Method and system for dynamically estimating understeer gradient of a vehicle

Abstract

The application discloses a method for dynamically estimating understeering gradient of a vehicle, and the method comprises the following steps: receiving a vehicle state signal; performing preliminary processing on the received vehicle state signal; establishing a vehicle kinematics model; dynamically estimating the understeering gradient of the vehicle by means of the established vehicle kinematics model; and optimizing the dynamically estimated understeering gradient. The application also discloses a system for dynamically estimating the understeering gradient of the vehicle.

Description

Technical Field

[0001] This application belongs to the field of vehicle control technology, specifically relating to a method and system for dynamically estimating the understeering gradient of a vehicle. Background Technology

[0002] With a fixed steering wheel angle, the vehicle's turning radius changes as speed increases during slow acceleration or constant speed travel. When the vehicle is in an understeer state, its turning radius increases. When the vehicle is in an oversteer state, its turning radius decreases. When the vehicle is in a neutral steering state, its turning radius remains constant. The understeer gradient is commonly used to describe the vehicle's steering characteristics. The understeer gradient is an important parameter for measuring the degree of vehicle steering and for implementing lateral control in autonomous driving vehicles.

[0003] In the vehicle design phase, the target value of the understeer gradient is typically derived theoretically. During the vehicle integration testing phase, the understeer gradient is obtained by the vehicle manufacturer through extensive experimentation. The understeer gradient obtained from both theoretical derivation in the vehicle design phase and experimentation in the vehicle integration testing phase has the following main problems: First, the target value of the understeer gradient derived theoretically is often not a fixed value, but rather a parameter range; understeer gradients within this range generally meet the design target. Second, although the understeer gradient obtained experimentally is a fixed value, this fixed value is measured under specific vehicle loads and tire types, while the understeer gradient of a vehicle in different states and environments is often dynamically changing. Finally, to obtain the corresponding understeer gradients for different states and environments to achieve full coverage, a large number of experimental scenarios, technical personnel, specialized equipment, and experimental time are required. Even then, the measured understeer gradient cannot be well applied to the lateral control of intelligent driving vehicles. In other words, obtaining the understeer gradient through extensive experimentation is a costly and impractical method. Summary of the Invention

[0004] In view of the aforementioned technical problems in the prior art, the purpose of this application is to propose a method for dynamically estimating the understeer gradient of a vehicle. This dynamic estimation method obtains the key factors affecting the understeer gradient at the vehicle level by modeling the whole vehicle kinematics. The established model can adapt to various vehicle masses, load distributions, and tire types.

[0005] This application proposes a method for dynamically estimating the understeer gradient of a vehicle, characterized by the following steps: receiving a vehicle state signal; performing preliminary processing on the received vehicle state signal; establishing a vehicle kinematic model; dynamically estimating the understeer gradient of the vehicle using the established vehicle kinematic model; and optimizing the dynamically estimated understeer gradient.

[0006] According to an optional implementation, the dynamic estimation method includes the following steps: transforming the estimated understeer gradient to obtain steering sensitivity and characteristic vehicle speed parameters; and outputting the estimated understeer gradient, steering sensitivity and / or characteristic vehicle speed parameters.

[0007] According to an optional implementation, the vehicle status signal includes a vehicle speed signal, a steering wheel angle signal, a yaw rate signal, and a signal validity flag. The vehicle status signal is provided by various sensors installed in the vehicle.

[0008] According to an optional implementation, various sensors installed in the vehicle include a vehicle speed sensor, a steering wheel angle sensor, and a yaw rate sensor.

[0009] According to an optional implementation, the preliminary processing includes verifying the validity of the vehicle status signal, compensating for the offset in the vehicle status signal, and smoothing the vehicle status signal.

[0010] According to an optional implementation, verifying the validity of the vehicle status signal includes: accepting the obtained vehicle status signal when the signal validity flag indicates that the vehicle status signal is valid, and designating the vehicle status signal as the default vehicle status signal when the signal validity flag indicates that the vehicle status signal is invalid.

[0011] According to an optional implementation, compensating for the offset in the vehicle status signal includes: correcting the vehicle status signal by means of the vehicle status signal offset, updating the vehicle status signal offset at a variable time interval, and if the difference between the updated vehicle status signal offset and the vehicle status signal offset before the update exceeds a threshold when updating the vehicle status signal offset, then the vehicle status signal before the update is still used after the update.

[0012] According to an optional implementation, smoothing the vehicle status signal includes applying a low-pass filter or a Kalman filter to the vehicle status signal.

