Method, data processing system, computer program and storage medium for determining vehicle trajectory using boundary cost function
By using a continuous and non-constant boundary cost function and a combination of multiple cost functions, the problems of insufficient user experience and motion control flexibility in vehicle trajectory calculation are solved, the vehicle trajectory is optimized and obstacle avoidance is achieved, and the user experience and control flexibility are improved.
Patent Information
- Application Number
- CN202011143313.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Priority Date
- 2019-10-24
- Filing Date
- 2020-10-23
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2040-10-23
AI Technical Summary
Existing technologies struggle to simultaneously optimize user experience and achieve flexible configuration of motion control when calculating vehicle trajectories, especially when dealing with boundary conditions of static and dynamic objects.
A continuous and non-constant boundary cost function is adopted, combined with comfort, driving route and target speed cost functions, the vehicle trajectory is calculated by solving the optimization problem, the basic cost function is configured to avoid collision and maintain a safe distance, and motion control is implemented through a data processing system.
It improves the user experience of vehicle trajectory calculation, enables simple and flexible configuration of motion control, can effectively avoid collisions with static and dynamic objects, and optimize the vehicle's driving path.
Smart Images
Figure CN112706761B_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a method for determining a vehicle trajectory by solving an optimization problem, wherein the overall cost function of the optimization problem depends on a boundary cost function having at least one continuous and non-constant section. Background Art
[0002] In the coming years, vehicles will increasingly feature assisted or autonomous driving functions. This will ease the burden on the driver and reduce the risk of accidents. Examples of already available functions include distance-controlled cruise control and lane-keeping assistance. These functions require the calculation of an environmental model based on sensor data (such as camera data, radar data, lidar data, and / or ultrasonic data). Based on this environmental model, the vehicle trajectory can be determined. This can be achieved by solving an optimization problem.
[0003] DE 10 2015 221 817 A1 describes a method for decentralized coordination of driving maneuvers of at least two motor vehicles. A planned trajectory and a desired trajectory are transmitted from a first motor vehicle to a second motor vehicle. Based on the planned trajectory and the desired trajectory of the first motor vehicle, the planned trajectory of the second motor vehicle is adapted, wherein an overall cost function is optimized, which includes at least the cost functions of the first and second motor vehicles. Summary of the Invention
[0004] Within the scope of the present invention, a method for calculating the trajectory of an ego vehicle is developed. In the following, the term "ego vehicle" refers to the vehicle whose behavior is to be influenced by the method. From the perspective of the ego vehicle, all other vehicles are strangers.
[0005] The object of the present invention is to calculate a better vehicle trajectory. In particular, the user experience when controlling the ego vehicle according to the calculated trajectory should be improved.
[0006] Furthermore, the task is to achieve a simple and flexible configuration of the motion control of the ego vehicle.
[0007] A first aspect of the invention relates to a computer-implemented method for calculating a trajectory of a mobile platform, wherein the method comprises solving an optimization problem, wherein the total cost function of the optimization problem depends on a first boundary cost function, wherein the first boundary cost function is a function of the position of the mobile platform, the function having a continuous and non-constant segment.
[0008] The mobile platform is preferably a vehicle, i.e., a mobile vehicle for transporting people or objects. However, the mobile platform may also be an automated or partially automated mobile robot that is not necessarily used for transporting people or objects. Although the present invention is generally applicable to mobile platforms, for the sake of clarity, the present invention is described below with respect to a vehicle.
[0009] The first boundary cost function may correspond to a first object that leads to the boundary of the permitted driving range. The first boundary cost function may depend on the position of the first object. The first object may be a static or dynamic object. A static first object may be, for example, a lane boundary line. A dynamic first object may be, for example, an unfamiliar vehicle traveling ahead. The first object may be assigned multiple positions. The multiple positions may correspond, for example, to the direction of a lane boundary line or the periphery of an unfamiliar vehicle. The first object may also have a left lane boundary line and a right lane boundary line, and the first object may also be assigned the positions of the left lane boundary line and the right lane boundary line. The first boundary cost function may also depend on multiple positions of the first object.
