CN114291074B - Vehicle rollover early warning method, device and storage medium - Google Patents
Vehicle rollover early warning method, device and storage medium Download PDFInfo
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Abstract
The application provides a vehicle rollover early warning method, a vehicle rollover early warning device and a storage medium, wherein the vehicle rollover early warning method comprises the following steps: acquiring current positioning information of a vehicle and a pre-aiming path of a current driving road, and acquiring a global path according to the current positioning information; fitting prediction is carried out according to the pre-aiming path and the global path, so as to obtain a predicted path; fitting the current positioning, the predicted path and the pre-aiming path of the vehicle to obtain a pre-advancing path of the vehicle; and predicting vehicle state data when the vehicle runs on each point of the pre-running path according to the current running parameters of the vehicle and the pre-running path of the vehicle, and triggering vehicle rollover risk early warning if the vehicle state data reaches a vehicle rollover state threshold value. According to the method, the pre-aiming path and the global positioning are fitted, the front blocked curve path is predicted, the distance of the pre-advancing path of the vehicle is prolonged, a longer path basis is provided for vehicle rollover early warning, and rollover accidents can be avoided earlier.
Description
Technical Field
The application relates to the technical field of active safety of automobiles, in particular to a vehicle rollover early warning method, a vehicle rollover early warning device and a storage medium.
Background
With the development of automobile technology, automobiles become necessary transportation means for each family, but due to the rapid increase of the number of automobiles, traffic accidents are frequently transmitted in recent years, and most of traffic accidents are caused by the fact that drivers do not pay attention to road conditions.
Rollover of a vehicle is an accident that occurs when the vehicle travels around a curve or trips over an obstacle. When there is a curve in the front, if the driver does not notice the slow down and slow down, the driver is most likely to rush out of the curve or turn fast due to the fact that the driver is not in the way of turning, and the vehicle turns over. The side-turning accident has large damage, and serious casualties and property loss are brought. The rollover early warning technology is developed, potential rollover risks are found in the early stage, and the method is an effective method for avoiding or reducing the rollover accidents of the vehicle.
In the prior art, the vehicle rollover prediction usually uses a vehicle sensor to acquire the current speed, steering wheel rotation angle and other parameters of the vehicle, and the vehicle is rollover predicted according to the speed and steering wheel rotation angle lamp parameters, so that the vehicle rollover state prediction is only effective in a short time, the long-time rollover state cannot be predicted, and if a rollover accident occurs, the response time of a driver is shorter, and the rollover accident cannot be timely avoided.
Disclosure of Invention
Based on the above, the application aims to provide a vehicle rollover early warning method, which solves the problems that the long-time rollover state cannot be predicted in the background technology, if rollover accidents occur, the response time of a driver is shorter, and the rollover accidents cannot be avoided in time.
The application provides a vehicle rollover early warning method, which comprises the following steps:
acquiring current positioning information of a vehicle and a pre-aiming path of a current driving road, and acquiring a global path according to the current positioning information;
fitting prediction is carried out according to the pre-aiming path and the global path, so as to obtain a predicted path;
fitting the current positioning, the predicted path and the pre-aiming path of the vehicle to obtain a pre-advancing path of the vehicle;
predicting vehicle state data when the vehicle runs on each point of the pre-running path according to the current running parameters of the vehicle and the pre-running path of the vehicle, wherein the vehicle state data comprise vehicle lateral acceleration, vehicle body roll angle and load transfer rate;
judging whether the vehicle state data reach a vehicle rollover state threshold value or not;
if yes, triggering vehicle rollover risk early warning.
In summary, in the vehicle rollover warning method in the above embodiment of the present application, the pre-aiming path of the vehicle is fitted to the global path, so as to predict a path that is not detected by the vision in front of the vehicle, and the predicted path and the pre-aiming path are combined to determine the path after the vehicle is currently positioned in the path as the vehicle pre-advancing path, so that the predicted traveling path of the vehicle is advanced to a longer distance in front, a long distance basis is provided for vehicle rollover warning, and by judging whether the vehicle reaches the rollover state threshold value in the pre-advancing path, the long-time vehicle rollover state prediction is realized, rollover accidents can be avoided earlier, and the problems that the long-time rollover state threshold value cannot be predicted in the background technology, the response time of the driver is shorter, and rollover accidents cannot be avoided in time are solved.
Further, the step of obtaining the current location of the vehicle includes:
acquiring a history path of a current driving road and a vehicle positioning;
and correcting the vehicle positioning to the current vehicle positioning according to the historical path and the pre-aiming path.
Further, the step of correcting the vehicle position to the current vehicle position includes:
and determining an intersection point between the history path and the pre-aiming path according to the history path and the pre-aiming path, and performing fitting comparison on the intersection point and the vehicle positioning to obtain the current positioning of the vehicle.