[0013] According to an optional implementation, dynamically estimating the understeer gradient of a vehicle includes: specifying the understeer gradient as the measured value of the understeer gradient when the vehicle status signal indicates that the vehicle speed is zero or the vehicle is traveling straight; and performing low-pass filtering on the estimated understeer gradient.

[0014] This application also proposes a system for dynamically estimating the understeer gradient of a vehicle, the system being configured to perform the dynamic estimation method according to this application.

[0015] The advantages of the method and system for dynamically estimating the understeer gradient of a vehicle proposed in this application are: it can adapt to changes in vehicle suspension, tires, mass, load distribution, road surface adhesion, etc., and has good adaptability; it performs real-time estimation during vehicle driving, and has good real-time performance; it only requires the collection of basic vehicle signals and does not require the addition of additional high-cost sensors, and has good practicality; the estimated understeer gradient can be applied to the lateral control of intelligent driving vehicles, improving the accuracy of vehicle lateral control. Attached Figure Description

[0016] A more complete understanding of the foregoing and other aspects of this application will be gained from the detailed description that follows, in conjunction with the accompanying drawings. It should be noted that the scale of the drawings may vary for clarity, but this will not affect the understanding of this application.

[0017] Figure 1 This is a flowchart of a method for dynamically estimating the understeering gradient of a vehicle according to this application.

[0018] Figure 2 This is a block diagram of a system 200 for dynamically estimating the understeering gradient of a vehicle according to this application. Detailed Implementation

[0019] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0020] Figure 1 This is a flowchart of a method for dynamically estimating the understeer gradient of a vehicle according to this application. The dynamic estimation method includes the following steps:

[0021] 110: Receives vehicle status signals;

[0022] 112: Perform preliminary processing on the received vehicle status signals;

[0023] 114: Establish a vehicle kinematic model;

[0024] 116: Using the established vehicle kinematics model, the understeer gradient of the vehicle is dynamically estimated through preliminary processing of the vehicle state signal; and

[0025] 118: Optimize the dynamically estimated understeering gradient.

[0026] The dynamic estimation method for alternative sites includes the following steps:

[0027] 120: The estimated understeering gradient is transformed to obtain steering sensitivity and characteristic speed parameters;

[0028] 122: Output the estimated understeering gradient, steering sensitivity, and / or characteristic speed parameters.

[0029] The execution process of each step is explained in detail below.

[0030] For step 110, the vehicle status signals include vehicle speed signal, steering wheel angle signal, yaw rate signal, and signal validity flag. These vehicle status signals are provided by various sensors installed in the vehicle, including but not limited to vehicle speed sensor, steering wheel angle sensor, and yaw rate sensor.

[0031] For step 112, the preliminary processing includes verifying the validity of the vehicle status signal, compensating for offsets in the vehicle status signal, and smoothing the vehicle status signal. Specifically, taking vehicle yaw rate as an example, the yaw rate signal and the corresponding signal validity flag are usually provided by the same sensor. After receiving the yaw rate signal and the signal validity flag, if the signal validity flag indicates that the yaw rate signal is valid (e.g., the value of the signal validity flag is "1" or other defined valid value), the yaw rate signal obtained from the yaw rate sensor is accepted; if the signal validity flag indicates that the yaw rate signal is invalid (e.g., the value of the signal validity flag is "0" or other defined invalid value), the yaw rate signal is designated as the default yaw rate signal. The default yaw rate signal is usually set to a signal corresponding to a yaw rate of 0 rad / s. Verifying the signal validity can improve the reliability of the yaw rate signal itself and subsequent estimations.

[0032] Various sensors are susceptible to zero-point drift. Taking vehicle yaw rate as an example, the actual yaw rate is 0 rad / s, but the yaw rate sensor may measure a non-zero value. Therefore, a yaw rate signal offset can be introduced to correct the yaw rate signal output by the yaw rate sensor. This offset can be determined experimentally, for example, by keeping the vehicle stationary on a flat road and then reading the yaw rate sensor output. Since the performance of the yaw rate sensor changes over time, the yaw rate signal offset is not static but can be automatically updated. Furthermore, to prevent the yaw rate signal offset from being updated to a clearly erroneous value due to random errors, an upper limit can be set for the difference in yaw rate signal offset at each update. When updating the yaw rate signal offset, if the difference between the updated and unupdated yaw rate signal offset exceeds the upper limit, a random error is considered to have occurred during the update process. In this case, the unupdated yaw rate signal offset is used after the update to eliminate the influence of random errors. Introducing a yaw rate signal offset can yield a more accurate yaw rate signal.

