CN110210339B - Method for identifying road jolt by fusing multiple sensors for ECAS system - Google Patents
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Abstract
The invention discloses a method for identifying road surface jolt by multi-sensor fusion for an ECAS system, which comprises the steps of firstly obtaining an identification model of road surface jolt degree through field test, acquiring vehicle attitude change information such as displacement, angular velocity and acceleration through an existing height sensor, a yaw sensor and a three-axis acceleration sensor of the ECAS in the test process of enabling a vehicle to pass through the road surface with known jolt degree, preprocessing the acquired vehicle attitude change information to improve signal quality and extract effective signals, and acquiring boundary threshold values of each effective signal aiming at the road surfaces with different jolt degrees; and then, when the road surface with unknown bump degree is on, the identification model is used for carrying out preliminary judgment on each effective signal, finally, the decision-level fusion algorithm is used for judging the bump degree of the road surface, and the final judgment result is sent to the ECAS system. The invention provides road surface bumping degree information for the self-adaptive adjustment of the air suspension by utilizing the existing sensor module of the ECAS system.
Description
Technical Field
The invention relates to the technical field of road information identification, in particular to a method for identifying road jolt by fusing multiple sensors for an ECAS system.
Background
With the increasing degree of intellectualization in the automobile electronic industry, it is increasingly challenging to obtain a high-quality road surface bump recognition result. The degree of road surface jolt affects not only the comfort of the passengers, but also the driving safety of the automobile. Different from a miniaturized passenger car, the large commercial vehicle has large load and high running strength, and has stronger requirements on rapid identification and response of road jolt. In particular, electronic control air suspension systems (ECASs) are gradually popularized on large commercial vehicle platforms in recent years, and uncontrolled oscillation of the air suspension is easily caused by continuously bumpy roads, so that the air suspension system is damaged, and the life safety of drivers and passengers is threatened. Therefore, the height of the vehicle frame needs to be accurately and adaptively adjusted according to the road surface bumping condition, and the method for identifying the road surface bumping, which is quick, efficient and stable, has important practical significance as the basis for adjusting the vehicle frame.
The existing identification method of road bump is usually based on a three-axis acceleration sensor or a wheel acceleration sensor, and does not fully utilize the current motion state and vehicle attitude information of a vehicle; in addition, the algorithm brings high hardware cost and is difficult to effectively develop under the condition of limited automobile hardware resources.
Disclosure of Invention
The invention provides a method for identifying road surface jolt by multi-sensor fusion for an ECAS (engineering-based application) system, which aims at the ECAS system, identifies the road surface jolt on the basis of not adding additional sensors, and has the characteristics of low cost, high real-time performance, high accuracy, good mobility and adaptability among application scenes and the like.
The purpose of the invention is realized by the following technical scheme: a method for identifying road bump by multi-sensor fusion for an ECAS system comprises the following steps:
(1) dividing the road surface into N classes according to the continuous fluctuation degree and the gradient information of the road surface, and recording the N classes as { x }1，x2，......，xN}; then, a field test is carried out, the vehicle passes through the road surfaces with different known bump degrees, and the vehicle attitude change signals are collected through a plurality of sensors of the ECAS system: suspension moving stroke signals of the front axle and the rear axle are respectively collected through a front axle and rear axle height sensor, longitudinal axis angular velocity signals are collected through a yaw sensor, and x-axis acceleration signals, y-axis acceleration signals and z-axis acceleration signals are collected through a three-axis acceleration sensor.
(2) Vehicle attitude change signals collected by multiple sensors are preprocessed to improve signal quality, and the method specifically comprises the following steps:
(2.1) filtering and denoising the vehicle attitude change signal;
(2.2) extracting effective signals in the filtered and noise-reduced vehicle attitude change signals:
for the suspension dynamic stroke signal, the height of the suspension height dynamic stroke signal collected by the front axle and the rear axle when the balance position is removed after the arithmetic mean of the suspension height dynamic stroke signal is carried out, a change signal of the suspension dynamic stroke is output, and the mean value signal i of the vehicle motion waveform is calculated1(t)；
For the longitudinal axis angular velocity signal, the angular acceleration signal i of the longitudinal axis swing is calculated2(t)；
For the acceleration signals of the x, y and z axes, the acceleration signals of the x and y axes are used for calculating a vehicle plane motion acceleration signal i3(t) calculating a variance signal i of a vehicle motion waveform using the z-axis acceleration signal4(t)。
(3) And (3) acquiring a boundary threshold value of each effective signal extracted in the step (2.2) aiming at the road surfaces with different bumping degrees, so as to establish a road bumping degree identification model.
