RADAR-BASED MODEL FOR ESTIMATE VEHICLE ODOMETRY

Abstract

Method for determining vehicle odometry, comprising: Receiving initial sensor data by a controller (34) of a vehicle (10); Receiving second sensor data by the controller (34) of the vehicle (10); Determining an initial longitudinal position, an initial lateral position and an initial heading of the vehicle (10) by the controller (34) of the vehicle (10) using the first sensor data, wherein the first sensor data are generated by an inertial measurement unit (IMU) of the vehicle (10), a wheel speed sensor (WSS) and a steering angle sensor (SAS) of the vehicle (10); Determining a longitudinal position error, a lateral position error and a course error of the vehicle (10) by the controller (34) of the vehicle (10) using the second sensor data, wherein the second sensor data are generated by a radar device of the vehicle (10); Determining a longitudinal speed of the vehicle (10) using the first sensor data and the second sensor data; Determining a lateral speed of the vehicle (10) using the first sensor data and the second sensor data; Determining a yaw rate of the vehicle (10) using the first sensor data and the second sensor data; Correcting the initial longitudinal position, initial lateral position, and initial heading of the vehicle (10) using the longitudinal position error, lateral position error, and heading error, respectively, thereby generating a corrected longitudinal position, a corrected lateral position, and a corrected heading of the vehicle (10), wherein the corrected longitudinal position, the corrected lateral position, and the corrected heading of the vehicle (10) are determined using an adaptive filter, wherein the corrected longitudinal position is based on the enhancement of a longitudinal position, the corrected lateral position is based on the enhancement of a lateral position, and the corrected heading is based on the enhancement of a heading. where the adaptive filter is implemented using a multitude of equations, and the multitude of equations includes: Δxk + 1 = Δxk + gk, xex, k Δ yk + 1 = Δ yk + gk , yey , k Δ ψ k + 1 = Δ ψ k + gk , ψ e ψ , k where: k is a time step; Δx k+1 is a filtered term for the initial longitudinal position of the vehicle (10); Δy k+1 is a filtered term for the initial lateral position of the vehicle (10); Δψ k+1 is a filtered term for the initial course of the vehicle (10); Δx k is a change in the longitudinal position of the vehicle (10) at time k; Δy k is a change in the lateral position of the vehicle (10) at time k; Δψ k is a change in the course of the vehicle (10) at time k; G k,x is the strengthening of the longitudinal position at time k; G k,y is the strengthening of the transverse position at time k; G k,ψ is the strengthening of the price at time k; ex,k is the error of the longitudinal position at time k; e y,k is the error of the transverse position at time k; and e ψ,k is the error of the course at time k; where the longitudinal position error, the transverse position error, and the course error are calculated using the following equations: ex = vs ( 1 ) . dt − Δ x ˜ ey = vs ( 2 ) . dt − Δ y ˜ e ψ = atan2 (vs (2) vs (1)) − Δ ψ ˜ where: v s is the scene speed of the vehicle as detected by the radar (10); e x is the error term for the x-position (i.e., the longitudinal position) of the vehicle (10); e y is the error term for the y-position (i.e., for the lateral position) of the vehicle (10); e ψ is the error term for the course of the vehicle (10); Δ x ˜ Delta x-position with uncertainty; Δ y ˜ Delta y-position with uncertainty; Δ ψ ˜ Delta price with uncertainty; and t is time; and Steering the vehicle (10) using the corrected longitudinal position, the corrected lateral position and the corrected course.

Description

INTRODUCTION

[0001] The present disclosure relates to methods and systems for estimating vehicle odometry using a radar-based model.

[0002] This introduction generally sets out the context of the disclosure. The work of the inventors mentioned herein, insofar as it is described in this introduction, as well as aspects of the description that may not have been part of the prior art at the time of filing, are neither expressly nor implicitly admitted as prior art in this disclosure.

[0003] Vehicle odometry and condition estimation play a key role in controlling advanced driver assistance systems (ADAS) in vehicles. Odometry estimation relies on various vehicle sensor data, which can change throughout the vehicle's lifetime. Some vehicles use sensor data from an inertial measurement unit (IMU), a wheel speed sensor (WSS), a steering wheel angle sensor (SAS), and a global navigation satellite system (GNSS) receiver. Radar information can provide orthogonal velocity and relative positional movements of the vehicle. Therefore, it is desirable to develop a system and method that uses radar data to adjust and improve the learned parameters, correct the adaptive filters of the odometry, and increase the accuracy of the odometry estimates.

