CN111806430B - Vehicle speed calculation method for automatic parking - Google Patents
Vehicle speed calculation method for automatic parking Download PDFInfo
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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
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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
- 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
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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
- 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
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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
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:
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:
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:
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:
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:
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:
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:
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:
wherein,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:
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);
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.
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:
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:
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.
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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 |
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