System and method for determining whether a vehicle is understeering or oversteering

Abstract

Systems and methods are provided for determining whether a vehicle is understeering or oversteering. The system includes a controller circuit coupled to an IMU and an EPS and programmed to, for a first axle of a steered wheel, use IMU measurements and EPS signals to calculate an axle-based tire drag distance and estimate a saturation level based on a distance between the axle-based tire drag distance and zero. The system estimates, for a second axle of an unsteered wheel, an axle lateral force curve relative to a slip angle of the second axle and estimates a saturation level based on when the axle lateral force curve relative to the slip angle transitions from a positive value to a negative value. The saturation levels of the first axle and the second axle are integrated. The system determines, based on the integrated saturation level, that the vehicle is understeering or oversteering.

Description

Technical Field

[0001] This disclosure generally relates to automated control for operation in a mobile platform, and more specifically to systems and methods for determining whether a vehicle having a first axle and a second axle is understeer or oversteer. Background Technology

[0002] With the development of automated control in vehicle operation, various control aspects have presented technical problems that need improvement or resolution. One such technical problem is the tendency to enter oversteering or understeering situations. Oversteering or understeering leads to an objectively uncomfortable passenger experience and increases vehicle wear.

[0003] In addition to addressing the related problems, the following disclosure provides technical solutions to these technical problems. Furthermore, other desirable features and characteristics of the system and method will become apparent from the following detailed description and appended claims, in conjunction with the accompanying drawings and the foregoing background information. Summary of the Invention

[0004] An embodiment of a processor-based method is provided for determining whether a vehicle having a first axle and a second axle is in an understeer or oversteer condition. The method includes: receiving IMU measurements of the vehicle from an inertial measurement unit (IMU), the IMU measurements including lateral acceleration and yaw rate; receiving longitudinal velocity; receiving EPS signals from an electric power-steering system (EPS), the EPS signals including steering angle, steering angular velocity, and torque measurements; determining that a first axle is steered by the EPS while a second axle is not; calculating, for the first axle, a wheel-axle-based tire trail using the IMU measurements, longitudinal velocity, and EPS signals; estimating the saturation level of the first axle based on the distance between the wheel-axle-based tire trail and zero; estimating a wheel-axle lateral force curve relative to the slip angle of the second axle based on the IMU measurements; and estimating the saturation level of the second axle based on when the wheel-axle lateral force curve relative to the slip angle of the second axle crosses zero in a manner transitioning from positive to negative; integrating the saturation levels of the first and second axles; and determining whether the vehicle is in an understeer or oversteer condition based on the integrated saturation level and the estimated understeer angle.

[0005] In one embodiment, the method further includes: estimating the understeer angle of the vehicle; and standardizing the slip angle of the second axle based on the axle-based tire trail of the first axle and the estimated understeer angle of the vehicle.

[0006] In one embodiment, the method further includes calculating the understeer angle of the vehicle.

[0007] In one embodiment, the method further includes: calculating the axle-based self-aligning torque of the first or second axle based on the EPS signal.

[0008] In one embodiment, the method further includes: calculating the axle-based self-aligning torque of the first or second axle based on the suspension in the vehicle.

[0009] In one embodiment, the method further includes: estimating a standardized first axle slip angle based on a calculated standardized tire trailing distance PT; and determining that the first axle is at its maximum saturation level when the standardized first axle slip angle increases beyond a predetermined value.

[0010] In one embodiment, the method further includes estimating the understeer angle of the vehicle, where the understeer angle represents the difference between the slip angle of the first axle and the slip angle of the second axle.

[0011] In one embodiment, the method further includes integrating the saturation levels of the first and second axial axes using a Kalman filter.

[0012] In one embodiment, the method further includes generating commands for actuators in the vehicle's drive system based on whether there is understeer or oversteer.

[0013] In one embodiment, the method further includes combining the integrated saturation level of the first and second wheel axles with the sensed vehicle response to steering commands from the EPS to determine whether the vehicle is in a state of terminal understeer or terminal oversteer.

