CN110182217B - Running task complexity quantitative evaluation method oriented to complex overtaking scene - Google Patents

Abstract

The invention discloses a running task complexity quantitative evaluation method facing a complex overtaking scene in the technical field of automatic driving, through the combination of the implementation flow of the running task complexity quantitative evaluation method of the complex overtaking scene and the implementation formula of the running task complexity quantitative evaluation method facing the complex overtaking scene, the problem that the existing environment task complexity quantitative evaluation method does not pay attention to the mutual influence of the road user and the running task of the main vehicle is solved, and a quantitative evaluation method for the complexity of the 'overtaking' driving task in a complex conflict scene is not provided, so that the problem that the influence of the existing algorithm on the complexity of the primary form task of the 'overtaking' decision of the main vehicle can not be quantitatively calculated, therefore, the influence of road users on the task complexity of the primary form of the 'overtaking' decision of the main vehicle is quantitatively calculated.

Description

Running task complexity quantitative evaluation method oriented to complex overtaking scene

Technical Field

The invention relates to the technical field of automatic driving, in particular to a running task complexity quantitative evaluation method for a complex overtaking scene.

Background

The automobile industry is an important component of national economy in China, outstanding contribution is made to steady and rapid increase of national economy, along with rapid development of the intelligent automobile industry, the market scale is increasingly large, developed countries are in a dispute and are in a leading position, the intelligent automobile industry in China starts relatively late, but the industry is rapidly developed, at present, the establishment and specification of intelligent automobile standards are bottlenecks for restricting the development of intelligent automobiles, and the research of the environment task complexity quantitative evaluation method can provide scientific basis and foundation for the establishment of the intelligent automobile standards.

The environment task complexity quantitative evaluation research is divided into two aspects of running environment complexity research and running task complexity research, wherein the running task complexity is difficult to carry out relatively comprehensive and objective quantitative evaluation on the vehicle running task due to the uncertainty of road users, road types, driving targets, constraints and the like and the uncertainty of related traffic rules.

In the current quantitative evaluation of the running task complexity, a traditional statistical method is still used, the elements such as the road attribute, the road user, the traffic control and the meteorological condition of the current automobile running need to be deconstructed and classified manually at the background, the prior knowledge of each element in the database on the running task complexity is contrasted, the current automobile running task complexity is quantitatively evaluated through weighting summation, a large amount of manpower is consumed to classify the running scenes and the elements, the influence of the road user on the running task complexity of the main automobile is not considered, the complexity quantization precision is extremely low, and the automatic driving technical standard is not easy to make and standardize.

The existing running task complexity quantitative evaluation method cannot describe the task complexity in 'overtaking' running, and the core functional modules required by the normal running of the intelligent automobile on the road are as follows: the system comprises a perception module, a decision-making module and a control module, wherein a behavior decision-making technology in the decision-making module is a key technology for restricting the development of automatic driving, and a overtaking driving task relates to typical complex scene series-parallel decision, and comprises the following steps: if a traditional quantitative evaluation method for the complexity of the driving task based on scene deconstruction is adopted, the complexity of the 'overtaking' driving task cannot be calculated quantitatively, and the influence of a road user on the 'overtaking' decision of a main vehicle and the complexity of the driving task cannot be calculated quantitatively by the conventional algorithm.

Disclosure of Invention

The invention aims to provide a running task complexity quantitative evaluation method for a complex overtaking scene, and aims to solve the problems that the existing environmental task complexity quantitative evaluation method proposed in the background technology does not pay attention to the mutual influence of a road user and a main vehicle running task, and the existing algorithm cannot quantitatively calculate the influence of the road user on the main vehicle 'overtaking' decision-making primary form task complexity due to the fact that the existing algorithm does not propose the quantitative evaluation method for the 'overtaking' running task complexity under the complex conflict scene.

