Steering oscillation detection and mitigation for automatic steering control

Abstract

Steering oscillation detection and mitigation for autonomous steering control is provided. In an example embodiment, a method and system for controlling oscillations for autonomous driving of a vehicle having a steering system including a steering wheel is provided. In one embodiment, the system includes one or more sensors of the vehicle and a processor. The one or more sensors are configured to obtain sensor data regarding a plurality of operating parameters of the vehicle, including regarding a commanded angle of the steering wheel and a measured angle of the steering wheel. The processor is coupled to the one or more sensors and is configured to facilitate at least: determining an oscillation measure of the vehicle based on the plurality of operating parameters of the vehicle, including based on a difference between the commanded angle of the steering wheel and the measured angle of the steering wheel over a period of time during operation of the vehicle.

Description

Technical Field

[0001] The technical field generally relates to vehicles, and more specifically to methods and systems for controlling oscillations in automatic steering control for vehicles. Background Technology

[0002] Some vehicles today are equipped with one or more autonomous driving features, including automatic steering control.

[0003] Accordingly, it is desirable to provide improved methods and systems for controlling the automatic steering of vehicles, including for controlling associated oscillations. Furthermore, other desirable features and characteristics of this disclosure will become apparent from the accompanying drawings and the foregoing technical and background information, based on the following detailed description and the appended claims. Summary of the Invention

[0004] According to an exemplary embodiment, a method for controlling oscillations for autonomous driving of a vehicle having a steering system including a steering wheel is provided. The method includes: acquiring sensor data via one or more sensors of the vehicle, the sensor data relating to a plurality of operating parameters of the vehicle, including a command angle and a measured angle of the steering wheel; and determining an oscillation measurement of the vehicle via a processor of the vehicle based on the plurality of operating parameters of the vehicle, including based on the difference between the command angle and the measured angle of the steering wheel over a period of time during vehicle operation.

[0005] Furthermore, in an exemplary embodiment, the oscillations include unexpected vehicle-level lateral motion oscillations of the vehicle during autonomous driving.

[0006] Furthermore, in an exemplary embodiment, the oscillation includes unexpected steering wheel oscillations during autonomous driving.

[0007] Furthermore, in an exemplary embodiment, the oscillation measurement is determined by the processor based on a plurality of operating parameters, which, in addition to the command angle and the measured angle of the steering wheel over a period of time during vehicle operation, include each of the following: vehicle acceleration, vehicle speed, and the corresponding angle of each of the plurality of wheels of the vehicle.

[0008] Furthermore, in an exemplary embodiment, the oscillation measurement is further determined by the processor based on each of the following: driver intent; roughness of the roadway on which the vehicle is operating; damping measurement for the vehicle; classification of the oscillation for the vehicle as involving an over-gain controller or an under-gain controller; and instability measurement associated with the oscillation.

[0009] Furthermore, in an exemplary embodiment, the method further includes: mitigating oscillations by adjusting the control gain of the power steering system according to instructions provided by the processor to the power steering system of the vehicle, the adjustment being based on oscillation measurements and a classification of whether the oscillations of the vehicle involve an over-gain controller or an under-gain controller.

[0010] Furthermore, in an exemplary embodiment, the method further includes determining, via a processor, that the oscillation involves an over-gain controller when the oscillation of the command handwheel angle coincides with the oscillation of the measured handwheel angle; and that the oscillation involves an under-gain controller when the frequency and amplitude of the oscillation are substantially the same but have a time delay greater than a predetermined time threshold.

[0011] Furthermore, in an exemplary embodiment, the mitigation of oscillations is further performed by the processor based on each of the following: quantification of the control effect on the oscillations; determination of the gain effect on the oscillations; and determination of whether the oscillations are temporary or permanent in nature.

[0012] Furthermore, in an exemplary embodiment, the processor determines that the oscillation is temporary when it is attributable to rough conditions of the roadway, the driver's intention, or both; and permanent when it is determined to exist in the absence of rough conditions of the roadway and the absence of the driver's intention.

[0013] In another exemplary embodiment, a system for controlling oscillations in autonomous driving of a vehicle is provided, the vehicle having a steering system including a steering wheel, the system including one or more sensors of the vehicle and a processor. The one or more sensors are configured to acquire sensor data relating to multiple operating parameters of the vehicle, including a command angle and a measured angle of the steering wheel. The processor is coupled to the one or more sensors and is configured to at least facilitate determining oscillation measurements of the vehicle based on the multiple operating parameters of the vehicle, including based on the difference between the command angle and the measured angle of the steering wheel over a period of time during vehicle operation.

[0014] Furthermore, in an exemplary embodiment, the oscillations include unexpected vehicle-level lateral motion oscillations of the vehicle during autonomous driving.

