Vehicle positioning method, system and in-vehicle terminal based on multi-data fusion

Abstract

The present invention provides a vehicle positioning method, system and vehicle-mounted terminal based on multi-data fusion, the method includes the steps of: calculating a first vehicle body change angle according to a wheel speed pulse signal, calculating a second vehicle body change angle according to a steering wheel signal, and calculating a third vehicle body change angle by an inertial measurement unit; fusing the second vehicle body change angle and the third vehicle body change angle to obtain a fusion change angle; when the first vehicle body change angle data is updated, performing filtering correction on the fusion change angle according to the first vehicle body change angle to obtain a filtering coefficient, and combining it with the vehicle body angle at a previous time to calculate the current vehicle body angle; and calculating the current vehicle body position of the vehicle according to the vehicle body position at a previous time, the vehicle body angle at a current time and the mileage of the vehicle. The present invention can greatly improve the data reliability of the vehicle change angle by fusing multiple types of data and then filtering it, and avoid the error of different data sources, thereby effectively improving the vehicle positioning accuracy.

Description

[Technical field]

[0001] The present invention relates to the technical field of autonomous driving, and in particular to a vehicle localization method and system based on multi-data fusion. [Background technology]

[0002] With the rapid development of autonomous driving, the automatic parking function equipped in automobiles is becoming more and more popular, and while the automatic parking function brings convenience to humans, it also brings about problems of poor experience, such as tilting and stopping at the stop line. The fundamental reason is that the vehicle positioning data in the automatic parking process is not accurate, and these positioning data include the vehicle body coordinates, the vehicle's heading angle, etc.

[0003] In the prior art, when a vehicle is generally located, most of the technical solutions are obtained by only one type of data, for example, the vehicle is located by calculating the vehicle's location by using wheel speed pulse signals. Although this method of location is more accurate, the data update frequency is relatively low due to the limitation of the recognition frequency of the Hall sensor, so the reliability of the data is unstable when the vehicle is located by using this method. In addition, the vehicle's angle is calculated by using the steering wheel signal, and the vehicle is located by combining it with the vehicle's travel path. However, this method is a repetitive calculation of data, so if the calculation is performed over a long period of time, errors will accumulate, and the data will become more and more uncertain. Some vehicles are equipped with an inertial measurement unit (IMU), which can calculate the movement trajectory by accumulating acceleration and rotation angle, thereby locating the vehicle, but the problem of data jumping during the calculation process is likely to occur.

[0004] Therefore, the key point of the above-mentioned prior art is how to determine the vehicle body change angle. However, due to problems of update frequency, error accumulation and data jump, the angle calculation is inaccurate, which makes automatic parking easily fail. When using the parking function, users feel that it is better to park by themselves, which is also detrimental to the development of automatic driving.

[0005] Based on this, it is necessary to provide a localization algorithm based on multi-data fusion. Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention provides a vehicle location method based on multi-data fusion, which solves the problem of inaccurate vehicle location caused by phenomena such as data update frequency, error accumulation and data jump. [Means for solving the problem]

[0007] The present invention provides a vehicle localization method based on multi-data fusion, the method comprising: Calculating a first vehicle body change angle according to the wheel speed pulse signal, calculating a second vehicle body change angle according to the handle signal, and calculating a third vehicle body change angle according to the inertial measurement unit; data fusing the second body change angle and the third body change angle to obtain a fusion change angle; When the first vehicle body change angle data is updated, performing a filtering correction on the fusion change angle according to the first vehicle body change angle to obtain a filtering coefficient, and combining the filtering coefficient with the vehicle body angle at a previous time to calculate a current vehicle body angle; and calculating a current body position of the vehicle based on the body position at a previous time, the body angle at the current time, and the travel distance of the vehicle.

[0008] When the first vehicle body change angle data is updated, the step of performing filtering correction on the fusion change angle by the first vehicle body change angle to obtain a corrected vehicle body change angle, A sub-step of determining a data update period of the first vehicle body change angle; A sub-step of calculating and acquiring a latest first body change angle at a first time of a current update period; calculating the sum of said fusion change angles within an update period; and a sub-step of determining a filtering coefficient based on a ratio of a latest first body change angle to the sum of the fusion change angles.

[0009] Optionally, the step of calculating a current body angle in combination with a body angle at a previous time point comprises: It involves integrating the product of the fusion change angle and the filtering coefficient from the previous time to the current time using an integral function.

