Vehicle speed estimation method and system based on multi-sensor fusion

Abstract

The invention discloses a vehicle speed estimation method and system based on multi-sensor fusion. The method comprises the steps that wheel speed information of wheels, steering wheel rotation angle information and vehicle acceleration information are collected; based on the steering wheel angle information, mapping the wheel speed information to a preset vehicle mass center to obtain a mass center wheel speed corresponding to the wheel speed information; screening the centroid wheel speeds according to a preset wheel speed diagnosis condition, and integrating screening results to obtain an effective vehicle speed; the effective vehicle speed and the vehicle acceleration information are fused through a Kalman filtering method, and the current estimated vehicle speed is obtained. The accuracy of vehicle speed estimation is improved.

Description

Technical Field

[0001] The present application belongs to the field of vehicle control, and specifically relates to a vehicle speed estimation method and system based on multi-sensor fusion. Background Art

[0002] With the development of intelligent vehicle technology, vehicle control is becoming more and more sophisticated, and high-precision vehicle speed information is the basis for realizing vehicle motion control. How to accurately estimate the current vehicle speed and thus achieve precise control of vehicle operation is the main research direction in the field of vehicle control.

[0003] At present, the method of estimating vehicle speed mainly uses the average of the wheel speeds of the two non-driving wheels. When the wheel speed of one of the two non-driving wheels is invalid, the other valid wheel speed is used as the vehicle speed; when the wheel speeds of both non-driving wheels are invalid, the wheel speeds of the two driving wheels are averaged; when the wheel speed of one of the two driving wheels is invalid, the other valid wheel speed is used as the vehicle speed. However, this vehicle speed calculation method is only applicable to two-wheel drive vehicles and not to four-wheel drive vehicles. Moreover, when braking suddenly, the wheel speed almost drops to zero because the wheel is locked, but at this time, the vehicle speed still exists due to inertia. At this time, it is unreasonable to estimate the current vehicle speed only by the wheel speed. In addition, when turning, the speed difference between the outer and inner wheels is large, and the speed difference between the front and rear wheels is also large, so the real vehicle speed cannot be reflected based on the wheel speed. Therefore, the application scope of the existing technology is small, and it is impossible to accurately estimate the current vehicle speed under various working conditions. Summary of the invention

[0004] This application proposes a vehicle speed estimation method and system based on multi-sensor fusion to improve the accuracy of vehicle speed estimation.

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

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

[0007] Based on the steering wheel angle information, the wheel speed information is mapped to a preset vehicle center of mass to obtain a center of mass wheel speed corresponding to the wheel speed information;

[0008] The center-of-mass wheel speed is screened according to a preset wheel speed diagnosis condition and the screening results are integrated to obtain an effective vehicle speed;

[0009] The effective vehicle speed and the vehicle acceleration information are fused through the Kalman filtering method to obtain the current estimated vehicle speed.

[0010] The above scheme collects the wheel speed information, steering wheel angle information and vehicle acceleration information of the vehicle through multiple sensors, and then maps the wheel speed information to the preset vehicle center of mass to obtain the corresponding center of mass wheel speed, and corrects the mapping result through the steering wheel angle information to reduce the error after wheel speed conversion and improve 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 seriously from the actual vehicle speed is deleted, and the wheel speed that is not much different from the actual vehicle speed is retained as the effective vehicle speed to estimate 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 filter method, and the increase or decrease of the vehicle speed is determined by the acceleration, rather than estimating the vehicle speed based solely on the wheel speed, which effectively avoids the interference of the estimation caused by the wheel speed returning to zero due to sudden braking, and obtains a more accurate current estimated vehicle speed.

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

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

[0013] If the driving state is straight-line driving, the mass center wheel speed is set to be equal to the wheel speed information;

[0014] If the driving state is curve driving, the wheel speed information is converted through a preset 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 above scheme first determines whether the current vehicle is turning or going straight through the steering wheel angle information, thereby determining the current wheel speed relative to the vehicle's center of mass, and obtaining the center of mass wheel speed that meets the current vehicle operating conditions, providing accurate data support for subsequent vehicle speed estimation.

