A steering intention decision method considering driver motion intensity driving habit

Abstract

This invention, applicable to the field of automotive driving technology, provides a steering intention decision-making method that considers a driver's driving habits based on their driving intensity. Using the driver's steering data under ideal driving conditions, a probabilistic model of their steering habits and driving intensity habits is constructed, linking the two. Based on the driver's actual operational input, the desired driving intensity (i.e., the lateral acceleration) is determined based on the driver's actual input. Because the model is derived from the target driver's driving data under ideal driving conditions, it better reflects the driver's natural driving habits. Therefore, the driver will feel more comfortable under this driving intensity, laying the foundation for designing personalized intelligent driving control strategies for the driver.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automobile driving, and particularly relates to a steering intention decision-making method considering driving habits of driver motion intensity. BACKGROUND

[0002] In recent years, with the rapid development and change of the automobile industry, the driving and riding experience of automobiles has been greatly improved, and consumers have more comprehensive expectations for the use of automobiles. Therefore, when consumers choose and purchase automobiles, they no longer only focus on meeting the basic needs of daily commuting, but further consider whether the experience during driving meets their psychological expectations. Therefore, domestic and foreign practitioners have carried out numerous researches, considering the influence of the driving style of the driver during the design of the automobile, providing different driving experiences for drivers of different styles to improve the driving satisfaction of consumers.

[0003] At present, the driver driving style recognition method and the personalized motion intensity control strategy designed for drivers of different driving styles generally have the following problems:

[0004] (1) The current driver classification method pursued by the industry divides a group of people into a category, and for some fuzzy individuals between two driving styles, there is a possibility that this category of people does not adapt to the control strategy corresponding to the classification result.

[0005] (2) There are also differences between drivers in the same category. Due to the differences between individual drivers, the preferred driving experience under the same category will also be different. Therefore, for drivers of the same style, the same motion intensity control strategy cannot well meet the individual needs of each driver.

[0006] For a single driver, by collecting the driving operation habit characteristics of the driver, the motion intensity of the driver under the steering working condition is analyzed and the relevant mapping is done, which is more in line with the motion intensity expectation of the driver and more matches the personalized experience expected by the driver. Therefore, in order to solve the above problems, a steering intention decision-making method considering driving habits of driver motion intensity is proposed. SUMMARY

[0007] The purpose of the embodiment of the application is to provide a steering intention decision-making method considering driving habits of driver motion intensity, which aims to solve the problems proposed in the background.

[0008] The embodiment of the application is implemented in the following way: a steering intention decision-making method considering driving habits of driver motion intensity, comprising the following steps:

[0009] Step 1: In ideal driving conditions, the steering wheel steering torque information of the driver during steering driving is collected, the steering wheel steering torque is used to represent the steering operation input of the driver, a probability model of driving input habit is constructed by using normal distribution, the mean and standard deviation estimators are calculated based on sample data by using moment estimation method to complete the parameter analysis of the model, and finally the probability model of operation habit is obtained;

[0010] Step 2: In ideal driving conditions, the lateral acceleration data of the driver during steering driving is collected, the lateral acceleration is used to represent the motion intensity during steering, and a probability model of motion intensity habit is constructed by using normal distribution; based on sample data, the corresponding mean and standard deviation estimators are calculated by using moment estimation method to complete the parameter analysis of the model, and then the probability model of driving motion intensity habit is obtained;

[0011] Step 3: The characteristic points in the normal distribution curve in the driving operation input habit probability model are matched with the characteristic points in the lateral acceleration motion intensity probability model during driving, since the steering operation is complex and the use frequency of the infrequently used operation input quantity needs to be considered, the lateral acceleration corresponding to the rollover threshold value in the general state of the vehicle is taken as a y =0.7g as the boundary point to limit the fitting range of the curve, and an exponential function is used to fit the mapping curve; thus, the lateral motion intensity to which the driving habit of the driver is more inclined can be obtained according to the steering operation input quantity of the driver.

[0012] Step 4: The steering wheel steering torque T sw_run during right turning is obtained, as well as the fitted characteristic points (2μ1-T sw_lim , 2μ2-a y_lim ), (T sw_lim , a y_lim ) and (T sw_max , 0.7g) of lateral acceleration, the fitting relationship between the two is constructed based on psychophysical method, and the function during left turning is supplemented as a complete odd function to cover all working conditions of left and right turning; according to the mapping relationship between the two, the corresponding lateral acceleration can be analyzed through the steering wheel steering torque input of the driver in the current state.