[0014] In one embodiment, a system is provided for determining whether a vehicle having a first wheel axle and a second wheel axle is in an understeer or oversteer condition. The system includes: an inertial measurement unit (IMU) configured to provide IMU measurements of the vehicle, including lateral acceleration and yaw rate; an electric power steering (EPS) system configured to provide EPS signals including steering angle, steering angular velocity, and torque measurements; and controller circuitry operatively coupled to the IMU and EPS, the controller circuitry being programmed to: determine that the first wheel axle is steered by the EPS while the second wheel axle is not; and, for the first wheel axle, calculate the first wheel axle's position using the IMU measurements and the EPS signals. The axle-based tire trail is used to estimate the saturation level of the first axle based on the distance between the axle-based tire trail and zero; the axle lateral force curve relative to the slip angle of the second axle is estimated based on IMU measurements; and the saturation level of the second axle is estimated based on when the axle lateral force curve relative to the slip angle of the second axle crosses zero in a manner that changes from a positive value to a negative value; the saturation levels of the first and second axles are integrated; and the vehicle is determined to be in an understeer or oversteer condition based on the integrated saturation level and the understeer angle.

[0015] In one embodiment, the controller circuit is also programmed to: estimate the understeer angle of the vehicle; and normalize the slip angle of the second axle based on the axle-based tire trail of the first axle and the estimated understeer angle of the vehicle.

[0016] In this embodiment, the controller circuit is also programmed to calculate the understeer angle.

[0017] In this embodiment, the controller circuit is also programmed to calculate the axle-based self-aligning torque of the first or second axle based on the EPS signal.

[0018] In one embodiment, the controller circuit is further programmed to calculate, based on the suspension in the vehicle, the axle-based self-aligning torque of the first or second axle.

[0019] In one embodiment, the controller circuit is further programmed to: estimate a standardized first axle slip angle based on a calculated standardized tire trailing distance PT; and determine that the first axle is at its maximum saturation level when the standardized first axle slip angle increases beyond a predetermined value.

[0020] In one embodiment, the controller circuit is further programmed to estimate the understeer angle of the vehicle, which represents the difference between the slip angle of the first axle and the slip angle of the second axle.

[0021] In one embodiment, the controller circuit is further programmed to integrate the saturation levels of the first and second wheelsets using a Kalman filter.

[0022] In one embodiment, the controller circuit is also programmed to generate commands for the actuators in the vehicle's drive system based on whether there is understeer or oversteer.

[0023] In one embodiment, the controller circuitry is further programmed to combine the integrated saturation level of the first and second wheel axles with the sensed vehicle response to steering commands from the EPS to determine whether the vehicle is in a state of terminal understeer or terminal oversteer. Attached Figure Description

[0024] Exemplary embodiments will be described below in conjunction with the following figures, wherein the same numerals denote the same elements, and wherein:

[0025] Figure 1 This is a schematic diagram illustrating a system implemented on a vehicle according to various embodiments for determining whether a vehicle having a first wheel axle and a second wheel axle is in an understeer or oversteer situation.

[0026] Figures 2 to 3 Provided information about Figure 1 Additional details about the two systems on the vehicle;

[0027] Figures 4 to 6 Used to show by Figure 1 The determinations made by various embodiments of the system; and

[0028] Figure 7 A process flowchart is provided to illustrate an example method, according to various embodiments, for determining whether a vehicle having a first axle and a second axle is in an understeer or oversteer situation. Detailed Implementation

[0029] 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 field, background art, summary of the invention, or the following detailed description.

[0030] Embodiments of this disclosure are described herein according to 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 may 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.

[0031] As used herein, the term "module" may refer alone or in any combination to any hardware, software, firmware, electronic control components, processing logic, and / or processor device. In various embodiments, a module is one or more of the following: application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), electronic circuitry, a computer system including a processor (shared, dedicated, or grouped) and memory executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide functionality attributable to the module.

[0032] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, machine learning models, radar, lidar, image analysis, and other functional aspects of the system (and its various operating components) will not be described in detail herein. Furthermore, the connecting lines shown in the various figures included herein are intended to represent example 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.