[0015] Furthermore, in an exemplary embodiment, the oscillation includes unexpected steering wheel oscillations during autonomous driving.

[0016] Furthermore, in an exemplary embodiment, the processor is further configured to at least facilitate the determination of oscillation measurements based on a plurality of operating parameters, which, in addition to the command angle and the measured angle of the steering wheel over a period of time during vehicle operation, include each of the following: vehicle acceleration, vehicle speed, and the corresponding angle of each of the plurality of wheels of the vehicle.

[0017] Furthermore, in an exemplary embodiment, the processor is further configured to facilitate the determination of oscillation measurements based on at least each of the following: driver intent; roughness of the roadway over which the vehicle is operating; damping measurements for the vehicle; classification of the oscillations for the vehicle as involving an over-gain controller or an under-gain controller; and instability measurements associated with the oscillations.

[0018] Furthermore, in an exemplary embodiment, the processor is further configured to at least facilitate: mitigating oscillations by adjusting the control gain of the power steering system according to instructions provided by the processor to the power steering system of the vehicle, the adjustment being based on oscillation measurements and a classification of whether the oscillations of the vehicle involve an over-gain controller or an under-gain controller.

[0019] Furthermore, in an exemplary embodiment, the processor is further configured to at least facilitate the determination that: when the oscillation of the command handwheel angle coincides with the oscillation of the measured handwheel angle, the oscillation involves an over-gain controller; and when the frequency and amplitude of the oscillation are substantially the same but have a time delay greater than a predetermined time threshold, the oscillation involves an under-gain controller.

[0020] Furthermore, in an exemplary embodiment, the processor is further configured to facilitate at least the mitigation of oscillations based on each of the following: quantification of the control effects of the oscillations; determination of the gain effects of the oscillations; and determination of whether the oscillations are temporary or permanent in nature.

[0021] Furthermore, in an exemplary embodiment, the processor is further configured to at least facilitate the determination that the oscillation is temporary when it is attributed to rough conditions of the roadway, the driver's intention, or both; and that the oscillation is permanent when it is determined to exist in the absence of rough conditions of the roadway and the absence of the driver's intention.

[0022] In another exemplary embodiment, a vehicle is provided, the vehicle including a body, a plurality of wheels, a drive system, a steering system, one or more sensors, and a processor. The drive system is configured to move the body via the plurality of wheels. The steering system includes a steering wheel and is coupled to the plurality of wheels. The one or more sensors are configured to acquire sensor data relating to a plurality of operating parameters of the vehicle, including a command angle of the steering wheel, a measured angle of the steering wheel, vehicle acceleration, vehicle speed, and a corresponding angle of each of the plurality of wheels. The processor is coupled to the one or more sensors and is configured to at least facilitate the determination of an oscillation measurement of the vehicle, the determination being based on the plurality of operating parameters of the vehicle, including based on each of: vehicle acceleration; vehicle speed; a corresponding angle of each of the plurality of wheels; a command angle of the steering wheel; and a measured angle of the steering wheel over a period of time during vehicle operation, including based on the difference between the command angle of the steering wheel and the measured angle of the steering wheel over a period of time during vehicle operation, wherein the oscillation includes unintended vehicle-level lateral motion oscillations of the vehicle during autonomous driving, unintended steering wheel oscillations of the vehicle's steering wheel during autonomous driving, or both. The processor is further configured to facilitate the determination of oscillation measurements based on at least each of the following: driver intent; roughness of the roadway over which the vehicle is operating; damping measurements for the vehicle; classification of the oscillations for the vehicle as involving an over-gain controller or an under-gain controller; and instability measurements associated with the oscillations.

[0023] Furthermore, in an exemplary embodiment, the processor is further configured to at least facilitate the determination that: when the oscillation of the command handwheel angle coincides with the oscillation of the measured handwheel angle, the oscillation involves an over-gain controller; and when the oscillations have approximately the same frequency and amplitude but a time delay greater than a predetermined time threshold, the oscillations involve an under-gain controller; and, according to instructions provided by the processor to the vehicle's power steering system, mitigate the oscillations by adjusting the control gain of the power steering system, the adjustment being based on oscillation measurements and a classification of whether the oscillations for the vehicle involve an over-gain controller or an under-gain controller; wherein the processor is further configured to at least facilitate the determination that: when the oscillations are attributable to rough road conditions, driver intent, or both; and when the oscillations are determined to be present in the absence of rough road conditions and driver intent, the oscillations are permanent; wherein the processor is further configured to at least facilitate oscillation mitigation based on each of the following: quantification of the control effect on the oscillations; determination of the gain effect on the oscillations; and determination of whether the oscillations are inherently temporary or permanent. Attached Figure Description

