Vehicle attitude estimation device and method, and storage medium

Abstract

Disclosed are a vehicle attitude estimation device and method, and a non-transitory computer-readable storage medium in which a program for executing the method is stored. A vehicle attitude estimation device according to one aspect of the present invention, for estimating an attitude of a vehicle, comprises: a first inertial sensor for measuring acceleration information of the vehicle; a second inertial sensor measuring angular velocity information of the vehicle; and a controller that inputs the acceleration information to a first estimation model obtained from the angular velocity information and generates first attitude estimation information of the vehicle using output information of the first estimation model, inputting the acceleration information and the angular velocity information to a second estimation model, and generating second attitude estimation information of the vehicle using output information of the second estimation model; and generates final attitude estimation information of the vehicle using at least one of the first attitude estimation information and the second attitude estimation information.

Description

Technical Field

[0001] The present invention relates to a vehicle attitude estimation device and method and a non-transitory computer-readable storage medium storing a program for executing the method. More specifically, the present invention relates to a vehicle attitude estimation device and method for estimating a vehicle attitude using measurement information of an inertial sensor provided in a vehicle, and a non-transitory computer-readable storage medium storing a program for executing the method. Background Art

[0002] In order to control the attitude of a vehicle, it is important to obtain accurate values of the state angles of the vehicle, that is, yaw, roll, and pitch. In order to calculate the yaw, roll, and pitch of the vehicle, an acceleration sensor and a gyro sensor provided in the vehicle can be applied.

[0003] The acceleration sensor is used to measure the physical quantity of acceleration. More specifically, the acceleration sensor can measure the gravitational acceleration applied to the X-axis, Y-axis, and Z-axis in a three-dimensional space. When using the measurement value of the acceleration sensor to estimate the angle related to the vehicle attitude, there is an advantage that no integration process is required, so errors are not accumulated. However, the disadvantage of the acceleration sensor is that it is difficult to accurately estimate changes due to errors in the measurement values.

[0004] Different from the acceleration sensor, the gyro sensor is used to measure the angular velocity. When using the measurement value of the gyro sensor, there is an advantage that the change in the vehicle attitude can be accurately estimated. However, when using the measurement value of the gyro sensor to estimate the angle related to the vehicle attitude, an integration process is involved. As a result, errors are accumulated over time, and there is a problem of drift during estimation.

[0005] Therefore, it is necessary to develop a technology to maximize the advantages of each of the acceleration sensor and the gyro sensor and compensate for the disadvantages, so as to accurately estimate the angle related to the vehicle attitude.

[0006] On the other hand, with the development of autonomous driving technology, there are increasing requirements for ensuring redundancy related to vehicle safety, robustness of measurement, etc. In this regard, there is an increasing need to develop a technology so that even when any one of the acceleration sensor and the gyro sensor installed in the vehicle fails, the angle related to the vehicle attitude can be estimated.

[0007] Patent Document 0001: Korean Patent No. 1549165 ("Vehicle Attitude Estimation Device and Vehicle Attitude Estimation Method", Authorization Date: August 26, 2015) Summary of the Invention

[0008] Problems to be Solved by the Invention

[0009] The present invention aims to solve the above-mentioned problems. An object of the present invention is to provide a vehicle attitude estimation device and method, and a non-transitory computer-readable storage medium storing a program for executing the method, which can accurately estimate an angle related to the vehicle attitude by comprehensively applying inertial sensors of different types from each other.

[0010] Another object of the present invention is to provide a vehicle attitude estimation device and method, and a non-transitory computer-readable storage medium storing a program for executing the method, which can accurately estimate an angle related to the vehicle attitude even when any one of a plurality of inertial sensors fails.

[0011] The problems of the present invention are not limited to the above-mentioned problems, and those of ordinary skill in the art will clearly understand other unmentioned problems through the following description.

[0012] Means for Solving the Problems

[0013] According to an aspect of the present invention, there is provided a vehicle attitude estimation device for estimating the attitude of a vehicle, including: a first inertial sensor for measuring the acceleration of the vehicle; a second inertial sensor for measuring the angular velocity of the vehicle; a first estimator for generating first attitude estimation information of the vehicle by using the acceleration and an estimated model variable obtained from the angular velocity; a second estimator for receiving the acceleration and the angular velocity to generate second attitude estimation information of the vehicle; and a controller for generating final attitude estimation information of the vehicle by using any one or more of the first attitude estimation information and the second attitude estimation information.

