Trajectory planning system for a vehicle, and vehicle

Abstract

The invention relates to a trajectory planning system (2) for a vehicle (1) comprising a sensor device (3) for measuring a present actual steering angle and an associated present corresponding actual yaw rate value of an actual curvature of an actual trajectory, and a storage unit (7) in which a target trajectory with corresponding target curvatures is stored, wherein the trajectory planning system (2) is designed to determine selected suitable self-steering sections (4) and to determine in each case an instantaneous self-steering gradient (K EG ) on the basis of the measured actual yaw rate value and the measured actual steering angle which are present in the self-steering section (4) selected in each case, and wherein a parameter estimator is provided, which is designed to recursively estimate a current self-steering gradient (K EG,tgt ) on the basis of an associated instantaneous self-steering gradient (K EG ) as input. Furthermore, the invention relates to a vehicle and to a method.

Description

[0001] Trajectory planning system for a vehicle and vehicle

[0002] The invention relates to a trajectory planning system for a vehicle, comprising a sensor device for measuring a current actual steering angle and a respective current corresponding actual yaw rate value of an actual curvature of an actual trajectory, and a memory unit in which a target trajectory with corresponding target curvatures is stored. Furthermore, the invention relates to a vehicle and a method for operating such a trajectory planning system.

[0003] Trajectory control for a vehicle in the context of automated or assisted driving is subject to high demands on tracking accuracy, stability, and robustness. These properties must be ensured across the entire operating range of the trajectory control. The steering system, as the primary interface between trajectory control and the vehicle's lateral dynamics, plays a key role. The specified guidance path (target path trajectory) is followed by electronically controlled steering axles, by retrieving and implementing the necessary steering angle settings (target curvature) from a memory based on the current vehicle position.

[0004] However, this is not easily possible, since the transmission chain from the target curvature to the actual curvature is subject to many variable or unknown influences, such as road contact, vehicle load, tires, etc.

[0005] Due to the influences described above, it is a great challenge to select the steering angle specification in such a way that the desired actual curvature is achieved.

[0006] The resulting inaccuracies in the follow-up control of the target curvature, which is calculated as part of a trajectory control, represent a strong limitation of the overall system performance and make it difficult to achieve the high accuracy requirements.

[0007] EP 2977297 B1 discloses a method for determining a current steering angle of a motor vehicle, in which a steering wheel angle specified by a steering device of the motor vehicle is detected by a sensor device of the motor vehicle, and the steering angle set as a result of the specification by means of the steering device is determined by means of a control device of the motor vehicle on the basis of a predetermined steering angle characteristic curve, which describes a dependence of the steering angle on the steering wheel angle, wherein a delay function, which describes a time delay between the specification of the steering wheel angle and the setting of the steering angle, is determined and the steering angle is additionally determined by means of the control device as a function of the delay function.

[0008] It is an object of the invention to provide an improved trajectory planning system for a vehicle and a vehicle itself as well as a method for operating such a trajectory planning system.

[0009] This object is achieved by a trajectory planning system having the features of claim 1 as well as a vehicle having the features of claim 14 and a method having the features of claim 15.

[0010] The subclaims list further advantageous measures which can be suitably combined with one another to achieve further advantages.

[0011] The object is achieved by a trajectory planning system for a vehicle comprising a sensor device for measuring a current actual steering angle and a respective current corresponding actual yaw rate value of an actual curvature of an actual trajectory, and a memory unit in which a target trajectory with corresponding target curvatures is stored, wherein the trajectory planning system is designed to determine selected suitable self-steering sections and to determine a respective instantaneous self-steering gradient based on the measured actual yaw rate value and the measured actual steering angle, which lie in the respectively selected self-steering section, and wherein a parameter estimator is provided which is designed to recursively estimate a current self-steering gradient based on a respective instantaneous self-steering gradient as input.A self-steering gradient is a vehicle-specific coefficient that can be used to characterize the driving behavior of a motor vehicle and which, for example, indicates whether the steering angle must be increased or decreased in order to be able to drive the same curve radius with increasing lateral acceleration, i.e. when cornering faster.