[0024] The present disclosure will be described below with reference to the following figures, wherein similar numerals denote similar elements, and wherein:

[0025] Figure 1 This is a functional block diagram of a vehicle including a control system according to an exemplary embodiment, the control system being used for automatic vehicle control, including for controlling automatic steering and associated oscillations;

[0026] Figure 2 This is a flowchart of a process for controlling automatic steering and associated oscillations according to an exemplary embodiment, and the process may be combined with... Figure 1 This is achieved through the vehicle and its control system; and

[0027] Figures 3-6 Depicting according to exemplary embodiments Figure 2 An exemplary implementation of the process. Detailed Implementation

[0028] The following detailed description is merely exemplary in nature and is not intended to limit this disclosure or its application and use. Furthermore, it is not intended to be bound by the foregoing background information or any theories set forth in the following detailed description.

[0029] Figure 1 A vehicle 100 according to an exemplary embodiment is shown. As described in further detail below, according to an exemplary embodiment, the vehicle 100 includes a control system 102 configured for automatic vehicle control, including for controlling automatic steering of the vehicle 100 and associated oscillations.

[0030] In various embodiments, vehicle 100 includes an automobile. Vehicle 100 can be any of several different types of automobiles, such as, for example, a sedan, van, truck, or sport utility vehicle (SUV), and in some embodiments can be two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD) and / or various other types of vehicles. In some embodiments, vehicle 100 may also include another type of vehicle, such as an aircraft, spacecraft, boat, etc.

[0031] Vehicle 100 includes a body 104 disposed on a chassis 106. The body 104 substantially surrounds the other components of vehicle 100. The body 104 and chassis 106 may together form a frame. Vehicle 100 also includes a plurality of wheels 112. Each wheel 112 is rotatably coupled to chassis 106 near a corresponding corner of body 104 to facilitate movement of vehicle 100. In one embodiment, vehicle 100 includes four wheels 112, although this may vary in other embodiments (e.g., for trucks and certain other vehicles).

[0032] like Figure 1As depicted, in an exemplary embodiment, a drive system 110 is mounted on a chassis 106 and drives wheels 112, for example, via axles 114. In some embodiments, the drive system 110 includes a propulsion system. In some exemplary embodiments, the drive system 110 includes a motor 113 coupled to its transmission, such as an internal combustion engine and / or an electric motor / generator. In some embodiments, the drive system 110 may vary, and / or two or more drive systems 110 may be used. Furthermore, in an exemplary embodiment, the drive system 110 controls the propulsion of the vehicle 100 based on input provided by the driver (i.e., via the accelerator pedal) and / or automatically controls the propulsion of the vehicle 100 via instructions provided by the control system 102.

[0033] like Figure 1 The vehicle depicted includes a steering system 115 that controls the steering of the vehicle 100. In various embodiments, the steering system 115 controls the steering of the vehicle 100 via steering components (e.g., including a steering column) coupled to axle 114 and / or wheels 112, and in various cases is automatically controlled via a control system 102, and in some cases is controlled via input provided by the driver through a steering wheel 116.

[0034] In an exemplary embodiment, the vehicle 100 also includes a braking system 118 that uses braking components to control the braking of the vehicle 100, the braking components being controlled via input provided by the driver (e.g., via the brake pedal) and / or automatically controlled via the control system 102.

[0035] exist Figure 1 In the depicted embodiments, the control system 102 is coupled to the steering system 115. In some embodiments, the control system 102 may also be coupled to the drive system 110, the braking system 118, and / or one or more other vehicle systems and / or components.

[0036] In various embodiments, as described above, the control system 102 controls automatic vehicle control, including controlling automatic steering of the vehicle 100. In various embodiments, the control system 102 controls automatic steering in a manner that controls oscillations relative to the vehicle 100, including by detecting and mitigating oscillations relative to the vehicle 100 related to the steering system 115. In various embodiments, the control system 102 according to... Figure 2 Process 200 and Figures 3-6 The implementation method is used to execute these features.

[0037] like Figure 1 As depicted, in various embodiments, as described in more detail below, the control system 102 includes a sensor array 120 and a controller 140. Furthermore, as... Figure 1As depicted in various embodiments, and as described in more detail below, the control system 102 also includes a display system 130, a transceiver 132, and a position system 134.

[0038] In various embodiments, sensor array 120 includes various sensors for acquiring sensor data about vehicle 100, including those for detecting and mitigating oscillations in relation to steering system 115 affecting vehicle 100. In the depicted embodiments, sensor array 120 includes one or more input sensors 122, steering sensor 124, detection sensor 125, rate sensor 126, and acceleration sensor 127. In some embodiments, sensor array 120 may also include one or more other sensors 128.

