METHOD FOR STEERING A VEHICLE

Abstract

A method for controlling a vehicle along a trajectory, in which the vehicle has a control device which plans the trajectory within a definable search space of the trajectory and can access actuators of the vehicle in order to control the vehicle , wherein at least one limit value is determined for at least one manipulated variable of an actuator and a search space of the manipulated variable is defined on the basis of the at least one limit value, wherein the search space is used to plan the trajectory.

Claims

1-15. (canceled)

16. A method for controlling a vehicle along a trajectory comprising accessing a plurality of actuators of the vehicle to control the vehicle with a control device; determining at least one limit value for at least one manipulated variable of one of the plurality of actuators; defining a search space of the variable on the basis of the at least one limit value; and planning the trajectory of the vehicle within the search space with the control device.

17. The method as claimed in claim 16, wherein the actuator is one of a steering system or an EPS motor of a steering system.

18. The method as claimed in claim 17, wherein at least one of a steering angle, a steering angle speed, a curvature of the road, and a motor torque of the EPS motor is the manipulated variable.

19. The method as claimed in claim 18, further comprising providing a maximum time progression of the manipulated variable to a left of the vehicle and a maximum time progression of the manipulated variable to a right of the vehicle as the limit value and are synchronized with a planner.

20. The method as claimed in claim 18, further comprising determining a difference between a force currently applied to the EPS motor and a maximum available force, and estimating a potential of the EPS motor using the difference.

21. The method as claimed in claim 16, further comprising determining non-linear frictional forces of the vehicle, and wherein determining the limit value is at least partially based on the non-linear frictional forces.

22. The method as claimed in claim 16, further comprising determining road forces on the vehicle, and wherein determining the limit value is at least partially based on the road forces.

23. The method as claimed in claim 22, wherein the determining the road forces further comprises using modeling based on a virtual spring.

24. The method as claimed in claim 23, wherein the determining the spring stiffness of the virtual spring further comprises using the vehicle speed and a motor torque.

25. The method as claimed in claim 24, wherein the determining the spring stiffness of the virtual spring further comprises using a least squares method.

26. The method as claimed in claim 25, wherein the determining the spring stiffness of the virtual spring further comprises using a recursive least squares method.

27. The method as claimed in claim 16, further comprising capturing the surroundings with at least one sensor.

28. The method as claimed in claim 27, wherein the capturing the surroundings with at least one sensor further comprises using at least one of a camera, a lidar sensor, a radar sensor, and an ultrasonic sensor.

29. The method as claimed in claim 27, wherein at least one of defining the search space and planning the for trajectory further comprises the using the captured surroundings.

30. The method as claimed in claim 16, wherein a computer program with program code for carrying out the method is executed on a computer.

31. The method as claimed in claim 16, wherein a computer-readable storage medium comprises instructions which cause the computer on which they are executed to carry out a method.

32. A control device for controlling a vehicle along a trajectory comprising accessing a plurality of actuators of the vehicle to control the vehicle with a control device; determining at least one limit value for at least one manipulated variable of one of the plurality of actuators; defining a search space of the variable on the basis of the at least one limit value; and planning the trajectory of the vehicle within the search space with the control device.

33. The control device as claimed in claim 32, wherein the actuator is one of a steering system or an EPS motor of a steering system.

34. The control device as claimed in claim 33, wherein the manipulated value is at least one of a steering angle, a steering angle speed, a curvature of the road, and a motor torque of the EPS motor.

35. The method as claimed in claim 32, wherein the limit value is at least partially based on the road forces.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present disclosure will become more fully understood from the detailed description and the accompanying drawings, wherein:

[0028] FIG. 1 shows a simplified schematic illustration of a vehicle in which a maximum manipulated variable is predicted;

[0029] FIG. 2 shows a simplified schematic illustration of a relationship between a virtual stiffness and the vehicle speed and maximum EPS torque;

[0030] FIG. 3 shows a simplified illustration of a search space of the steering angle that is limited using the method, and

[0031] FIG. 4 shows a simplified schematic illustration of a flowchart of the method.

