Comprehensive measurement

Abstract

This disclosure relates to a comprehensive range measurement system. A range measurement system includes multiple detectors and an electronic control unit (ECU). The detectors operate to detect vehicle movement events and vehicle stopping events. The ECU is coupled to the multiple detectors. The ECU includes a range state estimator, a feedback loop, and a low-speed motion controller. The range state estimator operates to generate an estimated range state of the vehicle in response to the movement and stopping events. The feedback loop wraps around the range state estimator and operates to enforce a zero-speed constraint in the estimated range state to correct inaccuracies in the estimated vehicle position. The low-speed motion controller operates to generate speed commands to control the longitudinal movement of the vehicle during automated parking assist maneuvers based on the estimated range state.

Description

Technical Field

[0001] This disclosure relates to a system and method for odometry. Background Technology

[0002] The longitudinal speed estimate based on the range typically suffers a delay of approximately 1 to 2 seconds during the stop-to-move and move-to-stop events. This delay can cause low-speed maneuvering control to overshoot torque requests by approximately 50%. During automated parking assist maneuvers, this torque overshoot can cause discomfort for the customer. This situation arises when there is a conflict between the data reported by the range and the data in the low-speed maneuvering command.

[0003] Therefore, those skilled in the art continue to make research and development efforts in the field of comprehensive measurement using adaptive weighting to mitigate longitudinal torque overshoot during low-speed maneuvering. Summary of the Invention

[0004] This document provides a range measurement system. The range measurement system includes multiple detectors and an electronic control unit. The multiple detectors operate to detect vehicle movement events and vehicle stopping events. The electronic control unit is coupled to the multiple detectors. The electronic control unit includes: a range state estimator that operates to generate an estimated range state of the vehicle in response to the movement event and the stopping event; a feedback loop that surrounds the range state estimator and operates to enforce a zero-speed constraint in the estimated range state to correct inaccuracies in the estimated position of the vehicle; and a low-speed motion controller that operates to generate a speed command to control the longitudinal movement of the vehicle during automated parking assist movement based on the estimated range state.

[0005] In one or more embodiments of the range system, the feedback loop further operates to adaptively weight the zero-velocity update, and the range state estimator includes an extended Kalman filter that operates to utilize the zero-velocity update during a movement event.

[0006] In one or more embodiments of the range system, the range state estimator further operates to detect a stopping event based on a velocity zero-crossing using an extended Kalman filter.

[0007] In one or more embodiments of the range measuring system, a plurality of detectors include: an axle torque detector, which operates to measure axle torque; a braking torque detector, which operates to measure braking torque; and a road slope detector, which operates to measure gravitational torque applied to the vehicle.

[0008] In one or more embodiments of the range measurement system, the range state estimator further operates to generate an estimated range state of the vehicle in response to axle torque, braking torque, and gravitational torque.

[0009] In one or more embodiments of the range system, the range state estimator further operates to: learn and adapt to creep torque; and also generate an estimated range state of the vehicle in response to creep torque.

[0010] In one or more embodiments of the range system, multiple detectors include inertial measurement units that operate to measure the longitudinal acceleration of the vehicle, and a range state estimator further operates to generate an estimated range state of the vehicle in response to the longitudinal acceleration.

[0011] In one or more embodiments of the range system, the range state estimator further operates to: arbitrate among multiple detectors; and generate vehicle motion indications and vehicle speeds based on movement and stop events, such as those arbitrated.

[0012] In one or more embodiments of the range measurement system, the range state estimator further operates to generate an estimated range state of the vehicle in response to vehicle motion indications and vehicle speed.

[0013] This paper presents a method for comprehensive range estimation. The method includes: detecting vehicle movement events and vehicle stopping events using multiple detectors; generating an estimated range state of the vehicle in response to the movement and stopping events using a range state estimator; forcing a zero-speed constraint in the estimated range state using a feedback loop to correct inaccuracies in the estimated vehicle position; and generating a speed command to control the longitudinal movement of the vehicle during automated parking assistance movement based on the estimated range state using a low-speed motion controller.

