CN111666634A - Method for establishing ellipse of driver's eye based on human motion simulation - Google Patents
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Abstract
The invention discloses a method for establishing an ellipse of a driver's eye based on human motion simulation, which belongs to the technical field of automobile human-machine engineering and comprises the following steps: generating a male and female mixed crowd sample according to the digital characteristics of the human body data distribution of the target crowd; establishing a kinematic model of driving sitting posture; establishing a mathematical model for predicting driving sitting postures according to the arrangement size of a cab and the size of a human body; and by using the generated crowd sample data, the positions of the eyepoints in the cab are obtained by predicting the driving sitting postures of the crowd. Performing statistical analysis calculation on the scattered data to obtain eye ellipse parameters corresponding to the layout size of the current vehicle type; and carrying out regression analysis on the obtained eye ellipse parameters of a series of vehicle types on the cab arrangement size parameters of the vehicle types to obtain eye ellipse mathematical models suitable for different vehicle types. The method can conveniently and quickly obtain the eye point distribution of the target crowd according to different target crowds, different vehicle types and different driving postures, and establish the eye ellipse.
Description
Technical Field
The invention belongs to the technical field of automobile human-machine engineering, and particularly relates to a method for establishing an ellipse of a driver's eye based on human motion simulation.
Background
The visual field design is an important link in the automobile design process, the distribution of the eyepoints is the basis of the visual field design, the distribution of the eyepoints is described by the eye ellipse, and the accurate description of the distribution of the eyepoints is important for the automobile visual field design. The eye ellipse refers to a statistical distribution graph of eye positions of drivers of different sizes in a vehicle coordinate system when the drivers sit at a proper driving position in a normal posture. It is called an eye ellipse because it is elliptical. The making of the eye ellipse provides scientific basis for the design of the automobile visual field, so that a designer can better master the visual field design result. The traditional eye ellipse establishment is obtained by measuring and statistically analyzing a certain number of drivers, wherein the proportion of male to female of the drivers is 1: 1, during measurement, a driver sits in a static vehicle, adjusts a steering wheel and a seat to a proper position according to own habits, watches a traffic scene played on a front screen by eyes, operates the vehicle as if the driver really drives, synchronously takes pictures of the positions of the glasses by two cameras in the front and the side, and can determine the positions of the eyes in a vehicle coordinate system through calculation. This method has the following disadvantages: a large number of samples are required to ensure the accuracy of the data, which results in a large amount of time and energy consumption when counting sample data; due to the fact that the sizes of human bodies of the driver samples are different regionally, statistical results of different regions are not universal; the statistical result is also influenced to a certain extent by different vehicle types used in a laboratory.
Aiming at the problems that the existing method for establishing the eye ellipse through experimental statistics wastes time and labor, and the change of the size of a human body and the size of a vehicle model influences the applicability of eye point distribution, a new method for obtaining the eye point distribution and establishing the eye ellipse is urgently needed.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for establishing the eye ellipse of the driver based on human motion simulation, which can effectively solve the problems that the existing method for establishing the eye ellipse through experimental statistics is time-consuming and labor-consuming, the change of the size of a human body and the size of a vehicle model can influence the applicability of eye spot distribution and the like, obtain the eye spot distribution of a target group, and establish the eye ellipse.
The invention is realized by the following technical scheme:
a method for establishing an ellipse of a driver's eye based on human motion simulation comprises the following steps:
the method comprises the following steps: the man and woman mixed population samples need to be generated through Monte Carlo simulation according to the digital characteristics (mean and covariance matrix) of the human body data distribution of the target population, and the capacity can be input according to needs, such as: sample data for 5000 samples for each of men and women may be generated.
Step two: establishing a kinematic model of driving sitting posture, and defining the posture of a human body; the established human body kinematics model is provided with main joint points and characteristic points (including eyepoints) of lower limbs, a trunk and a head.
Step three: a mathematical model for predicting the driving sitting posture according to the arrangement size of the cab and the size of the human body is established through research.
Step four: and by using the generated crowd sample data, the positions of the eyepoints in the cab are obtained by predicting the driving sitting postures of the crowd. And performing the same calculation on all the sample points to obtain the eye point coordinate scatter data.
Step five: and carrying out statistical analysis and calculation on the scattered data to obtain eye ellipse parameters (central position, angle of each axis and size of each axis) corresponding to the layout size of the current vehicle type. And calculating eye ellipse parameters corresponding to the cab arrangement size parameters of different vehicle types to obtain the eye ellipse parameters of a series of vehicle types.