Further, the step of acquiring the pre-aiming path includes:
acquiring a road image in front of the vehicle;
extracting lane line data of a road image according to a pre-trained deep learning algorithm model;
and fitting the lane line data with a pre-trained lane model to obtain a pre-aiming path.
Further, the step of predicting vehicle state data when the vehicle travels to each point of the pre-travel path includes:
according to the pre-running path, obtaining ideal lateral acceleration when the vehicle runs to the pre-running path;
obtaining an ideal steering wheel corner required by reaching the ideal lateral acceleration according to the ideal lateral acceleration;
acquiring actual lateral acceleration when the vehicle runs to a pre-running path, and calculating an error between the actual lateral acceleration and the ideal lateral acceleration;
correcting the ideal steering wheel angle according to the error to obtain the optimal steering wheel angle;
and inputting the optimal steering wheel rotation angle into a pre-trained side turning dynamics model to obtain lateral acceleration, a vehicle body side inclination angle and a load transfer rate.
Further, the rollover dynamics model includes:
acquiring the lateral acceleration, the roll angle and the steering wheel rotation angle of the vehicle;
inputting the lateral acceleration, the roll angle and the steering wheel rotation angle of the vehicle into a three-degree-of-freedom nonlinear dynamics model of the vehicle to obtain vehicle state data;
when the vehicle state data reach the rollover state threshold value, acquiring an actual measurement value of a vehicle structural parameter, and adjusting the vehicle structural parameter in the nonlinear dynamics model to the actual measurement value, wherein the vehicle structural parameter comprises a vehicle sprung mass, a centroid height and a centroid front-back position.
Further, the method further comprises:
acquiring the current steering wheel angle and the steering rate of the vehicle;
and inputting the current steering wheel angle and the current steering rate into the rollover dynamics model to obtain current vehicle state data.
Further, the method also comprises the steps of,
fusing the vehicle state data and the current vehicle state data under the pre-running path through an S-function;
and triggering rollover risk early warning if the fused vehicle state data reaches the rollover state threshold value.
The application also provides a vehicle rollover warning device, which comprises,
the path information acquisition module is used for acquiring current positioning information of the vehicle and a pre-aiming path of a current driving road and acquiring a global path according to the current positioning information;
the predicted path information acquisition module is used for carrying out fitting prediction according to the pre-aiming path and the global path to obtain a predicted path;
the pre-traveling path determining module is used for fitting the current positioning, the predicted path and the pre-aiming path of the vehicle to obtain a pre-traveling path of the vehicle;
the vehicle state calculation module is used for predicting vehicle state data when the vehicle runs on each point of the pre-running path according to the current running parameters of the vehicle and the pre-running path of the vehicle, wherein the vehicle state data comprises vehicle lateral acceleration, vehicle body roll angle and load transfer rate;
the judging module is used for judging whether the vehicle state data reach a vehicle rollover state threshold value or not;
the first execution unit is used for triggering vehicle rollover risk early warning when the vehicle state data reach the vehicle rollover state threshold value.
The application also provides a computer readable storage medium, wherein the computer readable storage medium is stored with a computer program, and when the computer program is executed by a processor, the vehicle rollover warning method of any one of the above is realized.
Drawings
FIG. 1 is a flow chart of a vehicle rollover warning method in accordance with a first embodiment of the present application;
FIG. 2 is a flow chart of a vehicle rollover warning method in a second embodiment of the present application;
FIG. 3 is a block diagram of a vehicle rollover warning device in a third embodiment of the present application;
FIG. 4 is a schematic diagram of the prediction of the present application;
FIG. 5 is a schematic diagram of a pretightening-following model of the present application;
FIG. 6 is a first schematic illustration of a vehicle rollover dynamics model in accordance with the present application;
FIG. 7 is a second schematic representation of the vehicle rollover dynamics model of the present application.
The application will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the application may be readily understood, a more complete description of the application will be rendered by reference to the appended drawings. Several embodiments of the application are presented in the figures. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
According to the vehicle rollover early warning method, long-time rollover risk prediction is achieved by acquiring the vehicle front pre-aiming path information in a long distance, specifically, the vehicle front pre-aiming path information is combined with a global path and the vehicle current position, front unknown prediction is obtained through methods such as curve fitting, namely, limited pre-aiming visible distance is advanced to front prediction, the front unknown prediction is input into a driver pre-aiming-following model, whether rollover risk is judged according to rollover dynamics, and accordingly prediction of rollover risk in an earlier period and longer time is achieved in an application stage.
