Yaw rate sensor bias estimation

Abstract

An example yaw rate related method includes obtaining a plurality of yaw rate measurements of a vehicle from a yaw rate sensor; and determining a yaw rate sensor deviation estimate for the yaw rate sensor based on at least one of a plurality of yaw rate measurements in response to: a plurality of position indications of the vehicle over time based on a global navigation satellite system signal representing a linear movement of the vehicle; or determining that the vehicle is stationary.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Application No. 18 / 363,498, filed on August 1, 2023, entitled “YAW RATE SENSORBIAS ESTIMATION,” which claims the benefit of U.S. Provisional Application No. 63 / 395,495, filed on August 5, 2022, entitled “YAW RATE SENSORBIAS ESTIMATION,” which is assigned to the assignee of this application, and the entire contents of both applications are hereby incorporated herein by reference for all purposes. Background Art Technical Field

[0003] The field of the present disclosure is yaw rate sensors, and more particularly to yaw rate sensor bias estimation. Background Art

[0005] Autonomous vehicles (also known as self-driving vehicles) are becoming increasingly popular. The demand for autonomous vehicles is driven by the potential to reduce vehicle collisions, improve traffic flow efficiency, increase vehicle efficiency (e.g., miles per gallon of gasoline, miles per kilowatt of electricity, etc.), increase user efficiency (e.g., freeing up future drivers to perform other tasks while transporting), etc. In order to gain approval and acceptance of autonomous vehicles, vehicle designers and manufacturers are working to address many considerations (especially safety considerations) so that the autonomous vehicles can have acceptable performance (e.g., acceptable collision avoidance).

[0006] Accurate data from the sensors of an autonomous vehicle helps ensure that decisions based on the data will be good and / or actions based on the data will be acceptable. For example, accurate yaw rate data can help ensure that when a blind stop is initiated, it can be performed without leaving the lane in which the autonomous vehicle is moving. A blind stop is when a vehicle stops without the assistance of normally available data (e.g., without image data from a forward-facing camera of the autonomous vehicle) due to a malfunction. Because yaw rate sensors inherently have bias (e.g., due to manufacturing tolerances, environmental conditions (e.g., temperature), etc.), and because this bias changes over time, it is desirable to update the bias estimate to determine a true yaw rate estimate. Summary of the invention

[0007] An example yaw rate correlation method includes: obtaining a plurality of yaw rate measurements of a vehicle from a yaw rate sensor; and determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: a plurality of position indications of the vehicle over time based on global navigation satellite system signals indicating straight-line movement of the vehicle; or determining that the vehicle is stationary.

[0008] An example apparatus includes: one or more memories; and one or more processors communicatively coupled to the one or more memories and configured to: obtain a plurality of yaw rate measurements of a vehicle from a yaw rate sensor; and determine a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: a plurality of position indications of the vehicle over time based on global navigation satellite system signals indicating straight-line movement of the vehicle; or a determination that the vehicle is stationary.

[0009] Another example apparatus includes: means for obtaining a plurality of yaw rate measurements of a vehicle from a yaw rate sensor; and means for determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: a plurality of position indications of the vehicle over time based on global navigation satellite system signals indicating linear movement of the vehicle; or a determination that the vehicle is stationary.

[0010] An example non-transitory processor-readable storage medium including processor-readable instructions for causing one or more processors to: obtain a plurality of yaw rate measurements of a vehicle from a yaw rate sensor; and determine a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: a plurality of position indications of the vehicle over time based on global navigation satellite system signals indicating straight-line movement of the vehicle; or a determination that the vehicle is stationary. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is an example of a simple driving environment.

[0012] Figure 2 is a block diagram of an example autonomous vehicle.

[0013] Figure 3 is a block diagram of another example autonomous vehicle.

[0014] Figure 4 yes Figure 3 A block diagram of an example of a yaw rate bias unit is shown.

[0015] Figure 5 yes Figure 4 A block diagram of an example of a straight driving detection unit is shown.

[0016] Figure 6 A graph of latitude and longitude of a position determined over time by a GNSS receiver.

[0017] Figure 7 is a flowchart of the yaw rate related method. DETAILED DESCRIPTION

[0018] Techniques for estimating yaw rate sensor bias and the use of these techniques are discussed herein. For example, a GNSS (Global Navigation Satellite System) receiver of a mobile device (e.g., an autonomous vehicle) may be used to detect straight-line driving and / or may detect that the mobile device is stationary. Detection of straight-line driving or stationary conditions may be used to trigger the use of a current yaw rate measurement (or a combination of recent yaw rate measurements (e.g., an average)) as an estimated yaw rate sensor bias. The current yaw rate measurement may be used as an estimated yaw rate only when the GNSS sensor (receiver) and the yaw rate sensor agree that the autonomous vehicle is (or was) driving in a straight line or satisfies a standstill (stationary) condition. The estimated yaw rate bias may be removed from the yaw rate sensor measurement to determine an estimated true yaw rate. The estimated true yaw rate may be used for one or more purposes, such as guiding the autonomous vehicle when performing a blind stop when the vehicle stops due to a failure within the system (e.g., loss of camera data). However, other configurations may be used.

[0019] The yaw rate sensor includes a time-varying error called bias in the output of the yaw rate sensor, and this error can be considered before the yaw rate is used in the car. Additionally, the yaw rate bias varies over time, so the initial estimate of the bias can become outdated over time. Conventional bias estimator algorithms are based on the principle that the bias should be zero when the vehicle is at a standstill or driving in a straight line. For non-stationary conditions of the vehicle, these algorithms assume that the vehicle is driving in a straight line when the current reading of the yaw rate sensor is within a margin of an earlier bias estimate. When such an assumption is true, these algorithms update the value of the bias estimate. This process is repeated periodically. However, the assumption that the vehicle is driving in a straight line may be incorrect. For example, when the actual yaw rate changes slowly, the bias estimate can be manipulated to an arbitrary value. The techniques discussed herein can use positioning data (e.g., satellite positioning system (SPS) data) in conjunction with yaw rate sensor data to determine whether the vehicle is driving in a straight line. If both systems are sufficiently consistent that the vehicle is driving in a straight line, the bias estimate can be updated. The bias estimate can be subtracted from the yaw rate sensor data to obtain the true yaw rate.

[0020] Items and / or techniques described herein may provide one or more of the following capabilities, as well as other capabilities not mentioned. Yaw rate sensor bias may be determined more accurately than in the prior art. Using yaw rate sensor measurements to estimate yaw rate sensor bias during non-zero true yaw rate may be avoided. Inaccurate yaw rate sensor bias estimates due to small, consistent, non-zero yaw rate measurements may be avoided. The confidence of yaw rate sensor bias compensation may be evaluated and / or improved. Other capabilities may be provided, and not every implementation according to the present disclosure necessarily provides any, much less all, of the capabilities discussed.

[0021] Discussion herein may focus on determining a yaw rate estimate for use in performing a blind stop of an autonomous vehicle. This is an example to help illustrate the concepts of the present disclosure. The disclosure herein (including the claims, unless otherwise specifically noted) is applicable to other example implementations, such as uses other than blind stops, uses for blind stops (and / or other functions) for various types of vehicles.