DETAILED DESCRIPTION

[0032] Reference number 1 in FIG. 1 denotes a vehicle with various actuators (steering system 3, motor 4, brake 5), which vehicle has a control device 2 (ECU, Electronic Control Unit) which can be used to carry out trajectory planning with regard to the actuator dynamic or actuator dynamics. The trajectory may be calculated in this case using a trajectory planner, wherein a maximum manipulated variable of the respective actuator is predicted, for example in the transverse direction with respect to the search space limitation of the trajectory planner, and may be used for trajectory planning. In this case, the trajectory planner may be in the form of a hardware module of the control device 2 or a pure software module. The vehicle 1 also has sensors for capturing the surroundings (camera 6, lidar sensor 7 and radar sensor 8), the sensor data may be used to recognize the surroundings and objects, with the result that various assistance functions, for example emergency brake assistant (EBA, Electronic Brake Assist), adaptive cruise control (ACC, Automatic Cruise Control), lane keeping control or a lane keeping assistant (LKA, Lane Keep Assist) or the like, can be implemented. In a practical manner, the assistance functions can also be carried out via the control device 2 or a dedicated control device.

[0033] The method can generally be applied to all actuators found in generic vehicles, and thus also to all steering types used in generic means of transport. Accordingly, it can also be applied to vehicles with excess actuators, i.e. also with front and rear axle steering. The method is described by way of example below with reference to a vehicle with front axle steering, wherein the steering angle δ is used as the manipulated variable, i.e. the current steering angle δ can first be measured as a starting value. It can be assumed here that the trajectory planner approach used can process a maximum steering angle δ_max. Alternatively or additionally, however, other manipulated variables, for example steering angle speed or curvature, would also be possible. The balance of forces in the steering system can be described mathematically as follows”

[00001]$\text{m\_EPS}\cdot \text{a}\text{=}\text{F\_Mot}\text{-}\text{d}\cdot \text{v}\text{-}\text{F\_Friction}\text{-}\text{F\_Load}$

[0034] . In this case, m_EPS is the accumulated mass of the steering system, a is the acceleration of the rack, F_Mot is the force provided by the EPS motor, d is the damping of the EPS, v is the speed of the rack, F_Friction is the non-linear friction of the EPS and F_Load is the load applied to the EPS, consisting of the road forces F_Str and the forces coming from the steering wheel. Such road forces are exerted on the vehicle wheels, for example when driving on a road. To dissipate the energy transfer of these road forces, spring or damper assemblies are generally used in the vehicle suspension system.

[0035] A maximum manipulated variable may be determined on the basis of the available actuator power without any interference. Interference variables such as the driver’s manual torque, which is included in the forces from the steering wheel, can be ignored in this case. Externally acting interference variables, for example side wind, are ignored, since such interference can be compensated for by the control, for example. The remaining road forces F_Str, on the other hand, and thus also F_Load, cannot be easily ignored, since they have a significant influence on the maximum steering angle and are therefore not regarded as interference, since they always occur. Looking at the road forces F_Str at vehicle level in a single-track model shows that they depend on the current steering angle, the vehicle speed and the road coefficient of friction. However, the influence of the road coefficient of friction can be ignored here, with the result that only scenarios with a high coefficient of friction are considered. This is possible because, although a reduced coefficient of friction leads to a higher maximum steering angle, this does not necessarily lead to a higher navigable curvature and therefore not to a navigable trajectory either. Accordingly, the dependence of the road forces F_Str on the steering angle and the vehicle speed remains. Due to the steering angle dependence for modeling the road forces F_Str, a virtual spring with the vehicle-speed-dependent spring stiffness c is used. The spring stiffness c is also dependent on the maximum set EPS torque M_Mot_max, as illustrated in FIG. 2. This results from non-linearities, such as the translation between rack travel and steering angle at the wheel or the steering-angle-dependent follow-on running.

[0036] The following thus results from equation (1):

[00002]$\begin{array}{l}\text{m\_EPS}\cdot \text{a}\text{=}\text{F\_Mot}\text{-}\text{d}\cdot \text{v}\text{-}\\ \text{F\_Friction}\text{-}\text{c}\left(\text{v\_veh,}\text{M\_Mot\_max}\right)\cdot \text{x}\text{.}\end{array}$

[0037] In this case, v_veh is the vehicle speed and x is the rack position which can be converted into a steering angle δ using a transmission ratio i. The term c (v_veh, M_Mot_max) is composed in this case of a purely speed-dependent part c1 (v-veh) and a speed-dependent and torque-dependent part c2 (v_veh, M_Mot_max):

[00003]$\text{c}\left(\text{v\_veh,}\text{M\_Mot\_max}\right)=\text{c1}\left(\text{v\_veh}\right)\text{c2}\left(\text{v\_veh,}\text{M\_Mot\_max}\right).$

[0038] This can be used to derive a lookup table for the spring stiffness c (according to FIG. 2) which may be derived, for example, from step excitations on the steering system at different speeds. For example, the spring stiffness c can be estimated and adapted on the basis of speed using an RLS (recursive least squares) algorithm. In this case, only c1 (v_veh) has to be adapted, since the term c2 (v_veh, M_Mot_max) reflects structural, unchanging relationships. As a result, a maximum settable steering angle δ to the left and to the right should be predicted in this case, in which case the maximum force that can still be set by the EPS motor is selected for F_Mot and applied as a step, which is filtered with a motor time constant T_Mot, according to

