Real-time GPS measurement lag compensation for vehicle state estimation

Abstract

A control system for a vehicle includes at least one parameter module configured to receive at least one sensor signal from at least one sensor and generate at least one parameter signal; a measurement comparison module configured to receive a measurement signal from a global positioning system receiver and the at least one parameter signal, and based on the measurement signal and the at least one parameter signal, determine a delay in the measurement signal and generate a delay signal indicative of the delay; and at least one parameter consumption and control module configured to control at least one operation of the vehicle based on the delay.

Description

[0001] Introduction The information provided in this section is for the purpose of generally presenting the context of the present disclosure. The work of the presently named inventors - to the extent it is described in this section - and aspects of this description that may not otherwise be eligible as prior art at the time of filing are neither expressly nor implicitly considered prior art to the present disclosure. Technical Field

[0002] The present disclosure relates to delays associated with signals received by a Global Positioning System (GPS) receiver. Background Art

[0003] A host vehicle may include a GPS receiver configured to receive parameter signals indicative of the state of the host vehicle. For example, the GPS receiver may indicate the position of the host vehicle and the estimated speed of the host vehicle. The host vehicle may also include various sensors for detecting the same parameters and other parameters related to the parameters indicated by the parameter signals. For example, the host vehicle may also include a wheel speed sensor, an inertial measurement sensor, an accelerometer, etc., based on which the speed of the vehicle can be determined. Summary of the Invention

[0004] A control system for a vehicle is disclosed, and the control system includes: at least one parameter module configured to receive at least one sensor signal from at least one sensor and generate at least one parameter signal; a measurement comparison module configured to receive a measurement signal from a Global Positioning System receiver and the at least one parameter signal, and based on the measurement signal and the at least one parameter signal, determine a delay in the measurement signal and generate a delay signal indicative of the delay; and at least one parameter consumption and control module configured to control at least one operation of the vehicle based on the delay.

[0005] In other features, the at least one parameter consumption and control module includes: a vehicle state estimation module configured to estimate vehicle parameters based on the measurement signal, the at least one parameter signal, and the delay signal; and an actuator control module configured to control at least one actuator based on the estimated parameters.

[0006] In other features, the at least one parameter consumption and control module performs at least one of a diagnostic operation and a safety operation based on the delay.

[0007] Among other features, the measurement comparison module is configured to receive a first uncertainty signal indicative of an uncertainty level of the measurement signal and generate a second uncertainty signal indicative of an uncertainty level of the delay indicated by the delay signal. The at least one parameter consumption and control module is configured to estimate the parameter based on the second uncertainty signal.

[0008] Among other features, the at least one parameter consumption and control module is configured to generate a third uncertainty signal based on the second uncertainty signal and estimate the parameter based on the third uncertainty signal.

[0009] Among other features, the measurement comparison module is configured to compare the measurement signal with the at least one parameter signal to generate the delay signal based on a timing difference between the measurement signal and the at least one parameter signal.

[0010] Among other features, a delay of the measurement signal relative to the at least one parameter signal varies with time.

[0011] Among other features, the measurement comparison module is configured to time-shift the measurement signal based on the delay. The at least one parameter consumption and control module is configured to estimate the parameter based on the time-shifted measurement signal.

[0012] Among other features, the at least one parameter consumption and control module is configured to receive the measurement signal having a timestamp and estimate the parameter based on the timestamp and the delay.

[0013] Among other features, the at least one parameter consumption and control module is configured to receive the measurement signal having a timestamp, time-shift the measurement signal, and estimate the parameter based on the time-shifted measurement signal.

[0014] Among other features, the measurement comparison module includes: a kinematic velocity estimation module configured to generate an estimated velocity signal indicative of an estimated velocity of a vehicle; and a delay determination module configured to determine the delay based on the estimated velocity signal.

[0015] Among other features, the at least one parameter signal includes a wheel rate signal and a steering angle signal. The kinematic velocity estimation module is configured to generate the estimated velocity signal based on the wheel rate signal and the steering angle signal.

[0016] Among other features, the kinematic velocity estimation module is configured to generate an enable signal based on at least one of the wheel rate signal and the steering angle signal. The delay determination module is configured to determine the delay based on the enable signal.

[0017] Among other features, a method for controlling the operation of a vehicle is disclosed. The method includes: receiving at least one sensor signal from at least one sensor and generating at least one parameter signal; receiving a measurement signal from a global positioning system receiver and the at least one parameter signal, and based on the measurement signal and the at least one parameter signal, determining a delay in the measurement signal and generating a delay signal indicative of the delay; and controlling at least one operation of the vehicle based on the delay.

[0018] Among other features, the method further includes: receiving a first uncertainty signal indicative of an uncertainty level of the measurement signal, and generating a second uncertainty signal indicative of an uncertainty level of the delay indicated by the delay signal; generating a third uncertainty signal based on the second uncertainty signal; and estimating the parameter based on the second uncertainty signal and the third uncertainty signal.

[0019] Among other features, the method further includes comparing the measurement signal with the at least one parameter signal to generate the delay signal based on a temporal difference between the measurement signal and the at least one parameter signal.

[0020] Among other features, the method further includes: time-shifting the measurement signal based on the delay; and estimating the parameter based on the time-shifted measurement signal.

[0021] Among other features, the method further includes: receiving the measurement signal having a timestamp; and estimating the parameter based on the timestamp and the delay.