[0022] refer to Figure 1, a driving environment 100 includes vehicles 110, 120, 130 disposed on a road 140 that intersects another road 150 at an intersection 160. The vehicle 110 may be an autonomous vehicle, capable of automated driving (i.e., without human input) as well as capable of manual driving (i.e., with human input). As part of autonomous driving, it may be desirable to stop the vehicle 110 (i.e., perform a blind stop) without using camera images, for example, if camera images are not available (e.g., because the vehicle's forward-facing camera is currently inoperable, etc.). In order to safely perform a blind stop, the true yaw rate of the vehicle 110 should be accurately known, for example, so that when initiating a stop, any deviations from straight-line travel within a lane 141 in which the vehicle resides can be taken into account, and these deviations are appropriately compensated to keep the vehicle within the lane 141 until the vehicle 110 stops. For example, with an accurate estimate of the true yaw rate of the vehicle 110, the vehicle 110 may be stopped blindly at the vehicle position 111 within the lane 141 (e.g., at or before the stop line 170 demarcating the edge of the intersection 160). Without an accurate estimate of the true yaw rate of the vehicle 110, the blind stop of the vehicle 110 may result in the vehicle leaving the lane 141, such as stopping at a vehicle position that is partially or completely beyond the lane 141, such as stopping in an adjacent lane of traffic, in an opposing lane of traffic, on a sidewalk (as shown by the vehicle position 112), etc. The vehicle 110 is an automobile, but other specific implementations of the vehicle may be used in other driving environments and / or other types of environments. Various surface-based vehicles may be used (e.g., ground-based vehicles such as automobiles (e.g., cars, trucks, motorcycles, etc.) and / or water-based vehicles such as boats, jet skis, etc.).

[0023] Also refer to Figure 2, the autonomous vehicle 200 (of which the vehicle 110 may be an example) may include a computing platform including a processor 210, a memory 220 including software (SW) 222, one or more sensors 213, a global navigation satellite system (GNSS) receiver 214, and one or more cameras 215. The processor 210, the memory 220, the sensor 213, the GNSS receiver 214, and the camera 215 may be communicatively coupled to each other via a bus 216 (which may be configured, for example, for optical communication and / or electrical communication). Even if mentioned in the singular, the processor 210 may include one or more processors, the memory 220 may include one or more memories, and / or the GNSS receiver 214 may include one or more GNSS receivers. One or more of the devices shown (e.g., the camera 215) may be omitted from the autonomous vehicle 200. The processor 210 may include one or more intelligent hardware devices, such as a central processing unit (CPU), a microcontroller, an application specific integrated circuit (ASIC), etc. The processor 210 may include multiple processors, including a general / application processor 230, a digital signal processor (DSP) 231, a modem processor 232, a video processor 233, and / or a sensor processor 234. One or more of the processors 230-234 may include multiple devices (e.g., multiple processors). For example, the sensor processor 234 may include, for example, a processor for RF (radio frequency) sensing (wherein one or more (cellular) wireless signals and reflections transmitted are used to identify, map, and / or track objects) and / or ultrasound, etc. The modem processor 232 may support dual SIM / dual connectivity (or even more SIMs). For example, a SIM (subscriber identity module or user identity module) may be used by an original equipment manufacturer (OEM), and another SIM may be used by an end user of the autonomous vehicle 200 to obtain connectivity. The memory 220 may be a non-transitory storage medium that may include a random access memory (RAM), a flash memory, a disk memory, and / or a read-only memory (ROM), etc. Memory 220 may store software 222, which may be non-transitory processor-readable, processor-executable software code containing instructions that may be configured to cause processor 210 to perform various functions described herein when executed. Alternatively, software 222 may not be directly executable by processor 210, but may be configured to cause processor 210 to perform these functions, for example, when compiled and executed. The description herein may refer to processor 210 performing functions, but this may include other specific implementations, such as specific implementations in which processor 210 executes software and / or firmware. This specification may refer to processor 210 performing functions as shorthand for one or more of processors 230-234 performing the function.This description may refer to autonomous vehicle 200 performing a function as shorthand for one or more appropriate components of autonomous vehicle 200 performing that function. Processor 210 may include memory with stored instructions in addition to and / or in lieu of memory 220. The functionality of processor 210 is discussed more fully below.

[0024] Figure 2 The configuration of autonomous vehicle 200 shown is an example and is not intended to limit the present disclosure, including the claims, and other configurations may be used. For example, an example configuration of an autonomous vehicle may include one or more of processors 230-234 in processor 210 and memory 220. Other example configurations may omit one or more of processors 230-234.

[0025] The autonomous vehicle 200 may include a sensor 213, which may include, for example, one or more of various types of sensors, such as one or more inertial sensors, one or more magnetometers, one or more environmental sensors, one or more optical sensors, one or more weight sensors, and / or one or more radio frequency (RF) sensors, etc. An inertial measurement unit (IMU) 217 ​​of the sensor 213 may include, for example, one or more yaw rate sensors, one or more accelerometers (e.g., collectively responsive to acceleration of the autonomous vehicle 200 in three dimensions), and / or one or more gyroscopes (e.g., three-dimensional gyroscopes). The sensor 213 may generate analog and / or digital signals, indications of which may be stored in the memory 220 and processed by the DSP 231 and / or the general / application processor 230 to support one or more applications, such as, for example, an application involving estimating a yaw rate sensor bias and removing the estimated yaw rate sensor bias from a yaw rate measurement to produce an estimated true yaw rate of the autonomous vehicle 200.

[0026] The IMU 217 may be configured to provide measurements of the direction of motion and / or velocity of motion of the autonomous vehicle 200, which may be used in various applications, such as blind stopping of the autonomous vehicle 200. For example, one or more accelerometers and / or one or more yaw rate sensors of the IMU 217 may detect the translational acceleration and angular velocity, respectively, of the autonomous vehicle 200. The translational acceleration and angular velocity measurements of the autonomous vehicle 200 may be integrated over time to determine the instantaneous direction of motion and displacement of the autonomous vehicle 200. The instantaneous direction of motion and displacement may be integrated to track the position of the autonomous vehicle 200 and compared to known displacement limits (e.g., lateral limits of the lane 141), and steering corrections may be made to help keep the autonomous vehicle 200 within the lane 141 when autonomously stopping (e.g., without the assistance of camera images).

[0027] The GNSS receiver 214 (e.g., a global positioning system (GPS) receiver) may be capable of receiving and acquiring GNSS signals 240 via a GNSS antenna 242. The GNSS antenna 242 may be configured to convert the GNSS signals 240 from wireless signals to wired signals (e.g., electrical signals or optical signals). The GNSS receiver 214 may be configured to process the acquired GNSS signals 240 in whole or in part to estimate the position of the autonomous vehicle 200. For example, the GNSS receiver 214 may be configured to determine the position of the autonomous vehicle 200 by performing trilateration using the GNSS signals 240. The general / application processor 230, the memory 220, the DSP 231, and / or one or more dedicated processors (not shown) may be used in conjunction with the GNSS receiver 214 to process the acquired GNSS signals in whole or in part, and / or calculate the estimated position of the autonomous vehicle 200. The memory 220 may store indications (e.g., measurements) of the GNSS signals 240 and / or other signals used to perform positioning operations (e.g., determining the position of the autonomous vehicle 200 over time and determining whether the autonomous vehicle 200 is moving in a straight line). The general / application processor 230, the DSP 231, and / or one or more special-purpose processors, and / or the memory 220 may provide and / or support a location engine for use in processing the measurements to estimate the position of the autonomous vehicle 200.

[0028] The autonomous vehicle 200 may include a camera 215 for capturing still or moving images. The camera 215 may include, for example, an imaging sensor (e.g., a charge coupled device or a CMOS (complementary metal oxide semiconductor) imager), a lens, analog-to-digital circuits, a frame buffer, etc. Additional processing, conditioning, encoding, and / or compression of signals representing captured images may be performed by the general / application processor 230 and / or the DSP 231. Additionally or alternatively, the video processor 233 may perform conditioning, encoding, compression, and / or manipulation of signals representing captured images. The video processor 233 may decode / decompress stored image data for presentation on a display device (not shown) of the autonomous vehicle 200.