Step six: and carrying out regression analysis on the obtained eye ellipse parameters of a series of vehicle types on the cab arrangement size parameters of the vehicle types to obtain eye ellipse mathematical models suitable for different vehicle types.
Further, the process of the first step is as follows:
in order to simulate the eye point position of the driver, a population sample is established according to the digital characteristics (mean and covariance matrix) of the human body data distribution of the target population. According to the invention, according to the digital characteristics of the distribution of three human body size variables, namely height S, sitting height H and human body mass M, a male and female mixed crowd sample is generated through Monte Carlo simulation, so that a data basis is provided for the prediction of the eyepoint position of a driver, for example: sample data of 5000 samples of each of men and women can be generated;
predicting and modeling the human body size by using an RBF (radial basis function) interpolation method, carrying out interpolation analysis on sample data with the sample capacity of 5000 generated by multi-dimensional joint simulation, establishing a human body size prediction model of the sample population, and verifying the prediction precision of the established human body size prediction model by carrying out error analysis on the predicted value and the sample value.
The invention mainly selects 20 human body sizes for prediction research, and the codes and meanings of all the sizes are shown in table 1.
Table 120 static body size designations and meanings
Note: wherein the us99 span refers to the length of the tip of the middle finger of two hands when the upper limb is horizontally straightened; the us126 hand grip position refers to the length of the hand grip center to the wrist mark.
The process of predicting the size of the human body is as follows: the application of the radial basis function interpolation method needs to be divided into two processes, namely a training process and a prediction process. First, a sample with a capacity of 5000 is divided into a training sample (4900) and a prediction sample (100), and then three macro sizes of us100 (height), us94 (sitting height) and us125 (weight) in the sample are selected as known variables of the whole process. Training process: taking the three known variables in the training sample as independent variables (first prediction variables) of the RBF in the training process, taking the residual variables as dependent variables, and solving a weighting coefficient matrix according to an RBF interpolation method so as to establish a function expression between the independent variables and the dependent variables; and (3) prediction process: three variables of us100, us94 and us125 in the prediction sample are also used as independent variables (secondary prediction variables) of the RBF, and the required variables are predicted by using a function rule between the independent variables and the dependent variables established by the training process. And establishing human body size prediction models of different target crowds based on an RBF interpolation method.
Further, the process of the second step is as follows:
the invention adopts a kinematic chain model to determine the driving posture, the tail end of the body is vertical, and the hand, the forearm, the upper arm, the thigh, the calf and the foot are connected through joints. Since the hand is typically placed on the steering wheel while driving, the hand and forearm are considered as a skeletal segment representing the driving posture, and the movement of the hand relative to the forearm is negligible in the prediction of the driving posture. Gesture capture is carried out by using a motion capture device, and Mark points attached to the surface of the body are used for positioning joint parts of the body.
The measurement of the driving posture is obtained by a motion capture device, and an infrared light sensitive Mark photosphere is required to be attached to a key part of a human body to determine the posture. Marking fifteen joint positions of left and right shoulder joints, neck, chest and central positions of eyes on left and right stepping joints, left and right knee joints, left and right thigh roots, left and right bowl joints, left and right elbow joints and points of a human body respectively to carry out Mark point marking.
After the comfortable driving sitting posture is adjusted by the human body, the driving sitting posture is captured by the motion capture equipment, so that a kinematic model of the driving sitting posture is obtained, and the driving sitting posture is defined.
Further, the process of the third step is as follows:
the present invention predicts the distribution of the eyepoint positions by considering the driving posture of the driver. And the description of the driving posture needs to be expressed by the angle of the trunk joint of the driver and the length of the joint. The present invention is intended to simulate the position of the driver's eyepoint, and therefore, mainly performs predictive analysis of the posture angle of the upper body, and includes: pelvic Angle (Pelvis Angle), abdominal Angle (Abdomen Angle), thoracic Angle (Thorax Angle), nape Angle (Neck Angle), head elevation Angle (HeadAngle), respectively
And (4) showing.And respectively establishing regression prediction models of the five angles based on the CPM model. The construction of the CPM model requires that cab layout parameters of a certain vehicle type are selected in combination with cab layout parameters of a real vehicle.
