CN111717217B - Driver intention identification method based on probability correction - Google Patents
Driver intention identification method based on probability correction Download PDFInfo
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Abstract
The invention relates to a driver intention identification method based on probability correction, and belongs to the field of unmanned automobiles. The method comprises the following steps: s1: collecting and preprocessing driving behavior data; s2: acquiring environmental road information, selecting characteristic data, carrying out primary identification on the intention of a driver by using an MGHMM model, and calculating to obtain initial probabilities of observation sequences corresponding to the intention models of the drivers; s3: initial probability P of driver intention model by combining environmental road information and collected driver data 1 Correction is carried out to respectively obtain correction probability P 2 And P 3 (ii) a S4: and inputting the corrected probability of the driver intention model into a PSO-SVM classifier for classification and identification, and identifying the final driver intention. The invention greatly improves the accuracy and the practicability of the intention recognition of the driver. The invention can be used for developing and designing a driver assistance system and realizing man-vehicle cooperative control of the unmanned vehicle.
Description
Technical Field
The invention belongs to the field of unmanned automobiles, relates to the field of driver intention identification and machine learning, and particularly relates to a driver intention identification method based on probability correction.
Background
The rapid development of the transportation industry and the steady increase of the automobile holding capacity bring convenience to the travel and life of people and also provide a severe test for road traffic safety. According to statistics, most road traffic safety accidents are directly or indirectly linked with driver misoperation, so that each automobile manufacturer continuously improves the active safety and the passive safety of own vehicle, thereby improving the safety performance of the vehicle. The driver intention is accurately and quickly identified, and a safer and more reliable collision assessment or collision early warning can be obtained, so that the safety performance of the vehicle is improved, and the life and property safety of the driver is ensured. In addition, the result of the driver intention recognition can also be used to activate various driver assistance systems, such as lane keeping systems, lane changing assistance systems, cruise control systems, etc.
In recent years, under the large background of the vigorous development of intelligent networked automobiles, human-vehicle cooperative control is widely concerned by researchers at home and abroad. On one hand, there are many key technologies to break through to achieve the L5 level of fully autonomous driving, and on the other hand, the driving will of the driver with driving interest cannot be ignored and deprived. The existing driver intention identification method has the problems of single identification information, insufficient utilization of man-vehicle-road comprehensive information, low accuracy, poor practicability and the like. Therefore, the driver intends to identify the key technology of the man-vehicle cooperative control, and needs to be further perfected and optimized to improve the safety and reliability of the man-vehicle cooperative control.
Disclosure of Invention
In view of the above, the present invention provides a driver intention identification method based on probability correction, which aims at solving the problems of single identification information, insufficient utilization of man-car-road comprehensive information, low accuracy, poor practicability, etc. of the existing driver intention identification method, improves the safety performance of vehicles, ensures the life and property safety of drivers, perfects and optimizes a man-car cooperative control method, and provides technical support for the development of unmanned driving technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a driver intention recognition method based on probability correction comprises the following steps:
s1: collecting data of driving behaviors, including vehicle data and driver data, and performing corresponding preprocessing;
s2: obtaining environment road information, selecting characteristic data, carrying out primary recognition of driver intention by using a Multi-dimensional Gaussian Hidden Markov Model (MGHMM), and calculating to obtain initial probabilities P of observation sequences corresponding to the driver intention models 1 ；
S3: initial probability P of driver intention model by combining environmental road information and collected driver data 1 Correction is carried out to respectively obtain correction probability P 2 And P 3 ；
S4: inputting the corrected probability P of the driver intention model into the SVM of which the parameters are optimized by a Particle Swarm Optimization (PSO), and carrying out classification and identification by a PSO-SVM classifier so as to identify the final driver intention.
Further, the step S1 specifically includes the following steps:
s11: collecting driving behavior data comprising vehicle data and driver data; vehicle data includes, but is not limited to, steering wheel angle, rate of change of steering wheel angle, throttle opening, brake pedal effort, speed, lateral speed, longitudinal speed, yaw rate, yaw angle, pitch angle, roll angle, and the like; driver data includes, but is not limited to, driver gaze number, gaze time, driver average saccade time, average saccade angle, average saccade velocity, driver eye horizontal movement, vertical movement, driver head yaw movement, roll movement, pitch movement, etc.;
s12: and preprocessing the acquired data, including but not limited to abnormal data elimination, missing value filling, filtering, data size range unification, class classification and the like.
