Method and system for lateral control of a vehicle

Abstract

The present disclosure provides methods and systems for lateral control of a vehicle. A computer-implemented method for lateral control of a vehicle, the method comprising the following steps performed by computer hardware components: determining a reference steering angle for the vehicle; determining at least one control variable based on a model predictive controller using the reference steering angle; and controlling the vehicle laterally based on the at least one control variable.

Description

Technical Field

[0001] This disclosure relates to methods and systems for lateral control of a vehicle, and more particularly to using at least one control variable to laterally control the vehicle, said control variable being determined using a reference steering angle. Background Technology

[0002] When a side collision with a detected object (such as another vehicle or obstacle) is imminent, the vehicle's side collision warning system alerts the driver. This is done by calculating the lateral distance between the detected object and the vehicle, and also estimating the time of collision between the vehicle and the detected object. If either of these variables falls below a corresponding threshold, the system outputs a side collision warning.

[0003] A side collision may occur due to the vehicle's unintentional deviation from the center of the road.

[0004] Therefore, effective and reliable lateral control of the vehicle is required. Summary of the Invention

[0005] This disclosure provides methods implemented by a computer, computer systems, and non-transitory computer-readable media. Embodiments are shown in the specification and accompanying drawings.

[0006] In one aspect, this disclosure relates to a computer-implemented method for lateral control of a vehicle, the method comprising the following steps implemented (in other words, executed) by computer hardware components: determining a reference steering angle of the vehicle; determining at least one control variable based on a model predictive controller using the reference steering angle; and laterally controlling the vehicle based on the at least one control variable.

[0007] In other words, a lateral control method is provided in the vehicle to safely and comfortably perform lane centering and merge into the center of the lane.

[0008] It has been found that using a baseline steering angle to determine control variables in a model predictive controller enhances vehicle control.

[0009] The vehicle can be a car, truck, van, bus, motorcycle, motorized bicycle, or any other steerable wheeled vehicle (in other words, the direction of movement can be controlled by manipulating at least one wheel of the vehicle).

[0010] The at least one control variable may include multiple variables or input data that can be used for the actual control of the vehicle.

[0011] According to the implementation method, the reference steering angle is based on the vehicle's wheelbase. Using the wheelbase to determine the reference steering angle provides an accurate determination of the reference steering angle.

[0012] According to the implementation method, the reference steering angle is based on the curvature of the road on which the vehicle travels. The curvature of the road allows for accurate determination of the reference steering angle.

[0013] According to the implementation method, the reference steering angle is based on the product of the vehicle's wheelbase and the curvature of the road on which the vehicle travels.

[0014] According to the implementation method, the reference steering angle is based on the understeer gradient. The understeer gradient can compensate for understeer in the vehicle. Therefore, the understeer gradient can be used to improve the accuracy of the reference steering angle.

[0015] According to the implementation, the understeering gradient is constant for the vehicle. Therefore, the methods according to various implementations can be configured to store the understeering gradient of the vehicle to be laterally controlled, and vehicle-specific understeering gradients can be used.

[0016] According to the implementation method, the reference steering angle is based on the vehicle's lateral acceleration. It has been found that lateral acceleration affects the reference steering angle.

[0017] According to the implementation method, the reference steering angle is based on the product of the understeering gradient and the vehicle's lateral acceleration.

[0018] According to the implementation method, the reference steering angle is based on the sum of a first product and a second product, wherein the first product is the product of the vehicle's wheelbase and the curvature of the road on which the vehicle travels, and wherein the second product is the product of the vehicle's understeer gradient and lateral acceleration.

[0019] According to the implementation method, the reference steering angle is based on the formula: δ ref =L*κ+K v *a y .

[0020] According to the implementation, the model prediction controller is based on a cost function that is based on a reference steering angle and at least one control variable, such as the difference between the reference steering angle and a determined steering angle for steering the vehicle.

[0021] In another aspect, this disclosure relates to a computer system comprising a plurality of computer hardware components configured to perform some or all of the steps of the computer-implemented methods described herein. The computer system may be part of a vehicle.