For i1(t) and i4(t) signals which form the plane waveform change of the automobile motion, and classifying all the tested signals through a K-means algorithm to obtain boundary thresholds corresponding to pavements with different bumping degrees;
for i2(t) and i3(t) signals constituting the three-dimensional waveform variations of the vehicle motion, all the signals obtained by the test being classified by means of a Support Vector Machine (SVM) algorithm, obtaining the differencesA boundary threshold value corresponding to a road surface with a bumping degree;
for ip(t) degree of road surface jounce x ═ xqThat is, when the grade q road surface jounce degree is reached, the boundary threshold value is recorded as (i)pq min，ipq max)。
(4) And when the vehicle passes through the road surface with unknown jolting degree, repeating the steps 1 and 2, and respectively comparing the extracted four effective signals with the boundary threshold values of the road surfaces with N types of jolting degrees to obtain 4N primary road surface jolting degree judgment results.
(5) And (4) obtaining a final road surface bumping degree judgment result by utilizing an optimal distributed detection fusion algorithm based on a parallel structure fusion system for the preliminary judgment result obtained in the step (4), and sending the final road surface bumping degree judgment result to an ECAS system suspension ECU through a CAN bus protocol.
Furthermore, the motion waveform of the vehicle passing through a bumpy road surface can be output according to the extracted effective signal, and an intuitive graphical human-computer interaction interface can be provided for users and debugging personnel.
Further, the implementation of the optimal distributed detection fusion algorithm based on the parallel structure fusion system in the step (5) is specifically as follows:
assuming that the parallel structure fusion system is composed of a fusion center and four effective signals, H0Indicating that the degree of such jolting is false, by H1Indicating that the degree of such jolting is true; the p-th effective signal is according to the observed value ipIndependently judging one type of road surface with the degree of bump (if N kinds of road surfaces with the degree of bump need to be subjected to information fusion for N times), and judging a result upSent to the fusion center. u. ofp0 denotes a decision H0Is true, up1 indicates that decision H1 is true. And the fusion center fuses the judgment of the effective signals and provides the final judgment result of the system.
Given a prior probability P0＝P(H0)、P1＝P(H1)，CijIs shown as HjIs true, and is judged as HiThe cost to be paid. CF＝P0(C10-C00)，CD＝P1(C01-C11)。
In order to obtain an optimal system decision rule that minimizes Bayes risk of a parallel structure fusion system, an optimal fusion rule of a fusion center needs to be obtained first.
Assuming that the decision rule of each valid signal has been determined, the optimal fusion rule is:
where Λ (u) is the likelihood ratio of the fusion center measurement, i.e., Λ (u) ═ P (u | H)1)/P(u|H0)，T＝CF/CDIs a decision threshold.
Assuming that the observations of the effective signals are independent, for any given fusion decision rule, the decision rule for optimizing the system detection performance is as follows:
in the formulap(ip) Likelihood ratios measured for a single effective signal, i.e.
j is 0, 1 is the p-th effective signal observation value ipIs determined as a conditional probability density function.
Wherein
And according to the obtained optimal judgment rule, giving the final road surface jolt degree judgment.
The invention has the following advantages and beneficial effects:
the invention only utilizes the existing sensor of the ECAS system to judge the road jolt degree, and realizes accurate and stable judgment of the road jolt degree through the information fusion technology of a plurality of sensors. The method provided by the invention is different from the existing road bump identification method, more comprehensively utilizes the vehicle motion attitude information acquired by various sensors, can be effectively implemented under the condition of limited hardware resources, is simple to operate, has good real-time performance, and is easy to implement in an actual system.
Drawings
FIG. 1 is a flow chart of the overall implementation of the method of the present invention;
FIG. 2 is a schematic diagram of the present invention for extracting valid signals and fusing information.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
As shown in fig. 1 and 2, the method for identifying road bump by multi-sensor fusion for an ECAS system provided by the present invention has the following specific implementation flow:
(1) the road surfaces were classified by the degree of jounce as shown in the following table according to the standard of the assessment of the state of road technology. The determination parameters are RQI (Riding qualityIndex) and a three-meter ruler.
Note: the RQI is preferably a rapid detection device or a high-precision section detection device. The three-meter ruler is the maximum vertical undulation degree of a section of road surface measured manually.
Then field testing was performed: the vehicle passes through the known road surfaces with different bumping degrees, and the vehicle attitude change signals are collected through a plurality of sensors of an ECAS system: suspension moving stroke signals of the front axle and the rear axle are respectively collected through a front axle and rear axle height sensor, longitudinal axis angular velocity signals are collected through a yaw sensor, and x-axis acceleration signals, y-axis acceleration signals and z-axis acceleration signals are collected through a three-axis acceleration sensor.