Example 1
Referring to fig. 1, a flow chart of a vehicle rollover warning method according to a first embodiment of the present application is shown, and the method includes steps S101 to S105:
s101, acquiring a pre-aiming path of the vehicle and the current positioning of the vehicle, and acquiring a global path according to the current positioning of the vehicle.
The method comprises the steps of acquiring a front image of a current driving road of a vehicle through a camera configured by the vehicle, analyzing the image information through a deep learning algorithm, namely acquiring road curvature information of the front road to obtain a pre-aiming path.
And acquiring a GPS global path of a vehicle driving road by using the GPS or Beidou navigation (about 10 m) of the vehicle configuration and taking the current positioning of the vehicle as the center.
S102, carrying out fitting prediction on the pre-aiming path and the global path to obtain a predicted path.
As shown in fig. 4, there is illustrated a historical path, a current location of the vehicle, a pre-aiming path, and a predicted path of the vehicle camera not pre-aimed.
And decomposing the pre-aiming path into a plurality of sections of path data, and performing curve fitting with the global path section by section to obtain a predicted path which is blocked in front of the vehicle or is not acquired by the camera.
The acquisition of the history path comprises the following steps:
as shown in fig. 4, when the vehicle is located at a certain position in front of a curve at time t, the vehicle moves to another position in front of the road after Δt time passes, and the path moving in the time t to (t+Δt) is a history path.
And S103, fitting the current positioning, the pre-aiming path and the predicted path of the vehicle to obtain the pre-travelling path of the vehicle.
And performing curve fitting by taking the current positioning of the vehicle as a starting point and combining the predicted path and the pre-aiming path to obtain a pre-traveling path which is close to an actual road and on which the vehicle is to travel.
S104, obtaining vehicle state data of each point on the pre-running path when the vehicle runs according to the current running parameters of the vehicle and the pre-running path of the vehicle.
S105, if the vehicle state data reach the rollover state threshold, the vehicle triggers rollover risk early warning.
According to the front pre-running path and the running parameters of the automobile at the current moment, the optimal lateral acceleration of the automobile is determined, so that the running track of the automobile after the pre-aiming time is passed can be minimized with the transverse displacement deviation of the pre-running path; meanwhile, considering the response lag of the driver and the nonlinearity of the lateral dynamics characteristic of the vehicle, correcting the steering wheel angle decided by the driver model in a lateral acceleration error feedback mode to obtain an optimal steering wheel angle.
According to the principle of rollover dynamics, the optimal steering wheel rotation angle is used as input, and rollover indexes such as vehicle lateral acceleration, vehicle body roll angle, load transfer rate and the like of a vehicle when the vehicle passes through a pre-running path are obtained.
The lateral acceleration, the roll angle, the load transfer rate and the like of the vehicle represent the risk of rollover of the vehicle, and when the lateral acceleration and the roll angle reach set thresholds, the vehicle represents that the rollover state threshold is reached, and in some alternative embodiments, the lateral acceleration threshold is 1.1g and the roll angle threshold is 70 degrees. When the vehicle load transfer rate is in a critical state, namely the absolute value is 1, the vehicle load transfer rate is characterized as reaching a rollover state threshold value.
Judging whether the side acceleration, the vehicle body side inclination angle and the load transfer rate reach the side turning state threshold value or not, if yes, triggering side turning risk early warning, warning a driver to safely drive or control the vehicle driving state, and timely avoiding side turning accidents;
if not, returning to the execution of the steps S101-S105, re-acquiring new predicted path information, and judging whether the vehicle has a rollover risk.
In summary, in the vehicle rollover warning method in the embodiment of the application, the pre-aiming path of the vehicle is fitted with the global path, the path which is not detected by the vision in front of the vehicle is predicted, and the pre-advancing path of the vehicle is obtained by fitting the current positioning of the vehicle and the pre-aiming path, so that the predicted traveling path of the vehicle is advanced to the front for a longer distance, a longer distance basis is provided for vehicle rollover warning, and the problem that the rollover accident cannot be avoided in time due to the fact that the rollover state threshold value of the vehicle cannot be predicted for a long time in the background technology is solved by judging whether the rollover state threshold value of the vehicle in the pre-advancing path reaches the rollover state threshold value of the vehicle.
Example two
Referring to fig. 2, a flow chart of a vehicle rollover warning method according to a second embodiment of the application is shown, and the method includes steps S201 to S211.
S201, acquiring a pre-aiming path of the vehicle and the current positioning of the vehicle, and acquiring a global path according to the current positioning of the vehicle.
The step of acquiring the pre-aiming path is as follows:
1) Establishing a lane model: the curve model is an assumption of the road shape, a conventional model is a parabolic (second order) model, a lane is generally composed of straight lines, circular arcs and gentle curves, the gentle curves are generally connecting transitions of circular arcs or straight lines with different curvatures, the curvatures of the transition curves are uniformly changed, and the spiral curves are a conventional form of the gentle curves.