In order to better compare the eye point distribution positions of different percentile crowds, the sample data are sequentially divided into 5th, 50th, 95th and 99th according to the height of a human body, and the trunk joint angles of the four percentile crowds are respectively obtained through prediction based on regression analysis.
The confidence interval of each joint angle of the human body can be obtained through calculation of the posture angle, and because a certain mutual relation exists among the joint angles, when one posture is changed, each joint angle can be mutually restrained to change. Thus, to
These five angles are used as prediction factors respectively, and the subsequent angle is predicted by using regression analysis in SPSS software, and the prediction factor is used as a prediction factor
(Pelvis Angle) as the first predictor,
(Abdomen Angle) as a second predictor; will be provided with
As
The regression-dependent variable of (a) is,
(Thorax Angle) as
The regression dependent variable is analogized, and a prediction model of each angle is established.
The remaining 17 human body size variables in table 1 have been predicted by using us100 (height), us94 (sitting height) and us125 (weight) as prediction factors based on a radial basis function interpolation method, and a mathematical model of the length of the upper torso joint link of the driver can be further established by simulating the driving posture of the driver according to the predicted human body size. The trunk length to be solved for the upper body is represented by L1 to L5.
According to the relation between the length of the trunk joint and the static human body size, a mathematical calculation model of the trunk joints L1-L5 is established. According to the trunk joint length calculation formula, the predicted sizes of all human bodies are substituted into the formula, and the average value of the trunk joint link lengths of different percentile crowds can be obtained.
Further, the process of the fourth step is as follows:
and estimating the eye point position of the driver by taking the crotch point position of the driver as a reference. hip is the position of the crotch point; h represents the position of the H point;
is a joint angle; eye is the eyepoint position; L1-L5 is the length of the trunk link. The eye point position of the relative crotch point is calculated according to the length and the angle of the trunk link, and the calculation formula is shown as the formula (1).
Since the position Of the crotch point Of the human body is not well determined and can be matched with the reference point Of the SAE standard, the reference point Of the eye point position is set at the accelerator pedal Heel point ahp (accelerator Heel point) in the Z direction, and the X direction is the bof (ball Of foot) reference point. It is necessary to calculate the distance of the hip point with respect to the Z-direction and X-direction reference points from the cab arrangement parameters. According to the corresponding position relationship, the calculation formula of the eyepoint position relative to the BOF reference point in the X direction and the calculation formula relative to the AHP reference point in the Z direction can be obtained as follows:
wherein X in the formulahip-BOFAnd Zhip-HCan be calculated according to CPM regression model, as shown in formulas (3), (4).
The eye point distribution positions of different percentile crowds can be calculated through a formula (1) and a formula (2), and a scatter diagram of eye point distribution is simulated according to coordinates (X, Z) of eye point distribution of different percentile crowds.
Further, the process of the step five is as follows:
and calculating relevant parameters of the eye ellipse according to the mathematical meaning of the eye ellipse, wherein the relevant parameters comprise a central position, a major axis, a minor axis and an inclination angle of the major axis.
The center position of the eye ellipse is determined by the eye point coordinate (X)eye，Zeye) Is estimated.
In the formula, Xc、ZcRespectively the coordinates of the center points of the distribution of the eyepoints; n is the sample volume. The calculation formula of the ellipse major and minor axes is:
l in the formulax、LzThe lengths of the major axis and the minor axis of the eyepoint distribution respectively; sigmaP1、σP2The standard deviations of the eyepoint distribution along the 1 st and 2 nd principal component directions are respectively needed to be estimated by using the sample variance; k1-α/2Is α/2 quantile on the standard normal distribution variable, and when the confidence coefficient is 0.95, K1-α21.96; when the confidence is 0.99, then K1-α/2＝2.575。
The formula for calculating the inclination angle of the major axis of the ellipse is shown in formula (7), wherein β is the inclination angle of the major axis, which is the included angle between the 1 st principal component direction of the eyepoint distribution and the X axis, and X is the included angle between the X axis and the 1 st principal component direction of the eyepoint distributionP、ZPEach representing X, Z coordinates of an arbitrary point in the positive direction of the 1 st principal component axis.
According to the eye ellipse parameter formulas (5), (6) and (7), calculating the eye ellipse parameters of 95 percent and 99 percent respectively.