[0039] In various embodiments, one or more input sensors 122 receive input from the driver of vehicle 100. In some embodiments, the input includes the driver's engagement of steering wheel 116. In some embodiments, the input may also include the driver's activation or deactivation of automatic steering control, the vehicle's desired destination and / or route, input regarding the driver's engagement of the accelerator pedal, brake pedal, and / or other devices and / or systems of vehicle 100, and so on.

[0040] In various embodiments, one or more steering sensors 124 acquire steering sensor data. In some embodiments, the steering sensor 124 measures the position and / or angle of the steering wheel 116, and / or one or more other steering angles (such as the angles of the wheels 112, steering column, and / or other components of the steering system 115). In some embodiments, the steering sensor 124 may also measure the driver's engagement with the steering wheel 116 (e.g., in some embodiments and cases, such a sensor may also involve an input sensor 122).

[0041] In various embodiments, one or more detection sensors 125 include one or more external cameras, radar, lidar, sonar, infrared and / or other detection sensors configured to acquire camera images and / or other detection sensor data about the environment surrounding the vehicle 100, including the terrain of the roadway or other path on which the vehicle 100 is traveling.

[0042] In various embodiments, one or more rate sensors 126 include one or more rate and / or speed sensors of the vehicle 100, such as wheel speed sensors in some embodiments. Additionally, in various embodiments, one or more acceleration sensors 127 include one or more accelerometers of the vehicle 100 (while in some embodiments, data from the rate sensors 126 may also be used to calculate the acceleration of the vehicle 100).

[0043] Additionally, in some embodiments, the sensor array 120 may also include one or more other sensors 128, such as one or more gyroscopes and / or other sensors configured to obtain sensor data about road conditions and / or other environmental and / or vehicle parameters.

[0044] In some embodiments, the display system 130 is configured to provide the driver with information, instructions, inquiries, alerts, settings, statuses, displays, and related information, including those relating to autonomous driving, including automatic steering and associated oscillations. In various embodiments, the display system 130 includes a visual component, which includes a display screen providing visual notifications to the driver. In some embodiments, the display system 130 may also include one or more audio components (e.g., including speakers) providing audio notifications to the driver, and / or one or more other components, such as haptic components (e.g., by vibrating the driver's seat) providing one or more haptic notifications to the driver.

[0045] In some embodiments, transceiver 132 is configured to communicate with one or more remote servers and / or remote service providers via one or more wireless networks, including, for example, with regard to road conditions adjacent to vehicle 100.

[0046] In some embodiments, the location system 134 obtains information about the geographic location of the vehicle 100. In some embodiments, the location system 134 includes or is part of or coupled to a satellite-based system, such as the Global Positioning System (GPS) as part of a navigation system for the vehicle 100.

[0047] In various embodiments, controller 140 is coupled to sensor array 120 and steering system 115. In some embodiments, controller 140 is also coupled to drive system 110, braking system 118, display system 130, transceiver 132, position system 134, and / or one or more other vehicle systems. Furthermore, in various embodiments, controller 140 includes a computer system (also referred to herein as computer system 140) and includes processor 142, memory 144, interface 146, storage device 148, and computer bus 150. In various embodiments, controller (or computer system) 140 provides automatic vehicle control, including controlling automatic steering of vehicle 100 and associated oscillations, as well as other functions. In various embodiments, controller 140 is based on… Figure 2 The process involves 200 steps and Figures 3-6 The implementation method is used to provide these and other functions.

[0048] In various embodiments, the controller 140 (and in some embodiments, the control system 102 itself) is housed within the body 104 of the vehicle 100. In one embodiment, the control system 102 is mounted on the chassis 106. In some other embodiments, the controller 140 and / or the control system 102 and / or one or more components thereof may be located outside the body 104, for example, on a remote server, in the cloud, or in other devices.

[0049] It will be understood that controller 140 may otherwise differ from... Figure 1 The embodiments depicted herein. For example, controller 140 may be coupled to or may otherwise utilize one or more remote computer systems and / or other control systems (e.g., as part of one or more of the equipment and systems of vehicle 100 described above).

[0050] In various embodiments, processor 142 performs the computational and control functions of controller 140 and may include any type of processor or multiple processors, a single integrated circuit such as a microprocessor, or any suitable number of integrated circuit devices and / or circuit boards that cooperate to perform the functions of the processing unit. During operation, processor 142 executes one or more programs 152 contained in memory 144 and thus controls the general operation of controller 140 and the computer system of controller 140, typically while performing the processes described herein, such as... Figure 2 Process 200 and Figures 3-6 The implementation method.