[0014] In one or more embodiments, the method includes: adaptively weighting zero-velocity updates using a feedback loop around the range state estimator; and utilizing the zero-velocity updates during a movement event using an extended Kalman filter of the range state estimator.

[0015] In one or more embodiments, the method includes: detecting a stopping event using a velocity zero-crossing based on an extended Kalman filter.

[0016] In one or more embodiments, the method includes: measuring axle torque using an axle torque detector among a plurality of detectors; measuring braking torque using a braking torque detector among the plurality of detectors; and measuring gravitational torque applied to the vehicle using a road slope detector among the plurality of detectors.

[0017] In one or more embodiments of the method, the generation of the estimated range state of the vehicle is also performed in response to axle torque, braking torque, and gravitational torque.

[0018] In one or more embodiments, the method includes: learning and adapting creep torque, wherein the generation of an estimated range state of the vehicle is also performed in response to the creep torque.

[0019] In one or more embodiments, the method includes: measuring the longitudinal acceleration of a vehicle using an inertial measurement unit among a plurality of detectors, wherein the generation of an estimated range state of the vehicle is also performed in response to the longitudinal acceleration.

[0020] In one or more embodiments, the method includes: arbitrating among a plurality of detectors; and generating vehicle motion indications and vehicle speeds based on movement events and stop events, such as those arbitrated.

[0021] In one or more embodiments of the method, the generation of the estimated range state of the vehicle is also performed in response to vehicle motion indication and vehicle speed.

[0022] This document provides a vehicle. The vehicle includes multiple detectors, an electronic control unit, and a drive system. The multiple detectors operate to detect vehicle movement events and vehicle stopping events. The electronic control unit is coupled to the multiple detectors. The electronic control unit includes: a range state estimator that operates to generate an estimated range state of the vehicle in response to the movement and stopping events; a feedback loop that operates to force a zero-speed constraint in the estimated range state to correct inaccuracies in the estimated position of the vehicle; and a low-speed motion controller that operates to generate a speed command to control the longitudinal movement of the vehicle during automated parking assist movement based on the estimated range state. The drive system operates to move the vehicle in response to the speed command.

[0023] In one or more embodiments of the vehicle, the feedback loop further operates to adaptively weight the zero-speed update, and the range state estimator includes an extended Kalman filter that operates to utilize the zero-speed update during a movement event.

[0024] The above features and advantages of this disclosure, as well as other features and advantages, will readily become apparent from the following detailed description of the best mode for carrying out this disclosure when taken in conjunction with the accompanying drawings.

[0025] This disclosure also includes the following technical solutions:

[0026] 1. A range measurement system, comprising:

[0027] Multiple detectors, which operate to detect vehicle movement events and vehicle stopping events; and

[0028] An electronic control unit, connected to the plurality of detectors, wherein the electronic control unit includes:

[0029] A range state estimator that operates to generate an estimated range state of the vehicle in response to the movement event and the stop event;

[0030] A feedback loop that surrounds the range state estimator and operates to force a zero-speed constraint in the estimated range state to correct for inaccuracies in the estimated position of the vehicle; and

[0031] A low-speed motion controller operates to generate speed commands for controlling the longitudinal movement of the vehicle during automated parking assist movement, based on the estimated range state.

[0032] 2. The range measurement system according to Scheme 1, wherein:

[0033] The feedback loop further operates to adaptively update at a weighted zero rate; and

[0034] The range state estimator includes an extended Kalman filter that operates to utilize the zero velocity update during the movement event.

[0035] 3. The range measurement system according to Scheme 2, wherein:

[0036] The range state estimator further operates to detect the stopping event based on the velocity zero crossing of the extended Kalman filter.

[0037] 4. The range measurement system according to Scheme 1, wherein the plurality of detectors includes:

[0038] Axle torque detector, which operates to measure axle torque;

[0039] A braking torque detector, which operates to measure braking torque; and

[0040] A road slope detector, which operates to measure the gravitational torque applied to the vehicle.

[0041] 5. The range measurement system according to Scheme 4, wherein the range state estimator further operates to:

[0042] The estimated range state of the vehicle is also generated in response to the axle torque, the braking torque, and the gravitational torque.