[0022] A computer system may include multiple computer hardware components (e.g., a processor, such as a processing unit or processing network; at least one memory, such as a memory cell or memory network; and at least one non-transitory data storage device). It should be understood that additional computer hardware components may be provided and used to perform the steps of the computer-implemented methods within the computer system. The non-transitory data storage and / or memory cell may include computer programs that instruct the computer, for example, to use the processing unit and at least one memory cell to perform some or all of the steps or aspects of the computer-implemented methods described herein.

[0023] In another aspect, this disclosure relates to a vehicle comprising a computer system as described herein, a sensor configured to determine an actual steering angle, and an actuator configured to steer the vehicle based on the at least one control variable and the actual steering angle.

[0024] According to an implementation, the sensor includes at least one of an angle sensor, a camera, a gyroscope, and an accelerometer.

[0025] On the other hand, this disclosure relates to a non-transitory computer-readable medium comprising instructions for performing several or all of the steps or aspects of the computer-implemented methods described herein. The computer-readable medium may be configured as: an optical medium, such as an optical disc (CD) or digital versatile disc (DVD); a magnetic medium, such as a hard disk drive (HDD); a solid-state drive (SSD); a read-only memory (ROM), such as flash memory; and so on. Furthermore, the computer-readable medium may be configured as data storage accessible via a data connection such as an Internet connection. The computer-readable medium may, for example, be an online database or cloud storage.

[0026] By utilizing methods and apparatus according to various aspects and implementations, and employing a cost function scheme that modifies the steering reference term for the model predictive controller, techniques for providing optimized steering wheel angles can be provided, for example, for lateral control applications (e.g., lane centering and lane changing). Reference steering values ​​can be provided for the model predictive control cost function. Attached Figure Description

[0027] This document describes exemplary embodiments and functions of the present disclosure in conjunction with the following schematically illustrated figures:

[0028] Figure 1 The model prediction controller is shown according to various implementations;

[0029] Figure 2 This shows a comparison of steering responses between different cost functions;

[0030] Figure 3This shows a comparison of cross-tracking errors between different cost functions;

[0031] Figure 4 This shows a comparison of orientation errors between different cost functions;

[0032] Figure 5 This is a flowchart illustrating a method for lateral control of a vehicle according to various embodiments; and

[0033] Figure 6 A computer system having multiple computer hardware components is shown, configured to perform steps of a computer-implemented method for lateral control of a vehicle according to various embodiments. Detailed Implementation

[0034] According to various implementations, by utilizing modified functions for model predictive controllers, devices and methods for providing optimized steering wheel angles can be provided, for example, for lateral control applications (e.g., lane centering and lane changing).

[0035] To design a model predictive controller that minimizes cost and provides an optimal solution for vehicle lateral control, a (mathematical) cost function and stable system dynamics are required. The cost function may include or consist of terms that minimize the control effort required to perform the operation. In this regard, a reference value for the steering wheel angle can be provided according to various implementations. This steering reference value may be based on the vehicle's understeer gradient.

[0036] Figure 1 A graph 100 of the model predictive controller is shown.

[0037] Model predictive controllers can be powerful controllers. As the name suggests, such controllers can predict the state of a vehicle, such as its lateral offset relative to a reference and its orientation error relative to the reference within a finite horizontal time (T). The reference values ​​for the vehicle can be calculated by a planning block, and then these values ​​can be incorporated into model predictive control (MPC) so that it compensates for the errors by generating control signals for the current moment.

[0038] exist Figure 1 The diagram shows a time axis 102 (with past time 104 and future time 106), a desired setpoint 108, a measured state 110, a closed-loop input 112, a remeasured state 114, a predicted state 116, an optimal input trajectory 118, a repredicted state 120, and a reoptimized input trajectory 122. A rolling time domain (t) is also shown. k )124 and the corresponding prediction time domain (T)126. The rolling time domain (t) is shown. k+1 )128 and the corresponding prediction time domain (T)130.

[0039] The following symbols can be used:

[0040] t k : Current time step;

[0041] t: The timeline moving forward in time;

[0042] T: Prediction time domain (duration of the predicted state);

[0043] Δt: Sampling period;

[0044] u * (τ k ): The optimal control value at the current time step;

[0045] x ref (t k ): The baseline trajectory for the state;

[0046] The predicted state.