[0010] Furthermore, the following formula

[0011]

number

[0012] and where P is the filtering coefficient and θ t1 is the current vehicle angle, and θ t0 is the vehicle angle at the previous time, In addition, when the data of the first vehicle body change angle Δrad1 is updated, the filtering coefficient is expressed by the following formula:

[0013]

number

[0014] and where t1 is the current time, t2 is the previous data update time of the first body change angle, When the first vehicle body change angle data is not updated, the filtering coefficient is: P=1.

[0015] As an option, the step of fusing the second body change angle and the third body change angle to obtain a fused change angle includes: a sub-step of determining whether the third body change angle jumps; If the answer is YES, the second body change angle is set as a fusion change angle, and if the answer is NO, a sub-step of calculating a relative deviation between the second body change angle and the third body change angle; a sub-step of calculating weights of the second body change angle and the third body change angle based on the relative deviation; and calculating a weighted average value of the second body change angle and the third body change angle to obtain a fusion change angle.

[0016] As an option, the step of calculating the first body change angle using the wheel speed pulse signal includes: a sub-step of calculating the travel distances of the left and right wheels of the vehicle according to the wheel speed pulse signals; calculating a difference between the mileage of the left wheel and the mileage of the right wheel; obtaining the treads of the left and right wheels; and a sub-step of calculating a ratio of the mileage difference and the tread to obtain the first body change angle.

[0017] As an option, the step of calculating the second body change angle according to the steering wheel signal further comprises: A sub-step of calculating a turning radius based on a steering angle sensor of a steering wheel and front and rear treads; a sub-step of acquiring a vehicle travel path; and a sub-step of calculating and acquiring a second vehicle body change angle based on the travel distance and the turning radius.

[0018] Optionally, the step of calculating a third body displacement angle by an inertial measurement unit comprises: calculating a yaw rate by an inertial measurement unit; and a sub-step of calculating a third body change angle based on the yaw rate and the measurement unit time.

[0019] Optionally, the step of calculating the current vehicle position based on the vehicle position at a previous time, the vehicle angle at the current time, and the vehicle travel distance may further include: Calculating a travel distance of the vehicle within a unit measurement time; Calculating an average angle of the vehicle body angle at the previous time and the vehicle body angle at the current time; and calculating the current coordinates of the vehicle body by using trigonometric functions based on the vehicle body position at the previous time, the average angle value, and the traveled distance of the vehicle.

[0020] In addition, the present invention further provides a vehicle location system based on multi-data fusion, the system comprising: a wheel speed pulse calculation unit for calculating a first body change angle according to a difference in travel distance between wheels on both sides of the body; a steering angle calculation unit for calculating a second vehicle body change angle according to a steering signal; an inertial measurement unit for measuring a third body displacement angle; A fusion calculation unit for fusing the second body change angle and the third body change angle, and performing filtering correction on the fusion change angle after the fusion using the first body change angle to obtain an accurate body change angle; The fusion calculation unit is further for calculating a current body position of the vehicle based on the body change angle and the position of the body at a previous time.

[0021] The present invention further provides a vehicle terminal, the vehicle terminal comprising a memory, a processor, and a data processing program stored in the memory and executable in the processor, and when the data processing program is executed by the processor, the vehicle position determination method based on the above-mentioned multi-data fusion is realized. Effect of the Invention

[0022] In the embodiment of the present invention, a number of vehicle body change angles are calculated and obtained by using the steering wheel signal and the inertial measurement unit, and data fusion is performed, and correction is performed by using the wheel speed pulse signal, so that the reliability of the vehicle change angle data can be greatly improved, and the error of different data sources can be avoided, thereby effectively improving the vehicle location accuracy. [Brief description of the drawings]

[0023] [Figure 1] FIG. 1 is a schematic diagram of a vehicle localization model according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a flowchart of a vehicle localization method based on multi-data fusion according to an embodiment of the present invention. [Diagram 3] FIG. 3 is a flow chart of combining the second vehicle body change angle and the third vehicle body change angle according to an embodiment of the present invention. [Figure 4] FIG. 4 is a flowchart illustrating a process for filtering a fusion change angle by using a first body change angle according to an embodiment of the present invention. [Diagram 5] FIG. 5 is a flow chart of calculating the first vehicle body variation angle based on the wheel speed pulse signal according to an embodiment of the present invention. [Figure 6] FIG. 6 is a schematic diagram of a geometric model for calculating a change angle based on a pulse wheel speed according to an embodiment of the present invention. [Figure 7] FIG. 7 is a flowchart of calculating the first vehicle body tilt angle based on a handle signal according to an embodiment of the present invention. [Figure 8] FIG. 8 is a schematic diagram of a geometric model for calculating a change angle based on a steering wheel signal according to an embodiment of the present invention. [Figure 9] FIG. 9 is a flowchart of calculating the third body displacement angle by the inertial measurement unit according to an embodiment of the present invention. [Figure 10] FIG. 10 is a structural schematic diagram of a vehicle location system based on multi-data fusion according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in more detail below by means of examples with reference to the drawings. It should be understood that the specific examples described herein are only for the purpose of illustrating the present invention, and are not intended to limit the present invention.

