CN112766306A - Deceleration strip area identification method based on SVM algorithm - Google Patents
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Abstract
The invention relates to the technical field of vehicle auxiliary driving, and discloses a deceleration strip area identification method based on an SVM algorithm, which comprises the following steps: acquiring real-time running state parameters of a vehicle, wherein the running state parameters comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement; and inputting the vehicle speed, the calculated tire vertical maximum vibration amplitude and the tire vertical maximum vibration frequency into a preset deceleration strip area recognition model based on an SVM algorithm, and recognizing whether the vehicle runs in a deceleration strip area or not through the deceleration strip area recognition model. The method establishes the deceleration strip area recognition model based on the SVM algorithm by simulating the data acquisition of the driving test, has the advantages of convenient data acquisition, low cost, small calculated amount, high calculating speed and good robustness, and overcomes the defects of the prior art.
Description
Technical Field
The invention relates to the technical field of vehicle auxiliary driving, in particular to a deceleration strip area identification method based on an SVM algorithm.
Background
Speed bumps are a common type of road surface that occurs during vehicle travel. For the automobile equipped with adjustable suspensions such as an air suspension, timely identification of the deceleration strip can help the ECU to properly adjust the suspension stiffness or damping, so that the vehicle can smoothly pass through the deceleration strip area, the comfort of passengers is improved, and the adverse effect of the deceleration strip is reduced.
The chinese patent with the application number of CN201910552000.5 and the name of "a deceleration strip recognition method and system" proposes a method for realizing deceleration strip recognition by using a convolutional neural network technology, which requires a large amount of memorability training of relevant images of a deceleration strip on the one hand, and on the other hand, in the actual application process, the image quality is sensitive to weather and illumination, and the robustness is difficult to guarantee.
The Chinese patent with the application number of CN202010489774.0 and the name of 'a deceleration strip detection method and device based on vision and a storage medium thereof' also provides a deceleration strip identification method based on a machine vision technology, and the method mainly uses a deep learning algorithm for modeling. This method is computationally intensive and presents similar problems as the aforementioned patent.
Therefore, a deceleration strip area identification method which is good in robustness and fast in calculation is needed.
Disclosure of Invention
The invention mainly aims to develop a deceleration strip area identification method based on an SVM (support vector machine) algorithm, which has the advantages of good robustness, small calculated amount and high calculation speed, so as to overcome the defects of the prior art.
In order to achieve the purpose, the invention provides a deceleration strip area identification method based on an SVM algorithm, which mainly comprises the following steps:
acquiring real-time running state parameters of a vehicle, wherein the running state parameters comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement;
and inputting the vehicle speed, the calculated tire vertical maximum vibration amplitude and the tire vertical maximum vibration frequency into a preset deceleration strip area recognition model based on an SVM algorithm, and recognizing whether the vehicle runs in a deceleration strip area or not through the deceleration strip area recognition model.
Preferably, the deceleration strip area identification model based on the SVM algorithm is obtained through the following modeling steps:
and (3) carrying out a simulated driving test: the method comprises the following steps that a driver uses a simulation driver to control a simulation vehicle to run through a virtual road comprising a deceleration strip area, and collected test data comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement;
processing test data: intercepting test data according to a certain time window length and a certain time window interval, solving the average vehicle speed and the average vehicle body vertical acceleration in each time window, and calculating through Fourier transform to obtain the tire vertical maximum vibration amplitude and the tire vertical maximum amplitude vibration frequency; marking all data in the deceleration strip area during driving as deceleration strip data to obtain a deceleration strip data set; marking all data in the driving process outside the deceleration zone as non-deceleration-zone data to obtain a non-deceleration-zone data set;
training a deceleration strip area recognition model based on an SVM algorithm: training to obtain a deceleration strip recognition model based on a deceleration strip data set, a non-deceleration strip data set and an SVM algorithm; in the training process, the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum vibration amplitude vibration frequency are used as independent variables, and data marks in data points are used as dependent variables; and judging whether the vehicle runs in the deceleration zone according to the calculated data mark when the obtained deceleration zone identification model based on the SVM algorithm is used.
Further preferably, in the simulation driving test, the virtual road environment comprises urban working conditions and high-speed working conditions; the total driving mileage exceeds 100km, wherein the length of a deceleration strip area accounts for 1/3 of the total mileage; the frequency of acquisition of the test data was 10 Hz.
Further preferably, when the test data is processed, the test data is intercepted with the certain time duration t being 2s as the window length and L being 0.5s as the time window interval, the average vehicle speed and the average vehicle body vertical acceleration in each time window are obtained, and the tire vertical maximum vibration amplitude and the tire vertical maximum amplitude vibration frequency are obtained through fourier transform calculation.