The calculation corresponds to the cab layout size parameter of a certain vehicle type, the cab layout size parameters of different vehicle types are replaced, the calculation is repeated, and the eye ellipse parameters corresponding to the cab layout size parameters of different vehicle types are calculated to obtain the eye ellipse parameters of a series of vehicle types.
Compared with the prior art, the invention has the following advantages:
the invention provides a method for establishing an eye ellipse of a driver based on human motion simulation, which avoids the problem that a large number of samples are needed to ensure the accuracy of data, so that a large amount of time and energy are consumed in the process of counting sample data; due to the fact that the sizes of human bodies of the driver samples are different regionally, statistical results of different regions are not universal; aiming at the problems that different vehicle types used in a laboratory can also cause certain influence on statistical results and the like, the eye ellipse can be established according to the eye point distribution of different target people, different vehicle types and different driving postures, so that the target people can be conveniently and quickly obtained.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for establishing an ellipse of a driver's eye based on human motion simulation according to the present invention;
FIG. 2 is a layout size of a cab of a method for building an ellipse of a driver's eye based on human motion simulation according to the present invention;
FIG. 3 is a human body dimension prediction flowchart of a method for establishing an ellipse of a driver's eye based on human body motion simulation according to the present invention;
FIG. 4 is a schematic view of a driving posture of a driver's eye ellipse establishing method based on human motion simulation according to the present invention;
FIG. 5 is a driving posture joint diagram of a driver eye ellipse establishing method based on human motion simulation according to the present invention;
FIG. 6 is a diagram showing the angles of the joints of the human body according to the method for establishing the ellipse of the driver's eye based on human body motion simulation of the present invention;
FIG. 7 is a driving posture of a driver's eye ellipse establishing method based on human motion simulation according to the present invention;
FIG. 8 is a relationship between a trunk length and a static body size of a method for establishing an ellipse of a driver's eye based on human motion simulation according to the present invention;
FIG. 9 is a driving posture diagram of a driver's eye ellipse establishing method based on human motion simulation according to the present invention;
FIG. 10 is a diagram illustrating eye point distributions of different percentile populations for a method for establishing an ellipse of a driver's eye based on human motion simulation according to the present invention;
FIG. 11 is an eye ellipse parameter of a driver's eye ellipse establishing method based on human motion simulation of the present invention.
Detailed Description
The following embodiments are only used for illustrating the technical solutions of the present invention more clearly, and therefore, the following embodiments are only used as examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
Example 1
A method for establishing an ellipse of a driver's eye based on human motion simulation comprises the following steps:
the method comprises the following steps: the man and woman mixed population samples need to be generated through Monte Carlo simulation according to the digital characteristics (mean and covariance matrix) of the human body data distribution of the target population, and the capacity can be input according to needs, such as: sample data for 5000 samples for each of men and women may be generated.
Step two: and establishing a kinematic model of the driving sitting posture, and defining the posture of a human body. The established human body kinematics model is provided with main joint points and characteristic points (including eyepoints) of lower limbs, a trunk and a head.
Step three: a mathematical model for predicting the driving sitting posture according to the arrangement size of the cab and the size of the human body is established through research.
Step four: and by using the generated crowd sample data, the positions of the eyepoints in the cab are obtained by predicting the driving sitting postures of the crowd. And performing the same calculation on all the sample points to obtain the eye point coordinate scatter data.
Step five: and carrying out statistical analysis and calculation on the scattered data to obtain eye ellipse parameters (central position, angle of each axis and size of each axis) corresponding to the layout size of the current vehicle type. And calculating eye ellipse parameters corresponding to the cab arrangement size parameters of different vehicle types to obtain the eye ellipse parameters of a series of vehicle types.
Step six: and carrying out regression analysis on the obtained eye ellipse parameters of a series of vehicle types on the cab arrangement size parameters of the vehicle types to obtain eye ellipse mathematical models suitable for different vehicle types.
Further, the process of the first step is as follows:
in order to simulate the eye point position of the driver, a population sample is established according to the digital characteristics (mean and covariance matrix) of the human body data distribution of the target population. According to the digital characteristics of the distribution of three human body size variables, namely height S, sitting height H and human body mass M, 5000 sample data of men and women in the United states are generated through Monte Carlo simulation, and a data basis is provided for the prediction of the eyespot position of a driver. The mean values of height S, sitting height H, and body mass M can be found in Table 2, and the covariance matrices for males and females are shown below.