[0043] 6. The range measurement system according to Scheme 5, wherein the range state estimator further operates to:

[0044] Learning and adapting to creep torque; and

[0045] The estimated range state of the vehicle is also generated in response to the creep torque.

[0046] 7. The range measurement system according to Scheme 1, wherein:

[0047] The plurality of detectors includes an inertial measurement unit that operates to measure the longitudinal acceleration of the vehicle; and

[0048] The range state estimator further operates to generate an estimated range state of the vehicle in response to the longitudinal acceleration.

[0049] 8. The range measurement system according to Scheme 1, wherein the range state estimator further operates to:

[0050] Arbitration is performed among the plurality of detectors; and

[0051] Vehicle motion indication and vehicle speed are generated based on the movement event and the stop event as described in the arbitration.

[0052] 9. The range measurement system according to Scheme 8, wherein the range state estimator further operates to:

[0053] It also generates an estimated range state of the vehicle in response to the vehicle motion indication and the vehicle speed.

[0054] 10. A method for comprehensive range measurement, comprising:

[0055] Multiple detectors are used to detect vehicle movement events and vehicle stopping events;

[0056] An estimated range state of the vehicle is generated in response to the movement event and the stop event using a range state estimator.

[0057] A feedback loop is used to force a zero-speed constraint under the estimated range state to correct the inaccuracy of the vehicle's estimated position; and

[0058] The low-speed motion controller generates speed commands to control the longitudinal movement of the vehicle during automated parking assist movement based on the estimated range state.

[0059] 11. The method according to Scheme 10 further includes:

[0060] Adaptive weighted zero-velocity updates are achieved using a feedback loop surrounding the range state estimator; and

[0061] The extended Kalman filter of the range state estimator is updated using the zero velocity during the movement event.

[0062] 12. The method according to Scheme 11 further includes:

[0063] The stop event is detected using the velocity zero-crossing method according to the extended Kalman filter.

[0064] 13. The method according to Scheme 10 further includes:

[0065] Wheel and axle torque is measured using the wheel and axle torque detector among the plurality of detectors;

[0066] Braking torque is measured using the braking torque detector among the plurality of detectors; and

[0067] The gravitational torque applied to the vehicle is measured using one of the plurality of detectors, namely the road slope detector.

[0068] 14. The method according to scheme 13, wherein:

[0069] The generation of the estimated range state of the vehicle is also in response to the axle torque, the braking torque, and the gravitational torque.

[0070] 15. The method according to Scheme 14 further includes:

[0071] Learning and adapting to creep torque, among which,

[0072] The generation of the estimated range state of the vehicle is also performed in response to the creep torque.

[0073] 16. The method according to Scheme 10, further comprising:

[0074] The longitudinal acceleration of the vehicle is measured using an inertial measurement unit among the plurality of detectors, wherein...

[0075] The generation of the estimated range state of the vehicle is also performed in response to the longitudinal acceleration.

[0076] 17. The method according to Scheme 10, further comprising:

[0077] Arbitration is performed among the plurality of detectors; and

[0078] Vehicle motion indication and vehicle speed are generated based on the movement event and the stop event as described in the arbitration.

[0079] 18. The method according to scheme 17, wherein:

[0080] The generation of the estimated range state of the vehicle is also performed in response to the vehicle motion indication and the vehicle speed.

[0081] 19. A vehicle comprising:

[0082] Multiple detectors, which operate to detect vehicle movement events and vehicle stopping events;

[0083] An electronic control unit, connected to the plurality of detectors, wherein the electronic control unit includes:

[0084] A range state estimator that operates to generate an estimated range state of the vehicle in response to the movement event and the stop event;

[0085] A feedback loop that operates to force a zero-speed constraint under the estimated range state to correct for inaccuracies in the estimated position of the vehicle; and

[0086] A low-speed motion controller, operating to generate speed commands for controlling the longitudinal movement of the vehicle during automated parking assist movement, based on the estimated range state; and

[0087] A drive system that operates to move the vehicle in response to the speed command.