[0025] The method disclosed in the embodiment of the present invention may be applied in fields such as automatic parking, assisted parking or automatic driving, and is mainly applied to the precise calculation of vehicle positioning information.

[0026] As shown in FIG. 1, FIG. 1 shows a schematic diagram of a vehicle localization model according to an embodiment of the present invention. When cornering along a curved road, a vehicle geometrically designed according to Ackermann steering utilizes the equal cranks of the four connecting rods to make the steering angle of the inner wheel larger than that of the outer wheel by about 2 to 4 degrees, so that the centers of the four wheel paths intersect with the Ackermann center on the extension line of the rear axle, thereby allowing the vehicle to corner smoothly. The localization information includes information such as the coordinate of the vehicle body in the world coordinate system and the vehicle body angle. It should be noted that the world coordinate system is a three-dimensional Cartesian coordinate system. In the localization process, in order to simplify the calculation, the coordinate of the vehicle's Z axis is initially set to 0, and the localization coordinate of the vehicle body is determined by the coordinates of the two axial directions of X and Y. In view of the vehicle's existing volume, the center point coordinate of the vehicle is generally regarded as the position coordinate of the vehicle.

[0027] In terms of the body angle, the magnitude of the radian that the traveling arc of the vehicle points in between two times is the body change angle, with the turning center corresponding to the traveling arc in the vehicle cornering process as the origin.

[0028] Therefore, the vehicle position identification information in the embodiment of the present invention is preferably (x, y, θ), where x represents the horizontal coordinate in the vehicle's world coordinate system, y represents the vertical coordinate in the vehicle's world coordinate system, and θ represents the vehicle body angle.

[0029] Example 1 FIG. 2 shows a process of a vehicle location method based on multi-data fusion according to the first embodiment of the present invention.

[0030] A vehicle positioning method based on multi-data fusion, the method includes the following steps 110 to 140.

[0031] Step 110: calculate a first body change angle according to the wheel speed pulse signal, calculate a second body change angle according to the handle signal, and calculate a third body change angle according to the inertial measurement unit.

[0032] In the embodiment of the present invention, the vehicle body angle change amount within a unit time period is calculated using three aspects: the wheel speed pulse signal, the steering wheel signal, and the inertial measurement unit.

[0033] Specifically, in the case of the wheel speed pulse signal, since the travel distances of the four wheels of the vehicle are different during cornering, the first body change angle can be determined according to the travel distance difference between the wheels and the trigonometric function relationship during the calculation process. In the embodiment of the present invention, the wheel speed pulse signal can be obtained by the Hall sensor and the rotor of the wheel speed sensor installed on the wheel and through calculation.

[0034] In the aspect in which the vehicle body change angle is calculated using a steering wheel signal, the steering of the vehicle is controlled by the steering wheel, and the vehicle tread, the rotation angle of the steering wheel, and the deflection angle relationship of the steered wheels are all constant. Therefore, by acquiring the rotation angle of the steering wheel, the turning radius can be determined, and by further combining this with the vehicle travel distance, the second vehicle body change angle can be determined.

[0035] In the embodiment of the inertial measurement unit, the inertial measurement unit is generally composed of a gyroscope, an accelerometer and an algorithm processing unit, and obtains its own motion trajectory by measuring acceleration and rotation angle. When calculating the body change angle, the inertial measurement unit measures the yaw angle of the vehicle at each time, and finally obtains the third body change angle by accumulating the magnitude of the total change angle within the unit time.

[0036] The vehicle body change angle value may be output in the form of an angle value or a radian value, and in the embodiment of the present invention, the value is output in radian value.

[0037] Step 120: fuse the second body change angle and the third body change angle to obtain a fused change angle.

[0038] The data fusion of the second body change angle and the third body change angle may be realized by different means, such as weighted average method, neural network algorithm, Kalman filtering evaluation, etc. In the selection process, the selection may be made according to the characteristics of the second body change angle and the third body change angle.