[0022] Among other features, the method further includes: receiving the measurement signal having a timestamp, time-shifting the measurement signal; and estimating the parameter based on the time-shifted measurement signal.

[0023] Among other features, the method further includes: generating a speed signal indicative of an estimate of the speed of the vehicle; and determining the delay based on the speed signal.

[0024] Further applicable fields of the present disclosure will become apparent from the detailed description, claims, and drawings. The detailed description and specific examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present disclosure will become more fully understood from the following detailed description and the accompanying drawings, in which: Figure 1 is a functional block diagram of an exemplary host vehicle including a control system according to the present disclosure, the control system having a measurement comparison module, a parameter module, and a parameter consumption and control module; Figure 2 is a functional block diagram of an exemplary vehicle control module implementing the measurement comparison module according to the present disclosure; Figure 3 is a functional block diagram of the measurement comparison module according to the present disclosure; Figure 4A and 4B (collectively FIG. 4) are collectively functional block diagrams of an enabling module according to the present disclosure; Figure 5 illustrates a control method based on parameter delay according to the present disclosure; Figure 6 illustrates a method for estimating the longitudinal kinematic speed of a vehicle according to the present disclosure; Figure 7 illustrates a method for updating global positioning system (GPS) lag estimation according to the present disclosure; In the drawings, reference numerals may be repeated to identify similar and / or identical elements. Detailed Description

[0026] In addition, the current GPS rate information received at the vehicle may have a long and variable delay (e.g., longer than 25 ms), resulting in uncertainty in measurements made using a GPS receiver, such as longitudinal speed measurements. GPS measurements provide an independent source of parameters (e.g., vehicle rate and position). GPS is particularly useful under low traction conditions, where other vehicle sensors, such as wheel speed sensors and accelerometers, may produce unreliable and noisy measurements. However, due to sensor delay, communication delay, and loop execution rate, GPS measurements exhibit delay. Thus, there are measurement confidence inaccuracies associated with GPS measurements. GPS measurements may violate measurement assumed probability distributions. Conventional systems are unable to determine GPS health and performance in real time. The lag may also result in inaccurate and lagged later vehicle rate and / or position estimates, as well as inaccurate and lagged vehicle slip angle estimates. Conventional systems also do not have an effective GPS measurement diagnosis.

[0027] The examples described herein further include a measurement comparison module, a GPS latency determination module, and a parameter consumption and control module - including a vehicle state estimation module and a profile generation module - for estimating GPS measurement latency, compensating for the latency to estimate the state of the host vehicle, and then performing various operations based on the estimated state of the host vehicle, such as controlling actuators, performing diagnostics, performing safety operations, performing acceleration and deceleration operations, etc. An accurate and robust vehicle state estimate is made, and includes a fused estimate based on available sensors and a GPS receiver. The real-time GPS latency is determined based on the sensor output, which enables the GPS measurements to be accurately synchronized with other sensor-based estimates and allows identification of GPS measurement outliers. These examples provide improved performance and reliability of vehicle state estimation using GPS measurements.

[0028] Figure 1 A host vehicle 100 is shown including an example control system 101 that includes a vehicle control module 102 that may include a measurement comparison module 103, a parameter module 104, and a parameter consumption and control module 105. The measurement comparison module 103, as further described below, determines the latency associated with signals received from the GPS and GNS receivers 116. The measurement comparison module 103 may also determine an uncertainty value associated with the determined latency. The parameter consumption and control module 105 then uses the determined latency and optionally the corresponding uncertainty value, and the parameter consumption and control module 105 performs various operations based on the latency. The parameter module 104 receives signals from sensors (such as any of the sensors mentioned herein) and determines corresponding parameters. The parameter consumption and control module 105 may include a vehicle state estimation module, an actuator control module, a diagnostic module, a safety module, and / or other modules (such as a brake system control module, a steering system control module, a propulsion system control module, and / or a power source control module) that may operate based on signals from modules 103, 104, and the sensors. Two or more of these modules may be implemented as a single module.

[0029] The vehicle state estimation module may determine one or more states of one or more parameters of the vehicle (such as the state of vehicle speed and / or position) based on signals received from the measurement comparison module 103. In one embodiment, the vehicle state estimation module also determines one or more uncertainty values respectively associated with the determined one or more states. The actuator module may control and adjust the state of one or more actuators (such as, a motor, an engine, a brake pressure, a steering angle, etc.) based on the estimated state and optionally the corresponding uncertainty value.

[0030] The diagnostic module can determine, based on the signals received from the measurement comparison module 103, whether, for example, one or more sensors and / or modules are not operating or are operating improperly. For example, when a vehicle speed estimate is generated based on the output of a vehicle sensor and the vehicle speed estimate differs from the estimate provided by a GPS and Global Navigation Satellite System (GNSS) receiver by more than a predetermined amount, then the vehicle sensor can be determined to be operating improperly. As another example, if a parameter signal generated by the GPS and GNSS receiver exceeds a predetermined threshold, the parameter signal can be determined to be out of range. These determinations can be based on the output of the parameter module 104 and / or the input (or sensor signal) received from the sensor. The safety module can perform various operations, such as adjusting the acceleration, deceleration, and / or steering angle of the vehicle, to, for example, avoid a collision. These operations can be performed based on the latency and optionally the corresponding uncertainty signal.