[0010] The total cost of the optimization problem can be determined by combining multiple boundary cost functions. Furthermore, the total cost function of the optimization problem can be determined by other cost functions, such as a comfort cost function, a driving route cost function, and / or a target speed cost function. For example, the total cost function of the optimization problem can be the sum of the comfort cost function, the driving route cost function, the target speed cost function, and one or more boundary cost functions.
[0011] The first boundary cost function is a continuous and non-constant function of the ego vehicle's position, at least in one section. Preferably, the first boundary cost function is a continuous function of the ego vehicle's position as a whole. In particular, when the ego vehicle's position reaches the boundary of the permitted driving range corresponding to the first object, the first boundary cost function does not suddenly rise from a minimum cost value to a maximum cost value. Alternatively, the first boundary cost function can rise continuously as the ego vehicle approaches the boundary of the permitted driving range. In particular, the boundary cost function adopts multiple different intermediate values between the minimum cost value and the maximum cost value as the ego vehicle approaches the boundary of the permitted driving range. This allows the ego vehicle's approach to the boundary of the permitted driving range, which depends on the position of the first object, to be charged with a cost at an early stage. Therefore, the closer the ego vehicle approaches the boundary of the permitted driving range, the more the ego vehicle's approach to the boundary of the permitted driving range corresponding to the first object can be countered with a continuously higher cost.
[0012] The optimization variables of the optimization problem can include, in particular, the trajectory of the ego vehicle, wherein the trajectory includes the time-dependent position of the ego vehicle.
[0013] Furthermore, the optimization problem can have one or more conditions. For example, the optimization problem can have the following as a condition: the acceleration and / or jerk do not exceed a corresponding maximum value in terms of quantity. The maximum value can depend on sensor data, which can, for example, indicate a dry, wet, or icy roadway. Furthermore, the optimization problem can have a condition that prevents exceeding the boundaries of the permitted driving range, where the boundaries can correspond to static and / or dynamic objects.
[0014] According to one specific embodiment, the method further includes determining a first boundary cost function, wherein the determination of the first boundary cost function is based on the classification of the first object as a static or dynamic object, the position of the first object, and a basic cost function from a set of basic cost functions. The set of basic cost functions includes a first basic cost function for avoiding collisions with static objects, a second basic cost function for maintaining a safe distance to static objects, a third basic cost function for maintaining a preferred static driving range, a fourth basic cost function for avoiding collisions with dynamic objects, a fifth basic cost function for maintaining a safe distance to dynamic objects, and / or a sixth basic cost function for maintaining a preferred dynamic driving range.
[0015] If the first object is a static object, the first boundary cost function may be based on the first, second or third basic cost function. On the other hand, if the first object is a dynamic object, the first boundary cost function may be based on the fourth, fifth or sixth basic cost function.
[0016] The first boundary cost function may depend on one or more positions of the first object. The first boundary cost function may also depend on the positions of other objects. The first boundary cost function may particularly depend on the positions of multiple static objects. Alternatively, the first boundary cost function may depend on the positions of multiple dynamic objects.
[0017] A particular advantage of this method may be that a basic cost function of the set of basic cost functions is determined only once but can then be applied to a plurality of static and dynamic objects.
[0018] The basic cost function may have a first, second, and third range. For example, in the first range, the basic cost function may be constantly equal to the minimum cost value. The minimum cost value may be zero. The second range of the basic cost function may immediately follow the first range. Furthermore, the second range of the basic cost function may reach the boundary of the permitted driving range. In the second range, the basic cost function preferably rises continuously from the minimum cost value. In particular, the basic cost function may rise to a maximum cost value in the second range. The basic cost function may have different minimum and maximum cost values. When referring to the slope of the basic cost function, this refers to a measure of the slope of the basic cost function in the second range. For example, the slope of the basic cost function may be the average or maximum slope of the basic cost function in the second range. The continuous and non-constant section of the first boundary cost function may correspond to the second range or a portion of the second range of the basic cost function. The basic cost function may also have a third range. The third range of the basic cost function may correspond to the boundary beyond the permitted driving range. In the third range, the boundary cost function may be constantly equal to the maximum cost value. The maximum cost value may correspond to a maximum value that can be digitally represented by the data processing system or a fraction of this maximum value. Preferably, the basic cost function is a continuous function as a whole. Furthermore, the basic cost function is preferably a convex function in its first and second regions.