[0039] Specifically, since the second body change angle is prone to accumulating errors and the third body change angle is likely to jump, the second body change angle may be corrected using the third body change angle after calculation over a long period of time, or the weight, reliability, etc. of the second body change angle may be reduced. Also, the deviation between the predicted value and the actual value may be corrected by a neural network algorithm.

[0040] Therefore, as can be understood, the fusion of the second vehicle body change angle and the third vehicle body change angle aims to reduce errors due to the characteristics of each data, thereby obtaining more reliable fusion data and forming a fusion change angle.

[0041] Step 130: if the first body change angle data is updated, perform filtering correction on the fusion change angle according to the first body change angle to obtain a filtering coefficient, which is combined with the body angle at the previous time to calculate the current body angle.

[0042] In order to obtain a more accurate body change angle, the embodiment of the present invention further filters the fusion data. The first body change angle is the data update time and has the highest accuracy. However, since the data update frequency is low, the reliability of the first body change angle becomes lower and lower from the current data update to the next data update.

[0043] Therefore, the embodiment of the present invention selects this precise value at the data update time of the first body change angle to perform filtering correction on the fusion change angle, and outputs the fusion change angle as the final body change angle at the remaining times, thereby ensuring the accuracy of the calculation result and the stability of the data.

[0044] In the embodiment of the filtering means, generally, a pay-per-use filtering method, a median filtering method, an arithmetic mean filtering method, a Kalman filtering method, etc. may be used. When the first vehicle body change angle data is updated, the first vehicle body change angle data at the time is compared with the fusion change angle, and if the difference is relatively large, the filtering coefficient is increased, and the vehicle body change angle finally output in the subsequent calculation is largely corrected; when the difference is relatively small, the filtering coefficient is relatively small, and the correction range of the vehicle body change angle finally output in the subsequent calculation process is also relatively small.

[0045] In addition, since the vehicle body position specifying information at the previous time is known, the vehicle body change angle at that time may be clearly grasped, and then the vehicle body course angle at the current time may be directly calculated and obtained.

[0046] Step 140 calculates the current vehicle position based on the vehicle position at the previous time, the vehicle angle at the current time, and the vehicle travel distance.

[0047] It should be noted that the time interval between the previous time and the current time is set according to the requirements of calculation accuracy, and if more accurate data is required, the interval between the two times may be set very small, for example, 5 ms, 10 ms, 20 ms, etc.

[0048] The body position at the previous time may then be determined by its location-specific information, such as abscissa and ordinate coordinates and body angle.

[0049] In addition, since the time interval between two times is generally relatively short, the calculation model can be simplified and the route traveled within this time interval is considered to be a straight line. The travel distance may be obtained by multiple different systems of the vehicle, and the travel distance is calculated, for example, by the product of speed and time or wheel pulse signals.

[0050] Then, after calculating the vehicle body angle at the current time, the coordinate of the vehicle body in the world coordinate system at the current time can be calculated based on the trigonometric function relationship, thereby completing the update of the vehicle position identification information.

[0051] Please refer to FIG. 3, which shows a schematic diagram of a merging process between the second body change angle and the third body change angle.

[0052] In one embodiment of the present invention, a specific process of fusing the second body change angle and the third body change angle includes the following steps 210 to 240.

[0053] In step 210, it is determined whether the third body change angle jumps.

[0054] The third vehicle body change angle is from the inertial measurement unit. Since the inertial measurement unit calculates the change in the heading angle by using a gyroscope, an acceleration sensor and the like, it is obvious that the data is prone to jump in the process of the sensor acquiring data. However, in the automatic parking scenario, since the vehicle speed is relatively slow, the vehicle body angle is unlikely to change significantly within a short period of time, and if the data jumps, the acquired result will generally be inaccurate.

[0055] Therefore, if the third body change angle jumps, for example suddenly becomes very large or very small, it needs to be verified.

[0056] The verification method may be to verify the second body change angle, and for example, if the difference between the third body change angle and the second body change angle becomes large, it may be considered that the third body change angle jumps.

[0057] In some preferred embodiments, the verification may be performed according to the following formula: |Δrad2-Δrad3|>110%*(Δrad2-Δrad3) / 2 Here, Δrad2 is the second vehicle body change angle, and Δrad3 is the third vehicle body change angle.

[0058] If the third body change angle jumps, the data needs to be discarded. If the third body change angle does not jump, the data can be merged.

