Vehicle location determination 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 on automobiles is becoming more and more popular. Although automatic parking function brings convenience to people, it also brings problems of poor experience, such as tilting and stopping at the stop line. The fundamental reason is that the vehicle positioning data during the automatic parking process is not accurate, and this positioning data includes the vehicle body coordinates, vehicle heading angle, etc.

[0003] In the prior art, most vehicle location solutions generally use only one type of data, such as wheel speed pulse signals to calculate the vehicle's location. While this method is more accurate, the data update frequency is relatively low due to the Hall sensor's recognition frequency. Therefore, when using this method to locate the vehicle, the data reliability is unstable. In addition, the vehicle's angle is sometimes calculated using steering signals and combined with the vehicle's travel path to locate the vehicle. However, this method requires repeated calculations of data, and over long periods of time, errors accumulate, making the data increasingly unreliable. Some vehicles are equipped with an inertial measurement unit (IMU), which can calculate the vehicle's motion trajectory by accumulating acceleration and rotation angle, thereby locating the vehicle. However, data jumps are likely to occur during the calculation process.

[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 is likely to cause automatic parking failure. When using the parking function, users will 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 body change angle according to the wheel speed pulse signal, calculating a second body change angle according to the steering wheel signal, and calculating a third body change angle according to the inertial measurement unit; performing data fusion of 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 based on the vehicle body position at a previous time, the vehicle body angle at the current time, and the distance traveled by the vehicle.

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

[0009] Optionally, the step of calculating the current body angle in combination with the 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 body angle, and θ t0 is the body 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 vehicle body change angle and the third vehicle body change angle to obtain a fused change angle includes: a sub-step of determining whether the third vehicle 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 substep 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 vehicle body change angle and the third vehicle body change angle based on the relative deviation; and a sub-step of obtaining a fusion change angle by calculating a weighted average value of the second body change angle and the third body 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 based on the wheel speed pulse signals; calculating a difference in mileage between the left wheel and the right wheel; obtaining the treads of the left and right wheels; and a sub-step of calculating a ratio between the mileage difference and the tread to obtain the first vehicle body change angle.

[0017] Optionally, the step of calculating the second body angle according to the steering wheel signal comprises: a sub-step of calculating a turning radius using 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 with 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.

[0019] Optionally, the step of calculating the current vehicle body position based on the vehicle body position at a previous time, the vehicle body angle at the current time, and the vehicle travel distance may further include: Calculating the traveled 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 trigonometric functions using 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 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 fused change angle after 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, which includes a memory, a processor, and a data processing program stored in the memory and executable on the processor, and which, when executed by the processor, realizes the vehicle location method based on multi-data fusion. [Effects of the Invention]

[0022] In the embodiment of the present invention, a plurality of vehicle body deviation angles are calculated and obtained by using the steering wheel signal and the inertial measurement unit, and then data fusion is performed and correction is performed by using the wheel speed pulse signal, so that the reliability of the vehicle deviation angle data can be greatly improved, and the error caused by different data sources can be avoided, thereby effectively improving the accuracy of vehicle location. [Brief explanation 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. [Figure 2] FIG. 2 is a flowchart of a vehicle localization method based on multi-data fusion according to an embodiment of the present invention. [Figure 3] FIG. 3 is a flowchart illustrating a process for 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 using a first body change angle according to an embodiment of the present invention. [Figure 5] FIG. 5 is a flowchart of calculating the first vehicle body change 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 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 illustrating a process for 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 INVENTION

[0024] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in 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 embodiments of the present invention may be applied in fields such as automatic parking, assisted parking or automatic driving, and is mainly applied to the accurate calculation of vehicle positioning information.

[0026] As shown in Figure 1, a vehicle localization model according to an embodiment of the present invention is diagrammatically illustrated. When cornering a curved road, a vehicle geometrically designed for Ackermann steering utilizes the equal cranks of the four connecting rods to steer the inner wheel at a steering angle approximately 2 to 4 degrees greater than that of the outer wheel, ensuring that the centers of the four wheel paths intersect approximately with the Ackermann center on the extension of the rear axle, thereby enabling the vehicle to corner smoothly. The localization information includes information such as the coordinates 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. To simplify calculations, the coordinate of the vehicle's Z axis is initially set to 0 during the localization process, and the localization coordinates of the vehicle body are determined by the coordinates of the X and Y axes. Considering the vehicle's volume, the center point coordinate of the vehicle is generally regarded as the vehicle's position coordinate.