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

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

[0018]

[0019] Wherein, V' is the wheel speed of the center of mass, 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 wheel width, α0 is the vehicle turning angle, δ is the steering wheel angle information, i is the steering gear ratio of the steering wheel, 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 center of mass, 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 previous moment.

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

[0021] Comparing the mass center wheel speed with the estimated vehicle speed at the last moment and the average value of the historical mass center wheel speeds, respectively, to obtain the mass center wheel speed that meets the wheel speed diagnosis condition as the screening result;

[0022] Obtain the actual total output torque and resistance torque of the current vehicle;

[0023] If the actual total output torque is greater than the resistance torque, selecting a minimum value from the screening results as the effective vehicle speed;

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

[0025] The above scheme compares the mass center wheel speed with the estimated vehicle speed at the previous moment and the average of the historical mass center wheel speeds to find the mass center wheel speed that deviates 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 a screening result that is not much different from the actual vehicle speed. Then, by comparing the actual total output torque and the resistance torque, the wheel speed that best meets the current vehicle working condition in the screening result is determined as the source of the estimated vehicle speed, and the effective vehicle speed that can accurately estimate the vehicle speed is obtained.

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

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

[0028] If the absolute error between the first wheel speed in the mass center wheel speed and the corresponding estimated vehicle speed is greater than a second threshold, it is considered that the first wheel speed does not meet the wheel speed diagnosis condition;

[0029] The results that do not meet the wheel speed diagnosis condition are deleted 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 historical mass center wheel speed is too large, it means that the vehicle's wheel speed has changed suddenly. It is unreasonable to use the sudden change in wheel speed to predict the current inertia speed, so it needs to be deleted. If the difference between the first wheel speed and the estimated speed at the previous moment is too large, it means that the current wheel has experienced transition slip, which is very different from the actual speed, so it cannot be used to estimate the current speed and needs to be deleted.

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

[0032] If all the mass center wheel speeds do not meet the wheel speed diagnosis condition, all the mass center wheel speeds are used as the screening results.

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

[0034] Predicting the effective vehicle speed through the vehicle acceleration information to obtain a vehicle speed prediction result;

[0035] adjusting the magnitude of the gain coefficient according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis condition;

[0036] According to the gain coefficient, the vehicle speed prediction result is integrated with the effective vehicle speed through the Kalman filter method 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] Where V predict is the vehicle speed prediction result, a k is the vehicle acceleration at the kth sampling point, Δt is the 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, V measure is the effective vehicle speed.

[0041] In a possible implementation method of the first aspect, the gain coefficient is adjusted according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis condition, specifically:

[0042] When the mass center wheel speed satisfies the wheel speed diagnosis condition, increasing the gain coefficient;

[0043] When the center of mass wheel speed that meets the wheel speed diagnosis condition does not exist, if the actual total output torque of the current vehicle is less than the third threshold and the maximum difference of the center of mass wheel speed is less than the fourth threshold, 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 reduced.

[0044] The above scheme determines whether the vehicle is accelerating or decelerating according to the vehicle acceleration information, determines whether the vehicle is in an emergency braking state or a stable state according to the actual total output torque, determines the number of wheel speeds in the currently obtained center of mass wheel speeds that are not much different from the actual vehicle speed according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis conditions, and thereby 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, and increases the weight of the reasonable wheel speed to reduce the error of the vehicle speed estimation and improve the robustness of the vehicle speed estimation.

[0045] A 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] Among them, the information collection module is used to collect wheel speed information, steering wheel angle information and vehicle acceleration information;

[0047] The wheel speed mapping module is used 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;

[0048] The effective speed screening module is used to screen the mass center wheel speed according to the preset wheel speed diagnosis condition and integrate the screening results to obtain the effective vehicle speed;

[0049] The speed estimation module is used to fuse the effective vehicle speed and the vehicle acceleration information through the Kalman filter method to obtain the current estimated vehicle speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1It is a specific flow chart of a vehicle speed estimation method based on multi-sensor fusion provided in a certain embodiment of the present application;

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

[0053] Figure 3 It is a specific structural diagram of a vehicle speed estimation system based on multi-sensor fusion provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0056] First embodiment

[0057] Vehicle speed is an important parameter that characterizes the motion state of a vehicle and is one of the basic parameters for vehicle kinematics and dynamics control. Determining the speed of the entire vehicle by wheel speed is a common method for determining vehicle speed. However, when the wheels slip or brake suddenly, the wheel speed is far from the actual vehicle speed. It is unreasonable to estimate the current vehicle speed only by wheel speed. Moreover, when turning, the wheel speed on the inside of the curve is smaller, and the wheel speed on the outside of the curve is larger. The front wheel speed is higher than the rear wheel speed. At this time, the current vehicle speed cannot be accurately obtained by wheel speed alone. Therefore, how to estimate the accurate vehicle speed based on wheel speed under various working conditions is the main research direction of the embodiments of this application.

[0058] like Figure 1 As shown, Figure 1 A specific flow chart of a vehicle speed estimation method based on multi-sensor fusion is provided for a certain embodiment of the present application. The vehicle speed estimation method based on multi-sensor fusion of this embodiment includes steps S1 to S4, which are described in detail as follows:

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

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

[0061] Because most vehicles are equipped with sensors, whether it is a four-wheel drive or two-wheel drive car, the current speed of the vehicle can be estimated by collecting the above data, which greatly expands the application scope of speed estimation.

[0062] Step S2: based on the steering wheel angle information, mapping the wheel speed information to a preset vehicle center of mass, to obtain the 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, it is necessary to first map the wheel speed information far away from the vehicle's center of mass to the vehicle's center of mass before calculating. The position of the vehicle's center of mass determines the dynamic characteristics of the vehicle during driving, and the factors that determine the position of the vehicle's center of mass mainly include the body structure and design, as well as the load distribution of the vehicle. By mapping the wheel speed information to the vehicle's center of mass, the estimated speed can be more consistent with the actual vehicle driving state, further improving the accuracy of the estimate.

[0064] First, the current driving state of the vehicle is determined based on the steering wheel angle information. If the absolute value of the steering wheel angle exceeds a set threshold, it can be considered that the steering wheel has rotated excessively and the vehicle is in a curved driving state; otherwise, it is considered that the vehicle is in a straight driving state.

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

[0066] When it is determined that the vehicle is in a straight-line driving state, the mass center wheel speed is made equal to the wheel speed information, that is, V lf ′=V lf 、V rf ′=V rf 、V lr ′=V lr 、V rr ′=V rr ; Among them, 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 center of mass, 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's center of mass; V lfis the left front wheel speed, V rf is the right front wheel speed, V lr is the left rear wheel speed, V rr 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 center of mass through a preset turning angle model. Because the wheel speed converted by the turning angle model may have errors, the model also needs to perform error correction on the mapping result according to the steering wheel angle information and the error value of the estimated vehicle speed at the previous moment to obtain the final center of mass wheel speed.

[0068] Optionally, in the embodiment of the present application, the Ackerman steering angle model is used to map the wheel speed information. Figure 2 The principle of mapping by the Ackerman steering model is shown in Figure 1. 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, right front wheel speed, left rear wheel speed and right rear wheel speed respectively, A is the distance from the center of mass of the vehicle to the front axle, B is the distance from the center of mass of the vehicle to the rear axle, C is the rear wheel width, R is the distance from the center of mass of the vehicle to the origin, R1, R2, R3 and R4 are the distance from the left front wheel to the origin, the distance from the right front wheel to the origin, the distance from the left rear wheel to the origin and the distance from the right rear wheel to the origin respectively. Among them, the ratio of the distance from the wheel 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 Ackerman steering angle model is:

[0070]

[0071] Wherein, V' is the wheel speed of the center of mass, 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 wheelbase, α0 is the vehicle turning angle, 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 center of mass, 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 previous moment.

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

[0073] The vehicle turning angle is specifically expressed as:

[0074]

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

[0076] Step S3, screening the center of mass wheel speed according to the preset wheel speed diagnosis condition and integrating the screening results to obtain the effective vehicle speed.

[0077] In an embodiment of the present application, when a wheel speed seriously deviates from the actual vehicle speed, the wheel speed will affect the accuracy of the vehicle speed estimation. Therefore, it is necessary to eliminate such obviously unreasonable data from the center of mass wheel speed according to the preset wheel speed diagnostic conditions so that it is not used for the subsequent vehicle speed estimation.

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

[0079] According to the wheel speed diagnosis condition, the center of mass wheel speed is compared with the estimated vehicle speed at the last moment and the average value of the historical center of mass wheel speed, and the center of mass wheel speed that meets the wheel speed diagnosis condition is obtained as the screening result.

[0080] The wheel speed diagnosis conditions are as follows:

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

[0082]

[0083] Where V' is the mass center wheel speed, is the estimated vehicle speed at the previous moment, ΔV2 is the second threshold, and k is the kth sampling point.

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

[0085]

[0086] Where m is the sampling point, ΔV1 is the first threshold, and V is the historical average wheel speed of the center of mass. 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 mutation, it can be considered that the corresponding center of mass wheel speed does not meet the wheel speed diagnosis condition and needs to be deleted from the data.

[0088] After all the center-of-mass wheel speeds are determined, a screening result that is not much different from the actual vehicle speed can be obtained. The center-of-mass wheel speeds in the screening result are then integrated, and the most appropriate center-of-mass wheel speed is selected as the effective vehicle speed to provide a data source for subsequent vehicle speed estimation.

[0089] If all the mass center wheel speeds do not satisfy the wheel speed diagnosis condition, all the mass center wheel speeds are taken as the screening results.

[0090] First, the actual total output torque and resistance torque of the current vehicle are obtained, and then the actual total output torque and resistance torque are compared, and the center of mass 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 results 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 results as the effective vehicle speed.

[0092] The calculation formula of resistance torque is:

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

[0094] Where, T f is the resistance torque, f0, f1, f2 are the sliding resistance coefficients, V is the estimated vehicle speed at the previous moment, and R is the wheel rolling radius.

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

[0096] Step S4, fusing the effective vehicle speed and the vehicle acceleration information through Kalman filtering to obtain the current estimated vehicle speed.

[0097] In the embodiment of the present application, considering that the wheel speeds are unreliable when multiple wheels are slipping or locked at the same time, the effective vehicle speed obtained alone may lead to large errors in the estimation results. Therefore, the effective vehicle speed is first predicted using the vehicle acceleration information to obtain a vehicle speed prediction result.

[0098] Then, the gain coefficient is adjusted according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis condition.

[0099] According to the gain coefficient, the vehicle speed prediction result is integrated with the effective vehicle speed through the Kalman filter 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 center-of-mass wheel speed that satisfies the wheel speed diagnosis condition, if the actual total output torque of the current vehicle is small and the difference between the maximum and minimum values ​​of the center-of-mass wheel speed is small, it can be considered that the vehicle is in a stable state and its wheel speed is reliable. On the contrary, the vehicle speed estimation is biased, and it is necessary to appropriately increase the gain coefficient to bring the estimated vehicle speed back to a normal value, thereby improving the robustness of the vehicle speed estimation.

[0102] (2) When there is no center of mass wheel speed that satisfies the wheel speed diagnosis condition, if the actual total output torque of the current vehicle is not small and the difference between the maximum and minimum values ​​of 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. When the vehicle speed is high, the gain coefficient can be reduced to 0.

[0103] (3) When there is a center-of-mass wheel speed that satisfies the wheel speed diagnosis condition, it is necessary to appropriately increase the gain coefficient to increase the wheel speed weight to reduce the impact of the acceleration error. When there is a center-of-mass wheel speed that satisfies the wheel speed diagnosis condition, it means that there is a center-of-mass wheel speed that satisfies the wheel speed diagnosis condition, so it is necessary to increase the weight of the wheel speed to increase the impact of a 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 flat ground.

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

[0105] The current estimated vehicle speed is specifically:

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

[0107]

[0108] Where 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; 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, V measure is the effective vehicle speed.

[0109] Implementing the embodiments of the present application has the following beneficial effects:

[0110] The embodiment of the present application collects the wheel speed information, steering wheel angle information and vehicle acceleration information of the vehicle through multiple sensors, and then maps the wheel speed information to the preset vehicle center of mass to obtain the corresponding center of mass wheel speed, and corrects the mapping result through the steering wheel angle information to reduce the error after wheel speed conversion and improve 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 seriously from the actual vehicle speed is deleted, and the wheel speed that is not much different from the actual vehicle speed is retained as the effective vehicle speed to estimate 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 filter method, and the increase or decrease of the vehicle speed is determined by the acceleration, rather than estimating the vehicle speed based solely on the wheel speed, which effectively avoids the interference of the estimation caused by the wheel speed returning to zero due to sudden braking, and obtains a more accurate current estimated vehicle speed.

[0111] Second embodiment

[0112] Furthermore, in order to execute the vehicle speed estimation system based on multi-sensor fusion corresponding to the above method embodiment to achieve 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 explanation, only the parts related to this embodiment are shown. The vehicle speed estimation system based on multi-sensor fusion provided by the embodiment of the present application includes:

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

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

[0115] Because most vehicles are equipped with sensors, whether it is a four-wheel drive or two-wheel drive car, the current speed of the vehicle can be estimated by collecting the above data, which greatly expands the application scope of speed estimation.

[0116] The wheel speed mapping module 202 is used 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 an embodiment of the present application, the current driving state of the vehicle is determined based on the steering wheel angle information.

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

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

[0120] The effective speed screening module 203 is used to screen the center of mass wheel speed according to the preset wheel speed diagnosis condition and integrate the screening results to obtain the effective vehicle speed.

[0121] In an embodiment of the present application, the center-of-mass wheel speed is compared with the estimated vehicle speed at the previous moment and the average value of the historical center-of-mass wheel speed, respectively, to obtain the center-of-mass wheel speed that meets the wheel speed diagnosis condition as the screening result.

[0122] Get the actual total output torque and resistance torque of the current vehicle.

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

[0125] The speed estimation module 204 is used to fuse the effective vehicle speed and the vehicle acceleration information through the Kalman filter method to obtain the current estimated vehicle speed.

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

[0127] According to the preset gain coefficient, the vehicle speed prediction result is integrated with the effective vehicle speed through the Kalman filter method to obtain the current estimated vehicle speed; wherein the size of the gain coefficient is related to the total number of effective vehicle speeds.

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

[0129] In order to accurately estimate the current vehicle speed, it is necessary to first map the wheel speed information far away from the vehicle's center of mass to the vehicle's center of mass before calculating. The position of the vehicle's center of mass determines the dynamic characteristics of the vehicle during driving. The factors that determine the position of the vehicle's center of mass mainly include the body structure and design, as well as the vehicle's load distribution. By mapping the wheel speed information to the vehicle's center of mass, the estimated speed can be more consistent with the actual vehicle driving state, further improving the accuracy of the estimate.

[0130] First, the current driving state of the vehicle is determined based on the steering wheel angle information. If the absolute value of the steering wheel angle exceeds a set threshold, it can be considered that the steering wheel has rotated excessively and the vehicle is in a curved driving state; otherwise, it is considered that the vehicle is in a straight driving state.