[0047] The cost function, according to various implementation methods, can be used by the optimizer in the model predictive controller to provide the optimal control value at the current time step. In this case, the steering angle can be the optimal control signal sequence (u... * This optimal control signal sequence is used to actuate the vehicle to minimize azimuth error and lateral offset (system state). Based on the designed cost function and system dynamics, trajectory prediction can also be calculated. The obtained control sequence can be applied to the vehicle's motion control sequence.

[0048] One approach is to minimize the error between the state and the baseline by introducing these terms into the following cost function J, while also minimizing the control effort required to do so:

[0049]

[0050] In equation (1):

[0051] J represents the cost function that needs to be minimized.

[0052] x represents the vehicle status (e.g., cross-tracking error).

[0053] x ref A baseline value (e.g., lane center) representing the state in which the error will be minimized.

[0054] k g This represents the tuning gain that minimizes the error between the state and the reference value.

[0055] u represents control input (e.g., steering input), and

[0056] ku This represents the tuning gain when the control input is minimized.

[0057] For a specific example that minimizes the vehicle's cross-tracking error relative to a reference and the corresponding steering angle, the following simple cost function J can be used:

[0058]

[0059] In equation (2):

[0060] J represents the cost function that needs to be minimized.

[0061] d represents the initial value of the lateral offset obtained from the vehicle sensor input.

[0062] d ref This indicates the reference value that the vehicle should follow, such as the center of the lane.

[0063] k d This represents the tuning gain when minimizing the cross-tracking error.

[0064] δ represents the steering angle or control input, and

[0065] k δ This represents the tuning gain when minimizing the control input.

[0066] The drawback of this cost approach is the inability to compensate for cross-tracking errors in the curve while keeping the steering at 0, which is how the cost function in equation (1) is determined. This leads to contradictory behavior in cost assessment, resulting in suboptimal handling commands generated from the MPC, leading to poor tracking of the baseline. This can become more apparent in the curve, where a certain steering angle is required to traverse it, but the cost will attempt to keep the steering angle as close to zero as possible while trying to take the curve and minimize cross-tracking errors (depending on how the error term is defined).

[0067] According to various implementations, instead of having a feedforward term, a steering reference term can be introduced into the cost function itself. Therefore, the steering angle required for steady-state turning is mathematically formulated as a cost function. Thus, the MPC can be well-informed about the curve it must traverse and can appropriately generate a steering angle, rather than keeping the steering at 0 while traversing the curve.

[0068] According to various implementation methods, the steering reference is given by the following formula (3):

[0069] δ ref =L*κ+K v *a y (3)

[0070] In equation (3):

[0071] δ ref This represents the steering value required for a steady-state turn.

[0072] L represents the wheelbase.

[0073] κ represents the curvature of the road.

[0074] K v This is the understeering gradient, which can be a constant for a given vehicle.

[0075] a y This indicates the lateral acceleration of the vehicle.

[0076] According to various implementation methods, the following cost function J can be provided:

[0077]

[0078] In equation (4):

[0079] J represents the cost function that needs to be minimized.

[0080] d represents the initial value of the lateral offset obtained from the vehicle sensor input.

[0081] d ref This indicates the reference value that the vehicle should follow, such as the center of the lane.

[0082] k d This represents the tuning gain when minimizing the cross-tracking error.

[0083] δ represents the steering angle.

[0084] δ ref The reference steering angle is represented (e.g., according to formula (3)), and

[0085] k δ This represents the tuning gain that minimizes steering error.

[0086] By employing a cost function as shown in equation (4), MPC can generate the desired outcome of navigating the curve rather than attempting to do so while simultaneously trying to keep the turning point as close to zero as possible. This allows the cost function to be mathematically balanced and avoids contradictory terms.

[0087] The determined steering angle can be referred to as a control variable. For example, a time series of steering angles (which can be a control variable) can be used to steer the vehicle for lateral control.

