A vehicle speed estimation method and system based on multi-sensor fusion

Abstract

The application discloses a vehicle speed estimation method and system based on multi-sensor fusion, wherein the method comprises the following steps: collecting wheel speed information, steering wheel angle information and vehicle acceleration information; mapping the wheel speed information to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information; screening the center of mass wheel speed according to a preset wheel speed diagnosis condition and integrating the screening result to obtain an effective vehicle speed; and fusing the effective vehicle speed and the vehicle acceleration information by a Kalman filtering method to obtain a current estimated vehicle speed. The application improves the accuracy of vehicle speed estimation.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of whole vehicle control, and particularly relates to a vehicle speed estimation method and system based on multi-sensor fusion. BACKGROUND

[0002] With the development of intelligent vehicle technology, the degree of refinement of vehicle control is higher and higher, and high-precision vehicle speed information is the basis for realizing vehicle motion control. How to accurately estimate the current vehicle speed to realize accurate control of vehicle operation is the main research direction in the field of whole vehicle control.

[0003] The current vehicle speed estimation method mainly takes the average of the wheel speeds of two non-driving wheels, when the wheel speed of one of the two non-driving wheels is invalid, the wheel speed of the other valid wheel is used as the vehicle speed; when the wheel speeds of the two non-driving wheels are both invalid, the average of the wheel speeds of the two driving wheels is taken; when the wheel speed of one of the two driving wheels is invalid, the wheel speed of the other valid wheel is used as the vehicle speed, but this vehicle speed calculation method is only applicable to two-wheel drive vehicles and is not applicable to four-wheel drive vehicles. Moreover, when emergency braking, the wheel speed almost drops to zero due to wheel lock, but at this time there is still vehicle speed due to inertia, so it is unreasonable to estimate the current vehicle speed only by the wheel speed of the wheel. In addition, when turning, the wheel speeds of the outer and inner wheels differ greatly, and the wheel speeds of the front and rear wheels also differ greatly, so the true vehicle speed cannot be reflected according to the wheel speed. Therefore, the existing technology has a small scope of application and cannot accurately estimate the current vehicle speed under various working conditions. SUMMARY

[0004] The application provides a vehicle speed estimation method and system based on multi-sensor fusion, which improves the accuracy of vehicle speed estimation.

[0005] The first aspect of the application provides a vehicle speed estimation method based on multi-sensor fusion, the method comprising:

[0006] collecting wheel speed information, steering wheel angle information and vehicle acceleration information;

[0007] mapping the wheel speed information to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information;

[0008] screening the center of mass wheel speed according to a preset wheel speed diagnosis condition and integrating the screening results to obtain an effective vehicle speed;

[0009] fusing the effective vehicle speed and the vehicle acceleration information by a Kalman filtering method to obtain a current estimated vehicle speed.

[0010] The scheme collects wheel speed information, steering wheel angle information and vehicle acceleration information through multiple sensors, maps the wheel speed information to a preset vehicle center of mass to obtain corresponding center of mass wheel speed, and corrects the mapping result through the steering wheel angle information, thereby reducing the error after wheel speed conversion and improving the accuracy of speed estimation. Then, the center of mass wheel speed is screened according to the wheel speed diagnosis condition, and the wheel speed that deviates greatly from the actual vehicle speed is deleted, and the wheel speed that deviates slightly from the actual vehicle speed is retained as an effective vehicle speed for estimation of the current vehicle speed. By eliminating unreasonable data, the accuracy of data prediction is further improved. Finally, the effective vehicle speed and the vehicle acceleration information are fused through the Kalman filtering method, the vehicle speed increase or decrease is determined through acceleration, and the vehicle speed is estimated based on the wheel speed, thereby effectively avoiding the interference of the wheel speed returning to zero caused by sudden braking, and obtaining a more accurate current estimated vehicle speed.

[0011] In a possible implementation method of the first aspect, the wheel speed information is mapped to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information, specifically:

[0012] The driving state of the current vehicle is determined according to the steering wheel angle information;

[0013] If the driving state is straight driving, the center of mass wheel speed is equal to the wheel speed information;

[0014] If the driving state is curve driving, the wheel speed information is converted through a preset steering angle model according to the vehicle center of mass and the steering wheel angle information to obtain the center of mass wheel speed.

[0015] The scheme first determines whether the current vehicle is turning or straight driving through the steering wheel angle information, thereby determining the speed of the current wheel speed relative to the vehicle center of mass, obtaining the center of mass wheel speed that meets the current vehicle working condition, and providing accurate data support for subsequent vehicle speed estimation.

[0016] In a possible implementation method of the first aspect, the steering angle model is specifically:

[0017] The wheel speed conversion formula of the steering angle model is:

[0018]

[0019] In the formula, V' is the center of mass wheel speed, V is the wheel speed information, L is the distance from the front axle to the rear axle of the wheel, B is the distance from the vehicle center of mass to the rear axle, C is the rear wheel track, a0 is the vehicle steering angle, δ is the steering wheel angle information, i is the steering wheel steering transmission ratio, V lf ' is the speed of the left front wheel speed mapped to the vehicle center of mass, V rfV is the speed of the right front wheel speed mapped to the vehicle mass center, V lr V is the speed of the left rear wheel speed mapped to the vehicle mass center, V rr V is the speed of the right rear wheel speed mapped to the vehicle mass center, V lf V is the speed of the right rear wheel speed mapped to the vehicle mass center, V rf V is the speed of the right rear wheel speed mapped to the vehicle mass center, V lr V is the speed of the right rear wheel speed mapped to the vehicle mass center, V rr V is the error value of the estimated wheel speed at the last time.