Further, the step S2 specifically includes the following steps:
s21: acquiring environmental road information according to a high-precision map and an environmental perception system, and selecting characteristic data for driver intention identification;
s22: training various driver intention models by using training data, and solving parameters lambda = (pi, A, C, mu, U) of the MGHMM model by adopting a Baum-Welch algorithm, wherein pi is initial state probability distribution, A is a state transition matrix, C is a mixed Gaussian element covariance matrix, mu is a mixed Gaussian element mean matrix, and U is a mixed Gaussian element covariance matrix;
s23: calculating output probability P of observation sequence to MGHMM model by adopting forward probability 1 ( o And lambda) preliminarily identifying the intention of the driver, and calculating the probability of the observation sequence corresponding to each intention model:
wherein alpha is T (i) Representing the forward probability, N being the number of states in the model, and T being the observation sequence length.
Further, the step S21 specifically includes: extracting characteristic data including but not limited to gradient, flatness and attachment coefficient of a road, position, running speed and direction of surrounding vehicles and the like for selecting intention identification of a driver from a high-precision map and an environment perception system; if the gradient change is large, the accelerator opening is not taken as characteristic data; and when the road is uneven, the pitch angle is removed from the characteristic data.
Further, the step S3 specifically includes the following steps:
s31: calculating the correction probability P of the driver by using the environmental road information acquired by the environmental perception system and the high-precision map 2 ；
S32: obtaining a driver intention correction probability P using driver data 3 ；
S33: initial probability P output by using MGHMM model 1 And the correction probability P 2 、P 3 And obtaining the final probability P of the driver intention model as follows: p = P 1 +P 2 +P 3 。
Further, the step S31 specifically includes: based on the environmental road information, a probability correction value of the driver intention model is obtained, and the accuracy of driver intention identification is improved. If the high-precision map displays that the road in front can only turn left, a larger left-turning probability correction value is generated based on the information, so that the left-turning probability of the final intention probability value of the driver is larger; when the environment perception system perceives that the left and right sides of the vehicle are provided with the vehicle to block the left and right turning of the vehicle, a larger straight-going probability correction value is generated based on the road condition information, so that the straight-going probability of the final intention probability value of the driver is larger.
Further, the step S32 specifically includes: in order to identify the driving intention, the motion states and the motion features of eyes and heads in the driver data are extracted, the relevance between each motion state and each feature and the driving intention is obtained by a statistical method, the motion states and the features with strong relevance are selected as feature data, the driver intention is identified by using an MGHMM model, and the driver intention correction probability P is obtained 3 。
Further, the step S4 specifically includes the following steps:
s41: and optimizing a penalty parameter c and a kernel function parameter g of the support vector machine by adopting a PSO algorithm to obtain optimal classifier parameters as far as possible, wherein the updating speed and position of the particles in the PSO algorithm are compared by the following formula:
where i is any ith particle (i =1, 2.. Ang., n), M is a dimension of the solution space (M =1, 2.. Ang., M), k represents the number of times the current iteration is performed, and the position of the ith particle is x i ＝(x i1 ，x i2 ，...，x iM ) T Velocity v i ＝(v i1 ，v i2 ，...，v iM ) T Individual extreme value of p i ＝(p i1 ，p i2 ，...，p iM ) T Population extremum is p g ＝(p g1 ，p g2 ，...，p gM ) T ，r 1 、r 2 Is [0,1 ]]A random number in between, ω is a non-negative inertial weight factor, c 1 、c 2 Is a learning factor;
s42: and (2) using a support vector machine, taking the probability value arrays of the final intentions of various drivers as input feature vectors, inputting the input feature vectors into a PSO-SVM classifier for classification and recognition to obtain a final intention recognition result of the drivers, wherein the decision function of the support vector machine is as follows:
wherein, in the known sample set G = { (x) i ，y i ) I = 1.. L., (x) i ，y i ) Denotes an arbitrary ith sample, α i Is Lagrange multiplier, K (x, x) i ) B is the offset for the support vector machine kernel.
