KR20190066229A - System and method for estimating positioning direction angle - Google Patents

Abstract

According to the present invention, a method for estimating a positioning direction angle comprises the following steps of: receiving yaw rate information, wheel speed information, and GNSS direction angle information from a sensor unit; calculating a dead reckoning direction angle by time-integrating the yaw rate information; detecting a curvature threshold value prepared in advance for each vehicle speed section corresponding to a current speed from the wheel speed information; comparing the detected curvature threshold value with current driving curvature; adjusting a reflection weighted value of the dead reckoning direction angle and the GNSS direction angle based on the comparison result; and estimating a positioning direction angle by reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weighted value.

Description

[0001] SYSTEM AND METHOD FOR ESTIMATING POSITIONING DIRECTION ANGLE [0002]

The present invention relates to a positioning direction angle estimation system and method.

1 is a view for explaining a positioning direction angle measuring system according to the prior art. FIG. 2 is a view for explaining the problem that the positioning direction angle error increases.

The positioning direction angle measuring system 10 according to the related art includes a direction angle calculating unit 11, a moving distance calculating unit 12, and a GNSS direction angle updating unit 13 as shown in FIG.

The direction angle calculator 11 calculates the dead reckoning direction angle by integrating the yaw rate output from the yaw rate sensor (YRS) 14.

The travel distance calculating unit 12 receives the wheel speed from the wheel speed sensor (WSS) 15 and calculates the travel distance to calculate the dead recording position.

The GNSS direction angle update unit 13 may calculate a positioning direction angle by adding a weight value to each of the direction angle output from the GNSS module 16 and the dead reckoning direction.

In this case, when the GNSS data sampling period (1 to 10 Hz) is slow as shown in FIG. 2, the GNSS direction angle error increases at high angular velocity (for example, 10 deg / s) or high curvature (1 / (1 / r ₁ <1 / r ₂ → Δφ _r1 <Δφ _r2 ).

The present invention can solve the problem of increasing the positioning direction angle error due to the GNSS direction angle error at high angular velocity or high curvature by selectively setting the measurement noise covariance R value affecting the gain of the Kalman filter according to the curvature The present invention provides a positioning direction angle estimation system and method.

It should be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.

According to a first aspect of the present invention, there is provided a positioning direction angle estimation method comprising: receiving yaw rate information, wheel speed information, and GNSS direction angle information from a sensor unit; Calculating a dead reckoning direction angle by integrating the yaw rate information with respect to time; Detecting a curvature threshold prepared in advance for each vehicle speed section corresponding to the current speed from the wheel speed information; Comparing the detected curvature threshold with a current curvature; Adjusting the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result and estimating the positioning direction angle by reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weight value do.

Adjusting the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the result of the comparison may include adjusting the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the following equation using the Kalman filter: .

[Mathematical Expression]

는 칼만필터의 Gain, 상기

는 시간 K에서의 오차 공분산(Error Covariance), 상기 H는 관측모델(Observation Model between the state and the observation)을 의미한다.At this time,

Gain of the Kalman filter,

Is the error covariance at time K, and H is the observation model between the state and the observation.

Wherein the step of adjusting the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result includes: increasing the reflecting weight of the dead reckoning direction angle when the current traveling curvature is equal to or greater than the threshold value, The reflection weights of the angles can be reduced.

Wherein the step of adjusting the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result includes the step of decreasing the reflecting weight of the dead reckoning direction angle when the current traveling curvature is less than the threshold value, The reflection weights of the angles can be increased.

Estimating the positioning direction angle by reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weight value may estimate the positioning direction angle based on the following equation.

[Mathematical Expression]

Positioning direction angle = Gain × GNSS direction angle + (1-Gain) × Dead re-cornering direction angle

The method of estimating a positioning direction angle according to the present invention includes the steps of calculating a first curvature by receiving steering angle information from a steering angle sensor included in the sensor unit; Calculating the second curvature by receiving the yaw rate information from the yaw rate sensor included in the sensor unit, and estimating the current curvature based on the calculated first and second curvatures have.

The positioning direction angle estimation method according to the present invention may further include comparing the current speed with a predetermined threshold speed and adjusting a weight of the first and second curvatures based on the comparison result. The comparing of the detected curvature threshold value and the current running curvature may compare the current running curvature in which the weights of the first and second curvatures are adjusted and the detected curvature threshold value.