[0031] Modules 102 - 105 can perform various operations based on the interaction with the driver of the host vehicle 100. The vehicle control module 102 can perform autonomous operations based on the interaction including the response received from the driver. The vehicle control module can be implemented as one or more control modules and / or one or more controllers.

[0032] Vehicle 100 further includes one or more power sources 106, a telematics module 107, an infotainment module 108, other control modules 109, and a propulsion system 110. The vehicle control module 102 can control the operation of vehicle 100 and modules 107, 108, and 109, and the propulsion system 110. The power source 106 can include one or more battery packs, generators, converters, control circuits, terminals for high - voltage and low - voltage loads, etc., and one or more battery sensors 111 for detecting the state of the power source 106, including voltage, current level, state of charge, etc.

[0033] The telematics module 107 provides wireless communication services within vehicle 100 and communicates wirelessly with service providers, back offices, central offices, cloud - based networks, enterprises, and devices external to vehicle 100. The telematics module 107 can support Bluetooth Low Energy (BLE), Near Field Communication (NFC), cellular, traditional (LG) Transmission Control Protocol (TCP), Long Term Evolution (LTE), and / or other wireless communications, and / or according to Operate using BLE, NFC, cellular, and / or other wireless communication protocols. The telematics module 106 may include one or more transceivers 112, and a navigation module 114 having a Global Positioning System (GPS) and GNSS receiver 116. The transceivers 112 communicate wirelessly with network devices inside and outside the vehicle 100, which include cloud-based network devices, central stations, back offices, and portable network devices. The transceivers 112 may perform pattern recognition, channel addressing, channel access control, and filtering operations.

[0034] The navigation module 114 executes navigation applications to provide navigation services. The navigation services may include a location identification service to identify where the vehicle 100 is located. The navigation services may also include guiding the driver and / or directing the vehicle 100 to a selected location. The navigation module 114 may communicate with a central station to collect map information that indicates traffic levels, transportation object identification and location (such as the location and type of signs), path information, turning locations, lane identification, ramp locations, etc. As an example, if the vehicle 100 is an autonomous vehicle, the navigation module 114 may direct the vehicle control module 102 along a selected route to a selected destination. The GPS and GNSS receiver 116 may provide vehicle state measurement information (such as the speed and / or direction (or heading) of the vehicle 100 and other vehicles and objects (such as pedestrians and cyclists)) and / or global clock timing information. The GPS and GNSS receiver 116 may generate GPS signals that are provided to the vehicle control module 102. The GPS signals and the corresponding vehicle state measurement information may be delayed (i.e., have an associated latency) when received at the vehicle control module 102. The latency time of the GPS signals may vary and depends on the bus traffic on the bus 117, satellite configuration, network traffic, the amount of acceleration of the vehicle 100, etc.

[0035] The infotainment module 108 may include and / or be connected to an audio system 122 and / or a video system, which includes one or more displays (one display 120 is shown). The display 120 and the audio system 122 may be part of the human-machine interface. The display may include a cluster and / or a central console display, a head-up display, etc. Messages may be displayed, audibly played out, and / or indicated via the display 120, the audio system 122, and / or via one or more other output devices. The infotainment module 108 may provide various proactive messages and information. The infotainment module 108 may, for example, guide the vehicle operator to a certain location, turn, change lanes, and / or display other information.

[0036] The propulsion system 110 may include one or more torque sources, such as one or more motors and / or one or more engines (such as an internal combustion engine).Figure 1 In the example shown, vehicle 100 includes an engine 130 and one or more motors 132. The torque sources are independently controlled. Propulsion system 110 includes a motor control system 134, which includes one or more motors 132 and a motor control module 136 that can control the operation of one or more motors 132 based on signals from vehicle control module 102.

[0037] Modules 102 - 105 and 107 - 109 can communicate with each other via one or more buses 117, such as a Controller Area Network (CAN) bus and / or other suitable interfaces. Vehicle control module 102 can control the operation of vehicle modules, devices, and systems based on feedback from sensors 150.

[0038] Sensors 150 can include external sensors, internal sensors, vehicle state sensors, and other sensors. For example, as shown, sensors 150 can include radar and / or lidar sensors 152, external imaging sensors (e.g., cameras) 154, wheel speed sensors 156, acceleration sensors (e.g., longitudinal and lateral acceleration sensors) 158, speed sensors (e.g., longitudinal and lateral speed sensors) 160, inertial measurement sensors 162, yaw rate sensors 164, and other sensors 166. Other sensors 168 can include wheel angle sensors and / or other vehicle state sensors and / or motion detection sensors.

[0039] External sensors can be used to detect objects outside vehicle 100 and / or in the path of vehicle 100. Internal sensors can include internal imaging sensors (e.g., cameras) and microphones or microphone arrays that can be used to monitor the driver's body activities, eye movements, and / or gaze direction and / or interact with the driver. Internal sensors can also be used to detect gestures made by the driver, detect the orientation of the driver's body, detect the driver's speech, etc.

[0040] Mode selection module 178 can select a vehicle operation mode. As an example, vehicle control module 102 can operate in a fully autonomous or partially autonomous mode and can control propulsion system 110, braking system 177, and steering system 201. Steering system 201 includes a steering wheel angle sensor 203. In one embodiment, vehicle control module 102 controls the operation of systems 101, 110, 177, and 201 based on interactions with the vehicle occupant (or driver). Vehicle control module 102 can i) perform autonomous operations, such as steering, braking, accelerating, etc., and / or ii) display and / or audibly play messages and / or output messages and / or corresponding signals via other output devices.