[0088] Below, graphs and plots are shown comparing the steering performance according to the cost functions of equations (1) and (2) with the steering performance according to the cost functions of equations (3) and (4). The use case of the following graph is a lane-changing behavior where the required lateral offset relative to the initial offset is approximately the lane width (3.5m in this case).

[0089] Figure 2 A graph 200 shows how the steering response has a smoother rise and a significantly lower amplitude for the same maneuver. In actual testing, this would result in lane changes that are comfortable for passengers. The horizontal axis 202 represents time, and the vertical axis 204 represents the steering angle. Curve 206 is obtained using the cost functions of equations (1) and (2), and curve 208 is obtained using the cost functions of equations (3) and (4).

[0090] Figure 3 A graph 300 shows a comparison between the lateral offsets of the two cases and how the modified cost (function) according to equations (2) and (3) makes the lateral offset rate smaller than the conventional lateral offset rate, again resulting in smoother manipulation. Moreover, differences can be noted in the overshoot between the plots, indicating that the cost according to equations (3) and (4) has a smaller overshoot for the same manipulation compared to the cost function according to equations (1) and (2). The horizontal axis 302 represents time, and the vertical axis 304 represents the cross-tracking error. Curve 306 is obtained using the cost function of equation (1), and curve 308 is obtained using the cost functions of equations (3) and (4).

[0091] Figure 4 A diagram 400 illustrates the difference in azimuth error. This can be significant, where compensation for the error with the modified cost is far superior to traditional compensation. The horizontal axis 402 represents time, and the vertical axis 404 represents azimuth error. Curve 406 is obtained using the cost functions of equations (1) and (2), and curve 408 is obtained using the cost functions of equations (3) and (4).

[0092] Figure 5 A flowchart 500 of a method for lateral control of a vehicle according to various embodiments is shown. At 502, a reference steering angle of the vehicle can be determined. At 504, at least one control variable can be determined based on a model predictive controller using the reference steering angle. At 506, the vehicle can be laterally controlled based on said plurality of at least one control variable.

[0093] According to various implementation methods, the reference steering angle can be based on the vehicle's wheelbase.

[0094] According to various implementation methods, the reference steering angle can be based on the curvature of the road on which the vehicle travels.

[0095] According to various implementation methods, the reference steering angle can be based on the product of the vehicle's wheelbase and the curvature of the road on which the vehicle travels.

[0096] According to various implementation methods, the reference steering angle can be based on the understeering gradient.

[0097] According to various implementation methods, the understeering gradient can be a constant for the vehicle.

[0098] According to various implementation methods, the reference steering angle can be based on the vehicle's lateral acceleration.

[0099] According to various implementation methods, the reference steering angle can be based on the product of the understeering gradient and the vehicle's lateral acceleration.

[0100] According to various implementations, the reference steering angle can be based on the sum of a first product and a second product, wherein the first product can be the product of the vehicle's wheelbase and the curvature of the road on which the vehicle travels, and wherein the second product can be the product of the vehicle's understeer gradient and lateral acceleration.

[0101] According to various implementation methods, the reference steering angle can be based on formula (3).

[0102] According to various implementations, the model predictive controller is based on a cost function that is based on a reference steering angle and at least one control variable (in other words: the cost function can be used as the cost function of the model predictive controller, which is used for the lateral control of the vehicle).

[0103] According to various implementation methods, the cost function can be based on formula (4).

[0104] Each of steps 502, 504, and 506, as well as the further steps described above, can be performed by computer hardware components.

[0105] Figure 6 A computer system 600 with multiple computer hardware components is shown, the multiple computer hardware components being configured to perform steps of a computer-implemented method for lateral control of a vehicle according to various embodiments. The computer system 600 may include a processor 602, a memory 604, and a non-transitory data storage unit 606. Sensors 608 and actuators 610 may be configured as part of the computer system 600 (e.g., Figure 6 (as shown), or it can be set outside the computer system 600.

[0106] Processor 602 can execute instructions provided in memory 604. Non-transitory data storage unit 606 can store computer programs, including instructions that can be transferred to memory 604 and then executed by processor 602. Sensor 608 can be used to determine the actual steering angle. Actuator 610 can be used to steer the vehicle based on at least one control variable and the actual steering angle.