[0051] Memory 144 can be any suitable type of memory. For example, memory 144 can include various types of dynamic random access memory (DRAM) (such as synchronous dynamic random access memory (SDRAM)), various types of static RAM (SRAM), and various types of non-volatile memory (such as programmable read-only memory (PROM) and flash memory). In some examples, memory 144 is located on and / or co-located with processor 142 on the same computer chip. In the depicted embodiment, memory 144 stores the aforementioned program 152 and stored values ​​157 (e.g., in various embodiments, for...). Figure 2 Process 200 and Figures 3-6 (The threshold of the implementation method).

[0052] Interface 146 allows communication, for example, from a system drive and / or another computer system to the computer system of controller 140, and can be implemented using any suitable methods and means. In one embodiment, interface 146 obtains various data from sensor array 120 and other possible data sources. Interface 146 may include one or more network interfaces for communicating with other systems or components. Interface 146 may also include one or more network interfaces for communicating with technicians, and / or one or more storage interfaces for connecting to storage devices, such as storage device 148.

[0053] Storage device 148 can be any suitable type of storage device, including various types of direct access storage and / or other memory devices. In one exemplary embodiment, storage device 148 includes a program product from which memory 144 can receive program 152, which performs one or more embodiments of one or more processes of this disclosure, such as Figure 2 The process involves 200 steps and Figures 3-6 The implementation method is as follows. In another exemplary embodiment, the program product may be directly stored in memory 144 and / or disk (e.g., disk 156) and / or otherwise accessed by memory 144 and / or disk, as referenced below.

[0054] Bus 150 is used to transmit programs, data, status, and other information or signals between various components of the computer system of controller 140. Bus 150 can be any suitable physical or logical device for connecting the computer system and components. This includes, but is not limited to, direct hardwired connections, fiber optic, infrared, and wireless bus technologies. During operation, program 152 is stored in memory 144 and executed by processor 142.

[0055] It will be understood that although this exemplary embodiment is described in the context of a full-featured computer system, those skilled in the art will recognize that the mechanisms of this disclosure can be distributed as program products in which one or more types of non-transitory computer-readable signal-bearing media are used to store their programs and instructions and to perform their distribution, such as non-transitory computer-readable media carrying programs and containing computer instructions stored therein for causing a computer processor (such as processor 142) to execute and run the program. Such program products can take various forms, and this disclosure applies equally regardless of the specific 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, and transmission media such as digital and analog communication links. It will be understood that cloud-based storage and / or other technologies may also be utilized in some embodiments. Similarly, it will be understood that the computer system of controller 140 may otherwise differ from other systems. Figure 1 In the depicted embodiments, for example, the computer system of controller 140 may be coupled to or may otherwise utilize one or more remote computer systems and / or other control systems.

[0056] Figure 2 This is a flowchart of a process 200 for controlling automatic steering and associated oscillations according to an exemplary embodiment. In various embodiments, process 200 may be combined with... Figure 1 This is achieved through the vehicle 100 and the control system 102. The following text combines... Figures 3-6 To describe process 200, Figures 3-6 An exemplary implementation of process 200 is described.

[0057] In various embodiments, process 200 begins when vehicle 100 is in an operating mode (e.g., while the vehicle is being driven). In one embodiment, the steps of process 200 are performed continuously when the driver is inside the vehicle and / or during operation of the vehicle.

[0058] like Figure 2 As depicted, in various embodiments, data is obtained (step 202). In various embodiments, sensor data is obtained from [the sensor data source] during step 202. Figure 1 The sensor array 120 obtained (and in some embodiments also via) Figure 1 The location system 134 and / or transceiver 132). In various embodiments, sensor data includes: (i) the vehicle's acceleration 204 (a x (ii) Vehicle speed 205 (V) x (iii) The vehicle's steering angle value 208, including the left front steering angle (ω) of each corresponding wheel 112 of the vehicle. LF Left rear turn angle (ω) LR Right front steering angle (ω) RF ) and right rear turn angle (ω) RR ); and (iv) correspond to Figure 1 The handwheel angle or steering wheel angle (θ) of the steering wheel 116. In various embodiments, as part of step 202, additional sensor data, including measurements of road roughness, driver input, etc., are also obtained.

[0059] In various embodiments, the data from step 202 is used in the steering performance monitor 212 of process 200. Specifically, in various embodiments, the data from step 202 is first used for oscillation detection 220 (corresponding to steps 222-228 described directly below) and for oscillation mitigation 230 (corresponding to steps 232-238 described further below). In some of these embodiments, as used throughout this application, the term "oscillation" refers to unintended lateral motion oscillations in autonomous driving, unintended steering wheel oscillations in autonomous driving, or both.