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

[0132] When it is determined that the vehicle is in a straight-line driving state, the mass center wheel speed is made equal to the wheel speed information, that is, V lf ′=V lf 、V rf ′=V rf 、V lr ′=V lr 、V rr ′=V rr ; Among them, 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 center of mass, 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's center of mass; V lf is the left front wheel speed, V rf is the right front wheel speed, V lr is the left rear wheel speed, V rr is the right rear wheel speed.

[0133] When it is determined that the vehicle is in a curve driving state, the wheel speed information is mapped to the vehicle center of mass through a preset turning angle model. Because the wheel speed converted by the turning angle model may have errors, the model also needs to perform error correction on the mapping result according to the steering wheel angle information and the error value of the estimated vehicle speed at the previous moment to obtain the final center of mass wheel speed.

[0134] Optionally, in the embodiment of the present application, an Ackerman 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] Wherein, V' is the wheel speed of the center of mass, 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 wheelbase, α0 is the vehicle turning angle, 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 center of mass, Vlr ′ 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 previous moment.

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

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

[0140]

[0141] Wherein, δ is the steering wheel angle information, and i is the steering gear ratio of the steering wheel.

[0142] In some embodiments, the effective speed screening module 203 is specifically:

[0143] When a wheel speed seriously deviates from the actual vehicle speed, the wheel speed will affect the accuracy of the vehicle speed estimation. Therefore, it is necessary to eliminate this obviously unreasonable data from the center of mass wheel speed according to the preset wheel speed diagnostic conditions so that it is not used in the subsequent vehicle speed estimation.

[0144] For example, when the vehicle is in an emergency braking state, the wheels will lock and the corresponding wheel speed will drop to almost zero, but the vehicle speed still exists due to inertia. If the vehicle speed is estimated by the wheel speed of the locked wheels, the estimated result will be very different from the actual vehicle speed, seriously reducing the estimation accuracy.

[0145] According to the wheel speed diagnosis condition, the center of mass wheel speed is compared with the estimated vehicle speed at the last moment and the average value of the historical center of mass wheel speed, and the center of mass wheel speed that meets the wheel speed diagnosis condition is obtained as the screening result.

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

[0147] (3) If the absolute error between the wheel speed at the center of mass and the estimated vehicle speed at the previous moment is greater than the second threshold, it is determined that the wheel has excessive slip, that is:

[0148]

[0149] Where V' is the mass center wheel speed, 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 mass center wheel speed and the historical mass center wheel speed average is greater than the first threshold, it is determined that the wheel speed has a sudden change, that is:

[0151]

[0152] Where m is the sampling point, ΔV1 is the first threshold, and V is the historical average wheel speed of the center of mass. Here, V takes the data of the first m sampling points in the historical data.

[0153] If it is determined that the wheel has excessive slip or mutation, it can be considered that the corresponding center of mass wheel speed does not meet the wheel speed diagnosis condition and needs to be deleted from the data.

[0154] If all the mass center wheel speeds do not satisfy the wheel speed diagnosis condition, all the mass center wheel speeds are taken as the screening results.

[0155] After all the center-of-mass wheel speeds are determined, a screening result that is not much different from the actual vehicle speed can be obtained. The center-of-mass wheel speeds in the screening result are then integrated, and the most appropriate center-of-mass wheel speed is selected as the effective vehicle speed to provide a data source for subsequent vehicle speed estimation.

[0156] First, the actual total output torque and resistance torque of the current vehicle are obtained, and then the actual total output torque and resistance torque are compared, and the center of mass 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 results 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 results as the effective vehicle speed.

[0158] The calculation formula of resistance torque is:

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

[0160] Where, T f is the resistance torque, f0, f1, f2 are the sliding resistance coefficients, V is the estimated vehicle speed at the previous moment, and R is the wheel rolling radius.

