SYNTHETIC ODOMETRY

Abstract

An odometry system comprising multiple detectors and an electronic control unit. The detectors are operable to detect a vehicle's movement and a vehicle stop event. The electronic control unit is coupled to the multiple detectors. It includes an odometry state estimator, a feedback loop, and a low-speed motion control. The odometry state estimator is operable to generate an estimated odometry state of the vehicle in response to the movement and stop events. The feedback loop, which wraps around the odometry state estimator, is operable to enforce a zero-speed limit within the estimated odometry state to correct for inaccuracies in the estimated vehicle position.The low-speed motion control can be operated to generate a speed command that controls a longitudinal movement of the vehicle during an automated parking assistance movement based on the estimated odometry state.

Description

The present description concerns a system and a method for synthesized odometry. Longitudinal velocity estimation from odometry typically suffers from a latency of 1 to 2 seconds during stop-to-move and move-to-stop events. This latency can lead to low-speed maneuver control exceeding torque requirements by approximately 50%. These torque overruns result in uncomfortable scenarios for customers during automated parking assistance maneuvers. These scenarios occur when there is a conflict between what the odometry reports and what the low-speed maneuvering commands actually are. GB 2 620 940 A describes a method for the autonomous control of the propulsion and braking process of a vehicle coming to a standstill, in which the control is switched from a first control scheme based on speed sensors to a second predictive control scheme based on vehicle forces in order to minimize the error in the stop position, provided a certain condition is met. The condition can be that the vehicle falls below a speed threshold of 0.8 m / s. The vehicle position can be derived from wheel speed sensors under the first control scheme. Speed ​​sensors are not used under the second control scheme. Vehicle forces can include propulsion force, braking force, road gradient, or longitudinal acceleration. A comfort limitation, e.g., acceleration or jerk, can be applied.A vehicle dynamics model can be created based on vehicle motion equations to determine a total force parameter for the vehicle that minimizes the error in maintaining its position. An estimator for unknown variables can correct this total force parameter. A position sensor can display the current vehicle position independently of the speed sensors. US 6,459,990 B1 describes a positioning method and system for water and land vehicles that enables highly accurate and autonomous operation. It is based on an inertial navigation system (INS) using a micro-MEMS IMU (microelectromechanical system), which forms the core of the positioning system. Several navigation sensors are integrated into the system to compensate for INS errors. A magnetic field sensor measures the vehicle's heading. An odometer measures the distance traveled when the vehicle is on land. An automated zero-speed update procedure is used to calibrate the constantly increasing INS errors. When the vehicle is in the water, a speedometer measures the water speed to assist the INS. Accordingly, the object of the invention is to reduce torque overrun in the longitudinal direction during low-speed maneuvering in the field of odometry synthesis with adaptive weighting. The object of the invention is achieved by means of an odometry system. The odometry system comprises a plurality of detectors and an electronic control unit. The plurality of detectors can be operated to detect a movement event of a vehicle and a stopping event of the vehicle. The electronic control unit is coupled to the plurality of detectors.The electronic control unit includes an odometry state estimator that is operational to generate an estimated odometry state of the vehicle in response to the motion event and the stop event; a feedback loop around the odometry state estimator that is operational to enforce a zero speed limit in the estimated odometry state to correct inaccuracies in an estimated position of the vehicle; and a low-speed motion control that is operational to generate a speed command that controls a longitudinal movement of the vehicle during an automated parking assistance movement based on the estimated odometry state. According to one embodiment, the feedback loop is further operable to adaptively weight updates of the zero velocity, and the odometry state estimator includes an enhanced Kalman filter that is operable to utilize the updates of the zero velocity during the motion event. According to another embodiment, the odometry state estimator can also be operated to detect the stopping event using one velocity zero crossing per extended Kalman filter. According to another embodiment, the plurality of detectors comprises an axle torque detector that is operable to measure an axle torque, a brake torque detector that is operable to measure a brake torque, and a road inclination detector that is operable to measure a gravitational torque exerted on the vehicle. According to another embodiment, the odometry state estimator can also be operated to generate the estimated odometry state of the vehicle as a further response to the axle torque, the braking torque and the gravitational torque. According to a further embodiment, the odometry state estimator can also be operated to