[0060] In various embodiments, driver intent detection (step 221) is performed as part of oscillation detection 220. In various embodiments, Figure 1 The processor 142 determines the driver's information about Figure 1 The intent of the steering wheel 116 includes determining whether any oscillations in the steering wheel 116 are due to the driver's intent (e.g., when manually steering the vehicle 100). In various embodiments, this is determined by... Figure 1 The data from the input sensor 122 and / or the steering sensor 124 (e.g., regarding the driver's engagement of the steering wheel as part of step 202) are determined. In various embodiments, the driver's intention is determined separately for each half-cycle and is stored in a buffer (e.g., as...). Figure 1 (A portion of the value 157 stored in memory 144).

[0061] Furthermore, in various embodiments, road roughness detection is performed (step 222). In various embodiments, Figure 1 The processor 142 determines roughness measurements (e.g., based on uneven surfaces, such as those from potholes and speed bumps, and / or other anomalies of the roadway or other path on which the vehicle 100 is traveling), including determining whether any oscillations in the steering wheel 116 are caused by the roughness of the roadway. In various embodiments, this is determined by a processor from... Figure 1 The data from the detection sensor 125 (and / or other sensors 128 in some embodiments) is determined as part of step 202. In various embodiments, the road roughness is determined separately for each half-cycle and stored in a buffer (e.g., as...). Figure 1 (A portion of the value 157 stored in memory 144).

[0062] Furthermore, in various embodiments, damping monitoring is performed (step 224). In various embodiments, damping monitoring is performed by processor 142 based on data from... Figure 1 The data from the steering sensor 124 is used to perform this (as part of step 202). Specifically, in various embodiments, Figure 1The processor 142 determines the damping of the steering system 115 for each half cycle according to the following formula. Estimate:

[0063] (Equation 1);

[0064] (Equation 2); and (Equation 3), where:

[0065] “x” indicates the error in the handwheel angle (HWA) (also known as “steering wheel angle”) of steering wheel 116;

[0066] “θr” and “θs” represent the command handwheel angle and the measurement handwheel angle, respectively.

[0067] “x1” and “x2” represent the corresponding HWA error peaks within consecutive half-cycles of oscillation; and

[0068] "" indicates the estimated damping at time point "1".

[0069] refer to Figure 3 In Figure 300, half a cycle is exemplarily represented. In an exemplary embodiment, Figure 300 includes an x-axis 301 representing time (t) (e.g., in seconds), and a y-axis 302 representing the HWA error x(t). Figure 3 As shown, an exemplary function 303 x(t) of the HWA error over time is provided. In the figure, the corresponding error values ​​x1 (304) and x2 (305) are shown (e.g., also corresponding to the magnitude of the function 303 x(t) of the HWA error over time).

[0070] Return to reference Figure 2 In various embodiments, oscillation classification is performed (step 226). In various embodiments, oscillation classification is also performed by processor 142 based on data from... Figure 1 The data from the steering sensor 124 is used to perform this (as part of step 202). Specifically, in various embodiments, Figure 1 The processor 142 classifies the handwheel oscillation into whether it has an over-gain controller or an under-gain controller, and identifies key characteristics for each category, including amplitude. and oscillation time period .

[0071] refer to Figure 4 This provides an exemplary implementation of the over-gain controller scenario for oscillation classification in step 226. Specifically, as... Figure 4The depiction, according to an exemplary embodiment, includes: (i) a first illustration 400 showing both the measured handwheel angle 402 and the command handwheel angle 404 as a function of time; and (ii) a second illustration 410 showing the vehicle lateral acceleration 412 as a function of time. Figure 4 As shown, with the over-gain controller, the command handwheel angle 404 does not oscillate significantly compared to the measured handwheel angle 402. Furthermore, in the depicted embodiment, the oscillation time period ranges from half a second to two seconds (i.e., 0.5 - 2 seconds).

[0072] refer to Figure 5 This provides an exemplary implementation of an undergain controller scenario for oscillation classification in step 226. Specifically, as Figure 5 The depiction, according to an exemplary embodiment, includes: (i) a first illustration 500 showing both the measured handwheel angle 502 and the command handwheel angle 504 as a function of time; (ii) a second illustration 510 showing the lateral deviation 512 from the vehicle's target path over time; and (iii) a third illustration 520 showing the error 522 relative to the target path as a function of time. Figure 5 As shown, in the undergain controller scenario, the command handwheel angle 504 and the measured handwheel angle 502 oscillate with the same frequency and amplitude (i.e., the frequency and amplitude difference is less than the corresponding threshold), but with a considerable time delay (e.g., a time delay greater than a predetermined time threshold). In one such embodiment, the oscillation time period in this controller ranges from four seconds (4 seconds) to eight seconds (8 seconds). Furthermore, in another such embodiment, the vehicle 100 also oscillates at the same frequency.