(2) Vehicle attitude change signals collected by multiple sensors are preprocessed to improve signal quality, and the method specifically comprises the following steps:
(2.1) filtering and denoising the vehicle attitude change signal;
(2.2) extracting effective signals in the filtered and noise-reduced vehicle attitude change signals:
for the suspension dynamic stroke signal, the height of the suspension height dynamic stroke signal collected by the front axle and the rear axle when the balance position is removed after the arithmetic mean of the suspension height dynamic stroke signal is carried out, a change signal of the suspension dynamic stroke is output, and the mean value signal i of the vehicle motion waveform is calculated1(t)；
For the longitudinal axis angular velocity signal, the angular acceleration signal i of the longitudinal axis swing is calculated2(t)；
For the acceleration signals of the x, y and z axes, the acceleration signals of the x and y axes are used for calculating a vehicle plane motion acceleration signal i3(t) calculating a variance signal i of a vehicle motion waveform using the z-axis acceleration signal4(t)。
(3) And (3) acquiring a boundary threshold value of the effective signal extracted in each step (2.2) aiming at the road surfaces with different bumping degrees, so as to establish a road bumping degree identification model.
For i1(t) motion waveform mean signal and i4And (t) the motion variance signal forms the waveform change of the plane motion of the automobile, and all signals obtained by testing are classified through a K-means algorithm to obtain a plurality of boundary threshold values of the road surfaces with different bumping degrees. For ip(t) for class q road surface jounce, x ═ xqWhen the boundary threshold is (i)pq min，ipq max). Wherein xq＝(x1，…，xq-1，xq+1，…，xN)。
For i2(t) and i3(t) signals which constitute the three-dimensional wave form change of the vehicle motion, so that the classification by means of a Support Vector Machine (SVM) algorithm yields the sameA demarcation threshold for the valid signal;
(4) and when the vehicle passes through the road surface with unknown jolting degree, repeating the steps 1 and 2, and respectively comparing the extracted four effective signals with the boundary threshold values of the road surfaces with N types of jolting degrees to obtain 4N primary road surface jolting degree judgment results.
(5) And (4) obtaining a final road surface bumping degree judgment result by utilizing an optimal distributed detection fusion algorithm based on a parallel structure fusion system for the preliminary judgment result obtained in the step (4), and sending the final road surface bumping degree judgment result to an ECAS system suspension ECU through a CAN bus protocol.
Assuming that the parallel structure fusion system is composed of a fusion center and four effective signals, H0Indicating that the degree of such jolting is false, by H1Indicating that the degree of such jolting is true; the p-th effective signal is according to the observed value ipIndependently judging one type of road surface with the degree of bump (if N kinds of road surfaces with the degree of bump need to be subjected to information fusion for N times), and judging a result upSent to the fusion center. u. ofp0 denotes a decision H0Is true, u p1 indicates that decision H1 is true. And the fusion center fuses the judgment of the effective signals and provides the final judgment result of the system.
Given a prior probability P0＝P(H0)、P1＝P(H1)，CijIs shown as HjIs true, and is judged as HiThe cost to be paid. CF＝P0(C10-C00)，CD＝P1(C01-C11)。
In order to obtain an optimal system decision rule that minimizes Bayes risk of a parallel structure fusion system, an optimal fusion rule of a fusion center needs to be obtained first.
Assuming that the decision rule of each valid signal has been determined, the optimal fusion rule is:
wherein Λ (u) is measured at the fusion centerLikelihood ratio, i.e. Λ (u) ═ P (u | H)1)/P(u|H0)，T＝CF/CDIs a decision threshold.
Assuming that the observations of the effective signals are independent, for any given fusion decision rule, the decision rule for optimizing the system detection performance is as follows:
in the formulap(ip) Likelihood ratios measured for a single effective signal, i.e.
j is 0, 1 is the p-th effective signal observation value ipIs determined as a conditional probability density function.
Wherein
The above-described embodiments are intended to illustrate the invention, not to limit the invention, which may be used for ECAS system control and also for road maintenance, and any modifications and variations made thereto within the spirit of the invention and the scope of the claims fall within the scope of the invention.