[0088] 20. The vehicle according to Scheme 19, wherein:

[0089] The feedback loop further operates to adaptively update at a weighted zero rate; and

[0090] The range state estimator includes an extended Kalman filter that operates to utilize the zero velocity update during the movement event. Attached Figure Description

[0091] Figure 1 It is a schematic plan view of the background of an illustrated system according to one or more exemplary embodiments.

[0092] Figure 2 This is a schematic diagram of a detector and an electronic control unit according to one or more exemplary embodiments.

[0093] Figure 3 It is a graph of vehicle speed estimation according to one or more exemplary embodiments.

[0094] Figure 4 It is a graph of longitudinal velocity estimation according to one or more exemplary embodiments.

[0095] Figure 5 It is a graph of vehicle speed estimation according to one or more exemplary embodiments.

[0096] Figure 6 This is a flowchart of a method for motion status monitoring according to one or more exemplary embodiments.

[0097] Figure 7It is a diagram of automatic parking assistance movement according to one or more exemplary embodiments.

[0098] Figure 8 It is a graph of stop detection according to one or more exemplary embodiments. Detailed Implementation

[0099] Embodiments of this disclosure provide a system and / or method for mitigating longitudinal torque overshoot during low-speed maneuvering by utilizing adaptive weighting for integrated range estimation. Closed-loop filtering around the range speed estimation behavior typically reduces the delay in reporting zero-speed events in addition to orthogonal sensor information. This delay reduction can be achieved by developing multiple (e.g., two) closed-loop behaviors, zero-crossing (stopping events), and adaptive weighting (movement events) along with sensor information from other vehicle detectors (e.g., inertial measurement unit, wheel speed detector, and axle torque detector). After taking gravity into account, data from the inertial measurement unit (IMU) (e.g., longitudinal acceleration) and total torque data are used to reduce the delay of movement events. Looking at the closed loop, when the speed crosses zero without shifting gears, the extended Kalman filter (EKF) behavior indicates a stopping event. With adjustments for gravity, the total torque can also be used for early detection of stopping events.

[0100] refer to Figure 1 The illustration shows a schematic plan view of the background of a system 70 according to one or more exemplary embodiments. System 70 may include gravity 72, a road surface 80, and a vehicle 90. Vehicle 90 typically includes multiple detectors 100, an electronic control unit 102 (ECU), and a drive system 104. The longitudinal direction 92 of vehicle 90 is generally oriented from front to rear of vehicle 90. Vehicle 90 rests on and moves along the road surface 80.

[0101] The road surface 80 may have a road slope 82. The road slope 82 and gravity 72 typically exert a downward force on the vehicle 90 along the road slope 82.

[0102] Vehicle 90 can be a gas-powered vehicle, an electric vehicle, a hybrid vehicle, or a plug-in hybrid vehicle. In various embodiments, vehicle 90 may include, but is not limited to, passenger vehicles, trucks, autonomous vehicles, motorcycles, boats, and / or aircraft. Other types of vehicle 90 may be implemented to meet design criteria for specific applications.

[0103] Detector 100 implements multiple detectors within vehicle 90, which operate to detect the movement of vehicle 90. Detector 100 may include, but is not limited to, an internal reference unit (IMU) detector 100a, one or more axle torque detectors 100b, one or more brake torque detectors 100c, a road slope detector 100d, and a wheel encoder 100e. Detector 100 may present sensor information (or data) 110 to ECU 102.

[0104] ECU 102 implements multiple digital computing circuits. These digital computing circuits can transfer data to each other across a communication bus. The digital computing circuits can be implemented in hardware, software executed on hardware, or a combination of both. ECU 102 can receive sensor information 110 from detector 100 and present drive commands 112 to drive system 104.

[0105] In various embodiments, the electronic control unit 102 typically includes at least one microcontroller. The at least one microcontroller may include one or more processors, each of which may be embodied as a separate processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a dedicated electronic control unit. The at least one microcontroller may be an electronic processor (implemented in hardware, software executed on hardware, or a combination of both). The at least one microcontroller may also include tangible, non-transient memory (e.g., read-only memory in the form of optical, magnetic, and / or flash memory). For example, the at least one microcontroller may include a quantity of random access memory, read-only memory, flash memory, and other types of electrically erasable programmable read-only memory suitable for the application, along with accompanying hardware (in the form of high-speed clocks or timers, analog-to-digital and digital-to-analog circuit systems, input / output circuit systems and devices, and appropriate signal conditioning and buffering circuit systems).