[0014] In the vehicle attitude estimation device according to an aspect of the present invention, the first attitude estimation information, the second attitude estimation information, and the final attitude estimation information may include Euler angles of the vehicle.

[0015] In the vehicle attitude estimation device according to an aspect of the present invention, the first inertial sensor may measure accelerations in three axes, and the second inertial sensor may measure angular velocities in three axes.

[0016] In the vehicle attitude estimation device according to an aspect of the present invention, the first estimator may generate the first attitude estimation information by using a Kalman filter.

[0017] In a vehicle attitude estimation device according to an aspect of the present invention, the acceleration in three axes can be converted into a quaternion to be input to the first estimator.

[0018] In a vehicle attitude estimation device according to an aspect of the present invention, the second estimator can generate the second attitude estimation information by using a sliding mode observer.

[0019] In a vehicle attitude estimation device according to an aspect of the present invention, when the controller receives a fault signal of the first inertial sensor from the outside and does not receive a fault signal of the second inertial sensor, the final attitude estimation information identical to the second attitude estimation information can be generated.

[0020] In a vehicle attitude estimation device according to an aspect of the present invention, when the controller receives a fault signal of the second inertial sensor from the outside and does not receive a fault signal of the first inertial sensor, the final attitude estimation information identical to the first attitude estimation information can be generated.

[0021] In a vehicle attitude estimation device according to an aspect of the present invention, when a difference above a specified reference occurs between the first attitude estimation information and the second attitude estimation information, the controller can determine it as a fault situation.

[0022] In a vehicle attitude estimation device according to an aspect of the present invention, when it is determined as the fault situation, the controller can generate the final attitude estimation information by mixing the first attitude estimation information and the second attitude estimation information.

[0023] In a vehicle attitude estimation device according to an aspect of the present invention, when it is determined as the fault situation, the controller can generate an average value of the first attitude estimation information and the second attitude estimation information as the final attitude estimation information.

[0024] According to another aspect of the present invention, there is provided a vehicle attitude estimation method for estimating the attitude of a vehicle, including: a step in which a first estimator generates first attitude estimation information of the vehicle by using acceleration measured by a first inertial sensor and a estimation model variable obtained from angular velocity measured by a second inertial sensor; a step in which a second estimator receives the acceleration and the angular velocity to generate second attitude estimation information of the vehicle; and a step in which a controller generates final attitude estimation information of the vehicle by using any one or more of the first attitude estimation information and the second attitude estimation information.

[0025] In a vehicle attitude estimation method according to an aspect of the present invention, the first attitude estimation information, the second attitude estimation information, and the final attitude estimation information may include Euler angles of the vehicle.

[0026] In a vehicle attitude estimation method according to an aspect of the present invention, the acceleration may include accelerations in three axes, and the angular velocity may include angular velocities in three axes.

[0027] In a vehicle attitude estimation method according to an aspect of the present invention, the first estimator may generate the first attitude estimation information by using a Kalman filter.

[0028] In a vehicle attitude estimation method according to an aspect of the present invention, the accelerations in the three axes may be converted into quaternions to be input into the first estimator.

[0029] In a vehicle attitude estimation method according to an aspect of the present invention, the second estimator may generate the second attitude estimation information by using a sliding mode observer.

[0030] In a vehicle attitude estimation method according to an aspect of the present invention, in the step of generating the final attitude estimation information, when the controller receives a failure signal of the first inertial sensor from the outside and does not receive a failure signal of the second inertial sensor, the final attitude estimation information identical to the second attitude estimation information may be generated.

[0031] In a vehicle attitude estimation method according to an aspect of the present invention, in the step of generating the final attitude estimation information, when the controller receives a failure signal of the second inertial sensor and does not receive a failure signal of the first inertial sensor, the final attitude estimation information identical to the first attitude estimation information may be generated.

[0032] In a vehicle attitude estimation method according to an aspect of the present invention, in the step of generating the final attitude estimation information, when the difference between the first attitude estimation information and the second attitude estimation information is equal to or greater than a specified reference, the controller may generate the final attitude estimation information by mixing the first attitude estimation information and the second attitude estimation information.

[0033] In a vehicle attitude estimation method according to an aspect of the present invention, when the controller mixes the first attitude estimation information and the second attitude estimation information, an average value of the first attitude estimation information and the second attitude estimation information may be generated as the final attitude estimation information.