[0029] Also refer to Figure 3, the autonomous vehicle 300 (of which the autonomous vehicle 200 may be an example) may include a computing platform including a processor 310, a memory 320 including software (SW) 322, a yaw rate sensor 330, a GNSS receiver 340, a yaw rate bias unit 350, and one or more yaw rate application units 360. The processor 310, the memory 320, the yaw rate sensor 330, and the GNSS receiver 340 may be communicatively coupled to each other via a bus 345 (e.g., which may be configured for optical communication and / or electrical communication). Even if mentioned in the singular, the processor 310 may include one or more processors, the memory 320 may include one or more memories, the yaw rate sensor 330 may include one or more yaw rate sensors, and / or the GNSS receiver 340 may include one or more GNSS receivers. One or more of the illustrated devices may be omitted from the autonomous vehicle 300. The processor 310 may include one or more intelligent hardware devices, such as a central processing unit (CPU), a microcontroller, an application specific integrated circuit (ASIC), etc. Processor 310 may include multiple processors (e.g., including a general / application processor, a DSP, a modem processor, a video processor, and / or a sensor processor, such as Figure 2 ). The memory 320 may be a non-transitory processor-readable storage medium that may include random access memory (RAM), flash memory, disk memory, and / or read-only memory (ROM), etc. The memory 320 may store software 322, which may be a processor-readable, processor-executable software code containing instructions that are configured to cause the processor 310 to perform the various functions described herein when executed. Alternatively, the software 322 may not be directly executable by the processor 310, but may be configured to cause the processor 310 to perform these functions, for example, when compiled and executed. Although the description herein may focus on the autonomous vehicle 300, the description may apply to other mobile devices and / or one or more of their components, for example, components of a mobile device (such as an ADAS (Advanced Driver Assistance System) engine). A mobile device is a device that is configured to move, but may be in motion or stationary at any given time.

[0030] The description herein may refer to processor 310 performing a function, but this may include other implementations, such as an implementation in which processor 310 executes software and / or firmware. The description herein may refer to processor 310 performing a function as shorthand for one or more of the processors included in processor 310 performing the function. The description herein may refer to autonomous vehicle 300 performing a function as shorthand for one or more appropriate components of autonomous vehicle 300 (e.g., processor 310 and memory 320) performing the function. Processor 310 may include a memory with stored instructions in addition to and / or in lieu of memory 320. Processor 310 and / or memory 320 may include one or more of yaw rate bias unit 350 and / or yaw rate application unit 360 or the yaw rate application unit. Additionally or alternatively, one or more of yaw rate bias unit 350 and / or yaw rate application unit 360 or the yaw rate application unit may be implemented with different hardware and / or firmware, or may be implemented in part by processor 310 and / or memory 320 and in part by other components (e.g., different hardware and / or firmware). Yaw rate bias unit 350 may be configured to determine a yaw rate sensor bias estimate and use the bias estimate to compensate the yaw rate sensor measurements, e.g., remove the yaw rate sensor bias estimate from the yaw rate sensor measurements. Yaw rate bias unit 350 is discussed further below, and the description may generally refer to processor 310 or generally refer to autonomous vehicle 300 performing any of the functions of yaw rate bias unit 350, where autonomous vehicle 300 is configured to perform those functions.

[0031] Figure 3 The configuration of autonomous vehicle 300 shown is an example and is not limiting of the present disclosure, including the claims, and other configurations may be used. For example, while processor 310 and memory 320 are shown and discussed as separate from yaw rate sensor 330, yaw rate sensor 330 may include a processor and memory, such as to implement yaw rate bias unit 350.

[0032] The yaw rate sensor 330 may have an inherent bias that may vary over time. The yaw rate sensor measurement includes the true yaw rate and the bias, and may be expressed as follows:

[0033] ψ 传感器 =ψ 真实 +b(1)

[0034] where ψ 传感器 is the yaw rate provided by the yaw rate sensor 330, ψ 真实is the actual or true yaw rate, and b is the yaw rate sensor bias. An estimate of the true yaw rate (e.g., for use by a blind stop application of the yaw rate application unit 360) may be estimated by estimating the yaw rate sensor bias and removing the bias estimate from the sensor measurement, as shown below

[0035] ψ 真实，估计 =ψ 传感器 -b 估计 (2)

[0036] where ψ 真实，估计 is the estimate of the true yaw rate (also called the estimated true yaw rate), and b 估计 is an estimate of the yaw rate sensor bias (of the yaw rate sensor 330). The estimated true yaw rate may be provided by the yaw rate bias unit 350 to the yaw rate application unit 360. Depending on the use of the estimate of the true yaw rate, the estimate of the true yaw rate may need to be within a threshold of the true yaw rate to be usable. For example, for a blind stop application, the estimate of the true yaw rate may need to be within an accuracy threshold of the true yaw rate to ensure that the autonomous vehicle 300 will stop within the lane in which the vehicle is traveling when the stop is initiated. The value of the accuracy threshold may depend on the speed of the vehicle 300 when the stop is initiated (e.g., the faster the initial speed, the smaller the threshold). The value of the accuracy threshold may be determined in any of a variety of ways, such as by finding the accuracy threshold in a lookup table of initial vehicle speeds and corresponding accuracy thresholds.

[0037] Prior art techniques for compensating for yaw rate sensor bias use the yaw rate indicated by the yaw rate sensor to determine when the true yaw rate is zero or near zero (e.g., due to the device containing the yaw rate sensor moving in a straight line (or near a straight line)). If the yaw rate sensor measurement provided by the yaw rate sensor is zero or near zero, the current (or possibly average) yaw rate measurement is stored as an estimate of the yaw rate sensor bias. For example, straight-line motion may be determined based on the absolute value of the difference between the yaw rate sensor measurement minus the yaw rate bias estimate being less than a threshold. Based on the straight-line motion determination, bias learning may be performed such that the current (or average) yaw rate measurement may be stored as the yaw rate sensor bias. However, the straight-line driving determination may be incorrect, resulting in the learned yaw rate sensor bias being incorrect. That is, the detection logic may detect straight-line motion at times of zero true yaw rate and at times of small non-zero true yaw rate. If the yaw rate bias estimate is updated (learned) based on the detection logic, the estimated yaw rate sensor bias may be inaccurate. Since we are trying to determine two unknown state variables (true yaw rate and yaw rate sensor bias) using a single known value (the measurement of yaw rate), it is not possible to accurately estimate the yaw rate sensor bias. That is, the observability condition (i.e., the ability to solve for one or more unknowns based on one or more knowns) is not satisfied.

[0038] The yaw rate bias unit 350 may be configured to use multiple measurements of different parameters, thus making the solution of the true yaw rate and the yaw rate sensor bias observable. The yaw rate bias unit 350 may add the ability to detect zero true yaw rate motion (e.g., driving in a straight line) via another sensor (i.e., in addition to the yaw rate sensor 330) (e.g., GNSS receiver 340). With the additional data source providing one or more additional data points, the observability condition (i.e., the measurement equals or exceeds the unknown) is satisfied.

[0039] Also refer to Figure 4 , the yaw rate bias unit 400 (which is an example of the yaw rate bias unit 350) may include a standstill detection unit 410, a straight driving detection unit 420, a bias estimator 430, an LPF 438 (low pass filter), a bias estimate removal unit 440, an LPF 450, and a ready unit 460. The configuration of the yaw rate bias unit 400 shown is an example, and other configurations may also be used. The yaw rate bias unit 400 may be configured to determine when the vehicle 300 has a zero true yaw rate, such as when the vehicle 300 is at a standstill or traveling in a straight line (or nearly a straight line). If the yaw rate measurement corresponds to a zero true yaw rate, the yaw rate measurement may be used to generate an estimate of the yaw rate sensor bias. For example, when the yaw rate bias unit 400 determines that the vehicle 300 has a zero true yaw rate, the low pass filtered output of the yaw rate sensor 330 may be averaged to generate an estimate of the yaw rate sensor bias through a Kalman filter. The estimate of the yaw rate sensor bias may be stored, thereby updating a previously stored estimate of the yaw rate sensor bias. If the yaw rate bias unit 400 determines that the vehicle has a non-zero true yaw rate, the stored yaw rate sensor bias may remain constant. The estimate of the yaw rate sensor bias may be removed from a raw yaw rate measurement (e.g., a low pass filtered raw yaw rate measurement) to produce an estimate of the true yaw rate.