[0033] Exemplary embodiments provide technical solutions to the problem of oversteering or understeering of vehicles. Oversteering or understeering leads to an objectively uncomfortable passenger experience and increases vehicle wear and tear.

[0034] The provided embodiments implement an algorithm that estimates the axle saturation levels of both steered and non-steered (also known as non-steering) wheel axles and uses the estimates to determine oversteer and understeer conditions. The central platform controller and drive system can use the outputs of the system and method to predict and smooth vehicle response, thereby providing an objectively improved passenger experience.

[0035] Figure 1This is a functional block diagram depicting an example mobility platform. The example mobility platform is vehicle 100, which is capable of moving, towing, and transporting passengers from one location to another. Vehicle 100 is depicted as a passenger car in the illustrated embodiment, but other vehicle types may also be used, including motorcycles, taxis, fleets, buses, vans, trucks, SUVs, other cars, recreational vehicles (RVs), locomotives, and other vehicles. As generally understood, vehicle 100 may include a body, chassis, and wheels 20, each wheel being rotatably coupled to the chassis near a corresponding corner of the body. Vehicle 100 is depicted as having four wheels 20, but the number of wheels 20 may vary in other embodiments. Vehicle 100 may be autonomous or semi-autonomous. Vehicle 100 includes at least a set of functional blocks (drive system 106), which generally includes known vehicle systems for vehicle operation, such as a propulsion system, a drivetrain, an electric power steering system (EPS 107), wheel actuators, wheel speed sensors providing longitudinal velocity, and a braking system. The drive system 106 can generate various signals, including vehicle speed and vehicle acceleration. In various embodiments, the drive system 106 is operatively coupled to one or more on-board components and systems via a communication bus 130.

[0036] In the environment surrounding vehicle 100, external sources 150 include one or more other mobile platforms outside vehicle 100 (also referred to herein as "road actors"). As described herein, a system generally shown as system 102 for determining whether vehicle 100 is in an understeer or oversteer condition (where the vehicle has a first axle and a second axle) includes controller circuitry 104 programmed or configured to function as a saturation determiner. In various embodiments, controller circuitry 104 is communicatively coupled to onboard systems and components via communication bus 130, as shown in connection 105. Controller circuitry 104 can transmit commands and controls for various onboard systems and components via connection 105 and communication bus 130. Controller circuitry 104 can obtain information from and about various road actors via onboard camera system 118 and sensors, and / or via transceiver 112.

[0037] Returning to vehicle 100, vehicle 100 may include one or more other components and / or onboard systems, each typically communicating with controller circuitry 104 via communication bus 130. Non-limiting examples of onboard components include drive system 106, central platform controller 108, user interface 114, transceiver 112, inertial measurement unit (IMU) 116, camera system 118 and sensors, mapping system 110, and navigation system 120. The function and operation of each of these components are described in more detail below.

[0038] In various embodiments, the central platform controller 108 can receive and integrate communications from various modules and systems known to exist in the vehicle 100 described above. Therefore, in some embodiments, the inputs provided by the central platform controller 108 to the controller circuit 104 may include or represent user inputs (including steering, braking, and speed requests), mobile application and system inputs, inputs from external communications (e.g., via transceiver 112), and inputs based on the inertial measurement unit (IMU 116), navigation system 120, mapping system 110, camera system 118 and sensors, and drive system 106.

[0039] User interface 114 can provide passengers in vehicle 100 with any combination of touch, voice / audio, cursor, button pressing, and gesture control. Therefore, user interface 114 may include display devices and audio devices, as known in the industry.

[0040] Transceiver 112 can be configured to enable communication between onboard components and systems and various external sources 150, such as cloud server systems. Therefore, in various embodiments, transceiver 112 includes hardware and software to support one or more communication protocols for wireless communication 151 (e.g., WiFi and Bluetooth) between controller circuitry 104 and external sources such as routers, the Internet, cloud, satellites, communication towers, and ground stations.