Relationship to arc length (road length): c=c 0 +C 1 *L，C 0 For starting point curvature, C 1 Is the rate of change of curvature. C (C) 0 ，C 1 When the road is 0, the road is a straight line; the method comprises the steps of carrying out a first treatment on the surface of the C (C) 1 At 0, C 0 The road is not 0, and the road is an arc; c (C) 1 If the road is not 0, the road is a moderation curve.
In the visible range of the camera, if the change direction of the lane is small, the road can be approximately represented by an arc:
the coordinates of a road can be generally expressed by arc length and curvature as:
y＝L
x＝0.5*C*L^2
if the lateral offset of the camera and the lane is d and the included angle between the camera and the lane is a, the lane model is
y＝L
x＝d+a*L+0.5*C*L^2
2) The road image in front of the vehicle is acquired by a vision sensor, such as a front camera, configured by the vehicle system. And obtaining a characteristic image of the road after processing by a deep learning algorithm (such as a convolutional neural network), extracting lane line pixel points in the characteristic image, and extracting each set of pixel points belonging to the lane lines from the foreground pixel point set to be used as a basis for fitting a lane line model.
3) And (3) fitting a lane line model, wherein the fitting method is a least square method, the optimal parameters of the curve digital model are determined according to the detected lane line pixel points, and finally, a pretightening path is obtained through fitting.
Acquiring the current positioning of the vehicle:
as shown in fig. 4, the vehicle positioning information is obtained through the GPS, the road intersection point between the history path and the pre-aiming path is determined, and curve fitting is performed on the road intersection point, the global path and the vehicle positioning information, so that the vehicle positioning is corrected to be the current vehicle positioning, and the corrected current vehicle positioning can more accurately position the vehicle.
Acquiring a global path:
and acquiring a GPS global path of a vehicle driving road by using a GPS or Beidou navigation (about 10 m) configured by the vehicle and taking the current positioning of the vehicle as a center, wherein the global path comprises road information of a certain distance in front of and behind the vehicle driving road. As shown in fig. 4, the global path includes: historical path, current positioning of the vehicle, pre-aiming path and predicted path information of areas where the vehicle cameras are not pre-aimed.
S202, carrying out fitting prediction on the pre-aiming path and the global path to obtain a predicted path.
Comparing the obtained pre-aiming path with the global path, substituting the pre-aiming path into the global path, and determining the blocked path in front of the vehicle running as a predicted path.
Specifically, the pre-aiming path is decomposed into multiple sections of path data, and curve fitting is carried out on the sections and the global path to obtain a predicted path which is blocked in front of the vehicle or is not obtained by a camera. Besides the curve fitting method, the prediction path can also be obtained by adopting methods such as Kalman filtering or neural network approximation.
Curve fitting refers to a data processing method that approximately characterizes or mimics a functional relationship between coordinates represented by a set of discrete points on a plane with a continuous curve. Besides the curve fitting method, the prediction path can also be obtained by adopting methods such as Kalman filtering or neural network approximation. In some other alternative embodiments, more sample data may be selected for curve fitting, such as fitting data of a historical path, current location of the vehicle, etc. with a pre-aiming path, the more reference sample data, the less predicted path error is obtained.
As shown in fig. 4, when the vehicle is located at a certain position in front of a curve at time t, the vehicle moves to another position in front of the road after Δt time passes, wherein the path moving in the time t to (t+Δt) is a history path. Vehicle travel information may be obtained by a vehicle sensor and a historical path may be derived from the path of travel. And (3) positioning the historical path and the vehicle, and performing curve fitting with the global path to obtain a more accurate predicted path.
And S203, fitting the current positioning, pre-aiming path and the predicted path of the vehicle to obtain a pre-travelling path of the vehicle.
And performing curve fitting by taking the current positioning of the vehicle as a starting point and combining the predicted path and the pre-aiming path to obtain a pre-traveling path which is close to an actual road and on which the vehicle is to travel.
S204, establishing a driver 'pre-aiming-following' model.