[0004] US Patent 2020 / 0309530A1 discloses a system and method for estimating a vehicle's position (location and / or orientation). The system and method employ a form of sensor fusion, combining output from vehicle dynamics sensors (such as accelerometers, gyroscopes, or encoders) with output from vehicle radar sensors to improve the accuracy of the vehicle position data. Uncorrected vehicle position data derived from the dynamics sensor data is compensated with correction data derived from occupancy grids based on radar sensor data. The occupancy grids, which are two-dimensional or three-dimensional mathematical objects similar to radar-based maps, must correspond to the same geographic location.

[0005] US 2018 / 0136665 A1 discloses a vehicle control system. The control system includes at least one controller. The controller is programmed to receive initial sensor values ​​from a first group of sensors and provide an initial vehicle position based on these initial sensor values. The initial vehicle position includes an initial location and orientation of the vehicle. The controller is also programmed to receive second sensor values ​​from a second group of sensors and provide a second vehicle position based on these second sensor values. The second vehicle position includes a second location and orientation of the vehicle. The control unit is further programmed to generate a diagnostic signal in response to the initial vehicle position being outside a predetermined range of the second vehicle position.

[0006] DE 10 2008 026 397 A1 discloses a system for estimating vehicle dynamics, including vehicle position and vehicle speed, using a stationary object. The system includes an object sensor that provides object signals from the stationary object. The system also includes vehicle-side sensors that provide signals representing vehicle motion. The system further includes a mapping processor that receives object signals and performs object tracking using multiple data frames. The system also includes a longitudinal state estimation processor that receives the object signals and the sensor signals and provides a correction to the vehicle speed in the forward direction. The system further includes a lateral state estimation processor that receives the object signals and the sensor signals and provides a correction to the vehicle speed in the lateral direction.

[0007] CN 1 11 102 978 A discloses a method and a device for determining the motion state of a vehicle. The method comprises determining a predicted value for the vehicle's motion state based on angular velocity and acceleration output by an inertial measurement unit using a strapdown inertial navigation algorithm. The method further comprises determining a predicted value for a motion error state variable of the vehicle by sequential Kalman filtering. SUMMARY

[0008] This disclosure describes a method for determining vehicle odometry using radar data. The method specifically utilizes radar data to adjust and improve learned parameters, correct adaptive odometry filters, and enhance the accuracy of odometry estimates. In one aspect of this disclosure, the method includes the reception of initial sensor data by a vehicle controller. This initial sensor data is generated by the vehicle's inertial measurement unit (IMU), wheel speed sensor (WSS), and steering angle sensor (SAS). The method further includes the vehicle controller determining an initial longitudinal position, lateral position, and heading using this initial sensor data. Additionally, the method includes the vehicle controller receiving secondary sensor data.The second set of sensor data is generated by the vehicle's radar. The method further includes determining the vehicle's longitudinal position error, lateral position error, and heading error using the second set of sensor data. The method also includes correcting the vehicle's initial longitudinal position, lateral position, and heading using these errors, resulting in corrected longitudinal position, lateral position, and heading. The method further includes determining the vehicle's initial longitudinal position, lateral position, and heading using an adaptive filter. The corrected longitudinal position is based on the gain of the longitudinal position. The corrected lateral position is based on the gain of the lateral position.The corrected course is based on the gain of the lateral position. The procedure further includes determining the vehicle's lateral speed, longitudinal speed, and yaw rate using the first and second sensor data. The adaptive filter is implemented using the following equations: Δxk+1=Δxk+gk,xex,k Δyk+1=Δyk+gk,yey,k Δψk+1=Δψk+gk,ψeψ,k where: k is a time step; Δx k+1 is a filtered term for the longitudinal position of the vehicle; Δy k+1 is a filtered term for the lateral position of the vehicle; Δψ k+1 is a filtered term for the vehicle's course; Δx k is a change in the longitudinal position of the vehicle at time k; Δy k is a change in the lateral position of the vehicle at time k; Δψ kis a change in the vehicle's course at time k; G k,x is the strengthening of the longitudinal position at time k; G k,y is the strengthening of the transverse position at time k; G k,ψ is the strengthening of the price at time k; e x,k is the error of the longitudinal position at time k; e y,k is the error of the transverse position at time k; and e ψ,k is the error of the course at time k.