[0012] The instantaneous self-steering gradient is the current self-steering gradient corresponding to the corresponding actual gir rate value and actual steering angle; the current self-steering gradient corresponds, for example, to a self-steering gradient calculated over a period of time.

[0013] By means of the trajectory planning system according to the invention, a quasi-stationary transfer behavior between the desired curvature and the actual curvature to be set can be approximated by estimating a current self-steering gradient.

[0014] A quasi-stationary process can be considered as a sequence of equilibrium states.

[0015] According to the invention, it was recognized that by estimating the quasi-stationary transfer behavior between the target curvature and the actual curvature, a reduction in the deviation in the follow-up control of the target curvature can be achieved. The trajectory planning system according to the invention can estimate the quasi-stationary transfer behavior between the target curvature and the actual curvature to be set based on the current self-steering gradient, despite the highly nonlinear variability and dependence of the quasi-stationary transfer behavior on numerous vehicle conditions, such as vehicle speed, lateral acceleration, longitudinal acceleration, and tire-road contact.

[0016] The current self-steering gradient is thus used to determine the quasi-stationary transfer behavior in a multidimensional space.

[0017] The trajectory planning system according to the invention allows a steering angle specification to be selected such that the desired actual curvature is based on the target steering angle. This avoids inaccuracies in the follow-up control of the target curvature, which is calculated by the trajectory control.

[0018] This improves the overall system performance and, for example, meets the high accuracy requirements for an autonomously operated vehicle.

[0019] The trajectory planning system according to the invention allows the multidimensional self-steering gradient surface of the current self-steering gradient to be determined recursively based on instantaneous self-steering gradients. The driving conditions on which the self-steering gradient depends can be integrated in a suitable form in the parameter estimator.

[0020] In further development, the trajectory planning system is designed to calculate the current self-steering gradient by the following difference: where K EG the current self-steering gradient, 5front: measured steering angle, l v the wheelbase is and ip mess is the measured actual yaw rate value.

[0021] This makes it easy to determine the current self-steering gradient.

[0022] Furthermore, in a further embodiment, the trajectory planning system can be designed to consider a suitable self-steering section as selected only if the vehicle speed is greater than a predetermined first limit value and the absolute value of the actual yaw rate value is greater than a predetermined second limit value and an absolute value of a steering angle change is less than a predetermined third limit value over a predetermined period of time. This means that such a self-steering section is only suitable at a vehicle speed that is greater than a predetermined first limit value; in particular, for example, a vehicle speed greater than 3 km / h was determined here. Furthermore, the absolute value of the actual yaw rate value in the selected self-steering section must be greater than a second limit value; in particular, for example, a second limit value of 5 degrees / s is suitable.In addition, the absolute value of the steering angle change, which can be calculated, for example, as a derivative of the measured actual steering angle, can preferably be less than a third limit value for a specified period of time. A third limit value of 3 degrees / s and a period of 2s are suitable, for example. Thus, in a preferred embodiment, the determination of the instantaneous self-steering gradient takes place only in suitable sections, the self-steering sections, in which the vehicle states do not change, for example, in a curve.

[0023] Furthermore, the parameter estimator is designed to determine a new current self-steering gradient only when a new instantaneous self-steering gradient is present. This saves computing capacity.

[0024] Furthermore, the previously determined current self-steering gradient can always be used to determine a steering angle input; real-time determination is therefore not absolutely necessary. This avoids excessive strain on the vehicle's computing capacity. This is particularly advantageous for autonomous / semi-autonomous vehicles.

[0025] Furthermore, in a further embodiment, the parameter estimator is designed to recursively estimate the current self-steering gradient based on the current self-steering gradient and the current vehicle parameters as input, wherein the vehicle parameters include at least the vehicle speed.

[0026] In a further embodiment, the parameter estimator can also be configured to recursively estimate the current self-steering gradient based on the current vehicle parameters and the current environmental data as input, wherein the vehicle parameters include at least the vehicle speed. These vehicle parameters / environmental data can be detected using a suitable sensor system, for example, a tachometer, or can be preset, for example, such as the vehicle type.