[0106] The computer-readable and executable instructions embodying this method may be recorded (or stored) in memory and executed as described herein. These executable instructions may be a series of instructions adopted to run an application (in the foreground or background) on at least one microcontroller. At least one microcontroller may receive commands and information in the form of one or more input signals from various controls or components and convey instructions to other electronic components.

[0107] The drive system 104 implements a semi-autonomous drive system. The drive system 104 operates to control the steering, acceleration, braking, and gear shifting of the vehicle 90 based on drive commands 112 received from the ECU 102. The drive commands 112 may include a steering command 112a, an acceleration command 112b, a braking command 112c, and a gear selection command 112d.

[0108] refer to Figure 2The diagram illustrates an example embodiment of detector 100 and ECU 102 according to one or more exemplary embodiments. Detector 100 typically includes a vehicle motion detector 120 and a vehicle stop detector 122. ECU 102 may include a range state estimator 130, a feedback loop filter 132, and a low-speed maneuvering (LVM) controller 134. Feedback loop 136 is configured to return from the output side of range state estimator 130 through feedback loop filter 132 to the input side of range state estimator 130.

[0109] Detector 100 generates sensor information 110 and presents it to range state estimator 130. Range state estimator 130 generates an estimated range state 138 for vehicle 90 and presents it to LVM controller 134. Speed ​​update 150 based on estimated range state 138 is routed along feedback loop 136, passes through feedback loop filter 132, and returns to range state estimator 130. LVM controller 134 generates and presents drive command 112 based on estimated range state 138.

[0110] The range state estimator 130 includes a mechanism block 140, an extended Kalman filter (EKF) 142, a calibration block (or corrector) 144, an integrity monitor 146, and an output block 148.

[0111] Feedback loop filter 132 implements a zero-rate update (ZUPT) adaptive zero-weighted filter. The ZUPT filter is dynamically adjusted in proportion to the rate residual determined by EKF 142.

[0112] To perform vehicle motion detection, a method similar to that used for moving to a stop event (or declaration) can be applied to reduce the delay from stop to movement event (or declaration). When vehicle 90 is considered to have stopped, EKF 142 enforces a zero-speed constraint to prevent inaccuracies in position estimation. Based on recent vehicle data (such as... Figure 3-5 As shown in the diagram, even if the LVM controller 134 issues a speed command, the zero constraint forces the speed to zero. The EKF 142 updates every few hundred milliseconds, thus producing a "zigzag" behavior in terms of speed. In this case, the vehicle 90 performs automatic parking while in reverse, and the speed becomes negative and then zero. This is precisely when the zero constraint begins to take effect. By monitoring this behavior along with other sensor information, we can also reduce the delay from stopping to moving.

[0113] refer to Figure 3 A graph of example vehicle speed estimation is shown according to one or more exemplary embodiments. Graph 160 has an X-axis 162 in units of time and a Y-axis 164 in units of speed.

[0114] Curve 166 illustrates the speed command from the longitudinal control of vehicle 90. Curve 168 is the estimated speed determined by EKF 142. Curve 170 is the raw longitudinal speed from the wheel speed sensor. Curve 172 is the total wheel axle torque. Curve 174 is the wheel axle torque and braking torque. Curve 176 is the longitudinal acceleration.

[0115] refer to Figure 4 A graph of example longitudinal velocity estimation is shown according to one or more exemplary embodiments. Graph 180 has an X-axis 182 in units of time and a Y-axis 184 in units of velocity.

[0116] Curve 186 illustrates the estimated longitudinal velocity. Point 188 illustrates the wheel speed sensor data from the right rear wheel.

[0117] refer to Figure 5 A graph of example vehicle speed estimation is shown according to one or more exemplary embodiments. Graph 200 has an X-axis 202 in units of time and a Y-axis 204 in units of speed.