[0073] Return to reference Figure 2 In various embodiments, instability detection is performed (step 228). In various embodiments, instability detection is performed based on previous determinations (including the determination of damping monitoring in step 224 and the determination of oscillation classification in step 226). Specifically, in various embodiments, Figure 1 The processor 142 determines instability based on whether the damping is less than a predetermined damping threshold within at least a predetermined number of consecutive half-cycle oscillations. In some embodiments, instability is determined to have occurred when the damping is less than the predetermined damping threshold within at least three consecutive half-cycle oscillations.

[0074] In some embodiments, low damping is determined according to the following formula:

[0075] (Equation 4); and

[0076] (Equation 5)

[0077] in “ represents the handwheel angle (HWA) error value at the corresponding time one (1), two (2) and three (3), and uses their respective absolute values ​​according to the above formula.

[0078] refer to Figure 6 An exemplary illustration 600 regarding instability detection is provided, based on a specific implementation. Figure 6 In the diagram, x-axis 601 represents time, and y-axis 602 represents the HWA error ( ).exist Figure 6 The HWA error is described in the text. The function 604 and the buffer as it changes over time. Figure 6 In the described implementation, (i) a first amplitude value 607 represents a low-damping region 608; (ii) a second amplitude value 610 (as part of a time window 605) represents a high-damping region 612; (iii) a third amplitude value 614 represents a low-damping region 616; (iv) a fourth amplitude value 618 represents a low-damping region 620; (v) a fifth amplitude value 622 represents a low-damping region 624; (vi) a sixth amplitude value 626 represents a low-damping region 627; and (vii) a seventh amplitude value 628 represents a low-damping region 629. Figure 6 In the example, because low damping occurred in at least three consecutive half-cycle oscillations, instability was determined to exist for this particular scenario.

[0079] Return to reference Figure 2 In various embodiments, once oscillation detection 220 is determined in steps 221-228 as described above, process 200 then performs oscillation mitigation 230 according to steps 232-238 as directly described below.

[0080] In various embodiments, during step 232, control effect quantification is performed. In various embodiments, Figure 1 The processor 142 determines the quantitative measurement of the control effect of the oscillation based on the various determinations made in steps 221-228 as described above.

[0081] Furthermore, in various embodiments, gain adaptation is performed during step 234. In various embodiments, Figure 1 The processor 142 determines the gain adaptation differently depending on whether the situation involves an over-gain controller or an under-gain controller (i.e., according to the oscillation classification previously performed in step 226 and described above in conjunction with it).

[0082] In various embodiments, during step 234, in the case of an over-gain controller, the controller gain is modified to dampen the oscillation. In some such embodiments, the proportional gain is applied in this manner. Specifically, in some embodiments with an over-gain controller, the level of gain modification is determined according to the following formula. Depends on vehicle speed ( ), target path curvature ( ) and the average HWA error magnitude in the buffer. :

[0083] (Formula 6).

[0084] Conversely, in various embodiments, during step 234, in the case of an undergain controller, the level of gain modification is determined according to the following formula. Depends on vehicle speed ( ), target path curvature ( The average HWA error magnitude in the buffer zone and the time delay at the zero point. :

[0085] (Equation 7).

[0086] Furthermore, in various embodiments, during step 236, the controller gain is temporarily adjusted where appropriate. In some embodiments, the processor 142 applies the temporary adjustment to the controller gain in situations such as when rough road conditions (e.g., speed bumps or potholes) or driver intentions (e.g., driver engagement of the steering wheel) are considered significant causes of oscillations, and therefore, the controller gain is determined to be attributable to one or more short-term conditions.

[0087] Specifically, in various embodiments where the controller gain is temporarily adjusted in step 236, the processor 142 updates the controller gain using the following formula:

[0088] (Equation 8), where:

[0089] " indicates the updated gain "P"; and

[0090] “ This represents the original "P" gain.

[0091] In various embodiments, when the oscillation is damped, according to Equation 8, the updated gain of step 236 gradually converges to the original gain.

[0092] Conversely, in various embodiments, during step 238, the controller gain is permanently adjusted, if otherwise appropriate. In some embodiments, the processor 142 applies the permanent adjustment to the controller gain when a problem occurs in the control system 102 or a permanent change occurs therein, wherein the controller gain is determined to be attributable to one or more permanent or long-term conditions.

[0093] Specifically, in various embodiments, such a permanent condition is considered to exist when the driver (or driver intention) does not cause oscillations and where the oscillations do not occur on rough roads (e.g., roads with speed bumps, potholes, etc.). In some such embodiments, a problem is considered to be a change in steering system specifications when oscillations occur frequently and occur after each temporary adjustment event. In such cases, the controller gain is permanently updated to address this issue. In some embodiments, a request for permanent controller gain adjustment is made when the number of temporary adjustment events exceeds a predetermined threshold and the following conditions are met: (i) the driver does not cause oscillations; and (ii) the oscillations do not occur on rough roads.