As shown in fig. 5, the pre-aiming-following model is used for deciding the ideal lateral acceleration and the ideal steering wheel corner of the vehicle according to the expected track of the road ahead and the running parameters of the vehicle at the current moment through a pre-aiming strategy, so that the lateral displacement deviation between the running track of the vehicle after a period of time and the expected track of the driver can be minimized; meanwhile, the steering wheel angle determined by the pre-aiming strategy is corrected by adopting a lateral acceleration error feedback mode in consideration of the response lag of a driver and the nonlinear factors of the lateral dynamics characteristics of the vehicle, so that an optimal steering wheel angle is obtained. The specific calculation process comprises the following steps:
let f (t) be the desired lateral displacement of the car and y (t) be the current actual lateral displacement of the car. Assuming that the pre-aiming time of the driver is T, the driver wants to make the actual lateral displacement y (t+T) of the vehicle approach the expected lateral displacement f of the automobile as much as possible after the pre-aiming time T is passed for the operation of the steering wheel e Wherein f e =f (t+t). The lateral displacement of the vehicle at the time t is y, and the lateral speed is v y If the lateral deviation between the pre-aiming point with the pre-aiming time of T and the vehicle is fp, fp is approximately equal to fe-y (t+T), assuming that the vehicle uniformly accelerates with an ideal lateral acceleration ay at the current moment, the vehicle can reach the expected track at the moment T, and then
y(t+T)＝y+v y T+ 1 / 2 (a y * )T 2 y(t+T)
Considering y (t+t) =f (t+t), then
The steady-state gain of the lateral acceleration of the vehicle to the steering wheel angle is Gay, which is set at a constant vehicle speed, to achieve the desired steering wheel angle to which the desired lateral acceleration ay should be applied.
The driver's neural response lag is represented by a transfer function exp (-tds), where td is the neural response lag time and s is the laplace operator. The driver's motion response lag is represented by the transfer function 1/(1+ths), where th is the motion response lag time. Considering the response lag of a driver, the nonlinearity of the lateral dynamics characteristic of the vehicle and the complex driving working condition of the automobile, the steering wheel angle decided by the driver model is corrected by adopting a lateral acceleration error feedback mode so as to obtain the optimal steering wheel angle.
Δδ＝(a y *-a y )P/(1+t h s)
S205, inputting the pre-running path and the current vehicle running parameters into a driver 'pre-aiming-following' model to obtain the optimal steering wheel angle.
Inputting the pre-running path and the current vehicle running parameters acquired in the step S203 into a driver 'pre-aiming-following' model, and calculating to obtain ideal lateral acceleration and optimal steering wheel rotation angle.
S206, building a vehicle rollover dynamics model.
Referring to fig. 6-7, first and second schematic diagrams of a vehicle rollover dynamics model are shown, and a three-degree-of-freedom nonlinear dynamics model including yaw motion, lateral motion and roll motion of a vehicle body is established in consideration of tire load transfer rate, nonlinear characteristics of tires and tire force coupling characteristics during steering.
Yaw motion:
lateral movement:
roll motion:
wheel load:
load transfer rate: ltr= (F z1 -F z2 )/(F z1 +F z2 )
Wherein I is z For yaw moment of inertia, I x Is a vehicle bodyThe moment of inertia of the roll,for yaw acceleration, l 1 And l 2 The distances from the mass center to the front axle and the rear axle are respectively shown, and delta is the wheel corner; f (F) zi (i=1, 2,3, 4) is the tire vertical load; f (F) yi (i=1, 2,3, 4) is four tire side forces, and F zi In relation, calculated from a nonlinear tire model; m, m s The mass of the whole car and the sprung mass are respectively; h is the sprung mass centroid to roll axis distance; a, a y For lateral acceleration, θ>And->The roll angle, the roll angle speed and the roll angle acceleration of the vehicle body are respectively; g is gravity acceleration, k θ C is the camber stiffness of the suspension θ Damping the roll angle of the vehicle body; l is the wheelbase, T w Is the track.
The lateral acceleration, the roll angle, the load transfer rate and the like of the vehicle represent the risk of rollover of the vehicle, and when the lateral acceleration and the roll angle reach set thresholds, the vehicle represents that the rollover state threshold is reached, and in some alternative embodiments, the lateral acceleration threshold is 1.1g and the roll angle threshold is 70 degrees. When the vehicle load transfer rate is in a critical state, namely the absolute value is 1, the vehicle load transfer rate is characterized as reaching a rollover state threshold value.
S207, carrying out parameter identification and correction on the vehicle structural parameters by combining experimental test data, and improving the accuracy of the rollover dynamics model.
In order to improve accuracy of rollover prediction, vehicle lateral acceleration, vehicle body roll angle and load transfer rate are taken as targets, vehicle structural parameters such as sprung mass, barycenter height, barycenter front and back positions and the like are taken as targets, sensitivity of the targets to the vehicle structural parameters is analyzed, when the vehicle reaches a rollover state threshold value, actual measurement values of the vehicle structural parameters are recorded, the vehicle structural parameters in a rollover dynamics model are corrected to be the actual measurement values, modeling accuracy is improved, and a foundation is laid for accurate prediction of the rollover state threshold value in the next step.
S208, inputting the optimal steering wheel rotation angle acquired under the pre-running path into a rollover dynamics model to obtain vehicle state data of the vehicle on the pre-running path.
