System and method for predicting and detecting vehicle instability

Abstract

Systems and methods for controlling a vehicle are provided. The systems and methods include a sensor system and a processor configured to execute program instructions to cause the at least one processor to: receive, from the sensor system, a yaw rate value, a lateral acceleration value, and a longitudinal speed value of the vehicle; determine a side slip angle parameter value based on the yaw rate value, the lateral acceleration value, and the longitudinal speed value; determine a phase plot angle based on the side slip angle parameter value and the yaw rate value, wherein the phase plot angle each represents an angle between the yaw rate and the side slip angle of the vehicle in a phase plot of the yaw rate and the side slip angle; detect or predict a vehicle instability based on at least the phase plot angle; and control a motion of the vehicle to at least partially correct the vehicle instability when the vehicle instability is detected or predicted.

Description

Technical Field

[0001] This disclosure generally relates to systems and methods for detecting and predicting vehicle instability in relation to vehicles. Background Technology

[0002] Some automated vehicle control systems aim to maintain vehicle stability. Some of these systems utilize model predictive control (MMC) to generate control commands based on various inputs from sensor systems, using a vehicle model as the basis. Some MMC algorithms rely on potentially inaccurate or imprecise data, such as tire type, road surface conditions, and lateral speed. Such MMC methods would be improved if vehicle instability could be accurately detected without relying on unreliable data inputs.

[0003] Therefore, it is desirable to provide systems and methods capable of predicting and detecting vehicle instability without requiring input data such as road conditions and tire type. Furthermore, it is desirable to provide vehicle instability detection and vehicle control using reliable input measurements, minimal number of measurements, and low computational budget. Moreover, other desirable features and characteristics of the invention will become apparent from the following detailed description and appended claims, in conjunction with the accompanying drawings and the foregoing technical and background information. Summary of the Invention

[0004] In one aspect, a system for controlling a vehicle is provided. The system includes a sensor system and a processor, the processor being operatively in communication with the sensor system, wherein the processor is configured to execute program instructions, wherein the program instructions are configured to cause the processor to: receive from the sensor system yaw rate values, lateral acceleration values, and longitudinal velocity values ​​of the vehicle; determine sideslip angle parameter values ​​based on the yaw rate values, lateral acceleration values, and longitudinal velocity values; determine phase diagram angles based on the sideslip angle parameter values ​​(e.g., the time derivative of the sideslip angle) and the yaw rate values ​​(e.g., the time derivative of the yaw rate), wherein the phase diagram angles each represent angles between the yaw rate and sideslip angle in the phase diagrams of the yaw rate and sideslip angle; detect or predict vehicle instability at least based on the phase diagram angles; and when vehicle instability is detected or predicted, control the motion of the vehicle to at least partially correct the vehicle instability.

[0005] In an embodiment, the sideslip angle parameter value is the time derivative of the sideslip angle value, and the program instructions are configured to cause the processor to determine the time derivative of the yaw rate value and determine the phase diagram angle based on the time derivative of the yaw rate value and the time derivative of the sideslip angle value.

[0006] In an embodiment, determining the phase diagram angle includes an arctangent function based on the sideslip angle parameter value and the yaw rate value.

[0007] In this embodiment, the program instructions are configured to cause the processor to determine a velocity amplitude value based on the sideslip angle parameter value and the yaw rate value, wherein the velocity amplitude value represents the speed of movement of the vehicle within the phase diagram based on the yaw rate and sideslip angle. Vehicle instability is detected based at least on the phase diagram angle and the velocity amplitude value.

[0008] In embodiments, detecting or predicting vehicle instability includes early prediction of vehicle instability and detection of vehicle instability. Early prediction of vehicle instability can be performed at least based on phase diagram angles and the minimum change in yaw rate values. Detection of vehicle instability can be performed at least based on phase diagram angles and the minimum change in sideslip angle parameter values. Early prediction of vehicle instability can be performed at least based on phase diagram angles representing a fundamental vertical path defined by a varying yaw rate and a substantially constant sideslip angle in the phase diagram. Detection of vehicle instability can be performed at least based on phase diagram angles representing a fundamental horizontal path defined by a varying sideslip angle and a substantially constant yaw rate in the phase diagram.