learn and adapt to a creep torque and to generate the estimated odometry state of the vehicle as a further reaction to the creep torque. According to another embodiment, the plurality of detectors includes an inertial measurement unit that can be operated to measure a longitudinal acceleration of the vehicle, and the odometry state estimator can further be operated to generate the estimated odometry state of the vehicle as a further response to the longitudinal acceleration. According to another embodiment, the odometry state estimator can also be operated to mediate between the plurality of detectors and to generate a vehicle motion indication and a vehicle speed based on the motion event and the stop event, as mediated. According to another embodiment, the odometry state estimator can also be operated to generate the estimated odometry state of the vehicle as a further reaction to the vehicle motion indicator and the vehicle speed. According to the invention, a method for synthesized odometry is provided. The method comprises detecting a movement event of a vehicle and a stop event of the vehicle with a plurality of detectors, generating an estimated odometry state of the vehicle with an odometry state estimator in response to the movement event and the stop event, enforcing a zero speed limit in the estimated odometry state with a feedback loop to correct inaccuracies in an estimated position of the vehicle, and generating a speed command with a low-speed motion controller that controls a longitudinal movement of the vehicle during an automated parking assistance movement based on the estimated odometry state. According to one embodiment, the method comprises adaptively weighting zero-velocity updates with a feedback loop around the odometry state estimator and utilizing the zero-velocity updates during the motion event with an extended Kalman filter of the odometry state estimator. In one or more embodiments, the method comprises detecting the stop event with one zero-velocity crossing per extended Kalman filter. According to a further embodiment, the method comprises measuring an axle torque with an axle torque detector of a plurality of detectors, measuring a braking torque with a braking torque detector of a plurality of detectors, and measuring a gravitational torque exerted on the vehicle with a road inclination detector of a plurality of detectors. According to one embodiment, the generation of the estimated odometry state of the vehicle occurs as a further reaction to the axle torque, the braking torque and the gravitational torque. According to another embodiment, the method includes learning and adapting to a creep torque, wherein the generation of the estimated odometry state of the vehicle is a further reaction to the creep torque. According to a further embodiment, the method comprises measuring a longitudinal acceleration of the vehicle with an inertial measurement unit of the plurality of detectors, wherein the generation of the estimated odometry state of the vehicle is carried out as a further reaction to the longitudinal acceleration. According to another embodiment, the method comprises mediating between the plurality of detectors and generating a vehicle motion indication and a vehicle speed based on the motion event and the stop event, as mediated. According to another embodiment of the method, the generation of the estimated odometry state of the vehicle is carried out as a further reaction to the vehicle motion indicator and the vehicle speed. In one use case, a vehicle is provided. The vehicle includes a variety of detectors, an electronic control unit, and a drive system. The variety of detectors can be operated to detect when the vehicle is moving or stopping. The electronic control unit is coupled to the variety of detectors.The electronic control unit comprises an odometry state estimator, which is operational to generate an estimated odometry state of the vehicle in response to the movement and stop events; a feedback loop, which is operational to enforce a zero-speed limit in the estimated odometry state to correct inaccuracies in the estimated position of the vehicle; and a low-speed motion controller, which is operational to generate a speed command that controls longitudinal movement of the vehicle during an automated parking assistance movement based on the estimated odometry state. The drive system is operational to move the vehicle in response to the speed command. The vehicle's feedback loop is furthermore operable to adaptively weight zero-speed updates, and the odometry state estimator includes an enhanced Kalman filter that is operable to utilize zero-speed updates during the motion event. The features and advantages mentioned above, as well as other features and advantages of the present description, will readily become apparent from the following detailed description of best practices of the description in conjunction with the accompanying drawings. Fig. 1 is a schematic plan diagram illustrating the context of a system. Fig. 2 is a schematic diagram of detectors and an electronic control unit. Fig. 3 is a diagram of vehicle speed estimations. Fig. 4 is a diagram of longitudinal speed estimation. Fig. 5 is a diagram of vehicle speed estimations. Fig. 6 is a flowchart of a motion state monitoring procedure. Fig. 7 is a diagram of an automatic parking assistance movement. Fig. 8 is a diagram of a stop detection. Embodiments of the description provide a system and / or method for synthesizing odometry with adaptive weighting