CN111806430B - Vehicle speed calculation method for automatic parking - Google Patents

Vehicle speed calculation method for automatic parking Download PDF

Info

Publication number
CN111806430B
CN111806430B CN202010492776.5A CN202010492776A CN111806430B CN 111806430 B CN111806430 B CN 111806430B CN 202010492776 A CN202010492776 A CN 202010492776A CN 111806430 B CN111806430 B CN 111806430B
Authority
CN
China
Prior art keywords
speed
current
vehicle
wheel
matrix
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010492776.5A
Other languages
Chinese (zh)
Other versions
CN111806430A (en
Inventor
仝乐斌
吕兵兵
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huizhou Desay SV Automotive Co Ltd
Original Assignee
Huizhou Desay SV Automotive Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huizhou Desay SV Automotive Co Ltd filed Critical Huizhou Desay SV Automotive Co Ltd
Priority to CN202010492776.5A priority Critical patent/CN111806430B/en
Publication of CN111806430A publication Critical patent/CN111806430A/en
Application granted granted Critical
Publication of CN111806430B publication Critical patent/CN111806430B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • B60W40/105—Speed

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The application relates to a vehicle speed calculation method for automatic parking, which comprises the steps of calculating the current actual vehicle speed through a Kalman filtering algorithm, and sending the current actual vehicle speed to a vehicle correction current system vehicle speed through a vehicle CAN bus; the beneficial effects are that: when one wheel slips or the rotating speed of one wheel is abnormal because of uneven road surface, the speed of the vehicle can be corrected by the method; the vehicle speed is not changed greatly. Meanwhile, the problem that the vehicle speed signal cannot be read under the low-speed working condition of the vehicle is solved.