[0009] In an embodiment, when vehicle instability is detected or predicted, program instructions are configured to cause the processor to determine the level of vehicle instability based on sideslip angle parameter values, and to control the vehicle's motion based at least on the level of instability to at least partially correct the vehicle instability.

[0010] In this embodiment, the motion of the vehicle is controlled by adjusting the constraints in the model predictive control algorithm to at least partially correct vehicle instability.

[0011] In this embodiment, the detection or prediction of vehicle instability is not based on road surface information or tire information.

[0012] In another aspect, a method for controlling a vehicle is provided. The method includes: receiving, via a processor, yaw rate values, lateral acceleration values, and longitudinal velocity values ​​of the vehicle from a sensor system; determining, via the processor, sideslip angle parameter values ​​based on the yaw rate values, lateral acceleration values, and longitudinal velocity values; determining, via the processor, phase diagram angles based on the sideslip angle parameter values ​​(e.g., their time derivatives) and the yaw rate values ​​(e.g., their time derivatives), wherein each phase diagram angle represents an angle between the yaw rate and sideslip angle (e.g., their time derivatives) in a phase diagram of the yaw rate and sideslip angle; detecting or predicting vehicle instability via the processor, at least based on the phase diagram angles; and when vehicle instability is detected or predicted, controlling the motion of the vehicle via the processor to at least partially correct the vehicle instability.

[0013] In this embodiment, the sideslip angle parameter value is the time derivative of the sideslip angle value. The method includes determining the time derivative of the yaw rate value via a processor. The phase diagram angle is determined based on the time derivatives of the yaw rate value and the sideslip angle value.

[0014] In an embodiment, determining the phase diagram angle includes an arctangent function based on the sideslip angle parameter value and the yaw rate value.

[0015] In one embodiment, the method includes determining a velocity amplitude value via a processor based on sideslip angle parameter values ​​and yaw rate values, wherein the velocity amplitude value represents the speed of movement of the vehicle within the phase diagram based on yaw rate and sideslip angle. Detection or prediction of vehicle instability is based at least on the phase diagram angle and velocity amplitude values.

[0016] In this embodiment, detecting or predicting vehicle instability includes both early prediction and detection of vehicle instability. Early prediction of vehicle instability is performed based at least on phase diagram angles and on the minimum change in yaw rate values. Detection of vehicle instability is performed based at least on phase diagram angles and on the minimum change in sideslip angle parameter values.

[0017] In another aspect, a vehicle is provided. The vehicle includes: a sensor system; and a processor operatively communicating with the sensor system. The processor is configured to execute program instructions to cause the processor to: receive from the sensor system a yaw rate value, a lateral acceleration value, and a longitudinal velocity value of the vehicle; determine a sideslip angle parameter value based on the yaw rate value, the lateral acceleration value, and the longitudinal velocity value; determine a phase diagram angle based on the sideslip angle parameter value and the yaw rate value, wherein the phase diagram angles each represent angles between the yaw rate and the sideslip angle in the phase diagrams of the yaw rate and the sideslip angle; detect or predict vehicle instability at least based on the phase diagram angles; and, when vehicle instability is detected or predicted, control the motion of the vehicle to at least partially correct the vehicle instability. Attached Figure Description

[0018] Exemplary embodiments will now be described in conjunction with the following accompanying drawings, wherein the same reference numerals denote the same elements, and in the drawings:

[0019] Figure 1 This is a functional block diagram of a vehicle associated with a system for detecting and predicting vehicle instability, according to various embodiments;

[0020] Figure 2 Phase diagrams according to various embodiments are depicted;

[0021] Figure 3 This is a functional block diagram of a system for detecting and predicting vehicle instability according to various embodiments; and

[0022] Figure 4 This is a flowchart illustrating a method for detecting and predicting vehicle instability according to various embodiments. Detailed Implementation

[0023] The following detailed description is exemplary in nature only and is not intended to limit application and use. Furthermore, it is not intended to be bound by any express or implied theory set forth in the foregoing technical fields, background art, summary of the invention, or the following detailed description. As used herein, the term "module" refers to any hardware, software, firmware, electronic control components, processing logic, and / or processor device, individually or in any combination, including, but not limited to: application-specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or grouped) and memories executing one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the described functionality.