[0033] After verifying the validity of the vehicle status signal and compensating for any offsets, a relatively accurate vehicle status signal can be obtained. However, this signal often contains significant noise. The sources of noise typically include interference from the external environment and the sensor's own structure. Therefore, it is necessary to smooth the vehicle status signal. Smoothing can be achieved by filtering the vehicle status signal. Specifically, low-pass filters or Kalman filters can be applied. The filtered vehicle status signal is smoother, which is beneficial for subsequent dynamic estimation.

[0034] For step 114, the established vehicle kinematic model is as follows:

[0035]

[0036] In equation (1):

[0037] δ fw It is the front wheel steering angle of the vehicle, which can be calculated based on the steering wheel angle and the transmission ratio of the vehicle's steering system.

[0038] L is the wheelbase of the vehicle;

[0039] γ is the yaw rate of the vehicle;

[0040] v is the vehicle's speed;

[0041] K v The insufficient steering gradient needs to be estimated;

[0042] Ay It is the vehicle's lateral acceleration.

[0043] G is the acceleration due to gravity; and

[0044] dr is the conversion factor.

[0045] For step 116, the expression for the insufficient steering gradient can be derived from equation (1):

[0046]

[0047] Substituting the vehicle speed, yaw rate and steering wheel angle obtained in step 112 into equation (2), the estimated value of the understeer gradient of the vehicle in the current state can be obtained.

[0048] When the vehicle speed is zero, i.e., when the vehicle is stationary, the understeer gradient cannot be estimated using equation (2). In this case, the understeer gradient can be specified as the understeer gradient measured experimentally.

[0049] For step 118, optimizing the dynamically estimated understeer gradient includes removing outliers and reducing noise in the estimated understeer gradient. The understeer gradient obtained in step 116 may contain outliers. For example, when the vehicle is traveling straight, the yaw rate is 0 rad / s, and the estimated understeer gradient in this case is an outlier. In this situation, the understeer gradient can be specified as a priori value, i.e., a value measured experimentally beforehand.

[0050] Although the vehicle state signal has been filtered in step 112, the estimated understeer gradient still contains some noise due to other variables that may be introduced into the algorithm and the amplification effect of the algorithm itself. Therefore, the estimated understeer gradient can be low-pass filtered and verified using prior values.

[0051] For step 120, the specific method for transforming the estimated understeer gradient is described below. The vehicle kinematics model of equation (1) can also be rewritten as:

[0052]

[0053] In equation (3):

[0054] K is the steering sensitivity; and

[0055] a y =A y *G.

[0056] From equations (1) and (3), the expression for steering sensitivity can be obtained:

[0057]

[0058] When a vehicle is in an understeer state, the steering sensitivity K and the characteristic vehicle speed V are used to determine the steering sensitivity K and the characteristic vehicle speed V. ch Relationship We can conclude that:

[0059]

[0060] In equation (5), K v A positive value for K corresponds to the vehicle being in an understeer state. v A value of zero corresponds to the vehicle being in a neutral steering state, while K... v A negative value corresponds to oversteer. Characteristic vehicle speed V ch This only applies to understeer; oversteer corresponds to the critical speed and is not within the scope of this application.

[0061] Figure 2 This is a block diagram of a system 200 for dynamically estimating the understeer gradient of a vehicle according to this application. System 200 includes a bus 202 or other communication mechanism for communicating information, and one or more hardware processors 204 coupled to the bus 202 to process the information. The hardware processors 204 may be, for example, one or more general-purpose microprocessors.

[0062] System 200 also includes a main memory 206 coupled to bus 202, such as random access memory (RAM), cache, and / or other dynamic storage devices, for storing information and instructions to be executed by processor 204. Hardware processor 204 executes the method described above for dynamically estimating the understeer gradient of a vehicle while running this information and instructions. Main memory 206 can also be used to store temporary variables or other intermediate information during the execution of instructions to be run by processor 204. Such instructions, when stored in a storage medium accessible to processor 204, make system 200 a special-purpose machine customized to perform the operations specified in the instructions. System 200 further includes a read-only memory (ROM) 208 or other static storage devices coupled to bus 202 for storing static information and instructions for processor 204. Storage devices 210, such as magnetic disks, optical disks, or USB drives (flash drives), are provided and coupled to bus 202 for storing information and instructions.

[0063] System 200 may implement the techniques described herein using custom hardwired logic, one or more ASICs or FPGAs, firmware, and / or program logic, which, when combined with a computer system, enable or program system 200 to become a special-purpose machine. According to one embodiment, the operations, methods, and processes described herein are executed by system 200 in response to processor 204 running one or more sequences of one or more instructions contained in main memory 206. Such instructions may be read into main memory 206 from another storage medium, such as storage device 210. Running the sequence of instructions contained in main memory 206 causes processor 204 to perform the processing steps described herein. In other embodiments, hardwired circuitry may be used instead of or in combination with software instructions.