[0041] The IMU 116 is a known inertial measurement unit in the mobile platform industry. The IMU 116 can interact with various external sources via transceiver 112 to provide information about the vehicle's position in three-dimensional space at any given time. IMU 116 measurements can be rooted in a Cartesian coordinate system 202, approximately located at the center of gravity of the vehicle 100, such as... Figure 2 As shown. Therefore, IMU 116 measurements can include the forward / longitudinal speed / velocity and acceleration, lateral speed and acceleration, yaw rate, and altitude / elevation speed and acceleration of vehicle 100. In various embodiments, IMU 116 measurements are used to estimate the axle lateral force Fy. Figure 2 The diagram shows the lateral force Fy 204 of the front / first axle 152 and the lateral force Fy 206 of the rear axle 154. In various embodiments, instead of the front and rear axles, axles 152 and 154 are referred to as the first and second axles, or as the steering axle and the non-steering axle.

[0042] Map system 110 includes a database for storing up-to-date and high-resolution maps of streets, environmental features, etc.

[0043] The navigation system 120 can acquire and process signals from various onboard components to determine current position, trajectory, speed, acceleration, etc., and coordinate with the central platform controller 108, IMU 116 and map system 110 to plan future position, trajectory, speed, acceleration, turning, etc.

[0044] Camera system 118 and sensors include one or more cameras and sensors for detecting the position and movement of road participants and features around the vehicle. Camera system 118 may include one or more optical cameras (e.g., forward, 360-degree, rearward, lateral, stereo, etc.), thermal (e.g., infrared) cameras, etc., mounted on the vehicle and capable of zooming in and out. Camera system 118 may include a front collision module (FCM), an augmented reality camera (ARC), etc., or may be part of them. In operation, the cameras in camera system 118 and sensors sense light levels, brightness, edges, contrast, light saturation, etc., and convert the sensed information into data that can be placed on communication bus 130. In an embodiment, camera system 118 includes object recognition software. Sensors in camera system 118 and sensors may be configured to send, receive, and process LiDAR, radar, or other signals to help determine the position and movement of nearby road participants.

[0045] In various embodiments, such as Figure 1As shown, the controller circuitry 104 is implemented as an enhanced computer system, including a computer-readable storage device or medium (memory 54) for storing instructions, algorithms, and / or programs (such as a vehicle target localization algorithm and multiple pre-programmed thresholds and parameters), a processor 50 for executing the program 56, and an input / output interface (I / O) 52. For example, the computer-readable storage device or medium (memory 54) may include volatile and non-volatile memory in the form of read-only memory (ROM), random access memory (RAM), and non-volatile memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operational variables when the processor 50 is powered off. Memory 54 can be implemented using any of many known memory devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combined memory device capable of storing data (some of which represents executable instructions used by the processor 50 when controlling the vehicle 100). In various embodiments, the processor 50 is configured to implement system 102. The memory 54 can also be used by the processor 50 to cache data, temporarily store comparison and analysis results, etc. During method initialization or installation operations, the information in the memory 54 can be organized and / or imported from external sources; this information can also be programmed via the user I / O interface.

[0046] Input / output interface (I / O) 52 can be operatively coupled to processor 50 via a bus and enables communication within circuit 104 as well as communication outside circuit 104. Input / output interface (I / O) 52 may include one or more wired and / or wireless network interfaces and may be implemented using any suitable methods and means. In various embodiments, input / output interface (I / O) 52 includes hardware and software to support one or more communication protocols for wireless communication between processor 50 and external sources such as satellites, clouds, communication towers, and ground stations. In various embodiments, input / output interface (I / O) 52 supports communication with technicians and / or for direct connection to one or more storage interfaces of a storage device.

[0047] During operation of system 102, processor 50 loads and executes one or more algorithms, instructions, and rules embodied in program 56, thereby controlling the general operation of system 102. During operation of system 102, processor 50 may receive data from communication bus 130 or external source 150. In various embodiments of system 102, controller circuitry 104 may: perform operations belonging to system 102 according to algorithms; perform operations according to state machine logic; and perform operations according to logic in a programmable logic array.

