CN112396120A - SVM algorithm-based vehicle lane change intention recognition modeling method - Google Patents
SVM algorithm-based vehicle lane change intention recognition modeling method Download PDFInfo
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Abstract
The invention relates to the technical field of vehicle safety, and particularly discloses a vehicle lane change intention recognition modeling method based on an SVM algorithm, which comprises the following steps: carrying out a simulated driving test and recording a video in the whole process, wherein the collected test data comprises a vehicle speed, a steering wheel corner and a steering wheel angular speed; intercepting test data according to the test video to obtain a lane keeping data set, a left conversion lane data set and a right conversion lane data set, and marking corresponding working condition type labels; randomly dividing the data in each data set according to a proportion to form a training data set and a testing data set; training a vehicle lane change intention recognition model based on an SVM algorithm by using a training data set, wherein during training, the vehicle speed, the steering wheel rotation angle and the steering wheel angular speed are used as input variables of the model, and a working condition type label is used as a dependent variable; the model is tested using the test data set. The invention adopts the simulated driver to carry out the test, has low cost and convenient and fast data acquisition, and the obtained model has the advantages of small calculated amount and high operation speed.
Description
Technical Field
The invention relates to the technical field of vehicle safety, in particular to a vehicle lane change intention recognition modeling method based on an SVM algorithm.
Background
With the continuous development of automatic driving and assistant driving technologies, automobiles are gradually entering an intelligent era. However, although driving assistance technologies such as AEB and LKA technologies have become mature, there are still a lot of accidents related to illegal lane changing of vehicles. How to effectively predict the lane change behavior of the vehicle so as to reduce related accidents and further maintain the life and property safety of people and the public property safety and social harmony is a problem worthy of being deeply researched.
The patent CN201910117515.2 proposes a method for establishing a probabilistic vehicle lane change output model based on driver lane change psychological analysis, which takes psychological factors into consideration in determining the vehicle lane change model, but the psychology is difficult to quantify and has strong randomness, so the validity of the method is questionable.
Patent CN201911172416.0 proposes an automatic driving lane changing model, an automatic driving lane changing method and a system thereof, which introduce speed bearing degree and space allowance degree to constrain lane changing behavior of an automatic driving vehicle so as to construct a matching model, and the model is complex in calculation, difficult to carry on an ECU with limited calculation capacity, and has certain limitation.
Therefore, a vehicle lane change prediction method with small calculation amount and low development cost is needed.
Disclosure of Invention
In order to solve the problems in the prior art, the invention mainly aims to provide a vehicle lane-changing intention recognition modeling method based on an SVM algorithm, so that vehicle driving related data obtained by a simulation driving test is used for establishing a vehicle lane-changing intention recognition model.
In order to achieve the aim, the invention provides a vehicle lane change intention recognition modeling method based on an SVM algorithm, which comprises the following steps:
performing a driver in-loop simulation driving test and recording a video in the whole process, wherein the collected test data comprises a vehicle speed, a steering wheel corner and a steering wheel angular speed;
intercepting test data according to time periods of the vehicle in the test video in three running conditions of maintaining the same lane running, the left switching lane and the right switching lane, obtaining a lane line keeping data set, a left switching lane data set and a right switching lane data set, and marking corresponding working condition type labels on data in each data set;
randomly dividing the data in each data set according to a proportion to form a training data set and a testing data set;
training a vehicle lane change intention recognition model based on an SVM algorithm by using a training data set, wherein during training, the vehicle speed, the steering wheel rotation angle and the steering wheel angular speed are taken as input variables of the model, and a working condition type label corresponding to the data is taken as a dependent variable;
the model is tested using the test data set.
Further, in the simulated driving test, the simulated driving system adopts a 1:1 virtual map of a real urban road and is provided with interference traffic.
Further, the test time of the driver in the simulated driving test is not less than 1 hour; the frequency of data acquisition in the experiment was 20 Hz.
Furthermore, when intercepting the test data, intercepting according to time periods of different working conditions, and the specific method comprises the following steps:
the intercepting time period of the lane line keeping data set takes the time when the vehicles keep running on the same lane line for 5s as the starting time and takes the time before the lane line is rolled for lane changing for 5s as the ending time;
the left lane change data set intercepting time period takes the time when a front wheel of a vehicle rolls a left lane line to start lane change as the starting time, and takes the time when a front wheel of the vehicle rolls the left lane line and then a steering wheel turns right for the first time as the finishing time;
the time period for intercepting the right switching lane data set takes the time when the front wheel of the vehicle rolls the right lane line to start switching as the starting time, and the time when the front wheel of the vehicle rolls the right lane line and the steering wheel returns to the right position for the first time as the ending time.
