which a safe distance must be maintained. Known driver assistance systems, for example active cruise control systems (in English: Adaptive Cruise Control) regulate the ego vehicle to a safe distance from the vehicle in front and maintain this distance until the vehicle in front leaves its lane or a previously set cruise control speed of the ego vehicle is exceeded becomes. The approach to vehicles ahead is known to take place with a predefined braking curve. This has the effect that the speed profile of the vehicle in front is followed and the consumption of the ego vehicle is thus largely determined by the driving behavior of the vehicle in front.

An object of the present invention can be seen in improving an autonomous driving function of a motor vehicle with regard to a more efficient driving style. The object is achieved by the subjects of the independent patent claims. Advantageous embodiments are the subject matter of the dependent claims, the following description and the figures.

The present invention proposes avoiding inefficient driving styles of the ego vehicle by planning the autonomous driving strategy, taking into account all restrictions of the ferry operation of the ego vehicle, so that an optimum in terms of speed and operating point selection of the ego vehicle is realized. At the same time, the energy consumption of the ego is optimized Vehicle while driving by knowing the operating data of the vehicle ahead. The basis for this is the most accurate possible prediction of the driving behavior of vehicles in front or vehicles in the vicinity of the ego vehicle. When using an MPC optimization algorithm as the driving strategy, the predicted trajectory of the vehicle ahead can be taken into account.

In this sense, according to a first aspect of the invention, a processor unit is provided for executing an autonomous driving function for an ego vehicle taking into account at least one vehicle in the vicinity of the ego vehicle, the autonomous driving function being model-based by an MPC algorithm predictive control of an ego vehicle is formed, the MPC algorithm containing a longitudinal dynamics model of the ego vehicle, a cost function to be minimized and a predicted trajectory of at least one vehicle located in the vicinity of the ego vehicle. The processor unit is set up to execute the MPC algorithm so that the ego vehicle drives autonomously based on the execution of the MPC algorithm.

The autonomous driving function can be formed at least in part by an MPC algorithm for model predictive control of the ego vehicle, the MPC algorithm being a longitudinal dynamics model of the ego vehicle, a predicted trajectory of at least one vehicle located in the vicinity of the ego vehicle and one to contains minimizing cost function. The processor unit is set up to execute the MPC algorithm so that the ego vehicle “drives autonomously based on the execution of the MPC algorithm, and by executing the MPC algorithm” - according to the trajectory of the vehicle in front as precisely as possible was predicted or predicted - to determine an input variable for the model-based predictive control of the ego vehicle, taking into account the longitudinal dynamics model of the ego vehicle, so that the cost function is minimized. In other words, the processor unit is set up to execute an autonomous driving function so that the ego vehicle drives autonomously based on the execution of the autonomous driving function. The model-based predictive control (MPC) method can be selected in order to find an optimal solution for a so-called “Driving Efficiency” function in every situation under given boundary conditions and restrictions. Methods of model-based predictive control (in English: Model Predictive Control or MPC for short) are used in the field of trajectory control, for example for engine control in the context of autonomous driving. The MPC method is based on a system model that describes the behavior of the system. Furthermore, the MPC method is based in particular on a target function or on a cost function that describes an optimization problem and determines which state variables are to be minimized. The state variables for the Driving Efficiency driving function can in particular be the vehicle speed or the kinetic energy, the remaining energy in the battery of an electric drive and the driving time. In addition, the cost function can also take into account a predicted trajectory of at least one vehicle in the vicinity of the ego vehicle. The optimization of energy consumption and travel time takes place in particular on the basis of the gradient of the route ahead and restrictions or secondary conditions for speed and driving force, as well as on the basis of the current system status.

The longitudinal dynamics model of the drive train can include a vehicle model with vehicle parameters and drive train losses (partly approximated maps). In particular, knowledge of the route topographies ahead (e.g. curves and gradients) can be incorporated into the longitudinal dynamics model of the drive train. Furthermore, knowledge of speed limits on the route ahead can also flow into the longitudinal dynamics model of the ego vehicle.

Current state variables can be measured, the corresponding data can be recorded and fed to the MPC algorithm. For example, route data from an electronic map for a forecast horizon or prediction horizon (for example up to 5 km) in front of the ego vehicle can, in particular, be updated cyclically. The route data can contain, for example, gradient information, curve information, and information about speed limits. Further a curve curvature can be converted into a speed limit for the ego vehicle using a maximum permissible lateral acceleration. In addition, the ego vehicle can be localized, in particular via a GNSS signal for precise localization on the electronic map.