The invention has the beneficial effects that: compared with the prior art, the method has the advantages that,
1) The invention selects the characteristic data by referring to the environment and road condition information, thereby reducing the model calculation amount and improving the identification speed of the method.
2) The invention utilizes the driver data to carry out the probability correction of the driver intention model, thereby improving the intention recognition precision.
3) The method and the system preprocess the driver data, select the strong association data and identify the intention of the driver by using the selected strong association characteristic data.
4) The invention introduces the driver data recognition result to carry out the probability correction of the driver intention model, thereby improving the accuracy of intention recognition.
5) The method uses the corrected final intention recognition probability of the driver to recognize the intention of the driver, and uses a PSO-SVM classifier to obtain a final intention recognition result.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general flow diagram of a driver intent recognition method of the present invention;
FIG. 2 is a schematic diagram of hidden Markov (HMM);
FIG. 3 is a Particle Swarm Optimization (PSO) flow chart;
fig. 4 is a schematic diagram of a Support Vector Machine (SVM).
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1 to 4, fig. 1 is a general schematic diagram of a method for recognizing a driver's intention based on probability correction according to an embodiment of the present invention, the method specifically includes the following steps:
s1: and (3) acquiring data (including vehicle data and driver data) of driving behaviors by using the vehicle-mounted sensor, the eye tracker and the built-in camera, and performing corresponding preprocessing. The method specifically comprises the following steps:
s11: performing driving behavior data acquisition including vehicle data and driver data; vehicle data includes, but is not limited to, steering wheel angle, rate of change of steering wheel angle, throttle opening, brake pedal effort, speed, lateral speed, longitudinal speed, yaw rate, yaw angle, pitch angle, roll angle, and the like.
S12: the driver data includes, but is not limited to, the number of times the driver gazes the left and right rear-view mirrors, the gazing time, the average saccade angle, the average saccade speed, the horizontal and vertical movements of the driver's eyes, the yaw, roll and pitch movements of the driver's head, and the like.
S13: and preprocessing the acquired data, including but not limited to abnormal data elimination, missing value filling, filtering, data size range unification, class classification and the like.
S2: according to the information obtained by the high-precision map and the environment perception system, the characteristic data is selected, the preliminary identification of the intention of the driver is carried out by using a multi-dimensional Gaussian hidden Markov model (MGHMM), and the initial probability P of the observation sequence corresponding to each intention model of the driver is obtained through calculation 1 . The method specifically comprises the following steps:
s21: selecting characteristic data for identifying the intention of the driver according to the high-precision map and the information acquired by the environment perception system; the method specifically comprises the following steps: characteristic data including, but not limited to, the gradient, flatness, and adhesion coefficient of a road, the position, traveling speed, direction, and the like of a surrounding vehicle are extracted from a high-precision map and an environment sensing system for extracting driver intention recognition. If the change of the gradient is large, the opening degree of the accelerator is no longer used as characteristic data, and when the road is uneven, the pitch angle is removed from the characteristic data.
S22: training various driver intention models by using training data, and solving parameters lambda = (pi, A, C, mu, U) of the MGHMM by adopting a Baum-Welch algorithm, wherein pi is initial state probability distribution, A is a state transition matrix, C is a mixed Gaussian element covariance matrix, mu is a mixed Gaussian element mean matrix, and U is a mixed Gaussian element covariance matrix.
S23: calculating output probability P of observation sequence to MGHMM model by adopting forward probability 1 (o | λ), performing primary recognition of the driver intention, and calculating the probability of each driver intention model corresponding to the observation sequence:
wherein alpha is T (i) Representing the forward probability, N being the number of states in the model, and T being the observation sequence length.
