CN114407877B - Automatic parking positioning method based on wheel speed pulse and IMU (inertial measurement Unit) - Google Patents

Automatic parking positioning method based on wheel speed pulse and IMU (inertial measurement Unit) Download PDF

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CN114407877B
CN114407877B CN202210182638.6A CN202210182638A CN114407877B CN 114407877 B CN114407877 B CN 114407877B CN 202210182638 A CN202210182638 A CN 202210182638A CN 114407877 B CN114407877 B CN 114407877B
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wheel
wheel speed
vehicle
speed pulse
imu
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CN114407877A (en
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秦明玉
于宏啸
夏天
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Beijing Liu Ma Chi Chi Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/06—Automatic manoeuvring for parking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/112—Roll movement
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B60W2050/0052—Filtering, filters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/20—Steering systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/28—Wheel speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention belongs to the technical field of automatic driving, and particularly relates to a real-time positioning method of an automatic driving parking system. An automatic parking positioning method based on wheel speed pulse and IMU comprises the following steps: s1, performing speed smooth compensation on a wheel speed pulse signal of a vehicle; s2, calculating the turning radius ratio of the inner wheel and the outer wheel; s3, further smoothing the wheel speed pulse signal after smoothing in the S1; s4, calculating the yaw angular velocity of the vehicle based on the wheel speed pulse signal after compensation and smoothing; s5, fusing the vehicle yaw velocity value obtained in the S4 and the yaw velocity value measured in real time by the IMU by using Kalman filtering; and S6, carrying out track calculation based on the fused yaw angular velocity. The invention effectively solves the problems of high time delay, high cost and insufficient course precision in the turning process of the existing automatic parking positioning method.