[0024] Embodiments of this disclosure can be described herein in terms of functional and / or logical block components and various processing steps. It should be understood that such block components can be implemented by any number of hardware, software, and / or firmware components configured to perform specified functions. For example, embodiments of this disclosure can employ various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, etc., which can perform various functions under the control of one or more microprocessors or other control devices. Furthermore, those skilled in the art will understand that embodiments of this disclosure can be practiced in conjunction with any number of systems, and the systems described herein are merely exemplary embodiments of this disclosure.

[0025] For the sake of brevity, conventional techniques relating to signal processing, data transmission, signaling, control, and other functional aspects of the system (and its individual operating components) may not be described in detail herein. Furthermore, the connecting lines shown in the various figures included herein are intended to represent exemplary functional relationships and / or physical connections between various elements. It should be noted that many alternative or additional functional relationships or physical connections may exist in the embodiments of this disclosure.

[0026] Reference Figure 1According to various embodiments, a system 200, generally shown as 200, for detecting and predicting vehicle instability, is associated with vehicle 10. Typically, system 200 for detecting and predicting vehicle instability provides a methodology and algorithmic structure to predict the behavior of the vehicle body of 10 and detect instability based on body motion patterns in a phase diagram. Predicted and / or detected information can be used to adjust constraints for vehicle motion control (or otherwise adjust vehicle motion control). System 200 for detecting and predicting vehicle instability can identify and utilize vehicle motion patterns in a β (sideslip angle) vs. r (yaw rate) phase diagram to predict and detect body instability of vehicle 10. According to some embodiments, body stability monitoring does not require tire model / information, and system 200 for detecting and predicting vehicle instability is independent of road conditions. In the embodiments described herein, system 200 for detecting and predicting vehicle instability uses a minimum (3) number of measurements and reliable estimates. In addition to detection, system 200 can also provide early indicators of body instability of vehicle 10.

[0027] like Figure 1 As shown, vehicle 10 typically includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and substantially surrounds the components of vehicle 10. The body 14 and chassis 12 may together form a frame. The wheels 16-18 are each rotatably connected to the chassis 12 near a corresponding corner of the body 14.

[0028] In various embodiments, vehicle 10 is an autonomous vehicle, and system 200 for detecting and predicting vehicle instability is associated with vehicle 10. Vehicle 10 is, for example, a vehicle automatically controlled to transport passengers from one location to another. Vehicle 10 is depicted as a passenger car in the illustrated embodiments, but it should be understood that any other vehicle may be used, including motorcycles, trucks, sports utility vehicles (SUVs), recreational vehicles (RVs), shared vehicles, long-haul buses, etc. In exemplary embodiments, vehicle 10 is a so-called Level 4 or Level 5 automation system. Level 4 system means “high automation,” referring to an automated driving system performing a specific driving mode for all aspects of a dynamic driving task even when a human driver does not properly respond to an intervention request. Level 5 system means “full automation,” referring to an automated driving system performing all aspects of a dynamic driving task at all times under all road and environmental conditions manageable by a human driver. However, in other embodiments, vehicle 10 has a lower level of automation and includes an advanced driver assistance system (ADAS).

[0029] As shown in the figure, vehicle 10 typically includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a connectivity system 36. In various embodiments, the propulsion system 20 may include an internal combustion engine, an electric motor such as a traction motor, and / or a fuel cell propulsion system. The transmission system 22 is configured to transmit power from the propulsion system 20 to the wheels 16-18 according to a selectable speed ratio. According to various embodiments, the transmission system 22 may include a stepped automatic transmission, a continuously variable transmission (CVT), or other suitable transmission. The braking system 26 is configured to provide braking torque to the wheels 16-18. In various embodiments, the braking system 26 may include friction brakes, brake-by-wire brakes, regenerative braking systems such as electric motors, and / or other suitable braking systems. The steering system 24 affects the position of the wheels 16-18. Although depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of this disclosure, the steering system 24 may not include a steering wheel.

[0030] Sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external and / or internal environment of vehicle 10. Sensing devices 40a-40n may include, but are not limited to, radar, lidar, GPS, optical cameras, thermal imaging cameras, ultrasonic sensors, and / or other sensors. Sensor system 28 includes an inertial measurement unit and a longitudinal velocity sensor. Sensor system 28 outputs yaw rate r and longitudinal velocity V. x and lateral acceleration A y This serves as data input to system 200, used for detecting and predicting vehicle instability. Actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, propulsion system 20, transmission system 22, steering system 24, and braking system 26. In various embodiments, vehicle features may also include internal and / or external vehicle features, such as, but not limited to, doors, trunk, and cabin features, such as air, music, lighting, etc. (not numbered).