to mitigate longitudinal torque overrun during low-speed maneuvering. Closed-loop filtering around an odometry speed estimation behavior generally reduces the latency of reporting zero-speed events in addition to orthogonal sensor information. This latency reduction can be achieved by developing multiple (e.g., two) closed-loop, zero-crossing (stop event), and adaptive-weighting (movement event) behaviors, along with sensor information from other vehicle detectors (e.g., an inertial measurement unit, wheel speed detectors, and axle torque detectors). Data from an inertial measurement unit (IMU) (e.g.,Longitudinal acceleration and total torque data are used, after accounting for gravity, to reduce the latency of motion events. Observing the behavior of the extended closed-loop Kalman filter (EKF) when the velocity crosses zero without a gear change indicates a stop event. The total torque, adjusted for gravity, can also be used for the early detection of stop events. With reference to Fig. 1, a schematic diagram illustrating the context of a system 70 according to one or more exemplary embodiments is shown. The system 70 may comprise gravity 72, a road surface 80, and a vehicle 90. The vehicle 90 generally comprises several detectors 100, an electronic control unit 102 (ECU), and a drive system 104. A longitudinal direction 92 of the vehicle 90 is generally oriented from front to rear of the vehicle 90. The vehicle 90 is located on the road surface 80 and moves along it. The road surface 80 can have a road gradient 82. The road gradient 82 and the force of gravity 72 generally exert a force on the vehicle 90 along the road gradient 82. Vehicle 90 implements a gasoline-powered vehicle, an electric vehicle, a hybrid vehicle, or a plug-in hybrid vehicle. In various embodiments, Vehicle 90 can include, but is not limited to, a passenger car, a truck, an autonomous vehicle, a motorcycle, a boat, and / or an aircraft. Other types of Vehicle 90 can be implemented to meet the design criteria of a particular application. The detectors 100 implement multiple detectors within the vehicle 90 that are operable to detect movement of the vehicle 90. The detectors 100 may include, but are not limited to, a detector 100a of an internal reference unit (IMU), one or more axle torque detectors 100b, one or more brake torque detectors 100c, a road inclination detector 100d, and a wheel encoder 100e. The detectors 100 can present sensor information (or data) 110 to the ECU 102. The ECU 102 implements several digital processing circuits. These digital processing circuits can transmit data to each other via a communication bus. The digital processing circuits can be implemented in hardware, software running on hardware, or a combination of both. The ECU 102 can receive sensor information 110 from the detectors 100 and present drive commands 112 to the drive system 104.In various embodiments, the electronic control unit 102 generally comprises at least one microcontroller. The at least one microcontroller may comprise one or more processors, each of which may be implemented 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 running on hardware, or a combination of both). The at least one microcontroller may also include tangible, non-volatile 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 application-appropriate amounts of random access memory, read-only memory, flash memory and other types of electrically erasable programmable read-only memory, as well as associated hardware in the form of a high-speed clock or timer, analog-to-digital and digital-to-analog circuits and input / output circuits and devices, as well as suitable signal conditioning and buffer circuits. Computer-readable and executable instructions embodying this method can be recorded (or stored) in memory and executed as described herein. The executable instructions can be a set of instructions used to run applications on the at least one microcontroller (either in the foreground or background). The at least one microcontroller can receive commands and information in the form of one or more input signals from various controllers or components and communicate instructions to the other electronic components. The drive system 104 implements a semi-autonomous drive system. The drive system 104 is capable of controlling the steering, acceleration, braking, and gear shifting of the vehicle 90 based on the drive commands 112 received from the ECU 102. The drive commands 112 can include a steering command 112a, an acceleration command 112b, a braking command 112c, and a gear selection command 112d. With reference to Fig. 2, a schematic diagram of an exemplary implementation of the detectors 100 and the ECU 102 according to one or more exemplary embodiments is shown. The detectors 100 generally comprise vehicle motion detectors 120 and vehicle stop detectors 122. The ECU 102 may include an odometry state estimator 130, a feedback loop filter 132, and a low-speed maneuvering (LVM) controller 134. A feedback loop 136 is formed by the feedback loop filter 132 from an output side of the odometry state estimator 130 back to an input side of the odometry state estimator 130. The detectors 100 can generate the sensor information 110 and present it to the odometry state estimator 130. The odometry state estimator 130 generates estimated odometry states 138 of the vehicle 90 and presents these to the LVM controller 134. Speed ​​updates 150 in the estimated odometry states 138 are routed along the feedback loop 136 through the