Further, the marking mode of the operating condition type label is as follows: the working condition type labels of the data in the lane line keeping data set are all 0; the working condition type labels of the data in the left conversion channel data set are all 1; the condition type labels of the data in the right transition trace dataset are all 2.
Further, when the data sets are divided, the data in the respective data sets are randomly divided at a ratio of 9: 1.
Further, when a test data set is used for carrying out vehicle lane change intention recognition model test, a working condition type label is obtained through model prediction by taking a steering wheel angle, a steering wheel angular speed and a vehicle speed as input variables; if the predicted working condition type label is the same as the originally marked working condition type label of the data, the model is successfully predicted in the data, otherwise, the model fails.
Further, if the prediction accuracy of the operating mode type labels of all the data in the test data set is more than 80%, the modeling is successful, otherwise, the simulated driving test is carried out again.
Due to the adoption of the technical scheme, the invention has the following beneficial effects: according to the method, the vehicle driving related data obtained by simulating the driver test is intercepted according to the time period of the vehicle driving working condition, so that the data sets of the vehicle driving, the left transition lane and the right transition lane along the same lane are obtained, the working condition type labels are marked, and then the vehicle lane change intention recognition model based on the SVM algorithm is established. The established vehicle lane change intention recognition model based on the SVM algorithm can accurately predict the vehicle lane change intention according to the real-time vehicle speed, the steering wheel rotation angle and the steering wheel angular speed.
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FIG. 1 is a schematic flow chart illustrating steps of a SVM algorithm-based vehicle lane-changing intention recognition modeling method according to the present 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 vehicle lane change intention recognition modeling method based on an SVM, including the following steps:
s1, testing and data acquisition:
a simulated driver is used for carrying out a real-time simulated driving test, and the driver can control an accelerator pedal, a steering wheel, gears and a brake pedal of a simulated vehicle. In the test, a 1:1 virtual map of a real urban road is adopted, and traffic interference such as pedestrian crossing the road and emergency braking of a front vehicle is arranged. The test data collected in the simulation driving test includes vehicle speed, steering wheel angle and steering wheel angular speed. The driver is required to drive according to the traffic rules during the simulated driving process, and the test time is not less than 1 hour. The sampling frequency in the test was 20 Hz. And (5) recording the whole driving test.
S2, processing test data:
and after the driving simulation test is finished, according to the test video and the time periods when the vehicle is in different working conditions, intercepting relevant data in the test data to obtain a lane line holding data set, a left conversion lane data set and a right conversion lane data set. The intercepting time period of the lane line keeping data set takes the time when the vehicles keep running on the same lane line for 5s as the starting time and takes the time before the lane line is rolled for lane changing for 5s as the ending time; the left lane change data set intercepting time period takes the time when a front wheel of a vehicle rolls a left lane line to start lane change as the starting time, and takes the time when a front wheel of the vehicle rolls the left lane line and then a steering wheel turns right for the first time as the finishing time; the time period for intercepting the right switching lane data set takes the time when the front wheel of the vehicle rolls the right lane line to start switching as the starting time, and the time when the front wheel of the vehicle rolls the right lane line and the steering wheel returns to the right position for the first time as the ending time. The data in each working condition data set needs to be added with a working condition type label, and in the embodiment, the working condition type labels of the data in the lane line keeping data set are all 0; the working condition type labels of the data in the left conversion channel data set are all 1; the condition type labels of the data in the right transition trace dataset are all 2.
S3, dividing a data set:
the data in each data set was randomly divided at a 9:1 ratio and composed into a training data set and a test data set.
S4, training a model:
training a vehicle lane change intention recognition model based on an SVM algorithm by using a training data set; during training, the steering wheel angle, the steering wheel angular speed and the vehicle speed are used as input variables, and the working condition type label corresponding to the data is used as a dependent variable.