[0020] In a possible implementation method of the first aspect, the mass center wheel speed is screened according to a preset wheel speed diagnosis condition, and a screening result is integrated to obtain an effective vehicle speed, specifically:

[0021] The mass center wheel speed is compared with an estimated vehicle speed at the last time and a historical mass center wheel speed average value respectively to obtain the mass center wheel speed satisfying the wheel speed diagnosis condition as the screening result.

[0022] An actual total output torque and a resistance torque of a current vehicle are obtained.

[0023] If the actual total output torque is greater than the resistance torque, a minimum value is selected from the screening result as the effective vehicle speed.

[0024] If the actual total output torque is not greater than the resistance torque, a maximum value is selected from the screening result as the effective vehicle speed.

[0025] The above scheme compares the mass center wheel speed with an estimated vehicle speed at the last time and a historical mass center wheel speed average value respectively to find the mass center wheel speed deviating seriously from the actual vehicle speed. Since the unreasonable mass center wheel speed will affect the estimation of the vehicle speed, the unreasonable mass center wheel speed is deleted to obtain the screening result with little difference from the actual vehicle speed. Then, the wheel speed in the screening result most conforming to the current vehicle working condition is determined as the source of the estimated vehicle speed through comparison of the actual total output torque and the resistance torque to obtain the effective vehicle speed capable of accurately estimating the vehicle speed.

[0026] In a possible implementation method of the first aspect, the mass center wheel speed satisfying the wheel speed diagnosis condition is obtained as the screening result, specifically:

[0027] If an absolute error between a first wheel speed in the mass center wheel speed and a corresponding historical mass center wheel speed average value is greater than a first threshold value, it is considered that the first wheel speed does not satisfy the wheel speed diagnosis condition.

[0028] If an absolute error between a first wheel speed in the mass center wheel speed and a corresponding estimated vehicle speed at the last time is greater than a second threshold value, it is considered that the first wheel speed does not satisfy the wheel speed diagnosis condition.

[0029] Delete the result not satisfying the wheel speed diagnostic condition from all the center of mass wheel speeds to obtain the screening result.

[0030] In the above scheme, if the difference between the first wheel speed and the average value of the historical center of mass wheel speeds is too large, it indicates that the wheel speed of the vehicle is suddenly changed, and it is unreasonable to use the suddenly changed wheel speed to predict the current vehicle speed with inertia, and thus the suddenly changed wheel speed needs to be deleted. If the difference between the first wheel speed and the estimated vehicle speed at the previous moment is too large, it indicates that the current wheel has transition slip, and the difference between the transition slip and the actual vehicle speed is large, and thus the wheel speed cannot be used to estimate the current vehicle speed, and needs to be deleted.

[0031] In a possible implementation method of the first aspect, the method further includes:

[0032] If all the center of mass wheel speeds do not satisfy the wheel speed diagnostic condition, all the center of mass wheel speeds are taken as the screening result.

[0033] In a possible implementation method of the first aspect, the effective vehicle speed and the vehicle acceleration information are fused by a Kalman filtering method to obtain the current estimated vehicle speed, and specifically, the method includes:

[0034] The effective vehicle speed is predicted by the vehicle acceleration information to obtain a vehicle speed prediction result;

[0035] The size of a gain coefficient is adjusted according to the total number of the center of mass wheel speeds satisfying the wheel speed diagnostic condition.

[0036] The vehicle speed prediction result and the effective vehicle speed are fused by the Kalman filtering method according to the gain coefficient to obtain the current estimated vehicle speed.

[0037] In a possible implementation method of the first aspect, the current estimated vehicle speed is specifically:

[0038] V predict (k)=V predict (k-1)+a k Δt

[0039]

[0040] In the formula, V predict is the vehicle speed prediction result, a k is the vehicle acceleration at the kth sampling point, and Δt is a prediction time interval, is the current estimated vehicle speed, V predict is the vehicle speed prediction result, k is the kth sampling point, L is the gain coefficient, and V measure is the effective vehicle speed.

[0041] In a possible implementation method of the first aspect, the size of the gain coefficient is adjusted according to the total number of the center of mass wheel speeds satisfying the wheel speed diagnostic condition, and specifically, the method includes:

[0042] when the centroid wheel speed satisfying the wheel speed diagnostic condition exists, increasing the gain coefficient;

[0043] when the centroid wheel speed satisfying the wheel speed diagnostic condition does not exist, if the actual total output torque of the current vehicle is less than a third threshold value and the maximum difference of the centroid wheel speed is less than a fourth threshold value, increasing the gain coefficient; otherwise, judging the vehicle acceleration information, if the vehicle acceleration information is not zero, decreasing the gain coefficient.

[0044] The above scheme determines whether the vehicle is in an accelerating or decelerating trend according to the vehicle acceleration information, determines whether the vehicle is in a hard braking state or a stable state according to the actual total output torque, determines the number of wheel speeds that are close to the actual vehicle speed among the currently obtained centroid wheel speeds according to the total number of the centroid wheel speeds satisfying the wheel speed diagnostic condition, and determines different working conditions of the vehicle to further select a suitable gain coefficient to control the fusion of the vehicle speed prediction result and the effective vehicle speed, increase the weight of reasonable wheel speeds to reduce the error of vehicle speed estimation and improve the robustness of vehicle speed estimation.

[0045] The second aspect of the present application provides a vehicle speed estimation system based on multi-sensor fusion, the system comprising: an information acquisition module, a wheel speed mapping module, an effective speed screening module and a speed estimation module;

[0046] The information acquisition module is configured to acquire wheel speed information, steering wheel angle information and vehicle acceleration information.

[0047] The wheel speed mapping module is configured to map the wheel speed information to a preset vehicle centroid based on the steering wheel angle information to obtain a centroid wheel speed corresponding to the wheel speed information.

[0048] The effective speed screening module is configured to screen the centroid wheel speed according to a preset wheel speed diagnostic condition and integrate the screening result to obtain an effective vehicle speed.