feedback loop filter 132 and back to the odometry state estimator 130. The LVM controller 134 generates and presents the drive commands 112 based on the estimated odometry states 138. The odometry state estimator 130 includes a mechanism block 140, an extended Kalman filter (EKF) 142, a correction block (or corrector) 144, an integrity monitor 146, and an output block 148. The feedback loop filter 132 implements an adaptive zero-weighting filter for updating the zero velocity (ZUPT). The ZUPT filter is dynamically adjusted proportionally to a residual velocity determined by the EKF 142. For vehicle motion detection, an approach similar to that used for move-to-stop events (or declarations) can be applied to reduce the latency for stop-to-move events (or declarations). The EKF 142 enforces a zero speed limit to prevent inaccuracies in position estimates when the vehicle is considered stopped. Based on real-time vehicle data (as shown in Figures 3-5), although the LVM controller 134 commands a speed, the zero limit enforces that the speed be zero. The EKF 142 updates every few hundred milliseconds and can therefore produce a "zigzag" behavior in the speed. In this case, the vehicle performs an automatic reverse parking maneuver, and the speed goes negative and then to zero. This is when the zero limit is triggered.By monitoring this behavior along with other sensor information, we can also reduce the stop-to-move latency. With reference to Fig. 3, a diagram of exemplary vehicle speed estimates according to one or more exemplary embodiments is shown. The diagram 160 has an X-axis 162 in time units and a Y-axis 164 in speed units. Curve 166 illustrates a speed command from the longitudinal control of vehicle 90. Curve 168 is an estimated speed determined by the EKF 142. Curve 170 is a raw longitudinal speed from wheel speed sensors. Curve 172 is a total axle torque. Curve 174 is an axle torque and a brake torque. Curve 176 is a longitudinal acceleration. With reference to Fig. 4, a diagram of an exemplary longitudinal velocity estimation according to one or more exemplary embodiments is shown. The diagram 180 has an X-axis 182 in time units and a Y-axis 184 in velocity units. Curve 186 illustrates an estimated longitudinal speed. Points 188 illustrate wheel speed sensor data from a right rear wheel. With reference to Fig. 5, a diagram of exemplary vehicle speed estimates according to one or more exemplary embodiments is shown. The diagram 200 has an X-axis 202 in time units and a Y-axis 204 in speed units. For vehicle stop detection, it can be observed that the longitudinal speed estimate from the EKF 142 crosses zero (e.g., becomes negative) before the vehicle comes to a complete stop at 90°. Curve 205 illustrates the LVM speed command. Curve 206 illustrates the EKF speed estimate. When the LVM speed command drops to zero (in window 208), the vehicle finally comes to a stop at 90°. While the speed drops below zero (becomes negative), wheel speed sensor information simultaneously reports zero speed, and the total torque is at a large threshold due to the high braking torque. Combining this information can overcome individual shortcomings in reducing latency during move-to-stop events. In window 208, the estimated speed crosses zero and becomes negative. Curve 210 illustrates wheel speed sensor information. In window 212, the wheel speed sensor information indicates that the vehicle has stopped at 90. 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, indicating high braking torque. Referring again to Fig. 2, the dynamic weighting in the feedback loop filter 132 implements a dynamic adjustment proportional to the EKF velocity residual. The velocity residual is a byproduct of the EKF 142, which estimates a non-zero velocity during the update phase, although the zero-velocity constraint is still applied. The update weight is changed according to equation (1) as follows: where σzupt is a standard deviation of the update of the zero velocity, v̂x is an estimated velocity of the EKF 142 and α is given by equation (2) as follows: Where τtotal is total torque, ax is longitudinal acceleration of the vehicle 90 from the IMU detector 100a and nxx is flank count from the wheel encoders 100e. With reference to Fig. 6, a flowchart of an exemplary method for motion state monitoring according to one or more exemplary embodiments is shown. Method 230 generally comprises steps 232 to 264, as illustrated. The sequence of steps is shown as a representative example. Other step sequences can be implemented to meet the criteria of a particular application. In step 232, wheel pulse edge counts from each wheel can be monitored. The wheel speed sensor detector can provide a total edge count and wheel consensus in step 234. A new motion event can be declared in step 236 based on the wheel speed sensor information. In step 242, the braking torque, axle torque, road inclination, and creep torque can be monitored. The torque detector can provide the total torque in step 244. A new motion event can be declared in step 246 based on the total torque information. In step 252, the longitudinal acceleration (Ax), the lateral acceleration (Ay), and the z-axis rotational velocity (Wz) can be monitored. The IMU detector 100a can provide a longitudinal damping factor for the longitudinal acceleration Ax and zero values ​​for the lateral acceleration Ay and the rotational velocity Wz in step 254. A new motion event can be declared in step 256 based on the IMU information. 