Further preferably, the deceleration strip data set and the non-deceleration strip data set are randomly divided into two parts according to a certain proportion to obtain a deceleration strip training data set, a deceleration strip test data set, a non-deceleration strip training data set and a non-deceleration strip test data set;
taking a set consisting of a deceleration strip training data set and a non-deceleration strip training data set as a training data set; taking a set consisting of a deceleration strip test data set and a non-deceleration strip test data set as a test data set; and training by using the training data set to obtain a deceleration strip area recognition model based on the SVM algorithm, and testing and training by using the test data set to obtain the deceleration strip area recognition model based on the SVM algorithm.
The training process of the SVM algorithm follows the following rules and steps:
the training data set is represented as:
T＝{(x1,y1),(x2,y2),...，(xn,yn)}
wherein x isiBelong to an n-dimensional space, and yiIs 1 or-1, and i ═ 1,2, n, xiIs the i-th feature vector, yiAre category labels. 1 represents a positive example; -1 represents a negative example. In the invention, the characteristic vector consists of the vehicle speed, the vehicle speed vertical acceleration and the tire vertical displacement, and the mark symbol is 1 to indicate that the vehicle is in a deceleration zone and 1 to indicate that the vehicle is in a non-deceleration zone.
1) Selecting a proper kernel function and a penalty parameter C >0, and constructing and solving a convex quadratic programming problem as shown in the following formula:
2) finding the optimal solution (typically using a gradient descent method):
3) calculation of b*
4) Classification decision function:
the invention uses a Gaussian kernel function, with a penalty parameter C equal to 15, to solve alpha*The gradient descent method is used, and the Gaussian kernel function is shown as the following formula:
further preferably, the deceleration strip data set is randomly divided into two parts according to a certain proportion k to 8:2 to obtain a deceleration strip training data set and a deceleration strip testing data set;
and randomly dividing the non-deceleration strip data set into two parts according to a certain proportion k to 8:2 to obtain a non-deceleration strip training data set and a non-deceleration strip testing data set.
More preferably, when the deceleration strip area identification model is tested, the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum vibration amplitude vibration frequency are used as input variables, and the output variables are data marks predicted by corresponding test data points; if the data mark of the test data point, which is predicted through model calculation, is consistent with the real data mark, the prediction of the deceleration strip area recognition model based on the SVM algorithm at the test data point is successful, otherwise, the prediction fails.
If the ratio of the successfully predicted test data points to the total data points in the test data set is greater than a certain threshold value alpha, the modeling is successful, otherwise, a supplementary simulation driving test is required and test data are collected.
In a specific embodiment, the threshold α is 85%. According to actual needs, the threshold value allows different values according to different precision requirements.
According to the method, a simulated driver is used for carrying out a simulated driving test to obtain test data of a deceleration zone and a non-deceleration zone of a vehicle, a deceleration zone identification model is obtained based on SVM algorithm training, and whether the vehicle is in the deceleration zone to drive or not can be accurately identified by inputting the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum amplitude vibration frequency of the vehicle according to the model, so that the vehicle is ensured to stably pass through the deceleration zone, the comfort and the safety are improved, and the limitations and the defects in the prior art are overcome. Compared with the prior art, the deceleration strip area identification method has the advantages that the model calculation amount is small, the calculation speed is high, the deceleration strip area identification method is not influenced by illumination and weather of the vehicle running environment, and the robustness is better.
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Fig. 1 is a schematic flow chart illustrating a modeling process in a deceleration strip region identification method based on an SVM algorithm according to the invention.
Detailed Description
In order to make the technical solution of the embodiments of the present invention better understood, the technical solution of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by equivalent changes and modifications by one skilled in the art based on the embodiments of the present invention, shall fall within the scope of the present invention.
Referring to fig. 1, the present embodiment provides a deceleration strip area identification method based on an SVM algorithm, which includes the steps of:
the method comprises the following steps: acquiring real-time running state parameters of a vehicle, wherein the running state parameters comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement;
step two: and inputting the vehicle speed, the calculated tire vertical maximum vibration amplitude and the tire vertical maximum vibration frequency into a preset deceleration strip area recognition model based on an SVM algorithm, and recognizing whether the vehicle runs in a deceleration strip area or not through the deceleration strip area recognition model.
Referring to fig. 1, the deceleration strip area recognition model based on the SVM algorithm preset in the step two is obtained through the following modeling steps S1-S4:
s1, performing a driver in-loop simulation driving test and acquiring data:
a driver uses a simulation driver to control a simulation vehicle to run through a virtual road comprising a deceleration strip area, and in a simulation driving test, a virtual road environment comprises urban working conditions and high-speed working conditions. The total mileage is driven over 100km, wherein the length of the deceleration strip area accounts for 1/3 of the total mileage. The acquired test data comprises the vehicle speed, the vehicle speed vertical acceleration and the tire vertical displacement, and the acquisition frequency of the test data is 10 Hz.