[0034] According to another aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing a program including at least one instruction for executing the vehicle attitude estimation method.

[0035] Effects of the Invention

[0036] According to the configuration, the vehicle attitude estimation device and method according to one aspect of the present invention and the non-transitory computer-readable storage medium storing a program for executing the method maximize the advantages of different types of inertial sensors and compensate for the disadvantages through a redundant structure, thereby accurately estimating the attitude of the vehicle.

[0037] The vehicle attitude estimation device and method according to one aspect of the present invention and the non-transitory computer-readable storage medium storing a program for executing the method select an estimated value according to a failure situation through a redundant structure, and can accurately estimate the attitude of the vehicle even when a part of a plurality of inertial sensors fails.

[0038] The effects of the present invention are not limited to the above effects, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention. Description of the Drawings

[0039] Figure 1 FIG. is a diagram showing the configuration of a vehicle attitude estimation device according to an embodiment of the present invention.

[0040] Figure 2 FIG. is a graph showing first attitude estimation information, second attitude estimation information, and actual vehicle attitude information obtained as a result of simulating a case where the first inertial sensor fails and the second inertial sensor is normal.

[0041] Figure 3 FIG. is a graph showing first attitude estimation information, second attitude estimation information, and actual vehicle attitude information obtained as a result of simulating a case where the first inertial sensor is normal and the second inertial sensor fails.

[0042] Figure 4 FIG. is a flowchart of a vehicle attitude estimation method according to an embodiment of the present invention.

[0043] Description of Reference Numerals

[0044] 100: Vehicle attitude estimation device

[0045] 110: First inertial sensor 120: Second inertial sensor

[0046] 130: First estimator 140: Second estimator

[0047] 150: Controller Detailed implementation manner

[0048] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art to which the present invention pertains can easily implement it. The present invention can be implemented in various different forms and is not limited to the embodiments described herein. To clearly explain the present invention, parts irrelevant to the description are omitted in the drawings, and the same reference numerals are assigned to the same or similar components throughout the specification.

[0049] The words and terms used in this specification and claims should not be construed as being limited to their ordinary or dictionary meanings, but rather, in order to best explain one's own invention, they should be construed as meanings and concepts corresponding to the technical idea of the present invention in accordance with the principle that the inventor can define terms and concepts.

[0050] In this specification, the term "comprising" or "having" is used to illustrate the existence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, rather than precluding the existence or additional possibility of one or more other features, numbers, steps, actions, components, parts, or combinations thereof in advance.

[0051] Figure 1 It is a diagram showing the configuration of a vehicle attitude estimation device according to an embodiment of the present invention.

[0052] A vehicle attitude estimation device 100 according to an embodiment of the present invention is used to estimate the attitude of a vehicle. The vehicle attitude estimation device 100 according to an embodiment of the present invention can comprehensively utilize mutually different types of inertial sensors to generate a plurality of attitude estimation information, and apply this information to generate final attitude estimation information that conforms to the actual attitude of the vehicle.

[0053] Among them, the attitude of the vehicle may include information on any one or more of roll, pitch, and yaw of the vehicle. In other words, the vehicle attitude estimation information may include any one or more of the roll angle, pitch angle, and yaw angle of the vehicle. In addition, the vehicle attitude estimation information may include Euler angles that display the direction in which the vehicle is placed in a three-dimensional space. However, the vehicle attitude estimation information may be any information representing the estimated attitude of the vehicle and is not limited to angles.

[0054] Refer to Figure 1, a vehicle attitude estimation device 100 according to an embodiment of the present invention is a device for estimating the vehicle attitude, and includes a first inertial sensor 110, a second inertial sensor 120, a first estimator 130, a second estimator 140, and a controller 150.

[0055] The first inertial sensor 110 is used to measure the acceleration of the vehicle. More specifically, the first inertial sensor 110 can measure the acceleration of three axes. The first inertial sensor 110 can be disposed on the vehicle. In other words, the first inertial sensor 110 can be an acceleration sensor disposed on the vehicle.

[0056] The second inertial sensor 120 is used to measure the angular velocity of the vehicle. More specifically, the second inertial sensor 120 can measure the angular velocity of three axes. The second inertial sensor 120 can be disposed on the vehicle. That is, the second inertial sensor 120 can be a gyro sensor disposed on the vehicle.