Male and female
TABLE 2 mean and standard deviation of U.S. human body size
Predicting and modeling the human body size by using an RBF (radial basis function) interpolation method, carrying out interpolation analysis on sample data with the sample capacity of 5000 generated by multi-dimensional joint simulation, establishing a human body size prediction model of the sample population, and verifying the prediction precision of the established human body size prediction model by carrying out error analysis on the predicted value and the sample value.
The prediction study was conducted with 20 human body sizes selected, and the codes and meanings of each size are shown in table 1.
The process of predicting the size of the human body is as follows: the application of the radial basis function interpolation method needs to be divided into two processes, namely a training process and a prediction process. First, a sample with a capacity of 5000 is divided into a training sample (4900) and a prediction sample (100), and then three macro sizes of us100 (height), us94 (sitting height) and us125 (weight) in the sample are selected as known variables of the whole process. Training process: taking the three known variables in the training sample as independent variables (first prediction variables) of the RBF in the training process, taking the residual variables as dependent variables, and solving a weighting coefficient matrix according to an RBF interpolation method so as to establish a function expression between the independent variables and the dependent variables; and (3) prediction process: three variables of us100, us94 and us125 in the prediction sample are also used as independent variables (secondary prediction variables) of the RBF, and the required variables are predicted by using a function rule between the independent variables and the dependent variables established by the training process. According to the flow chart of fig. 3, a human body size prediction model of different us populations is established based on an RBF interpolation method.
For space, the invention screens a plurality of sample data of human body sizes us88 and us73 from a plurality of sets of calculated sample data, and the prediction result is shown in Table 3.
TABLE 3 prediction of several individuals (mm) for us88 and us73
Further, the process of the second step is as follows:
the invention adopts a kinematic chain model to determine the driving posture, the tail end of the body is vertical, and the hand, the forearm, the upper arm, the thigh, the calf and the foot are connected through joints. Since the hand is typically placed on the steering wheel while driving, the hand and forearm are considered as a skeletal segment representing the driving posture, and the movement of the hand relative to the forearm is negligible in the prediction of the driving posture. Gesture capture is carried out by using a motion capture device, and Mark points attached to the surface of the body are used for positioning joint parts of the body.
The measurement of the driving posture is obtained by a motion capture device, and an infrared light sensitive Mark photosphere is required to be attached to a key part of a human body to determine the posture. Marking fifteen joint positions of left and right shoulder joints, neck, chest and central positions of eyes on left and right stepping joints, left and right knee joints, left and right thigh roots, left and right bowl joints, left and right elbow joints and points of a human body respectively to carry out Mark point marking.
After the comfortable driving sitting posture is adjusted by the human body, the driving sitting posture is captured by the motion capture equipment, so that a kinematic model of the driving sitting posture is obtained, and the driving sitting posture is defined. As shown in fig. 4 and 5.
Further, the process of the third step is as follows:
the present invention predicts the distribution of the eyepoint positions by considering the driving posture of the driver. And the description of the driving posture needs to be expressed by the angle of the trunk joint and the length of the joint of the driver, and the angle influencing the driving posture of the driver is shown in fig. 6. The present invention is intended to simulate the position of the driver's eyepoint and is therefore primarily intended to simulatePerforming predictive analysis of the posture angle of the upper body, comprising: pelvic Angle (Pelvis Angle), abdominal Angle (Abdomen Angle), thoracic Angle (Thorax Angle), nape Angle (Neck Angle), Head elevation Angle (Head Angle), respectively
And (4) showing. Regression prediction models for these six angles were established based on the CPM model, respectively, as shown in table 4. The construction of the CPM model needs to be combined with the cab layout parameters of a real vehicle, the cab layout parameters of a certain vehicle type are selected in the CPM model, see table 5, and the cab layout parameters are shown in fig. 2.
TABLE 4 CPM prediction model of joint angles
TABLE 5 certain vehicle type layout parameters
In order to better compare the eye point distribution positions of different percentile crowds, the sample data with the sample volume of 5000 is divided into 5th, 50th, 95th and 99th according to the height of a human body, the trunk joint angles of the four percentile crowds are respectively obtained through prediction based on regression analysis, and the table 6 is the average value of the trunk joint angles of the different percentile crowds obtained through prediction.