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 as moving in step 262. Otherwise, vehicle 90 is declared stopped in step 264. With reference to Fig. 7, a diagram of an exemplary automatic parking assistance movement according to one or more exemplary embodiments is shown. The diagram 280 comprises an X-axis 282 in time units and a Y-axis 284 in speed units. Because the IMU detector 100a is mounted on a spring mass, it is subject to vibrations when the wheels lock to a stop. These forward / backward vibrations manifest as longitudinal accelerations. Therefore, longitudinal acceleration and zero-crossing detection can reduce latency for scenarios such as automatic parking, which involve back-to-back move-to-stop and stop-to-move maneuvers. Diagram 280 illustrates how the problem of using longitudinal acceleration for stop / motion detection is solved by overcoming the oscillation behavior of the accelerometer. Curve 286 shows the velocity estimate from the EKF 142. Curve 288 shows a vehicle pitch angle. Damped oscillations of curve 290 can be seen in window 292. Curve 294 illustrates the wheel speed sensor pulses. Curve 296 illustrates a brake torque, and curve 298 illustrates a total axle torque. Window 300 shows the vehicle stop indication. To solve the oscillation problem, the frequency and amplitude of curve 290 are monitored to ensure that curve 290 dampens exclusively without any change in behavior. With reference to Fig. 8, a diagram of an exemplary stop detection according to one or more exemplary embodiments is shown. The diagram 320 comprises an X-axis 322 in time units and a Y-axis 324 in velocity units. Curve 326 illustrates a longitudinal control speed command. Curve 328 is the estimated EKF speed. Curve 330 is the raw speed from wheel speed sensors. Curve 332 is the brake torque. Curve 334 is the axle torque. Curve 336 is the total axle torque. Curve 338 illustrates longitudinal acceleration from the accelerometer, and curve 340 is the vehicle standstill indicator. Peaks A1, A2, and A3 are successive damped peaks of longitudinal acceleration. Periods P1 and P2 are time intervals between each peak A1, A2, and A3. Once the vehicle is stopped at 90 (as shown in the data in Fig. 8), a time factor and an amplitude factor can be calculated using historical data according to equations (3), (4) and (5) as follows: where x(n) is the index at which the amplitude occurs, TS is the sampling time at which data arrives, and Δt is the time difference (period) between two amplitudes according to equations (6) and (7) as follows: where Af is the amplitude factor, and where Tf is the time factor. If |Af| < Afavg+ εA AND |Tf| < Tfavg+ ∈ T, then damping is present. If both an amplitude factor and a time factor are within a tolerance, represented by ∈, the oscillation observed in the longitudinal velocity is declared as damping, and the behavior is consistent solely with spring-mass oscillation, and the vehicle is stopped. The factors are learned under the same condition as the creep torque, where the signal is buffered over a time T, from which the amplitude factor and time factors are learned. For the following cases, a decision can be made using two amplitude peaks according to equation (8) as follows: For internal combustion engine (ICE) type vehicles, implementing a technique solely based on total torque is not feasible. A stationary torque is approximately 434 Newton-meters with a zero-brake command. Actual torque varies from vehicle to vehicle, and therefore the technique can learn the stationary torque for a given vehicle and dynamically adjust the threshold. To overcome this problem, an internal creep torque learning methodology is implemented to compensate for a specific threshold for total torque. The learning methodology can be expressed as `if then τcreep = τ(t)`, where `vx(t)` is the velocity at time `t`, `T` is the time suitable to confidently declare the vehicle is stopped without other sensor information, `τ(t)` is the axle torque at that time, and `τcreep` can be the creep torque.The τcreep value can be used as an offset to the total torque threshold for stop-to-move or move-to-stop declarations. Similarly, the effect of the road gradient 82 can be taken into account using the total torque for the vehicle motion event decision. Equation (9) shows a complete total torque estimate to declare whether the vehicle 90 has stopped: Implementations of this description generally improve the customer experience when using parking maneuvers without additional hardware. The system / method generally adaptively weights zero-speed updates used by the EKF during movement events. Early detection of move-to-stop events can be achieved using the zero-crossing EKF speed. Axle torque, braking torque, and road inclination are taken into account to detect stop events and / or movement events. Different implementations can learn and adapt to creep torque for use in vehicle movement events to provide a common solution for internal combustion engine vehicles and electric vehicles with different operating modes, such as one-pedal driving, auto-hold, and the like.The longitudinal acceleration detected by an IMU can be used for the early detection of motion events. Furthermore, communication between different stop / motion detectors can be used to achieve improved performance for vehicle motion indication and vehicle speed. In general, embodiments of the described system provide an odometry system comprising multiple detectors and an electronic control unit. The detectors are operable to detect a vehicle's