[0057] The first estimator 130 can generate first attitude estimation information of the vehicle by using the estimated model variables obtained from the angular velocity and the acceleration. The first attitude estimation information can include any one or more of the roll angle, pitch angle, and yaw angle of the vehicle. In addition, the first attitude estimation information can include the Euler angle of the vehicle.

[0058] In an embodiment of the present invention, the first estimator 130 can use a Kalman filter to generate the first attitude estimation information. The Kalman filter is a recursive filter for estimating the state of a linear dynamic system based on measured values containing noise.

[0059] The Kalman filter can include: a prediction step: predicting an expected measured value when an input user input is applied in the state based on the state estimated at the previous moment; and a correction step: comparing the predicted measured value with the actual measured value and estimating the current state.

[0060] The estimated model variables of the estimation model for the Kalman filter can be obtained from the angular velocity information. More specifically, when the angular velocity includes the angular velocity of three axes, the angular velocity of three axes can be converted into a quaternion for use to avoid the singularity problem.

[0061] As described above, the state transition matrix A can be obtained as the estimated model variable by using the angular velocity of three axes converted into a quaternion. At this time, A can be composed of a 4×4 matrix.

[0062] More specifically, when the first estimator 130 executes the prediction step of the Kalman filter, it predicts the state value according to the following first mathematical formula obtained by using the state transition matrix A, and can predict the error covariance according to the following second mathematical formula.

[0063] First mathematical formula

[0064]

[0065] (X is the state value)

[0066] Second mathematical formula

[0067]

[0068] (P is the error covariance, Q is the system noise matrix)

[0069] Correspondingly, the first estimator 130 can set the initial value according to a specified reference. At this time, the three-axis acceleration information included in the acceleration information can be converted into quaternions to be input into the first and second mathematical formulas.

[0070] Next, the Kalman gain can be calculated and used to correct the previously predicted state value and error covariance. The calculation of the Kalman gain can be performed according to the following third mathematical formula, the correction of the state value using the result can be performed according to the following fourth mathematical formula, and the correction of the error covariance can be performed according to the following fifth mathematical formula.

[0071] Third mathematical formula

[0072]

[0073] (K is the Kalman gain, P is the error covariance, H is the output matrix, R is the measurement noise covariance matrix)

[0074] Fourth mathematical formula

[0075]

[0076] (X is the state value, Z is the measurement value, H is the output matrix)

[0077] Fifth mathematical formula

[0078]

[0079] (P is the error covariance, K is the Kalman gain, H is the output matrix)

[0080] The final predicted state value output by the first estimator 130 using the Kalman filter in the above manner has a quaternion form. The first estimator 130 generates the first attitude estimation information by converting the final predicted state value. The first attitude estimation information thus obtained may include the Euler angles of the vehicle.

[0081] The second estimator 140 receives the acceleration and the angular velocity to generate the second attitude estimation information of the vehicle. The second attitude estimation information may include any one or more of the roll angle, pitch angle, and yaw angle of the vehicle. In addition, the second attitude estimation information may include the Euler angles of the vehicle.

[0082] In an embodiment of the present invention, the second estimator 140 may use a sliding mode observer to generate the second attitude estimation information. The sliding mode observer has one or more dynamic models, and the second estimator 140 may generate the second attitude estimation information based on the result values derived by inputting the acceleration and the angular velocity into the dynamic models.

[0083] An observer is a general term for estimating an unmeasurable state variable using measurable state variables. A sliding mode observer is an observer based on sliding mode theory. Sliding mode theory is a non-linear control theory that is robust to uncertainties and disturbances. In an embodiment of the present invention, the sliding mode observer may be implemented by the following mathematical formula.

[0084] For example, the second estimator 140 may use dynamic models such as the sixth mathematical formula below and the seventh mathematical formula to estimate the roll angle of the vehicle.

[0085] Sixth mathematical formula

[0086]

[0087] (B and C are system variables obtained from the physical information of the vehicle, Gv is an error variable, is the estimated value of the lateral angular velocity, is the estimated value of the lateral acceleration, a y is the lateral acceleration)

[0088] Seventh mathematical formula

[0089]

[0090] ( is the estimated value of the lateral angular velocity, is the estimated value of the roll angle)

[0091] The lateral acceleration measured by the first inertial sensor 110 can be input into a dynamics model such as the sixth mathematical formula above, and the lateral angular velocity measured by the second inertial sensor 120 is provided as feedback so that the estimated value of the lateral angular velocity in the sixth mathematical formula can be corrected. The second estimator 140 can use the above dynamics model to derive an estimated value of the roll angle of the vehicle.