Description

Automatic parking positioning method based on wheel speed pulse and IMU (inertial measurement Unit)
Technical Field
The invention belongs to the technical field of automatic driving, and particularly relates to a real-time positioning method of an automatic driving parking system.
Background
At present, traffic congestion in cities is increasing day by day, and particularly, the cities are crowded in residential areas, entertainment places, tourist attractions and other places, and available parking spaces are less and less. And with the continuous improvement of living standard of people, new drivers of driving increase year by year, and due to the lack of experience, the new drivers are easy to have various accidents in the process of parking. The automatic parking system is gradually becoming the key point of the next automobile consumption demand of people. In the passenger car parking system, the real-time positioning algorithm is definitely the most important one.
In the environment with serious signal shielding, such as dense building areas, large shopping malls and the like, the GPS positioning accuracy is rapidly reduced or even the positioning cannot be carried out due to the deterioration of the signal quality. The technology commonly used in automatic parking systems in mass-produced automobiles either does not involve a fusion algorithm or does not process data accurately enough, and the following problems generally exist: 1. the system latency is too high. 2. The heading accuracy under the vehicle turning condition is insufficient. 3. The problems of high cost of positioning by adopting a visual or ultrasonic radar, insufficient precision under special working conditions and the like. Therefore, an environmentally adaptable and low-cost vehicle positioning solution is particularly important for an automatic parking system.
Disclosure of Invention
The purpose of the invention is: aiming at the defects of the existing automatic parking positioning method, the automatic parking positioning method based on wheel speed pulse and IMU is provided.
The technical scheme of the invention is as follows: an automatic parking positioning method based on wheel speed pulses and an IMU (inertial measurement Unit) comprises the following steps:
s1, aiming at wheel speed pulse signals of vehicles
Figure BDA0003521830740000015
The wheel speed pulse is collected, due to the limitation of the number of teeth, updating is unsmooth, if the pose of the vehicle body is updated by the wheel speed pulse only, the course shaking is severe, and meanwhile, the pose is also shaken seriously, so that the wheel speed pulse collected by a real vehicle needs to be processed, namely, the wheel speed pulse is subjected to smoothing processing by a speed compensation method to obtain the position/velocity ratio>
Figure BDA0003521830740000016
Figure BDA0003521830740000011
Wherein i is the number of frames,
Figure BDA0003521830740000012
the original pulse signal measured for the ith frame, <' >>
Figure BDA0003521830740000013
For a set initial pulse compensation value of width 10, <' > based on the pulse width>
Figure BDA0003521830740000014
The pulse signal difference measured for two consecutive frames;
because the turning radiuses of the left and right rear wheels are not consistent in the turning process of the vehicle, the actual increased values of the wheel speed pulses of the left and right rear wheels in the turning process of the vehicle are not consistent and have a corresponding relation with the turning radiuses, namely the ratio of the turning radiuses of the left and right wheels is actually equal to the ratio of the increased values of the wheel speed pulses, and the wheel speed pulses are further compensated by utilizing the characteristic so as to be smoother;
s2, obtaining a front wheel steering angle signal delta (i) based on the steering wheel steering angle signal and the transmission ratio of the steering system, calculating the ratio r (i) of the turning radius of the inner wheel and the turning radius of the outer wheel,
Figure BDA0003521830740000021
wherein L is A For vehicle wheel base, L W The wheel base of the rear axle of the vehicle;
s3, further smoothing the wheel speed pulse signals smoothed in the S1 based on the inner and outer wheel turning radius ratio r (i) obtained in the S2 to obtain wheel speed pulse signals
Figure BDA0003521830740000022
And &>
Figure BDA0003521830740000023
Figure BDA0003521830740000024
Figure BDA0003521830740000025
Wherein:
Figure BDA0003521830740000026
is the ith frame rotating angular speed of the right rear wheel, is greater than or equal to>
Figure BDA0003521830740000027
Rotating the angular velocity for the ith frame of the left rear wheel;
s4, calculating the yaw angular velocity of the vehicle based on the wheel speed pulse signal after compensation smoothing
Figure BDA0003521830740000028
Figure BDA0003521830740000029
Wherein r is R Is the rolling radius of the wheel, and theta is the wheel deflection angle;
s5, the vehicle yaw velocity value obtained in the S4 is subjected to
Figure BDA00035218307400000210
Yaw angular velocity value measured in real time with IMU
Figure BDA00035218307400000211
As the input of the Kalman filtering state value, the fused state value is used for outputting a new yaw angular velocity->
Figure BDA00035218307400000212
Kalman filtering involves a prediction and update process:
and (3) prediction process:
Figure BDA00035218307400000213
Figure BDA00035218307400000214
and (3) updating:
Figure BDA0003521830740000031
Figure BDA0003521830740000032