TABLE 6 different percentile human body posture angle (degree)
The confidence interval of each joint angle of the human body can be obtained through calculation of the posture angle, and because a certain mutual relation exists among the joint angles, when one posture is changed, each joint angle can be mutually restrained to change. Thus, to
These five angles are used as prediction factors respectively, and the subsequent angle is predicted by using regression analysis in SPSS software, and the prediction factor is used as a prediction factor
(Pelvis Angle) as the first predictor,
(Abdomen Angle) as a second predictor; will be provided with
As
The regression-dependent variable of (a) is,
(Thorax Angle) as
The regression dependent variables of (1) and so on, and a prediction model of each angle is established, see table 7.
TABLE 7 regression model of joint angles
The remaining 17 human body size variables in table 1 have been predicted by using us100 (height), us94 (sitting height) and us125 (weight) as prediction factors based on a radial basis function interpolation method, a mathematical model of the length of the joint link of the upper body trunk of the driver can be further established according to the predicted human body size by simulating the driving posture of the driver, the driving posture of the driver is shown in fig. 7, and the trunk length of the upper body to be solved is represented by L1-L5 in the figure.
From the relationship between the torso joint lengths and the static body dimensions of FIG. 8, mathematical computational models of the torso joints L1-L5 were constructed, as shown in Table 8. According to the trunk joint length calculation formula of table 8, the predicted sizes of the human bodies are substituted into the formula, and the average values of the trunk joint link lengths of different percentile crowds can be obtained, as shown in table 9.
TABLE 8 mathematical model of trunk joint length
Watch 9 different percentile human body joint segment length (mm)
Further, the process of the fourth step is as follows:
and estimating the eye point position of the driver by taking the crotch point position of the driver as a reference. Hip is the location of the crotch point as shown in fig. 9; h represents the position of the H point;
is a joint angle; eye is the eyepoint position; L1-L5 is the length of the trunk link. The eye point position of the relative crotch point is calculated according to the length and the angle of the trunk link, and the calculation formula is shown as the formula (1).
Since the position Of the crotch point Of the human body is not well determined and can be matched with the reference point Of SAE standard, the reference point Of the eye point position is set at the accelerator pedal Heel point ahp (accelerator Heel point) in the Z direction, and the X direction is the bof (ball Of foot) reference point, as shown in fig. 9. It is necessary to calculate the distance of the hip point with respect to the Z-direction and X-direction reference points from the cab arrangement parameters. From the corresponding positional relationship in fig. 9, the calculation formula of the eyepoint position relative to the BOF reference point in the X direction and the calculation formula relative to the AHP reference point in the Z direction can be obtained as follows:
wherein X in the formulahip-BOFAnd Zhip-HCan be calculated according to CPM regression model, as shown in formulas (3), (4).
The eye distribution positions of different percentile crowds can be calculated through the formula (1) and the formula (2), and a scatter diagram of eye distribution is simulated according to coordinates (X, Z) of eye distribution of different percentile crowds, as shown in fig. 10.
Further, the process of the step five is as follows:
the relevant parameters of the eye ellipse, including the center position, the major and minor axes and the inclination of the major axis, are calculated according to the mathematical meaning of the eye ellipse, and the parameters of the eye ellipse are shown in fig. 11.
The center position of the eye ellipse is determined by the eye point coordinate (X)eye，Zeye) Is estimated.
In the formula, Xc、ZcRespectively the coordinates of the center points of the distribution of the eyepoints; n is the sample volume. The calculation formula of the ellipse major and minor axes is:
l in the formulax、LzThe lengths of the major axis and the minor axis of the eyepoint distribution respectively; sigmaP1、σP2The standard deviations of the eyepoint distribution along the 1 st and 2 nd principal component directions are respectively needed to be estimated by using the sample variance; k1-α/2Is α/2 quantile on the standard normal distribution variable, and has a confidence of 0.95，K1-α21.96; when the confidence is 0.99, then K1-α/2＝2.575。
The formula for calculating the inclination angle of the major axis of the ellipse is shown in formula (7), wherein β is the inclination angle of the major axis, which is the included angle between the 1 st principal component direction of the eyepoint distribution and the X axis, and X is the included angle between the X axis and the 1 st principal component direction of the eyepoint distributionP、ZPEach representing X, Z coordinates of an arbitrary point in the positive direction of the 1 st principal component axis.
The 95% and 99% percentile eye ellipse parameters were calculated according to the eye ellipse parameter formulas (5), (6), and (7), respectively, as shown in table 10.