[0092] On the other hand, the second estimator 140 can also use dynamics models for estimating the pitch angle and yaw angle of the vehicle, respectively. In other words, the second estimator 140 uses a sliding mode observer and can use the dynamics model for estimating the pitch angle and the dynamics model for estimating the yaw angle to estimate the pitch angle and yaw angle of the vehicle, respectively.

[0093] The second estimator 140 can generate the second attitude estimation information by including the derived roll angle, pitch angle, and yaw angle of the vehicle. As described above, the second attitude estimation information can include the Euler angles of the vehicle.

[0094] The controller 150 uses any one or more of the first attitude estimation information and the second attitude estimation information to generate the final attitude estimation information of the vehicle. The final attitude estimation information can include the Euler angles of the vehicle.

[0095] The controller 150 can receive a fault signal of any one of the first inertial sensor 110 and the second inertial sensor 120 from the outside. The controller 150 can generate the final attitude estimation information as follows according to the content of the received fault signal.

[0096] First, when the controller 150 receives a fault signal of the first inertial sensor 110 from the outside and does not receive a fault signal of the second inertial sensor 120, the final attitude estimation information identical to the second attitude estimation information can be generated.

[0097] In an embodiment of the present invention, the first attitude estimation information generated by the first estimator 130 is relatively greatly affected by the acceleration of the vehicle in the estimation model input into the Kalman filter. Therefore, when the first inertial sensor 110 fails, the accuracy of the first attitude estimation information is lower than the accuracy of the second attitude estimation information.

[0098] Figure 2 It is a graph showing the first attitude estimation information, the second attitude estimation information, and the actual vehicle attitude information obtained from the results of a simulation for the case where the first inertial sensor fails and the second inertial sensor is normal.

[0099] Refer to Figure 2It can be confirmed that when the first inertial sensor 110 fails and the second inertial sensor 120 is normal, the first attitude estimation information generated by the first estimator 130 cannot estimate the actual vehicle attitude information. On the contrary, the second attitude estimation information generated by the second estimator 140 estimates the actual vehicle attitude information.

[0100] Considering this, when a failure signal of the first inertial sensor 110 is received and a failure signal of the second inertial sensor 120 is not received, the controller 150 can generate the final attitude estimation information identical to the second attitude estimation information. In other words, the controller 150 can select the second attitude estimation information generated by the second estimator 140 as the final attitude estimation information.

[0101] Next, when the controller 150 receives a failure signal of the second inertial sensor 120 from the outside and does not receive a failure signal of the first inertial sensor 110, the final attitude estimation information identical to the first attitude estimation information can be generated.

[0102] In an embodiment of the present invention, the second attitude estimation information generated by the second estimator 140 is relatively greatly affected by the angular velocity of the vehicle corrected by the dynamic model for the sliding mode observer. Therefore, when the second inertial sensor 120 fails, the accuracy of the second attitude estimation information is lower than the accuracy of the first attitude estimation information.

[0103] Figure 3 It is a graph showing the first attitude estimation information, the second attitude estimation information, and the actual vehicle attitude information obtained as a result of simulating the case where the first inertial sensor is normal and the second inertial sensor fails.

[0104] Refer to Figure 3 It can be confirmed that when the first inertial sensor 110 is normal and the second inertial sensor 120 fails, the first attitude estimation information generated by the first estimator 130 estimates the actual vehicle attitude information. On the contrary, the second attitude estimation information generated by the second estimator 140 cannot estimate the actual vehicle attitude information.

[0105] Considering this, when a failure signal of the second inertial sensor 120 is received and a failure signal of the first inertial sensor 110 is not received, the controller 150 can generate the final attitude estimation information identical to the first attitude estimation information. In other words, the controller 150 can select the first attitude estimation information generated by the first estimator 130 as the final attitude estimation information.

[0106] In addition, when there is a difference greater than or equal to a specified reference between the first attitude estimation information and the second attitude estimation information, the controller 150 may determine that a failure has occurred. The controller 150 can independently determine a failure situation based on whether there is a difference greater than or equal to a specified reference between the first attitude estimation information and the second attitude estimation information, regardless of whether a failure signal of any one of the first inertial sensor 110 and the second inertial sensor 120 is received from the outside.