[0049] The speed estimation module is configured to fuse the effective vehicle speed and the vehicle acceleration information by a Kalman filtering method to obtain a current estimated vehicle speed. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0051] Figure 1This is a schematic diagram of a specific process of a vehicle speed estimation method based on multi-sensor fusion provided in a certain embodiment of this application;

[0052] Figure 2 This is a schematic diagram of a turning angle model for a vehicle speed estimation method based on multi-sensor fusion provided in a certain embodiment of this application;

[0053] Figure 3 This is a structural diagram of a vehicle speed estimation system based on multi-sensor fusion provided in a certain embodiment of this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0055] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0056] First Embodiment

[0057] Vehicle speed is a crucial parameter characterizing a vehicle's motion state and is one of the fundamental parameters for vehicle kinematics and dynamics control. Determining vehicle speed using wheel speed is a common method. However, when wheel slippage or sudden braking occurs, the wheel speed differs significantly from the actual vehicle speed, making it unreasonable to estimate the current speed solely based on wheel speed. Furthermore, during cornering, the wheel speed on the inside of the curve is lower than that on the outside, and the front wheel speed is higher than the rear wheel speed. In these situations, relying solely on wheel speed is insufficient to accurately determine the current vehicle speed. Therefore, how to accurately estimate vehicle speed based on wheel speed under various operating conditions is the main research direction of this application's embodiments.

[0058] like Figure 1 As shown, Figure 1 This application provides a schematic flowchart of a vehicle speed estimation method based on multi-sensor fusion according to a certain embodiment. The vehicle speed estimation method based on multi-sensor fusion in this embodiment includes steps S1 to S4, which are described in detail below:

[0059] Step S1: Collect wheel speed information, steering wheel angle information, and vehicle acceleration information.

[0060] In the embodiment of the present application, firstly, wheel speed information, steering wheel angle information and vehicle acceleration information are collected according to wheel speed sensors, steering wheel angle sensors and acceleration sensors configured on the vehicle. The wheel speed information includes left front wheel speed, right front wheel speed, left rear wheel speed and right rear wheel speed. The vehicle acceleration information is longitudinal acceleration relative to the vehicle body.

[0061] Because sensors are configured on most vehicles, whether four-wheel drive or two-wheel drive type, the current vehicle speed can be estimated by collecting the above data, which greatly expands the application range of vehicle speed estimation.

[0062] In step S2, the wheel speed information is mapped to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information.

[0063] In the embodiment of the present application, in order to accurately estimate the current vehicle speed, the wheel speed information far from the vehicle center of mass needs to be mapped to the vehicle center of mass before calculation. The position of the vehicle center of mass determines the dynamic characteristics of the vehicle during driving, and the factors determining the position of the vehicle center of mass mainly include the vehicle body structure and design, and the load distribution of the vehicle. By mapping the wheel speed information to the vehicle center of mass, the estimated vehicle speed can be more consistent with the actual vehicle driving state, further improving the estimation accuracy.

[0064] Firstly, the driving state of the current vehicle is determined according to the steering wheel angle information. If the absolute value of the steering wheel angle exceeds a set threshold value, it is considered that the steering wheel has been excessively rotated, and at this time the vehicle is in a curve driving state; otherwise, it is considered that the vehicle is in a straight driving state.

[0065] Optionally, in the embodiment of the present application, when the absolute value of the steering wheel angle exceeds 54°, it is determined that the vehicle is in a curve driving state.

[0066] When it is determined that the vehicle is in a straight driving state, the center of mass wheel speed is equal to the wheel speed information, i.e. lf ′=V lf , rf ′=V rf , lr ′=V lr , rr ′=V rr ; wherein V lf ′ is the speed of the left front wheel speed mapped to the vehicle center of mass, V rf ′ is the speed of the right front wheel speed mapped to the vehicle center of mass, V lr ′ is the speed of the left rear wheel speed mapped to the vehicle center of mass, and V rr ′ is the speed of the right rear wheel speed mapped to the vehicle center of mass. lfV is the left front wheel speed, V rf V is the right front wheel speed, V lr V is the left rear wheel speed, V rr V is the right rear wheel speed.

[0067] When it is determined that the vehicle is in a curve driving state, the wheel speed information is mapped to the vehicle mass center through a preset corner model. Because the wheel speed after being converted through the corner model can have errors, the mapping result needs to be error corrected according to the steering wheel corner information and the error value of the estimated vehicle speed at the last moment in the model, so as to obtain the final mass center wheel speed.

[0068] Optionally, in the embodiment of the present application, the wheel speed information is mapped through an Ackermann corner model. In Figure 2 the principle of mapping through the Ackermann corner model is shown. As shown in the figure, in the coordinate system with point O as the origin, V1, V2, V3 and V4 are the left front wheel speed, the right front wheel speed, the left rear wheel speed and the right rear wheel speed, A is the distance from the vehicle mass center to the front axle, B is the distance from the vehicle mass center to the rear axle, C is the rear wheel track, R is the distance from the vehicle mass center to the origin, R1, R2, R3 and R4 are the distances from the left front wheel to the origin, the right front wheel to the origin, the left rear wheel to the origin and the right rear wheel to the origin respectively. Among them, the ratio of the wheel distance to the origin to R is equal to the ratio of the wheel speed to the wheel speed mapped to the origin.

[0069] The wheel speed conversion formula of the Ackermann corner model is:

[0070]

[0071] In the formula, V' is the mass center wheel speed, V is the wheel speed information, L is the distance from the front axle to the rear axle of the wheel, B is the distance from the vehicle mass center to the rear axle, C is the rear wheel track, α0 is the vehicle corner, V lf ' is the speed of the left front wheel speed mapped to the vehicle mass center, V rf ' is the speed of the right front wheel speed mapped to the vehicle mass center, V lr ' is the speed of the left rear wheel speed mapped to the vehicle mass center, V rr ' is the speed of the right rear wheel speed mapped to the vehicle mass center, ΔV lf , ΔV rf , ΔV lr , ΔV rr is the error value of the estimated wheel speed at the last moment.