[0009] The longitudinal position error, the transverse position error, and the course error are calculated using the following equations: ex=vs(1).dt−Δx˜ ey=vs(1).dt−Δy˜ eψ=atan2(vs(2)vs(1))−Δψ˜ where: v s The scene speed of the vehicle as detected by the radar is 10; e xThe error term for the x-position (i.e., the longitudinal position) of the vehicle is 10; e y The error term for the y-position (i.e., the lateral position) of the vehicle is 10; e ψ The error term for the course of the vehicle is 10; Δx˜ Delta x-position with uncertainty; Δy˜ Delta y-position with uncertainty; Δψ˜ Delta price with uncertainty; and t is time.

[0010] Furthermore, the procedure includes controlling the vehicle based on the corrected longitudinal position, the corrected lateral position, and the corrected heading. The procedure described in this section improves vehicle engineering by enabling robust vehicle control using improved (more accurate) estimation of the vehicle's position (i.e., longitudinal and lateral position) and heading. With the improved vehicle position and heading estimation, the vehicle can be controlled precisely and with minimal errors.

[0011] In one aspect of the present disclosure, the adaptive filter can be a Kalman filter. In one embodiment, the method further comprises determining the lateral speed, longitudinal speed, and yaw rate of the vehicle using the first sensor data, the second sensor data, and the adaptive filter.

[0012] The present disclosure also describes a system for determining vehicle odometry. The system comprises a variety of sensors and a controller that communicates with the sensors. The sensors include an inertial measurement unit (IMU), a wheel speed sensor (WSS), a steering angle sensor (SAS), and a radar. The controller is programmed to perform the procedure described above.

[0013] Further applications of this disclosure will become apparent from the detailed description given below. It is understood that the detailed description and specific examples serve only for illustration and are not intended to limit the scope of this disclosure.

[0014] The above features and advantages, as well as further features and advantages of the system and method disclosed herein, are readily apparent from the following detailed description, including the claims and exemplary embodiments, in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present disclosure will become more fully apparent from the detailed description and the accompanying drawings, whereby the following applies: Fig. Figure 1 is a schematic side view of a vehicle with a system for estimating vehicle odometry using a radar-based model. Fig. Figure 2 is a flowchart of a procedure for estimating vehicle odometry using a radar-based model. DETAILED DESCRIPTION

[0016] Several examples from the revelation, illustrated in the accompanying drawings, are now referred to in detail. Whenever possible, the same or similar reference numbers are used in the drawings and the description to indicate the same or similar parts or steps.

[0017] With reference to Fig. Figure 1 includes a vehicle 10 and a system 11 for odometry estimation (or is associated with it). Although the vehicle 10 is depicted as a coupe, it could also be another type of vehicle, such as a pickup truck, a sedan, a sport utility vehicle (SUV), a motorhome (RV), etc. The system 11 can be used for an accurate estimation of the odometry of the vehicle 10. The vehicle also includes a vehicle body 12 and one or more wheels 14 connected to the vehicle body 12.

[0018] Furthermore, the vehicle 10 comprises a controller 34 connected to the vehicle body 12 and one or more sensors 40 that communicate with the controller 34. The sensors 40 are coupled to the vehicle body 12 and collect information and generate sensor data that specify the external or internal vehicle parameters. Non-limiting examples of sensors 40 include one or more inertial measurement units (IMUs), yaw rate sensors, ride height sensors, wheel speed sensors (WSSs), lidars, radars, ultrasonic sensors, steering angle sensors (SASs), GNSS (Global Navigation Satellite System) transceivers or receivers, and cameras. The GNSS transceivers or receivers are configured to determine the location of the vehicle 10 on the globe. The WSSs are configured to detect the rotational speed of the wheels 14 of the vehicle 10.The yaw rate sensors are configured to determine the heading of the vehicle 10. The cameras' field of view can be large enough to capture images from the front, rear, and sides of the vehicle 10. The ride height sensors are configured to measure the correct height of the vehicle 10. The ultrasonic sensor can detect static and / or dynamic objects. The IMUs are configured to measure and report the specific force, angular velocity, acceleration, and orientation of the vehicle body 12, using a combination of accelerometers, gyroscopes, and sometimes magnetometers. The SASs are configured to measure the steering wheel rotation speed, wheel angle, and other parameters of the vehicle 10's steering wheel. All sensors 40 are configured to generate sensor data.For the sake of clarity, the sensor data in this disclosure can be divided into first and second sensor data. The first sensor data refers to the data generated at least partially by one or more IMUs, WSSs, and one or more steering angle sensors (SAS) of the vehicle 10. The second sensor data refers to the data generated at least partially by one or more radar devices of the vehicle 10.