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

[0028] Therefore, the vehicle position identification information according to 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 the process of a vehicle localization method based on multi-data fusion according to the first embodiment of the present invention.

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

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

[0032] In the embodiment of the present invention, the amount of change in the vehicle body angle 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 vehicle body change angle can be determined in the calculation process based on the travel distance difference between each wheel and the trigonometric function relationship. In an embodiment of the present invention, the wheel speed pulse signal can be obtained by calculation and cooperation between the Hall sensor and the rotor of the wheel speed sensor installed on the wheel.

[0034] In the mode 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 relationship between the vehicle tread, the rotation angle of the steering wheel, and the deflection angle of the steered wheels are all constant. Therefore, if the rotation angle of the steering wheel is obtained, the turning radius can be determined, and if this is further combined with the vehicle travel distance, the second vehicle body change angle can be determined.

[0035] In the case of an inertial measurement unit, the inertial measurement unit generally comprises a gyroscope, an accelerometer, and an algorithm processing unit, and acquires its own movement 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, the magnitude of the total change angle within a unit time is accumulated to obtain the third body change angle.

[0036] The value of the vehicle body change angle 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: The second body change angle and the third body change angle are data-fused to obtain a fused change angle.

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

[0039] Specifically, since the second body change angle is prone to accumulating errors and the third body change angle may 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, a neural network algorithm may be used to correct the deviation between the predicted value and the actual value.

[0040] Therefore, as can be understood, the purpose of combining the second vehicle body change angle and the third vehicle body change angle is 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 angle data is updated, the fusion angle is filtered and corrected by the first body angle to obtain a filtering coefficient, which is then combined with the body angle at the previous time to calculate the current body angle.

[0042] To obtain a more accurate body angle, an embodiment of the present invention further filters the fused data. The first body angle is the most accurate at the time of data update. However, because the data update frequency is low, its reliability decreases from one data update to the next.

[0043] Therefore, the embodiment of the present invention selects this precise value at the first body change angle data update time 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 results and the stability of the data.

[0044] In the form 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 this 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; if the difference is relatively small, the filtering coefficient is relatively small, and the correction width of the vehicle body change angle finally output in the subsequent calculation process is also relatively small.

[0045] Furthermore, since the vehicle body position specifying information at the previous time is known, the vehicle body angle at the current time may be directly calculated and acquired after the vehicle body angle change at that time is clearly grasped.

[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. If more accurate data is required, the interval between the two times can 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 position-specific information, such as horizontal and vertical 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 traveled distance may be obtained from multiple different systems in the vehicle, and the traveled distance may be 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] Referring to FIG. 3, FIG. 3 schematically shows the merging process of the second vehicle body change angle and the third vehicle body change angle.

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

[0053] Step 210 determines whether the third body change angle is a jump.

[0054] The third vehicle body change angle is from an inertial measurement unit. The inertial measurement unit calculates the change in heading angle using a gyroscope, an acceleration sensor, etc., so it is obvious that the data is prone to jumps when the sensor acquires the data. However, in an automatic parking scenario, the vehicle speed is relatively slow, so the vehicle body angle is unlikely to change significantly within a short period of time. If the data jumps, the acquired results 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, it may be verified by 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 must 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, the data fusion will cause a larger error in the result, so the second body change angle is directly output as the fusion change angle.

[0061] If the third body change angle does not jump, calculate the relative deviation between the second body change angle and the third body change angle, i.e., = 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 the weights of 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, and the two weights are related to the reliability thereof.

[0064] The specific calculation method is: factor2=dis2 / (dis2+dis3) factor3=dis3 / (dis2+dis3), However, factor 2 is the weight of the second body change angle, and factor 3 is the weight of the third body change angle.

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

[0066] In this embodiment, the data calculated by the fusion is relatively reliable, and its weight is relatively large, and its influence on the fusion data is also relatively large. Therefore, 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 change angle.