Figure BDA0003521830740000033
wherein Z (i) = [ omega ] M (i)ω IMU (i)]A is testThe quantity matrix is here [1 ]]Q is the measurement error, H is the state input matrix, here [1,1 ]]R is state input noise, and P is a covariance matrix;
s6, obtaining a new yaw velocity based on the S5
Figure BDA0003521830740000034
And carrying out dead reckoning to obtain the update of the vehicle position in the transverse direction and the longitudinal direction.
Has the advantages that: the invention utilizes various signals collected by the existing real vehicle and Kalman filtering fusion and other methods with IMU output data to calculate the pose of the vehicle body in real time, processes the vehicle body state information transmitted in real time and ensures the real-time property; only IMU (inertial measurement unit) arranged on the vehicle body and signals acquired by a real vehicle are utilized, and visual information or information of an ultrasonic radar is not utilized, so that the cost is greatly reduced; the wheel speed pulse is compensated by the ratio of the turning radius calculated by utilizing the steering wheel information, so that the accuracy of the vehicle in the turning process is ensured. The invention effectively solves the problems of high time delay, high cost and insufficient course precision in the turning process of the existing automatic parking positioning method.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
Embodiment 1, referring to fig. 1, an automatic parking position determining method based on wheel speed pulses and IMU includes the following steps:
s1, aiming at wheel speed pulse signals of vehicles
Figure BDA0003521830740000035
The wheel speed pulse is collected, due to the limitation of the number of teeth, updating is unsmooth, if the pose of the vehicle body is updated by the wheel speed pulse only, the course shaking is severe, and meanwhile, the pose is also shaken seriously, so that the wheel speed pulse collected by a real vehicle needs to be processed, namely, the wheel speed pulse is subjected to smoothing processing by a speed compensation method to obtain the position/velocity ratio>
Figure BDA00035218307400000310
Figure BDA0003521830740000036
Wherein i is the number of frames,
Figure BDA0003521830740000037
the original pulse signal measured for the ith frame, <' >>
Figure BDA0003521830740000038
For a set initial pulse compensation value of width 10>
Figure BDA0003521830740000039
The pulse signal difference measured for two consecutive frames;
because the turning radiuses of the left and right rear wheels are not consistent in the turning process of the vehicle, the actual increased values of the wheel speed pulses of the left and right rear wheels in the turning process of the vehicle are not consistent and have a corresponding relation with the turning radiuses, namely the ratio of the turning radiuses of the left and right wheels is actually equal to the ratio of the increased values of the wheel speed pulses, and the wheel speed pulses are further compensated by utilizing the characteristic so as to be smoother;
s2, obtaining a front wheel steering angle signal delta (i) based on the steering wheel steering angle signal and the transmission ratio of the steering system, calculating the ratio r (i) of the turning radius of the inner wheel and the turning radius of the outer wheel,
Figure BDA0003521830740000041
wherein L is A For vehicle wheel base, L W The wheel base of the rear axle of the vehicle;
s3, further smoothing the wheel speed pulse signals smoothed in the S1 based on the inner and outer wheel turning radius ratio r (i) obtained in the S2 to obtain wheel speed pulse signals
Figure BDA0003521830740000042
And &>
Figure BDA0003521830740000043
Figure BDA0003521830740000044
/>
Wherein:
Figure BDA0003521830740000045
is the ith frame rotation angular speed of the right rear wheel>
Figure BDA0003521830740000046
Rotating the angular velocity for the ith frame of the left rear wheel;
s4, calculating the yaw angular velocity of the vehicle based on the wheel speed pulse signals after compensation smoothing
Figure BDA0003521830740000047
Figure BDA0003521830740000048
Wherein r is R Is the rolling radius of the wheel, and theta is the wheel deflection angle;
s5, the vehicle yaw velocity value obtained in the S4 is subjected to
Figure BDA0003521830740000049
Yaw angular velocity value measured in real time with IMU
Figure BDA00035218307400000410
As the input of the Kalman filtering state value, the fused state value is used for outputting a new yaw angular velocity->
Figure BDA00035218307400000411
Kalman filtering involves a prediction and update process:
and (3) prediction process:
Figure BDA00035218307400000412
Figure BDA00035218307400000413
and (3) updating:
Figure BDA00035218307400000414
Figure BDA00035218307400000415
Figure BDA00035218307400000416
wherein Z (i) = [ omega ] M (i)ω IMU (i)]A is a measurement matrix here [1 ]]Q is the measurement error, H is the state input matrix, here [1,1 ]]R is state input noise, and P is a covariance matrix;
s6, obtaining a new yaw velocity based on the S5
Figure BDA00035218307400000417
Carrying out dead reckoning to obtain the update of the vehicle position in the transverse direction and the longitudinal direction;
Figure BDA00035218307400000418
Figure BDA0003521830740000051
wherein x '(i), y' (i) are respectively the updated values of the current real-time horizontal and vertical coordinates of the vehicle, R is the rolling radius of the wheel, and beta is the yaw angle of the vehicle body.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.