The step of adjusting the weight of the first and second curvatures may increase the weight of the first curvature and decrease the weight of the second curvature when the current speed is less than the threshold speed as a result of the comparison.

The adjusting the weight of the first and second curvatures may decrease the weight of the first curvature and increase the weight of the second curvature when the current speed is equal to or greater than the threshold speed.

A positioning direction angle estimation system according to a second aspect of the present invention includes a sensor information receiving unit for receiving yaw rate information, wheel speed information, and GNSS direction angle information from a sensor unit, and a sensor information receiving unit for time integrating the yaw rate information, Calculating a wheel curvature threshold value based on the wheel speed information, detecting a curvature threshold value prepared for each vehicle speed section corresponding to the current speed from the wheel speed information, comparing the detected curvature threshold value with the current running curvature, And a positioning direction angle estimator for adjusting a reflection weight of the GNSS direction angle and estimating a positioning direction angle reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weight value.

The positioning direction angle estimator may adjust the reflection weight of the dead reckoning direction angle and the GNSS direction angle based on the following equation using the Kalman filter.

[Mathematical Expression]

는 칼만필터의 Gain, 상기

는 시간 K에서의 오차 공분산(Error Covariance), 상기 H는 관측모델(Observation Model between the state and the observation)을 의미한다.remind

Gain of the Kalman filter,

Is the error covariance at time K, and H is the observation model between the state and the observation.

Wherein the positioning direction angle estimator increases the reflection weight of the dead reckoning direction angle and reduces the reflection weight of the GNSS direction angle when the current traveling curvature is greater than or equal to the threshold value, The reflection weights of the re-cornering direction angles can be reduced, and the reflection weights of the GNSS direction angle can be increased.

The positioning direction angle estimation system according to the present invention receives steering angle information from a steering angle sensor included in the sensor unit to calculate a first curvature, receives the yaw rate information to calculate a second curvature, And a curvature estimator for estimating the current curvature based on the second curvature.

Wherein the curvature estimator compares the current velocity with a predetermined threshold velocity and adjusts the weights of the first and second curvatures based on the comparison result, wherein the positioning direction angle estimator estimates a weight of the first and second curvatures Can be compared with the detected curvature threshold value.

Wherein the curvature estimator increases the weight of the first curvature and decreases the weight of the second curvature when the current speed is less than the threshold speed as a result of the comparison, and when the current speed is equal to or greater than the threshold speed, The weight of the curvature can be decreased and the weight of the second curvature can be increased.

According to any one of the above-mentioned objects of the present invention, it is possible to improve the performance of estimating the positioning direction angle by adjusting the reflection weights of the GNSS direction angle and the dead reckoning direction angle at the time of turning at a high angular velocity or a high curvature.

In addition, it is possible to improve the positioning direction angle estimation performance by estimating and reflecting the curvature by adjusting the weight of each curvature calculated by the yaw rate sensor and the steering angle sensor.

By using the estimated positioning direction angle, the movement distance and the position accuracy can be improved.

1 is a view for explaining a positioning direction angle measuring system according to the prior art.
FIG. 2 is a view for explaining the problem that the positioning direction angle error increases.
3 is a block diagram of a positioning direction angle estimation system according to an embodiment of the present invention.
4 is a flowchart of a positioning direction angle estimation method according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. It should be understood, however, that the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In the drawings, the same reference numbers are used throughout the specification to refer to the same or like parts.

Whenever a component is referred to as &quot; including &quot; an element throughout the specification, it is to be understood that the element may include other elements, not the exclusion of any other element, unless the context clearly dictates otherwise.

3 is a block diagram of a positioning direction angle estimation system 100 according to an embodiment of the present invention.

A positioning direction angle estimation system 100 according to an embodiment of the present invention includes a sensor information receiving unit 110 and a positioning direction angle estimating unit 120. [

The sensor information receiving unit 110 receives yaw rate information, wheel speed information, and GNSS direction angle information from the yaw rate sensor 142, the wheel speed sensor 143, and the GNSS module 144, respectively.

The positioning direction angle estimating unit 120 calculates the dead reckoning direction angle by time-integrating the yaw rate information. Then, a curvature threshold value prepared in advance for each vehicle speed section corresponding to the current speed is detected from the wheel speed information.