According to the optimal steering angle of the steering wheel of the driver under the pre-running path obtained in the step S205, and the lateral acceleration is measured through the IMU and is input into the rollover dynamics model, vehicle state data such as the lateral acceleration, the vehicle body roll angle, the load transfer rate and the like are realized, and whether the vehicle has rollover risks on the pre-running path can be judged according to the vehicle state data.
S209, acquiring the current steering wheel angle and steering speed of the vehicle, and inputting the steering wheel angle and the steering speed into a rollover dynamics model to obtain current vehicle state data.
The current steering wheel angle and steering rate of the vehicle are obtained through a steering wheel steering angle sensor of the vehicle, the current steering wheel angle and steering rate are input into a rollover dynamics model in the step S207, vehicle state data such as lateral acceleration and vehicle body roll angle of the vehicle, load transfer rate and the like in a short time are calculated, and whether the vehicle is at rollover risk currently can be judged according to the vehicle state data.
S210, fusing the vehicle state data in the pre-running path and the current vehicle state data through an S-function.
S211, triggering rollover risk early warning by the vehicle if the fused vehicle state data reaches a rollover state threshold value.
The formula of the vehicle state data fusion state is as follows:
T 3 ＝[1-E(t)]·T 1 +E(t)·T 2
wherein T is 1 For current vehicle state data, T 2 For pre-route vehicle status data, T 3 And the vehicle state data after the fusion of the two.
The S-function E (t), namely a system function, is written by MATLAB, and can be used for representing vehicle state data in two states by using one type of vehicle state data, and the specific expression is as follows:
a and b are normal numbers, and the matching degree of the fused track and the electronic map can be adjusted.
Judging whether the rollover indexes, the lateral acceleration, the vehicle body roll angle and the load transfer rate reach the rollover state threshold according to the fused vehicle state data, if so, triggering rollover risk early warning, warning a driver to safely drive or control the vehicle driving state, and timely avoiding rollover accidents; if not, returning to the execution of the steps S201-S211, acquiring new predicted path information again, and judging whether the rollover risk exists.
In summary, in the vehicle rollover warning method in the embodiment of the application, the pre-aiming path of the vehicle is fitted with the global path, the path which is not detected by the vision in front of the vehicle is predicted, and the pre-advancing path of the vehicle is obtained by fitting the current positioning of the vehicle and the pre-aiming path, so that the predicted traveling path of the vehicle is advanced to the front for a longer distance, a longer distance basis is provided for vehicle rollover warning, and the problem that the rollover accident cannot be avoided in time due to the fact that the rollover state threshold value of the vehicle cannot be predicted for a long time in the background technology is solved by judging whether the rollover state threshold value of the vehicle in the pre-advancing path reaches the rollover state threshold value of the vehicle.
Example III
In another aspect, referring to fig. 3, a schematic diagram of a vehicle rollover warning device is shown, and the vehicle rollover warning device is applied to a vehicle, where the device includes:
the path information acquisition module is used for acquiring current positioning information of the vehicle and a pre-aiming path of a current driving road and acquiring a global path according to the current positioning information;
the predicted path information acquisition module is used for carrying out fitting prediction according to the pre-aiming path and the global path to obtain a predicted path;
the pre-traveling path determining module is used for fitting the current positioning, the predicted path and the pre-aiming path of the vehicle to obtain a pre-traveling path of the vehicle;
the vehicle state acquisition module is used for predicting vehicle state data when the vehicle runs on each point of the pre-running path according to the current running parameters of the vehicle and the pre-running path of the vehicle, wherein the vehicle state data comprise vehicle lateral acceleration, vehicle body roll angle and load transfer rate;
the judging module is used for judging whether the vehicle state data reach a vehicle rollover state threshold value or not;
and the first execution unit is used for triggering vehicle rollover risk early warning when the vehicle state data reach a vehicle rollover state threshold value.
Further, in some optional embodiments of the present application, the path information obtaining module may further include:
the vehicle current positioning acquisition unit is used for acquiring the vehicle positioning and the history path of the current driving road;
and correcting the vehicle positioning to be the current positioning of the vehicle according to the historical path and the pre-aiming path.
Further, in some optional embodiments of the present application, the current location obtaining unit of the vehicle further includes:
and the vehicle current positioning correction subunit is used for determining an intersection point between the history path and the pre-aiming path according to the history path and the pre-aiming path, and performing fitting comparison on the intersection point and the vehicle positioning to obtain the vehicle current positioning.
Further, in some optional embodiments of the present application, the path information obtaining module further includes:
the pre-aiming path acquisition unit is used for acquiring a road image in front of the vehicle;
extracting lane line data of the road image according to a pre-trained deep learning algorithm model;
and fitting the lane line data with a pre-trained lane model to obtain the pre-aiming path.