Description

Vehicle speed calculation method for automatic parking
Technical Field
The application relates to the technical field of automobile electronics, in particular to a vehicle speed calculation method for automatic parking.
Background
The automatic parking system commonly used at present mainly comprises the following three major parts: the system comprises an identification system, a path planning system and a parking control system. Vehicle control systems can be broadly divided into speed control, gear control, and steering control. The gear control can switch the gears such as forward and backward parking according to the parking state and requirements. Steering control parking employs the steering angle control of the EPS, and the desired front wheel slip angle obtained from a planned parking curve CAN be controlled by the CAN bus in combination with the steering ratio.
The speed control is to control the vehicle to reach the expected speed by sending acceleration and deceleration signals according to the current speed and the expected speed. The vehicle speed control in automatic parking belongs to the control problem of short stroke high precision, if the current vehicle speed is not accurately estimated, the speed control can cause the speed of the vehicle to be over-adjusted, and the vehicle is directly deviated from a planned path, so that the automatic parking fails. Accurate estimation of the current vehicle speed is important.
In addition, the kinematic model of the vehicle:
Figure GDA0003146975090000011
wherein X is the X-axis coordinate of the vehicle, Y is the Y-axis coordinate of the vehicle, L is the wheelbase, delta is the front wheel deflection angle, and V is the vehicle speed.
Because the wheel base of the vehicle is a vehicle body parameter, the vehicle is fixed when leaving a factory, the front wheel deflection angle can be calculated through the angle of a steering wheel and the transmission ratio, and the vehicle speed is also calculated through a vehicle body sensor.
Therefore, it can be seen from the above formula that accurate estimation of the vehicle speed is very important for an automatic parking system to estimate the startle state of the vehicle by using the vehicle kinematics model.
The vehicle speed signals used by the parking system on the market are all vehicle speed signals directly sent by a chassis ESP through a CAN bus. The automatic parking work speed is less than 5km/h, and the speed measuring sensor is a Hall sensor; due to safety considerations and the limited resolution of the individual sensors, the vehicle speed from the chassis ESP is not accurate under low speed conditions, sometimes the vehicle speed is shown as 0 but the vehicle is not yet stationary. And the resolution of the vehicle speed is insufficient. The vehicle speed signal has a large step. When a certain wheel slips in the parking process, the wheel idles, other wheels are relatively stable, and if the idle wheel is added into the vehicle speed calculation, the result that the vehicle speed is higher than the real vehicle speed is caused at this time.
Disclosure of Invention
In order to solve the above technical problem, the present application provides a vehicle speed calculating method for automatic parking, which is applied to an automotive electronic product, and the method includes:
acquiring current vehicle data of a vehicle at the current moment;
obtaining a system speed matrix and a measuring wheel speed matrix according to the current vehicle data;
and outputting the current actual speed and correcting the current system speed in real time through a Kalman filtering algorithm according to the system speed matrix, the measured wheel speed matrix and the current vehicle data.
Optionally, the current vehicle data includes a current system vehicle speed, a current acceleration, a current wheel speed, a sampling period, a vehicle width, a wheel base, a covariance matrix, a noise covariance matrix, and a system noise
Optionally, the current vehicle data is acquired by connecting a CAN bus of the current vehicle.
Optionally, the obtaining a system vehicle speed matrix according to the current vehicle data includes:
acquiring the current system speed, the sampling period and the current acceleration,
obtaining a system speed matrix V according to the current system speed, the sampling period and the current accelerationt+1，t＝Vt+ a t. (1), wherein VtAnd the current system speed is a, the current acceleration is a, and the sampling period is t.
Optionally, obtaining a measured wheel speed matrix according to the current vehicle data includes obtaining a current system vehicle speed, a vehicle width, and a wheel base;
finding out the concentric circles where the wheels of the current vehicle are located and the turning radius of the center of the rear axle of the current vehicle according to the Ackerman steering principle;
obtaining the turning radius of the center of the rear axle and the ratio of the turning radius of each wheel to the turning radius of the center of the rear axle through the pythagorean theorem;