[0064] Main memory 206, ROM 208, and / or storage 210 may include non-transitory storage media. As used herein, the term "non-transitory media" and similar terms refer to any medium that stores data and / or instructions that cause the machine to operate in a particular manner. Such non-transitory media may include non-volatile media and / or volatile media. Non-volatile media include, for example, optical or magnetic disks, such as storage device 210. Volatile media include dynamic memory, such as main memory 206. Common forms of non-transitory media include, for example, floppy disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a perforated pattern, RAM, PROM and EPROM, FLASH-EPROM, NVRAM, any other memory chips or cartridges, and networking versions of the above.

[0065] System 200 also includes a network interface 218 coupled to bus 202. Network interface 218 provides bidirectional data communication coupling to one or more network links connected to one or more local networks 212. For example, network interface 218 may be an Integrated Services Digital Network (ISDN) card, a cable modem, a satellite modem, or a modem providing data communication connectivity to a corresponding type of telephone line. As another embodiment, network interface 218 may be a local area network (LAN) card to provide data communication connectivity to a compatible LAN (or a WAN component communicating with a WAN). Wireless links may also be implemented. In any such implementation, network interface 218 transmits and receives electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0066] System 200 can send messages and receive data, including program code, via a network, network link, and network interface 218. In an Internet-based embodiment, the server may transmit request code for an application via the Internet, an ISP, a local network, and network interface 218.

[0067] The received code can be executed by processor 204 upon receipt and / or stored in storage device 210, or other non-volatile storage, for later execution.

[0068] Each process, method, and algorithm described in the preceding sections can be embodied in a code module that runs by a computer processor comprising one or more computer systems or computer hardware, and can be fully or partially automated. Each process and algorithm can be implemented, partially or entirely, in a specific application circuit.

[0069] The various features and processes described above can be used independently or combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Various operations of the exemplary methods described herein can be performed at least in part by algorithms. Algorithms can consist of program code or instructions stored in memory (e.g., the non-transitory computer-readable storage medium described above). Such algorithms can include machine learning algorithms. In some embodiments, machine learning algorithms may not be explicitly programmed into a computer to perform a function, but can learn from training data to make predictive models for performing that function.

[0070] The various operations of the exemplary methods described herein can be performed at least in part by one or more processors, which are either temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation whose operations are to perform one or more of the operations or functions described herein.

[0071] Similarly, the methods described herein can be implemented at least in part by a processor, with one or more specific processors being embodiments of hardware. For example, at least a portion of the operation of the method can be performed by one or more processors or an engine implemented by the processor. Furthermore, the one or more processors can also be operated to support the related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a group of computers (as embodiments of machines including processors), and these operations can be accessed via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application programming interface APIs).

[0072] The performance of certain operations can be distributed among processors, residing not only within a single machine but also deployed across multiple machines. In some exemplary embodiments, the processor or processor-implemented engine may reside in a single geographic location. In other exemplary embodiments, the processor or processor-implemented engine may be distributed across many geographic locations.

[0073] In this specification, multiple instances may be implemented as components, operations, or structures described as a single instance. Although each operation of one or more methods is described and illustrated as a separate operation, one or more of the operations may be performed simultaneously, and nothing requires these operations to be performed in the order described. Structures and functions presented as independent components in exemplary configurations may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as independent components. These and other variations, modifications, additions, and improvements are all within the scope of this document.

[0074] Although the general outline of the subject matter has been described with reference to specific exemplary embodiments, various modifications and variations may be made to these embodiments without departing from the broader scope of implementation of this disclosure. Such embodiments of the subject matter may be referred to herein, individually or collectively, as an invention, merely for convenience, and are not intended to voluntarily limit the scope of this application to any single disclosure or concept, if in fact more than one concept is disclosed.

[0075] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the specific embodiments should not be considered limiting, and the scope of the various embodiments is defined only by the appended claims and all their equivalents.

[0076] Any process descriptions, elements, or blocks depicted in the flowcharts and / or figures described herein should be understood as modules, segments, or portions of code that may represent one or more operable instructions for implementing a specific logical function or step in the process. Alternative embodiments are included within the scope of the implementations described herein, wherein elements or functions may be omitted, or may not be executed in the order shown or discussed, including substantially simultaneously or in reverse order, depending on the functions involved, as will be understood by those skilled in the art.