[0072] Specifically, the distances between the wheels are provided by the whole vehicle simulation model.

[0073] The vehicle corner is specifically expressed as:

[0074]

[0075] wherein, δ is the steering wheel angle information, and i is the steering transmission ratio of the steering wheel.

[0076] In step S3, the center-of-mass wheel speed is filtered according to a preset wheel speed diagnosis condition, and the filtering result is integrated to obtain an effective vehicle speed.

[0077] In the embodiments of the present application, when a certain wheel speed deviates from the actual vehicle speed seriously, the wheel speed will affect the accuracy of the vehicle speed estimation, and thus it is necessary to remove such obviously unreasonable data from the center-of-mass wheel speed according to the preset wheel speed diagnosis condition, so that the data is not used for the subsequent vehicle speed estimation.

[0078] For example, when the vehicle is in a state of emergency braking, the wheel will be locked and the corresponding wheel speed will almost drop to zero, but there is still a vehicle speed due to inertia. If the vehicle speed is estimated by the wheel speed of the locked wheel, the estimation result will be greatly different from the actual vehicle speed, and the estimation accuracy will be seriously reduced.

[0079] According to the wheel speed diagnosis condition, the center-of-mass wheel speed is compared with the estimated vehicle speed at the previous time and the average value of the historical center-of-mass wheel speed respectively, and the center-of-mass wheel speed satisfying the wheel speed diagnosis condition is obtained as the filtering result.

[0080] The wheel speed diagnosis condition is as follows:

[0081] (1) If the absolute error between the center-of-mass wheel speed and the estimated vehicle speed at the previous time is greater than a second threshold value, it is determined that the wheel has excessive slip, i.e.:

[0082]

[0083] wherein, V' is the center-of-mass wheel speed, V' is the center-of-mass wheel speed, V is the estimated vehicle speed at the previous time, ΔV2 is the second threshold value, and k is the kth sampling point.

[0084] (2) If the absolute error between the center-of-mass wheel speed and the average value of the historical center-of-mass wheel speed is greater than a first threshold value, it is determined that the wheel speed has a sudden change, i.e.:

[0085]

[0086] wherein, m is the mth sampling point, ΔV1 is the first threshold value, and V is the average value of the historical center-of-mass wheel speed. Here, V takes the data of the first m sampling points in the historical data.

[0087] If it is determined that the wheel has excessive slip or a sudden change, it is considered that the corresponding center-of-mass wheel speed does not satisfy the wheel speed diagnosis condition and needs to be removed from the data.

[0088] After the determination of all the center wheel speeds, a screening result close to the actual vehicle speed is obtained, and then the center wheel speeds in the screening result are integrated to select the most appropriate center wheel speed as the effective vehicle speed to provide data sources for subsequent vehicle speed estimation.

[0089] If all the center wheel speeds do not meet the wheel speed diagnostic condition, all the center wheel speeds are taken as the screening result.

[0090] The actual total output torque and the resistance torque of the current vehicle are obtained, and then the actual total output torque and the resistance torque are compared, and the center wheel speed closest to the actual vehicle speed is selected as the effective vehicle speed according to the comparison result.

[0091] Specifically, if the actual total output torque is greater than the resistance torque, the minimum value is selected from the screening result as the effective vehicle speed; if the actual total output torque is not greater than the resistance torque, the maximum value is selected from the screening result as the effective vehicle speed.

[0092] The resistance torque calculation formula is:

[0093] T f =(f0+f1*V+f2*V*V)*R

[0094] In the formula, T f is the resistance torque, f0, f1 and f2 are the sliding resistance coefficients, V is the estimated vehicle speed at the last time, and R is the wheel rolling radius.

[0095] In addition, when the total number of the screening result is zero, that is, all the center wheel speeds do not meet the wheel speed diagnostic condition, all the center wheel speeds are taken as the effective vehicle speed.

[0096] In step S4, the effective vehicle speed and the vehicle acceleration information are fused by the Kalman filtering method to obtain the current estimated vehicle speed.

[0097] In the embodiments of the present application, when multiple wheel speeds are simultaneously slipping or locked, the wheel speed is not reliable, and the effective vehicle speed obtained alone may cause a large error in the estimation result, so the effective vehicle speed is first predicted by the vehicle acceleration information to obtain a vehicle speed prediction result.

[0098] Then, the size of the gain coefficient is adjusted according to the total number of the center wheel speeds meeting the wheel speed diagnostic condition.

[0099] According to the gain coefficient, the vehicle speed prediction result and the effective vehicle speed are fused by the Kalman filtering method to obtain the current estimated vehicle speed.

[0100] Specifically, the value of the gain coefficient follows the following rules:

[0101] (1) When there is no said center of mass wheel speed satisfying said wheel speed diagnostic condition, if the actual total output torque of the current vehicle is small and the difference between the maximum and minimum of said center of mass wheel speed is small, it can be considered that the vehicle is in a stable state, and the wheel speed is reliable, while the vehicle speed estimation deviates, and the gain coefficient needs to be appropriately increased to pull the estimated vehicle speed back to the normal value, thereby improving the robustness of the vehicle speed estimation.

[0102] (2) When there is no said center of mass wheel speed satisfying said wheel speed diagnostic condition, if the actual total output torque of the current vehicle is not small and the difference between the maximum and minimum of said center of mass wheel speed is not small, the vehicle acceleration information is judged; if the vehicle acceleration information is not zero, it means that the vehicle is currently in an acceleration or deceleration state, and the gain coefficient needs to be greatly reduced to reduce the error caused by the wheel speed, and when the vehicle speed is high, the gain coefficient can be reduced to 0.