For example, the curvature threshold value prepared in advance for each vehicle speed section may be stored as a matching table on the memory as follows. Since the curvature changes according to the vehicle speed, the curvature threshold value can be found in advance through the test according to the vehicle speed section and stored in the matching table as shown in Table 1.

Vehicle speed (kph) Curvature threshold (rad / m) ~ 15 A 15 to 30 B 30 to 60 C 60 to 90 D 90 ~ 200 E

The positioning direction estimating unit 120 adjusts the reflecting weight of the dead reckoning direction and the GNSS direction angle based on the result of comparison between the detected curvature threshold value and the current traveling curvature, The directional angle can be estimated by reflecting the direction angle and the GNSS direction angle.

Meanwhile, the positioning direction angle estimation system 100 according to an embodiment of the present invention may further include a curvature estimation unit 130. [

The curvature estimator 130 may calculate the first curvature by receiving the steering angle information from the steering angle sensor 141 included in the sensor unit 140 and may calculate the second curvature by receiving the yaw rate information. The current curvature can be estimated based on the calculated first and second curvatures.

At this time, the curvature estimator 130 compares the current speed with a preset threshold speed, and adjusts the weights of the first and second curvatures based on the comparison result. Accordingly, the positioning direction angle estimating unit 120 compares the curvature thresholds of the curvature threshold values prepared beforehand with the current traveling curvature of which the weights of the first and second curvatures are adjusted, Directional angle and the reflection weight of the GNSS directional angle.

Meanwhile, each component of the positioning direction angle estimation system 100 according to an embodiment of the present invention may be configured with a memory (not shown) and a processor (not shown) that executes a program stored in the memory.

Herein, the memory is collectively referred to as a non-volatile storage device and a volatile storage device which keep the stored information even when no power is supplied.

For example, the memory may be a memory card such as a compact flash (CF) card, a secure digital (SD) card, a memory stick, a solid-state drive (SSD) A magnetic computer storage device such as a NAND flash memory, a hard disk drive (HDD), and the like, and an optical disc drive such as a CD-ROM, a DVD-ROM, and the like.

3 may be implemented in hardware such as software or an FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit), and may perform predetermined roles can do.

However, 'components' are not meant to be limited to software or hardware, and each component may be configured to reside on an addressable storage medium and configured to play one or more processors.

Thus, by way of example, an element may comprise components such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, Routines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.

The components and functions provided within those components may be combined into a smaller number of components or further separated into additional components.

While the methods and systems of the present invention have been described in connection with specific embodiments, some or all of those elements or operations may be implemented using a computer system having a general purpose hardware architecture.

Hereinafter, a positioning direction angle estimation method in the positioning direction angle estimation system 100 according to an embodiment of the present invention will be described in more detail with reference to FIG.

4 is a flowchart of a positioning direction angle estimation method according to an embodiment of the present invention.

The positioning direction angle estimation method according to an embodiment of the present invention first calculates the yaw rate information from the yaw rate sensor 142 included in the sensor unit 140, the wheel speed information from the wheel speed sensor 143, And the GNSS direction angle information from the module 144 (S105).

Next, the dead-reckoning direction angle is calculated by integrating the yaw rate information with time (S110).

Next, a curvature threshold value prepared in advance for each vehicle speed section corresponding to the current speed is detected from the wheel speed information (S115), and the detected curvature threshold value is compared with the current running curvature (S120).

And adjusts the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result. At this time, an embodiment of the present invention can calculate the reflecting weight of the dead reckoning direction angle and the GNSS direction angle based on the following Equation 1 to which the Kalman filter is applied.

[Equation 1]

는 칼만필터의 Gain을 의미하고,

는 시간 K에서의 오차 공분산(Error Covariance)을 의미하며, H는 관측모델(Observation Model between the state and the observation)을 의미한다.At this time,

Means the gain of the Kalman filter,

Is the error covariance at time K, and H is the observation model between the state and the observation.

That is, an embodiment of the present invention can calculate the weight gain in the Kalman filter according to Equation (1).

At this time, the GNSS direction weight weight is adjusted by the measured noise covariance R value.