S3: the initial probability of the driver intention model is corrected by combining the environmental road information constructed by the environmental perception system and the high-precision map, the driver data collected by the eye tracker and the camera, and the corrected probabilities P are respectively obtained 2 ，P 3 . The method specifically comprises the following steps:
s31: calculating the probability correction value P of the driver by using the environmental perception system and the environmental road information of the high-precision map 2 。
Based on the environmental road information, a probability correction value of the driver intention model is obtained, and the accuracy of driver intention identification is improved. If the high-precision map displays that the front road can only turn left, a larger left-turn probability correction value is generated based on the information, so that the left-turn probability of the final intention probability value of the driver is larger; when the environment perception system perceives that the left and the right of the vehicle block the left and the right turn of the vehicle, a larger straight-going probability correction value is generated based on the road condition information, so that the straight-going probability of the final intention probability value of the driver is larger.
S32: obtaining a driver intention probability correction value P using driver data 3 。
In order to identify the driving intention, the motion states and the motion features of eyes and heads in the driver data are extracted, the relevance between each motion state and each feature and the driving intention is obtained by a statistical method, the motion states and the features with strong relevance are selected as feature data, the driver intention is identified by using an MGHMM (media gateway motion model), and the driver intention correction probability P is obtained 3 。
S33: probability P output by MGHMM model 1 And probability correction value P 2 ，P 3 And obtaining a final probability value P of the intention of the driver as follows: p = P 1 +P 2 +P 3 。
S4: and inputting the corrected probability P of the driver intention model into a Support Vector Machine (SVM) with parameters optimized by a Particle Swarm Optimization (PSO), and carrying out classification and identification by a PSO-SVM classifier so as to identify the final driver intention. The method specifically comprises the following steps:
s41: and optimizing a punishment parameter c and a kernel function parameter g of the support vector machine by adopting a particle swarm optimization algorithm to obtain the optimal classifier parameter as far as possible, wherein the update speed and the position of the particles in the particle swarm optimization algorithm are compared by the following formula:
where i is any ith particle (i =1, 2.. Once, n), M is the dimension of the solution space (M =1, 2.. Once, M), k represents the number of times the current iteration is performed, and the position of the ith particle is x i ＝(x i1 ，x i2 ，...，x iM ) T Velocity v i ＝(v i1 ，v i2 ，...，v iM ) T Individual extreme value of p i ＝(p i1 ，p i2 ，...，p iM ) T Population extremum is p g ＝(p g1 ，p g2 ，...，p gM ) T ，r 1 、r 2 Is [0,1 ]]A random number in between, ω is a non-negative inertial weight factor, c 1 、c 2 Is a learning factor;
s42: and (2) inputting the arrays of the probability values of the final intentions of various drivers serving as input feature vectors into a PSO-SVM classifier for classification and recognition by using a support vector machine to obtain a final intention recognition result of the drivers, wherein a decision function of the support vector machine is as follows:
wherein, in the known sample set G = { (x) i ，y i ) I = 1.. L., (x) i ，y i ) Denotes an arbitrary ith sample, α i Is Lagrange multiplier, K (x, x) i ) B is the offset for the support vector machine kernel.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A driver intention recognition method based on probability correction, characterized by comprising the steps of:
s1: collecting data of driving behaviors, including vehicle data and driver data, and performing corresponding preprocessing;
s2: obtaining environment road information according to a high-precision map and an environment perception system, selecting characteristic data for driver intention identification, carrying out primary identification on driver intention by using a Multi-dimensional Gaussian Hidden Markov Model (MGHMM), and calculating to obtain initial probabilities P of observation sequences corresponding to each driver intention Model 1 ；
The characteristic data extracted from the high-precision map comprises the gradient, the flatness and the adhesion coefficient of a road, and the characteristic data extracted from the environment perception system comprises the position, the running speed and the direction of a surrounding vehicle; if the gradient changes greatly, the accelerator opening is not taken as characteristic data; when the road is uneven, the pitch angle is removed from the characteristic data;
s3: initial probability P of driver intention model by combining environmental road information and collected driver data 1 Correction is carried out to respectively obtain correction probabilities P 2 And P 3 ；
S4: inputting the corrected probability P of the driver intention model into the SVM of which the parameters are optimized by a Particle Swarm Optimization (PSO), and carrying out classification and identification by a PSO-SVM classifier so as to identify the final driver intention.