[0048] Although exemplary embodiments of system 102 are described in the context of controller circuitry 104 implemented as a full-featured enhanced computer system, those skilled in the art will recognize that the mechanisms of this disclosure can be distributed as a program product including program 56 and predefined parameters. Such a program product may include an arrangement of instructions organized into multiple interdependent program code modules, each configured to implement a separate process and / or perform a separate algorithmic operation, arranged to manage data flow through system 102. Each program code module may each include an ordered list of executable instructions for implementing the logical functions of the process performed by system 102. When executed by a processor (e.g., processor 50), the instructions in the program code modules cause the processor to receive and process signals and perform the logic, calculations, methods, and / or algorithms described herein to automatically and in real-time perform vehicle target localization and generate relevant commands.

[0049] Once developed, the program code modules constituting the program product can be stored and distributed individually or together using one or more types of non-transitory computer-readable signal-bearing media (such as non-transitory computer-readable media) that can be used to store and distribute instructions. Such a program product can take many forms, and this disclosure applies equally regardless of the type of computer-readable signal-bearing medium used to perform the distribution. Examples of signal-bearing media include recordable media such as floppy disks, hard disks, memory cards, and optical disks, as well as transmission media such as digital and analog communication links. It should be understood that in some embodiments, cloud-based storage and / or other technologies can also be used as storage and as a program product for viewing time-based license requests.

[0050] Turning Figure 3 And continue to refer to Figures 1 to 2 , Figure 3 Details are provided showing the steering-related signals and measurements associated with the steering wheel axle 152 that can be provided to and / or sensed by the EPS 107. The steering wheel 302 is mechanically connected to the torsion bar 304. The system 102 is able to determine whether the wheel axle is turning. For the steering wheel axle, manually supplied torque, steering angle, and steering angular velocity 306 can be sensed by a block and converted by the EPS 107 into applied torque 307 to rotate the steering wheel axle 152. Simultaneously, the drive system 106 can control speed and acceleration 308. The applied torque 310 is responsive to commands from the EPS 107 and drive system 106. The wheels 20 (depending on the corresponding tires) can respond to the applied torque 310 with a trajectory 314 embodying a combination of measured values ​​312.

[0051] To explain the terminology used in this article, Figure 4An example of a wheel 20 on axle 152 is provided, with wheel 20 in contact with the driving surface. With vehicle 100 in operation and in response to driving conditions, the following measurements of tire 422 on wheel 20 are shown: the slip area 424 and contact surface 426 of tire 422. Furthermore, a combination of the aforementioned measurements 312 is depicted for tire 422; measurements 312 include yaw direction 416, slip angle (alpha) 404, and travel direction 420. In the graph on the left, the axle lateral force Fy 406 and self-aligning torque 408 are plotted on the Y-axis 402 as measures of the wheel slip angle alpha 404. It can be seen that the axle lateral force Fy 406 is at its maximum (i.e., maximum saturation) when the self-aligning torque 408 is at its minimum.

[0052] In another graph above the tire diagram, the axle lateral force Fy is plotted on the Y-axis, and the tire surface is plotted on the X-axis. Recursive least squares with a forgetting factor is used to calculate the axle-based tire trail. 410, Draw the tire trail. 410 illustrates the resulting lateral axle force Fy 406 (at this position, the tire grips the driving surface, and contact surface 426 begins), and the self-aligning torque (M) occurring along contact surface 426. z )414.

[0053] In response to the measured value 312, the system 102 can calculate the axle-based self-aligning torque (M) for a given axle. z 414. System 102 can first calculate the mechanical drag distance t. m (δ), which is a function of the suspension in vehicle 100 and EPS 107. Here, δ is the road wheel angle, incorporating the self-aligning torque (M). z )414 total trailing distanceΓ f Including mechanical trail and tire trail As shown in Equation 1.