[0161] In addition, when the total number of the screening results is zero, that is, all the mass center wheel speeds do not meet the wheel speed diagnosis condition, all the mass center wheel speeds are regarded as the effective vehicle speeds.

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

[0163] Considering that the wheel speed is unreliable when multiple wheels are slipping or locked at the same time, the effective vehicle speed obtained alone may lead to a large error in the estimation result. Therefore, the effective vehicle speed is first predicted by the vehicle acceleration information to obtain the vehicle speed prediction result.

[0164] Then, the gain coefficient is adjusted according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis condition, and then the vehicle speed prediction result is integrated with the effective vehicle speed through the Kalman filter method according to the gain coefficient 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 that satisfies the wheel speed diagnosis condition, if the actual total output torque of the current vehicle is small and the difference between the maximum and minimum values ​​of the center-of-mass wheel speed is small, it can be considered that the vehicle is in a stable state and its wheel speed is reliable. On the contrary, the vehicle speed estimation is biased, and it is necessary to appropriately increase the gain coefficient to bring the estimated vehicle speed back to a normal value, thereby improving the robustness of the vehicle speed estimation.

[0167] (2) When there is no center of mass wheel speed that satisfies the wheel speed diagnosis condition, if the actual total output torque of the current vehicle is not small and the difference between the maximum and minimum values ​​of 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. 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 that satisfies the wheel speed diagnosis condition, it is necessary to appropriately increase the gain coefficient to increase the wheel speed weight to reduce the impact of the acceleration error. When there is a center-of-mass wheel speed that satisfies the wheel speed diagnosis condition, it means that there is a center-of-mass wheel speed that satisfies the wheel speed diagnosis condition, so it is necessary to increase the weight of the wheel speed to increase the impact of a 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 flat ground.

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

[0170] The current estimated vehicle speed is specifically:

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

[0172]

[0173] Where 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; 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, V measure is the effective vehicle speed.

[0174] Implementing the embodiments of the present application has the following beneficial effects:

[0175] The embodiment of the present application collects the wheel speed information, steering wheel angle information and vehicle acceleration information of the vehicle through multiple sensors, and then maps the wheel speed information to the preset vehicle center of mass to obtain the corresponding center of mass wheel speed, and corrects the mapping result through the steering wheel angle information to reduce the error after wheel speed conversion and improve 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 seriously from the actual vehicle speed is deleted, and the wheel speed that is not much different from the actual vehicle speed is retained as the effective vehicle speed to estimate 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 filter method, and the increase or decrease of the vehicle speed is determined by the acceleration, rather than estimating the vehicle speed based solely on the wheel speed, which effectively avoids the interference of the estimation caused by the wheel speed returning to zero due to sudden braking, and obtains a more accurate current estimated vehicle speed.

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

Claims

1. A vehicle speed estimation method based on multi-sensor fusion, characterized in that: include: Collect wheel speed information, steering wheel angle information and vehicle acceleration information; Based on the steering wheel angle information, the wheel speed information is mapped to a preset vehicle center of mass to obtain a center of mass wheel speed corresponding to the wheel speed information; The center-of-mass wheel speed is screened according to a preset wheel speed diagnosis condition and the screening results are integrated to obtain an effective vehicle speed; The effective vehicle speed and the vehicle acceleration information are fused through the Kalman filtering method to obtain the current estimated vehicle speed.

2. The vehicle speed estimation method based on multi-sensor fusion according to claim 1 is characterized in that: Based on the steering wheel angle information, the wheel speed information is mapped to a preset vehicle center of mass to obtain the center of mass wheel speed corresponding to the wheel speed information, specifically: Determining the current driving state of the vehicle according to the steering wheel angle information; If the driving state is straight-line driving, the mass center wheel speed is set to be equal to the wheel speed information; If the driving state is curve driving, the wheel speed information is converted through a preset 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 vehicle speed estimation method based on multi-sensor fusion according to claim 2 is characterized in that: The corner model is specifically: The wheel speed conversion formula of the turning angle model is: Wherein, V' is the wheel speed of the center of mass, 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 wheel width, α0 is the vehicle turning angle, δ is the steering wheel angle information, i is the steering gear ratio of the steering wheel, 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 center of mass, 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 previous moment.