[0107] When there is a difference greater than or equal to a specified reference between the first attitude estimation information and the second attitude estimation information, the controller 150 regards any one of the first inertial sensor 110 and the second inertial sensor 120 as having failed. When a failure occurs, the first inertial sensor 110 or the second inertial sensor 120 may output a signal value that has nothing to do with the actual vehicle state. In this case, it is difficult for the controller 150 to accurately determine which one of the first inertial sensor 110 and the second inertial sensor 120 has failed.

[0108] Taking this into account, when it is determined that the failure situation has occurred, the controller 150 may generate the final attitude estimation information by mixing the first attitude estimation information and the second attitude estimation information. In a situation where it is not clear which of the first attitude estimation information and the second attitude estimation information estimates the actual vehicle state, the controller 150 maintains an accuracy level above a certain level by using the two types of information through blending.

[0109] In an embodiment of the present invention, when it is determined that the failure situation has occurred, the controller 150 may generate an average value of the first attitude estimation information and the second attitude estimation information as the final attitude estimation information.

[0110] On the other hand, the first estimator 130, the second estimator 140, and the controller 150 may be integrally implemented in the form of an Electronic Control Unit (ECU) or a Micro Controller Unit (MCU). In other words, the first estimator 130, the second estimator 140, and the controller 150 correspond to the logical configuration according to the estimation process, and these configurations can basically be implemented by the memory and the processor on the ECU or the MCU.

[0111] At this time, the memory may include any one or more of semiconductor device-based storage media such as RAM, ROM, and flash memory, magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as Compact Disk Read Only Memory (CD-ROM) and Digital Video Disk (DVD), and magneto-optical media such as floptical disks. Of course, the memory may also have different hardware configurations.

[0112] In addition, the processor may be a hardware unit that performs calculations and controls within a computer. For example, the processor may include one or more arithmetic logic units (ALUs) and processing registers.

[0113] The vehicle attitude estimation device 100 according to an embodiment of the present invention has been described in detail above. Hereinafter, a vehicle attitude estimation method according to an embodiment of the present invention will be described.

[0114] Figure 4 It is a flowchart of a vehicle attitude estimation method according to an embodiment of the present invention.

[0115] The vehicle attitude estimation method S100 according to an embodiment of the present invention is used to estimate the attitude of a vehicle. Among them, the vehicle attitude estimation information may include any one or more of the roll angle, pitch angle, and yaw angle of the vehicle. In addition, the vehicle attitude estimation information may include Euler angles that display the direction in which the vehicle is located in a three-dimensional space.

[0116] The vehicle attitude estimation method S100 according to an embodiment of the present invention may be executed by the vehicle attitude estimation device 100 according to an embodiment of the present invention.

[0117] Refer to Figure 4 , the vehicle attitude estimation method S100 according to an embodiment of the present invention may be executed as follows.

[0118] First, the first estimator 130 generates first attitude estimation information S110 of the vehicle by using the acceleration measured by the first inertial sensor 110 and the estimation model variables obtained from the angular velocity measured by the second inertial sensor 120.

[0119] At this time, the acceleration may include accelerations of three axes, and the angular velocity may include angular velocities of three axes. The first attitude estimation information may include any one or more of the roll angle, pitch angle, and yaw angle of the vehicle. In addition, the attitude estimation information may include the Euler angles of the vehicle.

[0120] In an embodiment of the present invention, the first estimator 130 may use a Kalman filter to generate the first attitude estimation information. In this regard, the accelerations of the three axes may be converted into quaternions and input to the first estimator 130.

[0121] The generation of the first attitude estimation information using a Kalman filter is the same as the description of the vehicle attitude estimation device 100 according to an embodiment of the present invention. Therefore, detailed description will be omitted.

[0122] Next, the second estimator 140 receives the acceleration and the angular velocity to generate the second attitude estimation information S120 of the vehicle.

[0123] At this time, the second attitude estimation information may include any one or more of the roll angle, pitch angle, and yaw angle of the vehicle. In addition, the attitude estimation information may include the Euler angles of the vehicle.

[0124] In an embodiment of the present invention, the second estimator 140 may use a sliding mode observer to generate the second attitude estimation information. In this regard, the second estimator 140 may apply one or more dynamic models.