Claims (2)

1. An automatic parking positioning method based on wheel speed pulse and IMU is characterized by comprising the following steps:
s1, aiming at wheel speed pulse signals of vehicles
Figure FDA0003986117810000011
Collecting, and smoothing with speed compensation method
Figure FDA0003986117810000012
Figure FDA0003986117810000013
Wherein i is the number of frames,
Figure FDA0003986117810000014
the raw pulse signal measured for the ith frame,
Figure FDA0003986117810000015
for the initial pulse compensation value of 10 width set,
Figure FDA0003986117810000016
the pulse signal difference measured for two consecutive frames;
s2, obtaining a front wheel steering angle signal delta (i) based on the steering wheel steering angle signal and the transmission ratio of the steering system, calculating the ratio r (i) of the turning radius of the inner wheel and the turning radius of the outer wheel,
Figure FDA0003986117810000017
wherein L is A Is the vehicle wheel base, L W The wheel base of the rear axle of the vehicle;
s3, further smoothing the wheel speed pulse signals smoothed in the S1 based on the inner and outer wheel turning radius ratio r (i) obtained in the S2 to obtain wheel speed pulse signals
Figure FDA0003986117810000018
Figure FDA0003986117810000019
Wherein:
Figure FDA00039861178100000110
the i-th frame rotational angular velocity of the right rear wheel,
Figure FDA00039861178100000111
the i-th frame rotation angular velocity for the left rear wheel,
Figure FDA00039861178100000112
respectively obtaining left and right wheel speed pulse signals after speed compensation smoothing processing;
s4, calculating the yaw angular velocity of the vehicle based on the wheel speed pulse signal after compensation smoothing
Figure FDA00039861178100000113
Figure FDA00039861178100000114
Wherein r is R Is the rolling radius of the wheel, theta is the wheel deflection angle;
s5, the vehicle yaw velocity value obtained in the S4 is subjected to
Figure FDA00039861178100000115
Yaw angular velocity value measured in real time with IMU
Figure FDA00039861178100000116
As Kalman filtering state value input, obtaining new yaw velocity output after fusion
Figure FDA0003986117810000021
Kalman filtering involves a prediction and update process:
and (3) prediction process:
Figure FDA0003986117810000022
Figure FDA0003986117810000023
and (3) updating:
Figure FDA0003986117810000024
Figure FDA0003986117810000025
Figure FDA0003986117810000026
wherein Z (i) = [ omega ] M (i) ω IMU (i)]，ω M (i) And omega IMU (i) Respectively a vehicle yaw angular velocity value after Kalman filtering and an IMU yaw angular velocity value after Kalman filtering, A is a measurement matrix and is [1 ]]Q is the measurement error, H is the state input matrix, here [1,1 ]]R is state input noise; p is a covariance matrix, wherein,
Figure FDA0003986117810000027
in order to predict the covariance matrix of the process,
Figure FDA0003986117810000028
updating the covariance matrix of the process, wherein the covariance matrix and the covariance matrix are both intermediate variables of Kalman filtering;
s6, obtaining a new yaw velocity based on the S5
Figure FDA0003986117810000029
And carrying out dead reckoning to obtain the update of the vehicle position in the transverse direction and the longitudinal direction.
2. The automatic parking positioning method based on wheel speed pulse and IMU as claimed in claim 1, wherein in S6, the dead reckoning method is:
Figure FDA00039861178100000210
Figure FDA00039861178100000211
wherein x '(i), y' (i) are respectively the updated values of the current real-time horizontal and vertical coordinates of the vehicle, R is the rolling radius of the wheel, and beta is the yaw angle of the vehicle body.
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KR101940726B1 (en) * 2012-05-04 2019-01-22 현대모비스 주식회사 Smart Parking Assist System and the method for Wheel Radius Correction thereof
CN105835721B (en) * 2016-03-31 2017-10-31 电子科技大学 A kind of four-wheel wheel hub electric automobile method for controlling driving speed
EP3470947B1 (en) * 2017-10-12 2021-07-28 Volvo Car Corporation Method and system for guiding an autonomous vehicle
CN109405837B (en) * 2018-11-14 2021-06-08 蔚来(安徽)控股有限公司 Object positioning method, application and vehicle
CN109945858B (en) * 2019-03-20 2021-04-13 浙江零跑科技有限公司 Multi-sensing fusion positioning method for low-speed parking driving scene
CN110077392B (en) * 2019-03-28 2020-08-25 惠州市德赛西威汽车电子股份有限公司 Dead reckoning method of automatic parking positioning system
CN111780756A (en) * 2020-07-20 2020-10-16 北京百度网讯科技有限公司 Vehicle dead reckoning method, device, equipment and storage medium
CN113276862B (en) * 2021-06-21 2022-12-06 智新控制系统有限公司 Vehicle driving state estimation method
CN113561969B (en) * 2021-08-31 2023-07-28 岚图汽车科技有限公司 Reversing auxiliary method and device

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