[0107] The processor 602, memory 604, and non-transitory data storage unit 606 may be connected to each other, for example, via electrical connection 612 (e.g., cable or computer bus) or via any other suitable electrical connection to exchange electrical signals. The sensor 608 may be connected to the computer system 600, for example, via an external interface, or may be provided as part of the computer system (in other words: inside the computer system, for example, connected via electrical connection 612).

[0108] The terms “connection” or “link” are intended to include direct “connection” (e.g., via a physical link) or direct “link” as well as indirect “connection” or indirect “link” (e.g., via a logical link).

[0109] It should be understood that the above description of one of the methods can be similarly applied to computer system 600.

[0110] List of reference numerals

[0111] 100 Model Predictive Controllers Based on Various Implementation Methods

[0112] 102 Timeline

[0113] 104 past time

[0114] 106 Future Time

[0115] 108 Expected Set Point

[0116] The state measured by 110

[0117] 112 Closed-loop input

[0118] 114 Remeasured State

[0119] 116 Predicted State

[0120] 118 Optimal Input Trajectory

[0121] 120 Re-predicted State

[0122] 122 Re-optimized Input Trajectory

[0123] 124 Scrolling Time Domain

[0124] 126 Prediction Time Domain

[0125] 128 rolling time domain

[0126] 130 Prediction Time Domain

[0127] Comparison of steering responses among 200 different cost functions

[0128] 202 horizontal axis

[0129] 204 vertical axis

[0130] 206 curve

[0131] 208 curve

[0132] Comparison of cross-tracking errors among 300 different cost functions

[0133] 302 horizontal axis

[0134] 304 vertical axis

[0135] 306 curve

[0136] 308 curve

[0137] Comparison of azimuth errors among 400 different cost functions

[0138] 402 horizontal axis

[0139] 404 vertical axis

[0140] 406 curve

[0141] 408 curve

[0142] 500 flowcharts illustrating methods for lateral control of vehicles according to various embodiments

[0143] 502 Steps for determining the reference steering angle of the vehicle

[0144] 504. Steps for determining at least one control variable using a reference steering angle based on a model predictive controller.

[0145] 506 Steps for Lateral Control of a Vehicle Based on at Least One Control Variable

[0146] 600 Computer systems according to various implementation methods

[0147] 602 processor

[0148] 604 memory

[0149] 606 Non-Temporary Data Storage Department

[0150] 608 sensor

[0151] 610 actuator

[0152] 612 connection

Claims

1. A computer-implemented method for lateral control of a vehicle, the method comprising the following steps performed by computer hardware components: The reference steering angle of the vehicle (502) is determined based on the sum of the first product and the second product, wherein, The first product is the product of the vehicle's wheelbase and the curvature of the road on which the vehicle travels, and the second product is the product of the vehicle's understeer gradient and lateral acceleration. At least one control variable (504) is determined using the reference steering angle based on a model predictive controller, wherein the model predictive controller is based on a cost function, the cost function being based on the reference steering angle and the at least one control variable; and The vehicle is laterally controlled (506) based on the at least one control variable.

2. The computer-implemented method according to claim 1, in, The understeering gradient is a constant for the vehicle.

3. The computer-implemented method according to claim 1, in, The reference steering angle is based on the formula: ， in, This represents the steering value required for a steady-state turn. This indicates the wheelbase of the vehicle. The curvature of the road is represented. This is the insufficient steering gradient. This refers to the lateral acceleration of the vehicle.

4. A computer system (600) comprising a plurality of computer hardware components configured to perform the steps of a computer-implemented method according to any one of claims 1 to 3.

5. A vehicle comprising a sensor (608), an actuator (610), and a computer system (600) according to claim 4, wherein the sensor is configured to determine an actual steering angle, and the actuator is configured to steer the vehicle based on the at least one control variable and the actual steering angle.

6. The vehicle according to claim 5, in, The sensor (608) includes at least one of an angle sensor, a camera, a gyroscope, and an accelerometer.

7. A non-transitory computer-readable medium comprising instructions for performing the computer-implemented method of any one of claims 1 to 3.

Images