Further, in some optional embodiments of the present application, the vehicle state data calculation module may further include:
the optimal steering wheel rotation angle acquisition unit is used for acquiring ideal lateral acceleration when the vehicle runs to the pre-running path according to the pre-running path;
obtaining an ideal steering wheel angle required by reaching the ideal lateral acceleration according to the ideal lateral acceleration;
acquiring actual lateral acceleration when the vehicle runs to the pre-running path, and calculating an error between the actual lateral acceleration and the ideal lateral acceleration;
correcting the ideal steering wheel angle according to the error to obtain the optimal steering wheel angle;
and the vehicle state data acquisition unit is used for inputting the optimal steering wheel angle into a pre-trained rollover dynamics model to obtain the lateral acceleration, the vehicle body roll angle and the load transfer rate.
Further, in some optional embodiments of the present application, the vehicle state data acquisition unit may further include:
the side turning dynamics model subunit is used for acquiring the lateral acceleration, the side inclination angle and the steering wheel rotation angle of the vehicle; inputting the vehicle lateral acceleration, the vehicle body roll angle and the steering wheel corner into a three-degree-of-freedom nonlinear dynamics model of a vehicle to obtain the vehicle state data;
and the rollover dynamics model correction subunit is used for acquiring an actual measurement value of a vehicle structural parameter when the vehicle state data reach a rollover state threshold value, and adjusting the vehicle structural parameter in the nonlinear dynamics model to the actual measurement value, wherein the vehicle structural parameter comprises a vehicle sprung mass, a centroid height and a centroid front-rear position.
Further, in some optional embodiments of the present application, the apparatus may further include:
the current vehicle state acquisition module is used for acquiring the current steering wheel rotation angle and the steering rate of the vehicle; and inputting the current steering wheel angle and the current steering rate into the rollover dynamics model to obtain current vehicle state data.
Further, in some optional embodiments of the present application, the apparatus may further include:
the vehicle state fusion module is used for fusing the vehicle state data under the pre-running path and the current vehicle state data through an S-function; triggering rollover risk early warning if the fused vehicle state data reaches a rollover state threshold value;
a judging module for judging whether the fused vehicle state data reaches a rollover state threshold value,
and the first execution unit is used for triggering rollover risk early warning when the fused vehicle state data reaches a rollover state threshold value.
In summary, in the vehicle rollover warning device in the above embodiment of the present application, the pre-aiming path of the vehicle is fitted to the global path to predict the path that is not detected by the vision in front of the vehicle, and the current positioning of the vehicle is combined with the predicted path and the pre-aiming path to fit the predicted path to obtain the pre-advancing path of the vehicle, so that the predicted traveling path of the vehicle is advanced to a longer distance in front, a longer distance basis is provided for vehicle rollover warning, and by judging whether the vehicle in the pre-advancing path reaches the rollover state threshold value, the long-time vehicle rollover state prediction is realized, the rollover accident can be avoided earlier, and the problems that the long-time rollover state threshold value cannot be predicted in the background technology, the response time of the driver is shorter, and the rollover accident cannot be avoided in time are solved.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the vehicle rollover warning method in any one of the above embodiments.
Example IV
The application also provides a vehicle rollover warning system, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the vehicle rollover warning method in the embodiment is realized when the processor executes the program. The processor may be an Electronic Control Unit (ECU), a central processing unit (CentralProcessingUnit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, for executing program codes or processing data stored in the memory, such as executing an access restriction program, or the like.
Wherein the memory comprises at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory may in some embodiments be an internal storage unit of the vehicle, such as a hard disk of the vehicle. The memory may in other embodiments also be an external storage device of the vehicle, such as a plug-in hard disk provided on the vehicle, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), or the like. Further, the memory may also include both internal storage units and external storage devices of the vehicle. The memory may be used not only for storing application software installed in a vehicle and various types of data, but also for temporarily storing data that has been output or is to be output.
In summary, in the vehicle rollover warning system in the above embodiment of the present application, the pre-aiming path of the vehicle is fitted to the global path, so as to predict the path that is not detected by the vision in front of the vehicle, and the current positioning of the vehicle is combined with the predicted path and the pre-aiming path to fit the predicted path to obtain the pre-advancing path of the vehicle, so that the predicted traveling path of the vehicle is advanced to a longer distance in front, a longer distance basis is provided for vehicle rollover warning, and by judging whether the vehicle reaches the rollover state threshold value in the pre-advancing path, the long-time vehicle rollover state prediction is realized, the rollover accident can be avoided earlier, and the problems that the long-time rollover state threshold value cannot be predicted in the background technology, the response time of the driver is shorter, and the rollover accident cannot be avoided in time are solved.