and obtaining a measuring wheel speed matrix according to the ratio of the turning radius of each wheel of the current vehicle to the turning radius of the center of the rear axle and the current coefficient vehicle speed.
Optionally, the measured wheel speed matrix is calculated by the following formula:
Figure GDA0003146975090000031
wherein R is0The turning radius of the center of the rear axle of the vehicle, wide is the vehicle width, LFR is the wheelbase, V0、VVFor the current system speed, VRR、VRL、VFR、VFLThe wheel speeds of the right rear wheel, the left rear wheel, the right front wheel and the left front wheel are respectively.
Optionally, the outputting the current actual vehicle speed and correcting the current system vehicle speed in real time according to the system vehicle speed matrix, the measured wheel speed matrix and the current vehicle data through a kalman filter algorithm includes:
acquiring a system vehicle speed matrix, a covariance matrix, a noise covariance matrix and system noise;
calculating a correction coefficient through the covariance matrix, the noise covariance matrix and the system noise;
and calculating the current actual vehicle speed according to the correction coefficient and the system vehicle speed matrix.
Optionally, the correction coefficient is calculated by the following formula:
Figure GDA0003146975090000041
Pt+1＝(I-Kt+1Ct+1)Pt+1，t...(4)，
wherein, Pt+1，t＝Pt+Qt.. (6) is an estimate of the covariance matrix at time t +1, Wt+1Systematic noise at time t +1, Ct+1Is a matrix of ratios of the turning radius of each wheel to the turning radius of the centre of the rear axle, QtIs a system noise covariance matrix; obtaining the correction coefficient K through simultaneous equations (3) and (4)t。
Optionally, the calculating the speed of the current vehicle according to the correction coefficient and the system vehicle speed matrix includes:
Vt+1＝Vt+1，t+Kt[Yt-Gt]...(5)
wherein, Vt+1，tIs a system speed matrix, YtFor the current wheel speed, GtMeasuring a wheel speed matrix; obtaining the current actual speed V through simultaneous formulas (5) and (1)t。
Optionally, correcting the current system vehicle speed in real time, comprising: and outputting the current actual speed to a CAN bus, and correcting the current system speed.
According to the vehicle speed calculation method for automatic parking, the current actual vehicle speed is calculated through a Kalman filtering algorithm, and the current actual vehicle speed is sent to a vehicle correction current system vehicle speed through a vehicle CAN bus; the beneficial effects are that: when one wheel slips or the rotating speed of one wheel is abnormal because of uneven road surface, the speed of the vehicle can be corrected by the method; the vehicle speed is not changed greatly. Meanwhile, the problem that the vehicle speed signal cannot be read under the low-speed working condition of the vehicle is solved.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of ackermann steering principle according to an embodiment of the present application.
Detailed Description
The following detailed description of the preferred embodiments of the present application, taken in conjunction with the accompanying drawings, will make the advantages and features of the present application more readily appreciated by those skilled in the art, and thus will more clearly define the scope of the invention.
In an embodiment shown in fig. 1, the present application provides a vehicle speed calculation method for automatic parking, which is applied to an automobile electronic product, and comprises the following steps:
100, acquiring current vehicle data of a vehicle at the current moment;
in step 100, current vehicle data is acquired by connecting a CAN bus of a current vehicle; the current vehicle data includes a current system vehicle speed, a current acceleration, a current wheel speed, a sampling period, a vehicle width, a wheel base, a covariance matrix, a noise covariance matrix, and system noise. The current system speed can be acquired through a chassis ESP; the acceleration can be obtained through a gyroscope, and the wheel speed of each wheel can be obtained through a Hall sensor.
200, acquiring a system speed matrix and a measured wheel speed matrix according to current vehicle data;