[0040] The standstill detection unit 410 may be configured to use the input 415 to determine whether the vehicle 300 is considered to be stationary. For example, the standstill detection unit 410 may use the vehicle motion state, the wheel rotation direction, the acceleration (raw acceleration measurement), the raw yaw rate (i.e., the yaw rate sensor measurement including the true yaw rate and the yaw rate sensor bias), and one or more parameters (e.g., thresholds) to determine whether the vehicle 300 is considered to be at a standstill using one or more known techniques. For example, the standstill detection unit 410 may determine the relative motion of the vehicle 300 relative to the surface on which the vehicle 110 is parked and determine whether the surface is stationary. The standstill detection unit 410 may be configured to output a stationary vehicle indication 412, such as a Boolean indicator, such as a value of "1" indicating that the vehicle 300 is at a standstill (i.e., has a zero true yaw rate), and a value of "0" indicating that the vehicle 300 is not at a standstill.

[0041] The straight driving detection unit 420 may be configured to use the input 425 (including the GNSS input) to determine whether the vehicle 300 is considered to be in a straight driving state. For example, the straight driving detection unit 420 may use the GNSS coordinates (corresponding to positions at different times determined by the GNSS receiver 340), the GNSS HDOP (horizontal dilution of precision) data, the raw yaw rate, the GNSS virtual zero yaw rate, and one or more parameters (e.g., thresholds such as the axle ratio threshold discussed further below) to determine whether the vehicle 300 is considered to be driving in a straight line. The straight driving detection unit 420 may be configured to output a driving straight line indication 422, such as a Boolean indicator, such as a value of "1" indicating that the vehicle 300 is considered to be moving in a straight line (i.e., having a zero true yaw rate), and a value of "0" indicating that the vehicle 300 is considered not to be moving in a straight line (i.e., having a non-zero true yaw rate or a true yaw rate above a threshold).

[0042] The deviation estimator 430 may be configured to determine a yaw rate deviation estimate in response to the vehicle 300 having a zero true yaw rate, as determined by the standstill detection unit 410 or the straight driving detection unit 420. The deviation estimator 430 may be configured to analyze the stationary vehicle indication 412 and the driving straight line indication 422 to determine whether the vehicle 300 has a zero true yaw rate. One or both of the indications 412, 422 may include an indication of a confidence level in the correctness of the indications 412, 422. The deviation estimator 430 (e.g., a Kalman filter) may use the confidence level in conjunction with the respective indications to determine that the vehicle 300 has a zero true yaw rate (e.g., the deviation estimator 430 may determine with at least a threshold confidence level that the vehicle 300 is at a standstill (i.e., considered to be at a standstill) or is driving in a straight line (i.e., considered to be driving in a straight line)). The LPF 438 may filter an input 435 of a raw yaw rate measurement from the yaw rate sensor 330 to produce a filtered raw yaw rate 439 that may be provided to the bias estimator 430. In response to the bias estimator 430 determining that the vehicle has a true yaw rate of zero, the bias estimator 430 (including a Kalman filter) may use the filtered raw yaw rate 439 from the LPF 438 to obtain an estimate of the yaw rate sensor bias. The input 435 includes the true yaw rate and the yaw rate sensor bias, but in the case where the current true yaw rate is estimated to be zero by the standstill detection unit 410 or the straight driving detection unit 420, the raw yaw rate may be estimated to include only the yaw rate sensor bias. The bias estimator 430 may be a Kalman filter that estimates the yaw rate sensor bias based on the filtered yaw rate measurement (i.e., may determine one unknown number based on one measurement). The deviation estimator 430 may gate on the stationary vehicle indication 412 and the driving straight line indication 422 (i.e., may estimate the deviation based on either of the indications 412, 422 indicating a true yaw rate of zero (i.e., the vehicle 300 is considered stationary or is considered to be moving in a straight line)). The deviation estimator 430 may replace the previously stored estimated yaw rate sensor bias with the newly obtained yaw rate sensor bias estimate, thereby updating the estimated yaw rate sensor bias. An indication of a zero true yaw rate from either of the units 410, 420 may trigger an update of the estimated yaw rate sensor bias. The deviation estimator 430 may use the filtered raw yaw rate 439 based on the indicated true yaw rate of zero, so that the deviation estimator 430 may treat the yaw rate sensor 330 as a sensor having an output that is only usable when the filtered raw yaw rate 439 has a true yaw rate of zero. If the bias estimator 430 determines that the true yaw rate (ie, the filtered raw yaw rate minus the yaw rate sensor bias) has a non-zero value, the bias estimator 430 may keep the yaw rate sensor bias constant. The bias estimator 430 may output a yaw rate bias estimate indication 432 to the bias estimate removal unit 440 .Indication 432 can be the actual value of the estimated deviation, or a shortened indication (e.g., a differential indication relative to a reference (e.g., the most recent estimated deviation output by the deviation estimator)), or a decoded indication (e.g., an index that can be mapped to an estimated deviation in a lookup table, etc.).

[0043] The bias estimator 430 may provide a yaw rate bias estimate covariance indication 434 to a readiness unit 460, which may determine readiness of the yaw rate bias estimate based on the indication 434 and may indicate readiness (ready or not ready) with a readiness indicator 462. The Kalman filter of the bias estimator 430 may estimate the yaw rate sensor bias and may estimate uncertainty in the estimate of the raw rate sensor bias. Each time the bias estimator 430 determines not to update the yaw rate sensor bias (in response to either of the indications 412, 422 indicating a non-zero true yaw rate), the covariance of the yaw rate bias estimate is increased (e.g., to account for the ramp rate of the yaw rate sensor 330, etc.). If the yaw rate sensor bias estimate covariance indicated by the indication 434 exceeds a first threshold (an application-specific accuracy threshold), the estimated yaw rate sensor bias may no longer be reliable and the readiness indicator 462 may provide a not ready indication. In response to a not ready indication (e.g., having a ready indicator provided to one or more of the yaw rate application units 360), the estimated yaw rate sensor bias may be ignored or otherwise not used, and / or one or more of the yaw rate application units 360 may be disabled, e.g., to disable an autonomous blind stop and hand control of the stop to the driver of the vehicle 300. In the case of a low initial covariance, the covariance of the unupdated yaw rate sensor bias estimate may not exceed a first threshold for several minutes. Once the covariance exceeds the first threshold, the new estimated yaw rate sensor bias may not be used until the covariance drops below a second threshold that is below the first threshold, thereby providing hysteresis for determining whether to use the estimated yaw rate sensor bias. The covariance threshold may be a speed-dependent value (e.g., a higher initial vehicle speed corresponds to a lower threshold), resulting in a shorter time window without a yaw rate sensor bias update before determining that the yaw rate sensor bias estimate is unreliable.

[0044] The bias estimate removal unit 440 may be configured to remove the estimated yaw rate sensor bias from the filtered yaw rate sensor measurement. The input 435 (raw yaw rate measurement) may be filtered by the LPF 450, which may provide the filtered raw yaw rate 452 to the bias estimate removal unit 440. The bias estimate removal unit 440 may subtract the estimated yaw rate bias as indicated by the yaw rate bias estimate indication 432 from the filtered raw yaw rate 452 according to equation (2) to produce a yaw rate estimate 442, which is an estimate of the true yaw rate of the vehicle 300, and the bias estimate removal unit 440 may be configured to output the yaw rate estimate, for example, to the yaw rate application 360.