The processor unit is preferably set up to use the MPC algorithm to regulate a drive machine of a drive train of the ego vehicle, the MPC algorithm being a longitudinal dynamics model of the drive train and a predicted trajectory of at least one vehicle in the vicinity of the ego vehicle contains. The processor unit is set up to determine an input variable for the control of the drive machine by executing the MPC algorithm taking into account the stored driver intervention, so that the ego vehicle is driven autonomously by the drive machine and so that the cost function is minimized.

The cost function preferably contains as the first term an electrical energy weighted with a first weighting factor and predicted according to the longitudinal dynamics model and according to the predicted trajectory of the at least one vehicle located in the vicinity of the ego vehicle, which within a prediction horizon from a battery of the drive train to the Drive of the prime mover is provided, wherein the cost function also contains as a second term a driving time weighted with a second weighting factor and predicted according to the longitudinal dynamics model and according to the predicted trajectory of the at least one vehicle located in the vicinity of the ego vehicle, which the ego vehicle for the Covering the entire distance predicted within the prediction horizon is required. The processor unit is set up to determine an input variable for regulating the drive machine of the ego vehicle by executing the MPC algorithm as a function of the first term and as a function of the second term, so that the cost function is minimized.

The cost function only has linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear secondary conditions and a convex problem results, which is good and fast can be solved. The target function or the cost function can be set up with a weighting device (weighting factors), with in particular energy efficiency, travel time and travel comfort being calculated and weighted. An energy-optimal speed trajectory can be calculated online for a horizon ahead on the processor unit, which can in particular form a component of a central control device of the ego vehicle. By using the MPC method, the target speed of the ego vehicle can also be recalculated cyclically based on the current driving status and the route information available ahead.

The cost function of the MPC algorithm ‘minimizes the travel time for the prediction horizon and minimizes the energy consumed. Alternatively, the cost function of the MPC algorithm ‘can specify a maximum speed for the prediction horizon. In one embodiment, there is also a minimization of torque changes for the prediction horizon. As far as the input for the model-based predictive control is concerned, the MPC algorithm can be supplied with secondary conditions, e.g. speed limits, physical limits for the torque and speeds of the drive machine. The MPC algorithm can also be supplied with control variables for optimization as input, in particular the speed of the vehicle (which can be proportional to the speed), the torque of the prime mover and the state of the battery charge. As an output of the optimization, the MPC algorithm can deliver an optimal speed and an optimal torque for calculated points in the forecast horizon. As far as the implementation of the MPC control in the vehicle is concerned, the MPC algorithm can be followed by a software module which determines a currently relevant state and forwards it to power electronics.

Energy consumption and travel time or maximum speed can be evaluated and weighted at the end of the horizon. This term is only active for the last point on the horizon. In this sense, the cost function in one embodiment contains a final energy consumption value weighted with the first weighting factor, which the predicted electrical energy at the end of the prediction on horizon, and the cost function contains a final travel time value weighted with the second weighting factor, which the predicted travel time assumes at the end of the prediction horizon.

In order to ensure comfortable driving, additional terms can be introduced to punish momentary jumps. In this sense, the cost function can have a third term with a third weighting factor, the third term containing a value of a torque predicted according to the longitudinal dynamics model, which the prime mover provides for driving the ego vehicle, and the processor unit is set up for this To determine the input variable for the prime mover by executing the MPC algorithm as a function of the first term, as a function of the second term and as a function of the third term, so that the cost function is minimized.

For the first point on the horizon, the deviation from the last set moment can be assessed negatively in order to ensure that there is a seamless and jerk-free transition when switching between the old and the new trajectory. In this sense, the third term can contain a first value, weighted with the third weighting factor, of a torque predicted according to the longitudinal dynamics model, which the drive machine provides for driving the ego vehicle to a first waypoint within the prediction horizon. The third term can contain a zeroth value of a torque weighted with the third weighting factor, which the drive machine provides for driving the ego vehicle to a zeroth waypoint which is immediately before the first waypoint. The zeroth torque can in particular be a real - in not merely predicted - torque provided by the drive machine. In the cost function, the zeroth value of the torque can be subtracted from the first value of the torque.

Alternatively, the third term can contain a first value, weighted with the third weighting factor, of a drive force predicted according to the longitudinal dynamics model, which the drive machine for driving the ego vehicle to a first Provides waypoint within the prediction horizon. The third term contains a zero value, weighted with the third weighting factor, of a drive force which the drive machine provides to drive the ego vehicle to a zero waypoint which is immediately before the first waypoint, with the zero value of the drive force in the cost function is deducted from the first value of the drive force.

The waypoints which are taken into account by the MPC algorithm are, in particular, discrete waypoints which, for example, follow one another at a certain frequency. In this sense, the zeroth waypoint and the first waypoint represent discrete waypoints, with the first waypoint immediately following the zeroth waypoint. The zeroth waypoint can be before the prediction horizon. The zeroth torque value can be measured or determined for the zeroth waypoint. The first waypoint in particular represents the first waypoint within the prediction horizon. The first torque value can be predicted for the first waypoint. The zeroth torque value actually determined can thus be compared with the predicted first torque value.

In addition, torque gradients that are too high within the horizon are disadvantageous, so that in one embodiment they are already penalized in the objective function. For this purpose, the square deviation of the driving force per meter can be weighted and minimized in the objective function. In this sense, the cost function can have a fourth term with a fourth weighting factor, the fourth term containing a gradient of the torque predicted according to the longitudinal dynamics model or an indicator value for a gradient of the torque predicted according to the longitudinal dynamics model. The processor unit is set up to determine the input variable for the drive machine by executing the MPC algorithm as a function of the first term, as a function of the second term, as a function of the third term and as a function of the fourth term so that the cost function is minimized.

In one embodiment, the fourth term contains a quadratic deviation of the gradient des multiplied by the fourth weighting factor and added up Torque. Furthermore, the cost function can contain a quadratic deviation, summed up with the fourth weighting factor, of a driving force which the prime mover provides in order to move the ego vehicle one meter in the longitudinal direction. In this sense, the fourth term can contain a quadratic deviation, multiplied by the fourth weighting factor and summed up, of a driving force which the prime mover provides in order to move the ego vehicle one meter in the longitudinal direction.

Speed limits, which can be set for example by a traffic route, are hard limits for optimization that should not be exceeded. In reality, it is always permissible to slightly exceed the speed limits and, above all, it is the normal case when passing from one speed zone to a second zone. In dynamic environments, in which speed limits shift from one computing cycle to the next, it can happen that no valid solution can be found for a speed curve if the limits are very hard. In order to increase the stability of the calculation algorithm, a so-called “soft constraint” can be introduced into the objective function. In particular, a so-called “slip variable” or “slack variable” can become active in a predetermined narrow range before the hard speed limit is reached. Solutions that are very close to this speed limit can be rated worse, that is, solutions whose speed trajectory keep a certain distance from the hard limit. In this sense, the cost function can contain, as the fifth term, a Slack variable weighted with a fifth weighting factor, the processor unit being set up to, by executing the MPC algorithm, “depending on the first term, depending on the second term, to determine the input variable for the drive machine as a function of the third term, as a function of the fourth term and as a function of the fifth term, so that the cost function is minimized.

In order to respect the physical limits of the drive train components, the tractive force can be limited by restricting the engine map become. For example, the battery is the limiting element for maximum recuperation. In order not to damage them, the performance value should not fall below a certain negative value.

The predicted trajectory of the respective vehicle located in the environment of the ego vehicle preferably includes a predicted speed profile of the vehicle. The predicted trajectory of the respective vehicle in the environment of the ego vehicle is taken into account as an additional boundary condition in the MPC algorithm, which additionally limits the solution space for minimizing the cost function. The predicted trajectory is generated from information from the vehicle in front, with a maximum speed in the forecast horizon, for example, being derived from the predicted trajectory, taking into account the longitudinal dynamics model of the ego vehicle.

The ego vehicle preferably includes a sensor system that supplies sensor data on the vehicle driving in front of the ego vehicle. With additional information, such as map data, in particular the route topography, or speed limits, such as tight bends or traffic lights, the processor unit can predict or predict driving behavior, in this case in particular a speed profile of the vehicle in front. Likewise, an optimal operating point or operating point curve of the vehicle driving in front of the ego vehicle can also be predicted.