[0059] In step 220, if YES, the second body change angle is set as the fusion change angle, and if NO, the relative deviation between the second body change angle and the third body change angle is calculated.

[0060] When the third body change angle jumps, if the data is fused, the result will have a larger error, so the second body change angle is directly output as the fused change angle.

[0061] And, when the third body change angle does not jump, calculate the relative deviation between the second body change angle and the third body change angle, that is, = dis2=|Δrad2-(Δrad2-Δrad3) / 2| dis3=|Δrad2-(Δrad2-Δrad3) / 2|, Here, dis2 is the relative deviation of the second vehicle body change angle, and dis3 is the relative deviation of the third vehicle body change angle.

[0062] Step 230 calculates weights for the second and third body change angles based on the relative deviations.

[0063] The two relative deviation values ​​are used to perform normalization to obtain two weights for the second and third body change angles, which are related to their reliability.

[0064] The specific calculation method is as follows: factor2=dis2 / (dis2+dis3) factor3=dis3 / (dis2+dis3), Here, factor2 is the weight of the second body change angle, and factor3 is the weight of the third body change angle.

[0065] Step 240: obtain a fusion change angle by calculating a weighted average value of the second body change angle and the third body change angle.

[0066] In this embodiment, the data calculated by fusion is relatively reliable, and the weight is relatively large, and the influence on the fusion data is also relatively large, so that a more accurate fusion change angle can be obtained by using the weighted average method. The specific calculation method is as follows: Δθ=(Δrad2*factor2)+(Δrad3*factor3), where Δθ is the fusion transition angle.

[0067] Referring to FIG. 4, FIG. 4 provides a process of filtering a fusion change angle using a first body change angle.

[0068] In a preferred embodiment of the present invention, a specific method for filtering the fusion change angle by using the first body change angle is as follows.

[0069] It should be noted that since the first vehicle body change angle is from the wheel pulse signal, when the vehicle speed is relatively slow, the pulse signal update speed is relatively slow, so only when the first vehicle body change angle data is updated, the data is most accurate and may be used for filtering. At this time, the step of performing filtering correction on the fusion change angle by the first vehicle body change angle to obtain the corrected vehicle body change angle includes the following sub-steps 310 to 340.

[0070] In step 310, the data update period for the first vehicle body angle is determined.

[0071] In an automatic parking scenario, the vehicle's running speed may be considered to be relatively slow and generally move at a constant speed, and therefore the period of the wheel speed pulse signal may be considered to be unchanged. The time interval between the peaks of two pulse signals may be determined as one update period, and as can be understood, the time interval of the same number of multiple pulse signals may be one data update period. Also, when the vehicle speed is obviously not constant, the update period may be determined as one change value, and the time interval between adjacent peaks of the pulse signal is one period.

[0072] Step 320: At the first time in the current update period, the latest first vehicle body change angle is calculated and obtained.

[0073] At this time, the travel distance of each wheel is very accurate, so the first body change angle calculated at this time is very accurate.

[0074] Step 330 calculates the sum of the fusion change angles within the update period.

[0075] Because the time interval between update periods is generally relatively large, the data update frequency of the second vehicle body change angle and the third vehicle body change angle is not synchronized with the update frequency of the first vehicle body change angle, and in many cases, the first vehicle body change angle is updated once after the second vehicle body change angle and the third vehicle body change angle are updated multiple times.

[0076] In order to synchronize the data on a timeline, it is necessary to obtain the fusion change angle within the update period by summing the multiple fusion change angles calculated and obtained within the update period of the first body change angle.

[0077] Step 340: Determine a filtering coefficient based on the ratio of the latest first body change angle to the sum of the fusion change angles.

[0078] The filtering coefficient needs to take into consideration two situations: the update time of the first vehicle body change angle data and a time other than the update time of the first vehicle body change angle data.

[0079] It may be considered that there is no need to filter the fusion change angle within the time when the first body change angle is not updated, and therefore the filtering coefficient P is determined as a constant, typically P=1.

[0080] The specific calculation method for calculating the first vehicle body change angle at the data update time is as follows:

[0081]

number

[0082] and Here, t1 is the current time, t2 is the previous data update time of the first vehicle body variation angle, and Δrad1 is the first vehicle body variation angle.

[0083] The correction coefficients at each time can be obtained by the above method, and before the fusion change angle is output, they are all corrected by the same calculation method.

[0084] Furthermore, in the embodiment of the present invention, when calculating the vehicle body angle at the current time, the calculation is performed in combination with the vehicle body angle at the previous time.