[0045] Also refer to Figure 5 , the straight driving detection unit 500 (which is an example of the straight driving detection unit 420) may include a principal component analysis unit 510, a check sensor consistency unit 520, and a logical AND 530. The straight driving detection unit 500 may be configured to make an initial GNSS virtual zero yaw rate determination that the vehicle 300 is moving in a straight line and is therefore assumed to have a zero true yaw rate based on GNSS data (e.g., positions determined by the GNSS receiver 340 at different times for the vehicle 300). The straight driving detection unit 500 may be configured to determine whether the yaw rate sensor measurement and the GNSS virtual zero yaw rate determination are consistent, and provide the driving straight line indication 422 based on the initial driving straight line determination and the determination regarding sensor consistency. The GNSS receiver 340 may act as a virtual zero yaw rate sensor by providing information from which a virtual zero (near zero) yaw rate may be determined (e.g., based on moving in a straight line).

[0046] The principal component analysis unit 510 may be configured to analyze the GNSS data to determine whether the vehicle 300 is driving in a straight line (i.e., driving within a threshold deviation of a straight line and therefore considered to be driving in a straight line). For example, the principal component analysis unit 510 (PCA unit 510) may receive a GNSS input 515 from the GNSS receiver 340. The GNSS input 515 may include an indication of GNSS coordinates of a plurality of positions (referred to as GNSS positions) of the vehicle 300 corresponding to different times. The PCA unit 510 may map the GNSS coordinates to a local coordinate system in a plane tangent to the surface of the earth, use a linear approximation for the GNSS positions (which is a very good approximation within a small span (e.g., 400m or less) of the GNSS positions), and may use differential coordinates (where the coordinates are represented by a difference relative to a reference (e.g., the coordinates of one of the received positions)). This may greatly reduce the processing requirements for determining whether the vehicle 300 is driving in a straight line, for example, this may allow for single precision floating point representation of the coordinates and may eliminate mathematical conversion of GNSS latitude and longitude coordinates to Cartesian coordinates. GNSS input 515 may include GNSS HDOP data, and PCA unit 510 may use the HDOP data and the GNSS coordinates to determine, for each of the received GNSS positions, whether to use the GNSS position to determine whether vehicle 300 is driving in a straight line. Each GNSS position that PCA unit 510 decides to use to determine whether vehicle 300 is driving in a straight line may be referred to as a valid GNSS position.

[0047] Also refer to Figure 6 , the PCA unit 510 may analyze the valid GNSS positions, as graphically shown in the graph 600. The PCA unit 510 may be configured to analyze the coordinates of the plurality of valid GNSS positions 610 to determine a line of travel 620, a midpoint 630 of the line of travel 620, an ellipse 640 defined by a major axis standard deviation 650 (along the line of travel 620) and a minor axis standard deviation 660 (perpendicular to the line of travel 620). The PCA unit 510 may use SVD (singular value decomposition) to determine the major axis standard deviation 650 and the minor axis standard deviation 660 as the principal components of the ellipse 640. The PCA unit 510 may be configured to calculate the axis ratio according to the following formula:

[0048]

[0049] where σ short is the minor axis standard deviation 660 and σ longis the major axis standard deviation 650. Thus, the axis ratio is the ratio of the orthogonal standard deviations. The PCA unit 510 may determine that the vehicle 300 is driving in a straight line based on the axis ratio being below a threshold axis ratio. The threshold axis ratio may be determined experimentally and may vary based on a variety of factors, such as vehicle speed, maximum vehicle speed, quality of the GNSS receiver 340 (e.g., rate at which GNSS is received (e.g., 1 Hz, 10 Hz, etc.), etc. For example, based on a maximum vehicle speed of 80 kph, an axis ratio threshold of 0.008 may be used. The threshold axis ratio may be set based on an accuracy tradeoff (setting the threshold axis ratio very low may result in a very accurate yaw rate bias estimate, but may also result in undesirably infrequent updates of the yaw rate bias estimate, while setting the threshold axis ratio very low may result in a very accurate yaw rate bias estimate, but may also result in undesirably infrequent updates of the yaw rate bias estimate, whereas ... The PCA unit 510 may be configured to provide an initial driving straight line indication 512 based on whether the vehicle 300 is driving in a straight line, as determined by the PCA unit 510 based on the GNSS input 515. The indication 512 may be a Boolean value, for example, a value of "1" indicates driving in a straight line, and a value of "0" indicates driving not in a straight line. The initial driving straight line indication 512 may be provided to the check sensor consistency unit 520 and the logical AND 530.

[0050] Reference again Figure 5, the check sensor consistency unit 520 may be configured to determine whether the sensors (here, the yaw rate sensor 330 and the GNSS receiver 340) agree that the vehicle 300 is driving in a straight line. For example, the check sensor consistency unit 520 may receive a sensor input 525 including a raw yaw rate from the yaw rate sensor 330, and may use a GNSS virtual zero yaw rate as a GNSS sensor yaw rate measurement to determine whether there is sensor consistency. The GNSS virtual zero yaw rate may be provided to the check sensor consistency unit 520 in response to the true yaw rate as determined using the GNSS receiver 214 being zero. The GNSS virtual zero yaw rate may be provided as an input to a Kalman filter of the check sensor consistency unit 520. A simulated value of zero may be presented to the check sensor consistency unit 520, and the Kalman filter of the check sensor consistency unit 520 may use the input based on the initial driving straight line indication 512 indicating driving in a straight line. The check sensor consistency unit 520 may determine whether there is sensor consistency in response to the initial driving straight line indication 512 indicating driving in a straight line (and may not check sensor consistency otherwise). The Kalman filter of the check sensor consistency unit 520 may weight the GNSS virtual zero yaw rate (considered to be a GNSS sensor measurement) based on a confidence level that the value of the initial driving straight line indication 512 is correct. For example, if the yaw rate of the vehicle 300 must change faster than expected (e.g., faster than reasonably possible), the check sensor consistency unit 520 may weight the GNSS virtual zero yaw rate very low. The check sensor consistency unit 520 may compare the raw yaw rate from the yaw rate sensor 330 to the GNSS virtual zero yaw rate (where either or both of these sensor values ​​are weighted by the Kalman filter based on confidence in accuracy). The Kalman filter may monitor the yaw rate measurement residual, which is the difference between the actual yaw rate measurement and the predicted yaw rate measurement. If the yaw rate measurement residual value and the residual variance are acceptably low (e.g., below the residual threshold and the variance threshold, such that the residual is close to zero and has a small standard deviation), then the check sensor consistency unit 520 can conclude that the sensor is consistent. For example, an example yaw rate measurement residual threshold can be 0.003 rad / sec, and an example yaw rate measurement residual variance threshold can be 0.0002 rad / sec. 2 / sec 2 , an example GNSS measurement residual threshold may be 0.0004 rad / sec, and an example GNSS measurement residual threshold may be 0.0002 rad 2 / sec 2The check sensor consistency unit 520 may be configured to provide a sensor consistency indication 522 to the logical AND 530. The sensor consistency indication 522 may be a Boolean indication, e.g., a value of "1" indicates sensor consistency, and a value of "0" indicates sensor inconsistency. If the sensors are inconsistent (e.g., due to one sensor operating more slowly and thus may indicate straight driving after the vehicle 300 has changed from straight driving (zero true yaw rate) to non-straight driving (non-zero true yaw rate)), then the estimated yaw rate sensor bias should not be updated, e.g., with the current yaw rate measurement. By implementing this check, updating the estimated yaw rate bias estimate based on a false positive of straight driving may be avoided (which may avoid updating the yaw rate bias estimate with an inaccurate value).