As an alternative or in addition, the predicted trajectory of the respective vehicle located in the vicinity of the ego vehicle includes a current lane of the vehicle. The processor unit can take into account the lane in which the vehicle in front is located. The processor unit therefore makes a different decision about the optimal speed of the ego vehicle, depending on whether the vehicle in the vicinity is in the same lane in front of the ego vehicle or in one of the adjacent lanes. Furthermore, the predicted trajectory of the respective vehicle located in the environment of the ego vehicle preferably includes a position of the vehicle relative to the ego vehicle. In other words, a distance from the vehicle in front is detected. In particular, the speed profile of the ego vehicle can be selected or planned in such a way that a minimum or safety distance from the vehicle driving ahead is always maintained.

The processor unit can also be set up to store a second longitudinal dynamics model of the respective vehicle located in the vicinity of the ego vehicle. For example, via a wireless connection or a cloud system, the ego vehicle can receive and save vehicle data from the vehicle in the vicinity of the ego vehicle. This data can be continuously called up or compared so that the autonomous driving function of the ego vehicle takes place with updated information from the vehicle in front.

The processor unit is preferably set up to determine the input variable for regulating the drive machine of the ego vehicle, taking into account the second longitudinal dynamics model of the vehicle in the vicinity of the ego vehicle, so that the cost function is minimized. The longitudinal dynamics model of the respective vehicle located in the surroundings of the ego vehicle can also be a longitudinal dynamics model of the drive train of the respective vehicle located in the surroundings of the ego vehicle and comprise a vehicle model with vehicle parameters and drive train losses. This longitudinal dynamics model can in particular include knowledge of the route topographies lying ahead. Furthermore, knowledge of speed limits on the route ahead can also flow into the longitudinal dynamics model of the respective vehicle located in the vicinity of the ego vehicle.

According to a second aspect of the invention, an ego vehicle is provided. The vehicle comprises a drive train with a drive machine and a driver assistance system. Furthermore, the drive train includes, in particular, a battery, it being possible for the drive machine to be designed as an electrical machine. Remote The drive train can in particular comprise a transmission. The driver assistance system is set up to access an input variable for the drive machine by means of a communication interface, the input variable having been determined by a processor unit according to the first aspect of the invention. Furthermore, the driver assistance system can be set up to control the ego vehicle or the drive machine based on the input variable. The ego vehicle is, for example, a motor vehicle, such as a car (e.g. a passenger vehicle weighing less than 3.5 t), motorcycle, scooter, moped, bicycle, e-bike, bus or truck (bus and Trucks, e.g. with a weight of over 3.5 t). The ego vehicle can, for example, belong to a vehicle fleet.

According to a third aspect of the invention, a method is provided for executing an autonomous driving function for an ego vehicle taking into account at least one vehicle in the vicinity of the ego vehicle, the autonomous driving function being provided by an MPC algorithm for model-based predictive control of a vehicle Ego vehicle is formed. The method comprises the steps

- Execution of the MPC algorithm for model-based predictive control of an ego vehicle by means of a processor unit, the MPC algorithm being a longitudinal dynamics model of the ego vehicle, a predicted trajectory of at least one vehicle in the vicinity of the ego vehicle and a cost function to be minimized contains,

Determination of an input variable for the drive machine, taking into account the longitudinal dynamics model of the ego vehicle and the predicted trajectory of the at least one vehicle located in the vicinity of the ego vehicle, so that the cost function is minimized.

According to a fourth aspect of the invention, a computer program product is provided for executing an autonomous driving function for an ego vehicle, taking into account at least one vehicle located in the vicinity of the ego vehicle, the computer program product when it is on a processor unit is executed, instructs the processor unit to run an MPC algorithm for model-based predictive control of an ego vehicle, the MPC algorithm being a longitudinal dynamics model of the ego vehicle, a predicted trajectory of at least one vehicle in the vicinity of the ego vehicle and contains a cost function to be minimized, and by executing the MPC algorithm, taking into account the longitudinal dynamics model of the ego vehicle and the predicted trajectory of the at least one vehicle located in the vicinity of the ego vehicle, to determine an input variable for the drive machine, so that the cost function is minimized.

The above definitions and statements on technical effects, advantages and advantageous embodiments of the processor unit also apply mutatis mutandis to the vehicle according to the second aspect of the invention, to the method according to the third aspect of the invention and to the computer program product according to the fourth aspect of the invention.