[0019] The controller 34 is connected to the sensors 40 and is programmed to receive sensor data from them. The controller 34 comprises at least one processor 44 and a non-transient, computer-readable storage device or medium 46. The processor 44 can be a custom processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors connected to the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or, more generally, an instruction-executing device. The computer-readable storage devices or media 46 can include volatile and non-volatile memory, such as read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM).KAM is a persistent or non-volatile memory that can be used to store various operating variables while the processor 44 is powered off. The computer-readable storage device(s) or media of the controller 34 can be implemented using a variety of storage devices such as PROMs (programmable read-only memory), EPROMs (electrical PROMs), EEPROMs (electrically erasable PROMs), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which are executable instructions used by the controller 34 in controlling the vehicle 10.

[0020] The instructions can comprise one or more separate programs, each containing an ordered list of executable instructions for implementing logical functions. When executed by processor 44, the instructions receive and process signals from the sensors, perform logic, calculations, procedures, and / or algorithms for the automatic control of the vehicle 10's components, and generate control signals for the actuators to automatically control the vehicle 10's components based on the logic, calculations, procedures, and / or algorithms. Although in Fig. While a single controller 34 is shown in Figure 1, the system 11 can comprise a plurality of controllers 34 that communicate and cooperate via a suitable communication medium or combination of communication media to process the sensor signals, perform logic, calculations, procedures and / or algorithms, and generate control signals to automatically control functions of the system 11. In various embodiments, one or more instructions of the controller 34 are integrated into the system 11. The non-transitory computer-readable storage device or medium 46 contains machine-readable instructions (which, for example, are written in a script). Fig. 2 are shown), which, when executed by one or more processors, cause processors 44 to execute procedure 100 ( Fig. 2) to execute.

[0021] The vehicle 10 also includes one or more vehicle actuators 42, which control one or more vehicle functions, such as the drive system, the transmission system, the accelerator pedal, the brake pedal, the electronic power steering system, the steering wheel, and the braking system. The vehicle actuators 42 are connected to the controller 34. Therefore, the controller 34 is programmed to control the operation of the vehicle actuators 42.

[0022] Fig.Figure 2 is a flowchart of a procedure 100 for odometry estimation using radar information. The procedure 100 uses radar data to adjust and improve the learned parameters, correct the adaptive filters of the odometry, and increase the accuracy of the odometry estimates. The procedure 100 begins in block 102. In block 102, the controller 34 receives input data or inputs. The input data includes real-time sensor data from the sensors 40, such as the first sensor data and the second sensor data. As described above, the first sensor data refers to data generated at least partially by one or more IMUs, WSSs, and one or more SASs of the vehicle 10, and the second sensor data refers to data generated at least partially by one or more radars of the vehicle 10. The first sensor data may also include GNSS data generated by the GNSS transceiver or receiver.

[0023] Non-restrictive examples of input data include wheel speed from the wheel speed sensor over time, steering angle measurements over time from the steering angle sensor, rear axle steering measurements over time from the active rear axle steering system, wheel impulse inertial measurements over time from the inertial measurement unit (IMU), global navigation satellite system (GNSS) data from the GNSS transceiver, and data from a communication gateway module. Procedure 100 then continues with block 104.

[0024] Block 104 involves the use of an adaptive and stochastic filter to determine the longitudinal position, lateral position, heading, longitudinal velocity, lateral velocity, and yaw rate of vehicle 10 in real time using sensor data (i.e., the first set of sensor data generated by vehicle 10's IMU, WSS, GNSS receiver, and SAS, and the second set of sensor data generated by vehicle 10's radar). In Block 104, a suitable state observer can be used to determine an initial longitudinal position, an initial lateral position, an initial heading, an initial lateral velocity, an initial longitudinal velocity, and an initial yaw rate. These state observers can be referred to as model-based observers. These model-based observers can be corrected using radar data generated by vehicle 10's radar.The radar data can be described as second sensor data. To correct the model-based observers, the controller 34 uses a velocity scaling factor, an IMU scaling factor, and corrections for the suspension dynamics.

[0025] The radar of vehicle 10 can be used to determine its course, longitudinal speed, and lateral speed in real time. For example, the controller 34 uses the azimuth between the radar and a stationary object in front of vehicle 10 to determine its course. In a driving scene with sufficient reflection, the radar is able to determine the vehicle's speed relative to the scene. This data can be clustered and made available to us in the speed domain. The model-based observers provide an odometry estimate in the form of relative vehicle motion using the initial sensor data. The initial sensor data may contain uncertainties due to unmodeled dynamics, measurement noise, and environmental uncertainties, which can be expressed as follows: Δx˜=Δx+wx Δy˜=Δy+wy Δψ˜=Δψ+wψ where: Δx is the delta (or change) of the longitudinal position (i.e., the x-position) of the vehicle 10; Δy is the delta (or change) of the lateral position (i.e., the y-position) of vehicle 10; Δ Ψ is the delta (or change) of the course of vehicle 10; w x is the uncertainty in Δx; w y is the uncertainty in Δy; w ψ is the uncertainty in Δψ; Δx˜ Delta x-position with uncertainty; Δy˜ Delta y-position with uncertainty; and Δψ˜ Delta price with uncertainty.

[0026] An estimation error can be calculated using the radar data and the following equations: ex=vs(1).dt−Δx˜ ey=vs(2).dt−Δy˜ eψ=atan2(vs(2)vs(1))−Δψ˜ where: v s The scene speed of the vehicle as detected by the radar is 10; e xThe error term for the x-position (i.e., the longitudinal position) of the vehicle is 10; e y The error term for the y-position (i.e., the lateral position) of the vehicle is 10; e ψ The error term for the course of the vehicle is 10; Δx˜ Delta x-position with uncertainty; Δy˜ Delta y-position with uncertainty; Δψ˜ Delta price with uncertainty; and t is time.

[0027] After calculating the errors, the adaptive filter can be implemented using the following equations: Δxk+1=Δxk+gk,xex,k Δyk+1=Δyk+gk,yey,k Δψk+1=Δψk+gk,ψeψ,k where: k is a time step; Δx k+1 is a filtered term for the longitudinal position of the vehicle at time k + 1; Δy k+1 is a filtered term for the lateral position of the vehicle at time k + 1; Δψ k+1 is the filtered term for the vehicle's course at time k + 1; Δx k is a change in the longitudinal position of the vehicle at time k; Δy k is a change in the lateral position of the vehicle at time k; Δψ k is a change in the vehicle's course at time k; G k,x is the strengthening of the longitudinal position at time k; G k,y is the strengthening of the transverse position at time k; G k,ψ is the strengthening of the price at time k; e x,k is the error of the longitudinal position at time k; e y,k is the error of the transverse position at time k; and e ψ,k is the error of the course at time k.

[0028] The longitudinal position gain, the lateral position gain, and the heading gain are calculated using the recursive least-squares method. Using the corrected adaptive filters described above, controller 34 corrects the initial longitudinal position, initial longitudinal speed, initial lateral position, initial lateral speed, initial yaw rate, and initial heading of the vehicle using the longitudinal position error, the lateral position error, and the heading error, respectively, thereby generating a corrected longitudinal position, a corrected lateral position, a corrected lateral speed, a corrected longitudinal speed, a corrected yaw rate, and a corrected heading for vehicle 10. The process 100 then proceeds to block 106.

[0029] In block 106, the controller 34 performs an integrity check of the distance measurement, or odometry, using radar data from the vehicle 10's radar. First, the controller 34 detects a faulty sensor signal from the sensors 40 (i.e., WSS, IMU, SAS, radar). If one or more faulty sensor signals are detected, the controller 34 performs a correction procedure. Then, the controller 34 performs a physical check. For this purpose, the controller 34 checks for implausible values ​​for longitudinal speed, implausible values ​​for lateral speed, implausible yaw rates, implausible pitch, and implausible roll. The controller 34 also performs a consistency check between the yaw rate and the longitudinal speed of the vehicle 10, as well as between the yaw rate and the lateral speed of the vehicle 10. The controller 34 also performs a check of the estimation characteristics.The examination of the estimation characteristics includes a check for negative covariance matrix diagonal elements and outlier detection. Procedure 100 then proceeds to block 108.

[0030] In block 108, the controller 34 controls the vehicle 10 using the corrected longitudinal position, corrected lateral position, corrected lateral speed, corrected longitudinal speed, corrected yaw rate, and corrected heading of the vehicle 10. For example, the controller 34 can control one or more ADAS of the vehicle 10 using the corrected longitudinal position, corrected lateral position, corrected lateral speed, corrected longitudinal speed, corrected yaw rate, and corrected heading of the vehicle 10.

[0031] While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms encompassed by the claims. The words used in the description are descriptive rather than limiting, and it is understood that various modifications may be made without departing from the spirit and scope of the disclosure. As previously described, the features of different embodiments can be combined to form further embodiments of the system and method disclosed herein, which may not be explicitly described or illustrated.While various embodiments might be described as advantageous or preferred over other embodiments or implementations of the prior art with respect to one or more desired properties, those skilled in the art recognize that one or more features or properties may be compromised to achieve the desired overall system properties, which depend on the specific application and implementation. These attributes may include, but are not limited to, cost, strength, durability, life-cycle costs, marketability, appearance, packaging, size, usability, weight, manufacturability, ease of assembly, etc.Therefore, embodiments that are described as less desirable than other embodiments or implementations of the prior art with respect to one or more features are not outside the scope of protection of the disclosure and may be desirable for certain applications.

[0032] The drawings are simplified and not to scale. For the sake of simplicity and clarity only, directional terms such as top, bottom, left, right, on, above, above, below, under, back, and front may be used in relation to the drawings. These and similar directional terms are not to be interpreted as limiting the scope of the disclosure in any way.

[0033] Embodiments of the present disclosure are described herein. However, it is understood that the disclosed embodiments are merely examples and that other embodiments may take different and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of certain components. Therefore, specific structural and functional details disclosed herein are not to be understood as limiting, but merely as a representative basis for teaching those skilled in the art to use the present disclosed system and method in various ways. As is clear to those skilled in the art, various features illustrated and described with reference to one of the figures can be combined with features illustrated in one or more other figures to create embodiments that are not expressly illustrated or described.The illustrated feature combinations represent representative embodiments for typical applications. However, different combinations and modifications of the features, consistent with the teachings of this disclosure, might be desirable for certain applications or implementations.

[0034] Embodiments of the present disclosure can be described here in the form of functional and / or logical block components and various processing steps. It should be clear that such block components can be implemented by a number of hardware, software, and / or firmware components configured to perform the specified functions. For example, in one embodiment of the present disclosure, various integrated circuit components, such as memory elements, digital signal processing elements, logic elements, lookup tables, or the like, can be used, which can perform a variety of functions under the control of one or more microprocessors or other control devices.Furthermore, it is clear to those skilled in the art that embodiments of the present disclosure can be used in connection with a number of systems and that the systems described here are merely exemplary embodiments of the present disclosure.

[0035] For the sake of brevity, techniques of signal processing, data fusion, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) are not described in detail here. Furthermore, the connecting lines shown in the various figures are intended to represent exemplary functional relationships and / or physical connections between the various elements. It should be noted that alternative or additional functional relationships or physical connections may exist in an embodiment of this disclosure.

[0036] This description is merely explanatory and is not intended to limit the revelation, its application, or use. The comprehensive teachings of revelation can be implemented in a multitude of forms. Although this revelation contains certain examples, the true scope of revelation should therefore not be so restricted, since other modifications are evident upon study of the drawings, the description, and the following claims.

Images