[0051] The logical AND 530 may be configured to generate the driving straight line indication 422 based on the initial driving straight line indication 512 and the sensor consistency indication 522. The logical AND 530 may generate the driving straight line indication indicating straight driving (e.g., a Boolean value of "1") based on the initial driving straight line indication 512 indicating straight driving and the sensor consistency indication 522 indicating sensor consistency of zero true yaw rate (e.g., both indications 512, 522 have a logical value of "1"). Otherwise, for example, if the initial driving straight line indication 512 indicates non-straight driving and / or the sensor consistency indication 522 indicates a lack of sensor consistency (e.g., either or both indications 512, 522 have a logical value of "0"), the logical AND 530 may generate the driving straight line indication indicating non-straight driving (e.g., a Boolean value of "0").

[0052] refer to Figure 7 , and further reference Figures 1 to 6 , the yaw rate correlation method 700 includes the stages shown. However, the method 700 is merely an example and is not limiting. The method 700 may be modified, for example, by adding, removing, rearranging, combining, performing one or more stages concurrently, and / or by splitting one or more single stages into multiple stages.

[0053] At stage 710, method 700 includes obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor. For example, yaw rate sensor 330 may measure the yaw rate of vehicle 300 and provide an indication of the yaw rate, e.g., as part of input 415, 425, 435, 525. Processor 310 (possibly in combination with memory 320) may include means for obtaining a plurality of yaw rate measurements.

[0054] At stage 720, method 700 includes determining a yaw rate sensor bias estimate for a yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: a plurality of position indications of the vehicle over time based on global navigation satellite system signals indicating straight-line movement of the vehicle; or a determination that the vehicle is stationary. For example, straight-line driving detection unit 420 may use the GNSS position of input 425 to determine that vehicle 300 may be considered to be driving in a straight line (e.g., driving in a straight line or within an acceptable threshold of driving in a straight line), and this determination may trigger a yaw rate sensor bias estimate by bias estimator 430. As another example, standstill detection unit 410 may use input 415 to determine that vehicle 300 may be considered to be stationary (e.g., stationary or within an acceptable threshold of stationary), and this determination may trigger a yaw rate sensor bias estimate by bias estimator 430. Bias estimator 430 may determine the yaw rate sensor bias estimate based on input 435 in response to one or more indications of zero true yaw rate from standstill detection unit 410 or straight-line driving detection unit 420. Processor 310 (possibly in combination with memory 320 ) may include components for determining a yaw rate sensor bias estimate.

[0055] Implementations of the method 700 may include one or more of the following features. In an example implementation, further in response to at least one yaw rate measurement in the plurality of yaw rate measurements indicating straight-line movement of the vehicle, a yaw rate sensor bias estimate for the yaw rate sensor is determined. For example, the straight-line driving detection unit 420 may use the raw yaw rate of the input 425 to determine that the vehicle 300 may be considered to be driving in a straight line (e.g., driving in a straight line or within an acceptable threshold of driving in a straight line), and this determination may be used to trigger the yaw rate sensor bias estimation performed by the bias estimator 430. In another example implementation, determining the yaw rate sensor bias estimate for the yaw rate sensor includes determining that the yaw rate measurement residual is below one or more corresponding residual thresholds. For example, the bias estimator 430 may determine the yaw rate bias estimate indication 432 based on the check sensor consistency unit 520 determining that the residual of the raw yaw rate in the input 525 and the GNSS virtual zero yaw rate is below the residual threshold (such that the check sensor consistency unit 520 determines that the sensors agree that the vehicle 300 may be considered to be driving in a straight line).

[0056] Additionally or alternatively, implementations of the method 700 may include one or more of the following features. In an example implementation, the method 700 includes providing a yaw rate indication including at least one yaw rate measurement from a plurality of yaw rate measurements with a yaw rate sensor bias estimate removed. For example, the bias estimate removal unit 440 may provide the yaw rate estimate 442 to, for example, the yaw rate application 360. The processor 310 (possibly in conjunction with the memory 320) may include means for providing the yaw rate indication. In another example implementation, the vehicle is an autonomous vehicle, and the yaw rate related method further includes disabling a blind stop function of the vehicle based on the yaw rate sensor bias estimate covariance exceeding an accuracy threshold. For example, the readiness unit 460 may provide a readiness indicator 462 (the readiness indicator indicating that the yaw rate bias estimate is not ready) to an autonomous mobility assistance system (e.g., an ADAS of a car) of the yaw rate application 360 to inhibit a blind stop of the vehicle 300. The processor 310 (possibly in combination with the memory 320) may include components for disabling the blind stop function. In another example implementation, the method 700 includes determining an accuracy threshold based on the speed of the autonomous vehicle. For example, the processor 310 may determine the accuracy threshold such that the threshold is smaller for faster vehicle speeds. The processor 310 (possibly in combination with the memory 320, in combination with one or more sensors in the sensor 213 (e.g., a speed sensor)) may include components for determining the accuracy threshold.

[0057] Additionally or alternatively, an implementation of method 700 may include one or more of the following features. In an example implementation, method 700 includes determining that multiple position indications of a vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the multiple position indications of the vehicle is less than a straight-line threshold. For example, PCA unit 510 may determine that vehicle 300 is driving in a straight line based on an axle ratio determined according to equation (3) being less than a threshold axle ratio. Processor 310 (possibly in combination with memory 320, one or more sensors in sensor 213 (e.g., a speed sensor), and / or GNSS receiver 340) may include components for determining that multiple position indications of a vehicle represent straight-line movement of the vehicle.

[0058] Experimental Results

[0059] Experiments have shown that using GNSS input to determine straight-line movement and triggering an update of the estimated yaw rate sensor bias can reduce errors in the estimated yaw rate sensor bias compared to updating the yaw rate sensor bias based on the yaw rate indicated by the yaw rate sensor being within a range of values ​​close to a true yaw rate of zero. Furthermore, experiments have shown that updating the estimated yaw rate sensor bias in response to agreement between yaw rate sensor measurements and GNSS sensor measurements can improve the accuracy of the estimated yaw rate sensor bias, for example, avoiding inaccurate updates of the estimated yaw rate sensor bias when changing from driving in a straight line to driving on a curve.

[0060] Specific implementation examples

[0061] Specific implementation examples are provided in the following numbered clauses.

[0062] Clause 1. A yaw rate correlation method, the yaw rate correlation method comprising:

[0063] obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; and

[0064] In response to at least one yaw rate measurement of the plurality of yaw rate measurements based on

[0065] To determine the yaw rate sensor bias estimate for the yaw rate sensor:

[0066] The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or

[0067] It is determined that the vehicle is stationary.

[0068] Clause 2. The yaw rate correlation method of Clause 1, wherein the yaw rate sensor bias estimate for the yaw rate sensor is determined further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle.

[0069] Clause 3. The yaw rate correlation method of Clause 2, wherein determining the yaw rate sensor bias estimate for the yaw rate sensor comprises determining that a yaw rate measurement residual is below one or more respective residual thresholds.

[0070] Clause 4. The yaw rate correlation method of clause 1, further comprising providing a yaw rate indication, the yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

[0071] Clause 5. The yaw rate correlation method of Clause 1, wherein the vehicle is an autonomous vehicle, and the yaw rate correlation method further comprises disabling a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

[0072] Clause 6. The yaw rate correlation method of Clause 5, further comprising determining the accuracy threshold based on a speed of the autonomous vehicle.

[0073] Clause 7. The yaw rate correlation method of clause 1, further comprising determining that the plurality of position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the plurality of position indications of the vehicle is less than a straight-line threshold.