[0027] This allows relevant influencing factors such as vehicle speed, tire specifications, road surface conditions, longitudinal acceleration, lateral acceleration, tire-road contact, etc., as well as other environmental / vehicle data, to be taken into account. The parameter estimator thus determines the current self-steering gradient based on the current / previous results as well as the current environmental / vehicle data.

[0028] This means that the parameter estimator can determine the multidimensional self-steering gradient surface depending on the properties such as vehicle speed, longitudinal acceleration, lateral acceleration, and tire-road contact.

[0029] This allows the quasi-stationary transmission behavior of the vehicle to be determined depending on various influencing factors.

[0030] In a further development, the parameter estimator can be implemented as a recursive least squares estimator. In particular, the recursive least squares estimator can include a forgetting factor to forget older eigensteering gradients, i.e., older results.

[0031] This allows for a stable and rapid determination of the current self-steering gradient. Recursiveness allows online use with currently accumulating data while maintaining the same complexity in each recursion step. In particular, a forgetting factor can be introduced that forgets the results that are too old, i.e., self-steering gradients. This allows historical data to become less important for optimization, and the current data to be given greater weight. Alternatively, the parameter estimator can be implemented as an artificial neural network, for example, with nodes to be determined.

[0032] Furthermore, in a further embodiment, the parameter estimator has internal parameters to be optimized for determining the current self-steering gradient as output. These can be, for example, the nodes if the parameter estimator is designed as an artificial neural network. Preferably, the parameter estimator is designed to use the parameters to be optimized internally, for example, the nodes, as output for the future estimation of the current self-steering gradient, starting from an achieved accuracy value.

[0033] This means that from a certain quality onwards, the parameter estimator is only used as a known optimising function in space, in which the input data is entered and on the basis of which, for example, with the already optimised nodes, if the parameter estimator is an artificial neural network, the current self-steering gradient is determined as output.

[0034] Furthermore, in a further embodiment, the trajectory planning system can be designed to form a pre-steering angle based on the current self-steering gradient, so that the target curvature can be converted into the desired actual curvature in a timely manner.

[0035] Furthermore, the trajectory planning system can be designed to determine the pilot angle by to determine, where ßf f the pilot angle, l v the current wheelbase, v CR the vehicle speed, K EGitgt the current self-steering gradient and K CR tgt is the desired curvature.

[0036] Furthermore, in a further embodiment, a steering system with steering parameters for setting a steering angle is provided, which is designed to continuously adjust the steering parameters based on the pilot control angle.

[0037] A target steering angle, which is sent to the steering system as a request, can be composed of the pilot steering angle and a feedback steering angle. In a disturbance-free, quasi-stationary situation, the pilot steering angle, with a known self-steering gradient, preferentially leads directly to the desired vehicle behavior, and the feedback steering angle is zero.

[0038] Furthermore, the object is achieved by a vehicle with a trajectory planning system as described above, wherein the vehicle has a receiving unit for receiving a desired trajectory to be set with desired curvatures at predetermined positions from one or more preceding vehicles and / or a trajectory generation system for generating a desired trajectory based on at least navigation data and environmental data.

[0039] For example, it could be a tracking vehicle that follows a leading vehicle, or an autonomously operated vehicle that creates the target trajectories based on navigation data and environmental data recorded by a sensor system. The trajectory planning system according to the invention, which places only minimal demands on computing resources, does not place additional strain on these resources, particularly in an autonomously driving vehicle, which requires high computing resources.

[0040] In addition, the object is achieved by a method for operating a trajectory planning system for a vehicle comprising the steps:

[0041] - Measuring a current actual steering angle and a respective current corresponding actual yaw rate value of an actual curvature of an actual trajectory,

[0042] - Providing a target trajectory with corresponding target curvatures,

[0043] - Determination of selected suitable self-steering sections by the trajectory planning system,

[0044] - Determining a respective instantaneous self-steering gradient based on the measured actual yaw rate value and the measured actual steering angle, which lie in a respectively selected suitable self-steering section,

[0045] - Input of the respective instantaneous self-steering gradients into a parameter estimator and recursively estimating a current self-steering gradient based on the parameter estimator. This can, in particular, be the trajectory planning system described above. Furthermore, the advantages of the trajectory planning system can also be applied to the method.