[0125] The generation of the second attitude estimation information using a sliding mode observer is the same as the description of the vehicle attitude estimation device 100 according to an embodiment of the present invention. Therefore, detailed description will be omitted.

[0126] On the other hand, the step S110 of generating the first attitude estimation information and the step S120 of generating the second attitude estimation information may be executed simultaneously. In addition, the step S120 of generating the second attitude estimation information may be executed first, and then the step S110 of generating the first attitude estimation information may be executed.

[0127] Finally, the controller 150 uses any one or more of the first attitude estimation information and the second attitude estimation information to generate the final attitude estimation information S130 of the vehicle.

[0128] The step S130 of generating the final attitude estimation information of the vehicle may be executed as follows.

[0129] First, when the controller 150 receives a fault signal from the first inertial sensor 110 from the outside and does not receive a fault signal from the second inertial sensor 120, the controller 150 may generate the final attitude estimation information identical to the second attitude estimation information.

[0130] As described above, the first attitude estimation information generated by the first estimator 130 is relatively greatly affected by the acceleration of the vehicle input to the estimation model of the Kalman filter. Therefore, when the first inertial sensor 110 fails, the accuracy of the first attitude estimation information is lower than the accuracy of the second attitude estimation information.

[0131] Considering this, when the controller 150 receives a fault signal from the first inertial sensor 110 and does not receive a fault signal from the second inertial sensor 120, the controller 150 may select the second attitude estimation information generated by the second estimator 140 as the final attitude estimation information.

[0132] In addition, when the controller 150 receives a fault signal from the second inertial sensor 120 and does not receive a fault signal from the first inertial sensor 110, the controller 150 may generate the final attitude estimation information identical to the first attitude estimation information.

[0133] As described above, the second attitude estimation information generated by the second estimator 140 is relatively greatly affected by the angular velocity of the vehicle used for the correction of the dynamic model of the sliding mode observer. Therefore, when the second inertial sensor 120 fails, the accuracy of the second attitude estimation information is lower than the accuracy of the first attitude estimation information.

[0134] Considering this, when the controller 150 receives a fault signal from the second inertial sensor 120 and does not receive a fault signal from the first inertial sensor 110, the controller 150 may select the first attitude estimation information generated by the first estimator 130 as the final attitude estimation information.

[0135] On the other hand, when there is a difference above a specified reference between the first attitude estimation information and the second attitude estimation information, the controller 150 may generate the final attitude estimation information by mixing the first attitude estimation information and the second attitude estimation information. More specifically, when mixing the first attitude estimation information and the second attitude estimation information, the controller 150 may generate an average value of the first attitude estimation information and the second attitude estimation information as the final attitude estimation information.

[0136] When there is a difference above a specified reference between the first attitude estimation information and the second attitude estimation information, it can be regarded that any one of the first inertial sensor 110 and the second inertial sensor 120 has failed. In this case, it is difficult for the controller 150 to accurately determine which one of the first inertial sensor 110 and the second inertial sensor 120 has failed.

[0137] Considering this, when there is a difference above a specified reference between the first attitude estimation information and the second attitude estimation information, the controller 150 can generate the final attitude estimation information by mixing the first attitude estimation information and the second attitude estimation information. More specifically, when it is determined that the failure situation occurs, the controller 150 can generate the average value of the first attitude estimation information and the second attitude estimation information as the final attitude estimation information.

[0138] The present invention provides a non-transitory computer-readable storage medium that stores a program for executing the vehicle attitude estimation method S100. Specifically, the present invention can provide a non-transitory computer-readable storage medium that stores a program including at least one instruction for executing the vehicle attitude estimation method S100.

[0139] At this time, the instruction can include not only machine code generated by a compiler but also high-level language code executable by a computer. In addition, the recording medium can include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as compact disk read-only memories (CD-ROMs), digital video disks (DVDs), magneto-optical media such as floptical disks, and hardware devices configured to store and execute program instructions such as ROMs, RAMs, and flash memories.

[0140] Although embodiments of the present invention have been described, the spirit of the present invention is not limited to the embodiments presented in this specification, and those skilled in the art who understand the spirit of the present invention can easily propose other embodiments by adding, changing, deleting, adding, etc. to the components within the scope of the same spirit. However, this will also be considered to fall within the spirit of the present invention.