[0041] Vehicle 100 may further include a memory 180. The memory 180 may store sensor data 182, parameters 184, applications 186, algorithms 188, historical data 190, and other data 192. The algorithms 188 may include any algorithms implemented by modules 102, 103, 104, 105 and / or algorithms of other modules mentioned herein. These parameters may include, for example, sensor parameters and data from the sensors 150. The applications 186 may include applications executed by modules 102 - 105, 108, 109.

[0042] Although the memory 180 and the vehicle control module 102 are shown as separate devices, the memory 180 and the vehicle control module 102 may be implemented as a single device. The memory 180 may also store historical data 190 and other data 192, such as driver driving patterns, data collected and / or generated by modules 102 - 105 and 107, traffic data, navigation data, map data, GPS data, path data, sensor data, etc.

[0043] The vehicle control module 102 may control the operation of the propulsion system 110, the video system including the display 120, the audio system 122, the braking system 177, the steering system 201, and / or other devices and systems according to the parameters set by modules 102 - 105, 108, 201. The vehicle control module 102 may set at least some of the parameters based on signals received from the sensors 150.

[0044] The vehicle control module 102 may receive power from the power source 106, and this power may be supplied to the propulsion system 110, the braking system 177, the steering system 201, etc. The power supplied to the motor 132, the braking system 177, the steering system 201, and / or their actuators may be controlled by the vehicle control module 102 to, for example, adjust: motor speed, torque, and / or acceleration; braking pressure; steering wheel angle; pedal position; etc. This control may be based on the outputs of the sensors 150, the navigation module 114, the GPS and GNSS receivers 116, and the data and information stored in the memory 180.

[0045] Module 102 and / or 103 may determine various parameters, including vehicle speed and acceleration, yaw rate, inertia momentum, wheel angle, steering wheel angle, gear state, accelerator position, brake pedal position, and / or other information. Modules 102, 103 may further determine lane boundaries, lane position, road position, turning position, speed limits, object position, environmental conditions, etc.

[0046] Figure 2 The vehicle control module is illustrated (e.g., Figure 1of the vehicle control module 102), which includes a measurement comparison module 103, a parameter module 104, a parameter consumption and control module 105, and a reference clock 512. The vehicle control module 102 receives signals from the GPS and GNSS receiver 116, the steering angle sensor 203, and other sensors 502 (such as Figure 1 any of the sensors 150). The GPS and GNSS receiver 116 generates a GPS measurement signal 516 and an uncertainty signal 518. The steering angle sensor generates a steering angle signal 520 and may generate an uncertainty signal 522. The uncertainty signals 518 and 522 indicate the uncertainty in the signals 516 and 520. The sensor 502 generates an output signal that is provided to the parameter module 104 and may be provided to the parameter consumption and control module 105 and may be used by the parameter consumption and control module 105. One or more of the parameter consumption and control module 105 may not use the output of one or more of the sensors 502.

[0047] The measurement comparison module 103 determines a delay 505 of the GPS measurement signal 516 received from the GPS and GNSS receiver 116 (e.g., ) compared to other measurement signals generated by the sensor 502 The other measurement signal does not have a lag associated with the GPS measurement signal 516. As an example, the GPS measurement signal 516 may indicate a speed estimate of the corresponding host vehicle and has an associated lag relative to the output of the parameter module 104. The measurement comparison module 103 determines the delay based on the output of the parameter module 104 and the reference clock signal from the reference clock 512 The delay is sent to the parameter consumption and control module 105. The measurement comparison module 103 may also determine the uncertainty level 530 in the delay and provide the uncertainty level 530 to the parameter consumption and control module 105. In one embodiment, the measurement comparison module 103 outputs the GPS measurement signal 516 to the parameter consumption and control module 105 along with a timestamp (labeled 532).

[0048] The parameter module 104 determines parameters based on the output signals from the sensors 502 respectively. In one embodiment, one or more parameter modules 104 generate a measurement signal, which is then compared with the GPS measurement signal 516 generated by the GPS and GNSS receiver 116. In one embodiment, the comparison occurs at the measurement comparison module 103. In one embodiment, the parameter module 104 may include an IMU compensation module, an accelerometer module, and a wheel rate module. The IMU compensation module is based on Figure 1The output of the inertial measurement sensor 162 is used to estimate the vehicle's speed. The accelerometer module determines the vehicle speed based on the output of the accelerometer. The wheel speed module estimates the vehicle's speed based on the separately detected wheel speeds. The vehicle speed estimate can be provided directly from sensors, such as from radar sensors and / or cameras. The same can be done for the vehicle position and / or other parameters provided by the GPS and GNSS receivers 116.

[0049] The measurement comparison module 103 and / or the parameter consumption and control module 105 can compare the speed estimate from the parameter module 104 with the GPS measurement signal 516 to estimate the vehicle's speed. In one embodiment, one or more of the modules 103, 105 time-shift the GPS measurement (or speed) signal 516 based on the determined delay 505 of the GPS measurement signal 516 to synchronize it with the output time of the sensor 502. In one embodiment, when estimating the state of the vehicle (e.g., vehicle speed) 540, the parameter consumption and control module 105 can add or subtract the delay 505 from the timestamped GPS measurement signal 532 based on the reference clock signal. In one embodiment, one of the parameter consumption and control modules 105 (e.g., the vehicle state estimation module) can output the determined state 540 and the corresponding uncertainty signal 542 to the actuator control module and / or other modules in the parameter consumption and control module 105. Examples of the signals 540, 542 are shown.