[0031] The connectivity system 36 is configured to wirelessly transmit information to and from other entities 48, such as, but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems, and / or personal devices. In an exemplary embodiment, the connectivity system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or by using cellular data communication. However, additional or alternative communication methods (such as dedicated short-range communication (DSRC) channels) are also considered to be within the scope of this disclosure. A DSRC channel refers to a one-way or two-way short-to-medium-range wireless communication channel designed specifically for automotive use, along with a set of corresponding protocols and standards.

[0032] Data storage device 32 stores data for automatically controlling vehicle 10. In various embodiments, data storage device 32 stores a defined map of the navigable environment. In various embodiments, the defined map may be predefined by and obtained from a remote system (e.g., a cloud processing system). For example, the defined map may be assembled by a remote system and transmitted (wirelessly and / or wired) to vehicle 10 and stored in data storage device 32. As will be understood, data storage device 32 may be part of controller 34, separate from controller 34, or part of controller 34 and a separate system. Data storage device 32 may store reference data 238 (see...) Figure 3 ), for use in system 200 for detecting and predicting vehicle instability.

[0033] The controller 34 includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 may be any custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), auxiliary processor among several processors associated with the controller 34, semiconductor-based microprocessor (in the form of a microchip or chipset), macroprocessor, any combination thereof, or any device typically used for executing instructions. For example, the computer-readable storage device or medium 46 may include volatile and non-volatile storage devices in read-only memory (ROM), random access memory (RAM), and non-fail-to-recovery memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operational variables when the processor 44 is powered off. The computer-readable storage device or medium 46 may be implemented using any of a number of known memory devices, such as PROM (programmable read-only memory), EPROM (electrically programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), flash memory, or any other electrical memory device, magnetic memory device, optical memory device, or combined memory device capable of storing data, some of which represents executable instructions used by controller 34 to control vehicle 10.

[0034] The instructions may include one or more separate programs, each including an ordered list of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals from sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling components of vehicle 10, and generate control signals for actuator system 30 to automatically control components of vehicle 10 based on logic, calculations, methods, and / or algorithms. Although in Figure 1 Only one controller 34 is shown, but embodiments of vehicle 10 may include any number of controllers 34 that communicate and cooperate via any suitable communication medium or combination of communication media to process sensor signals, perform logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of vehicle 10.

[0035] In various embodiments, one or more instructions of the controller 34 are included in the system 200 for detecting and predicting vehicle instability, and when executed by the processor 44, perform instructions regarding... Figure 3 The system description of functions and about Figure 4The steps of the described method are as follows. Specifically, the processor 44 is configured by instructions to receive input data representing the state of the vehicle at the center of gravity of the vehicle 10, to predict impending vehicle body instability or detect current vehicle body instability. The system 200 for detecting and predicting vehicle body instability operates based on parameters evaluating vehicle body motion patterns in a phase diagram. The system 200 for detecting and predicting vehicle instability can output data representing the predicted or detected vehicle instability, which can be used to adjust the vehicle motion control system 220 (see [link to system description]). Figure 3 The constraints or other parameters of the motion predictive control algorithm are adjusted based on vehicle instability data output from the system 200 used for detecting and predicting vehicle instability. This allows for enhanced control performance of the vehicle 10.