S5, testing a model:
when the test data set is used for vehicle lane change intention recognition model test, the operating condition type label is obtained through model prediction by taking the steering wheel angle, the steering wheel angular speed and the vehicle speed as input variables. If the predicted working condition type label is the same as the original working condition type label of the data, the model is successfully predicted in the data, otherwise, the model fails. If the recognition accuracy is more than 80%, the modeling is successful, otherwise, the simulation driving test is carried out again.
The vehicle lane-changing intention recognition model which is qualified in test is obtained through the modeling method, and the lane-changing intention of the vehicle can be accurately predicted according to the real-time vehicle speed, the steering wheel turning angle and the steering wheel angular speed of the vehicle. Compared with the prior art, the method adopts the simulated driver to carry out the test, has low cost and convenient and fast data acquisition, uses the SVM algorithm to carry out modeling, has small calculated amount and high operation speed of the obtained model, and overcomes the limitation of the prior art to a certain extent.
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 (8)
1. A vehicle lane change intention recognition modeling method based on an SVM algorithm is characterized by comprising the following steps:
performing a driver in-loop simulation driving test and recording a video in the whole process, wherein the collected test data comprises a vehicle speed, a steering wheel corner and a steering wheel angular speed;
intercepting test data according to time periods of the vehicle in the test video in three running conditions of maintaining the same lane running, the left switching lane and the right switching lane, obtaining a lane line keeping data set, a left switching lane data set and a right switching lane data set, and marking corresponding working condition type labels on data in each data set;
randomly dividing the data in each data set according to a proportion to form a training data set and a testing data set;
training a vehicle lane change intention recognition model based on an SVM algorithm by using a training data set, wherein during training, the vehicle speed, the steering wheel rotation angle and the steering wheel angular speed are taken as input variables of the model, and a working condition type label corresponding to the data is taken as a dependent variable;
the model is tested using the test data set.
2. The SVM algorithm-based vehicle lane change intention recognition modeling method as claimed in claim 1, wherein in the simulated driving test, a simulated driving system adopts a 1:1 virtual map of a real urban road and is provided with interference traffic.
3. The SVM algorithm-based vehicle lane-changing intention recognition modeling method as claimed in claim 1, wherein the test duration of a driver in a simulated driving test is not less than 1 hour; the frequency of data acquisition in the experiment was 20 Hz.
4. The SVM algorithm-based vehicle lane change intention recognition modeling method as claimed in claim 1, wherein the interception is performed according to time periods of different working conditions when the test data is intercepted, and the specific method is as follows:
the intercepting time period of the lane line keeping data set takes the time when the vehicles keep running on the same lane line for 5s as the starting time and takes the time before the lane line is rolled for lane changing for 5s as the ending time;
the left lane change data set intercepting time period takes the time when a front wheel of a vehicle rolls a left lane line to start lane change as the starting time, and takes the time when a front wheel of the vehicle rolls the left lane line and then a steering wheel turns right for the first time as the finishing time;
the time period for intercepting the right switching lane data set takes the time when the front wheel of the vehicle rolls the right lane line to start switching as the starting time, and the time when the front wheel of the vehicle rolls the right lane line and the steering wheel returns to the right position for the first time as the ending time.
5. The SVM algorithm-based vehicle lane change intention recognition modeling method according to claim 1, wherein the labeling manner of the operating condition type label is as follows: the working condition type labels of the data in the lane line keeping data set are all 0; the working condition type labels of the data in the left conversion channel data set are all 1; the condition type labels of the data in the right transition trace dataset are all 2.
6. The SVM algorithm-based vehicle lane-change intention recognition modeling method of claim 1, wherein the data sets are divided, and the data in each data set is randomly divided in a 9:1 ratio.
7. The SVM algorithm-based vehicle lane-changing intention recognition modeling method according to claim 1, wherein when a test data set is used for vehicle lane-changing intention recognition model test, a working condition type label is obtained through model prediction by using a steering wheel angle, a steering wheel angular speed and a vehicle speed as input variables; if the predicted working condition type label is the same as the originally marked working condition type label of the data, the model is successfully predicted in the data, otherwise, the model fails.
8. The SVM algorithm-based vehicle lane-changing intention recognition modeling method of claim 7, wherein if the prediction accuracy of the operating condition type labels of all the data in the test data set is more than 80%, the modeling is successful, otherwise, the simulation driving test is carried out again.
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