[0085] Specifically, an integral function is used to integrate the product of the fusion change angle from the previous time to the current time and the filtering coefficient. The specific calculation method is as follows:

[0086]

number

[0087] and where P is the filtering coefficient and θ t1 is the current vehicle angle, and θ t0 is the vehicle angle at the previous time.

[0088] In this way, the accurate vehicle body angle at the current time can be obtained by the above calculation method.

[0089] 5 and 6, Fig. 5 shows a process of calculating a first vehicle body change angle according to a wheel speed pulse signal, and Fig. 6 shows a geometric model of calculating a change angle according to a pulse wheel speed.

[0090] Specifically, the process includes the following steps 410 to 440.

[0091] Step 410 calculates the travel distances of the left and right wheels of the vehicle according to the wheel speed pulse signals.

[0092] Taking Fig. 6 as an example, Fig. 6 shows a schematic geometric model of the process of a vehicle turning right. Therefore, L1 is the travel distance of the left wheel, L2 is the travel distance of the right wheel, R1 is the tread, and R2 is the distance from the turning center to the right wheel.

[0093] As can be seen from the geometric model, the distance traveled by the left wheel is: L1=Δrad1*(R1+R2), The mileage of the right wheel is L2=Δrad1*R2.

[0094] The travel distances of the left and right wheels can be obtained by direct calculation according to the wheel speed pulse signals, and are therefore known in the above formula.

[0095] Step 420 calculates the difference in mileage between the left wheel mileage and the right wheel mileage.

[0096] That is, L1-L2=Δrad1*R1.

[0097] Step 430 obtains the treads of the left and right wheels.

[0098] In the formula of step 420, the difference in mileage between the left wheel and the right wheel may be directly calculated and obtained, and the tread R1 is known and may be directly obtained, and then the change of the first body may be obtained through further calculation.

[0099] Step 440: Calculate the ratio of the mileage difference to the tread to obtain the first body change angle.

[0100] The specific calculation method is as follows:

[0101]

number

[0102] and This makes it possible to obtain the first vehicle body variation angle.

[0103] 7 and 8, Fig. 7 shows a schematic process of calculating a first vehicle body change angle according to a handle signal, and Fig. 8 shows a schematic geometric model of calculating a change angle according to a handle signal.

[0104] The step of calculating the second vehicle body change angle based on the handle signal includes the following sub-steps 510 to 530.

[0105] In step 510, the turning radius is calculated based on the steering wheel steering angle sensor and the front and rear treads.

[0106] First, the steering ratio may be obtained from the positioning of an actual vehicle, and thus the wheel angle θ1 fitting to the steering angle sensor SAS (Steering Angle Sensor) and the steering ratio of the vehicle can be obtained, that is, θ1=SAS angle / ratio, However, SAS angle is the steering angle, and ratio is the steering ratio.

[0107] Next, in FIG. 8, S is the front and rear tread of the vehicle, which is a constant and may be obtained based on the vehicle body parameters, and the turning radius can be obtained using trigonometric functions (the following equation).

[0108]

number

[0109] Step 520 acquires the vehicle travel path.

[0110] The vehicle travel distance L may be acquired by a plurality of methods, for example, a wheel speed pulse signal, a vehicle speed signal, a highly accurate positioning signal, etc. In this embodiment, detailed description is omitted.

[0111] In step 530, a second vehicle body change angle is calculated according to the travel distance and the turning radius.

[0112] It may be calculated using the radian formula. The specific calculation method is as follows: Δrad2=L / R, This makes it possible to obtain the second vehicle body variation angle.

[0113] Please refer to FIG. 9, which illustrates a process of calculating the third body displacement angle by the inertial measurement unit.

[0114] The step of calculating the third body change angle by the inertial measurement unit includes the following sub-step 610 and sub-step 620.

[0115] Step 610 calculates yaw rate by the inertial measurement unit.

[0116] Yaw rate may be understood as the offset range of the vehicle heading angle in a unit time, and it is explained that the larger the offset range, the larger the change range of the body angle, and conversely, the smaller the change range of the body angle.

[0117] Step 620 calculates a third body change angle based on the yaw rate and the measurement unit time.

[0118] Since the sampling rate of the inertial measurement unit is relatively high, the yaw width accumulation amount can be calculated based on the measurement period in the calculation process.

[0119] For example, within the sampling period of the inertial measurement unit, the yaw width is 0.1°, but the change period of the body angle calculated by the system is 10 times the sampling period of the inertial measurement unit, so within the period in which the system calculates the change in the body angle, it is calculated that the change in the body angle is 0.1°*10=1°.