in step 200, a system vehicle speed matrix is obtained according to the current vehicle data, and the method comprises the following steps: acquiring the current system speed, the sampling period and the current acceleration, and acquiring a system speed matrix V according to the current system speed, the sampling period and the current accelerationt+1，t＝Vt+ a t. (1), wherein VtAnd the current system speed is a, the current acceleration is a, and the sampling period is t. Obtaining a measured wheel speed matrix, package, from current vehicle dataAcquiring the speed, the width and the wheelbase of a current system; finding out the concentric circles where the wheels of the current vehicle are located and the turning radius of the center of the rear axle of the current vehicle according to the Ackerman steering principle; obtaining the turning radius of the center of the rear axle and the ratio of the turning radius of each wheel to the turning radius of the center of the rear axle through the pythagorean theorem; and obtaining a measuring wheel speed matrix according to the ratio of the turning radius of each wheel of the current vehicle to the turning radius of the center of the rear axle and the current coefficient vehicle speed.
And 300, outputting the current actual speed and correcting the current system speed in real time through a Kalman filtering algorithm according to the system speed matrix, the measured wheel speed matrix and the current vehicle data.
In step 300, a system vehicle speed matrix, a covariance matrix, a noise covariance matrix, and system noise are obtained; calculating a correction coefficient through the covariance matrix, the noise covariance matrix and the system noise; and calculating the current actual vehicle speed according to the correction coefficient and the system vehicle speed matrix.
According to the vehicle speed calculation method for automatic parking, the current actual vehicle speed is calculated through a Kalman filtering algorithm, and the current actual vehicle speed is sent to a vehicle correction current system vehicle speed through a vehicle CAN bus; when one wheel slips or the rotating speed of one wheel is abnormal because of uneven road surface, the speed of the vehicle can be corrected by the method; the vehicle speed is not changed greatly. Meanwhile, the problem that the vehicle speed signal cannot be read under the low-speed working condition of the vehicle is solved.
In some embodiments, the current vehicle data includes a current system vehicle speed, a current acceleration, a current wheel speed, a sampling period, a vehicle width, a wheel base, a covariance matrix, a noise covariance matrix, and system noise. And the current vehicle data is acquired by connecting a CAN bus of the current vehicle. The CAN bus of the vehicle is connected with each device of the vehicle to obtain each data of the vehicle; the current system speed can be obtained through a chassis ESP; the acceleration can be obtained through a gyroscope, and the wheel speed of each wheel can be obtained through a Hall sensor. The sampling period, the vehicle width, the wheel base, the covariance matrix, the noise covariance matrix, the system noise and the like can be obtained through vehicle-mounted or vehicle-mounted control information and an automatic parking system. In the embodiment, the sampling period, the vehicle width, the wheel base, the covariance matrix, the noise covariance matrix and the system noise are acquired in the vehicle inertial navigation module through the CAN bus.
In some embodiments, obtaining a system vehicle speed matrix from the current vehicle data comprises:
acquiring the current system speed, the sampling period and the current acceleration, and acquiring a system speed matrix V according to the current system speed, the sampling period and the current accelerationt+1，t＝Vt+ a t. (1), wherein VtAnd the current system speed is a, the current acceleration is a, and the sampling period is t.
In some embodiments, referring to FIG. 2, a measured wheel speed matrix is obtained from current vehicle data, including
Acquiring the speed, the width and the wheelbase of a current system;
finding out the concentric circles where the wheels of the current vehicle are located and the turning radius of the center of the rear axle of the current vehicle according to the Ackerman steering principle; wherein. Ackerman steering is a steering mode of a modern automobile, when the automobile turns, the turning angles of an inner wheel and an outer wheel are different, and the turning radius of an inner side tire is smaller than that of an outer side tire. The lower graph is the ideal ackermann steering. In the embodiment, the ackermann steering principle can be realized, and the wheel speeds of the four wheels have a certain geometrical relationship because the four wheels are on the same concentric circle. Since the reference point of the vehicle body is generally the midpoint of the rear axle, assume VRRVRLVFRVFLThe wheel speeds of the right rear wheel, the left rear wheel, the right front wheel and the left front wheel are respectively, and the ratio relation between the four wheel speeds and the midpoint speed of the rear shaft is as follows:
Figure GDA0003146975090000081
obtaining the turning radius of the center of the rear axle and the ratio of the turning radius of each wheel to the turning radius of the center of the rear axle through the pythagorean theorem; wherein R isRR、RRL、RFR、RFLAre respectively the rightThe turning radius of the rear wheel, the left rear wheel, the right front wheel and the left front wheel. R0The turning radius at the midpoint of the rear axle, and V0 is the speed at the midpoint of the rear axle. And the geometrical relationship of four wheels is obtained by the Pythagorean theorem:
Figure GDA0003146975090000082
and obtaining a measuring wheel speed matrix according to the ratio of the turning radius of each wheel of the current vehicle to the turning radius of the center of the rear axle and the current coefficient vehicle speed.
To sum up, the wheel speed matrix is measured and calculated by the following formula:
Figure GDA0003146975090000091
wherein R is0The turning radius of the center of the rear axle of the vehicle, wide is the vehicle width, LFR is the wheelbase, V0、VVFor the current system speed, VRR、VRL、VFR、VFLThe wheel speeds of the right rear wheel, the left rear wheel, the right front wheel and the left front wheel are respectively. Middle VVThe vehicle speed is the rear axle midpoint speed, and therefore the coefficient is 1.
In some embodiments, outputting the current actual vehicle speed and correcting the current system vehicle speed in real time by a kalman filtering algorithm according to the system vehicle speed matrix, the measured wheel speed matrix and the current vehicle data, comprises:
acquiring a system vehicle speed matrix, a covariance matrix, a noise covariance matrix and system noise;
calculating a correction coefficient through the covariance matrix, the noise covariance matrix and the system noise;
and calculating the current actual vehicle speed according to the correction coefficient and the system vehicle speed matrix.
In some embodiments, the correction factor is calculated by the following formula:
Figure GDA0003146975090000092
Pt+1＝(I-Kt+1Ct+1)Pt+1，t...(4)，
wherein, Pt+1，t＝Pt+QtIs an estimate of the covariance matrix at time t +1, Wt+1Systematic noise at time t +1, Ct+1The ratio matrix of the turning radius of each wheel and the turning radius of the center of the rear axle is shown, and Qt is a system noise covariance matrix; obtaining the correction coefficient K through simultaneous equations (3) and (4)t。
In some embodiments, calculating the current vehicle speed based on the correction factor and the system vehicle speed matrix comprises:
Vt+1＝Vt+1，t+Kt[Yt-Gt]...(5)
Vt+1，t＝Vt+a*t...(6)
wherein, Vt+1，tIs a system speed matrix, YtFor the current wheel speed, GtMeasuring a wheel speed matrix; obtaining the current actual speed V through simultaneous formulas (5) and (6)t。
In an implementation manner of the foregoing embodiment, according to the kalman filter algorithm, the prediction step calculates the equation as:
Figure GDA0003146975090000101
Figure GDA0003146975090000102
wherein,
Figure GDA0003146975090000103
is the estimated value of the system state vector at the time t +1, Pt+1，tIs an estimate of the covariance matrix at time t +1, Qt+1Is the covariance matrix of the system noise at time t +1, the vehicle speed matrix of the system at time t +1, WtIs the system noise. Further, linearityThe correction process equation of the Kalman filtering algorithm is as follows:
Figure GDA0003146975090000104
Figure GDA0003146975090000105
Pt+1＝(I-Kt+1Ck)Pt+1，t；
substituting (1), (2), (3), (4) and (5) into the above formula can obtain:
Vt+1，t＝Vt+a*t...(1)；
Figure GDA0003146975090000111
Figure GDA0003146975090000112
Pt+1＝(I-Kt+1Ct+1)Pt+1，t..(4)
Vt+1＝Vt+1，t+Kt[Yt-Gt]...(5)
Pt+1，t＝Pt+Qt..(6)
the Kalman filtering algorithm is an algorithm for performing optimal estimation on the system state by using a linear system state equation and inputting and outputting observation data through the system. The optimal estimation can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system.
Figure GDA0003146975090000113
YtCurrent system wheel speed;
Figure GDA0003146975090000114
gt is the measured wheel speed matrix.
Through the simultaneous (1), (2), (3), (4), (5) and (6), the current actual vehicle speed V can be obtained through continuous rolling iterationt。
In some embodiments, correcting the current system vehicle speed in real-time includes: and outputting the current actual speed to a CAN bus, and correcting the current system speed. According to the vehicle speed calculation method for automatic parking, the current actual vehicle speed is calculated through a Kalman filtering algorithm, and the current actual vehicle speed is sent to a vehicle correction current system vehicle speed through a vehicle CAN bus; when one wheel slips or the rotating speed of one wheel is abnormal because of uneven road surface, the speed of the vehicle can be corrected by the method; the vehicle speed is not changed greatly. Meanwhile, the problem that the vehicle speed signal cannot be read under the low-speed working condition of the vehicle is solved.
The embodiments of the present application have been described in detail with reference to the drawings, but the present application is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present application within the knowledge of those skilled in the art.