[0019] The first basic cost function can be implemented to avoid collisions with static objects. A boundary cost function based on the first basic cost function can be implemented, for example, to limit the permitted driving range at the location of a guardrail. The first basic cost function can have a high slope.
[0020] The second basic cost function may be implemented to maintain a safe distance from static objects (eg, guardrails).The second basic cost function may have a moderate slope.
[0021] The third basic cost function can be implemented to maintain a preferred static driving range. The ego vehicle should only leave the preferred static driving range for comfort reasons. Within the preferred static driving range, the third basic cost function can be constantly equal to zero. The greater the distance from the preferred static driving range, the larger the value of the third basic cost function can be. The third basic cost function preferably has a low slope.
[0022] The fourth basic cost function can be implemented to avoid collisions with dynamic objects. A boundary cost function based on the fourth basic cost function can, for example, be implemented to limit the permitted driving range based on the position of the rear bumper of an unfamiliar vehicle traveling ahead. The fourth basic cost function can have a high slope.
[0023] The fifth basic cost function may be implemented to maintain a safe distance from dynamic objects (eg, unfamiliar vehicles). The fifth basic cost function may have a medium slope.
[0024] The sixth basic cost function can be implemented to maintain a preferred dynamic driving range. The ego vehicle should only leave the preferred dynamic driving range for comfort reasons. Within the preferred dynamic driving range, the sixth basic cost function can be constantly equal to zero. The greater the distance from the preferred dynamic driving range, the larger the value of the sixth basic cost function can be. The sixth basic cost function preferably has a low slope.
[0025] If the first object is a static object and the first boundary cost function is based on the i-th basic cost function (1≤i≤3), the first boundary cost function can be expressed as follows, for example:
[0026]
[0027] Among them, t0 is the starting time of the ego vehicle trajectory, t end is the end moment of the ego vehicle trajectory, f i,static is the instantaneous boundary cost function based on the i-th basic cost function, k i,static (t) is a possible time-dependent weight, x(t) = (x(t), y(t)) T is the time-dependent position of the ego vehicle in the global coordinate system, and y i,left (x i,left ) and y i,right (x i,right ) represents the left and right limits of the permitted driving range, which are dependent on the position of the first object and are also specified in the global coordinate system. i,left (x i,left ) and y i,right (x i,right ) may depend on one or more positions of the first object. In addition, the boundary y i,left (x i,left ) and y i,right (x i,right ) can depend on the position of one or more other static objects.
[0028] If the first object is a dynamic object and the first boundary cost function is based on the i-th basic cost function (4≤i≤6), the first boundary cost function can be expressed as follows, for example:
[0029]
[0030] Among them, f i,dynamic is the instantaneous boundary cost function based on the i-th basic cost function, ki,dynamic(t) are weights that may be time-dependent, and x 1,i (t)=(x 1,i (t),y 1,i (t)) T represents the limit of the permitted driving range depending on the position of the first dynamic object. 1,i (t) may depend on one or more positions of the first object. In addition, f i,dynamic Can depend on multiple boundaries x j,i (t)(1≤j≤J)(not considered in the above formula), where the jth boundary may depend on the position of the jth dynamic object, and J is the number of dynamic objects.
[0031] According to another embodiment of the method, the total cost function of the optimization problem further depends on a second boundary cost function, wherein the second boundary cost function depends on the position of the first object, wherein the first and second boundary cost functions are based on different basic cost functions from the set of basic cost functions.
[0032] Thus, the first and second boundary cost functions may depend on the position of the first object, wherein the first and second boundary cost functions are based on different elementary cost functions from the set of elementary cost functions. The total cost function of the optimization problem may also depend on three boundary cost functions, which in turn depend on the position of the first object, wherein the three boundary cost functions are each based on a different elementary cost function. The three boundary cost functions may in particular be based on the first, second, and third elementary cost functions. Alternatively, the three boundary cost functions may be based on a fourth, fifth, and sixth elementary cost function.