[0118] For vehicle stop detection, the longitudinal speed estimate from EKF 142 is observed to cross zero (e.g., become negative) before the vehicle 90 comes to a complete stop. Curve 205 illustrates the LVM speed command. Curve 206 illustrates the EKF speed estimate. As the LVM speed command drops to zero (in window 208), the vehicle 90 eventually comes to a stop. While the speed drops below zero (negative), wheel speed sensor information simultaneously reports zero speed, and the total torque is at a large threshold due to high braking torque. Using this information together can overcome various shortcomings to reduce delays in the movement to a stop event.

[0119] In window 208, the estimated speed crosses zero and becomes negative. Curve 210 illustrates the wheel speed sensor information. In window 212, the wheel speed sensor information indicates that vehicle 90 has stopped. Curve 214 is the braking torque. Curve 216 is the axle torque. Curve 218 is the total axle torque. In window 220, the total torque has a large negative value, which indicates high braking torque.

[0120] Return to Figure 2 The dynamic weighting in feedback loop filter 132 is dynamically adjusted in proportion to the EKF velocity residual. The velocity residual is a byproduct of the non-zero velocity estimation in EKF 142 during the update phase, but the zero velocity constraint still applies. The updated weights are changed according to equation (1) as follows:

[0121]

[0122] Where σ zupt It is the standard deviation of zero-speed updates. The velocity is the estimated velocity of EKF 142, and α is given by equation (2) as follows:

[0123] α=f(τ 总 a x n xx Equation (2)

[0124] Where, τ 总 It is the total torque, a x It is the longitudinal acceleration of vehicle 90 from IMU detector 100a, and n xx It is the edge count from the wheel encoder 100e.

[0125] refer to Figure 6 A flowchart of an example method for motion state monitoring is shown according to one or more exemplary embodiments. As illustrated, method 230 typically includes steps 232 through 264. The sequence of steps is shown as a representative example. Other sequence of steps may be implemented to meet the criteria of a particular application.

[0126] In step 232, wheel pulse edge counts from each wheel can be monitored. In step 234, the wheel speed sensor detector can provide the total edge count and wheel consensus. In step 236, new motion events can be declared based on the wheel speed sensor information.

[0127] In step 242, braking torque, axle torque, road gradient, and creep torque can be monitored. In step 244, the torque detector can provide the total torque. In step 246, a new motion event can be declared based on the total torque information.

[0128] In step 252, longitudinal acceleration (Ax), lateral acceleration (Ay), and Z-axis rotational speed (Wz) can be monitored. In step 254, IMU detector 100a can provide a longitudinal damping factor for longitudinal acceleration Ax and zeroing of lateral acceleration Ay and rotational speed Wz. In step 256, new motion events can be declared based on the IMU information.

[0129] In step 258, a Boolean OR of the new motion events from steps 236, 246, and 256 is determined. If one or more motion events are detected in step 260, vehicle 90 is declared to be in motion in step 262. Otherwise, vehicle 90 is declared to be stopped in step 264.

[0130] refer to Figure 7 A graph illustrating example automatic parking assist movement is shown according to one or more exemplary embodiments. Graph 280 includes an X-axis 282 in units of time and a Y-axis 284 in units of speed.

[0131] Because the IMU detector 100a is mounted on the spring block, it experiences oscillations when the wheels lock to a stop. These oscillations occur as longitudinal acceleration in the forward / reverse direction. Therefore, longitudinal acceleration and zero-crossing detection can reduce latency in scenarios such as automatic parking, which involves the transition from movement to a stop and from a stop to movement.

[0132] Figure 280 illustrates how to address the problem of stop / move detection using longitudinal acceleration by overcoming the oscillating behavior of the accelerometer. Curve 286 shows the speed estimate from EKF 142. Curve 288 shows the vehicle pitch angle. Damped oscillations of curve 290 can be seen in window 292. Curve 294 illustrates the wheel speed sensor pulse. Curve 296 illustrates the braking torque, and curve 298 illustrates the total wheel axle torque. Window 300 shows the vehicle stop indication. To address the oscillation problem, the frequency and amplitude of curve 290 are observed to ensure that curve 290 only exhibits damping without behavioral changes.