On the other hand, the measurement noise covariance R can be tuned to a single constant value, the method of raising the reflection GNSS direction reflection weights by making the measurement noise covariance different for each urban area and the plain region, and GSNN Horizontal Dilution of Precision (HDOP) The method of raising the GNSS weights can be applied.

In this case, when the GNSS data sampling period (1 to 10 Hz) is slow, the GNSS direction angle error increases at high angular speed (for example, 10 deg / s) or at high curvature (1 / r) An embodiment of the present invention increases the reflection weight of the dead reckoning direction angle when the current curvature is greater than or equal to the threshold value at step S125, The reflection weight of the direction angle can be reduced (S130).

Alternatively, if the current curvature is less than the threshold value, the reflection weight of the dead reckoning direction angle may be decreased (S135), and the reflection weight of the GNSS direction angle may be increased (S140).

After the reflection weighting values of the dead reckoning direction and the GNSS direction are adjusted, the positioning direction angle is estimated by reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weight values (S145).

At this time, the positioning direction angle can be calculated and estimated by the following equation (2).

&Quot; (2) &quot;

Positioning direction angle = Gain × GNSS direction angle + (1-Gain) × Dead re-cornering direction angle

Meanwhile, the positioning direction angle estimation method according to an embodiment of the present invention can additionally perform a process for improving the accuracy of the current traveling curvature used to improve the accuracy of the positioning direction angle estimation.

For this, an embodiment of the present invention receives the steering angle information from the steering angle sensor 141 included in the sensor unit 140 and calculates the first curvature. Then, the second curvature is calculated by receiving the yaw rate information from the yaw rate sensor 142 included in the sensor unit (S150, S155).

At this time, the first and second curvatures can be calculated based on the following equations (3) and (4). Here,? _SAS denotes the steering angle, and WheelLength denotes the distance between the wheel base, that is, the front axle of the vehicle and the rear axle.

&Quot; (3) &quot;

&Quot; (4) &quot;

, γ:요 레이트,υ: 속도

, y: yorate, v: speed

After the first and second curvatures are calculated in this manner, the current curvature is estimated based on the first and second curvatures.

Specifically, an embodiment of the present invention compares the current speed with a preset threshold speed (S160) and adjusts the weights of the first and second curvatures based on the comparison result. Then, the current traveling curvature in which the weights of the first and second curvatures are adjusted is compared with the detected curvature threshold value.

At this time, since the slip may occur when the current speed is equal to or higher than the critical speed, that is, when the vehicle is traveling at a high speed, the weight of the first curvature is decreased (S165) and the weight of the second curvature is increased (S170).

In contrast, if the current speed is less than the threshold speed, that is, there is almost no slip at low speed driving, the weight of the first curvature may be increased (S175) and the weight of the second curvature may be decreased (S180).

In order to adjust the weights of the first and second curvatures, a Kalman filter according to Equation (1) can be used.

The current curvature is calculated based on the first and second curvatures thus adjusted, and the calculated current curvature can be applied to step S120 described above (S185).

In the above description, steps S105 to S185 may be further divided into further steps or combined into fewer steps, according to an embodiment of the present invention. Also, some of the steps may be omitted as necessary, and the order between the steps may be changed. In addition, the contents already described with respect to the positioning direction angle estimation system 100 in FIG. 3 apply to the positioning direction angle estimation method in FIG. 4, even if other contents are omitted.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: Positioning direction angle estimation system
110: sensor information receiver
120: positioning direction angle estimating unit
130: Curvature estimation unit
140:
141: Steering angle sensor
142: Yaw rate sensor
143: Wheel speed sensor
144: GNSS module

Claims (15)

Receiving yaw rate information, wheel speed information, and GNSS direction angle information from the sensor unit;
Calculating a dead reckoning direction angle by integrating the yaw rate information with respect to time;
Detecting a curvature threshold prepared in advance for each vehicle speed section corresponding to the current speed from the wheel speed information;
Comparing the detected curvature threshold with a current curvature;
Adjusting the reflection weights of the dead reckoning direction angle and the GNSS direction angle based on the comparison result, and
Estimating a positioning direction angle by reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weight value. The method according to claim 1,
Adjusting the reflection weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result,
Wherein the reflecting weighting values of the dead reckoning direction and the GNSS direction angle are adjusted based on the following equation to which the Kalman filter is applied.
[Mathematical Expression]