[0120] This makes it possible to obtain the third vehicle body change angle.

[0121] Referring to FIG. 1, according to the model in FIG. 1, in some invention embodiments, the process of calculating the current body position of the vehicle based on the body position at the previous time, the body angle at the current time, and the mileage of the vehicle includes:

[0122] Calculate the vehicle's travel distance within a unit measurement time.

[0123] The travel distance may be calculated based on the wheel speed pulse signal, and the travel distance of the left and right wheels of the vehicle can be clearly known, and thus the travel distance may be obtained by calculating the average value thereof, that is,

[0124]

number

[0125] and Here, L is the distance traveled by the vehicle within a unit measurement time, L1 is the distance traveled by the left wheel, and L2 is the distance traveled by the right wheel.

[0126] The average angle of the vehicle body angle at the previous time and the vehicle body angle at the current time is calculated.

[0127] Generally, within a shorter time, e.g., 20 ms, the vehicle's driving path may be regarded as approximately straight, and therefore the angle of the driving path can also be calculated; specifically, the driving direction can be obtained by averaging the vehicle body angle at two times.

[0128] The current coordinates of the vehicle are calculated using trigonometric functions based on the vehicle position at the previous time, the average angle, and the traveled distance of the vehicle.

[0129] If the vehicle's travel route is considered to be a substantially straight line and the travel direction is understood, the coordinates of the vehicle body at the current time are obtained by performing the following calculation using trigonometric functions. x=sin ((θ t0 +θ t1 ) / 2)*L y=cos ((θ t0 +θ t1 ) / 2)*L

[0130] Where θ t1 is the current vehicle angle, and θ t0 is the vehicle angle at the previous time, and L is the traveled distance of the vehicle within the unit measurement time.

[0131] Example 2 The present invention further provides a vehicle location system based on multi-data fusion, refer to FIG. 10, which shows a schematic structure of a vehicle location system based on multi-data fusion.

[0132] The vehicle localization system based on multi-data fusion 10 comprises a wheel speed pulse calculation unit 11, a steering angle calculation unit 12, an inertial measurement unit 13 and a fusion calculation unit 14.

[0133] The wheel speed pulse calculation unit 11 is for calculating a first vehicle body change angle according to the travel distance difference between the wheels on both sides of the vehicle body.

[0134] The steering angle calculation unit 12 is for calculating a second vehicle body change angle according to a steering signal.

[0135] The inertial measurement unit 13 is for measuring the third body deflection angle; and The fusion calculation unit 14 is for fusing the second body change angle and the third body change angle, and performing filtering correction on the fusion change angle after the fusion using the first body change angle to obtain an accurate body change angle; Besides, the fusion calculation unit 14 is further for calculating a current body position of the vehicle based on the body change angle and the position of the body at a previous time.

[0136] In the embodiments of the present application, the vehicle location determination system and the vehicle location method in the above embodiments belong to the same concept, and the vehicle location determination system may execute any one of the method steps of the embodiments of the vehicle location determination method, and the specific implementation process details may be referred to the embodiments of the vehicle location determination method, and may be arbitrarily combined to form optional embodiments of the present application, and detailed descriptions thereof will be omitted here.

[0137] Example 3 The present invention further provides a vehicle terminal, which includes a memory, a processor, and a data processing program that is stored in the memory and can be executed in the processor, and when the data processing program is executed by the processor, it realizes the vehicle location determination method based on multi-data fusion of the above embodiment 1.

[0138] As will be understood by those skilled in the art, all or some of the steps in the various methods of the above embodiments may be completed by instructing relevant hardware by a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, etc.

[0139] The above description is merely a preferred embodiment of the present invention, and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included within the protection scope of the present invention.

Claims

1. A vehicle localization method based on multi-data fusion, comprising: calculating a first vehicle body change angle according to the wheel speed pulse signal, calculating a second vehicle body change angle according to the handle signal, and calculating a third vehicle body change angle according to the inertial measurement unit; data fusing the second vehicle body change angle and the third vehicle body change angle to obtain a fusion change angle; When the first vehicle body change angle data is updated, performing a filtering correction on the fusion change angle according to the first vehicle body change angle to obtain a filtering coefficient, and combining the filtering coefficient with the vehicle body angle at a previous time to calculate a current vehicle body angle; and calculating a current vehicle body position of the vehicle based on the vehicle body position at a previous time, the vehicle body angle at the current time, and the vehicle travel distance.