S2, processing test data:
performing Fourier transform on the vertical displacement of the tire according to a certain time window length t being 2s and a time window interval L being 0.5s to obtain the maximum vibration amplitude and the corresponding vibration frequency of the tire, namely the maximum vibration amplitude in the vertical direction of the tire and the maximum vibration frequency in the vertical direction of the tire; and (3) calculating the average vertical acceleration of the vehicle body and the average vehicle body according to the certain time window length t being 2s and the time window interval L being 0.05 s.
Marking all data in the deceleration strip area during driving as deceleration strip data to obtain a deceleration strip data set; and marking all data in the driving process outside the deceleration zone as non-deceleration-zone data to obtain a non-deceleration-zone data set. In the present embodiment, the data flag of the deceleration strip data is represented by the number "1", and the data flag of the non-deceleration strip data is represented by the number "0".
Randomly dividing a deceleration strip data set into two parts according to a certain proportion k to 8:2 to obtain a deceleration strip training data set and a deceleration strip testing data set; and randomly dividing the non-deceleration strip data set into two parts according to a certain proportion k to 8:2 to obtain a non-deceleration strip training data set and a non-deceleration strip testing data set.
Taking a set consisting of a deceleration strip training data set and a non-deceleration strip training data set as a training data set; and taking a set consisting of the deceleration strip test data set and the non-deceleration strip test data set as a test data set.
S3, training deceleration strip area recognition model based on SVM algorithm
When the identification model is trained, training is carried out by using a training data set to obtain a deceleration strip area identification model based on an SVM algorithm; in the training process, the vehicle speed, the maximum vibration amplitude of the tire vertical direction and the vibration frequency of the maximum vibration amplitude of the tire vertical direction are used as independent variables, and data marks in data points are used as dependent variables.
The training process of the SVM algorithm follows the following rules and steps:
the training data set is represented as:
T＝{(x1,y1),(x2,y2),...，(xn,yn)}
wherein x isiBelong to an n-dimensional space, and yiIs 1 or-1, and i ═ 1,2, n, xiIs the i-th feature vector, yiAre category labels. 1 represents a positive example; -1 represents a negative example. In the invention, the characteristic vector consists of the vehicle speed, the vehicle speed vertical acceleration and the tire vertical displacement, and the mark symbol is 1 to indicate that the vehicle is in a deceleration zone and 1 to indicate that the vehicle is in a non-deceleration zone.
1) Selecting a proper kernel function and a penalty parameter C >0, and constructing and solving a convex quadratic programming problem as shown in the following formula:
2) finding the optimal solution (typically using a gradient descent method):
3) calculation of b*
4) Classification decision function:
the invention uses a Gaussian kernel function, with a penalty parameter C equal to 15, to solve alpha*When the method adopts a gradient descent method,the gaussian kernel function is shown by the following formula:
s4, testing and identifying the model:
and testing the deceleration strip area recognition model based on the SVM algorithm obtained by training by using the test data set. When the deceleration strip area identification model is tested, the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum amplitude vibration frequency are used as input variables, and the output variables are data marks predicted by corresponding test data points; if the data mark of the test data point, which is predicted through model calculation, is consistent with the real data mark, the prediction of the deceleration strip area recognition model based on the SVM algorithm at the test data point is successful, otherwise, the prediction fails.
If the ratio of the successfully predicted test data points to the total data points in the test data set is greater than a certain threshold value alpha, the modeling is successful, otherwise, a supplementary simulation driving test is required and test data are collected. In this embodiment, the threshold α is 85%. The threshold value allows different values according to different precision requirements.
After modeling is completed, the deceleration zone recognition method carries out deceleration zone recognition according to the obtained deceleration zone recognition model based on the SVM algorithm. In the running process of the vehicle, collecting running state parameters of the vehicle in real time, wherein the running state parameters comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement, calculating average vehicle speed and average vehicle body vertical acceleration by taking 2s as the length of a time window, and calculating to obtain tire vertical maximum vibration amplitude and tire vertical maximum amplitude vibration frequency through Fourier transform; and inputting the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum vibration amplitude vibration frequency into a deceleration strip area recognition model based on an SVM algorithm to obtain a predicted data mark, and judging that the vehicle runs in a deceleration strip area if the output value of the data mark is 1. At this time, the ECU of the vehicle can appropriately adjust the suspension stiffness or damping to smooth the vehicle through the deceleration strip region. And if the output value of the data mark is 0, judging that the vehicle runs in a non-deceleration zone.
Compared with the prior art, the method has the advantages of convenience in modeling data acquisition, low modeling cost, small model calculation amount and high calculation speed, only needs to detect the driving state parameters of the vehicle in the model using step, is not interfered by the road light environment and weather, is more accurate in identification of the deceleration strip, is better in robustness, and effectively overcomes the limitations of the prior art.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention; also, the above description should be understood as being readily apparent to those skilled in the relevant art and can be implemented, and therefore, other equivalent changes and modifications without departing from the concept disclosed herein are intended to be included within the scope of the present invention.