[0050] One or more of the parameter consumption and control modules 105 can control the operation of one or more actuators based on the signals received from the modules 103, 104, sensor 502, and reference clock 512. The diagnostic module can determine that one or more of the sensors 502 are operating improperly and prevent the use of the output from one or more of the sensors 502 or discard the output from one or more of the sensors 502. When, for example, one or more of the determined parameters exceed the corresponding predetermined thresholds, the safety module can adjust the vehicle's speed, acceleration, deceleration, and / or steering.

[0051] Figure 3Shows an example of the measurement comparison module 103, which in this example includes a kinematic speed estimation module 600 and a GPS delay determination module 602. The kinematic speed estimation module 600 includes an enabling module 604. The GPS delay determination module 602 includes an Extended Kalman Filter (EKF) 606. In one embodiment, instead of or in addition to the EKF 606, one or more other filters are used, such as a Kalman filter, an Unscented Kalman Filter (UKF), and / or a particle filter. In one embodiment, instead of or in addition to the filter, other computational methods are used, such as machine learning algorithms. The kinematic speed estimation module 600 receives input signals ω i , θ i , v x and a threshold κ thresh , where ω i refers to the vehicle wheel speed in radians per second, θ i refers to the wheel angle in radians, v x refers to the estimation of the longitudinal speed in meters per second (m / s), κ thresh refers to the wheel slip rate threshold, and refers to the minimum longitudinal rate threshold in meters per second. The kinematic speed estimation module 600 generates an estimated vehicle speed i based on the input signals ω , the left side average speed R and the tire radius r The estimated vehicle speed refers to the kinematic speed estimation of the vehicle in meters per second (or the longitudinal speed estimation of the vehicle's center of gravity). The enabling module 604 generates an enabling signal ENABLE 610 based on the input signals ω i , θ i , v x and the threshold κ thresh , The enabling signal ENABLE 610 enables the GPS delay estimation (or learning of the GPS delay) determined by the GPS delay determination module 602.

[0052] The GPS delay determination module 602 estimates the delay based on i) the GPS estimated speed of the vehicle ii) the enabling signal ENABLE 610, the estimated speed of the vehicle x and the acceleration a where refers to the longitudinal speed in meters per second indicated by the GPS receiver, and a x refers to in meters per second squared (m / s 2The longitudinal acceleration at the center of gravity of the vehicle in units of Refers to the longitudinal acceleration threshold in units of meters per square second, and Refers to the estimated delay in GPS measurements (e.g., the delay in GPS estimation of vehicle speed). In one embodiment, if the estimated speed is determined to be inaccurate, the GPS delay determination module 602 may reject the estimated speed. This may occur, for example, when wheel slip has exceeded the threshold on two wheels on a single side of the vehicle. If at least one wheel on a single side of the vehicle has not exceeded the threshold, the estimated speed may not be rejected.

[0053] GPS delay EKF formula The GPS delay determination module 602 can use the following equations 1 - 7 and the definitions of equations 8 - 13 provided below to determine the GPS delay The GPS delay can be determined by the EKF 606. P k|k =(I - K k H k )P k|k-1 (7)

[0054] Definition for GPS delay determination F k =1 (12) H k =-a x (13) In the above equations 1 - 13, Qk refers to the process covariance, R k refers to the observation noise covariance, refers to the estimate of the GPS delay based on the previous estimate of the function f, P k|k-1 refers to the estimate of the GPS delay estimation covariance based on the previous time delay estimate of the function f (or the predicted time delay uncertainty), refers to how much the observation differs from the observation predicted by the model, S kRefers to the covariance (or the predicted uncertainty of the measurement error), K k Refers to the Kalman gain of the Kalman filter (or relative to How much weight to place on the current measurement), Refers to the updated state estimate of the GPS delay, P k|k Refers to the uncertainty of the updated GPS delay estimate, Refers to the function for predicting the GPS delay based on the previous estimate of the GPS delay (this is called the state transition function), z k Refers to the current observation (or the current measurement reading), Refers to the function for mapping the current state estimate to what the corresponding observation will be (this is called the observation function), F k Refers to the Jacobian matrix of the state transition function (or the linearized derivative of the state transition function), H k Refers to the Jacobian matrix of the observation function (or the linearized derivative of the observation function), I refers to the identity matrix, t delay Refers to the estimated delay of the GPS measurement (e.g., the delay in the GPS speed signal), Refers to the GPS longitudinal speed, Refers to the estimated speed from 600, and a x Refers to the measured longitudinal acceleration.

[0055] For Equation 8, it can be assumed that the GPS delay is constant over a short time interval. Equation 10 is used to determine the difference between the GPS speed and the speed estimate based on the wheel rate. This is an example of using the EKF 606 to estimate the delay. However, other filters can be used to determine the delay. In addition, machine learning methods can be used to determine the delay and perform corresponding calculations.

[0056] Figure 4 shows an example of enabling module 604. The enabling module 604 can include slip rate modules 700, 701, 702, 703, lateral stability module 704, absolute value module 705, comparator 706, absolute value modules 710, 712, 714, 716 and comparators 718, 720, 722, 724. The slip rate modules 700 - 703 calculate the slip rate based on the wheel rates ω LF , ω RF , ω LR , ω RR and based on the speed v x The slip rate can be calculated using Equation 14, where κ i is the slip rate, ω i is the wheel rate in radians, ri is the effective wheel radius in meters, v x is the estimated speed, and is the minimum speed threshold.