TABLE 10 Ocular ellipse parameters
The calculation corresponds to the cab layout size parameter of a certain vehicle type, the cab layout size parameters of different vehicle types are replaced, the calculation is repeated, and the eye ellipse parameters corresponding to the cab layout size parameters of different vehicle types are calculated to obtain the eye ellipse parameters of a series of vehicle types. Since the calculation process is repeated, it is not given here.
Further, the process of the step six is as follows:
and establishing a regression prediction model according to the obtained eye ellipse parameters of a series of vehicle types and the arrangement size parameters of the cabs of different vehicle types, and performing regression analysis to obtain eye ellipse mathematical models suitable for different vehicle types.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (8)
1. A method for establishing an ellipse of a driver's eye based on human motion simulation is characterized by comprising the following steps:
the method comprises the following steps: according to the digital characteristics of the human body data distribution of the target population, a male and female mixed population sample is generated through Monte Carlo simulation, and the capacity can be input according to the requirement, such as: sample data of 5000 samples of each of men and women can be generated;
step two: establishing a kinematic model of driving sitting posture, and defining the posture of a human body; the established human body kinematics model is provided with main joint points and characteristic points of lower limbs, a trunk and a head;
step three: establishing a mathematical model for predicting driving sitting postures according to the arrangement size of a cab and the size of a human body through research;
step four: and by using the generated crowd sample data, the positions of the eyepoints in the cab are obtained by predicting the driving sitting postures of the crowd. Performing the same calculation on all the sample points to obtain eye point coordinate scatter data;
step five: performing statistical analysis calculation on the scattered data to obtain eye ellipse parameters corresponding to the layout size of the current vehicle type; calculating eye ellipse parameters corresponding to the arrangement size parameters of cabs of different vehicle types to obtain eye ellipse parameters of a series of vehicle types;
step six: and carrying out regression analysis on the obtained eye ellipse parameters of a series of vehicle types on the cab arrangement size parameters of the vehicle types to obtain eye ellipse mathematical models suitable for different vehicle types.
2. The method for creating the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 1, wherein the process of the first step is as follows:
according to the digital characteristics of the distribution of three human body size variables, namely height S, sitting height H and human body mass M, a male and female mixed crowd sample is generated through MonteCarlo simulation, a data base is provided for the prediction of the eye point position of a driver, the prediction and modeling of the human body size are carried out through a RBF interpolation method, the sample data generated through multi-dimensional joint simulation is subjected to interpolation analysis, a human body size prediction model of the sample population is established, and the prediction accuracy of the established human body size prediction model is verified through error analysis of the predicted value and the sample value.
3. The method for creating the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 2, wherein the selected human body size, the code and the meaning of each size are shown in table 1:
table 120 static body size designations and meanings
4. The method for establishing the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 2, wherein the process of predicting the human size is specifically as follows: the application of the radial basis function interpolation method needs to be divided into two processes, namely a training process and a prediction process; firstly, dividing a sample into a training sample and a prediction sample, and then selecting three macro sizes of us100 (height), us94 (sitting height) and us125 (weight) in the sample as known variables of the whole process; training process: taking the three known variables in the training sample as independent variables of the RBF in the training process, taking the residual variables as dependent variables, and solving a weighting coefficient matrix according to an RBF interpolation method so as to establish a function expression between the independent variables and the dependent variables; and (3) prediction process: the three variables of us100, us94 and us125 in the prediction sample are also used as the independent variables of the RBF, and the required variables are predicted by utilizing the function rule between the independent variables and the dependent variables established in the training process; and establishing human body size prediction models of different target crowds based on an RBF interpolation method.
5. The method for creating the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 1, wherein the process of the second step is as follows:
determining a driving posture by adopting a kinematic chain model, wherein the tail end of a body is vertical, hands, forearms, upper arms, thighs, cruses and feet are connected through joints, posture capture is carried out by using motion capture equipment, and Mark photospheres attached to the surface of the body are used for positioning joint parts of the body; marking fifteen joint positions of left and right shoulder joints, neck, chest and central positions of eyes on left and right stepping joints, left and right knee joints, left and right thigh roots, left and right bowl joints, left and right elbow joints and points of a human body respectively to carry out Mark point marking.