[0094] In some embodiments, for the permanent adjustment in step 238, the processor 142 adjusts the permanent gain " according to the following formula. "Calculated as the average of all previously modified gains (e.g., in some embodiments, all previously temporary modified gains):"

[0095] (Equation 9).

[0096] In various embodiments, at step 239, processor 142 provides torque relief accordingly by providing a torque adjustment “τ” to the power steering system (i.e., steering system 115 in the exemplary embodiment corresponding to the figure). Furthermore, in various embodiments, as process 200 continues from step 221 in a new iteration, and as process 200 is subsequently repeated in new iterations, the resulting updated torque “τ” and handwheel angle “θ” values ​​are provided accordingly to oscillation detection 220.

[0097] Therefore, methods, systems, and vehicles for controlling automatic steering and associated oscillations of a vehicle are disclosed. Specifically, in various embodiments, the vehicle's processor uses various types of sensor data to perform both oscillation detection and, where appropriate, temporary and permanent mitigation of steering system oscillations.

[0098] It will be understood that the systems, vehicles, and methods may differ from those depicted in the accompanying drawings and described herein. For example, Figure 1 100 vehicles Figure 1The control system 102 and / or its components may vary in different embodiments. Similarly, it will be understood that the steps of process 200 may differ. Figure 2 The steps described, and / or the various steps of process 200, can occur simultaneously and / or be related to... Figure 2 The order in which they are described occurs is different from the order in which they occur. It will also be understood that... Figures 3-6 The implementation method can also vary in different embodiments.

[0099] While 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 method for controlling oscillations in automated driving of a vehicle, the vehicle having a steering system including a steering wheel, the method comprising: Sensor data is obtained via one or more sensors of the vehicle, the sensor data relating to multiple operating parameters of the vehicle, including command angles and measured angles of the steering wheel; as well as The vehicle's oscillation measurement is determined by the vehicle's processor based on the vehicle's multiple operating parameters, including the difference between the command angle of the steering wheel and the measured angle of the steering wheel over a period of time during the vehicle's operation.

2. The method of claim 1, wherein the oscillation includes unexpected vehicle-level lateral motion oscillations of the vehicle during autonomous driving.

3. The method of claim 1, wherein the oscillation includes unexpected steering wheel oscillations of the vehicle's steering wheel during autonomous driving.

4. The method of claim 1, wherein the oscillation measurement is determined by the processor based on the plurality of operating parameters, which, in addition to the command angle of the steering wheel and the measured angle of the steering wheel during the time period of operation of the vehicle, include each of the following: the acceleration of the vehicle, the speed of the vehicle, and the corresponding angle of each of the plurality of wheels of the vehicle.

5. The method of claim 1, wherein the oscillation measurement is further determined by the processor based on each of the following: Driver's intention; The roughness of the roadway on which the vehicle is operating; Damping measurements for the vehicle; The classification of the oscillations in the vehicle as involving either an over-gain controller or an under-gain controller; and Instability measurements associated with the oscillations.

6. The method according to claim 5, further comprising: The oscillation is mitigated by adjusting the control gain of the power steering system according to instructions provided by the processor to the power steering system of the vehicle. The adjustment is based on the oscillation measurement and the classification of whether the oscillation of the vehicle involves an over-gain controller or an under-gain controller.

7. The method according to claim 6, further comprising: Determined via the processor: When the oscillation of the command handwheel angle coincides with the oscillation of the measured handwheel angle, the oscillation involves the gain controller; as well as When the frequency and amplitude of the oscillation are approximately the same but have a time delay greater than a predetermined time threshold, the oscillation involves an undergain controller.

8. The method of claim 6, wherein the mitigation of the oscillation is further performed by the processor based on each of the following: Quantification of the control effect of the oscillation; Determining the gain effect of the oscillation; and Whether the oscillation is temporary or permanent in nature is a matter of determination.

9. The method of claim 8, wherein the oscillation is determined by the processor as: The oscillation is temporary when it is attributable to rough conditions of the roadway, the driver's intention, or both; and The oscillation is permanent when it is determined to exist without the rough conditions of the roadway and without the driver's intention.

10. A system for controlling oscillations during automated driving of a vehicle, the vehicle having a steering system including a steering wheel, the system comprising: One or more sensors of the vehicle are configured to acquire sensor data relating to multiple operating parameters of the vehicle, including command angles and measured angles of the steering wheel. as well as A processor, coupled to the one or more sensors, and configured to at least facilitate: determining an oscillation measurement of the vehicle based on the plurality of operating parameters of the vehicle, including based on the difference between the command angle of the steering wheel and the measured angle of the steering wheel over a period of time during operation of the vehicle.

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