[0013] Another purpose of the embodiment of the application is a steering intention decision module considering the driving habit of the driver's motion intensity, which is based on the steering intention decision method considering the driving habit of the driver's motion intensity, and includes a probability model construction module, an operation input information collection module and a steering driving intention analysis module.

[0014] The probability model construction module is used to construct the steering wheel steering torque operation habit probability model of the driver and the motion intensity driving habit probability model of the driver.

[0015] The operation input information collection module is used for collecting steering wheel steering torque information and sending data to the steering driving intention analysis module.

[0016] The steering driving intention analysis module is used for analyzing the lateral acceleration of the driver habit corresponding to the current steering wheel steering torque.

[0017] The steering intention decision method considering the driving habit of the driver's motion intensity provided by the embodiment of the application, by the steering driving data of the driver under ideal driving conditions, constructs the probability model of the operation habit and the motion intensity habit, and establishes the connection between the two, according to the actual operation input of the driver, the motion intensity conforming to the driving habit of the driver at this time, that is, the expected lateral acceleration, can be obtained. Since the data for constructing the model is derived from the driving data of the target driver under ideal driving conditions, the natural driving habit of the current driver can be better reflected, so the driver will feel more comfortable under this motion intensity, which lays a foundation for designing the personalized intelligent driving control strategy of the driver. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is a schematic diagram of system principle;

[0019] Figure 2 It is a schematic diagram of probability model construction process;

[0020] Figure 3 It is a mapping fitting curve. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0022] The specific implementation of the application is described in detail below in combination with specific embodiments.

[0023] The steering intention decision method considering the driving habit of the driver's motion intensity provided by one embodiment of the application, comprising the following steps:

[0024] Step 1, under ideal driving conditions, collect the steering driving related data of the driver. Use normal distribution to construct the probability model of driving input habit, select the steering torque of the steering wheel as the steering driving operation input of the driver, since the steering is divided into left steering and right steering, the collected steering torque T sw of the steering wheel for left steering is marked as a positive value, assuming that the sample data of the steering wheel collected in the experiment is N, which conforms to the following normal distribution:

[0025]

[0026] Firstly, sample data T sw_i Calculate sample mean and sample variance S 2 :

[0027]

[0028] Then, according to the population moments equal to sample moments, the equation is established by using the method of moments:

[0029]

[0030] Combined with the expectation and variance formula of normal distribution:

[0031] E(T sw ) = μ1

[0032] The moment estimators of μ1 and can be calculated, that is, and

[0033]

[0034] Substitute the estimators of mean and standard deviation into the equation, and get the probability model of steering input operation habit of the driver;

[0035] Step 2, select lateral acceleration to represent the motion intensity, and according to the lateral acceleration data collected under ideal driving conditions, use the same way to process, get lateral acceleration a y The probability model of motion intensity habit:

[0036]

[0037] Step 3, the probability model of driving operation input habit reflects the probability distribution of the steering torque applied by the driver, the greater the probability density corresponding to the value of the steering torque, the more often the driver uses the corresponding steering torque to operate; on the contrary, it means that the driver rarely applies the corresponding steering torque, which well describes the steering operation habit of the driver. Similarly, the probability model of motion intensity reflects the probability of the driver selecting different lateral accelerations during driving, which well describes the motion intensity habit of the driver when steering.

[0038] After the construction of two probability models, the mapping operation between the two is carried out, and the feature points of the normal distribution curve in the driving operation input habit probability model are matched with the feature points in the lateral acceleration motion intensity probability model during driving to build a corresponding relationship. Since the steering operation is relatively complex and the use frequency of the infrequently used operation input quantity needs to be considered, the lateral acceleration corresponding to the rollover threshold in the general state of the vehicle is taken as the boundary point to limit the fitting range of the curve, and an exponential function is used to fit the mapping curve. Thus, the lateral motion intensity to which the driver's steering operation input quantity is more inclined can be obtained according to the driver's steering operation input quantity; y = 0.7g as the boundary point to limit the fitting range of the curve, and an exponential function is used to fit the mapping curve. Thus, the lateral motion intensity to which the driver's steering operation input quantity is more inclined can be obtained according to the driver's steering operation input quantity;

[0039] Step 4: According to the psychophysical method, the operation habit and the motion intensity habit are mapped by fitting the curve with the feature points, as follows:

[0040] First, the steering torque of the driver's steering wheel and the lateral acceleration data of the vehicle are constructed to build a mapping relationship. This involves the knowledge of psychophysics. Psychophysics is a branch of psychology that studies the quantitative relationship (functional relationship) between "psychological quantities" and "physical quantities". It establishes a connection between human perception and experimental data, describes the relationship between complex psychological perception and external stimuli through simple mathematical models, and explores the numerical rules of human perception of external stimuli.