Those of skill in the art will appreciate that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A vehicle rollover warning method, the method comprising:
acquiring current positioning information of a vehicle and a pre-aiming path of a current driving road, and acquiring a global path according to the current positioning information;
fitting and predicting according to the pre-aiming path and the global path to obtain a predicted path;
fitting the current positioning, the predicted path and the pre-aiming path of the vehicle to obtain a pre-advancing path of the vehicle;
predicting vehicle state data when the vehicle runs on each point of the pre-running path according to the current running parameters of the vehicle and the pre-running path of the vehicle, wherein the vehicle state data comprise vehicle lateral acceleration, vehicle body roll angle and load transfer rate;
judging whether the vehicle state data reach a vehicle rollover state threshold value or not;
if yes, triggering vehicle rollover risk early warning.
2. The vehicle rollover warning method according to claim 1, wherein the step of obtaining the current location of the vehicle comprises:
acquiring a history path of a current driving road and a vehicle positioning;
and correcting the vehicle positioning to be the current positioning of the vehicle according to the historical path and the pre-aiming path.
3. The vehicle rollover warning method according to claim 2, wherein the step of correcting the vehicle positioning to the current vehicle positioning includes:
and determining an intersection point between the history path and the pre-aiming path according to the history path and the pre-aiming path, and performing fitting comparison on the intersection point and the vehicle positioning to obtain the current positioning of the vehicle.
4. The vehicle rollover warning method according to claim 1, wherein the step of acquiring the pre-aiming path includes:
acquiring a road image in front of the vehicle;
extracting lane line data of the road image according to a pre-trained deep learning algorithm model;
and fitting the lane line data with a pre-trained lane model to obtain the pre-aiming path.
5. The vehicle rollover warning method according to claim 1, wherein the step of predicting vehicle state data when the vehicle is traveling on each point of the pre-travel path includes:
according to the pre-running path, obtaining ideal lateral acceleration when the vehicle runs to the pre-running path;
obtaining an ideal steering wheel angle required by reaching the ideal lateral acceleration according to the ideal lateral acceleration;
acquiring actual lateral acceleration when the vehicle runs to the pre-running path, and calculating an error between the actual lateral acceleration and the ideal lateral acceleration;
correcting the ideal steering wheel angle according to the error to obtain an optimal steering wheel angle;
and inputting the optimal steering wheel rotation angle into a pre-trained rollover dynamics model to obtain the lateral acceleration, the vehicle body roll angle and the load transfer rate.
6. The vehicle rollover warning method according to claim 5, wherein the rollover dynamics model includes:
acquiring the lateral acceleration, the roll angle and the steering wheel rotation angle of the vehicle;
inputting the vehicle lateral acceleration, the vehicle body roll angle and the steering wheel corner into a three-degree-of-freedom nonlinear dynamics model of a vehicle to obtain the vehicle state data;
when the vehicle state data reach the rollover state threshold, acquiring an actual measurement value of a vehicle structural parameter, and adjusting the vehicle structural parameter in the nonlinear dynamics model to the actual measurement value, wherein the vehicle structural parameter comprises a vehicle sprung mass, a centroid height and a centroid front-rear position.
7. The vehicle rollover warning method according to claim 6, wherein the method further comprises:
acquiring the current steering wheel angle and the steering rate of the vehicle;
and inputting the current steering wheel angle and the current steering rate into the rollover dynamics model to obtain current vehicle state data.
8. The vehicle rollover warning method according to claim 7, wherein the method further comprises,
fusing the vehicle state data under the pre-running path and the current vehicle state data through an S-function;
and triggering rollover risk early warning if the fused vehicle state data reaches the rollover state threshold value.
9. A vehicle rollover warning device is characterized in that the device comprises,
the path information acquisition module is used for acquiring current positioning information of the vehicle and a pre-aiming path of a current driving road and acquiring a global path according to the current positioning information;
the predicted path information acquisition module is used for carrying out fitting prediction according to the pre-aiming path and the global path to obtain a predicted path;
the pre-traveling path determining module is used for fitting the current positioning, the predicted path and the pre-aiming path of the vehicle to obtain a pre-traveling path of the vehicle;
the vehicle state calculation module is used for predicting vehicle state data when the vehicle runs on each point of the pre-running path according to the current running parameters of the vehicle and the pre-running path of the vehicle, wherein the vehicle state data comprise vehicle lateral acceleration, vehicle body roll angle and load transfer rate;
the judging module is used for judging whether the vehicle state data reach a vehicle rollover state threshold value or not;
and the first execution unit is used for triggering vehicle rollover risk early warning when the vehicle state data reach a vehicle rollover state threshold value.
10. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when executed by a processor, the computer program implements the vehicle rollover warning method according to any one of claims 1-8.
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