[0103] (3) When there is said center of mass wheel speed satisfying said wheel speed diagnostic condition, the gain coefficient needs to be appropriately increased to increase the weight of the wheel speed, so as to reduce the influence of the acceleration error. Wherein, when there is said center of mass wheel speed satisfying said wheel speed diagnostic condition, it means that there is a center of mass wheel speed satisfying said wheel speed diagnostic condition, so the weight of the wheel speed needs to be increased to increase the influence of the reasonable center of mass wheel speed on the estimation result. In addition, the gain coefficient when the vehicle is on a slope is greater than the gain coefficient when the vehicle is on a flat ground.

[0104] By adjusting the gain coefficient in the data fusion process according to the above rules, the influence of the errors of the wheel speed and the vehicle acceleration on the vehicle speed estimation can be reduced, and finally the accurate current estimated vehicle speed is obtained for controlling the vehicle driving.

[0105] Wherein, the current estimated vehicle speed is specifically:

[0106] V predict (k)=V predict (k-1)+a k Δt

[0107]

[0108] In the formula, V predict is the vehicle speed prediction result, a k is the vehicle acceleration at the kth sampling point, and Δt is the prediction time interval. Wherein, the initial value of the vehicle speed prediction result is 0; is the current estimated vehicle speed, V predict is the vehicle speed prediction result, k is the kth sampling point, L is the gain coefficient, and V measure is the effective vehicle speed.

[0109] The embodiment of the application has the following beneficial effects:

[0110] The embodiment of the application collects wheel speed information, steering wheel angle information and vehicle acceleration information of a vehicle through multiple sensors, then maps the wheel speed information to a preset vehicle center of mass to obtain corresponding center of mass wheel speed, and corrects the mapping result through the steering wheel angle information, reduces the error after wheel speed conversion, and improves the accuracy of speed estimation; then the center of mass wheel speed is screened according to the wheel speed diagnosis condition, the wheel speed that deviates from the actual vehicle speed is deleted, and the wheel speed that does not differ much from the actual vehicle speed is retained as an effective vehicle speed for estimation of the current vehicle speed, and the accuracy of data prediction is further improved by eliminating unreasonable data. Finally, the effective vehicle speed and the vehicle acceleration information are fused through the Kalman filtering method, the vehicle speed increase or decrease is determined through acceleration, instead of estimating the vehicle speed based on the wheel speed only, the interference of wheel speed zero caused by sudden braking on the estimation is effectively avoided, and a more accurate current estimated vehicle speed is obtained.

[0111] Second embodiment

[0112] Further, in order to execute the vehicle speed estimation system based on multi-sensor fusion corresponding to the above-mentioned method embodiment, to realize the corresponding functions and technical effects, Figure 3 A structural diagram of a vehicle speed estimation system based on multi-sensor fusion is provided. For ease of illustration, only the part related to the present embodiment is shown. The vehicle speed estimation system based on multi-sensor fusion provided by the embodiment of the application comprises:

[0113] An information collection module 201 is configured to collect wheel speed information, steering wheel angle information and vehicle acceleration information.

[0114] In the embodiment of the application, the wheel speed information, the steering wheel angle information and the vehicle acceleration information are collected according to the wheel speed sensor, the steering wheel angle sensor and the acceleration sensor configured on the vehicle. The wheel speed information includes left front wheel speed, right front wheel speed, left rear wheel speed and right rear wheel speed; and the vehicle acceleration information is the longitudinal acceleration relative to the vehicle body.

[0115] Because sensors are configured on most vehicles, the current vehicle speed can be estimated through the collection of the above-mentioned data, whether the vehicle is a four-wheel drive or a two-wheel drive type, which greatly expands the application range of vehicle speed estimation.

[0116] A wheel speed mapping module 202 is configured to map the wheel speed information to a preset vehicle center of mass based on the steering wheel angle information, to obtain the center of mass wheel speed corresponding to the wheel speed information.

[0117] In the embodiment of the application, the driving state of the current vehicle is determined according to the steering wheel angle information.

[0118] If the driving state is straight driving, the center-of-mass wheel speed is equal to the wheel speed information.

[0119] If the driving state is curve driving, the wheel speed information is converted by a preset steering angle model according to the vehicle center-of-mass and the steering wheel steering angle information, to obtain the center-of-mass wheel speed.

[0120] The effective speed screening module 203 is configured to screen the center-of-mass wheel speed according to a preset wheel speed diagnostic condition and integrate the screening result to obtain an effective vehicle speed.

[0121] In the embodiment of the application, the center-of-mass wheel speed is compared with an estimated vehicle speed at a previous time and a historical average center-of-mass wheel speed respectively, and the center-of-mass wheel speed satisfying the wheel speed diagnostic condition is obtained as the screening result.

[0122] The actual total output torque and the resistance torque of the current vehicle are obtained.

[0123] If the actual total output torque is greater than the resistance torque, the minimum value is selected from the screening result as the effective vehicle speed.

[0124] If the actual total output torque is not greater than the resistance torque, the maximum value is selected from the screening result as the effective vehicle speed.

[0125] The speed estimation module 204 is configured to fuse the effective vehicle speed and the vehicle acceleration information by Kalman filtering to obtain a current estimated vehicle speed.

[0126] In the embodiment of the application, the effective vehicle speed is predicted by the vehicle acceleration information to obtain a vehicle speed prediction result.

[0127] The vehicle speed prediction result and the effective vehicle speed are fused by Kalman filtering according to a preset gain coefficient to obtain the current estimated vehicle speed, wherein the size of the gain coefficient is related to the total number of the effective vehicle speed.