[0133] refer to Figure 8 A graph illustrating an example stop detection is shown according to one or more exemplary embodiments. Graph 320 includes an X-axis 322 in units of time and a Y-axis 324 in units of velocity.

[0134] Curve 326 illustrates the longitudinal control speed command. Curve 328 is the speed estimated by the EKF. Curve 330 is the raw speed from the wheel speed sensor. Curve 332 is the braking torque. Curve 334 is the axle torque. Curve 336 is the total axle torque. Curve 338 illustrates the longitudinal acceleration from the accelerometer, and curve 340 is the vehicle stall indication. Peaks A1, A2, and A3 are the continuous damped peak values ​​of the longitudinal acceleration. Periods P1 and P2 are the time periods between each peak A1, A2, and A3.

[0135] Once the vehicle stops at 90 degrees (as in...) Figure 8 As shown in the data, the time factor and amplitude factor can be calculated using historical data according to equations (3), (4) and (5) as follows:

[0136] x(n1)=A1, x(n2)=A2, x(n3)=A3 Equation (3)

[0137] Δt1=T s Equation (4) (n2-n1)

[0138] Δt2=T s Equation (5) (n3-n2)

[0139] Where x(n) is the exponent of the amplitude, T SIt is the sampling time that generates the data, and Δt is the time difference (period) between the two amplitudes according to equations (6) and (7) as follows:

[0140]

[0141] Where A f It is the amplitude factor.

[0142]

[0143] Where T f It is a time factor.

[0144] if Then a x It has a damping effect. If both the amplitude factor and the time factor are within the tolerance denoted by ∈, the oscillation observed in the longitudinal velocity is declared to have a damping effect and behaves only as if the spring block oscillates and the vehicle 90 has stopped. This factor is learned under the same conditions as the creep torque given below, where the signal is buffered over time T, from which the amplitude factor and the time factor are learned. For the following case, a decision can be made using the two amplitude peaks according to equation (8):

[0145]

[0146] For vehicles with internal combustion engines (ICE) type 90, techniques that only utilize total torque may not be feasible. With zero braking command, the torque at a standstill is approximately 434 Nm. Actual torque varies from vehicle to vehicle, and therefore this technique learns the standstill torque of a particular vehicle and dynamically adjusts the threshold. To overcome this problem, an internal creep torque learning method is fed in to compensate for a specific threshold of total torque. This learning method can be... To express, therefore τ 蠕变 =τ(t). Where v x τ(t) is the velocity at time t, where T is the time at which the vehicle can be confidently declared to have stopped without other sensor information, and τ(t) is the wheel axle torque at this time, and τ 蠕变 It could be creep torque. τ 蠕变 The value can be used as compensation for a threshold of total torque in order to make a stop-to-move or move-to-stop declaration.

[0147] Similarly, the total torque can be used to account for the effect of road gradient 82 in order to make decisions about vehicle motion events. Equation (9) shows the complete total torque estimate to indicate whether the vehicle 90 has stopped:

[0148] τ 停止 =(τ 轮轴 +τ 蠕变 )-τ 制动—m veh Equation (9) is given by *g*z*sin(θ).

[0149] Embodiments of this disclosure typically improve the customer experience when using parking maneuvers without additional hardware. The system / method is typically adaptively weighted by zero-speed updates used by the EKF during movement events. Zero-crossing of the EKF speed can be used to achieve early detection of movement-to-stop events. Wheel axle torque, braking torque, and road gradient are considered to detect stop events and / or movement events. Various embodiments can learn and adapt creep torque to provide a common solution for internal combustion engine vehicles and electric vehicles with various operating modes (such as one-pedal driving, automatic hold, etc.) during vehicle movement events. Longitudinal acceleration detected by the IMU can be used for early detection of movement events. Furthermore, arbitration between various stop / movement detectors can be used to achieve improved execution of vehicle motion indication and vehicle speed.