In the following, exemplary embodiments of the invention are explained in more detail with reference to the schematic drawings, the same or similar elements being provided with the same reference numerals. Here shows

1 shows a schematic illustration of a vehicle with a drive train that comprises an electrical machine and a battery, and

FIG. 2 shows a map of a drive machine for the vehicle according to FIG. 1.

Fig. 1 shows an ego vehicle 1, which can be, for example, a passenger vehicle. The ego vehicle 1 comprises a system 2 for executing an automated driving function of the ego vehicle 1, in the exemplary embodiment shown for model-based predictive control of the ego vehicle 1. In particular, the system 2 can be set up for the model-based predictive control of an electrical machine 8 of a drive train 7 of the ego vehicle 1. In the exemplary embodiment shown, the system 2 comprises a processor purity 3, a memory unit 4, a communication interface 5 and a detection unit 6 for detecting status data relating to the ego vehicle 1. The detection unit 6 is also provided to monitor an area around the ego vehicle 1 and in the area of the Ego vehicle 1 located vehicles 19 to recognize.

The ego vehicle 1 furthermore comprises a drive train 7 which, for example, can comprise an electric machine 8, which can be operated as a motor and as a generator, a battery 9 and a transmission 10. In motor mode, the electric machine 8 can drive the wheels of the ego vehicle 1 via the transmission 10, which can have a constant gear ratio, for example. The electrical energy required for this can be provided by the battery 9. The battery 9 can be charged by the electric machine 8 when the electric machine 8 is operated in generator mode (recuperation). The battery 9 can optionally also be charged at an external charging station. The drive train of the ego vehicle 1 can also optionally have an internal combustion engine 17 which, as an alternative or in addition to the electric machine 8, can drive the ego vehicle 1. The internal combustion engine 17 can also drive the electric machine 8 in order to charge the battery 9.

A computer program product 11 can be stored on the storage unit 4. The computer program product 11 can be executed on the processor unit 3, for which purpose the processor unit 3 and the memory unit 4 are connected to one another by means of the communication interface 5. When the computer program product 11 is executed on the processor unit 3, it instructs the processor unit 3 to fulfill the functions described below or to carry out method steps.

The computer program product 11 contains an MPC algorithm 13 for executing the autonomous driving function. The MPC algorithm 13 in turn contains a longitudinal dynamics model 14 of the drive train 7 of the ego vehicle 1, a predicted trajectory 20 of at least one in the vicinity of the ego vehicle 1 located vehicle 19 and a cost function 15 to be minimized. The processor unit 3 executes the MPC algorithm 13 and thereby predicts a behavior of the ego vehicle 1 based on the longitudinal dynamics model 14 and the predicted trajectory 20, the cost function 15 being minimized. In the exemplary embodiment shown, the output of the optimization by the MPC algorithm 13 can result in an optimal speed and an optimal torque of the electric machine 8 for calculated waypoints in the forecast horizon. For this purpose, the processor unit 3 can determine an input variable for the electrical machine 8, so that the optimum speed and the optimum torque are set. The processor unit 3 can control the electrical machine 8 based on the determined input variable. Furthermore, however, this can also be done by a driver assistance system 16. In this way, the ego vehicle 1 can drive autonomously based on the output of the MPC algorithm 13 executed.

The detection unit 6 can measure current state variables of the ego vehicle 1, record corresponding data and feed them to the MPC algorithm 13. For example, route data from an electronic map for a forecast horizon or prediction horizon (for example 5 km) in front of the ego vehicle 1 can be updated cyclically. The route data can contain, for example, gradient information, curve information and information about speed limits. Furthermore, a curve curvature can be converted into a speed limit for the ego vehicle 1 via a maximum permissible transverse acceleration. In addition, the ego vehicle 1 can be localized by means of the detection unit 6, in particular via a GNSS signal generated by a GNSS sensor 12 for precise localization on the electronic map. Furthermore, the detection unit 6 can include sensors for determining the loading weight of the ego vehicle 1, for detecting the number of vehicle occupants and a time measuring and recording module. The processor unit 3 can access information or data generated by the sensors mentioned, for example via the communication interface 5. In addition, the detection unit 6 can recognize vehicles 19 located in the vicinity of the ego vehicle 1. The recorded data flow into the predicted trajectory 20 of the MPC algorithm 13, with these data showing a predicted speed profile of the vehicle 19, a current lane of the vehicle 19, a position of the vehicle 19 relative to the ego Vehicle 1 and / or a longitudinal dynamics model 18 of vehicle 19. In other words, an energy-efficient driving strategy of the ego vehicle 1, for example an optimal speed profile, an optimal operating point profile and / or an optimal driving time based on the current vehicle status of the ego vehicle 1, the route topography ahead and based on the traffic ahead , in particular of the preceding vehicle 19 planned. The vehicle 19 driving ahead is only relevant for minimizing the cost function if it restricts the maximum permitted speed of the ego vehicle 1 more than road-related or road-topography-related speed limits and / or other speed limits do. As an alternative to a maximum speed, temporal boundary conditions of the vehicle 19 in front can also be derived.