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

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

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

[0070] Step 310: The data update period for the first vehicle body angle change is determined.

[0071] In an automatic parking scenario, the vehicle's traveling 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 two pulse signal peaks may be determined as one update period, and it is understood that the time interval between the same number of multiple pulse signals may be considered as one data update period. Furthermore, if the vehicle speed is obviously not constant, the update period may be determined as one changing value, and the time interval between adjacent peaks of the pulse signal may be considered as 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 cycles is generally relatively long, the data update frequency of the second and third body change angles is not synchronized with the update frequency of the first body change angle, and the first body change angle is often updated once after the second and third body change angles have been updated multiple times.

[0076] In order to synchronize the data on the 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.

[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 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 for the first vehicle body angle change, and Δrad1 is the first vehicle body angle change.

[0083] The correction coefficients at each time point can be obtained by the above method, and before outputting the fusion change angle, 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.

[0086]

number

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

[0088] In this way, the above calculation method makes it possible to obtain an accurate vehicle body angle at the current time.

[0089] 5 and 6, Fig. 5 shows a schematic diagram of a process for calculating the first vehicle body change angle based on the wheel speed pulse signal, and Fig. 6 shows a schematic diagram of a geometric model for calculating the change angle based on the pulse wheel speed.

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

[0091] Step 410 calculates the travel distance of the left and right wheels of the vehicle based on the wheel speed pulse signals.

[0092] Taking Figure 6 as an example, Figure 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 distance traveled by the right wheel is L2=Δrad1*R2.

[0094] The travel distances of the left and right wheels can be obtained by direct calculation from 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 and the right wheel.

[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 can be directly calculated and obtained, and the tread R1 is known and can be directly obtained, at which point the change in the first body can be determined by 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 diagram of a process for calculating a first vehicle body change angle based on a steering wheel signal, and Fig. 8 shows a schematic diagram of a geometric model for calculating a change angle based on a steering wheel 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 angle sensor of the steering wheel and the front and rear treads.

[0106] First, the steering ratio may be obtained from the positioning of the actual vehicle, and therefore, the wheel angle θ1 fitting can be obtained by the automobile steering wheel turning angle sensor SAS (Steering Angle Sensor) and the steering ratio of the vehicle, 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, the tread 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 distance.

[0110] The vehicle travel distance L may be acquired by a plurality of methods, such as a wheel speed pulse signal, a vehicle speed signal, or a method of highly accurate position identification, etc. Detailed description thereof will be omitted in this embodiment.

[0111] Step 530: Calculate and obtain a second vehicle body change angle according to the travel distance and turning radius.

[0112] It can be calculated using the radian formula, and 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 shows a schematic diagram of the process of calculating the third body displacement angle by the inertial measurement unit.

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

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

[0116] Yaw rate may be understood as the offset range of the vehicle's heading angle within a unit time, and it is explained that the larger the offset range, the larger the change range of the vehicle body angle, and conversely, the smaller the change range of the vehicle 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 amplitude accumulation amount can be calculated based on the measurement period in the calculation process.

[0119] For example, if the yaw width is 0.1° within the sampling period of the inertial measurement unit, but the change period of the body angle calculated by the system is 10 times the sampling period of the inertial measurement unit, then 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 embodiments of the invention, 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 can 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 can be obtained by calculating the average value thereof, that is,

[0124]

number

[0125] and Here, L is the distance traveled by the vehicle within the 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, for example, 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 using 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 known, 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] However, θ t1 is the current body angle, and θ t0 is the vehicle body angle at the previous time, and L is the vehicle travel distance 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 10 based on multi-data fusion includes 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 the first vehicle body change angle according to the difference in travel distance between the wheels on both sides of the vehicle body.

[0134] The steering angle calculation unit 12 is for calculating a second vehicle body angle according to the 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 the current body position of the vehicle based on the body change angle and the position of the body at the 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 in the embodiments of the vehicle location determination method. For details of the specific implementation process, please refer to the embodiments of the vehicle location determination method, and they can 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 stored in the memory and executable in the processor, and when the data processing program is executed by the processor, realizes the vehicle location method based on multi-data fusion according to the above-mentioned Example 1.

[0138] As will be understood by those skilled in the art, all or part 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, which may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, etc.