4. The vehicle speed estimation method based on multi-sensor fusion according to claim 1, characterized in that: The center of mass wheel speed is screened according to the preset wheel speed diagnosis condition and the screening results are integrated to obtain the effective vehicle speed, which is specifically: Comparing the mass center wheel speed with the estimated vehicle speed at the last moment and the average value of the historical mass center wheel speeds, respectively, to obtain the mass center wheel speed that meets the wheel speed diagnosis condition as the screening result; Obtain the actual total output torque and resistance torque of the current vehicle; If the actual total output torque is greater than the resistance torque, selecting a minimum value from the screening results 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 results as the effective vehicle speed.

5. The vehicle speed estimation method based on multi-sensor fusion according to claim 4 is characterized in that: The obtaining of the center of mass wheel speed satisfying the wheel speed diagnosis condition as the screening result is specifically: If the absolute error between the first wheel speed in the mass center wheel speed and the corresponding historical mass center wheel speed average value is greater than a first threshold, it is considered that the first wheel speed does not meet the wheel speed diagnosis condition; If the absolute error between the first wheel speed in the mass center wheel speed and the corresponding estimated vehicle speed is greater than a second threshold, it is considered that the first wheel speed does not meet the wheel speed diagnosis condition; The results that do not meet the wheel speed diagnosis condition are deleted from all the center of mass wheel speeds to obtain the screening result.

6. The vehicle speed estimation method based on multi-sensor fusion according to claim 4 is characterized in that: Also includes: If all the mass center wheel speeds do not meet the wheel speed diagnosis condition, all the mass center wheel speeds are used as the screening results.

7. The vehicle speed estimation method based on multi-sensor fusion according to claim 1 is characterized in that: The effective vehicle speed and the vehicle acceleration information are fused by the Kalman filter method to obtain the current estimated vehicle speed, specifically: Predicting the effective vehicle speed through the vehicle acceleration information to obtain a vehicle speed prediction result; adjusting the magnitude of the gain coefficient according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis condition; According to the gain coefficient, the vehicle speed prediction result is integrated with the effective vehicle speed through the Kalman filter method to obtain the current estimated vehicle speed.

8. The vehicle speed estimation method based on multi-sensor fusion according to claim 7 is characterized in that: The current estimated vehicle speed is specifically: V predict (k)=V predict (k-1)+a k Δt Where V predict is the vehicle speed prediction result, a k is the vehicle acceleration at the kth sampling point, Δt is the 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, V measure is the effective vehicle speed.

9. The vehicle speed estimation method based on multi-sensor fusion according to claim 7, characterized in that: The gain coefficient is adjusted according to the total number of the center of mass wheel speeds that meet the wheel speed diagnosis condition, specifically: When the mass center wheel speed satisfies the wheel speed diagnosis condition, increasing the gain coefficient; When the center of mass wheel speed that meets the wheel speed diagnosis condition does not exist, if the actual total output torque of the current vehicle is less than the third threshold and the maximum difference of the center of mass wheel speed is less than the fourth threshold, 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 reduced.

10. A vehicle speed estimation system based on multi-sensor fusion, characterized in that: include: Information collection module, wheel speed mapping module, effective speed screening module and speed estimation module; Among them, the information collection module is used to collect wheel speed information, steering wheel angle information and vehicle acceleration information; The wheel speed mapping module is used 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; The effective speed screening module is used to screen the mass center wheel speed according to the preset wheel speed diagnosis condition and integrate the screening results to obtain the effective vehicle speed; The speed estimation module is used to fuse the effective vehicle speed and the vehicle acceleration information through the Kalman filter method to obtain the current estimated vehicle speed.

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