Claims (8)

1. A vehicle speed calculation method for automatic parking is applied to automobile electronic products, and is characterized by comprising the following steps:
acquiring current vehicle data of a vehicle at the current moment;
obtaining a system speed matrix and a measuring wheel speed matrix according to the current vehicle data;
acquiring a covariance matrix, a noise covariance matrix and system noise according to the current vehicle data, and calculating a correction coefficient through a Kalman filtering algorithm according to a system vehicle speed matrix, the covariance matrix, the noise covariance matrix and the system noise;
calculating the current actual vehicle speed and correcting the current system vehicle speed in real time according to the correction coefficient, the system vehicle speed matrix and the measured wheel speed matrix;
the obtaining a system speed matrix according to the current vehicle data includes: vehicle for acquiring current systemThe speed, the sampling period and the current acceleration are calculated according to the current system speed, the sampling period and the current acceleration by the following formula: vt+1,t＝Vt+ a t … (1) obtains a system vehicle speed matrix, where VtAnd the current system speed is a, the current acceleration is a, and the sampling period is t.
2. The vehicle speed calculation method for automatic parking according to claim 1, wherein the current vehicle data includes a current system vehicle speed, a current acceleration, a current wheel speed, a sampling period, a vehicle width, a wheel base, a covariance matrix, a noise covariance matrix, and system noise.
3. The method for calculating a vehicle speed for automatic parking according to claim 1, wherein the current vehicle data is acquired through a CAN bus connected to the current vehicle.
4. The method of claim 3, wherein obtaining a measured wheel speed matrix based on the current vehicle data comprises
Acquiring the speed, the width and the wheelbase of a current system;
finding out the concentric circles where the wheels of the current vehicle are located and the turning radius of the center of the rear axle of the current vehicle according to the Ackerman steering principle;
obtaining the turning radius of the center of the rear axle and the ratio of the turning radius of each wheel to the turning radius of the center of the rear axle through the pythagorean theorem;
and obtaining a measuring wheel speed matrix according to the ratio of the turning radius of each wheel of the current vehicle to the turning radius of the center of the rear axle and the current coefficient vehicle speed.
5. The vehicle speed calculation method for automatic parking according to claim 4, wherein the measured wheel speed matrix is calculated by the following formula:
Figure FDA0003290219390000021
wherein R is0The turning radius of the center of the rear axle of the vehicle, wide is the vehicle width, LFR is the wheelbase, V0、VvFor the current system speed, VRR、VRL、VFR、VFLThe wheel speeds of the right rear wheel, the left rear wheel, the right front wheel and the left front wheel are respectively.
6. A vehicle speed calculation method for automatic parking according to claim 1, wherein the correction coefficient is calculated by the following formula:
Figure FDA0003290219390000022
Pt+1＝(I-Kt+1Ct+1)Pt+1，t...(4)，
wherein, Pt+1，t＝Pt+Qt... (6) is the estimate of the covariance matrix at time t +1, Wt+1Systematic noise at time t +1, Ct+1Is a matrix of ratios of the turning radius of each wheel to the turning radius of the centre of the rear axle, QtIs a system noise covariance matrix; obtaining the correction coefficient K through simultaneous equations (3) and (4)t。
7. The vehicle speed calculation method for automatic parking according to claim 6, wherein calculating the current actual vehicle speed based on the correction factor, the system vehicle speed matrix and the measured wheel speed matrix comprises:
Vt+1＝Vt+1，t+Kt[Yt-Gt]...(5)
wherein, Vt+1，tIs a system speed matrix, YtFor the current wheel speed, GtMeasuring a wheel speed matrix; obtaining the current actual speed V through simultaneous formulas (1) and (5)t。
8. The vehicle speed calculation method for automatic parking according to claim 1, wherein the correcting the current system vehicle speed in real time includes: and outputting the current actual speed to a CAN bus, and correcting the current system speed.
CN202010492776.5A 2020-06-03 2020-06-03 Vehicle speed calculation method for automatic parking Active CN111806430B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010492776.5A CN111806430B (en) 2020-06-03 2020-06-03 Vehicle speed calculation method for automatic parking