[0057] The absolute value modules 710, 712, 714, 716 determine the absolute value of the slip ratio. The comparators 718, 720, 722, 724 determine whether the absolute value is less than the slip threshold K thresh . If so, an output indicates that the wheel is stable. If not, the wheel is unstable. The comparators 718, 720, 722, 724 output signals 730, 732, 734, 736 indicating whether the wheels are stable respectively. These operations are performed to determine whether each wheel has a low enough slip that will allow use in the estimated speed calculation.

[0058] The lateral stability module 704 determines whether the vehicle is laterally stable based on the steering angles θ FrtRWA , θ RrRWA to determine whether the vehicle is laterally stable, where the steering angles θ FrtRWA , θ RrRWA are the front and rear wheel angles. This can include looking up the lateral steering angle in a look-up table to determine whether the lateral steering angle is associated with lateral stability. The lateral stability module 704 outputs a signal 738 indicating whether the vehicle is laterally stable. The speed and wheel angles are used to help determine the body slip angle. If the body slip angle is large enough, this may cause the calculated wheel-based vehicle speed to be erroneously higher than the actual vehicle speed, and thus prevent the determination of an accurate GPS lag estimate. Therefore, when the body slip angle is too high, GPS lag determination is not enabled. The body slip angle can refer to the difference between the direction in which the vehicle is moving forward and the direction in which the vehicle's body is pointing (or the true forward direction).

[0059] The absolute value module 705 determines the absolute value of the speed v x where the speed v x can be estimated based on the wheel rates ω LF , ω RF , ω LR , ω RR , ω x . The comparator 706 determines whether the absolute value of the speed v thresh is greater than the threshold speed threshold v thresh . If it is greater than v thresh , then the vehicle is moving at a rate greater than a predetermined rate. The predetermined rate is greater than or equal to 0. A signal 740 is generated to indicate whether the vehicle is moving and / or is moving at a rate greater than a predetermined rate. GPS delay is not calculated unless the vehicle is moving. This helps avoid dividing by zero when calculating the slip ratio.

[0060] The enabling module 604 may further include comparators 750, 752 and an AND gate 754. Signals 730, 732 are received at comparator 750, and comparator 750 determines whether signals 730, 732 are equal. If so, the output of comparator 750 is high (or 1), otherwise, the output is low (or 0). Signals 734, 736 are provided to comparator 752, and comparator 752 determines whether signals 734, 736 are equal. If so, the output of comparator 752 is high (or 1), otherwise, the output is low (or 0). The outputs of comparators 750, 752 and signals 738, 740 are provided to AND gate 754. The output of AND gate 754 is provided as the enabling signal ENABLE610, also referred to as the enabling learning signal. Comparators 750, 752 take into account the differences in wheel speeds when the vehicle is turning. "Stable" wheels are required on each side of the vehicle in order to enable learning of the GPS speed measurement delay.

[0061] Figure 5 A control method based on parametric delay is shown. The following operations may be performed iteratively.

[0062] At 800, sensor signals and GPS receiver signals are received at vehicle control module 102, as Figure 1 shown. At 802, the output of parametric module 104 is generated. At 804, the conditions determined for enabling GPS delay are checked, as shown in FIG. 4.

[0063] At 806, if the conditions are met, operation 808 is performed, otherwise operation 800 is performed. At 808, a reference parameter determination algorithm is executed to generate a reference parameter (e.g., an estimated speed). An example of this algorithm is Figure 6 described.

[0064] At 810, the GPS delay and GPS delay uncertainty are determined, as described above. The GPS delay determination algorithm may be performed based on the reference parameter to determine the GPS delay and GPS uncertainty.

[0065] At 812, the delay of the GPS measurement signal is considered, which may include time-shifting the GPS measurement signal. This operation may be performed at 810. The amount of time-shifting may not be uniform and may change over time.

[0066] At 814, a parameter is estimated based on the parametric GPS measurement and one or more other estimates of the parameter made based on the sensor signals. At 816, 818, 820, 822, operations are performed based on the estimate of the parameter determined at 814. These operations are provided as examples and may be Figure 1-2 performed by the parameter consumption and control module 105, and other operations may be performed.

[0067] At 816, one or more in the parameter consumption and control module 105 control the operation of one or more actuators based on at least one of the estimate and optionally a corresponding uncertainty value, as described above. At 818, one or more in the parameter consumption and control module 105 perform diagnostic operations based on at least one of the estimate and optionally a corresponding uncertainty value, as described above. At 820, one or more in the parameter consumption and control module 105 perform safety operations based on at least one of the estimate and optionally a corresponding uncertainty value, as described above. At 822, one or more in the parameter consumption and control module 105 perform other operations based on at least one of the estimate and optionally a corresponding uncertainty value.

[0068] Figure 6 A method for estimating the longitudinal kinematic speed of a vehicle is shown. The following operations can be performed iteratively. When the condition for learning GPS latency is met, the estimated speed is determined. The estimated speed is determined based on which wheels are stable. In one embodiment, the left speed is determined, the right speed is determined, and the left and right speeds are averaged to determine the estimated speed.

[0069] At 900, execute the GPS learning enable algorithm for enabling the execution of GPS latency determination - performed by Figure 3 the kinematic speed estimation module 600 - to generate the enable signal ENABLE 610. At 902, determine whether GPS latency determination is enabled. If so, operation 904 can be performed, otherwise, operation 900 can be performed.

[0070] At 904, determine whether the left front wheel is stable. If so, perform operation 906, otherwise perform operation 908.

[0071] At 906, determine whether the left rear wheel is stable. If so, perform operation 910, otherwise perform operation 912.

[0072] At 908, determine the left speed based on the left rear wheel rate and the tire radius of the left rear wheel. This can be done using Equation 15.

[0073] At 910, determine the left average value of the estimated speed based on both the left front wheel rate and the left rear wheel rate and the tire radius. This can be done using Equation 16.

[0074] At 912, determine the left speed based on the left front wheel rate and the tire radius of the left front wheel. This can be done using Equation 17.

[0075] At 914, it is determined whether the right front wheel is stable. If so, operation 916 is performed; otherwise, operation 918 is performed. At 916, it is determined whether the right rear wheel is stable. If so, operation 920 is performed; otherwise, operation 922 is performed.

[0076] At 918, an estimated speed is determined based on the right rear wheel speed and the tire radius of the right rear wheel. This can be done using Equation 18.

[0077] At 920, an estimated speed is determined based on both the right front wheel speed and the right rear wheel speed and the tire radius. This can be done using Equation 19.

[0078] At 922, an estimated speed is determined based on the right front wheel speed and the tire radius of the left front wheel. This can be done using Equation 20.

[0079] Figure 7 A method for updating the GPS lag (or delay) estimate is shown. The following operations can be performed iteratively.

[0080] At 1000, a call to the GPS delay algorithm is executed. The GPS delay algorithm can be executed by Figure 3 the EKF 606, and the EKF 606 can receive the call. The call can be generated by Figure 1 a module of the vehicle control module 102 or another module.

[0081] At 1002, it is determined whether learning of the GPS delay is enabled. If so, operation 1004 is performed; otherwise, operation 1000 can be performed.

[0082] At 1004, it is determined whether the absolute value of the vehicle acceleration is greater than the acceleration threshold a xthresh . If so, operation 1006 is performed; otherwise, operation 1008 is performed. In one embodiment, if the vehicle is accelerating or decelerating, the GPS lag is updated, and if the vehicle is not accelerating or decelerating, the GPS lag is not updated.

[0083] At 1006, the GPS lag determination is updated (i.e., determined again) as described above. At 1008, as opposed to updating the GPS lag, the previously determined GPS lag is maintained and used for estimating the state of the vehicle.

[0084] Figure 5-7The above operations are meant to be illustrative examples. Depending on the application, these operations may be performed sequentially, synchronously, simultaneously, continuously, during overlapping time periods, or in a different order. Additionally, depending on the implementation and / or sequence of events, any of these operations may not be performed or may be skipped.

[0085] The above examples include using wheel rate and acceleration to learn the reported GPS rate time delay. Vehicle accelerometer information and steering angle measurements are used to determine when to enable learning of the GPS measurement delay. Wheel stability is monitored to select wheel measurements for learning the GPS measurement delay. The learned time delay is used to align the GPS measurement with the output of vehicle sensors and / or estimates made based on the vehicle sensor output.

[0086] In one embodiment, optical measurements and / or radar measurements are used in conjunction with wheel rate to determine the GPS measurement delay. The learned GPS measurement delay is used to eliminate the fusion of GPS rate measurement outliers and inaccuracies. The stochastic nature of the learned GPS measurement delay is used to determine the health of the GPS measurement. The learned GPS measurement delay is also used to update the GPS measurement uncertainty for algorithms such as consumption control, estimation, diagnosis, security, etc. This may be done with or without realigning the measurements.

[0087] The foregoing description is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. The broad teachings of the disclosure may be implemented in a variety of forms. Thus, while the disclosure includes specific examples, the true scope of the disclosure should not be so limited since other modifications will become apparent upon study of the drawings, the specification, and the following claims. It should be understood that one or more steps within a method may be performed in a different order (or simultaneously) without altering the principles of the disclosure. Additionally, although each embodiment is described above as having certain features, any one or more of those features described with respect to any embodiment of the disclosure may be implemented in and / or combined with the features of any other embodiment, even if the combination is not explicitly described. In other words, the described embodiments are not mutually exclusive, and permutations of one or more of the embodiments with each other are still within the scope of the disclosure.

[0088] The spatial and functional relationships between components (e.g., between modules, circuit components, semiconductor layers, etc.) are described using various terms, including "connected", "engaged", "coupled", "adjacent", "immediately adjacent", "on", "above", "below", and "disposed". Unless explicitly described as "direct", when describing the relationship between a first and a second component in the foregoing disclosure, the relationship can be a direct relationship in which no other intervening components exist between the first and second components, but can also be an indirect relationship in which one or more intervening components (either spatially or functionally) exist between the first and second components. As used herein, the phrase "at least one of A, B, and C" should be interpreted to mean a logical (A or B or C) using a non-exclusive logical "or", and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C".

[0089] In the various figures, the direction of an arrow, as indicated by the arrowhead, generally indicates the information flow (such as data or instructions) of interest for that figure. For example, when component A and component B exchange various information, but the information transmitted from component A to component B is relevant to that figure, the arrow can point from component A to component B. This one-way arrow does not imply that no other information is transmitted from component B to component A. Additionally, for the information sent from component A to component B, component B can send a request for that information or receive an acknowledgement to component A.