[0046] Further features and advantages of the present invention will become apparent from the following description with reference to the accompanying figures. Variations may be devised by those skilled in the art without departing from the scope of the invention as defined by the following claims.

[0047] The figures show schematically:

[0048] FIG 1 : a vehicle with a trajectory planning system according to the invention,

[0049] FIG 2: a determination of self-steering sections,

[0050] FIG 3: the instantaneous self-steering gradient K EG and the current self-steering gradient K EGitgt ,

[0051] FIG 4: the instantaneous self-steering gradient K EG and the current self-steering gradient K EG tgt ,

[0052] FIG 5: an overview of the trajectory planning system according to the invention and its method.

[0053] FIG 1 shows a vehicle 1 with a trajectory planning system 2 according to the invention schematically.

[0054] If vehicle 1 is configured as an autonomously operated vehicle, a trajectory generation system (not shown) may be present for generating a target trajectory from navigation data and environmental data, in this case, for example, environmental data such as other road users and static objects. The target trajectory is stored with its target curvature in a memory unit 7.

[0055] Furthermore, the trajectory planning system 2 comprises a sensor device 3. The sensor device 3 is designed to detect the current actual yaw rate values ​​of an actual curvature and the corresponding current actual steering angle. For this purpose, the sensor device 3 can comprise rotation angle sensors and other sensors for detecting the actual yaw rate values ​​and the actual steering angle.

[0056] In addition, a steering system 5 is provided with steering control variables for transferring the desired curvatures to actual curvatures based on the steering control variables. The steering system 5 can have actuators, such as rotary actuators and sensors, etc., for this purpose.

[0057] Vehicle 1 can also be designed as a tracking vehicle. Such a vehicle receives the target yaw rate values ​​to be set from a preceding vehicle that has the same target trajectory. For this purpose, vehicle 1 can have a corresponding receiving unit (not shown), for example, for receiving the target curvature via radio (V2V connection).

[0058] Trajectory control, especially for adjusting the curvature of a vehicle 1 in the context of automated or assisted driving, is subject to high demands on tracking accuracy, stability, and robustness. These properties must be ensured across the entire operating range of the trajectory control. A key challenge for trajectory control is to follow the target curvature with high accuracy.

[0059] Within the framework of such a trajectory control, the target steering angle is calculated based on the target curvature using a kinematic vehicle model and the self-steering gradient as follows: where the target steering angle 3f ront , which is sent as a request to the steering system 5, from the pilot steering angle 8 ft - and the feedback steering angle 8 fb composed.

[0060] To determine the required pilot steering angle 8ff The trajectory planning system 2 determines from the measured actual yaw rate value ip mess and the measured steering angle 8 mess the current self-steering gradient: where l v is the current wheelbase, and where the curvature for quasi-stationary vehicle states is given by the yaw rate is trained.

[0061] The instantaneous self-steering gradient is only calculated if a suitable self-steering section 4 is present. A self-steering section 4 is always present if:

[0062] - the vehicle speed is greater than a specified first limit. The first limit is preferably greater than 3 km / h.

[0063] - the absolute value of the current actual yaw rate is greater than a predefined second limit. For example, the second limit can be set to 5 degrees / s.

[0064] - the absolute value of the steering angle change must be less than a specified third limit value for at least a specified period of time. The steering angle change can be determined as a derivative of the measured actual steering angle. The third limit value can be specified, for example, as 3 degrees / s and the period as 2 seconds.

[0065] Overall, this means that the vehicle states must not change beyond the specified time limit, for example in a curve.

[0066] If a self-steering section 4 is present, the equation can be used: with ^Pmess equal to the measured actual yaw rate value, 8 mess the measured steering angle, and l v the current wheelbase, the determination of instantaneous self-steering gradients K EG in the respective self-steering section 4.

[0067] FIG 2 shows the determination of self-steering sections 4, in which such a calculation is possible, as a diagram.

[0068] The first diagram above shows the current vehicle speed over time.