[0139] The above description is only 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 position identification method based on multi-data fusion that fuses a first vehicle body change angle, a second vehicle body change angle, and a third vehicle body change angle, comprising the steps of calculating the first vehicle body change angle based on a difference in travel distance between each wheel and a trigonometric function relationship determined by a wheel speed pulse signal, calculating the second vehicle body change angle based on a steering wheel rotation angle and vehicle travel distance obtained by a steering wheel signal, and calculating the third vehicle body change angle based on an acceleration and rotation angle measured by an inertial measurement unit; fusing the second vehicle body change angle and the third vehicle body change angle to obtain a fused change angle; When the first body change angle is updated, obtaining a filtering coefficient based on the first body angle and the fusion change angle, performing filtering correction on the fusion change angle using the first body change angle, and calculating a body angle at a current time by combining the body angle at a previous time; 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 traveling distance of the vehicle.

2. When the first vehicle body change angle is updated, the step of performing filtering correction on the fusion change angle using the first vehicle body change angle to obtain a corrected vehicle body change angle includes: a sub-step of determining a data update period of the first vehicle body angle; a sub-step of calculating and acquiring the latest first vehicle body change angle at a first time in the current update period; a sub-step of calculating the sum of the fusion change angles within an update period; 2. The vehicle position determination 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 fused change angles.

3. The step of calculating the current body angle by combining with the body angle at a previous time point comprises:

3. The vehicle localization method based on multi-data fusion according to claim 2, further comprising: integrating the product of the fusion change angle from the previous time to the current time and the filtering coefficient using an integral function.

4. The method for calculating the current body angle by combining it with the body angle at the previous time is as follows: [Equation 8] and where P is the filtering coefficient and θ ｔ１ is the current body angle, and θ ｔ０ is the body angle at the previous time, And the first vehicle body change angle Δrad １ When the data is updated, the filtering coefficients are [Equation 9] and where t1 is the current time, t2 is the previous data update time of the first vehicle body angle change, When the first vehicle body change angle 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 fused change angle includes: a sub-step of determining whether the third vehicle body change angle jumps; If the answer is YES, the second vehicle body change angle is set as a fusion change angle, and if the answer is NO, a substep of calculating a relative deviation between the second vehicle body change angle and the third vehicle body change angle; a sub-step of calculating weights of the second vehicle body change angle and the third vehicle 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 fused change angle.

6. The step of calculating the 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 based on the wheel speed pulse signals; calculating a difference in mileage between the left wheel and 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: a sub-step of calculating a ratio between the mileage difference and the tread to obtain the first vehicle body change angle.

7. The step of calculating the second vehicle body angle based on the steering wheel signal includes: a sub-step of calculating a turning radius using a steering angle sensor of a steering wheel and front and rear treads; a sub-step of acquiring a vehicle travel path; The vehicle position determination 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 vehicle body travel distance and the turning radius.

8. The step of calculating a third body displacement angle by an inertial measurement unit comprises: calculating a yaw rate with 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 the current vehicle body position based on the vehicle body position at a previous time, the vehicle body angle at the current time, and the vehicle travel distance includes: Calculating the traveled 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 coordinates of the current vehicle body by trigonometric functions using the vehicle body position at a previous time, the average angle, and the traveled distance of the vehicle.

10. A vehicle position identification system based on multi-data fusion that fuses a first vehicle body change angle, a second vehicle body change angle, and a third vehicle body change angle, comprising: a wheel speed pulse calculation unit for calculating the first vehicle body change angle based on a difference in travel distance between each wheel and a trigonometric function relationship using wheel speed pulse signals; a steering angle calculation unit for calculating the second vehicle body change angle based on a steering wheel rotation angle and vehicle body travel distance obtained using a steering wheel signal; and a steering angle calculation unit for calculating the third vehicle body change angle based on an acceleration and rotation angle measured by an inertial measurement unit; a fusion calculation unit for fusing the second vehicle body change angle and the third vehicle body change angle, and performing filtering correction on the fused change angle after fusion using the first vehicle body change angle to obtain an accurate vehicle 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 based on 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, wherein the vehicle terminal realizes the vehicle position determination method according to claim 1 when the data processing program is executed by the processor.

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