[0090] In this application, including the definitions below, the term "module" or the term "controller" can be replaced with the term "circuit". The term "module" can refer to, be part of, or include the following: application specific integrated circuit (ASIC); digital, analog, or mixed analog / digital discrete circuit; digital, analog, or mixed analog / digital integrated circuit; combinational logic circuit; field programmable gate array (FPGA); processor circuit (shared, dedicated, or grouped) that executes code; memory circuit (shared, dedicated, or grouped) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system on a chip.

[0091] A module can include one or more interface circuits. In some examples, the interface circuit can include a wired or wireless interface connected to a local area network (LAN), the Internet, a wide area network (WAN), or a combination thereof. The functionality of any given module of the present disclosure can be distributed among multiple modules connected via the interface circuit. For example, multiple modules can allow load balancing. In a further example, a server (also referred to as remote or cloud) module can implement some functions on behalf of a client module.

[0092] As used above, the term "code" can include software, firmware, and / or microcode, and can refer to programs, routines, functions, classes, data structures, and / or objects. The term "shared processor circuit" encompasses a single processor circuit that executes some or all of the code from multiple modules. The term "group processor circuit" encompasses a processor circuit that executes some or all of the code from one or more modules in combination with additional processor circuits. References to multiple processor circuits encompass multiple processor circuits on separate die, multiple processor circuits on a single die, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or combinations of the above. The term "shared memory circuit" encompasses a single memory circuit that stores some or all of the code from multiple modules. The term "group memory circuit" includes a memory circuit that stores some or all of the code from one or more modules in combination with additional memory.

[0093] The term "memory circuit" is a subset of the term "computer-readable medium". As used herein, the term "computer-readable medium" does not encompass non-transitory electrical or electromagnetic signals propagated through a medium (such as on a carrier wave); thus, the term "computer-readable medium" can be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media are non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits, or mask ROM circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog or digital tape or hard disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0094] The devices and methods described in this application can be implemented in part or in whole by a special-purpose computer created by configuring a general-purpose computer to execute one or more specific functions embodied in a computer program. The functional blocks, flowchart components, and other elements described above serve as a software specification that can be translated into a computer program by the routine work of a skilled technician or programmer.

[0095] A computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program can also include or rely on stored data. The computer program can encompass a basic input / output system (BIOS) that interacts with the hardware of the special-purpose computer, device drivers that interact with specific devices of the special-purpose computer, one or more operating systems, user applications, background services, background applications, and the like.

[0096] A computer program may include: (i) descriptive text to be parsed, such as HTML (HyperText Markup Language), XML (eXtensible Markup Language), or JSON (JavaScript Object Notation), (ii) assembly code, ( i ) object code generated by a compiler from source code, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. By way of example only, the source code may be written using the syntax of a language including: C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Fortran, Perl, Pascal, Curl, OCaml, HTML5 (Fifth Edition of HyperText Markup Language), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Visual Lua, MATLAB, SIMULINK, and

Claims

1. A control system for a vehicle, the control system comprising: at least one parameter module configured to receive at least one sensor signal from at least one sensor and generate at least one parameter signal; a measurement comparison module configured to receive a measurement signal from a global positioning system receiver and the at least one parameter signal and, based on the measurement signal and the at least one parameter signal, determine a delay in the measurement signal and generate a delay signal indicative of the delay; as well as At least one parameter consumption and control module is configured to control at least one operation of the vehicle based on the delay.

2. The control system of claim 1, wherein said at least one parameter consumption and control module comprises: a vehicle state estimation module configured to estimate a parameter of the vehicle based on the measurement signal, the at least one parameter signal, and the delayed signal; as well as An actuator control module is configured to control at least one actuator based on the estimate of the parameter. 3 . The control system of claim 1 , wherein said at least one parameter consumption and control module performs at least one of a diagnostic operation and a safety operation based on said delay.

4. The control system according to claim 1, wherein: The measurement comparison module is configured to receive a first uncertainty signal indicative of an uncertainty level of the measurement signal and to generate a second uncertainty signal indicative of an uncertainty level of the delay indicated by the delay signal; and The at least one parameter consumption and control module is configured to estimate the parameter based on a second uncertainty signal. 5 . The control system of claim 4 , wherein the at least one parameter consumption and control module is configured to generate a third uncertainty signal based on the second uncertainty signal and to estimate the parameter based on the third uncertainty signal. 6 . The control system of claim 1 , wherein the measurement comparison module is configured to compare the measurement signal with the at least one parameter signal to generate the delay signal based on a temporal difference between the measurement signal and the at least one parameter signal.

7. The control system of claim 1, wherein the delay of the measurement signal relative to the at least one parameter signal varies with time.

8. The control system of claim 1, wherein: The measurement comparison module is configured to time-shift the measurement signal based on the delay; and The at least one parameter consumption and control module is configured to estimate the parameter based on the time-shifted measurement signal.

9. The control system of claim 1, wherein the at least one parameter consumption and control module is configured to receive the measurement signal with a time stamp and estimate the parameter based on the time stamp and the delay.

10. The control system of claim 1, wherein the at least one parameter consumption and control module is configured to receive the measurement signal with a time stamp, time-shift the measurement signal, and estimate the parameter based on the time-shifted measurement signal.

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