[0069] The second diagram below shows the measured current actual yaw rate values ​​over time.

[0070] The third diagram shows the measured current actual steering angle over time. The fourth diagram shows the self-steering sections 4 in which the vehicle speed is greater than a first, predefined limit value and the absolute value of the current actual yaw rate is greater than a second, predefined limit value, and the absolute value of the steering angle change is less than a third, predefined limit value for at least a predefined period of time.

[0071] The trajectory planning system 2 also includes a parameter estimator, which is specifically designed as a recursive least squares estimator 6. This estimator delivers fast and reliable results. The recursive nature of the estimator allows online use with currently available data while maintaining the same complexity in each recursion step. Alternatively, the parameter estimator can be designed as an artificial neural network, for example.

[0072] The Recursive Least Squares Estimator 6 receives the instantaneous self-steering gradients K as input data. EG of the current self-steering section 4. The Recursive Least Squares estimator assigns 6 internal parameters to be optimized to determine the current self-steering gradient K EGttgtas output. In addition, the Recursive Least Squares Estimator receives six vehicle parameters, specifically the vehicle speed and environmental data such as road conditions and, for example, the coefficient of friction, longitudinal acceleration, lateral acceleration, and tire-road contact, as input data.

[0073] Based on the vehicle parameters and the environmental data and the current instantaneous self-steering gradients K EG a current self-steering gradient K EGt tgt determined recursively by the Recursive Least Squares Estimator 6.

[0074] Such a current self-steering gradient can be determined online with reasonable computational and storage effort.

[0075] A forgetting factor can be introduced which forgets the results that are too old.

[0076] Likewise, the Recursive Least Squares Estimator 6 can be designed to use the parameters to be optimized internally for the future estimation of the current self-steering gradient K EG tgt to be used as an output.

[0077] This means that from a certain quality level onwards, the Recursive Least Squares Estimator 6 is only used as a known function in space in which the input data is entered, and on the basis of which the current self-steering gradient is determined as output.

[0078] The trajectory planning system 2 only determines a new current self-steering gradient K using the recursive least squares estimator 6 EG , tgt , if a newly determined instantaneous self-steering gradient K EG and a new current self-steering section 4 is present. Since the previous current self-steering gradient is used, K EG ,t gt is used, no real-time calculation with excessive computing capacity is necessary. This means that the vehicle's computing capacity is not excessively burdened. This is particularly advantageous for autonomous / semi-autonomous vehicles. The trajectory planning system 2 can then calculate the current self-steering gradient K determined in this way. EG tgt to achieve a pilot steering angle 8 f f, which here is the nominal curvature K CR ITGT into a target steering angle 8 front , transferred to determine: where l v the wheelbase and v CR indicates the vehicle speed.

[0079] The target steering angle 8f ront , which is sent as a request to the steering system 5, from the pilot steering angle and the feedback steering angle Sf b together:

[0080] In the disturbance-free quasi-stationary case, the pilot steering angle 8 ffwith a known self-steering gradient directly to the desired vehicle behavior and the feedback steering angle 8f b is zero.

[0081] The current self-steering gradient K EGitgt is then continuously used for the current driving condition for the target curvature control, so that the current self-steering gradient K estimated for the current driving condition is always used. EG-tgt is taken into account in the pre-control. This means that the current self-steering gradient K EGitgt is continuously used in the curvature control, ie in equation 3 and equation 1.

[0082] If the target curvature K CR tgt equal to zero, the pilot steering angle is 8 ff in equation 3 is also zero.

[0083] By means of the trajectory planning system 2 according to the invention and the current self-steering gradient K determined according to the invention EG tgtAn online estimate of the quasi-stationary transmission behavior can be achieved, thus reducing the deviation between the target and actual curvature. The trajectory planning system 2 according to the invention takes into account the strong nonlinearity of the quasi-stationary transmission behavior of a vehicle and the dependence of the quasi-stationary transmission behavior on the numerous vehicle conditions, such as vehicle speed, lateral acceleration, longitudinal acceleration, tire-road contact, etc.