[0074] Clause 8. An apparatus comprising:

[0075] one or more memories; and

[0076] One or more processors, the one or more processors being communicatively coupled to the

[0077] One or more memories and are configured as:

[0078] obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; and

[0079] determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to:

[0080] The plurality of position indications of the vehicle over time based on the GNSS signals indicates linear movement of the vehicle; or determines that the vehicle is stationary.

[0081] Clause 9. The apparatus of clause 8, wherein the one or more processors are configured to determine the yaw rate sensor bias estimate for the yaw rate sensor further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle.

[0082] Clause 10. The apparatus of Clause 9, wherein to determine the yaw rate sensor bias estimate for the yaw rate sensor, the one or more processors are configured to determine that a yaw rate measurement residual is below a residual threshold.

[0083] Clause 11. The apparatus of Clause 8, wherein the one or more processors are configured to provide a yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

[0084] Clause 12. The apparatus of clause 8, wherein the vehicle is an autonomous vehicle, and the one or more processors are further configured to disable a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

[0085] Clause 13. The apparatus of Clause 12, wherein the one or more processors are configured to determine the accuracy threshold based on a speed of the autonomous vehicle.

[0086] Clause 14. An apparatus according to Clause 8, wherein the one or more processors are configured to determine that the multiple position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the multiple position indications of the vehicle is less than a straight-line threshold.

[0087] Clause 15. An apparatus comprising:

[0088] means for obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; and

[0089] means for determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to:

[0090] The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or

[0091] It is determined that the vehicle is stationary.

[0092] Clause 16. The apparatus of clause 15, wherein the means for determining the yaw rate sensor bias estimate for the yaw rate sensor comprises means for determining the yaw rate sensor bias estimate for the yaw rate sensor further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle.

[0093] Clause 17. The apparatus of Clause 16, wherein the means for determining the yaw rate sensor bias estimate for the yaw rate sensor comprises means for determining that a yaw rate measurement residual is below a residual threshold.

[0094] Clause 18. The apparatus of Clause 15, further comprising means for providing a yaw rate indication, the yaw rate indication comprising at least one yaw rate measurement of the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

[0095] Clause 19. The apparatus of Clause 15, wherein the vehicle is an autonomous vehicle, and the apparatus further comprises means for disabling a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

[0096] Clause 20. The apparatus of Clause 19, further comprising means for determining the accuracy threshold based on a speed of the autonomous vehicle.

[0097] Clause 21. The apparatus of clause 15, further comprising means for determining that the plurality of position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the plurality of position indications of the vehicle is less than a straight-line threshold.

[0098] Clause 22. A non-transitory processor-readable storage medium comprising processor-readable instructions for causing one or more processors to:

[0099] obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; and

[0100] In response to at least one yaw rate measurement of the plurality of yaw rate measurements based on

[0101] To determine the yaw rate sensor bias estimate for the yaw rate sensor:

[0102] The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or

[0103] It is determined that the vehicle is stationary.

[0104] Clause 23. The non-transitory processor-readable storage medium of clause 22, wherein the processor-readable instructions for causing the one or more processors to determine the yaw rate sensor bias estimate for the yaw rate sensor include processor-readable instructions for causing the one or more processors to further determine the yaw rate sensor bias estimate for the yaw rate sensor in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle.

[0105] Clause 24. The non-transitory processor-readable storage medium of clause 23, wherein the processor-readable instructions for causing the one or more processors to determine the yaw rate sensor bias estimate for the yaw rate sensor include processor-readable instructions for causing the one or more processors to determine that the yaw rate measurement residual is below a residual threshold.

[0106] Clause 25. The non-transitory processor-readable storage medium of clause 22, further comprising processor-readable instructions for causing the one or more processors to provide a yaw rate indication, the yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

[0107] Clause 26. A non-transitory processor-readable storage medium as described in clause 22, wherein the vehicle is an autonomous vehicle and the non-transitory processor-readable storage medium further comprises processor-readable instructions for causing the one or more processors to disable a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

[0108] Clause 27. The non-transitory processor-readable storage medium of Clause 26, further comprising processor-readable instructions for causing the one or more processors to determine the accuracy threshold based on a speed of the autonomous vehicle.

[0109] Item 28. A non-volatile processor-readable storage medium according to Item 22, wherein the non-volatile processor-readable storage medium further comprises processor-readable instructions for causing the one or more processors to determine that the multiple position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the multiple position indications of the vehicle is less than a straight-line threshold.

[0110] Other considerations

[0111] Other examples and implementations are within the scope of this disclosure and the appended claims. For example, due to the nature of software and computers, the functions described above may be implemented using software executed by a processor, hardware, firmware, hard wiring, or any combination thereof. Features that implement a function may also be physically located at different locations, including being distributed so that portions of a function are implemented at different physical locations.

[0112] As used herein, the singular forms "a", "an", and "the" also include the plural forms, unless the context clearly indicates otherwise. Therefore, a device referred to in the singular form (e.g., "device", "the / said device"), included in the claims, includes one or more of such devices (e.g., "processor" includes one or more processors, "the / said processor" includes one or more processors, "memory" includes one or more memories, "the / said memory" includes one or more memories, etc.). As used herein, the terms "comprising", "including" indicate the presence of the recited features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0113] Likewise, as used herein, “or” used in a list of items (possibly followed by “at least one of” or followed by “one or more of”) indicates a disjunctive list, so that, for example, a list of “at least one of A, B, or C,” or a list of “one or more of A, B, or C,” or a list of “A or B or C” means A or B or C, or AB (A and B), or AC (A and C), or BC (B and C), or ABC (i.e., A and B and C), or a combination having more than one feature (e.g., AA, AAB, ABBC, etc.). Thus, a statement that an item (e.g., a processor) is configured to perform a function with respect to at least one of A or B, or a statement that an item is configured to perform function A or function B means that the item can be configured to perform a function with respect to A, or can be configured to perform a function with respect to B, or can be configured to perform functions with respect to both A and B. For example, the phrase "a processor configured to measure at least one of A or B" or "a processor configured to measure A or measure B" means that the processor may be configured to measure A (and may or may not be configured to measure B), or may be configured to measure B (and may or may not be configured to measure A), or may be configured to measure A and measure B (and may be configured to select which of A and B to measure or both). Similarly, a statement about a component for measuring at least one of A or B includes: a component for measuring A (which may or may not be able to measure B), or a component for measuring B (and may or may not be configured to measure A), or a component for measuring A and B (which may be able to select which of A and B to measure or both). As another example, a statement that an item (e.g., a processor) is configured to perform at least one of function X or to perform function Y means that the item may be configured to perform function X, or may be configured to perform function Y, or may be configured to perform function X and perform function Y. For example, the phrase "a processor configured to measure at least one of X or Y" means that the processor may be configured to measure X (and may or may not be configured to measure Y), or may be configured to measure Y (and may or may not be configured to measure X), or may be configured to measure X and measure Y (and may be configured to select which or both of X and Y to measure).

[0114] As used herein, unless otherwise stated, a statement that a function or operation is "based on" an item or condition means that the function or operation is based on the stated item or condition, and may be based on one or more items and / or conditions other than the stated item or condition.

[0115] Substantial changes may be made according to specific requirements. For example, customized hardware may also be used, and / or specific elements may be implemented in hardware, in software (including portable software, such as applets, etc.) executed by a processor, or in both. In addition, connections with other computing devices such as network input / output devices may be employed. Unless otherwise indicated, components (functional or otherwise) shown in the figures and / or discussed herein as being connected or communicating with each other are communicatively coupled. That is, these components may be connected directly or indirectly to enable communication between them.