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010492776.5A CN111806430B (en) 2020-06-03 2020-06-03 Vehicle speed calculation method for automatic parking

Publications (2)

Publication Number Publication Date
CN111806430A CN111806430A (en) 2020-10-23
CN111806430B true CN111806430B (en) 2021-12-31

Family

ID=72847933

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010492776.5A Active CN111806430B (en) 2020-06-03 2020-06-03 Vehicle speed calculation method for automatic parking

Country Status (1)

Country Link
CN (1) CN111806430B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112277929B (en) * 2020-11-05 2022-03-01 中国第一汽车股份有限公司 Vehicle wheel slip rate control method and device, vehicle and storage medium
CN113009173B (en) * 2021-02-02 2023-10-03 惠州市德赛西威汽车电子股份有限公司 High-precision vehicle speed calculation method
CN114394113B (en) * 2022-01-19 2023-08-25 广州小鹏自动驾驶科技有限公司 Vehicle track re-planning method and device, electronic equipment and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102009654A (en) * 2010-11-12 2011-04-13 清华大学 Longitudinal speed evaluation method of full-wheel electrically-driven vehicle
CN106394561A (en) * 2015-11-10 2017-02-15 北京中科易电信息科技股份有限公司 Estimation method and device for longitudinal vehicle speed of vehicle
CN106696965A (en) * 2015-11-16 2017-05-24 恩德 High-precision motor vehicle speed measuring method and device based on single chip microcomputer
DE102017011029A1 (en) * 2017-11-29 2018-07-12 Daimler Ag Method for correcting a first speed value of the vehicle determined by means of a rotational speed of at least one moving component of a drive train and / or chassis of a vehicle
CN110920629A (en) * 2019-12-13 2020-03-27 华人运通(江苏)技术有限公司 Vehicle speed calculation method and device and computer readable storage medium

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3597499B1 (en) * 2017-06-16 2023-01-18 Hitachi Astemo, Ltd. Parking assistance device

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102009654A (en) * 2010-11-12 2011-04-13 清华大学 Longitudinal speed evaluation method of full-wheel electrically-driven vehicle
CN106394561A (en) * 2015-11-10 2017-02-15 北京中科易电信息科技股份有限公司 Estimation method and device for longitudinal vehicle speed of vehicle
CN106696965A (en) * 2015-11-16 2017-05-24 恩德 High-precision motor vehicle speed measuring method and device based on single chip microcomputer
DE102017011029A1 (en) * 2017-11-29 2018-07-12 Daimler Ag Method for correcting a first speed value of the vehicle determined by means of a rotational speed of at least one moving component of a drive train and / or chassis of a vehicle
CN110920629A (en) * 2019-12-13 2020-03-27 华人运通(江苏)技术有限公司 Vehicle speed calculation method and device and computer readable storage medium

Also Published As

Publication number Publication date
CN111806430A (en) 2020-10-23

Similar Documents

Publication Publication Date Title
CN111806430B (en) Vehicle speed calculation method for automatic parking
CN108297872B (en) Full-working-condition vehicle-mounted road gradient estimation device and method
CN102556075B (en) Vehicle operating state estimation method based on improved extended Kalman filter
CN107627900B (en) Differential torque control system and control method for double-wheel-side motor of electric vehicle
US6522956B2 (en) Method and device for estimating a transverse acceleration at an axle of a semitrailer or a trailer of a vehicle combination
CN110077392B (en) Dead reckoning method of automatic parking positioning system
Lian et al. Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information
CN108819950B (en) Vehicle speed estimation method and system of vehicle stability control system
CN105835889B (en) A kind of method of estimation of the vehicle centroid side drift angle based on Second Order Sliding Mode observer
CN105151047A (en) Automobile gravity center slip angle measuring method
CN1862232B (en) System and method for estimating side drift angle of motor vehicle
CN102582626B (en) Method for estimating heavy semitrailer status
CN110979026B (en) Distributed driving bus torque distribution method based on real-time road conditions
CN110987470B (en) Model iteration-based automobile quality online estimation method
CN105691403A (en) Road adhesion coefficient estimation method for four-wheel all-drive electric automobile
CN113247004A (en) Joint estimation method for vehicle mass and road transverse gradient
CN110341714B (en) Method for simultaneously estimating vehicle mass center slip angle and disturbance
CN113799783B (en) Road transverse gradient measuring method and system applied to vehicle
CN111006884B (en) Method for measuring wheel axle slip angle and slip stiffness based on Fourier transform
CN113887060B (en) Novel automatic parking system vehicle positioning method
US6853886B2 (en) Method of estimating quantities that represent state of vehicle
CN108394413B (en) A kind of electronic vehicle attitude and parameter correcting method of four motorized wheels and steering
CN112026749B (en) Stability control method for wire-controlled steering system
JP2000074727A (en) Apparatus for estimating mass of vehicle
CN111572552B (en) Vehicle active safety control method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant