Acceleration distribution characteristic-based driver driving style identification method

Abstract

The invention is suitable for the technical field of driver driving style recognition, and provides a driver driving style recognition method based on acceleration distribution characteristics, which comprises the following steps: obtaining a driver acceleration style recognition reference boundary and a steering style reference boundary through statistical analysis; the decoupled acceleration style and steering style can be obtained only by collecting the speed, longitudinal acceleration and lateral acceleration data of the vehicle. The method can obtain acceleration and steering styles of the driver at the same time, thereby providing a basis for behavior analysis of the driver. In addition, a comprehensive driving style after comprehensive weighting of the acceleration style and the steering style can be obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of driver driving style recognition, and in particular relates to a driver driving style recognition method based on acceleration distribution characteristics. Background Art

[0002] With the advancement of the new "four trends" of "intelligence, electrification, networking, and sharing", the automotive industry is undergoing an unprecedented technological and product transformation. At the same time, users' demand for driving experience has gradually shifted from single driving comfort to personalized performance requirements. Drivers are no longer satisfied with the traditional standard driving mode, but expect the vehicle to be able to adaptively adjust according to their personal driving style. Therefore, the research focus of academia and industry has gradually shifted to the identification and application of driver driving style, and a variety of driving mode designs have been proposed to cope with the increasingly diverse user needs. The driver's driving style is the stable driving behavior exhibited by the driver when operating the vehicle. Usually, the driver's driving style is stable in the short term, which is related to factors such as the driver's personality and driving experience. The current methods for identifying the driver's driving style have the following limitations: First, the methods based on machine learning or deep learning require a large amount of driver data with driving style labels, and the algorithms are complex. In addition, it is necessary to select and identify feature parameters of the driver data, and then cluster and train the feature parameters to obtain a driving style recognition model. Secondly, the learning-based methods have common problems, that is, poor interpretability and opaque recognition process. Finally, since the operating style of each vehicle is different, the driver's acceleration operation amount, including accelerator pedal opening, accelerator pedal change rate, brake pedal opening, brake pedal opening change rate, and gear position as the driver's driving style recognition features is not universal. Drivers with the same driving style have different driver operation amounts when operating different vehicles. At the same time, the current driving style recognition algorithm can only estimate the driver's comprehensive driving style, but cannot estimate the driver's acceleration style and steering style separately. The two identified operating styles are not decoupled, which has certain limitations when applying matching driving modes. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a method for identifying a driver's driving style based on acceleration distribution characteristics, aiming to solve the problems raised in the above background technology.

[0004] The embodiment of the present invention is implemented as follows: a method for identifying a driver's driving style based on acceleration distribution characteristics, comprising the following steps: Step 1: Based on a publicly available existing driver driving behavior dataset, extract the existing driver's three-dimensional natural driving data in the dataset, including the existing driver's longitudinal speed, longitudinal acceleration, and lateral acceleration; Step 2: Divide the obtained existing natural driving data of the driver into a speed-longitudinal acceleration two-dimensional data set and a speed-lateral acceleration two-dimensional data set; Based on the natural driving data of existing drivers, the speed-longitudinal acceleration acceleration style recognition area and the speed-lateral acceleration steering style recognition area are obtained to analyze the acceleration style and steering style of existing drivers respectively; For accelerated style analysis, a triangular closed curve is used to fit the accelerated style recognition benchmark boundary, where is the probability distribution of the existing driver's acceleration small probability data, is the probability distribution of common data of existing driver acceleration, which can be solved by the following formula: i : ; in, i Representative i A triangular closed curve; N Represents the total amount of existing driver data; Representative i The number of data points contained in a closed-loop curve; Representative i The probability that the data points contained in the triangular closed curve account for the total data; By solving the above formula, the total amount of data is The triangular envelope is used as the benchmark boundary for identifying the acceleration style of existing drivers; Similarly, for the steering style analysis, a triangular closed curve is used to fit the steering style recognition boundary, where is the probability distribution of the existing driver's acceleration small probability data, is the probability distribution of common data of existing driver acceleration, which can be solved by the following formula: j : ; in, j Representative j A triangular closed curve; N Represents the total amount of existing driver data; Representative j The number of data points contained in a closed-loop curve; Representative j The probability that the data points contained in the triangular closed curve account for the total data; By solving the above formula, the total amount of data is The triangular envelope of the steering style is used as the reference boundary for identifying the steering style of existing drivers; Step 3: Determine the driving style recognition time window T w, within a time window, longitudinal speed, longitudinal acceleration and lateral acceleration data of the driver to be tested are collected through vehicle sensors, and the collected data are used as data input for the recognition of the driving style of the driver to be tested; Step 4: Based on the existing driver acceleration style recognition benchmark boundary and steering style recognition benchmark boundary obtained in step 2, the driving style of the driver to be tested is identified. By separately identifying the acceleration style and steering style of the driver to be tested, the decoupling of acceleration style and steering style recognition is achieved.

[0005] As a preferred embodiment of the present invention, in step 1, the longitudinal acceleration is obtained according to the ratio of two speed sampling points of the existing driver to the sampling time, that is: ; In the formula, is the longitudinal velocity at the k+1th moment, is the longitudinal velocity at the kth moment, is the timestamp of the k+1th moment, is the timestamp of the kth moment, is the acceleration at the kth moment.

[0006] According to a further technical solution, in step 3, the longitudinal velocity, longitudinal acceleration and lateral acceleration data of the vehicle are obtained by using an IMU inertial measurement unit or GPS.

[0007] According to a further technical solution, step 4 comprises the following specific steps: Step 4.1: Acceleration style recognition: Based on the acceleration style recognition benchmark boundary, define the probability interval of drivers with normal acceleration style, where the upper limit of the interval is , the lower bound of the interval is ; Define the lower bound of the probability interval of aggressive acceleration style drivers as , the upper bound is ; Define the upper bound of the probability interval of conservative acceleration style drivers as , the lower bound is , the specific interval is expressed as follows: ; The probability that the driving data of the driver to be tested exceeds the benchmark boundary of acceleration style recognition accounts for the total data is ,like If the acceleration style falls within the conservative acceleration style range, the acceleration style of the driver to be tested is determined to be conservative; If the acceleration style falls within the normal acceleration style range, the acceleration style of the driver to be tested is judged to be normal; If the acceleration style falls within the aggressive acceleration style range, the acceleration style of the driver to be tested is determined to be aggressive; Step 4.2: Quantify the accelerated style and normalize the accelerated style, that is, by Combined with the accelerated style interval Converted to an acceleration style coefficient between 0 and 1 ; Step 4.3: Steering style recognition. Similarly, based on the steering style recognition benchmark boundary, define the probability interval of drivers with normal steering style, where the upper limit of the interval is , the lower bound of the interval is ; Define the lower bound of the probability interval of aggressive steering style drivers as , the upper bound is ; Define the upper bound of the probability interval of conservative steering style drivers as , the lower bound is ; The specific interval expression is shown in the following formula: ; The probability that the driving data of the driver to be tested exceeds the reference boundary of steering style recognition accounts for the total data is ,like If the steering style falls within the conservative steering style range, the steering style of the driver to be tested is determined to be conservative; If the steering style falls within the normal range, the driver's steering style is judged to be normal. If the steering style falls within the radical steering style range, the steering style of the driver to be tested is determined to be radical; Simultaneously quantify the steering style and determine the steering style coefficient ; Step 4.4: Determine the comprehensive driving style coefficient based on the obtained acceleration style coefficient and the steering style coefficient , combined with the acceleration style weight coefficient and the steering style coefficient The comprehensive driving style coefficient can be obtained. The calculation formula of the comprehensive driving style coefficient is as follows: ; In the formula, represents the comprehensive driving style coefficient; and Represent the acceleration style weight coefficient and the steering style coefficient respectively. and Take 0.5 for both.

[0008] As a preferred embodiment of the present invention, in step 4.2, the acceleration style coefficient The calculation formula is as follows: ; Among them, the ordinary acceleration style coefficient is 0.5, the radical type is greater than 0.5, and the conservative type is less than 0.5.

[0009] As a preferred embodiment of the present invention, in step 4.3, the steering style coefficient The calculation formula is as follows: ; Among them, the ordinary steering style coefficient is 0.5, the aggressive steering style coefficient is greater than 0.5 and less than 1, and the conservative steering style coefficient is less than 0.5 and greater than 0.

[0010] The embodiment of the present invention provides a method for identifying a driver's driving style based on acceleration distribution characteristics. The driver's acceleration style identification reference boundary and steering style reference boundary are obtained through statistical analysis. The decoupled acceleration style and steering style can be obtained by only collecting the vehicle's speed, longitudinal acceleration and lateral acceleration data. The method can simultaneously obtain the driver's acceleration and steering styles, thereby providing a basis for driver behavior analysis. In addition, a comprehensive driving style after comprehensive weighting of the acceleration style and the steering style can also be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of a method for identifying a driver's driving style based on acceleration distribution characteristics provided by an embodiment of the present invention; Figure 2 A reference boundary for identifying acceleration and steering styles in a method for identifying a driver's driving style based on acceleration distribution characteristics provided by an embodiment of the present invention; Figure 3 The data point distribution characteristics of the aggressive and conservative acceleration styles. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0013] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0014] like Figure 1 and 2 As shown, a method for identifying a driver's driving style based on acceleration distribution characteristics is provided in one embodiment of the present invention, comprising the following steps: Step 1: Based on the public existing driver driving behavior dataset, extract the existing driver's three-dimensional natural driving data in the dataset, including the existing driver's longitudinal speed, longitudinal acceleration and lateral acceleration. Since this data is needed as the identification benchmark condition, a large amount of diverse existing driver driving data is needed, including various driving conditions, so as to characterize the universal driving habits of existing drivers.

[0015] Step 2: Divide the existing natural driving data of the driver into a speed-longitudinal acceleration two-dimensional data set and a speed-lateral acceleration two-dimensional data set. Based on the existing natural driving data of the driver, the speed-longitudinal acceleration acceleration style recognition area and the speed-lateral acceleration steering style recognition area are obtained to analyze the acceleration style and steering style of the existing driver respectively. The driving style recognition benchmark boundary is finally obtained as follows: Figure 2 As shown in (where a is the velocity-longitudinal acceleration distribution characteristic; b is the velocity-lateral acceleration distribution characteristic). For acceleration style analysis, a triangular closed curve is used to fit the acceleration style recognition benchmark boundary, where is the probability distribution of the existing driver's acceleration small probability data, is the probability distribution of common data of existing driver acceleration, which can be solved by the following formula: i : ; in, i Representative i A triangular closed curve; N Represents the total amount of existing driver data; Representative i The number of data points contained in a closed-loop curve; Representative i The probability that the data points contained in the triangular closed curve account for the total data.

[0016] The above formula can be used to solve the problem that the amount of data is The triangular envelope is used as the benchmark boundary for identifying the acceleration style of existing drivers.

[0017] Similarly, for the steering style analysis, a triangular closed curve is used to fit the steering style recognition boundary, where is the probability distribution of the existing driver acceleration small probability data, is the probability distribution of common data of existing driver acceleration, which can be solved by the following formula: j : ; in, j Representative j A triangular closed curve; NRepresents the total amount of existing driver data; Representative j The number of data points contained in a closed-loop curve; Representative j The probability that the data points contained in the triangular closed curve account for the total data.

[0018] By solving the above formula, the total amount of data is The triangular envelope of the existing driver is used as the reference boundary for identifying the steering style of the existing driver. The reference boundary for identifying the driving style of the existing driver is a precondition for identifying the driving style.

[0019] Step 3: Determine the driving style recognition time window T w , within a time window, the longitudinal speed, longitudinal acceleration, and lateral acceleration data of the driver to be tested are collected through vehicle sensors, and the collected data are used as data input for identifying the driving style of the driver to be tested.

[0020] Step 4: Based on the existing driver acceleration style recognition benchmark boundary and steering style recognition benchmark boundary obtained in step 2, the driving style of the driver to be tested is identified. The existing driver driving style recognition benchmark boundary represents the average acceleration distribution characteristics of various drivers, which can characterize the driving habits of ordinary existing drivers, that is, the commonly used acceleration distribution, and has universal applicability. In addition, the percentage of driving data of the driver to be tested that is outside the benchmark boundary is positively correlated with the degree of aggressiveness of the driver to be tested, that is, the greater the percentage of data outside the benchmark boundary, the more aggressive the driver to be tested, and conversely, the smaller the percentage of data outside the benchmark boundary, the more conservative the driver to be tested. By separately identifying the acceleration style and steering style of the driver to be tested, the decoupling of acceleration style and steering style recognition is achieved.

[0021] As a preferred embodiment of the present invention, in step 1, the longitudinal acceleration can be obtained according to the ratio of the two speed sampling points of the driver to the sampling time, that is: ; In the formula, is the longitudinal velocity at the k+1th moment, is the longitudinal velocity at the kth moment, is the timestamp of the k+1th moment, is the timestamp of the kth moment, is the acceleration at the kth moment.

[0022] As a preferred embodiment of the present invention, in step 3, data such as the longitudinal velocity, longitudinal acceleration and lateral acceleration of the vehicle can be obtained through an IMU inertial measurement unit or a GPS.

[0023] As a preferred embodiment of the present invention, step 4 includes the following specific steps: Step 4.1: Acceleration style recognition: Based on the acceleration style recognition benchmark boundary, define the probability interval of drivers with normal acceleration style, where the upper limit of the interval is , the lower bound of the interval is ; Define the lower bound of the probability interval of aggressive acceleration style drivers as , the upper bound is ; Define the upper bound of the probability interval of conservative acceleration style drivers as , the lower bound is , the specific interval is expressed as follows: ; The distribution characteristics of data points of radical and conservative acceleration styles are as follows Figure 3 As shown in the figure, the probability that the driving data of the driver to be tested exceeds the benchmark boundary of acceleration style recognition accounts for the total data is ,like If the acceleration style falls within the conservative acceleration style range, the acceleration style of the driver to be tested is determined to be conservative; If the acceleration style falls within the normal acceleration style range, the acceleration style of the driver to be tested is judged to be normal; If the acceleration style falls within the aggressive acceleration style range, the acceleration style of the driver to be tested is determined to be aggressive; Step 4.2: Quantify the acceleration style to accurately identify the acceleration style requirements of the driver to be tested; normalize the acceleration style, that is, by Combined with the accelerated style interval Converted to an acceleration style coefficient between 0 and 1 ; Step 4.3: Steering style recognition. Similarly, based on the steering style recognition benchmark boundary, define the probability interval of drivers with normal steering style, where the upper limit of the interval is , the lower bound of the interval is ; Define the lower bound of the probability interval of aggressive steering style drivers as , the upper bound is ; Define the upper bound of the probability interval of conservative steering style drivers as , the lower bound is ; The specific interval expression is shown in the following formula: ; The probability that the driving data of the driver to be tested exceeds the reference boundary of steering style recognition accounts for the total data is ,like If the steering style falls within the conservative steering style range, the steering style of the driver to be tested is determined to be conservative; If the steering style falls within the normal range, the driver's steering style is judged to be normal. If the steering style falls within the radical steering style range, the driver's steering style is determined to be radical. Similarly, the steering style is quantified and the steering style coefficient is determined. .

[0024] Step 4.4: Determine the comprehensive driving style coefficient based on the obtained acceleration style coefficient and the steering style coefficient , combined with the acceleration style weight coefficient and the steering style coefficient The comprehensive driving style coefficient can be obtained.

[0025] As a preferred embodiment of the present invention, in step 4.2, the acceleration style coefficient The calculation formula is as follows: ; Among them, the ordinary acceleration style coefficient is 0.5, the radical type is greater than 0.5, and the conservative type is less than 0.5.

[0026] As a preferred embodiment of the present invention, in step 4.3, the steering style coefficient The calculation formula is as follows: ; Among them, the ordinary steering style coefficient is 0.5, the aggressive steering style coefficient is greater than 0.5 and less than 1, and the conservative steering style coefficient is less than 0.5 and greater than 0.

[0027] As a preferred embodiment of the present invention, in step 4.4, the calculation formula of the comprehensive driving style coefficient is as follows: ; In the formula, represents the comprehensive driving style coefficient; and Represent the acceleration style weight coefficient and the steering style coefficient respectively. and Take 0.5 for both.

[0028] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying a driver's driving style based on acceleration distribution characteristics, characterized in that: The following steps are involved: Step 1: Based on a publicly available existing driver driving behavior dataset, extract the existing driver's three-dimensional natural driving data in the dataset, including the existing driver's longitudinal speed, longitudinal acceleration, and lateral acceleration; Step 2: dividing the obtained existing driver's three-dimensional natural driving data into a speed-longitudinal acceleration two-dimensional data set and a speed-lateral acceleration two-dimensional data set; Based on the natural driving data of existing drivers, the speed-longitudinal acceleration acceleration style recognition area and the speed-lateral acceleration steering style recognition area are obtained to analyze the acceleration style and steering style of the driver to be tested respectively; Step 3: Determine the driving style recognition time window T w , within a time window, the longitudinal speed, longitudinal acceleration and lateral acceleration data of the driver to be tested are collected through the vehicle sensor, and the collected data are used as the data input for the driving style recognition of the driver to be tested; Step 4: Based on the existing driver acceleration style recognition benchmark boundary and steering style recognition benchmark boundary obtained in step 2, the driving style of the driver to be tested is identified. By separately identifying the acceleration style and steering style of the driver to be tested, the decoupling of acceleration style and steering style recognition is achieved.

2. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 1, characterized in that: In step 1, the longitudinal acceleration is obtained according to the ratio of the two speed sampling points of the existing driver to the sampling time, that is: ； In the formula, is the longitudinal velocity at the k+1th moment, is the longitudinal velocity at the kth moment, is the timestamp of the k+1th moment, is the timestamp of the kth moment, is the acceleration at the kth moment.

3. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 1, characterized in that: In step 2, for the accelerated style analysis, a triangular closed curve is used to fit the accelerated style recognition reference boundary, where is the probability distribution of the existing driver's acceleration small probability data, is the probability distribution of common data of existing driver acceleration, which can be solved by the following formula: i : ； in, i Representative i A triangular closed curve; N Represents the total amount of existing driver data; Representative i The number of data points contained in a closed-loop curve; Representative i The probability that the data points contained in the triangular closed curve account for the total data; By solving the above formula, the total amount of data is The triangular envelope is used as the benchmark boundary for identifying the acceleration style of existing drivers; Similarly, for the steering style analysis, a triangular closed curve is used to fit the steering style recognition boundary, where is the probability distribution of the existing driver's acceleration small probability data, is the probability distribution of common data of existing driver acceleration, which can be solved by the following formula: j : ； in, j Representative j A triangular closed curve; N Represents the total amount of existing driver data; Representative j The number of data points contained in a closed-loop curve; Representative j The probability that the data points contained in the triangular closed curve account for the total data; By solving the above formula, the total amount of data is The triangular envelope is used as the reference boundary for identifying the steering style of existing drivers.

4. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 3, characterized in that: In step 3, the longitudinal velocity, longitudinal acceleration and lateral acceleration data of the vehicle are obtained through an IMU inertial measurement unit or a GPS.

5. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 3, characterized in that: The step 4 comprises the following specific steps: Step 4.1: Acceleration style recognition: Based on the acceleration style recognition benchmark boundary, define the probability interval of drivers with normal acceleration style, where the upper limit of the interval is , the lower bound of the interval is ; Define the lower bound of the probability interval of aggressive acceleration style drivers as , the upper bound is ; The upper bound of the probability interval of conservative acceleration style drivers is defined as , the lower bound is , the specific interval is expressed as follows: ； The probability that the driving data of the driver to be tested exceeds the benchmark boundary of acceleration style recognition accounts for the total data is ,like If the acceleration style falls within the conservative acceleration style range, the acceleration style of the driver to be tested is determined to be conservative; If the acceleration style falls within the normal acceleration style range, the acceleration style of the driver to be tested is judged to be normal; If the acceleration style falls within the aggressive acceleration style range, the acceleration style of the driver to be tested is determined to be aggressive; Step 4.2: Quantify the accelerated style and normalize the accelerated style, that is, by The combination with the accelerated style interval will Converted to an acceleration style coefficient between 0 and 1 ; Step 4.3: Steering style recognition. Similarly, based on the steering style recognition benchmark boundary, define the probability interval of drivers with normal steering style, where the upper limit of the interval is , the lower bound of the interval is ; The lower bound of the probability interval of aggressive steering style drivers is defined as , the upper bound is ; Define the upper bound of the probability interval of conservative steering style drivers as , the lower bound is ; The specific interval expression is shown in the following formula: ； The probability that the driving data of the driver to be tested exceeds the reference boundary of steering style recognition accounts for the total data is ,like If the steering style falls within the conservative steering style range, the steering style of the driver to be tested is determined to be conservative; If the steering style falls within the normal range, the driver's steering style is judged to be normal. If the steering style falls within the radical steering style range, the steering style of the driver to be tested is determined to be radical; Simultaneously quantify the steering style and determine the steering style coefficient ; Step 4.4: Determine the comprehensive driving style coefficient based on the obtained acceleration style coefficient and the steering style coefficient , combined with the acceleration style weight coefficient and the steering style coefficient The comprehensive driving style coefficient can be obtained. The calculation formula of the comprehensive driving style coefficient is as follows: ； In the formula, represents the comprehensive driving style coefficient; and Represent the acceleration style weight coefficient and the steering style coefficient respectively.

6. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 5, characterized in that: In step 4.2, the acceleration style coefficient The calculation formula is as follows: ； Among them, the ordinary acceleration style coefficient is 0.5, the radical type is greater than 0.5, and the conservative type is less than 0.

5.

7. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 6, characterized in that: In step 4.3, the style coefficient The calculation formula is as follows: ； Among them, the ordinary steering style coefficient is 0.5, the aggressive steering style coefficient is greater than 0.5 and less than 1, and the conservative steering style coefficient is less than 0.5 and greater than 0.

8. The method for identifying a driver's driving style based on acceleration distribution characteristics according to claim 7, characterized in that: In step 4.4, and Take 0.5 for both.

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