2. The method for recognizing the driver' S intention based on the probability correction as claimed in claim 1, wherein the step S1 specifically includes the steps of:
s11: collecting driving behavior data comprising vehicle data and driver data; the vehicle data includes a steering wheel angle, a rate of change of the steering wheel angle, an accelerator opening, a brake pedal force, a velocity, a lateral velocity, a longitudinal velocity, a yaw rate, a yaw angle, a pitch angle, and a roll angle; the driver data comprises the fixation times and fixation time of the left and right rear-view mirrors of the driver, the average saccade time, the average saccade angle and the average saccade speed of the driver, the horizontal movement and the vertical movement of the eyes of the driver, the yaw movement, the roll movement and the pitch movement of the head of the driver;
s12: and preprocessing the acquired data.
3. The method for recognizing the driver' S intention based on the probability correction as claimed in claim 1, wherein the step S2 specifically includes the steps of:
s21: acquiring environmental road information according to a high-precision map and an environmental perception system, and selecting characteristic data for driver intention identification;
s22: training various driver intention models by using training data, and solving parameters lambda = (pi, A, C, mu, U) of the MGHMM model by adopting a Baum-Welch algorithm, wherein pi is initial state probability distribution, A is a state transition matrix, C is a mixed Gaussian element covariance matrix, mu is a mixed Gaussian element mean matrix, and U is a mixed Gaussian element covariance matrix;
s23: calculating output probability P of observation sequence to MGHMM model by adopting forward probability 1 (o | λ), performing primary recognition of the driver intention, and calculating the probability of each driver intention model corresponding to the observation sequence:
wherein alpha is T (i) Representing the forward probability, N being the number of states in the model, and T being the observation sequence length.
4. The method for recognizing the intention of the driver based on the probability correction as claimed in claim 3, wherein the step S3 specifically comprises the steps of:
s31: calculating the correction probability P of the driver by using the environmental road information acquired by the environmental perception system and the high-precision map 2 ；
S32: obtaining a driver intention correction probability P using driver data 3 ；
S33: initial probability P output by using MGHMM model 1 And the correction probability P 2 、P 3 And obtaining the final probability P of the driver intention model as follows: p = P 1 +P 2 +P 3 。
5. The method for recognizing the driver' S intention based on the probability correction as claimed in claim 4, wherein the step S32 specifically includes: obtaining the relevance between each motion state and characteristic and the driving intention by a statistical method, selecting the motion state and characteristic with strong relevance as characteristic data, identifying the driver intention by using an MGHMM model, and obtaining the driver intention correction probability P 3 。
6. The probability correction-based driver intention recognition method as claimed in claim 4, wherein the step S4 specifically comprises the steps of:
s41: and optimizing a penalty parameter c and a kernel function parameter g of the support vector machine by adopting a PSO algorithm to obtain an optimal classifier parameter, wherein the particle in the PSO algorithm compares the updating speed and the position by the following formula:
wherein, i is any ith particle, i =1,2, \8230, n; m is the dimension of the solution space, M =1,2, \8230;, M, k denotes the number of times the current iteration is performed, the position of the ith particle is x i ＝(x i1 ,x i2 ,...,x iM ) T Velocity v i ＝(v i1 ,v i2 ,...,v iM ) T Individual extreme value of p i ＝(p i1 ,p i2 ,...,p iM ) T Population extremum is p g ＝(p g1 ,p g2 ,...,p gM ) T ，r 1 、r 2 Is [0,1 ]]A random number in between, ω is a non-negative inertial weight factor, c 1 、c 2 Is a learning factor;
s42: and (2) using a support vector machine, taking the probability value arrays of the final intentions of various drivers as input feature vectors, inputting the input feature vectors into a PSO-SVM classifier for classification and recognition to obtain a final intention recognition result of the drivers, wherein the decision function of the support vector machine is as follows:
wherein, in the known sample set G = { (x) i ,y i ) I =1, \ 8230;, l }, wherein (x) i ，y i ) Denotes an arbitrary ith sample, α i Is Lagrange multiplier, K (x, x) i ) B is the bias.
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