[0054]

[0055] Summarize Equation 1 and Figures 1 to 4 Based on the information provided, system 102 can estimate the saturation level of the steering wheel axle according to the tire trail length based on the axle, where zero length represents maximum saturation and maximum length represents unsaturation. This determination of saturation is independent of road conditions and is also applicable to complex slip conditions.

[0056] System 102 can determine that vehicle 100 may have one or more non-steering axles, also referred to as non-steering axles. To estimate the saturation level of the non-steering axles, system 102 can employ a first method that utilizes the aforementioned slip angle (alpha, α) along with lateral acceleration, yaw rate, and longitudinal velocity. In some embodiments, the lateral acceleration and yaw rate are derived from an IMU, while the longitudinal velocity is derived from sensors on the wheels.

[0057] System 102 determines the maximum saturation level. In a first embodiment, the first axle is at the maximum saturation level when the curve of the standardized saturation level changes from a positive value to a negative value. In another embodiment, system 102 estimates the standardized first axle slip angle based on the calculated standardized tire trailing distance PT; and determines that the first axle is at the maximum saturation level when the standardized first axle slip angle increases beyond a predetermined value.

[0058] Turning Figure 5 The algorithm in program 56 estimates the slope of the normalized wheel axle lateral force curve 506 (measured relative to the Y-axis 502, the normalized wheel axle lateral force Fy) relative to the slip angle (alpha, α) measured along the X-axis 504, based on IMU 116 measurements and the vehicle's longitudinal velocity (this slope is sometimes called the "slip slope"). When the slope exhibits a change in sign from positive to negative, the point 508 where the sign changes indicates that the wheel axle has used all its lateral load capacity and is saturated. This determination of the saturation of a wheel axle without steering is independent of road conditions and also applies to complex slip conditions.

[0059] Slope estimation requires first calculating the derivatives of the lateral force Fy on the wheel axle and the slip angle (alpha). Then, a recursive least squares method with a forgetting factor can be used to estimate the slip slope. In this embodiment, a bicycle model is used, as shown in Equations 2 through 4, where line 510 represents the following C. R .

[0060]

[0061]

[0062]

[0063] Turning Figure 6 In various embodiments, system 102 can, for example, use Equation 5, a tire model approximation, to estimate the standardized front (first axle) slip angle based on the calculated standardized tire trailing distance PT. Figure 6 In the diagram, Y-axis 602 represents tire trail, while X-axis 604 represents slip angle.

[0064]

[0065] Where 606 is the curve graph of equation 5, and and (608) is the front tire saturation slip angle. Minimum tire trailing distance (PT) is plotted at 610. min ), and depicted the maximum slip angle (α) max )612.

[0066] In addition to estimating the understeer angle, this estimate can also be used to estimate the rear / second axle slip angle. In this estimation method, the understeer angle represents the difference between the front / first axle slip angle and the rear / second axle slip angle, as shown in Equation 6.

[0067]

[0068] Therefore, the standardized backslip angle is shown in Equation 7.

[0069]

[0070] in, and

[0071] In Equation 7, for similar front and rear tires and in the absence of load transfer, G = 1.

[0072] Now, let's describe understeer in more detail. Understeer angle (δ) u This reflects the difference between the front and rear wheel axle slip angles, and therefore also their saturation levels. A large understeer angle indicates both end-of-pivot understeer and end-of-pivot oversteer. The algorithm in program 56 combines the actual road wheel angle (δ) sensed by the local sensor as part of the measurement value 312 with the desired neutral steering angle (δ). n This is compared to estimate vehicle level information such as the understeering angle (sometimes abbreviated as understeer angle). The desired neutral steering angle δ is calculated. n and equal to Where L is the predefined wheelbase length of the vehicle, r is the yaw rate, and V x It is longitudinal speed. Understeer angle is a characterization of the response to steering inputs (such as...) Figure 3 The continuous signal of the vehicle response (306 in the equation). Next, starting with equation 8, we can calculate equation 9.

[0073] δ u =δ-δ n Equation 8

[0074] -|α * |>δu *sign(a y )>|α * Equation 9

[0075] In equation 9, α * Predefined in memory 54 and calculated based on tire characteristics / data. The left side of Equation 9 represents the end-point oversteer case, and the right side of Equation 9 represents the end-point understeer case.