[0077] As used herein, the term "or" can be interpreted in an inclusive or exclusive sense. Furthermore, multiple instances of the resources, operations, or structures described herein can be provided as a single instance. Moreover, the boundaries between various resources, operations, engines, and data storage are somewhat arbitrary, and a particular operation is illustrated within the context of a particular illustrative configuration. The allocation of other functionalities is conceivable and can fall within the scope of various embodiments of this disclosure. Generally, structures and functions presented as separate resources in exemplary configurations can be implemented as combined structures or resources. Similarly, structures and functions presented as single resources can be implemented as separate resources. These and other variations, modifications, additions, and improvements are all within the scope of embodiments of this disclosure represented by the appended claims. Therefore, this specification and drawings should be viewed in an illustrative rather than restrictive sense.

[0078] Conditional language, such as “can,” “may,” or “able,” unless specifically stated or otherwise understood in the context of its use, is generally intended to express that certain implementations include certain features, elements, and / or steps, while other implementations do not. Therefore, such conditional language generally does not imply that a feature, element, and / or step is necessary in any way for one or more implementations, or that one or more implementations must include logic for determining whether such features, elements, and / or steps are included or performed in any particular implementation, with or without user input or prompting.

Claims

1. A method for dynamically estimating an understeer gradient of a vehicle, characterized by, The method includes the following steps: Receive vehicle status signals; The received vehicle status signal is preliminarily processed to obtain the vehicle speed, yaw rate and steering wheel angle; Establish a vehicle kinematic model; Using the established vehicle kinematics model, the understeer gradient of the vehicle is dynamically estimated; and the dynamically estimated understeer gradient is optimized. The vehicle kinematic model is as follows: ;δ fw The front wheel steering angle of the vehicle is calculated based on the steering wheel angle and the transmission ratio of the vehicle's steering system. L is the wheelbase of the vehicle, γ is the yaw rate, v is the vehicle speed, and K... v It is the insufficient steering gradient, A y It is the lateral acceleration of the vehicle. G is the acceleration due to gravity, d r It is the conversion factor; The expression for the understeer gradient, determined based on the vehicle kinematics model, is as follows: ; Substitute the vehicle speed, the yaw rate, and the steering wheel angle into the expression for the understeer gradient to obtain the understeer gradient of the vehicle in the current state.

2. The method according to claim 1, characterized in that, The method further includes the following steps: The estimated understeering gradient is transformed to obtain steering sensitivity and characteristic vehicle speed parameters; and Output the estimated understeering gradient, steering sensitivity, and / or characteristic speed parameters.

3. The method according to claim 1 or 2, characterized in that, Vehicle status signals include vehicle speed signal, steering wheel angle signal, yaw rate signal, and signal validity flag. Vehicle status signals are provided by various sensors installed in the vehicle.

4. The method according to claim 3, characterized in that, Various sensors installed in the vehicle include vehicle speed sensors, steering wheel angle sensors, and yaw rate sensors.

5. The method according to claim 3, characterized in that, The preliminary processing includes verifying the validity of the vehicle status signal, compensating for the offset in the vehicle status signal, and smoothing the vehicle status signal.

6. The method according to claim 5, characterized in that, Verifying the validity of vehicle status signals includes: If the signal validity flag indicates that the vehicle status signal is valid, the obtained vehicle status signal is accepted. If the signal validity flag indicates that the vehicle status signal is invalid, the vehicle status signal will be assigned the default value.

7. The method according to claim 5, characterized in that, Compensation for offsets in vehicle status signals includes: The vehicle status signal is corrected by using the vehicle status signal offset. The vehicle status signal offset is updated at variable time intervals. When updating the vehicle status signal offset, if the difference between the updated vehicle status signal offset and the original vehicle status signal offset exceeds a threshold, the original vehicle status signal will still be used after the update.

8. The method according to claim 5, characterized in that, Smoothing vehicle status signals includes: Apply low-pass filtering or Kalman filtering to the vehicle status signal.

9. The method according to claim 1 or 2, characterized in that, Dynamically estimating the understeer gradient of a vehicle includes: When the vehicle status signal indicates that the vehicle speed is zero or the vehicle is traveling straight, the understeer gradient is specified as the measured value of the understeer gradient; and The estimated understeering gradient is subjected to low-pass filtering.

10. A system for dynamically estimating the understeer gradient of a vehicle, characterized in that, The system includes: Bus (202), A hardware processor (204), coupled to a bus (202) to process information, A main memory (206), coupled to a bus (202), stores information and instructions to be executed by a hardware processor (204), which, when executing the information and instructions, performs a method for dynamically estimating the understeer gradient of a vehicle according to any one of claims 1-9. A static storage device (208), coupled to the bus (202) and storing static information and instructions for the hardware processor (204), and A storage device (210) is coupled to a bus (202) and stores information and instructions.

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