Claims (9)
1. A deceleration strip area identification method based on an SVM algorithm is characterized by comprising the following steps:
acquiring real-time running state parameters of a vehicle, wherein the running state parameters comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement;
and inputting the vehicle speed, the calculated tire vertical maximum vibration amplitude and the tire vertical maximum vibration frequency into a preset deceleration strip area recognition model based on an SVM algorithm, and recognizing whether the vehicle runs in a deceleration strip area or not through the deceleration strip area recognition model.
2. The deceleration strip area identification method based on the SVM algorithm according to claim 1, wherein: the deceleration strip area identification model based on the SVM algorithm is obtained through the following modeling steps:
and (3) carrying out a simulated driving test: the method comprises the following steps that a driver uses a simulation driver to control a simulation vehicle to run through a virtual road comprising a deceleration strip area, and collected test data comprise vehicle speed, vehicle speed vertical acceleration and tire vertical displacement;
processing test data: intercepting test data according to a certain time window length and a certain time window interval, solving the average vehicle speed and the average vehicle body vertical acceleration in each time window, and calculating through Fourier transform to obtain the tire vertical maximum vibration amplitude and the tire vertical maximum amplitude vibration frequency; marking all data in the deceleration strip area during driving as deceleration strip data to obtain a deceleration strip data set; marking all data in the driving process outside the deceleration zone as non-deceleration-zone data to obtain a non-deceleration-zone data set;
training a deceleration strip area recognition model based on an SVM algorithm: training to obtain a deceleration strip recognition model based on a deceleration strip data set, a non-deceleration strip data set and an SVM algorithm; in the training process, the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum vibration amplitude vibration frequency are used as independent variables, and data marks in data points are used as dependent variables; and judging whether the vehicle runs in the deceleration zone according to the calculated data mark when the obtained deceleration zone identification model based on the SVM algorithm is used.
3. The deceleration strip area identification method based on the SVM algorithm according to claim 2, wherein: in the simulation driving test, the virtual road environment comprises urban working conditions and high-speed working conditions; the total driving mileage exceeds 100km, wherein the length of a deceleration strip area accounts for 1/3 of the total mileage; the frequency of acquisition of the test data was 10 Hz.
4. The deceleration strip area identification method based on the SVM algorithm according to claim 2, wherein: and when the test data are processed, intercepting the test data by taking the certain time length t equal to 2s as the window length and L equal to 0.5s as the time window interval, solving the average vehicle speed and the average vehicle body vertical acceleration in each time window, and calculating by Fourier transform to obtain the tire vertical maximum vibration amplitude and the tire vertical maximum vibration frequency.
5. The deceleration strip area identification method based on the SVM algorithm according to claim 2, wherein: randomly dividing a deceleration strip data set and a non-deceleration strip data set into two parts according to a certain proportion to obtain a deceleration strip training data set, a deceleration strip test data set, a non-deceleration strip training data set and a non-deceleration strip test data set;
taking a set consisting of a deceleration strip training data set and a non-deceleration strip training data set as a training data set; taking a set consisting of a deceleration strip test data set and a non-deceleration strip test data set as a test data set; and training by using the training data set to obtain a deceleration strip area recognition model based on the SVM algorithm, and testing and training by using the test data set to obtain the deceleration strip area recognition model based on the SVM algorithm.
6. The deceleration strip area identification method based on the SVM algorithm according to claim 5, wherein:
randomly dividing a deceleration strip data set into two parts according to a certain proportion k to 8:2 to obtain a deceleration strip training data set and a deceleration strip testing data set;
and randomly dividing the non-deceleration strip data set into two parts according to a certain proportion k to 8:2 to obtain a non-deceleration strip training data set and a non-deceleration strip testing data set.
7. The deceleration strip area identification method based on the SVM algorithm according to claim 5 or 6, wherein: when the deceleration strip area identification model is tested, the vehicle speed, the tire vertical maximum vibration amplitude and the tire vertical maximum amplitude vibration frequency are used as input variables, and the output variables are data marks predicted by corresponding test data points; if the data mark of the test data point, which is predicted through model calculation, is consistent with the real data mark, the prediction of the deceleration strip area recognition model based on the SVM algorithm at the test data point is successful, otherwise, the prediction fails.
8. The deceleration strip area identification method based on the SVM algorithm according to claim 7, wherein if the ratio of the successfully predicted test data points to the total number of data points in the test data set is greater than a certain threshold value α, the modeling is successful, otherwise, a supplementary simulation driving test is required and the test data are collected.
9. The method for identifying deceleration strip areas based on SVM algorithm according to claim 8, wherein the threshold α is 85%.
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