movement and a vehicle's stop. The electronic control unit is coupled to the detectors.The electronic control unit includes an odometry state estimator that is operational to generate an estimated odometry state of the vehicle in response to the motion event and the stop event; a feedback loop around the odometry state estimator that is operational to enforce a zero speed limit in the estimated odometry state to correct inaccuracies in an estimated position of the vehicle; and a low-speed motion control that is operational to generate a speed command that controls a longitudinal movement of the vehicle during an automated parking assistance movement based on the estimated odometry state. Numerical values ​​of parameters (e.g., of quantities or conditions) in this specification, including the appended claims, are to be understood as being modified in every case by the term "approximately," regardless of whether "approximately" actually precedes the numerical value or not. "Approximately" indicates that the stated numerical value permits a slight degree of inaccuracy (with some allowance for accuracy in the value; approximately or reasonably close to the value; nearly). If the inaccuracy provided by "approximately" is not otherwise understood in the technical language with this ordinary meaning, then, as used herein, "approximately" indicates at least variations that may arise from ordinary methods of measuring and using such parameters. Additionally, the description of ranges includes the description of values ​​and further subdivided ranges within the overall range.Each value within a range and the endpoints of a range are hereby described as a separate embodiment.

Claims

Odometry system comprising: a plurality of detectors (100) operable to detect a motion event of a vehicle (90) and a stop event of the vehicle (90); and an electronic control unit (102) coupled to the plurality of detectors (100), the electronic control unit (102) comprising: an odometry state estimator (130) operable to generate an estimated odometry state of the vehicle (90) in response to the motion event and the stop event; a feedback loop (136) around the odometry state estimator (130) operable to enforce a zero speed limit in the estimated odometry state in order to correct inaccuracies in an estimated position of the vehicle (90);and a low-speed motion control that is operable to generate a speed command that controls a longitudinal movement of the vehicle (90) during an automated parking assistance movement based on the estimated odometry state.; Odometry system according to claim 1, wherein: the feedback loop (136) is further operable to adaptively weight updates of the zero velocity; and the odometry state estimator (130) comprises an extended Kalman filter (142) operable to utilize the updates of the zero velocity during the motion event. Odometry system according to claim 2, wherein: the odometry state estimator (130) is further operable to detect the stopping event using one velocity zero crossing per extended Kalman filter (142). Odometry system according to claim 1, wherein the plurality of detectors (100) comprises: an axle torque detector (100b) that is operable to measure an axle torque; a brake torque detector (100c) that is operable to measure a brake torque; and a road inclination detector (100d) that is operable to measure a gravitational torque exerted on the vehicle (90). Odometry system according to claim 4, wherein the odometry state estimator (130) is further operable to: generate the estimated odometry state of the vehicle (90) as a further response to the axle torque, the braking torque and the gravitational torque. Odometry system according to claim 5, wherein the odometry state estimator (130) is further operable to: learn and adapt to a creep torque; and generate the estimated odometry state of the vehicle (90) as a further response to the creep torque. Odometry system according to claim 1, wherein: the plurality of detectors (100) comprises an inertial measurement unit that is operable to measure a longitudinal acceleration of the vehicle (90); and the odometry state estimator (130) is further operable to generate the estimated odometry state of the vehicle (90) as a further response to the longitudinal acceleration. Odometry system according to claim 1, wherein the odometry state estimator (130) is further operable to: mediate between the plurality of detectors (100); and generate a vehicle motion indication and a vehicle speed based on the motion event and the stop event, as mediated. Odometry system according to claim 8, wherein the odometry state estimator (130) is further operable to: generate the estimated odometry state of the vehicle (90) as a further response to the vehicle motion indicator and the vehicle speed. Method (230) for synthesized odometry, comprising: detecting (236) a movement event of a vehicle (90) and a stop event of the vehicle (90) with a plurality of detectors (100); generating an estimated odometry state of the vehicle (90) with an odometry state estimator (130) in response to the movement event and the stop event; enforcing a zero speed limit in the estimated odometry state with a feedback loop (136) to correct inaccuracies in an estimated position of the vehicle (90); and generating a speed command with a low-speed motion controller that controls a longitudinal movement of the vehicle (90) during an automated parking assistance movement based on the estimated odometry state.

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