[0128] In some embodiments, the wheel speed mapping module 202 further comprises:

[0129] In order to accurately estimate the vehicle speed of the current vehicle, it is necessary to map the wheel speed information away from the vehicle center-of-mass to the vehicle center-of-mass for calculation. The position of the vehicle center-of-mass determines the dynamic characteristics of the vehicle during driving, and the factors determining the position of the vehicle center-of-mass mainly include the vehicle body structure and design, and the load distribution of the vehicle. By mapping the wheel speed information to the vehicle center-of-mass, the estimated vehicle speed can be more consistent with the actual vehicle driving state, and the estimation accuracy is further improved.

[0130] First, the driving state of the vehicle is determined according to the steering wheel angle information. If the absolute value of the steering wheel angle exceeds a set threshold, it is considered that the steering wheel is excessively rotated, and the vehicle is in a curve driving state; otherwise, it is considered that the vehicle is in a straight driving state.

[0131] Optionally, in the embodiment of the present application, the vehicle is determined to be in a curve driving state when the absolute value of the steering wheel angle exceeds 54°.

[0132] When the vehicle is determined to be in a straight driving state, the center of mass wheel speed is equal to the wheel speed information, i.e. lf ′=V lf , V rf ′=V rf , V lr ′=V lr , V rr ′=V rr ; wherein V lf ′ is the speed of the left front wheel speed mapped to the center of mass of the vehicle, V rf ′ is the speed of the right front wheel speed mapped to the center of mass of the vehicle, V lr ′ is the speed of the left rear wheel speed mapped to the center of mass of the vehicle, V rr ′ is the speed of the right rear wheel speed mapped to the center of mass of the vehicle; V lf is the left front wheel speed, V rf is the right front wheel speed, V lr is the left rear wheel speed, and V rr is the right rear wheel speed.

[0133] When the vehicle is determined to be in a curve driving state, the wheel speed information is mapped to the center of mass of the vehicle through a preset steering angle model. Because there may be errors in the wheel speed converted through the steering angle model, the mapping result needs to be error corrected according to the steering wheel angle information and the error value of the estimated speed at the last time in the model, so as to obtain the final center of mass wheel speed.

[0134] Optionally, in the embodiment of the present application, the Ackerman steering angle model is used to map the wheel speed information.

[0135] The wheel speed conversion formula of the Ackerman steering angle model is:

[0136]

[0137] In the formula, V' is the center of mass wheel speed, V is the wheel speed information, L is the distance from the front axle to the rear axle of the wheel, B is the distance from the center of mass of the vehicle to the rear axle, C is the rear track, α0 is the vehicle steering angle, V lf ′ is the speed of the left front wheel speed mapped to the center of mass of the vehicle, V rf ′ is the speed of the right front wheel speed mapped to the center of mass of the vehicle, Vlr V' is the speed of the left rear wheel mapped to the vehicle's center of gravity. rr ' is the speed of the right rear wheel mapped to the vehicle's center of gravity, ΔV lf ΔV rf ΔV lr ΔV rr This represents the error value of the estimated wheel speed at the previous moment.

[0138] Specifically, the distances between the wheels are provided by the vehicle simulation model.

[0139] The vehicle turning angle is specifically expressed as follows:

[0140]

[0141] In the formula, δ represents the steering wheel angle information, and i represents the steering wheel transmission ratio.

[0142] In some embodiments, the effective speed filtering module 203 specifically comprises:

[0143] When a wheel speed deviates significantly from the actual vehicle speed, it will affect the accuracy of the vehicle speed estimation. Therefore, it is necessary to remove such obviously unreasonable data from the center-of-gravity wheel speed according to the preset wheel speed diagnosis conditions so that it is not used for subsequent vehicle speed estimation.

[0144] For example, when a vehicle is under emergency braking, the wheels will lock up and the corresponding wheel speed will drop to almost zero, but due to inertia, there will still be some vehicle speed. If the vehicle speed is estimated based on the wheel speed of the locked wheels, the estimated result will differ greatly from the actual vehicle speed, severely reducing the accuracy of the estimation.

[0145] Based on the wheel speed diagnosis criteria, the center-of-gravity wheel speed is compared with the estimated vehicle speed at the previous moment and the historical average center-of-gravity wheel speed, respectively, and the center-of-gravity wheel speed that meets the wheel speed diagnosis criteria is used as the screening result.

[0146] The wheel speed diagnostic conditions are as follows:

[0147] (3) If the absolute error between the wheel speed at the center of gravity and the estimated vehicle speed at the previous moment is greater than the second threshold, then the wheel is determined to have excessive slippage, i.e.:

[0148]

[0149] In the formula, V′ is the speed of the center of mass wheel. ΔV2 is the estimated vehicle speed at the previous moment, ΔV2 is the second threshold, and k is the kth sampling point.

[0150] (4) If the absolute error between the center-of-mass wheel speed and the historical average center-of-mass wheel speed is greater than the first threshold, then a sudden change in wheel speed is determined, i.e.:

[0151]

[0152] In the formula, m is the first sampling point, ΔV1 is the first threshold value, and V is the average value of the historical centroid wheel speed. Here, V takes the data of the first m sampling points in the historical data.

[0153] If it is determined that the wheel slips or mutates, it is considered that the corresponding centroid wheel speed does not meet the wheel speed diagnostic condition and needs to be deleted from the data.

[0154] If all the centroid wheel speeds do not meet the wheel speed diagnostic condition, all the centroid wheel speeds are taken as the screening result.

[0155] After the determination of all the centroid wheel speeds, the screening result close to the actual vehicle speed is obtained, and then the centroid wheel speed in the screening result is integrated, and the most suitable centroid wheel speed is selected as the effective vehicle speed to provide data source for subsequent vehicle speed estimation.