[0076] Readers may notice that two separate sources of information and methods have been provided to estimate the saturation level of the rear axle or second axle: estimating the saturation level of the second axle based on when the axle lateral force curve relative to the slip angle crosses zero (from a positive to a negative value); and standardizing the slip angle of the second axle based on the axle-based tire trail of the first axle and the estimated understeer angle of the vehicle.

[0077] System 102 can employ a Kalman filter by combining the vehicle's lateral dynamics equations, thereby fusing these methods and integrating the saturation levels of the first and second wheel axles, as shown in Equation 10.

[0078]

[0079] The corresponding Kalman filter has status, Input and The measured values. Among them Depend on estimate, and It is the standardized backslip angle and is estimated by Equation 7.

[0080] Recursive least squares with a forgetting factor is used to calculate the final rear axle saturation based on Equation 11, which is similar to Equation 4.

[0081]

[0082] Using the above determination, the understeer state interpreter module programmed in the controller circuit 104 can combine the estimated saturation level with the understeer angle estimate (e.g., using predefined rule-based logic) to determine whether the vehicle 100 is currently or about to enter an end-stage understeer or end-stage oversteer state.

[0083] Turn now Figure 7 The following description of method 700 can be referred to in conjunction with the above. Figures 1 to 2The mentioned components. In various embodiments, portions of method 700 may be performed by different components of the described system 102. It should be understood that method 700 may include any number of additional or alternative operations and tasks. Figure 7 The tasks shown do not need to be performed in the illustrated order, and method 700 can be incorporated into a more comprehensive process or method with additional functionality not described in detail herein. Furthermore, if the intended overall functionality remains intact, it can be omitted from embodiments of method 700. Figure 7 One or more tasks are shown.

[0084] At 702, system 102 is initialized and begins receiving measurements from IMU 116 as the vehicle operates. At 704, EPS 107 signals are received. Figures 1 to 3 Referring to the discussion of details regarding IMU measurements and EPS signals, at 706, system 102 determines which axles are steerable and which are not. In one embodiment, the front axles are steerable while the rear axles are not, but this can vary in other embodiments. Determining which axles are steerable can be hardwired into system 102 or detected via software polling of EPS 107. At 708, system 102 begins the task of estimating the saturation level of the steerable axles. As described above, this task relies on first calculating the tire trail based on the axle using IMU measurements and EPS signals for the steerable axles. At 706, the estimate of the saturation level of the first axle is a function of the tire trail length based on the axle.

[0085] At 710, system 102 proceeds to the task of estimating the saturation level of the unsteering axle. At 710, this includes calculating the axle lateral force curve relative to the slip angle of the second axle based on IMU measurements; and estimating the saturation level of the second axle based on when the axle lateral force curve relative to the slip angle of the second axle crosses zero (from a positive value to a negative value).

[0086] At 712, system 102 performs the task of integrating saturation levels. In various embodiments, this includes integrating the saturation levels of the first and second wheelsets using a Kalman filter.

[0087] At 714, system 102 performs the task of determining whether the vehicle is in an understeer or oversteer condition. As described above, both understeer and oversteer conditions can be estimated using rule-based interpreter logic, which uses the saturation level and an estimated understeer angle to determine whether the vehicle is in or about to enter an understeer or oversteer terminal state.

[0088] After 714, method 700 may end or proceed to other steps, such as generating commands for the actuators in the drive system 106 of vehicle 100 based on understeer or oversteer conditions; or combining the integrated saturation level of the first and second wheel axles with the sensed vehicle response to steering commands from EPS to determine whether the vehicle is in a state of terminal understeer or terminal oversteer.

[0089] Therefore, the provided system 102 and method 700 offer a technical solution to the technical problems of oversteer or understeer encountered by available autonomous driving systems and methods. The provided embodiments estimate the saturation levels of the front and rear axles to determine oversteer and understeer conditions, which translates into an objectively improved passenger riding experience.