The longitudinal dynamics model 14 of the ego vehicle 1 can be expressed mathematically as follows:

Where: v the speed of the ego vehicle;

Ftrac Tractive force exerted by the engine or the brakes on the wheels of the ego vehicle;

For the rolling resistance force, which is an effect of the deformation of the tires when rolling and depends on the load on the wheels (the normal force between the wheel and the road) and thus on the angle of inclination of the road;

Fgr is the gradient resistance force, which is a longitudinal component of the

Describes gravity, which acts on the ego vehicle in uphill or downhill operation, depending on the inclination of the road;

Fd is the drag force of the ego vehicle; and meq is the equivalent mass of the ego vehicle; the equivalent mass includes in particular the inertia of the rotating parts of the drive train, which are exposed to the acceleration of the ego vehicle (engine, transmission drive shafts, wheels).

By converting time dependency into path dependency

and coordinate transformation to eliminate the quadratic speed term in air resistance with e _kin = - * m _eq * vt) ² follows

So that the problem can be solved quickly and easily by the MPC algorithm 13, the dynamics equation of the longitudinal dynamics model 14 can be linearized in that the speed is expressed by coordinate transformation using kinetic energy dekin. As a result, the quadratic term for calculating the air resistance Fd is replaced by a linear term and, at the same time, the longitudinal dynamic model 14 of the ego vehicle 1 is no longer described as a function of time as usual, but as a function of the path. This fits well with the optimization problem insofar as the forecast information of the electrical horizon is path-based.

In addition to the kinetic energy, there are two other state variables which, in the sense of a simple optimization problem, must also be described linearly and path-dependently. On the one hand, the electrical energy consumption of the drive train 7 is usually described in the form of a map as a function of torque and engine speed. In the exemplary embodiment shown, the ego vehicle 1 has a fixed transmission ratio between the electrical machine 8 and the road on which the ego vehicle 1 is moving. As a result, the speed of the electrical machine 8 can be converted directly into a speed of the ego vehicle 1 or even into a kinetic energy of the ego vehicle 1. Furthermore, the electrical power of the electrical machine 8 can be converted into energy consumption per meter by dividing the corresponding speed. As a result, the characteristics map of the electrical machine 8 is given the form as shown in FIG. 2. In order to be able to use this map for the optimization, it is approximated linearly: Your gy _perMeter > a * * e _kin + b _t * F _trac for all i. An exemplary cost function 15 to be minimized can be expressed mathematically as follows:

Where is:

Wßat Weighting factor for the energy consumption of the battery

Eßat energy consumption of the battery

S distance

SE-I Distance one time step before the end of the prediction horizon

FA Driving force, which is provided by the electric machine, is constantly translated by a transmission and is applied to a wheel of the ego vehicle

WTem Weighting factor for torque gradients

WTemstart Weighting factor for momentary jumps

T Time that the vehicle needs to cover the entire distance predicted within the prediction period WTime Weighting factor for the time T

SE Distance to the end of the horizon ws lack Weighting factor for the Slack variable

Varsiack Slack variable

The cost function 15 has only linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved quickly and easily.

The cost function 15 contains as the first term an electrical weighted with a first weighting factor Wßat and predicted according to the longitudinal dynamics model Energy Eßat, which is provided by the battery 9 of the drive train 7 for driving the electric machine 8 within a prediction horizon.

The cost function 15 contains as a second term a travel time T weighted with a second weighting factor Wnme and predicted according to the longitudinal dynamics model 14, which the ego vehicle 1 needs to cover the predicted distance low speed is not always rated as optimal and so there is no longer the problem that the resulting speed is always at the lower limit of the permitted speed.