[0156] First, the actual total output torque and the resistance torque of the current vehicle are obtained, and then the actual total output torque and the resistance torque are compared, and the centroid wheel speed closest to the actual vehicle speed is selected as the effective vehicle speed according to the comparison result.

[0157] Specifically, if the actual total output torque is greater than the resistance torque, the minimum value is selected from the screening result as the effective vehicle speed; if the actual total output torque is not greater than the resistance torque, the maximum value is selected from the screening result as the effective vehicle speed.

[0158] The resistance torque calculation formula is:

[0159] T f =(f0+f1*V+f2*V*V)*R

[0160] In the formula, T f is the resistance torque, f0, f1, and f2 are the sliding resistance coefficients, V is the estimated vehicle speed at the last time, and R is the wheel rolling radius.

[0161] In addition, when the total number of the screening result is zero, that is, all the centroid wheel speeds do not meet the wheel speed diagnostic condition, all the centroid wheel speeds are taken as the effective vehicle speed.

[0162] In some embodiments, the speed estimation module 204 further comprises:

[0163] Considering that when multiple wheel speeds slip or lock at the same time, the wheel speed is not reliable, and only the effective vehicle speed obtained may cause a large error in the estimation result, the effective vehicle speed is first predicted by the vehicle acceleration information to obtain a vehicle speed prediction result.

[0164] Then, according to the total number of the center of mass wheel speeds satisfying the wheel speed diagnostic condition, the size of the gain coefficient is adjusted, and then according to the gain coefficient, the vehicle speed prediction result and the effective vehicle speed are fused by Kalman filtering method to obtain the current estimated vehicle speed.

[0165] Specifically, the value of the gain coefficient follows the following rules:

[0166] (1) When there is no center of mass wheel speed satisfying the wheel speed diagnostic condition, if the actual total output torque of the current vehicle is small and the difference between the maximum value and the minimum value in the center of mass wheel speed is small, it can be considered that the vehicle is in a stable state, and the wheel speed is reliable, while the vehicle speed estimation deviates, and the gain coefficient needs to be appropriately increased to pull the estimated vehicle speed back to the normal value, thereby improving the robustness of the vehicle speed estimation.

[0167] (2) When there is no center of mass wheel speed satisfying the wheel speed diagnostic condition, if the actual total output torque of the current vehicle is not small and the difference between the maximum value and the minimum value in the center of mass wheel speed is not small, the vehicle acceleration information is judged; if the vehicle acceleration information is not zero, it means that the vehicle is currently in an acceleration or deceleration state, and the gain coefficient needs to be greatly reduced to reduce the error caused by the wheel speed, and when the vehicle speed is high, the gain coefficient can be reduced to 0.

[0168] (3) When there is a center of mass wheel speed satisfying the wheel speed diagnostic condition, the gain coefficient needs to be appropriately increased to increase the weight of the wheel speed, so as to reduce the influence of the acceleration error. When there is a center of mass wheel speed satisfying the wheel speed diagnostic condition, it means that there is a center of mass wheel speed satisfying the wheel speed diagnostic condition, so the weight of the wheel speed needs to be increased to increase the influence of the reasonable center of mass wheel speed on the estimation result. In addition, the gain coefficient when the vehicle is on a slope is greater than the gain coefficient when the vehicle is on a flat ground.

[0169] By adjusting the gain coefficient in the data fusion process according to the above rules, the influence of the errors of the wheel speed and the vehicle acceleration on the vehicle speed estimation can be reduced, and finally the accurate current estimated vehicle speed is obtained for controlling the vehicle to travel.

[0170] The current estimated vehicle speed is specifically:

[0171] V predict (k)=V predict (k-1)+a k Δt

[0172]

[0173] In the formula, V predict is the vehicle speed prediction result, a k is the vehicle acceleration at the kth sampling point, and Δt is the prediction time interval. The initial value of the vehicle speed prediction result is 0. V is a current estimated vehicle speed, V predict V is a vehicle speed prediction result, k is a kth sampling point, L is the gain coefficient, V measure V is an effective vehicle speed.

[0174] The embodiments of the present application have the following beneficial effects:

[0175] The embodiments of the present application collect wheel speed information, steering wheel angle information and vehicle acceleration information of the vehicle through multiple sensors, then map the wheel speed information to a preset vehicle center of mass to obtain corresponding center of mass wheel speed, and correct the mapping result through the steering wheel angle information, thereby reducing the error after wheel speed conversion and improving the accuracy of speed estimation; then the center of mass wheel speed is screened according to the wheel speed diagnosis condition, the wheel speed seriously deviating from the actual vehicle speed is deleted, and the wheel speed with little difference from the actual vehicle speed is kept as the effective vehicle speed to estimate the current vehicle speed, thereby further improving the accuracy of data prediction by eliminating unreasonable data. Finally, the effective vehicle speed and the vehicle acceleration information are fused through the Kalman filtering method, the vehicle speed increase or decrease is determined through acceleration, instead of estimating the vehicle speed based on the wheel speed only, thereby effectively avoiding the interference of wheel speed returning to zero caused by sudden braking on the estimation, and obtaining a more accurate current estimated vehicle speed of the vehicle.