[0116] The systems and devices discussed above are examples. Various configurations may appropriately omit, replace, or add various procedures or components. For example, features described with respect to certain configurations may be combined in various other configurations. Different aspects and elements of a configuration may be combined in a similar manner. In addition, technology is constantly evolving, and therefore many elements are examples and do not limit the scope of the present disclosure or claims.

[0117] Specific details are given in this specification to provide a thorough understanding of exemplary configurations (including specific implementations). However, configurations can be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques have been shown without unnecessary details to avoid confusing these configurations. The description herein provides example configurations without limiting the scope, applicability, or configuration of the claims. On the contrary, the previous description of the configuration provides a description for implementing the described technology. Various changes can be made to the functions and arrangements of the elements.

[0118] As used herein, the terms "processor-readable medium," "machine-readable medium," and "computer-readable medium" refer to any medium that participates in providing data that causes a machine to operate in a particular manner. Using a computing platform, various processor-readable media may be involved in providing instructions / codes to a processor for execution, and / or may be used to store and / or carry such instructions / codes (e.g., as signals). In many specific implementations, processor-readable media are physical and / or tangible storage media. Such media may take many forms, including, but not limited to, non-volatile media and volatile media. Non-volatile media include, for example, optical disks and / or magnetic disks. Volatile media include, but are not limited to, dynamic memory.

[0119] After describing several example configurations, various modifications, alternative constructions, and equivalents may be used. For example, the above elements may be components of a larger system, in which other rules may take precedence over the application of the present disclosure or otherwise modify the application of the present disclosure. In addition, several operations may be taken before, during, or after considering the above elements. Accordingly, the above description does not limit the scope of the claims.

[0120] Unless otherwise indicated, "approximately" and / or "about" as used herein in reference to a measurable value (such as an amount, a duration of time, etc.) encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specified value, as appropriate in the context of the systems, devices, circuits, methods, and other specific implementations described herein. Unless otherwise indicated, "substantially" as used herein in reference to a measurable value (such as an amount, a duration of time, a physical property (such as frequency), etc.) also encompasses variations of ±20% or ±10%, ±5%, or ±0.1% from the specified value, as appropriate in the context of the systems, devices, circuits, methods, and other specific implementations described herein.

[0121] A statement that a value exceeds (or is greater than or above) a first threshold is equivalent to a statement that the value meets or exceeds a second threshold that is slightly greater than the first threshold, e.g., the second threshold is one value higher than the first threshold at the resolution of the computing system. A statement that a value is less than (or within or below) a first threshold is equivalent to a statement that the value is less than or equal to a second threshold that is slightly less than the first threshold, e.g., the second threshold is one value lower than the first threshold at the resolution of the computing system.

Claims

1. A yaw rate correlation method, the yaw rate correlation method comprising: obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; as well as determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or It is determined that the vehicle is stationary.

2. The yaw rate correlation method of claim 1 , wherein the yaw rate sensor bias estimate for the yaw rate sensor is determined further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle. 3 . The yaw rate correlation method of claim 2 , wherein determining the yaw rate sensor bias estimate for the yaw rate sensor comprises determining that a yaw rate measurement residual is below one or more respective residual thresholds. 4 .

4. The yaw rate correlation method of claim 1 , further comprising providing a yaw rate indication, the yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

5. The yaw rate correlation method of claim 1 , wherein the vehicle is an autonomous vehicle, and the yaw rate correlation method further comprises disabling a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold. 6 . The yaw rate correlation method of claim 5 , further comprising determining the accuracy threshold based on a speed of the autonomous vehicle. 7 .

7. The yaw rate correlation method of claim 1 , further comprising determining that the plurality of position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the plurality of position indications of the vehicle is less than a straight-line threshold.

8. A device, comprising: one or more memories; and one or more processors communicatively coupled to the one or more memories and configured to: obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; and determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or It is determined that the vehicle is stationary.

9. The apparatus of claim 8, wherein the one or more processors are configured to determine the yaw rate sensor bias estimate for the yaw rate sensor further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle. 10 . The apparatus of claim 9 , wherein to determine the yaw rate sensor bias estimate for the yaw rate sensor, the one or more processors are configured to determine that a yaw rate measurement residual is below a residual threshold.

11. The device of claim 8, wherein the one or more processors are configured to provide a yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

12. The apparatus of claim 8, wherein the vehicle is an autonomous vehicle, and the one or more processors are further configured to disable a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

13. The apparatus of claim 12, wherein the one or more processors are configured to determine the accuracy threshold based on a speed of the autonomous vehicle.

14. The apparatus of claim 8, wherein the one or more processors are configured to determine that the multiple position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the multiple position indications of the vehicle is less than a straight-line threshold.

15. A device, comprising: means for obtaining a plurality of yaw rate measurements of a vehicle from a yaw rate sensor; and means for determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or It is determined that the vehicle is stationary.

16. The apparatus of claim 15, wherein means for determining the yaw rate sensor bias estimate for the yaw rate sensor comprises means for determining the yaw rate sensor bias estimate for the yaw rate sensor further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle.

17. The apparatus of claim 16, wherein means for determining the yaw rate sensor bias estimate for the yaw rate sensor comprises means for determining that a yaw rate measurement residual is below a residual threshold.

18. The apparatus of claim 15, further comprising means for providing a yaw rate indication, the yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

19. The apparatus of claim 15, wherein the vehicle is an autonomous vehicle, and the apparatus further comprises means for disabling a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

20. The apparatus of claim 19, further comprising means for determining the accuracy threshold based on a speed of the autonomous vehicle.

21. The apparatus of claim 15, further comprising means for determining that the plurality of position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the plurality of position indications of the vehicle is less than a straight-line threshold.

22. A non-transitory processor-readable storage medium comprising processor-readable instructions for causing one or more processors to: obtaining a plurality of yaw rate measurements of the vehicle from a yaw rate sensor; and determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one yaw rate measurement of the plurality of yaw rate measurements in response to: The plurality of position indications of the vehicle over time based on global navigation satellite system signals represent linear movement of the vehicle; or It is determined that the vehicle is stationary.

23. The non-transitory processor-readable storage medium of claim 22, wherein the processor-readable instructions for causing the one or more processors to determine the yaw rate sensor bias estimate for the yaw rate sensor include processor-readable instructions for causing the one or more processors to determine the yaw rate sensor bias estimate for the yaw rate sensor further in response to the at least one yaw rate measurement of the plurality of yaw rate measurements indicating straight-line movement of the vehicle.

24. The non-transitory processor-readable storage medium of claim 23, wherein the processor-readable instructions for causing the one or more processors to determine the yaw rate sensor bias estimate for the yaw rate sensor include processor-readable instructions for causing the one or more processors to determine that the yaw rate measurement residual is below a residual threshold.

25. The non-transitory processor-readable storage medium of claim 22, further comprising processor-readable instructions for causing the one or more processors to provide a yaw rate indication, the yaw rate indication comprising at least one yaw rate measurement from the plurality of yaw rate measurements with the yaw rate sensor bias estimate removed.

26. The non-transitory processor-readable storage medium of claim 22, wherein the vehicle is an autonomous vehicle and the non-transitory processor-readable storage medium further comprises processor-readable instructions for causing the one or more processors to disable a blind stop function of the autonomous vehicle based on a yaw rate sensor bias estimate covariance exceeding an accuracy threshold.

27. The non-transitory processor-readable storage medium of claim 26, further comprising processor-readable instructions for causing the one or more processors to determine the accuracy threshold based on a speed of the autonomous vehicle.

28. The non-volatile processor-readable storage medium of claim 22, further comprising processor-readable instructions for causing the one or more processors to determine that the multiple position indications of the vehicle represent straight-line movement of the vehicle by determining that a ratio of orthogonal standard deviations of the multiple position indications of the vehicle is less than a straight-line threshold.

Images