6. The method for establishing the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 1, wherein the process of the third step is as follows:
performing predictive analysis of the posture angle of the upper body, comprising: pelvic angle, abdominal angle, thoracic angle, cervical angle, and head pitch angle, respectively
Represents; respectively establishing regression prediction models of the five angles based on the CPM model; the construction of the CPM model needs to be combined with cab arrangement parameters of a real vehicle;
dividing the sample data into 5th, 50th, 95th and 99th according to the height of the human body, and respectively predicting the trunk joint angles of the four percentile crowds based on regression analysis;
to be provided with
These five angles are used as prediction factors respectively, and the subsequent angle is predicted by using regression analysis in SPSS software, and the prediction factor is used as a prediction factor
(Pelvis Angle) as the first predictor,
(Abdomen Angle) as a second predictor; will be provided with
As
The regression-dependent variable of (a) is,
(Thorax Angle) as
The regression dependent variable is analogized, and a prediction model of each angle is established.
7. The method for establishing the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 1, wherein the process of the fourth step is as follows:
calculating the eye point position of the driver by taking the crotch point position of the driver as a reference, wherein hip is the position of the crotch point; h represents the position of the H point;
is a joint angle; eye is the eyepoint position; L1-L5 is the length of the trunk link. The eye point position of the relative crotch point is calculated according to the length and the angle of the trunk link, and the calculation formula is shown as the formula (1).
Since the position Of the crotch point Of the human body is not well determined and can be matched with the reference point Of SAE standard, the reference point Of the eye point position is set at the position Of the accelerator pedal Heel point AHP (accelerator Heel point) in the Z direction, the X direction is the BOF (ball Of foot) reference point, so that the distance Of the hip point with respect to the Z direction and the reference point in the X direction needs to be calculated according to the cab arrangement parameters, and the eye point position with respect to the BOF reference point in the X direction is obtained according to the corresponding positional relationship, and the calculation formula with respect to the AHP reference point in the Z direction is:
wherein X in the formulahip-BOFAnd Zhip-HCan be calculated according to CPM regression model, as shown in formulas (3), (4):
the eye point distribution positions of different percentile crowds can be calculated through a formula (1) and a formula (2), and a scatter diagram of eye point distribution is simulated according to coordinates (X, Z) of eye point distribution of different percentile crowds.
8. The method for establishing the ellipse of the eye of the driver based on the human motion simulation as claimed in claim 1, wherein the process of the fifth step is as follows:
calculating relevant parameters of the eye ellipse according to the mathematical meaning of the eye ellipse, wherein the relevant parameters comprise a central position, a major axis, a minor axis and an inclination angle of the major axis;
the center position of the eye ellipse is determined by the eye point coordinate (X)eye，Zeye) To estimate the mean of:
in the formula, Xc、ZcRespectively the coordinates of the center points of the distribution of the eyepoints; n is the sample capacity, and the calculation formula of the major axis and the minor axis of the ellipse is as follows:
l in the formulax、LzThe lengths of the major axis and the minor axis of the eyepoint distribution respectively; sigmaP1、σP2The standard deviations of the eyepoint distribution along the 1 st and 2 nd principal component directions are respectively needed to be estimated by using the sample variance; k1-α/2Is α/2 quantile on the standard normal distribution variable, and when the confidence coefficient is 0.95, K1-α/21.96; when the confidence is 0.99, then K1-α/2＝2.575。
The formula for calculating the inclination angle of the long axis of the ellipse is shown in formula (7), wherein β is the inclination angle of the long axis, which is the included angle between the 1 st principal component direction of the eyepoint distribution and the X axis, and X is the included angle between the X axis and the 1 st principal component direction of the eyepoint distributionP、ZPX, Z coordinates of any point in the positive direction of the 1 st principal component axis:
according to the eye ellipse parameter formulas (5), (6) and (7), calculating the eye ellipse parameters of 95 percent and 99 percent respectively.
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| CN111898210A (en) * | 2020-08-03 | 2020-11-06 | 吉林大学 | Method for establishing ellipse of driver eye |
| CN112373405A (en) * | 2020-10-19 | 2021-02-19 | 东风汽车集团有限公司 | Automobile display screen adjusting method and device |
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| CN114354213A (en) * | 2021-12-28 | 2022-04-15 | 重庆长安汽车股份有限公司 | Method for verifying operation comfort of gear shifting handle |
| CN116822260A (en) * | 2023-08-31 | 2023-09-29 | 天河超级计算淮海分中心 | Eyeball simulation method based on numerical conversion, electronic equipment and storage medium |
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