[0036] Reference Figure 2 A phase diagram 100 depicts the yaw rate r along the y-axis 104 and the sideslip angle β along the x-axis 105. Vehicle motion data points 109 are plotted on the phase diagram. Furthermore, a stable region 108 of the phase diagram is depicted, thus showing the boundaries of the vehicle motion data points 109, which generally represent vehicle stability. According to this disclosure, the discovery of patterns in the vehicle motion data points 109 provides early prediction and actual detection of vehicle instability. In particular, a prediction pattern 110 can be identified when the vehicle motion data points 109 exhibit a generally vertical trend in the phase diagram 100 at a sufficient speed (a substantially constant sideslip angle β and a varying yaw rate r), which provides an early prediction of vehicle instability. A detection pattern 112 can be identified when the vehicle motion data points 109 exhibit a generally horizontal trend in the phase diagram 100 (a substantially constant yaw rate r and a varying sideslip angle β), which indicates the detection of a current vehicle instability event. System 200 for detecting and predicting vehicle instability derives phase diagram parameters and determines whether conditions describing prediction mode 110 or detection mode 112 are met. In an embodiment, the phase diagram parameters include a phase diagram angle θ106 between the yaw rate r and sideslip angle β of a sample window of multiple vehicle motion data points 109, and a moving speed M of the sample window of the vehicle motion data points 109. The moving speed M will be lower for consecutive vehicle motion data points 109 that are relatively close together, and higher for consecutive vehicle motion data points 109 that are relatively spaced apart.

[0037] Now refer to Figure 3The system 200 for detecting and predicting vehicle instability is described in more detail. The system 200 includes a reference data source 214, a sensor system 28, a vehicle instability processing system 202, and a vehicle motion control system 220. The sensor system 28 provides sensor data 240, allowing the vehicle instability processing system 202 to derive parameters indicating phase diagram motion patterns. The vehicle instability processing system 202 evaluates the parameters indicating phase diagram motion patterns to provide early predictions of vehicle body instability and detect current vehicle body instability. The vehicle instability processing system 202 outputs vehicle instability data 226 reflecting the prediction and / or detection of vehicle body instability, which is used to correct the constraints or other parameters of the vehicle motion control system 220 to bring the vehicle into a more stable state. The vehicle instability processing system 202 performs stability recovery detection to reset the vehicle instability and prediction flags set by the vehicle instability processing system 202.

[0038] exist Figure 3 In an exemplary embodiment, sensor system 28 provides sensor data 240, which includes the yaw rate r and lateral acceleration A of vehicle 10. y and longitudinal velocity V x The measured or estimated values ​​are obtained. Sensor data 240 is provided to vehicle instability processing system 202 to predict and detect vehicle instability without using road condition data and tire model / information. Vehicle instability processing system 202 includes a preprocessing module 208 configured to receive sensor data 240 and perform various operations on it. The preprocessing module determines the time derivative of the yaw rate according to the following equation. and the time derivative of the sideslip angle

[0039]

[0040]

[0041] Furthermore, in some embodiments, the preprocessing module 208 applies an averaging function to the yaw rate. and the time derivative of the sideslip angle Alternatively, it can be applied to the incoming sensor data 240. The averaging function can be a moving average function. The preprocessing module 208 outputs preprocessed measurement data 232, which includes the derivatives of the yaw rate and the sideslip angle in pairs, which are expressed as... Figure 2 The described vehicle motion data points 109 can be used to derive phase diagram parameters indicating vehicle instability.

[0042] The vehicle instability handling system 202 includes a vehicle instability prediction module 204 and a vehicle instability detection module 206. The vehicle instability prediction module 204 implements a series of conditions, evaluates these conditions with respect to phase diagram parameters derived from preprocessed measurement data 232, to identify instability issues related to vehicle instability. Figure 2 The predicted pattern 110 is described. The vehicle instability detection module 206 implements a series of conditions, which are evaluated with respect to phase diagram parameters derived from preprocessed measurement data 232, to identify instability related to... Figure 2 The detection mode described is 112.

[0043] The vehicle instability prediction module 204 receives a sample window of p preprocessed measurement data 232. The preprocessed measurement data 232 is time series data, where the most recent data point is labeled t. k The sample window size is defined as t. k-p :t k The vehicle instability prediction module 204 identifies prediction pattern 110 based on the following conditions:

[0044] For t = t k-p :t k ,

[0045]

[0046]

[0047]

[0048] Equation 3 represents the determination of the phase diagram angle θ106. The phase diagram angle θ106 should correspond to a sample window of preprocessed measurement data 232 corresponding to the fundamental vertical pattern in phase diagram 100. Equation 4 represents the moving speed (or propagation range) M of the data points included in the sample window of preprocessed measurement data 232. K1 and K2 are calibrable weighting factors provided in reference data 238 from reference data source 214. C1 is a calibrable constant that provides a threshold for the moving speed, exceeding which the predicted pattern is identifiable. Equation 5 represents the third condition, which requires the yaw rate r to have a sufficient amount (greater than the calibrable constant C2) of total movement / change within the sample window. Equations 3, 4, and 5 should be satisfied for the predicted pattern 110 to be identified. However, each condition in Equations 3, 4, and 5 indicates the predicted pattern 110 and can be applied independently or in any combination. When the vehicle instability prediction module 204 evaluates the existence of prediction mode 110 based on equations 3, 4, and 5, it provides instability prediction output data 228 indicating this situation. The instability prediction output data 228 may include Boolean prediction flags or more granular variables representing the determinism of the prediction.