The energy consumption and travel time can be evaluated and weighted at the end of the horizon. These terms are then only active for the last point on the horizon.

Too high torque gradients within the horizon are disadvantageous. Torque gradients are therefore already penalized in the cost function 15, namely by the term w _Tem D ' ^e quadratic deviation of An

Driving force per meter is weighted with a weighting factor WTem and minimized in the cost function. As an alternative to the driving force FA per meter, the torque MEM provided by the electrical machine 8 can also be used and weighted with the weighting factor WTem, so that the alternative term w _Tem results. Due to the constant ratio of the gear 10

the driving force and the torque are directly proportional to each other.

In order to ensure comfortable driving, a further term for punishing momentary jumps is introduced in the cost function 15, namely w _TemStart

(F _A (S _I ) - F _A (S ₀ )). As an alternative to the driving force FA, the torque MEM provided by the electrical machine 8 can also be used here, so that the alternative term w _TemStart

- M _EM (S ₀ )) results. For the first point in the prediction horizon, the deviation from the last specified moment is assessed negatively and weighted with a weighting factor WTemStart to ensure that there is a seamless and jerk-free transition when switching between old and new trajectories.

For optimization purposes, speed limits are hard limits that must not be exceeded. In reality, it is always permissible to slightly exceed the speed limits and, above all, it is more the norm when passing from one speed zone to a second zone. In dynamic environments, where speed limits shift from one computing cycle to the next, it can happen that if the limits are very hard, no valid solution can be found for a speed curve. In order to increase the stability of the calculation algorithm, a soft constraint is introduced into the cost function 15. A Slack variable Varsiack weighted with a weighting factor Wsiack becomes active in a predetermined narrow range before the hard speed limit is reached. Solutions that are very close to this speed limit are rated worse, that is, solutions whose speed trajectory keep a certain distance from the hard limit.

The regulation of the electrical machine 8 of the ego vehicle 1 by means of the MPC algorithm 13 is suitable for automation levels below level 5 (e.g. according to SAE J3016), in particular up to level 3, with a driver of the ego vehicle 1 still having the option to influence the journey or to access the MPC-based autonomous driving function of the ego vehicle 1 described above.

Bezuqszeichen ego vehicle system processor unit storage unit communication interface detection unit drive train drive machine battery transmission computer program product GNSS sensor MPC algorithm longitudinal dynamics model cost function driver assistance system internal combustion engine longitudinal dynamics model of the vehicle in front Vehicle in front Trajectory of the vehicle in front

Claims

1. Processor unit (3) for executing an autonomous driving function for an ego vehicle (1) taking into account at least one vehicle (19) located in the vicinity of the ego vehicle (1), the autonomous driving function being carried out by an MPC algorithm (13 ) is formed for the model-based predictive control of an ego vehicle (1), wherein

- the MPC algorithm (13) contains a longitudinal dynamics model (14) of the ego vehicle (1),

- the MPC algorithm (13) contains a cost function (15) to be minimized,

- the MPC algorithm (13) contains a predicted trajectory (20) of at least one vehicle (19) located in the vicinity of the ego vehicle (1),

- The processor unit (3) is set up to execute the MPC algorithm (13) so that the ego vehicle (1) drives autonomously based on the execution of the MPC algorithm (13), and

- The processor unit (3) is set up by executing the MPC algorithm (13) taking into account the longitudinal dynamics model (14) of the ego vehicle (1) and the predicted trajectory (20) of the at least one in the vicinity of the ego -Fahrzeugs (1) located vehicle (19) to determine an input variable for the model-based predictive control of the ego vehicle (1) so that the cost function is minimized.

2. processor unit (3) according to claim 1, wherein

- the processor unit (3) is set up to regulate a drive machine (8) of a drive train (7) of the ego vehicle (1) by means of the MPC algorithm (13),

- The MPC algorithm (13) contains a longitudinal dynamics model (14) of the drive train (7),

- The MPC algorithm (13) contains a predicted trajectory (20) of at least one vehicle (19) located in the vicinity of the ego vehicle (1), and

- The processor unit (3) is set up to, by executing the MPC algorithm "(13), taking into account the stored driver intervention, an input large to determine for the control of the drive machine (8), so that the ego vehicle (1) is driven autonomously by the drive machine (8) and so that the cost function is minimized.