[0041] Therefore, based on the psychophysical method, the normal distribution center mean values μ1 and μ2 of the driving operation input habit probability model and the lateral acceleration motion intensity habit probability model are selected as the first pair of feature points (μ1, μ2) for mutual mapping.

[0042] Since the mapping is based on the driver's habits, the small probability events at both ends may correspond to emergency or special situations, which are not suitable for representing the driver's habits. Therefore, the majority of the normal distribution center is selected for mapping operation, and the part with 90% of the normal distribution area centered at the mean values μ1 and μ2 is selected for mapping operation, i.e.:

[0043] P(|T sw -μ1|≤T sw_lim )=0.90

[0044] P(|a y -μ2|≤a y_lim )=0.90

[0045] 2μ1-T sw_lim ≤T sw ≤T sw_lim

[0046] 2μ2-a y_lim ≤a y ≤ay_lim

[0047] respectively as the steering wheel steering torque and steering driving motion habit characteristic point in the driver's usual driving habit range; sw_lim , 2μ2-a y_lim ), (T sw_lim , a y_lim ) as the steering wheel steering torque and steering driving motion habit characteristic point in the driver's usual driving habit range;

[0048] At the same time, the steering wheel steering torque T y corresponding to the lateral acceleration threshold (considered as a sw_max =0.7g) of the vehicle driven by the driver reaching the rollover threshold is measured as the boundary critical characteristic point (T sw_max , 0.7g).

[0049] Assuming that the steering wheel steering torque to be mapped at this time is T sw_run , first according to the exponential function algebraic relationship:

[0050]

[0051] The three characteristic points are respectively substituted into the above algebraic relationship to fit the mapped motion intensity function relationship for calibration:

[0052]

[0053] Since the steering wheel steering torque and lateral acceleration information during left turning are used in the previous data processing, and the former is calibrated as a positive value, the mapping relationship of right turning is missing. To this end, we simply supplement the function during left turning as a complete odd function, and explicitly define the right turning steering wheel steering torque as a negative value, that is, the function relationship mapping under all steering torques is still fitted according to the above mapping function relationship, and is combined into the final complete mapping function relationship.

[0054] Another embodiment of the present application provides a steering intention decision module considering the driving habit of the driver's motion intensity, based on the above-mentioned steering intention decision method considering the driving habit of the driver's motion intensity, comprising a probability model construction module, an operation input information acquisition module and a steering driving intention analysis module;

[0055] The probability model construction module is used to construct a driver steering wheel steering torque operation habit probability model and a motion intensity driving habit probability model;

[0056] The operation input information acquisition module is used to acquire steering wheel steering torque information and send data to the steering driving intention analysis module;

[0057] The turning driving intention analysis module is configured to analyze the lateral acceleration of the driver habit corresponding to the current steering wheel steering torque.

[0058] The above merely provides the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall fall within the scope of protection of the present application.

Claims

1. A steering intention decision method considering the driver's driving habit of sports intensity, characterized by: The following steps are involved: Step 1: Under ideal driving conditions, the driver's steering wheel torque information is collected during steering. This torque is used to represent the driver's steering input. A probability model of driving input habits is constructed using a normal distribution. The mean and standard deviation estimators are calculated using the moment estimation method based on the sample data to complete the model parameter analysis, ultimately obtaining a probability model of driving habits. Step 2: Under ideal driving conditions, collect the driver's lateral acceleration data during steering, use lateral acceleration to represent the intensity of steering movement, and use normal distribution to construct a probability model of movement intensity habits; Based on the sample data, the method of moment estimation is used to calculate the corresponding mean and standard deviation estimators to complete the parameter analysis of the model, and then the probability model that drives exercise intensity habits is obtained; Step 3: Take the characteristic points of the normal distribution curve in the driving operation input habit probability model and establish a corresponding relationship with the characteristic points in the normal distribution curve of the lateral acceleration motion intensity probability model during driving, and take the lateral acceleration corresponding to the rollover threshold of the car in the general state as a y = 0.7g as the boundary point to limit the fitting range of the curve, and use an exponential function to fit the mapping curve; thus, the lateral movement intensity that the driver tends to prefer can be obtained based on the steering input of the driver; Step 4: Obtain the steering wheel torque T when turning right sw_run , and the fitting characteristic points of lateral acceleration (2μ1-T sw_lim , 2μ2-a y_lim )、(T sw_lim , a y_lim ) and (T sw_max , 0.7g), a fitting relationship between the two is constructed based on psychophysics methods, and the function when turning left is supplemented into a complete odd function to cover all working conditions of left and right turns; according to the mapping relationship between the two, the corresponding lateral acceleration can be analyzed through the steering torque input of the driver's steering wheel in the current state.