[0176] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only for specific embodiments of the present application and is not used to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for estimating vehicle speed based on multi-sensor fusion, characterized in that, The method comprises the following steps: Collecting wheel speed information, steering wheel angle information and vehicle acceleration information of a vehicle; Mapping the wheel speed information to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information; Screening the center of mass wheel speed according to a preset wheel speed diagnosis condition and integrating the screening results to obtain an effective vehicle speed, specifically: comparing the center of mass wheel speed with an estimated vehicle speed at a previous time and a historical average center of mass wheel speed respectively to obtain the center of mass wheel speed satisfying the wheel speed diagnosis condition as the screening result; obtaining an actual total output torque and a resistance torque of a current vehicle; If the actual total output torque is greater than the resistance torque, selecting a minimum value from the screening result as the effective vehicle speed; if the actual total output torque is not greater than the resistance torque, selecting a maximum value from the screening result as the effective vehicle speed; Fusing the effective vehicle speed and the vehicle acceleration information by Kalman filtering method to obtain a current estimated vehicle speed, specifically: predicting the effective vehicle speed by the vehicle acceleration information to obtain a vehicle speed prediction result; adjusting the size of a gain coefficient according to the total number of the center of mass wheel speeds satisfying the wheel speed diagnosis condition; fusing the vehicle speed prediction result and the effective vehicle speed by Kalman filtering method according to the gain coefficient to obtain the current estimated vehicle speed.

2. The multi-sensor fusion based vehicle speed estimation method according to claim 1, wherein, The method of mapping the wheel speed information to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information specifically comprises the following steps: Determining a driving state of a current vehicle according to the steering wheel angle information; If the driving state is straight driving, the center of mass wheel speed is equal to the wheel speed information; If the driving state is curve driving, converting the wheel speed information by a preset steering angle model according to the vehicle center of mass and the steering wheel angle information to obtain the center of mass wheel speed.

3. The multi-sensor fusion based vehicle speed estimation method according to claim 2, wherein, The steering angle model specifically comprises: The wheel speed conversion formula of the steering angle model is: where V' is the mass center wheel speed, V is the wheel speed information, L is the distance from the front axle to the rear axle of the wheel, B is the distance from the mass center of the vehicle to the rear axle, C is the rear track width, is the vehicle turn angle, is the steering wheel turn angle information, i is the steering wheel steering ratio, is the left front wheel speed mapped to the vehicle mass center speed, is the right front wheel speed mapped to the vehicle mass center speed, is the left rear wheel speed mapped to the vehicle mass center speed, is the right rear wheel speed mapped to the vehicle mass center speed, , , , is the error value of the estimated wheel speed at the previous time. 4.The multi-sensor fusion based vehicle speed estimation method according to claim 1, wherein, The method of obtaining the center of mass wheel speed satisfying the wheel speed diagnosis condition as the screening result specifically comprises the following steps: If an absolute error between a first wheel speed in the center of mass wheel speed and a corresponding historical average center of mass wheel speed is greater than a first threshold value, it is considered that the first wheel speed does not satisfy the wheel speed diagnosis condition; If an absolute error between the first wheel speed in the center of mass wheel speed and a corresponding estimated vehicle speed is greater than a second threshold value, it is considered that the first wheel speed does not satisfy the wheel speed diagnosis condition; Deleting the results not satisfying the wheel speed diagnosis condition from all the center of mass wheel speeds to obtain the screening result. 5.The multi-sensor fusion based vehicle speed estimation method according to claim 1, wherein, The method further comprises the following steps: If all the center of mass wheel speeds do not satisfy the wheel speed diagnosis condition, all the center of mass wheel speeds are taken as the screening result. 6.The multi-sensor fusion based vehicle speed estimation method according to claim 1, wherein, The current estimated vehicle speed, in particular: wherein, is the vehicle speed prediction result, a k is the vehicle acceleration at the kth sampling point, is the prediction time interval, is the current estimated vehicle speed, is the vehicle speed prediction result, k is the kth sampling point, and L is the gain coefficient, is the effective vehicle speed. 7.The multi-sensor fusion based vehicle speed estimation method according to claim 1, wherein, The method of adjusting the size of the gain coefficient according to the total number of the center of mass wheel speeds satisfying the wheel speed diagnosis condition specifically comprises the following steps: When there is the center of mass wheel speed satisfying the wheel speed diagnosis condition, the gain coefficient is increased. When the center of mass wheel speed satisfying the wheel speed diagnostic condition does not exist, if the actual total output torque of the current vehicle is less than a third threshold value and the maximum difference of the center of mass wheel speed is less than a fourth threshold value, the gain coefficient is increased; otherwise, the vehicle acceleration information is judged, and if the vehicle acceleration information is not zero, the gain coefficient is decreased.

8. A multi-sensor fusion based vehicle speed estimation system, characterized by, The method comprises the following steps: An information acquisition module, a wheel speed mapping module, an effective speed screening module, and a speed estimation module are included. The information acquisition module is configured to acquire wheel speed information, steering wheel angle information, and vehicle acceleration information. The wheel speed mapping module is configured to map the wheel speed information to a preset vehicle center of mass based on the steering wheel angle information to obtain a center of mass wheel speed corresponding to the wheel speed information. The effective speed screening module is configured to screen the center of mass wheel speed according to a preset wheel speed diagnostic condition and integrate the screening results to obtain an effective vehicle speed. Specifically, the center of mass wheel speed is compared with an estimated vehicle speed at a previous time and a historical average center of mass wheel speed, respectively, to obtain the center of mass wheel speed satisfying the wheel speed diagnostic condition as the screening result. The actual total output torque and the resistance torque of the current vehicle are obtained. If the actual total output torque is greater than the resistance torque, the minimum value is selected from the screening result as the effective vehicle speed. If the actual total output torque is not greater than the resistance torque, the maximum value is selected from the screening result as the effective vehicle speed. The speed estimation module is configured to fuse the effective vehicle speed and the vehicle acceleration information by a Kalman filtering method to obtain a current estimated vehicle speed. Specifically, the effective vehicle speed is predicted by the vehicle acceleration information to obtain a vehicle speed prediction result. The size of a gain coefficient is adjusted according to the total number of the center of mass wheel speed satisfying the wheel speed diagnostic condition. The vehicle speed prediction result and the effective vehicle speed are fused by the Kalman filtering method according to the gain coefficient to obtain the current estimated vehicle speed.

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