[00004]$\text{F\_Mot}\text{=}\text{1}/\left(\text{T\_Mot}\cdot \text{s}\text{+}\text{1}\right)\cdot \text{F\_Mot\_max}\text{.}$

[0039] In this case, F_Mot_max is the difference between the currently applied force and the maximum available force. The maximum available force can be determined using the power of the EPS or is made available to the prediction by the EPS as an input signal. This makes it possible, for example, to represent different degradation levels of the EPS motor if only some of the power is available. The non-linear friction F_Friction corresponds to the static friction in the system and can also be taken into account via a so-called dead zone in the motor force F_Mot, since only one constant direction of movement is ever considered and the hysteresis effects of static friction are therefore not relevant. The result is therefore:

[00005]$\begin{array}{ll}\text{F\_Mot\_F}\text{=}\text{0}\text{if}\hfill & \text{I}\text{F\_Mot}\text{I}\text{<}\text{F\_Haft}\hfill \\ \text{F\_Mot}\text{-}\text{F\_Haft if F\_Mot}\text{>}\text{F\_Haft}\hfill & \hfill \\ \text{F\_Mot}\text{+}\text{F\_Haft}\hfill & \text{if}\text{-F\_Mot}\text{<}\text{F\_Haft}\text{.}\hfill \end{array}$

[0040] Here, F_Haft is the amplitude of the adhesive force. Furthermore, equation (2) results in equation (6),

[00006]$\text{m\_EPS}\cdot \text{a}\text{=}\text{F\_Mot\_Fric}\text{-}\text{d}\cdot \text{v}\text{-}\text{c}\left(\text{v\_veh,}\text{M\_Mot\_max}\right)\cdot \text{x,}$

[0041] which corresponds to a second-order delay element. The damping d can be chosen to be constant in this case.

[0042] A maximum rack position or a maximum steering angle δ_max can thus be predicted by switching according to the acceleration in equation (6) and double integration. The resulting vectors for the maximum steering angle to the right (δ_max_re) and to the left (δ_max_li) over time t can then be sampled in order to reduce the amount of data to be sent and forwarded to the planner as a feedback signal. The two vectors indicate the upper and lower limits of the search space of the manipulated variable in which the trajectory planner can search for an optimal solution, as shown in FIG. 3 using the limited search space 9 illustrated using dotted lines between the two vectors δ_max_re, δ_max_li.

[0043] In the exemplary embodiment of a method sequence according to FIG. 4, a predicted maximum time progression of the steering angle to the left and to the right is output for a vehicle with front axle steering. In this case, the steering angle δ is first determined or measured as the starting value (steering angle determination 12) and the spring stiffness c is determined, for example, using the described look-up table (cf. FIG. 2) (determination of the spring stiffness 10). In addition, the engine property and characteristics 11a (on the left) and 11b (on the right) are determined, inter alia, based on the currently applied motor torque (motor torque detection), i.e. the currently applied motor torque M_Mot. The engine property and characteristics can be different to the left and to the right, for example due to asymmetries in the steering or artificially introduced asymmetries, e.g. in the course of an LDP (Lane Departure Protection) function, in which the steering is more limited in the direction of the closer lane boundary. The progression of the maximum manipulated variable can then be predicted on the basis of the spring stiffness and engine property and characteristics, in this case the maximum steering angle to the left (prediction on the left 13) and the maximum steering angle to the right (prediction on the right 14). The predicted steering angles are then forwarded to planner 15. If the vehicle also has rear axle steering, it is possible to determine the rear axle steering angle in the same way as the front axle steering angle, i.e. there are two further vectors on the rear axle for the rear axle steering, one for the maximum steering angle to the left and one for the maximum steering angle to the right. Accordingly, the method sequence in FIG. 4 describes, on the one hand, the method sequence for determining the maximum steering angles for the front axle or the rear axle. Alternatively or additionally, the curvature to be navigated can be used as a manipulated variable, regardless of whether or not there is rear axle steering. The advantage of this configuration is that only two vectors arise (maximum curvature to the left and to the right) even when there is rear axle steering. However, a vehicle model should then again be provided for determination.

[0044] In a practical way, the predicted manipulated variable limit can also be used for anti-wind-up concepts in the controller. Furthermore, the lookup table for the stiffness and the relationship according to FIG. 2 can also be used estimate a load of the vehicle.