[0049] The vehicle instability detection module 206 receives a sample window of p preprocessed measurement data 232. The number p used by the vehicle instability detection module 206 may differ from the number p used by the vehicle instability prediction module 204. The preprocessed measurement data 232 is time-series data, where the most recent data point is labeled t. k The sample window size is defined as t. k-p :t k The vehicle instability detection module 206 identifies the detection mode 112 based on the following conditions:

[0050] For t = t k-p :t k ,

[0051]

[0052]

[0053]

[0054] Equation 6 represents the determination of the phase diagram angle θ106. The phase diagram angle θ106 should correspond to a sample window of preprocessed measurement data 232 corresponding to the basic horizontal pattern in phase diagram 100. Equation 7 represents the moving speed (or propagation range) M of the data points included in the sample window of preprocessed measurement data 232. K1 and K2 are calibrable weighting factors provided in reference data 238 from reference data source 214. C3 is a calibrable constant that provides a threshold for the moving speed, exceeding which the detection pattern is identifiable. Equation 8 represents a third condition that requires a sufficient amount (greater than the calibrable constant C4) of total movement / change in the sideslip angle β within the sample window. Equations 6, 7, and 8 should be satisfied for the detection pattern 112 to be identified. However, each condition in Equations 6, 7, and 8 indicates the detection pattern 112 and can be applied individually or in any combination. When the vehicle instability detection module 206 evaluates the presence of detection mode 112 based on equations 6, 7, and 8, it provides instability detection output data 230 indicating this situation. The instability detection output data 230 may include Boolean detection flags or more granular variables representing the determinism of the detection.

[0055] In one embodiment, instability detection output data 230 (e.g., detection flag) and instability prediction output data 228 (e.g., prediction flag) are output as part of vehicle instability data 226 for further processing by the vehicle motion control system 220. In one embodiment, a stability level determination module (not shown) responds to the detection flag from the vehicle instability detection module 206 to determine the instability level. The instability level can be calculated according to Equation 9:

[0056]

[0057] t1 represents the time when the vehicle instability detection module 206 first determines instability. t2 is a later point in time (e.g., approximately 2 seconds later) when the integral of the sideslip rate reasonably and accurately indicates the level of instability. The quantification of the instability level can be included as part of the vehicle instability data 226 for use by the vehicle motion control system 220.

[0058] In some embodiments, the vehicle instability handling system 202 includes a stability recovery detection module 212 for detecting when conditions for instability prediction or detection no longer exist, and accordingly changing the instability prediction output data 228 or the instability detection output data 230 (e.g., by resetting the prediction or detection flag). A variety of conditions can be used to detect when instability prediction is no longer valid, including one or more of the following: the phase diagram angle 106 is not perpendicular (e.g., the condition of Equation 3 is not satisfied); the velocity amplitude M has been sufficiently reduced (e.g., the condition of Equation 4 is no longer satisfied); the yaw rate is close to or zero; the ratio of the current yaw rate to the yaw rate that caused the vehicle instability prediction module 204 is less than a predetermined constant; and the longitudinal velocity of the vehicle is close to or zero. Several conditions can be used to detect when instability detection is no longer effective. These conditions include one or more of the following: the velocity amplitude M has been sufficiently reduced (e.g., the conditions of Equation 7 are no longer satisfied); the longitudinal velocity of vehicle 10 is close to zero or zero; the yaw rate is close to zero or zero; and the integral of Equation 9 from the time (t1) when vehicle instability was detected by vehicle instability detection module 206 to the current time is close to zero. Alternatively, other conditions for resetting the prediction or detection flag can be used. The change in instability prediction output data 228 or instability detection output data 230 upon detection of stability recovery is provided to vehicle motion control system 220 as part of vehicle instability data 226. Vehicle motion control system can respond to such changes by ceasing to take corrective actions to stabilize the vehicle, since the actions already taken were effective.