[0084] The resulting inaccuracies in the follow-up control of the target curvature, which is calculated by the trajectory control, are corrected by the trajectory planning system 2, resulting in increased overall system performance and meeting the high accuracy requirements, particularly for autonomously operated or tracked vehicles.

[0085] FIG 3 shows the determined instantaneous self-steering gradient K EG and the current self-steering gradient K EG>tgt as a function of the vehicle speed. The first upper diagram shows the instantaneous self-steering gradient K EG (act ssg immediate) compared to the current self-steering gradient K EG>tgt (act ssg estimated) over time.

[0086] In addition, the middle second diagram shows the self-steering section 4 formed over time, in which the vehicle speed is greater than the first predetermined limit value and the absolute value of the current actual yaw rate value is greater than the second predetermined limit value and the absolute value of the steering angle change is less than the third predetermined limit value for at least a predetermined period of time.

[0087] The lower third graph shows the vehicle speed over time.

[0088] FIG 4 shows the determined instantaneous self-steering gradient K EG and the current self-steering gradient K EGitgt depending on the vehicle speed and the actual yaw rate and the target yaw rate. The first upper diagram shows the current self-steering gradient K EG (act ssg immediate) compared to the current self-steering gradient K E G,tgt ( act ssg estimated) over time.

[0089] Furthermore, the middle second diagram shows the actual yaw rate values ​​(act yaw rate) and the target yaw rate values ​​(tgt yaw rate) over time. Through the continuous use of the current self-steering gradient K EG tgt This results in a resulting agreement between the target yaw rate values ​​(act yaw rate) and the actual yaw rate values ​​(tgt yaw rate) and thus between the target and actual curvature in quasi-stationary driving conditions.

[0090] The lower third graph shows the vehicle speed over time.

[0091] FIG 5 shows an overview of the trajectory planning system 2 and its method.

[0092] In a first step, the target curvatures to be set are stored in a memory unit 7. A respective current actual yaw rate value is also recorded, along with a corresponding current actual steering angle.

[0093] The vehicle speed and the steering angle change are then determined.

[0094] In addition, self-steering sections 4 are determined in which the vehicle speed is greater than a first limit value, the absolute value of the current actual yaw rate value is greater than a second limit value and the absolute value of the steering angle change is less than a third limit value for at least a predetermined period of time.

[0095] The actual yaw rate values ​​in these self-steering sections 4 and a corresponding actual steering angle are used to determine the instantaneous self-steering gradients K EG used: with ^Pmess equal to the measured actual yaw rate value, 8 mess the measured steering angle, and l v the current wheelbase.

[0096] Subsequently, the instantaneous self-steering gradients K EG together with at least the vehicle speed and the coefficient of friction / road surface, longitudinal acceleration, lateral acceleration, tire-road contact as parameters in the Recursive Least Squares Estimator 6, which, based on at least some previous results, calculates the current self-steering gradient K EGitgt appreciates.

[0097] Based on the current self-steering gradient K EGitgt:a pilot control angle is determined, which is input into the steering system 5 for curvature control in order to compensate for a deviation in the transmission from the desired curvature to the actual curvature to be set.

[0098] List of reference symbols Vehicle Trajectory planning system Sensor device Self-steering section Steering system Recursive least squares estimator Storage unit

Claims

Patent claims 1. Trajectory planning system (2) for a vehicle (1) comprising a sensor device (3) for measuring a current actual steering angle and a respective current corresponding actual yaw rate value of an actual curvature of an actual trajectory, and a storage unit (7) in which a desired trajectory with corresponding desired curvatures is stored, characterized in that the trajectory planning system (2) is designed to determine selected suitable self-steering sections (4) and in each case a current self-steering gradient (K EG ) based on the measured actual yaw rate value and the measured actual steering angle, which lie in the respectively selected self-steering section (4), and wherein a parameter estimator is provided which is designed to use a respective instantaneous self-steering gradient ( K EG ) as input recursively a current self-steering gradient (K EG,tgt) to be appreciated.

