A method for online identification of human driver style

Abstract

The present invention discloses a method for online identification of human driver style, comprising the following steps: Step 1: constructing an online identification model of human driving behavior using the modeling approach of inverse optimal control; Step 2: simplifying the online identification model of human driving behavior based on the Pontryagin minimum principle; Step 3: transforming the model into a linear least squares problem based on the simplified model result; Step 4: solving the model based on the model transformation using the Lagrange multiplier method to obtain driving behavior parameters that describe the human driver's style preferences, thereby identifying the human driver's driving style. The method of the present invention can accurately identify the driving style of human drivers of environmental vehicles, thereby effectively improving the responsiveness and adaptability of the autonomous driving system, and enhancing driving safety and comfort.

Description

Technical Field

[0001] The present invention relates to a human driving behavior analysis method, and in particular to an online driver style identification method. Background Art

[0002] Accurately identifying the driving styles of human drivers in surrounding vehicles is crucial for the current research and iteration of autonomous driving systems. By deeply understanding the diverse driving styles of individual drivers, we can effectively improve the responsiveness and adaptability of autonomous driving systems, enhancing driving safety and comfort.

[0003] Currently, the analysis and modeling of human driving behavior primarily utilize mathematical modeling or machine learning. However, traditional mathematical modeling methods often rely on numerous assumptions, resulting in a narrow scope of applicability and weak generalization capabilities. Machine learning-based methods, on the other hand, require massive amounts of data and suffer from poor interpretability. Furthermore, these methods all overlook the diverse driving styles of individual drivers and fail to implement personalized strategies to proactively protect right-of-way. A few studies have considered heterogeneous driving styles, but these primarily use offline data to refine driving styles, making them difficult to adapt to complex and changing driving scenarios.

[0004] Therefore, based on the above analysis, there is an urgent need for an online identification method of human driver style that can adapt to highly complex and dynamic driving environments. Summary of the Invention

[0005] Purpose of the Invention: This invention aims to provide an online human driver style identification method that can accurately identify the driving style of human drivers in an environmental vehicle, effectively improving the responsiveness and adaptability of the autonomous driving system, and enhancing driving safety and comfort. Based on inverse optimal control theory, this method analyzes and characterizes the diverse driving behavior characteristics of human drivers and analyzes the multi-objective coupling mechanism of human driving behavior.

[0006] Technical solution: The method for online identification of human driver style described in the present invention comprises the following steps:

[0007] Step 1: Build an online recognition model for human driving behavior;

[0008] Step 2: Based on the Pontryagin minimum principle, simplify the online recognition model of human driving behavior:

[0009] Step 3: Based on the model simplification results, transform the model into a linear least squares problem;

[0010] Step 4: Based on the model transformation, the Lagrange multiplier method is used to solve the model and obtain the driving behavior parameters that describe the style preferences of human drivers.

[0011] Preferably, in step 1, for building an online recognition model of human driving behavior:

[0012] Using the modeling idea of ​​inverse optimal control and based on the Pontryagin minimum principle, the Hamiltonian equation is listed:

[0013]

[0014] Among them, x n -System status;

[0015] a CV，n -Human-driven vehicle acceleration;

[0016] β-driving behavior parameter;

[0017] β T - transpose of driving behavior parameter β;

[0018]

[0019] u EV，n -Control quantity of the main vehicle;

[0020] u CV，n -Amount of human-driven vehicle control;

[0021] A n , B n , C n -System state equation parameters;

[0022] - co-morphic variables;

[0023] Based on the Hamiltonian equation, the necessary conditions that the optimal control input in the PMP method should satisfy are listed, namely the regularization equation and the extreme value condition:

[0024]

[0025] in,

[0026] γ n - co-morphic variables;

[0027] -Calculate the gradient of the Hamiltonian function using the system state as a variable;

[0028] H n -Hamiltonian function;

[0029] x n -System status;

[0030] a CV，n -Human-driven vehicle acceleration;

[0031] β-driving behavior parameter;

[0032] -Calculate the gradient of the Hamiltonian function with acceleration as the variable;

[0033] Preferably, the online identification model of human driving behavior in step 2 is simplified: first, the Hamiltonian equation is substituted into the canonical equation to obtain:

[0034]

[0035] Note n =(β,γ n ) T , we can get the following recursive equation:

[0036] z n+1 =K n z n

[0037] in,

[0038]

[0039] The derived recursive equation is reversed and recursively converted to the following equation:

[0040] z n+1 =K n K n-1 K n-2 ...K0z0=K n z0

[0041] z0=(β,γ0) T

[0042] in,

[0043] I - unit vector;

[0044] 0-zero matrix;

[0045] γ0-initial costate vector;

[0046] β-driving behavior parameter;

[0047] A n -System state equation parameters;

[0048] x n -System status;

[0049]

[0050] -Calculate the gradient of the Hamiltonian function using the system state as a variable;

[0051] Similarly, substituting the Hamiltonian equation into the extreme value condition, we can obtain:

[0052]

[0053] remember Then we can get the following equation:

[0054] L n z n+1 =0

[0055] in,

[0056] -Find the gradient of the Hamiltonian function with acceleration as the variable.

[0057]

[0058] Substituting the equation derived by reverse recursion into the above equation, we can get the following equation:

[0059] L n K n z0=0

[0060] Preferably, the simplified model for online identification of human driving behavior in step 3 is transformed into:

[0061] Considering the noise in the collected data and the errors in the model, it is actually difficult to solve a set of β according to the equation finally obtained in step 2. Therefore, the equation obtained in step 2 is amplified and converted into a linear least squares problem.

[0062]

[0063] stez0=β1=a

[0064] in,

[0065] remember

[0066] P n -positive semidefinite matrix;

[0067]

[0068] a (a>0) – magnification factor, which makes the value of the first variable of z0 equal to a.

[0069] β1 - the first term of driving behavior parameters;

[0070]

[0071] K=K n K n-1 Kn-2 ...K0;

[0072]

[0073] Preferably, after the simplified model of online identification of human driving behavior is transformed in step 4, the following solution is obtained:

[0074] For the least squares problem above, the Lagrange multiplier method is used to solve it. The Lagrange equation is:

[0075] 2P n z0+pe T =0

[0076] in,

[0077] p is the Lagrange multiplier, P n - As defined above, z0 - Same as above definition, z0 = (β, γ0) T , e T - As defined above, The transpose of

[0078] To express z0 more clearly, let the matrix P k Block, get the following formula:

[0079]

[0080] in,

[0081] -Matrix P k The element in the first row and first column;

[0082] q k - Remove elements The matrix P k The first column of

[0083] -q k The transpose of

[0084] -Matrix P k submatrix of ;

[0085] -The vector z0 after the element a is deleted.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] 1. Traditional methods for analyzing and modeling human driving behavior primarily rely on mathematical modeling or machine learning. These methods often overlook the diverse driving styles of individual drivers and fail to tailor driving strategies to each individual. However, the online human driver style identification algorithm proposed in this paper considers the heterogeneity of driver driving styles and utilizes online data to refine driving styles, making it adaptable to complex and changing driving scenarios.

[0088] 2. The online human driver style identification method proposed in this paper deconstructs the multi-objective coupling mechanism of human driving behavior, enabling autonomous vehicles to identify the driving styles and behaviors of human and rival vehicles in real time. As a result, autonomous vehicles can adopt driving strategies tailored to individual drivers, thereby improving driving efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 Flowchart of the online human driver style identification algorithm;

[0090] Figure 2 Diagram of the modeling ideas for inverse optimal control. DETAILED DESCRIPTION

[0091] The following is a more detailed description of the human driver style online identification method of the present invention, with reference to a schematic diagram. A preferred embodiment of the present invention is shown. It should be understood that those skilled in the art may modify the present invention described herein while still achieving the beneficial effects of the present invention. Therefore, the following description should be understood as a general guide for those skilled in the art and not as a limitation of the present invention.

[0092] like Figures 1-2 The online driver style identification method of this embodiment specifically includes the following steps:

[0093] Step 1: Build an online recognition model for human driving behavior.

[0094] Using the modeling idea of ​​inverse optimal control and based on the Pontryagin minimum principle, the Hamiltonian equation is listed:

[0095]

[0096] Among them, x n -System status;

[0097] a CV，n -Human-driven vehicle acceleration;

[0098] β-driving behavior parameter;

[0099] β T - transpose of driving behavior parameter β;

[0100]

[0101] u EV，n -Control quantity of the main vehicle;

[0102] u CV，n -Amount of human-driven vehicle control;

[0103] A n , B n , C n -System dynamic parameters.

[0104] Based on the Hamiltonian equation, the necessary conditions that the optimal control input in the PMP method should satisfy are listed, namely the regularization equation and the extreme value condition:

[0105]

[0106] Among them, λ n -co-state vector, the number of parameters in the co-state vector is equal to the number of variables in the system model;

[0107] -Calculate the gradient of the Hamiltonian function using the system state as a variable;

[0108] H n -Hamiltonian function;

[0109] x n -System status;

[0110] a CV，n -Human-driven vehicle acceleration;

[0111] β-driving behavior parameter;

[0112] -Find the gradient of the Hamiltonian function with acceleration as the variable.

[0113] Step 2: Based on the Pontryagin minimum principle, simplify the online recognition model of human driving behavior.

[0114] First, substitute the Hamiltonian equation into the canonical equation to obtain:

[0115]

[0116] Note n =(β,γ n ) T , we can get the following recursive equation:

[0117] z n+1 =K n z n

[0118] in,

[0119]

[0120] The derived recursive equation is reversed and recursively converted to the following equation:

[0121] z n+1 =K n K n-1 K n-2 ...K0z0=K n z0

[0122] z0=(β,γ0) T

[0123] Where, I - unit vector;

[0124] 0-zero matrix;

[0125] γ0-initial costate vector.

[0126] β-driving behavior parameter;

[0127] A n -System state equation parameters;

[0128] x n -System status;

[0129]

[0130] -Calculate the gradient of the Hamiltonian function using the system state as a variable;

[0131] Similarly, substituting the Hamiltonian equation into the extreme value condition, we can obtain:

[0132]

[0133] remember Then we can get the following equation:

[0134] L n z n+1 =0

[0135] in,

[0136] -Calculate the gradient of the Hamiltonian function with acceleration as the variable;

[0137]

[0138] Substituting the equation derived by reverse recursion into the above equation, we can get the following equation:

[0139] L n K n z0=0

[0140] Step 3: Based on the model simplification results, transform the model into a linear least squares problem.

[0141] Considering the noise in the collected data and the errors in the model, it is actually difficult to solve a set of β according to the equation finally obtained in step 2. Therefore, the equation obtained in step 2 is amplified and converted into a linear least squares problem.

[0142]

[0143] stez0=β1=a

[0144] Among them, P n -positive semidefinite matrix;

[0145]

[0146] a (a>0) - magnification factor, so that the value of the first variable of z0 is equal to a;

[0147] β1 - the first term of driving behavior parameters;

[0148]

[0149] K=K n K n-1 K n-2 ...K0;

[0150]

[0151] Step 4: Based on the model transformation, the Lagrange multiplier method is used to solve the model and obtain the driving behavior parameters that describe the style preferences of human drivers.

[0152] For the least squares problem above, the Lagrange multiplier method is used to solve it. The Lagrange equation is:

[0153] 2P n z0+pe T =0

[0154] Where p is the Lagrange multiplier.

[0155] To express z0 more clearly, let the matrix P k Block, get the following formula:

[0156]

[0157] in, -Matrix P k The element in the first row and first column;

[0158] q k- Remove elements The matrix P k The first column of

[0159] -Matrix P k submatrix of ;

[0160] -The vector z0 after the element a is deleted.

[0161] The goal of this invention for parameter identification is to find the parameter β that can make the optimal solution meet the necessary conditions under the premise of knowing the optimal trajectory data online. The modeling idea is as follows Figure 2 shown.

[0162] Figure 2 This paper presents a framework for solving human driver style parameters based on trajectory data, including forward and inverse optimization control analysis. Both approaches form a closed-loop feedback loop, centered around optimal control conditions. The forward analysis calculates driving behavior parameters using trajectory data and an optimal control algorithm, while the inverse analysis employed in this paper uses trajectory data and an inverse optimization algorithm to derive the parameter β that satisfies the necessary conditions for an optimal solution.

[0163] The above description is merely a preferred embodiment of the present invention and does not limit the present invention in any way. Any person skilled in the art who, without departing from the scope of the present invention, makes any equivalent substitution, modification, or other changes to the technical solution and technical content disclosed in the present invention shall be deemed to be within the scope of the present invention and still fall within the scope of protection of the present invention.

Claims

1. A method for online identification of human driver style, characterized in that: The following steps are involved: Step 1: Use the inverse optimal control modeling approach to build an online recognition model for human driving behavior; Step 2: Based on the Pontryagin minimum principle, combined with the canonical equation and extreme value conditions, the online identification model of human driving behavior is simplified; Step 3: Convert the online human driving behavior identification model into a linear least squares problem; Step 4: Based on the model transformation, the Lagrange multiplier method is used to solve the model and obtain the driving behavior parameters that describe the style preferences of human drivers. Using the modeling idea of ​​inverse optimal control and based on the Pontryagin minimum principle, the Hamiltonian equation is listed: ； in, -System status; -Human-driven vehicle acceleration; - Driving behavior parameters; - Driving behavior parameters The transpose of ; is the stage loss function vector; -Control quantity of the main vehicle; -Amount of human-driven vehicle control; , , -System state equation parameters; - co-morphic variables; Based on the Hamiltonian equation, the necessary conditions that the optimal control input should satisfy in the Pontryagin minimum principle are listed, namely the regularization equation and the extreme value condition: ； ； in, - co-morphic variables; -Calculate the gradient of the Hamiltonian function using the system state as a variable; -Hamiltonian function; -System status; -Human-driven vehicle acceleration; - Driving behavior parameters; -Find the gradient of the Hamiltonian function with acceleration as the variable.

2. The method for online identification of human driver style according to claim 1, characterized in that: The method for simplifying the online recognition model of human driving behavior in step 2 is as follows: First, substitute the Hamiltonian equation into the canonical equation to obtain: ； remember , we can get the following recursive equation: ； in, ； The derived recursive equation is reversed and recursively converted to the following equation: ； ； in, -unit vector; - zero matrix; - initial costate vector; - Driving behavior parameters; -System state equation parameters; -System status; ； Calculate the gradient of the Hamiltonian function with the system state as the variable; Similarly, substituting the Hamiltonian equation into the extreme value condition, we can obtain: ； remember , we can get the following equation: ； in, -Calculate the gradient of the Hamiltonian function with acceleration as the variable; ； Substituting the equation derived by reverse recursion into the above equation, we can get the following equation: 。 3. The method for online identification of human driver style according to claim 2, characterized in that: The conversion method in step 3: Considering the noise of the collected data and the error of the model, it is actually difficult to solve the equation obtained in step 2 to obtain a set of Therefore, the equation obtained in step 2 is amplified and converted into a linear least squares problem. ； ； in, remember ; positive semidefinite matrix ; ( >0)-magnification, so The value of the first variable is equal to , -The first item of driving behavior parameters ； ； ； ； 。 4. The method for online identification of human driver style according to claim 3, characterized in that: After the simplified model of online identification of human driving behavior in step 4 is transformed, the solution is obtained: the Lagrange multiplier method is used to solve it. The Lagrange equation is: ； in, is the Lagrange multiplier, - As defined above, , - As defined above, , - As defined above, The transpose of To express more clearly , the matrix Block, get the following formula: ； in, -matrix The element in the first row and first column; - Remove elements Matrix The first column of - The transpose of -matrix submatrix of ; -vector Delete elements The vector after.

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