3. processor unit (3) according to claim 2, wherein

- the cost function (15) as the first term an electrical energy weighted with a first weighting factor and predicted according to the longitudinal dynamics model (14) and according to the predicted trajectory (20) of the at least one vehicle (19) located in the vicinity of the ego vehicle (1) contains, which is provided within a prediction horizon by a battery (9) of the drive train (7) for driving the drive machine (8),

- The cost function (15) contains as a second term a driving time weighted with a second weighting factor and predicted according to the longitudinal dynamics model (14) and according to the predicted trajectory (20) of the at least one vehicle (19) located in the vicinity of the ego vehicle (1) which the ego vehicle (1) needs to cover the entire distance predicted within the prediction horizon, and

- The processor unit (3) is set up to generate an input variable for regulating the drive machine (8) of the ego vehicle by executing the MPC algorithm (13) as a function of the first term and as a function of the second term (1) so that the cost function (15) is minimized.

4. processor unit (3) according to any one of the preceding claims, wherein the predicted trajectory (20) of the respective vehicle (19) in the vicinity of the ego vehicle (1) includes a predicted speed profile of the vehicle (19).

5. processor unit (3) according to any one of the preceding claims, wherein the predicted trajectory (20) of the respective in the environment of the ego vehicle (1) be sensitive vehicle (19) comprises a current lane of the vehicle (19).

6. processor unit (3) according to any one of the preceding claims, wherein the predicted trajectory (20) of the respective vehicle (19) in the vicinity of the ego vehicle (1) includes a position of the vehicle (19) relative to the ego vehicle (1).

7. Processor unit (3) according to one of the preceding claims, wherein the processor unit (3) is set up to store a second longitudinal dynamics model (18) of the respective vehicle (19) located in the vicinity of the ego vehicle (1).

8. processor unit (3) according to claim 7, wherein the processor unit (3) is set up to the input variable for the control of the drive machine (8) of the ego vehicle (1) taking into account the second longitudinal dynamics model (18) of the respective in the environment of the Ego vehicle (1) located vehicle (19) to determine so that the cost function (15) is minimized.

9. Motor vehicle (3), comprising a driver assistance system (16) and a drive train (7) with a drive machine (8), wherein the driver assistance system (16) is set up to

- Using a communication interface to access an input variable for the model-based predictive control of the ego vehicle (1) or for the drive machine (8) of the ego vehicle (1), the input variable from a processor unit (3) after a of the preceding claims has been determined, and

- To control the ego vehicle (1) or the prime mover (8) based on the input variable.

10. A method for executing an autonomous driving function for an ego vehicle (1) taking into account at least one vehicle (19) located in the vicinity of the ego vehicle (1), the autonomous driving function using an MPC algorithm (13) is formed for the model-based predictive control of an ego vehicle (1), the method comprising the steps - Execution of the MPC algorithm (13) for model-based predictive control of an ego vehicle (1) by means of a processor unit (3), the MPC algorithm (13) being a longitudinal dynamics model (14) of the ego vehicle (1 ), a predicted trajectory (20) of at least one vehicle (19) located in the environment of the ego vehicle (1) and a cost function (15) to be minimized,

- Determination of an input variable for the drive machine (8) taking into account the longitudinal dynamics model (14) of the ego vehicle (1) and the predicted trajectory (20) of the at least one vehicle (19) located in the vicinity of the ego vehicle (1) so that the cost function (15) is minimized.

11. Computer program product (11) for executing an autonomous driving function for an ego vehicle (1) taking into account at least one vehicle () located in the environment of the ego vehicle (1), the computer program product (11) when it is on a processor unit (3 ) is executed, instructs the processor unit (3),

- to execute an MPC algorithm (13) for model-based predictive control of an ego vehicle (1), the MPC algorithm (13) being a longitudinal dynamics model (14) of the ego vehicle (1), a predicted trajectory (20) contains at least one vehicle (19) located in the vicinity of the ego vehicle (1) and a cost function (15) to be minimized,

- by executing the MPC algorithm (13) taking into account the longitudinal dynamic model (14) of the ego vehicle (1) and the predicted trajectory () of the at least one vehicle () located in the vicinity of the ego vehicle (1) an input variable for the drive machine (8) to be determined so that the cost function (15) is minimized.