2. Trajectory planning system (2) according to claim 1, characterized in that the trajectory planning system (2) is designed to form the instantaneous self-steering gradient ( / QJG) by the following difference: where (Sfront.mess) is the measured steering angle, (l v ) is the wheelbase and (jp mess ) is the measured actual yaw rate value.

3. Trajectory planning system (2) according to one of the preceding claims, characterized in that the trajectory planning system (2) is designed to consider a suitable self-steering section (4) as selected only if the vehicle speed is greater than a predetermined first limit value and the absolute value of the actual yaw rate value is greater than a predetermined second limit value and an absolute value of a steering angle change is less than a predetermined third limit value over a predetermined period of time.

4. Trajectory planning system (2) according to claim 3, characterized in that the parameter estimator is designed to calculate a new current self-steering gradient (K EGitgt ) can only be determined if a new instantaneous self-steering gradient ( K EG ) is present.

5. Trajectory planning system (2) according to one of the preceding claims, characterized in that the parameter estimator is designed to use the instantaneous self-steering gradient ( K EG ) and the current vehicle parameters as input, the current self-steering gradient (K EGitgt ) recursively, where the vehicle parameters include at least the vehicle speed.

6. Trajectory planning system (2) according to one of the preceding claims 1 to 4, characterized in that the parameter estimator is designed to use the instantaneous self-steering gradient ( K EG) and the current vehicle parameters as well as the current environmental data as input, the current self-steering gradient (K EG tgt ) recursively, where the vehicle parameters include at least the vehicle speed.

7. Trajectory planning system (2) according to claim 6, characterized in that the environmental data comprise at least the road condition and / or a detected friction coefficient.

8. Trajectory planning system (2) according to one of the preceding claims, characterized in that the parameter estimator is designed as a recursive least squares estimator (6).

9. Trajectory planning system (2) according to claim 8, characterized in that the recursive least squares estimator (6) has a forgetting factor for forgetting results of the recursive least squares estimator (6) from a predetermined point in time.

10. Trajectory planning system (2) according to one of the preceding claims, characterized in that the parameter estimator determines internal parameters to be determined for determining the current self-steering gradient as output, wherein the parameter estimator is designed to use the parameters to be determined internally for the future estimation of the current self-steering gradient (K EG tgt ) as an output.

11. Trajectory planning system (2) according to one of the preceding claims, characterized in that the trajectory planning system (2) is designed to use the current self-steering gradient (K EGitgt ) to form a pilot angle (Sff) so that the target curvature can be converted into the desired actual curvature in good time.

12. Trajectory planning system (2) according to claim 11, characterized in that the trajectory planning system (2) is designed to calculate the pilot control angle (6 ff ) by fyf = actan( / v Kcß.tpt) + K EG v GR K CRitgt to determine, where (5 / jf ) the pilot angle, / ( v ) the current wheelbase, (y CR ) the vehicle speed, (K EG tgt ) the current self-steering gradient and (K CRitgt ) is the target curvature.

13. Trajectory planning system (2) according to claim 11 or 12, characterized in that the trajectory planning system (2) has a steering system (5) with steering parameters for setting a steering angle, which is designed to determine the steering parameters based on the pilot control angle (8 ff ) continuously.

14. Vehicle (1) with a trajectory planning system (2) according to one of the preceding claims, wherein the vehicle (1) has a receiving unit for receiving a desired trajectory to be set with desired curvatures at predetermined positions by one or more preceding vehicles and / or a Trajectory generation system for generating a target trajectory based on at least navigation data and environmental data.

15. Method for operating a trajectory planning system (2) for a vehicle (1 ) comprising the steps: - Measuring a current actual steering angle and a respective current corresponding actual yaw rate value of an actual curvature of an actual trajectory, - Providing a target trajectory with corresponding target curvatures, - Determination of selected suitable self-steering sections (4) by the trajectory planning system (2), - Determination of a respective instantaneous self-steering gradient ( K EG ) based on the measured actual yaw rate value and the measured actual steering angle, which lie in a respectively selected suitable self-steering section (4), - Input of the respective current self-steering gradients ( K EG ) into a parameter estimator and recursively estimating a current self-steering gradient (K EGitgt ) using the parameter estimator.

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