[0033] According to another embodiment of the method, the first basic cost function has a higher slope than the second basic cost function, the second basic cost function has a higher slope than the third basic cost function, the fourth basic cost function has a higher slope than the fifth basic cost function, and / or the fifth basic cost function has a higher slope than the sixth basic cost function.
[0034] According to another embodiment, the basic cost function on which the first boundary cost function is based is continuous and non-constant in at least one section.
[0035] Preferably, all basic cost functions from the basic cost function set are continuous functions in their entirety, thereby ensuring that the corresponding boundary cost function is a continuous function of the position of the ego vehicle.
[0036] The continuous and non-constant section of the basic cost function may be the aforementioned second region of the basic cost function, in which the basic cost function rises from a minimum cost value to a maximum cost value. The continuous and non-constant section of the basic cost function may also be part of the second region of the basic cost function.
[0037] According to another embodiment, the method comprises the following steps: receiving user input and configuring a specific basic cost function from the set of basic cost functions based on the user input. Here, the configuration of the specific basic cost function includes configuring an offset parameter and / or configuring a slope of the basic cost function.
[0038] Here, the specific basic cost function is any basic cost function from the set of basic cost functions. For example, the computer-implemented method can be implemented to require the user to configure the specific basic cost function in the initial stage. Alternatively, the computer-implemented method can be implemented to enable the user to select the configuration of the specific basic cost function.
[0039] This computer-implemented method can be implemented to directly configure the function value of a specific basic cost function. Alternatively, the computer-implemented method can be implemented to configure one or more parameters of the specific basic cost function. In particular, the method can be implemented to configure an offset parameter of the specific basic cost function. For example, the offset parameter can be the distance between an object that defines the boundary of the driving range and the point at which the first and second regions of the specific basic cost function intersect. Alternatively, the offset parameter can be the distance between an object that defines the boundary of the driving range and the point at which the second and third regions of the specific basic cost function intersect.
[0040] According to another embodiment of the method, the total cost function of the optimization problem further depends on the comfort cost function, the driving route cost function and / or the target speed cost function.
[0041] A comfort cost function can be implemented to prevent high acceleration and / or high jerk. Specifically, when the ego vehicle's speed is constant, the comfort cost function can achieve a minimum cost value. The comfort cost function can represent the sum of an acceleration cost function and a jerk cost function. The greater the ego vehicle's acceleration, the higher the value of the acceleration cost function. Similarly, the greater the ego vehicle's jerk, the higher the value of the jerk cost function.
[0042] The driving path cost function can be implemented to provide an impetus for the ego vehicle to advance along the arc length of the lane. The further the ego vehicle's trajectory extends along the arc length of the lane, the lower the driving path cost function can be. On the other hand, when the ego vehicle is not moving, the driving path cost function can be higher.
[0043] The computer-implemented method can be implemented to allow a user to input a target speed via a human-machine interface. If the speed of the ego vehicle is equal to the target speed, the target speed cost function can assume a value of zero. The further the speed of the ego vehicle deviates from the target speed, the target speed cost function can assume a higher value.
[0044] A second aspect of the invention relates to a data processing system comprising means for carrying out the method according to the invention.
[0045] The data processing system is, for example, a control device. It has at least one processor and a memory unit. The processor can be, for example, a microprocessor, a microcontroller, or a dedicated processor. The memory unit preferably comprises a non-volatile memory unit on which a computer program is stored, which is written to implement the method according to the present invention. The data processing system can have a number of other components, such as a communication unit, via which it can communicate with a server, so that parts of the computer program according to the present invention can be stored and / or executed on the server.
[0046] Therefore, a third aspect of the present invention relates to a computer program, wherein the computer program comprises instructions which, when the program is executed by a data processing system, cause the data processing system to carry out the method according to the invention.
[0047] According to one specific embodiment, the computer program comprises a configuration module and a motion control module, wherein the configuration module is designed to configure a basic cost function based on user input, wherein the motion control module is designed to solve an optimization problem.
[0048] Since the basic cost functions can be used for a plurality of different objects, a simple and flexible configuration of the motion control can be achieved by means of these functions.Thus, one advantage can be that a simple and flexible interface for the configuration of the motion control is provided.
[0049] A fourth aspect of the present invention relates to a computer-readable storage medium on which a computer program according to the present invention is stored.