[0090] 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. Various changes may 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 processor-based method for determining whether a vehicle having a first axle and a second axle is in an understeer or oversteer condition, the method comprising: The vehicle receives inertial measurement unit (IMU) measurements, including lateral acceleration and yaw rate, from the IMU. Receive longitudinal velocity; Receive electric steering system signals from the electric steering system, the electric steering system signals including steering angle, steering angular velocity and torque measurement values; It is determined that the first axle is steered by the electric steering system, while the second axle is not steered by the electric steering system; For the first axle, the tire trail based on the axle is calculated using the inertial measurement unit measurement, the longitudinal velocity, and the electric steering system signal; The saturation level of the first axle is estimated based on the distance between the tire trailing distance based on the axle and zero. Based on the measurements taken by the inertial measurement unit, the lateral force curve of the wheel axle relative to the slip angle of the second wheel axle is estimated; and The saturation level of the second wheel axle is estimated based on when the lateral force curve of the wheel axle relative to the slip angle crosses zero in a manner that changes from a positive value to a negative value. Integrate the saturation levels of the first wheel axle and the second wheel axle; as well as Based on the integrated saturation level and understeer angle estimate, the vehicle is determined to be in an understeer or oversteer situation.

2. The processor-implemented method according to claim 1 further includes: Estimate the understeer angle of the vehicle; as well as The slip angle of the second axle is standardized based on the axle-based tire trail of the first axle and the estimated understeer angle of the vehicle.

3. The processor-implemented method according to claim 1 further includes: Based on the electric steering system signal, calculate the axle-based self-aligning torque of the first axle or the second axle.

4. The processor-implemented method according to claim 3 further includes: Further, based on the suspension in the vehicle, the axle-based self-aligning torque of the first axle or the second axle is calculated.

5. The processor-implemented method according to claim 1, further comprising: Based on the calculated standardized tire trailing distance PT, estimate the standardized first axle slip angle; as well as When the standardized first wheel axle slip angle increases beyond a predetermined value, the first wheel axle is determined to be at the maximum saturation level.

6. The processor-implemented method according to claim 5, further comprising: Estimate the understeer angle of the vehicle, the understeer angle representing the difference between the slip angle of the first axle and the slip angle of the second axle.

7. The processor-implemented method according to claim 1, further comprising: Based on the understeer or oversteer situation, commands are generated for the actuators in the vehicle's drive system.

8. A system for determining whether a vehicle having a first axle and a second axle is in an understeer or oversteer condition, the system comprising: An inertial measurement unit is configured to provide inertial measurement unit measurements of the vehicle, the inertial measurement unit measurements including lateral acceleration and yaw rate; An electric steering system configured to provide electric steering system signals, the electric steering system signals including steering angle, steering angular velocity, and torque measurements; as well as A controller circuit, operably connected to the inertial measurement unit and the electric steering system, is programmed to: It is determined that the first axle is steered by the electric steering system, while the second axle is not steered by the electric steering system; For the first axle, the tire trail based on the axle is calculated using the inertial measurement unit measurement value and the electric steering system signal; The saturation level of the first axle is estimated based on the distance between the tire trailing distance based on the axle and zero. Based on the measurements taken by the inertial measurement unit, the lateral force curve of the wheel axle relative to the slip angle of the second wheel axle is estimated; and The saturation level of the second wheel axle is estimated based on when the lateral force curve of the wheel axle relative to the slip angle crosses zero in a manner that changes from a positive value to a negative value. Integrate the saturation levels of the first wheel axle and the second wheel axle; as well as Based on the integrated saturation level and understeer angle, the vehicle is determined to be in an understeer or oversteer situation.

9. The system according to claim 8, wherein, The controller circuit is also programmed to calculate the axle-based self-aligning torque of the first axle or the second axle based on the electric steering system signal.

10. The system according to claim 9, wherein, The controller circuit is also programmed to combine the integrated saturation level of the first and second axles with the sensed vehicle response to steering commands from the electric steering system to determine whether the vehicle is in a state of terminal understeer or terminal oversteer.

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