[0150] Embodiments of this disclosure typically provide a range measurement system including multiple detectors and an electronic control unit (ECU). The detectors operate to detect vehicle movement events and vehicle stopping events. The ECU is coupled to the detectors. The ECU includes: a range state estimator that operates to generate an estimated range state of the vehicle in response to the movement and stopping events; a feedback loop surrounding the range state estimator and operating to enforce a zero-speed constraint in the estimated range state to correct inaccuracies in the estimated vehicle position; and a low-speed motion controller that operates to generate speed commands for controlling the longitudinal movement of the vehicle during automated parking assist maneuvers based on the estimated range state.

[0151] The numerical values ​​of parameters (e.g., quantities or conditions) in this specification (including the appended claims) will be understood to be modified by the term “about” in each instance, regardless of whether “about” actually appears before the numerical value. “About” indicates that the stated numerical value allows for some slight imprecision (approximately close to exact in terms of value; about or reasonably close to the value; almost). If the imprecision provided by “about” is not otherwise understood in this common sense in the art, then “about” as used herein at least indicates variations that may result from common methods of measuring and using such parameters. Additionally, the disclosure of ranges includes the disclosure of values ​​and further divisions of the range throughout. Thus, each value within the range and the endpoints of the range are disclosed as separate embodiments.

[0152] While the best mode for carrying out this disclosure has been described in detail, those skilled in the art will recognize various alternative designs and embodiments for practicing this disclosure within the scope of the appended claims.

Claims

1. A range measurement system, comprising: Multiple detectors operate to detect vehicle movement events and vehicle stopping events; as well as An electronic control unit, connected to the plurality of detectors, wherein the electronic control unit includes: A range state estimator that operates to generate an estimated range state of the vehicle in response to the movement event and the stop event; A feedback loop that surrounds the range state estimator and operates to force a zero-speed constraint in the estimated range state to correct for inaccuracies in the estimated position of the vehicle; and A low-speed motion controller operates to generate speed commands for controlling the longitudinal movement of the vehicle during automated parking assist movement, based on the estimated range state.

2. The range measurement system according to claim 1, wherein: The feedback loop further operates to adaptively update at a weighted zero rate; and The range state estimator includes an extended Kalman filter that operates to utilize the zero velocity update during the movement event.

3. The range measurement system according to claim 2, wherein: The range state estimator further operates to detect the stopping event based on the velocity zero crossing of the extended Kalman filter.

4. The range measurement system according to claim 1, wherein, The plurality of detectors includes: Axle torque detector, which operates to measure axle torque; A braking torque detector, which operates to measure braking torque; and A road slope detector, which operates to measure the gravitational torque applied to the vehicle.

5. The range measurement system according to claim 4, wherein, The range state estimator further operates to: The estimated range state of the vehicle is also generated in response to the axle torque, the braking torque, and the gravitational torque.

6. The range measurement system according to claim 5, wherein, The range state estimator further operates to: Learning and adapting to creep torque; and The estimated range state of the vehicle is also generated in response to the creep torque.

7. The range measurement system according to claim 1, wherein: The plurality of detectors includes an inertial measurement unit that operates to measure the longitudinal acceleration of the vehicle; and The range state estimator further operates to generate an estimated range state of the vehicle in response to the longitudinal acceleration.

8. The range measurement system according to claim 1, wherein, The range state estimator further operates to: Arbitration is performed among the plurality of detectors; and Vehicle motion indication and vehicle speed are generated based on the movement event and the stop event as described in the arbitration.

9. The range measurement system according to claim 8, wherein, The range state estimator further operates to: It also generates an estimated range state of the vehicle in response to the vehicle motion indication and the vehicle speed.

10. A method for comprehensive range measurement, comprising: Multiple detectors are used to detect vehicle movement events and vehicle stopping events; An estimated range state of the vehicle is generated in response to the movement event and the stop event using a range state estimator. A feedback loop is used to force a zero-speed constraint under the estimated range state to correct the inaccuracy of the estimated position of the vehicle. as well as The low-speed motion controller generates speed commands to control the longitudinal movement of the vehicle during automated parking assist movement based on the estimated range state.

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