Claims (3)
1. A method for identifying road bump by multi-sensor fusion for an ECAS system is characterized by comprising the following steps:
(1) according to the degree of continuous undulation of the road surface and the slopeThe degree information divides the road surface into N classes according to the bumping degree x, and the N classes are marked as { x1，x2，......，xN}; then, a field test is carried out, the vehicle passes through the road surfaces with different known bump degrees, and the vehicle attitude change signals are collected through a plurality of sensors of the ECAS system: respectively acquiring suspension moving stroke signals of a front axle and a rear axle through a front axle and rear axle height sensor, acquiring longitudinal axis angular velocity signals through a yaw sensor, and acquiring x-axis acceleration signals, y-axis acceleration signals and z-axis acceleration signals through a three-axis acceleration sensor;
(2) vehicle attitude change signals collected by multiple sensors are preprocessed to improve signal quality, and the method specifically comprises the following steps:
(2.1) filtering and denoising the vehicle attitude change signal;
(2.2) extracting effective signals in the filtered and noise-reduced vehicle attitude change signals:
for the suspension dynamic stroke signal, the height of the suspension height dynamic stroke signal collected by the front axle and the rear axle when the balance position is removed after the arithmetic mean of the suspension height dynamic stroke signal is carried out, a change signal of the suspension dynamic stroke is output, and the mean value signal i of the vehicle motion waveform is calculated1(t)；
For the longitudinal axis angular velocity signal, the angular acceleration signal i of the longitudinal axis swing is calculated2(t)；
For the acceleration signals of the x, y and z axes, the acceleration signals of the x and y axes are used for calculating a vehicle plane motion acceleration signal i3(t) calculating a variance signal i of a vehicle motion waveform using the z-axis acceleration signal4(t)；
(3) Acquiring a boundary threshold value of each effective signal extracted in the step (2.2) aiming at the road surfaces with different bumping degrees, thereby establishing a road surface bumping degree identification model;
for i1(t) and i4(t) signals which form the plane waveform change of the automobile motion, and classifying all the tested signals through a K-means algorithm to obtain boundary thresholds corresponding to pavements with different bumping degrees;
for i2(t) and i3(t) signals constituting the three-dimensional wave shape variations of the vehicle motion, paired by Support Vector Machine (SVM) algorithmsClassifying all the tested signals to obtain boundary threshold values corresponding to the pavements with different bumping degrees;
for ip(t) degree of road surface jounce x ═ xqThat is, when the grade q road surface jounce degree is reached, the boundary threshold value is recorded as (i)pq min，ipq max)；
(4) When the vehicle passes through a road surface with unknown jolting degree, repeating the step (1) and the step (2), and respectively comparing the extracted four effective signals with boundary threshold values of N types of jolting degree road surfaces to obtain 4N primary road surface jolting degree judgment results;
(5) and (4) obtaining a final road surface bumping degree judgment result by utilizing an optimal distributed detection fusion algorithm based on a parallel structure fusion system for the preliminary judgment result obtained in the step (4), and sending the final road surface bumping degree judgment result to an ECAS system suspension ECU through a CAN bus protocol.
2. The method for identifying the road bump through the fusion of the multiple sensors of the ECAS system as claimed in claim 1, wherein the optimal distributed detection fusion algorithm based on the parallel structure fusion system in the step (5) is implemented as follows:
assuming that the parallel structure fusion system is composed of a fusion center and four effective signals, H0Indicating that the degree of such jolting is false, by H1Indicating that the degree of such jolting is true; the p-th effective signal is according to the observed value ipIndependently judging one type of road surface with the degree of bump, and judging the result upSending to a fusion center; u. ofp0 denotes a decision H0Is true, up1 denotes decision H1Is true; the fusion center fuses the judgment of a plurality of effective signals and provides the final judgment result of the system;
given a prior probability P0＝P(H0)、P1＝P(H1)，CijIs shown as HjIs true, and is judged as HiThe cost to be paid; cF＝P0(C10-C00)，CD＝P1(C01-C11)；
In order to obtain an optimal system judgment rule which minimizes Bayes risk of a parallel structure fusion system, firstly, an optimal fusion rule of a fusion center needs to be obtained;
assuming that the decision rule of each valid signal has been determined, the optimal fusion rule is:
where Λ (u) is the likelihood ratio of the fusion center measurement, i.e., Λ (u) ═ P (u | H)1)/P(u|H0)，T＝CF/CDIs a decision threshold;
assuming that the observations of the effective signals are independent, for any given fusion decision rule, the decision rule for optimizing the system detection performance is as follows:
in the formulap(ip) Likelihood ratios measured for a single effective signal, i.e.
For the p-th effective signal observation value ipThe conditional probability density function of (1);
wherein
And according to the obtained optimal judgment rule, giving the final road surface jolt degree judgment.
3. The method for identifying the road jolt through the fusion of the multiple sensors of the ECAS system as claimed in claim 1, wherein the waveform of the motion of the vehicle passing through the jolt road is outputted according to the extracted effective signal, which can be used to provide an intuitive graphical human-computer interaction interface for users and debugging personnel.
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| CN113252369B (en) * | 2021-06-24 | 2023-04-07 | 中国第一汽车股份有限公司 | Method for determining road parameters suitable for air suspension test |
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| CN114435376A (en) * | 2022-02-25 | 2022-05-06 | 重庆长安汽车股份有限公司 | Method for controlling running speed of vehicle on bumpy road surface, electronic equipment and storage medium |
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