2. The steering intention decision method considering the driver's driving habit and sports intensity according to claim 1 is characterized in that: In step 1, the construction of the driver's steering input operation habit probability model includes the following specific steps: The probability model of driving input habits is constructed using normal distribution. The steering torque of the steering wheel is selected as the driver's steering operation input. Since steering is divided into left steering and right steering, the collected left steering wheel steering torque T sw The calibration is positive. Assume that the sample data of the steering wheel collected in the experiment is N, which conforms to the following normal distribution: First, using the sample data t sw_i Calculate the sample mean and sample variance S 2 : Then, using the moment estimation method, we establish an equation based on the population moment being equal to the sample moment: Combining the expectation and variance formulas of the normal distribution: E(T sw )=μ1 μ1 and The moment estimator of and Substituting the estimated values ​​of the mean and standard deviation, we can obtain the probability model of the driver's steering input operation habits.

3. The steering intention decision method considering the driver's driving habit and sports intensity according to claim 2 is characterized in that: In step 2, lateral acceleration is selected to represent the intensity of movement. Based on the lateral acceleration data collected under ideal driving conditions, the data is processed in the same manner as in step 1 to obtain a probability model of lateral acceleration intensity habit:

4. The steering intention decision method considering the driver's driving habit and sports intensity according to claim 3 is characterized in that: The step 4 includes the following specific steps: First, a mapping relationship is established between the driver's steering wheel torque and the vehicle's lateral acceleration data. Based on the psychophysics method, the normal distribution center means μ1 and μ2 of the driving operation input habit probability model and the lateral acceleration motion intensity habit probability model are selected as the first pair of mutually mapped feature points (μ1, μ2). The 90% normal distribution area centered on the means μ1 and μ2 is selected for the mapping operation, namely: P(|T sw -μ1|≤T sw_lim )=0.90 P(|a y -μ2|≤a y_lim )=0.90 2μ1-T sw_lim ≤T sw ≤T sw_lim 2m2-a y_lim ≤a y ≤a y_lim (2μ1-T sw_lim , 2μ2-a y_lim ), (T sw_lim , a y_lim ) as the characteristic points of the steering wheel steering torque and steering driving motion habits within the range of the driver's common driving habits; At the same time, the steering wheel torque T corresponding to the lateral acceleration threshold near the rollover threshold of the vehicle driven by the driver is measured. sw_max , as the critical feature point of the boundary (T sw_max , 0.7g); Assume that the steering wheel steering torque to be mapped is T sw_run , first according to the algebraic relationship of the exponential function: Substitute the three feature points into the above algebraic relationship to fit the mapped motion intensity function relationship for calibration: The function for turning left is supplemented into a complete odd function, and it is clear that the steering torque of the steering wheel when turning right is a negative value, that is, the functional relationship mapping under all steering torques is still fitted with the above mapping function relationship and merged into the final complete mapping function relationship.

5. A steering intention decision module considering a driver's driving habits based on the steering intention decision method considering a driver's driving habits based on any one of claims 1 to 4, characterized in that: It includes a probability model building module, an operation input information collection module, and a steering driving intention analysis module; The probability model building module is used to build a probability model of the driver's steering wheel steering torque operation habit and a probability model of the driving intensity driving habit; The operation input information acquisition module is used to collect steering wheel steering torque information and send the data to the steering driving intention analysis module; The steering driving intention analysis module is used to analyze the corresponding lateral acceleration of the driver according to the current steering wheel steering torque.

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