[0059] According to various embodiments, the vehicle motion control system 220 includes a vehicle motion control module 222 and a vehicle motion control regulator 224. The vehicle motion control module 222 generates vehicle motion control command data 242, which reflects the control actions to be taken by the actuator system 30 to control the vehicle. See also... Figure 1 The vehicle motion control command data 242 can control one or more actuator devices 42a-42n to control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. The vehicle motion control module 222 implements vehicle motion control algorithms, such as one or more of model predictive control algorithms and feedforward control algorithms. The vehicle motion control module 222 can utilize various constraints and variables to generate the vehicle motion control command data 242. The vehicle motion control regulator 224 responds to vehicle instability data 226 to adjust the vehicle motion control module 222 based on the vehicle instability data to correct vehicle instability conditions. In one embodiment, the vehicle motion control regulator 224 can implement a constraint calculator to adjust the constraints or variables used by the vehicle motion control module 222. Such adjustments to the constraints of model predictive control algorithms (e.g.) can restore vehicle stability control based on the vehicle instability data 226 determined by the vehicle instability handling system 202.

[0060] Now refer to Figure 4 And continue to refer to Figures 1 to 3 The flowchart illustrates method 400 according to this disclosure, which can be performed by system 200 for detecting and predicting vehicle instability. As will be understood from this disclosure, the sequence of operations within the method is not limited to... Figure 4 The method 400 may be executed in the order shown, but may be executed in one or more different orders as applicable and in accordance with this disclosure. In various embodiments, method 400 may be scheduled to run based on one or more predetermined events, and / or may run continuously during the operation of vehicle 10.

[0061] In step 410, the system 200 for detecting and predicting vehicle instability receives sensor data 240 from the sensor system 28. Sensor data 240 includes yaw rate, lateral acceleration, and longitudinal velocity. In step 420, the sensor data 240 is preprocessed, which may include applying a moving average function and calculating the time derivatives of the yaw rate and sideslip angle. The moving average function may be applied to the sensor data 240 itself or to the time derivatives of the yaw rate and sideslip angle. In step 430, phase diagram parameters are calculated based on the time derivatives of the yaw rate and sideslip angle. The phase diagram parameters include the phase diagram angle 106 and velocity amplitude M of the most recent sample window from the preprocessed measurement data 232 from step 420. The phase diagram angle 106 represents the angle between the yaw rate and sideslip angle in the phase diagram, and the velocity amplitude M represents the propagation or change rate of the data points in the phase diagram (each data point includes a pair of yaw rate and sideslip angle).

[0062] In step 440, the phase diagram parameters from step 430 are used to evaluate phase diagram conditions that predict upcoming vehicle instability events. Specifically, these phase diagram conditions include the basic vertical trend of data points in phase diagram 100 whose rate of change exceeds a certain minimum threshold, which will indicate prediction pattern 110. In one embodiment, equations 3 to 5 are evaluated in step 440 to confirm whether a vehicle instability prediction should be made.

[0063] In step 450, the phase diagram parameters from step 430 are used to evaluate phase diagram conditions that detect current vehicle instability events. Specifically, these phase diagram conditions include a basic horizontal trend of data points in phase diagram 100 whose rate of change exceeds a certain minimum threshold, which will indicate detection mode 110. In one embodiment, equations 6 to 8 are evaluated in step 450 to confirm whether vehicle instability detection should be performed. In some embodiments, only one of the prediction step 440 and the detection step 450 is performed.

[0064] In step 460, when steps 440 and / or 450 indicate a predicted or detected vehicle instability, vehicle motion is controlled to correct the instability. For example, the constraints of the model predictive control algorithm may be adjusted based on the detected or predicted vehicle instability. In some embodiments, an instability level is determined according to Equation 9, which serves as the basis for constraint adjustment. In step 470, stability recovery is detected, resulting in a reset of the prediction or detection flag. Vehicle motion control may cease to be adjusted to correct vehicle instability. Stability recovery can be determined in various ways. In particular, the termination of the conditions leading to the prediction or detection of vehicle instability in steps 440 and 450 at least partially determines the vehicle stability recovery detection. In the absence of a detected vehicle stability recovery, vehicle motion control continues to be adjusted to bring the vehicle into a more stable state.