[0050] In order to travel the calculated trajectory, in particular a steering system, a drive system and / or a braking system of the host vehicle may be activated. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] The following further illustrates other details in conjunction with the description of the preferred embodiments of the present invention based on the accompanying drawings.
[0052] The accompanying drawings show:
[0053] Figure 1 An exemplary traffic situation is shown with visualization of a boundary cost function;
[0054] Figure 2 An exemplary basic cost function is shown. DETAILED DESCRIPTION
[0055] Figure 1 An exemplary traffic situation 1 is shown with a diagram of the boundary cost function, which is of particular importance here. A two-lane roadway is shown with lane boundaries 10a and 10b. A dashed center line 11 separates the two lanes. Own vehicle 2 moves from the right lane to the left lane, for example, to overtake stranger vehicle 3a traveling ahead. Stranger vehicles 3b and 3c are also shown traveling in the left lane.
[0056] The first boundary cost function is shown by the dot-dash lines 4a and 4b. The first boundary cost function can be based on a first basic cost function, wherein the first basic cost function is implemented for avoiding collisions with static objects. Figure 1 In the example, the left and right guardrails of the roadway correspond to static objects. The dashed lines 4a and 4b also depict the path of the guardrails. The first boundary cost function can be constant at a maximum cost value to the right of curve 4a and to the left of curve 4b. The first boundary cost function can decrease from the boundaries of the permitted driving range, indicated by lines 4a and 4b, toward the center of the roadway. The first boundary cost function preferably decreases rapidly to a minimum cost value, which can be zero.
[0057] The second boundary cost function is shown by dashed lines 5a and 5b. The second boundary cost function can be based on a second basic cost function, wherein the second basic cost function is implemented to maintain a safe distance to static objects. The left and right guardrails of the roadway can in turn correspond to static objects. Figure 1 In the example, dashed lines 5a and 5b for maintaining a safe distance from the guardrail indicate a narrower permitted driving range than dashed lines 4a and 4b for avoiding collisions with the guardrail. The second boundary cost function can be constant at a maximum cost value to the right of dashed curve 5a and to the left of dashed curve 5b. The second boundary cost function can descend from the boundaries 5a and 5b of the permitted driving range toward the center of the lane. The second boundary cost function preferably descends at a moderate slope to a minimum cost value, wherein the minimum cost value can be zero.
[0058] The third boundary cost function is shown by dotted lines 6a and 6b. The third boundary cost function can be based on a third basic cost function, wherein the third basic cost function is implemented to maintain a preferred static driving range. Since the ego vehicle 2 is preparing to change to the left lane, the preferred static driving range is located in the left lane. The preferred static driving range can depend on the position of the lane centerline and the position of the left lane boundary line. In this case, the preferred static driving range can have a certain distance from the lane centerline and the left lane boundary line. The third boundary cost function can be constantly equal to a minimum cost value between dotted lines 6a and 6b, and this minimum cost value can be zero. In addition, to the right of the dotted curve 6a and to the left of the dotted curve 6b, the third boundary cost function can increase with increasing distance from the left lane. In particular, the third boundary cost function can increase monotonically in these ranges, wherein the increase preferably has a low slope.
[0059] The fourth boundary cost function is indicated by reference numerals 7a, 7b and 7c. The fourth boundary cost function can be based on a fourth basic cost function, wherein the fourth basic cost function is implemented for avoiding collisions with dynamic objects. Figure 1 The dynamic objects in the image are particularly unfamiliar vehicles 3a, 3b, and 3c. Markers 7a, 7b, and 7c can be located at the front and rear bumpers of unfamiliar vehicles 3a, 3b, and 3c. The fourth boundary cost function can be constantly equal to a maximum cost value at the locations of the unfamiliar vehicles, and the fourth boundary cost function can decrease from the maximum cost value at markers 7a, 7b, and 7c, with the fourth boundary cost function decreasing away from the respective unfamiliar vehicle. The fourth boundary cost function preferably decreases rapidly to a minimum cost value, which can be zero.