2. When the first vehicle body change angle data is updated, the step of performing filtering correction on the fusion change angle by the first vehicle body change angle to obtain a corrected vehicle body change angle, A sub-step of determining a data update period of the first vehicle body change angle; A sub-step of calculating and acquiring a latest first vehicle body change angle at a first time in a current update period; calculating the sum of said fusion change angles within an update period; The vehicle positioning method based on multi-data fusion according to claim 1 , further comprising: a sub-step of determining a filtering coefficient based on a ratio of the latest first vehicle body change angle to the sum of the fusion change angles.

3. The step of calculating a current body angle by combining with a body angle at a previous time point, The vehicle positioning method based on multi-data fusion as claimed in claim 2, further comprising: integrating the product of the fusion change angle from a previous time to a current time and the filtering coefficient using an integral function.

4. The specific calculation method for calculating the current body angle by combining with the body angle at the previous time is as follows: [0080] and where P is the filtering coefficient and θ ｔ１ is the current vehicle angle, and θ ｔ０ is the vehicle angle at the previous time, And the first vehicle body change angle Δrad １ When the data is updated, the filtering coefficient is [0090] and where t1 is the current time, t2 is the previous data update time of the first vehicle body change angle, When the first vehicle body change angle data is not updated, the filtering coefficient is: The vehicle localization method based on multi-data fusion as claimed in claim 3, wherein P=1.

5. The step of fusing the second vehicle body change angle and the third vehicle body change angle to obtain a fusion change angle includes: a sub-step of determining whether the third body change angle jumps; If the answer is YES, the second body change angle is set as a fusion change angle, and if the answer is NO, a sub-step of calculating a relative deviation between the second body change angle and a third body change angle; a sub-step of calculating weights of the second body change angle and the third body change angle based on the relative deviation; and a sub-step of calculating a weighted average value of the second vehicle body change angle and the third vehicle body change angle to obtain a fusion change angle.

6. The step of calculating a first vehicle body change angle based on the wheel speed pulse signal includes: a sub-step of calculating the travel distances of the left and right wheels of the vehicle according to the wheel speed pulse signals; calculating a difference between the mileage of the left wheel and the mileage of the right wheel; obtaining the treads of the left and right wheels; 2. The vehicle positioning method based on multi-data fusion according to claim 1, further comprising the sub-step of: calculating a ratio of the mileage difference and the tread to obtain the first body change angle.

7. The step of calculating the second vehicle body change angle based on the handle signal includes: A sub-step of calculating a turning radius based on a steering angle sensor of a steering wheel and front and rear treads; a sub-step of acquiring a vehicle travel path; The vehicle positioning method based on multi-data fusion according to claim 1 , further comprising: a sub-step of calculating and obtaining a second vehicle body change angle according to the travel distance and the turning radius.

8. The step of calculating a third body displacement angle by an inertial measurement unit includes: calculating a yaw rate by an inertial measurement unit; and a sub-step of calculating a third vehicle body change angle based on the yaw rate and the measurement unit time.

9. The step of calculating a current vehicle position based on a vehicle position at a previous time, a vehicle angle at the current time, and a travel distance of the vehicle, Calculating a travel distance of the vehicle within a unit measurement time; Calculating an average angle of the vehicle body angle at the previous time and the vehicle body angle at the current time; 2. The vehicle position determination method based on multi-data fusion according to claim 1, further comprising: calculating a current vehicle coordinate by a trigonometric function using the vehicle position at a previous time, the average angle, and the traveled distance of the vehicle.

10. A vehicle location system based on multi-data fusion, comprising: a wheel speed pulse calculation unit for calculating a first vehicle body change angle according to a difference in travel distance between wheels on both sides of the vehicle body; a steering angle calculation unit for calculating a second vehicle body change angle according to the steering signal; an inertial measurement unit for measuring a third body displacement angle; A fusion calculation unit for fusing the second body change angle and the third body change angle, and performing filtering correction on the fusion change angle after fusion using the first body change angle to obtain an accurate body change angle; The vehicle positioning system based on multi-data fusion, wherein the fusion calculation unit is further for calculating a current body position of the vehicle according to the body change angle and the position of the body at a previous time.

11. A vehicle terminal, A vehicle terminal comprising a memory, a processor, and a data processing program stored in the memory and executable by the processor, the vehicle terminal realizing the vehicle position determination method according to claim 1 when the data processing program is executed by the processor.

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