[0065] According to the system and method described in this paper, phase diagram parameters are derived to identify phase diagram patterns for vehicle stability monitoring. An early indication algorithm predicts the stability behavior of the vehicle body. Parameters derived from the phase diagram are used to identify the onset of vehicle instability. The method and system are independent of tire type and road conditions. Vehicle instability can be predicted and detected using a minimum number of measurements and reliable estimates, thus ensuring a low computational budget. Furthermore, an early indication and detection indication reset mechanism is provided.

[0066] Although at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that numerous variations exist. It should also be understood that the exemplary embodiments or multiple exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing the exemplary embodiments or multiple exemplary embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope of this disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A system for controlling a vehicle, the system comprising: Sensor systems; and At least one processor, operatively communicating with the sensor system, wherein the at least one processor is configured to execute program instructions, wherein the program instructions are configured to cause the at least one processor to: The sensor system receives the vehicle's yaw rate, lateral acceleration, and longitudinal velocity values. The sideslip angle parameter value is determined based on the yaw rate value, lateral acceleration value, and longitudinal velocity value. The phase diagram angle is determined based on the sideslip angle parameter value and the yaw rate value, wherein each phase diagram angle represents the angle between the yaw rate and the sideslip angle of the vehicle in the phase diagram of the yaw rate and the sideslip angle. Vehicle instability is detected or predicted based at least on the phase diagram angles; as well as When vehicle instability is detected or predicted, the vehicle is controlled to at least partially correct the vehicle instability. The program instructions are configured to cause the at least one processor to determine a speed amplitude value based on the sideslip angle parameter value and the yaw rate value, wherein the speed amplitude value represents the speed of movement of the vehicle within the phase diagram based on the yaw rate and sideslip angle, and wherein the detection or prediction of vehicle instability is based at least on the phase diagram angle and the speed amplitude value.

2. The system of claim 1, wherein the sideslip angle parameter value is the time derivative of the sideslip angle value, and wherein the program instructions are configured to cause the at least one processor to determine the time derivative of the yaw rate value and to determine the phase diagram angle based on the time derivative of the yaw rate value and the time derivative of the sideslip angle value.

3. The system of claim 1, wherein determining the phase diagram angle comprises an arctangent function based on the sideslip angle parameter value and the yaw rate value.

4. The system of claim 1, wherein detecting or predicting vehicle instability includes early prediction of vehicle instability and detection of vehicle instability.

5. The system of claim 4, wherein the early prediction of vehicle instability is performed based at least on the phase diagram angle and on the minimum change in the yaw rate value.

6. The system of claim 4, wherein the detection of vehicle instability is performed based at least on the phase diagram angle and on the minimum change in the sideslip angle parameter value.

7. The system of claim 4, wherein the early prediction of vehicle instability is performed at least based on the phase diagram angles representing a substantially vertical path defined by a varying yaw rate and a substantially constant sideslip angle in the phase diagram.

8. The system of claim 4, wherein the detection of vehicle instability is performed at least based on the phase diagram angles representing a basic horizontal path defined by a varying sideslip angle and a substantially constant yaw rate in the phase diagram.

9. A method for controlling a vehicle, the method comprising: The vehicle receives yaw rate, lateral acceleration, and longitudinal velocity values ​​from the sensor system via at least one processor. The sideslip angle parameter value is determined by the at least one processor based on the yaw rate value, lateral acceleration value, and longitudinal velocity value; The phase diagram angle is determined by the at least one processor based on the sideslip angle parameter value and the yaw rate value, wherein each phase diagram angle represents the angle between the yaw rate and the sideslip angle of the vehicle in the phase diagram of the yaw rate and the sideslip angle. Vehicle instability is detected or predicted via the at least one processor, based at least on the phase diagram angle; as well as When vehicle instability is detected or predicted, the movement of the vehicle is controlled via the at least one processor to at least partially correct the vehicle instability. The method further includes determining a speed amplitude value via the at least one processor based on the sideslip angle parameter value and the yaw rate value, wherein the speed amplitude value represents the speed of movement of the vehicle within the phase diagram based on the yaw rate and sideslip angle, and wherein the detection or prediction of vehicle instability is based at least on the phase diagram angle and the speed amplitude value.

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