[0060] The fifth boundary cost function is shown by reference numerals 8a, 8b and 8c. The fifth boundary cost function can be based on a fifth basic cost function, wherein the fifth basic cost function is implemented for maintaining a safe distance to dynamic objects. Figure 1 In the example, stranger vehicles 3a, 3b, and 3c represent dynamic objects. Markers 8a, 8b, and 8c for maintaining a safe distance from vehicles in the environment of ego vehicle 2 have a smaller permitted driving range than markers 7a, 7b, and 7c for avoiding collisions with stranger vehicles. The fifth boundary cost function can decrease from a maximum cost value at markers 8a, 8b, and 8c, with the decrease occurring primarily in the direction of the ego vehicle. The fifth boundary cost function preferably decreases with a moderate slope to a minimum cost value, which can be zero.
[0061] The sixth boundary cost function is shown by the reference numerals 9a and 9b. The sixth boundary cost function can be based on a sixth basic cost function, wherein the sixth basic cost function is implemented to maintain a preferred dynamic driving range. Figure 1 In the example, the preferred dynamic driving range is in the left lane between markings 9a and 9b. The preferred dynamic driving range may depend on the positions of stranger vehicles 3b and 3c. The sixth boundary cost function may be constant at a minimum cost value in the left lane between markings 9a and 9b, which may be zero. Furthermore, the sixth boundary cost function may rise in the left lane from markings 9a and 9b in the direction of the respective stranger vehicle and outward beyond these markings. In particular, the sixth boundary cost function may rise monotonically within these ranges, with the rise preferably having a low slope.
[0062] The six boundary cost functions described above are preferably continuous, so that a small change in the position of the ego vehicle 2 results in a small change in the total cost function.
[0063] Figure 2 The basic cost functions are shown as examples. The first basic cost function is shown by the dot-dash curve 20. In a first region to the left of the marker 21, the first basic cost function 20 is constantly equal to a minimum cost value, which can be zero. In a second region between the markers 21 and 22, the first basic cost function 20 rises to a maximum cost value 23. Here, the first basic cost function has a high slope. In a third region to the right of the marker 22, the first basic cost function is constantly equal to the maximum cost value 23. Figure 2 The mark 22 in the Figure 1 In other words, it can be determined that Figure 1 The first boundary cost function is obtained by aligning the first basic cost function so that the mark 22 is located on the lines 4a and 4b. Figure 1 In FIG, the dot-dash lines 4a and 4b illustrate those locations where the first boundary cost function drops from the maximum cost value 23.
[0064] also, Figure 2 The second basic cost function is shown by a dashed curve 30. In a first region to the left of the marker 31, the second basic cost function 30 is constantly equal to a minimum cost value, which can be zero. In a second region between the markers 31 and 32, the second basic cost function 30 rises to a maximum cost value 33. Here, the second basic cost function has a moderate slope. In a third region to the right of the marker 32, the second basic cost function is constantly equal to the maximum cost value 33. Figure 2 The mark 32 in the Figure 1 In other words, it can be determined that Figure 1The second boundary cost function is obtained by aligning the second basic cost function so that the mark 32 is located on the lines 5a and 5b. Figure 1 In FIG, dashed lines 5a and 5b illustrate those positions at which the second boundary cost function drops from the maximum cost value 33. The first basic cost function is implemented for avoiding collisions with static objects, while the second basic cost function is implemented for maintaining a safe distance from static objects. Figure 2 In FIG. 1 , the safety distance corresponds to the distance between marks 22 and 32 .
[0065] also, Figure 2 The third basic cost function is shown by a dotted curve 40. In a first region to the left of the marker 41, the first basic cost function 40 is constant at a minimum cost value, which can be zero. In a second region to the right of the marker 41, the third basic cost function rises. Here, the third basic cost function has a low slope. Figure 2 The mark 41 in the Figure 1 In other words, it can be determined that Figure 1 The third boundary cost function is obtained by aligning the third basic cost function so that the mark 41 is located on the lines 6a and 6b. Figure 1 In FIG, dotted lines 6a and 6b illustrate those locations where the third boundary cost function starts to rise.