Claims

1. A vehicle posture estimation device, the vehicle posture estimation device is used to estimate the posture of a vehicle, characterized in that: include: a first inertial sensor for measuring the acceleration of the vehicle, a second inertial sensor for measuring the angular velocity of the vehicle, a first estimator for generating first posture estimation information of the vehicle using the acceleration and an estimation model variable obtained from the angular velocity, a second estimator receiving the acceleration and the angular velocity to generate second posture estimation information of the vehicle, and The controller generates final posture estimation information of the vehicle using at least one of the first posture estimation information and the second posture estimation information.

2. The vehicle posture estimation device according to claim 1, characterized in that: The first posture estimation information, the second posture estimation information, and the final posture estimation information include Euler angles of the vehicle.

3. The vehicle posture estimation device according to claim 1, characterized in that: The first inertial sensor measures accelerations of three axes, and the second inertial sensor measures angular velocities of three axes.

4. The vehicle posture estimation device according to claim 3, characterized in that: The first estimator generates the first posture estimation information using a Kalman filter.

5. The vehicle posture estimation device according to claim 1, characterized in that: The second estimator generates the second posture estimation information using a sliding mode observer.

6. The vehicle posture estimation device according to claim 1, characterized in that: The controller generates the final attitude estimation information which is the same as the second attitude estimation information when the controller receives a failure signal of the first inertial sensor from the outside and does not receive a failure signal of the second inertial sensor.

7. The vehicle posture estimation device according to claim 1, characterized in that: The controller generates the final attitude estimation information which is the same as the first attitude estimation information when the controller receives a failure signal of the second inertial sensor from the outside and does not receive a failure signal of the first inertial sensor.

8. The vehicle posture estimation device according to claim 1, characterized in that: When the first posture estimation information and the second posture estimation information differ by a predetermined reference or more, the controller determines that a failure occurs.

9. The vehicle posture estimation device according to claim 8, characterized in that: When it is determined to be the failure situation, the controller generates the final posture estimation information by mixing the first posture estimation information and the second posture estimation information.

10. The vehicle posture estimation device according to claim 9, characterized in that: When it is determined to be the failure situation, the controller generates an average value of the first posture estimation information and the second posture estimation information as the final posture estimation information.

11. A vehicle posture estimation method, the vehicle posture estimation method is used to estimate the posture of a vehicle, characterized in that: include: a step of generating first posture estimation information of the vehicle by a first estimator using an acceleration measured by a first inertial sensor and an estimation model variable obtained from an angular velocity measured by a second inertial sensor, a step of receiving the acceleration and the angular velocity by a second estimator to generate second posture estimation information of the vehicle, and A controller generates final posture estimation information of the vehicle using at least one of the first posture estimation information and the second posture estimation information.

12. The vehicle posture estimation method according to claim 11, characterized in that: The first posture estimation information, the second posture estimation information, and the final posture estimation information include Euler angles of the vehicle.

13. The vehicle posture estimation method according to claim 11, characterized in that: The acceleration includes accelerations of three axes, and the angular velocity includes angular velocity of three axes.

14. The vehicle posture estimation method according to claim 13, characterized in that: The first estimator generates the first posture estimation information using a Kalman filter.

15. The vehicle posture estimation method according to claim 11, characterized in that: The second estimator generates the second posture estimation information using a sliding mode observer.

16. The vehicle posture estimation method according to claim 11, characterized in that: In the step of generating the final posture estimation information, when the controller receives a failure signal of the first inertial sensor from the outside and does not receive a failure signal of the second inertial sensor, the controller generates the final posture estimation information which is the same as the second posture estimation information.

17. The vehicle posture estimation method according to claim 11, characterized in that: In the step of generating the final posture estimation information, when the controller receives the failure signal of the second inertial sensor and does not receive the failure signal of the first inertial sensor, the controller generates the final posture estimation information that is the same as the first posture estimation information.

18. The vehicle posture estimation method according to claim 11, characterized in that: In the step of generating the final posture estimation information, when the first posture estimation information and the second posture estimation information differ by a predetermined reference or more, the controller generates the final posture estimation information by mixing the first posture estimation information and the second posture estimation information.

19. The vehicle posture estimation method according to claim 18, characterized in that: When the controller mixes the first posture estimation information and the second posture estimation information, an average value of the first posture estimation information and the second posture estimation information is generated as the final posture estimation information. 20 . A non-transitory computer-readable storage medium storing a program including at least one instruction for executing the vehicle posture estimation method according to claim 11 .

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