remind

Gain of the Kalman filter,

Is the error covariance at time K, and H is the observation model between the state and the observation The method according to claim 1,
Adjusting the reflection weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result,
If the current curvature is greater than or equal to the threshold value, increase the reflection weight of the dead reckoning direction angle and decrease the reflection weight of the GNSS direction angle. The method according to claim 1,
Adjusting the reflection weight of the dead reckoning direction angle and the GNSS direction angle based on the comparison result,
Wherein when the current curvature is less than the threshold value, the reflection weight of the dead reckoning direction angle is decreased and the reflection weight of the GNSS direction angle is increased. The method according to claim 1,
Estimating a positioning direction angle reflecting the dead reckoning direction angle and the GNSS direction angle according to the adjusted weight value,
And the positioning direction angle is estimated based on the following equation.
[Mathematical Expression]
Positioning direction angle = Gain × GNSS direction angle + (1-Gain) × Dead re-cornering direction angle The method according to claim 1,
Receiving the steering angle information from the steering angle sensor included in the sensor unit and calculating a first curvature;
Calculating a second curvature by receiving the yaw rate information from a yaw rate sensor included in the sensor unit, and
And estimating the current traveling curvature based on the calculated first and second curvatures. The method according to claim 6,
Comparing the current speed with a preset threshold speed; and
Adjusting a weight of the first and second curvatures based on the comparison result,
Wherein the comparing the detected curvature threshold value and the current traveling curvature comprises comparing the current traveling curvature with the weight of the first and second curvatures adjusted and the detected curvature threshold value. 8. The method of claim 7,
Wherein adjusting the weights of the first and second curvatures comprises:
And if the current speed is less than the threshold speed, increasing the weight of the first curvature and decreasing the weight of the second curvature. 8. The method of claim 7,
Wherein adjusting the weights of the first and second curvatures comprises:
And decreasing the weight of the first curvature and increasing the weight of the second curvature when the current speed is equal to or greater than the threshold speed. In the positioning direction angle estimation system,
A sensor information receiving unit for receiving yaw rate information, wheel speed information, and GNSS direction angle information from the sensor unit,
Calculating a dead reckoning directional angle by time-integrating the yaw rate information, detecting a curvature threshold value prepared in advance for each vehicle speed section corresponding to the current speed from the wheel speed information, detecting the curvature threshold value and the current driving curvature And adjusting a dead reckoning direction angle and a reflection weight of the GNSS direction angle based on the comparison result and calculating a positioning direction direction in which the dead reckoning direction angle and the GNSS direction angle are reflected according to the adjusted weight value, And estimating means for estimating the positioning direction. 11. The method of claim 10,
Wherein the positioning direction angle estimator adjusts the reflection weight of the dead reckoning direction angle and the GNSS direction angle based on the following equation to which the Kalman filter is applied.
[Mathematical Expression]

remind

Gain of the Kalman filter,

Is the error covariance at time K, and H is the observation model between the state and the observation 11. The method of claim 10,
Wherein the positioning direction angle estimator increases the reflection weight of the dead reckoning direction angle and reduces the reflection weight of the GNSS direction angle when the current traveling curvature is greater than or equal to the threshold value, To reduce the reflection weights of the re-cornering direction angles and to increase the reflection weights of the GNSS direction angles. 11. The method of claim 10,
Calculating a first curvature by receiving steering angle information from a steering angle sensor included in the sensor unit, receiving the yaw rate information to calculate a second curvature, calculating a second curvature based on the calculated first and second curvatures, And a curvature estimator for estimating the curvature of the measurement target. 14. The method of claim 13,
Wherein the curvature estimator compares the current velocity with a predetermined threshold velocity and adjusts the weights of the first and second curvatures based on the comparison result,
Wherein the positioning direction angle estimating unit compares the current traveling curvature with the weight of the first and second curvatures adjusted and the detected curvature threshold value. 15. The method of claim 14,
Wherein the curvature estimator increases the weight of the first curvature and decreases the weight of the second curvature when the current speed is less than the threshold speed as a result of the comparison, and when the current speed is equal to or greater than the threshold speed, The weight of the curvature is decreased and the weight of the second curvature is increased.

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