[0066] Figure 2 It is shown that the basic cost functions can have different slopes. When determining the boundary cost function, the corresponding basic cost function is aligned at the object. In this case, the offset parameter can play the following role: this offset parameter can adopt different values for different basic cost functions. Generally, the offset parameter is the distance between the reference point of the basic cost function and the position of the following object: the basic cost function is aligned at the object when determining the corresponding boundary cost function. For example, if the mark 22 is selected as the reference point for the first basic cost function and, when determining the first boundary cost function, this reference point coincides with the position of the object to be avoided, the offset parameter will be zero.
[0067] Conversely, if, for example, marker 32 is selected as the reference point for the second basic cost function, the offset parameter of the second basic cost function will correspond to the safe distance to be maintained from static objects. For the third basic cost function, for example, marker 41 can be used as a reference point.
[0068] In principle, the fourth, fifth, and sixth basic cost functions can have similar trends to the first, second, and third basic cost functions, respectively. However, in particular with respect to slope and offset parameters, the fourth, fifth, and sixth basic cost functions can differ significantly from the first, second, and third basic cost functions. For example, the safety distance to be maintained with dynamic objects can be significantly greater than the safety distance to be maintained with static objects, resulting in different offset parameters for the second and fifth basic cost functions. Similarly, the distance between the preferred static driving range and static objects (such as lane boundaries) can be less than the distance between the preferred dynamic driving range and dynamic objects (such as unfamiliar vehicles). Therefore, in this regard, different offset parameters are also derived for the third and sixth basic cost functions.
[0069] The basic cost function is preferably a continuous function in order to avoid abrupt changes in the motion control. Furthermore, the basic cost function is preferably a convex function in its first and second regions in order to simplify the search for a global total cost minimum.
Claims
1. A computer-implemented method for calculating a trajectory of a mobile platform, The method has the feature of calculating the trajectory by solving an optimization problem, in, The total cost function of the optimization problem depends on the first boundary cost function, wherein the first boundary cost function is a function of the position of the mobile platform, the function having a continuous and non-constant section, Based on the calculated trajectory, control signals for manipulating a steering system, a drive system and / or a braking system of the mobile platform are provided, and / or warning signals for warning an occupant of the mobile platform are provided.
2. The method according to claim 1, further comprising the following steps: Determine the first boundary cost function, where the determining being based on a classification of the first object as a static or dynamic object, a position of the first object and a basic cost function from a set of basic cost functions, The set of basic cost functions includes: a first basic cost function for avoiding collisions with static objects, a second basic cost function for maintaining a safe distance to static objects, a third basic cost function for maintaining a preferred static driving range, a fourth basic cost function for avoiding collisions with dynamic objects, a fifth basic cost function for maintaining a safe distance to dynamic objects and / or a sixth basic cost function for maintaining a preferred dynamic driving range.
3. The method according to claim 2, in, The total cost function of the optimization problem also depends on a second boundary cost function, wherein the second boundary cost function depends on the position of the first object, The first boundary cost function and the second boundary cost function are based on different basic cost functions.
4. The method according to claim 2 or 3, in, The first basic cost function has a higher slope than the second basic cost function, the second basic cost function has a higher slope than the third basic cost function, the fourth basic cost function has a higher slope than the fifth basic cost function, and / or the fifth basic cost function has a higher slope than the sixth basic cost function.
5. The method according to claim 2 or 3, in, The basic cost function on which the first boundary cost function is based is continuous and non-constant in at least one section.
6. The method according to claim 2 or 3, further comprising the following steps: Receive user input, configuring a specific base cost function from the set of base cost functions based on the user input, in, The configuration of the specific basic cost function includes the configuration of an offset parameter and / or the configuration of a slope of the basic cost function.
7. The method according to any one of claims 1 to 3, in, The total cost function of the optimization problem further depends on a comfort cost function, a driving route cost function and / or a target speed cost function.
8. A data processing system comprising means for implementing the method according to any one of claims 1 to 7.
9. A computer program product comprising instructions which, when executed by a data processing system, cause the data processing system to carry out the method according to any one of claims 1 to 7.
10. The computer program product according to claim 9, in, The computer program product has a configuration module and a motion control module, wherein the configuration module is implemented to configure the basic cost function based on user input, The motion control module is implemented to solve the optimization problem. 11 . A computer-readable storage medium having stored thereon the computer program product according to claim 9 or 10.