In order to achieve the purpose, the invention provides the following technical scheme: a running task complexity quantitative evaluation method for a complex overtaking scene is implemented by the following flows:

1) acquisition of "overtaking" scene prior knowledge

Using SCANER studio to establish overtaking scene, and obstacle vehicle B is in front of main vehicle A by X_abThe longitudinal distance between a conflicted vehicle C in the opposite traffic flow and an obstacle vehicle B is X_bcMeter, setting the initial speed of the obstacle vehicle B to V_bkm/h, initial speed of subject vehicle A V_ckm/h, initial velocity of colliding vehicle C is V_akm/h, with a scene function of S (X)_ab2X_bc2V_a2V_b2V_c) Indicating a passing scene, V, set from the above initial state_a、V_b、V_cThe speed selection depends on the traffic regulation speed limit value of the corresponding road, x drivers with the driving ages of 3-5 years are selected, three driving simulators are used for carrying out combined simulation in the built scene at the same time, and in order to utilize human resources to the maximum degree, the x drivers can design A by utilizing the permutation and combination principle^x _xGroup tests, each group of tests was repeated y times, totaling y A^x _xThe test is performed by recording the running states of the host vehicle A, the obstacle vehicle B and the collision vehicle C in each test, and using N_b1、N_b2、N_b3、N_c1、N_c2、N_c3Respectively represents three driving behaviors of accelerating, uniform speed and decelerating of the vehicle B, C, M_a1、M_a2、M_a3、M_a4、M_a5、M_a6Respectively representing six driving behaviors of accelerating non-lane change, accelerating lane change, constant-speed non-lane change, constant-speed lane change, decelerating non-lane change and decelerating lane change of the main vehicle A;

let the scene function be S (X)_ab,X_bc,V_a,V_b,V_c) The decision probability P of the acceleration of the vehicle B_b,1Uniform speed running decision probability P_b,2And a deceleration driving decision probability P_b,3Acceleration decision probability P of conflicting vehicle C_c,1Uniform speed running decision probability P_c,2And a deceleration driving decision probability P_c,3Setting the driving behaviors of the obstacle vehicle B and the conflict vehicle C as N respectively_bi、N_cjUnder the scene, the driving behavior of the main vehicle A is M_akHas a decision probability of P_k|ijThe calculation formula is as follows:

changing the scene function S (X)_ab,X_bc,V_a,V_b,V_c) Initial values of variables, S (X) being obtained using the above calculation method_ab,X_bc,V_a,V_b,V_c) And P_b,i、P_c,j、P_k|ijThe mapping table of (1), namely the overtaking scene prior knowledge;

2) "overtaking" decision probability extraction

Reading in a specific overtaking driving case aiming at a 'overtaking' driving task in a bidirectional two-lane conflict scene, respectively calculating an accelerated driving decision probability, a constant speed driving decision probability and a decelerated driving decision probability of a front obstacle vehicle B and a lane change conflict vehicle C at the current moment through priori knowledge, establishing a mixed decision joint probability of the overtaking scene of a main vehicle A at the current moment based on a statistical analysis theory, extracting a conditional probability of each decision of the main vehicle A by utilizing the priori knowledge, and calculating an accelerated non-lane-changing decision probability, an accelerated lane-changing decision probability, a constant speed non-lane-changing decision probability, a constant speed lane-changing decision probability, a decelerated non-lane-changing decision probability and a decelerated lane-changing decision probability of the main vehicle A under each decision of the overtaking scene at the current moment;

3) quantitative evaluation of 'overtaking' driving task complexity

The method comprises the steps of counting the occurrence probability of each behavior decision of a main vehicle A in a overtaking scene, calculating the average decision probability of overtaking, selecting a reasonable task complexity normalization parameter, establishing a task complexity function of the main vehicle A in the overtaking scene at the current time by utilizing the principle that the larger the covariance of the actual behavior decision probability and the average decision probability of the main vehicle A is, the higher the complexity of the overtaking driving task at the current time is, popularizing the task complexity quantitative evaluation method to the complex overtaking scene, and selecting the maximum value of the task complexity function for quantitatively evaluating the overall complexity of the overtaking driving task.

Preferably, the implementation formula of the running task complexity quantitative evaluation method for the complex overtaking scene is as follows:

reading t_pScene of overtaking at any moment

(1) Determining t from a priori knowledge_pAcceleration driving decision probability P of preceding obstacle vehicle B at time_b,1(t_p) Uniform speed running decision probability P_b,2(t_p) And a deceleration driving decision probability P_b,3(t_p)；

(2) Determining t from a priori knowledge_pAcceleration driving decision probability P of vehicle C with conflict of lane change at any moment_c,1(t_p) Uniform speed running decision probability P_c,2(t_p) And a deceleration driving decision probability P_c,3(t_p)；

(3) Calculating t according to the accelerated driving decision probability, the uniform speed driving decision probability and the decelerated driving decision probability of the front obstacle vehicle B and the lane change conflict vehicle C_pDecision joint probability P of time overtaking scene_bc,ij(t_p) Where i is {1,2,3}, and j is {1,2,3}, the following formula:

P_bc,ij(t_p)＝P_c,i(t_p)×P_c,j(t_p) (4)；

(4) known calculation of t_pLower master of each decision joint probability in time overtaking sceneConditional probability of the body vehicle A, i.e. the subject vehicle acceleration lane-change-free conditional probability P_t,1|ij(t_p) Accelerated lane change probability P_t,2|ij(t_p) Constant velocity non-changing track probability P_t,3|ij(t_p) Uniform speed lane changing probability P_t,4|ij(t_p) Deceleration non-lane-changing probability P_t,5|ij(t_p) And deceleration lane change probability P_t,6|ij(t_p)；

(5) According to t_pJoint probability P of each decision in moment overtaking scene_bc,ij(t_p) I ═ 1,2,3, and j ═ 1,2,3, and the conditional probability P of the subject vehicle a_t,k|ij(t_p) And k is {1,2,3,4,5,6}, and the probability P of occurrence of each decision-making behavior of the subject vehicle A is calculated_k(t_p) The formula is as follows:

(6) knowing t_pThe average decision probability of the time overtaking scene is P_a(t_p) 1/n, n-6 represents the actual decision number of the main vehicle A, and a task normalization parameter W is selected_c>0, quantitative calculation of t_pThe overtaking task complexity of the main vehicle A in the overtaking scene at any moment is as follows:

(7) reading the overtaking scene at the next moment, wherein p + tau, tau is 1, …, N, repeating the steps (1) - (6) to calculate the overtaking task complexity (formula (3)) at each moment, and selecting the maximum value as a quantitative evaluation index of the overtaking task complexity in the overtaking process, wherein the formula is as follows:

compared with the prior art, the invention has the beneficial effects that: the invention has practical significance for the formulation and specification of the automatic driving technical standard of the intelligent automobile, and definitely provides a quantitative evaluation method of the running task complexity facing to the 'overtaking' scene.

Drawings

FIG. 1 is a decision probability extraction diagram of the present invention;

FIG. 2 is a diagram illustrating a driving complexity quantitative evaluation according to the present invention;

FIG. 3 is a cut-in view of the present invention;

FIG. 4 shows the present invention t_p+1、t_p+2、t_p+3And (5) a moment overtaking scene schematic diagram.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.

The invention provides the following technical scheme: a running task complexity quantitative evaluation method for a complex overtaking scene is used for establishing a new running task complexity quantitative evaluation standard for quantitative evaluation of 'overtaking' running tasks in a two-way two-lane conflict scene, and the implementation flow of the running task complexity quantitative evaluation method for the complex overtaking scene is as follows:

1) acquisition of "overtaking" scene prior knowledge

Using SCANER studio to create a cut-in scenario As shown in FIG. 3, an obstacle vehicle B is in front of a subject vehicle A by X_abThe longitudinal distance between a conflicted vehicle C in the opposite traffic flow and an obstacle vehicle B is X_bcRice, setting upInitial speed of obstacle vehicle B is V_bkm/h, initial speed of subject vehicle A V_ckm/h, initial velocity of colliding vehicle C is V_akm/h, with a scene function of S (X)_ab2X_bc2V_a2V_b2V_c) Indicating a passing scene, V, set from the above initial state_a、V_b、V_cThe speed selection depends on the traffic regulation speed limit value of the corresponding road, x drivers with the driving ages of 3-5 years are selected, three driving simulators are used for carrying out combined simulation in the built scene at the same time, and in order to utilize human resources to the maximum degree, the x drivers can design A by utilizing the permutation and combination principle^x _xGroup tests, each group of tests was repeated y times, totaling y A^x _xIn the secondary test, the running states of the host vehicle a, the obstacle vehicle B, and the collision vehicle C in each test are recorded, and N is used as shown in tables 1 and 2_b1、N_b2、N_b3、N_c1、N_c2、N_c3Respectively represents three driving behaviors of the vehicle B, C, namely acceleration driving, constant speed driving and deceleration driving; m_a1、M_a2、M_a3、M_a4、M_a5、M_a6Respectively representing six driving behaviors of accelerating non-lane change, accelerating lane change, constant-speed non-lane change, constant-speed lane change, decelerating non-lane change and decelerating lane change of the main vehicle A;

TABLE 1 test chart

TABLE 2 summary of tests for subject vehicle A

Ma1

Ma2

Ma3

Ma4

Ma5

Ma6

Nb1Nc1

…

NbiNcj

ak|ij

…

Nb3Nc3

Note: a is_k|ijN represents the driving behaviors of the obstacle vehicle B and the conflict vehicle C_bi、N_cjUnder the scene, the driving behavior M of the subject vehicle A_akThe number of hours;

let the scene function be S (X)_ab,X_bc,V_a,V_b,V_c) The decision probability P of the acceleration of the vehicle B_b,1Uniform speed running decision probability P_b,2And a deceleration driving decision probability P_b,3Acceleration decision probability P of conflicting vehicle C_c,1Uniform speed running decision probability P_c,2And a deceleration driving decision probability P_c,3Setting the driving behaviors of the obstacle vehicle B and the conflict vehicle C as N respectively_bi、N_cjUnder the scene, the driving behavior of the main vehicle A is M_akHas a decision probability of P_k|ijThe calculation formula is as follows:

changing the scene function S (X)_ab,X_bc,V_a,V_b,V_c) Initial values of variables, S (X) being obtained using the above calculation method_ab,X_bc,V_a,V_b,V_c) And P_b,i、P_c,j、P_k|ijThe mapping table of (1), namely the overtaking scene prior knowledge;

2) "overtaking" decision probability extraction

The overtaking decision probability extraction process is as shown in fig. 1, a specific overtaking driving case is read in aiming at an overtaking driving task in a bidirectional two-lane conflict scene, the accelerating driving decision probability, the constant-speed driving decision probability and the decelerating driving decision probability of a front obstacle vehicle B and a lane-changing conflict vehicle C at the current moment are respectively calculated through priori knowledge, the mixed decision joint probability of the overtaking scene of a main vehicle A at the current moment is established based on a statistical analysis theory, the conditional probability of each decision of the main vehicle A is extracted by utilizing the priori knowledge, and the accelerating non-lane-changing decision probability, the accelerating lane-changing decision probability, the constant-speed non-lane-changing decision probability, the decelerating non-lane-changing decision probability and the decelerating lane-changing decision probability of the main vehicle A at each decision of the overtaking scene at the current moment are calculated;

3) quantitative evaluation of 'overtaking' driving task complexity

The process of quantitative evaluation of the complexity of the overtaking driving task is shown in fig. 2, the occurrence probability of each behavior decision of the main vehicle A under the overtaking scene is counted, the average decision probability of overtaking is calculated, reasonable task complexity normalization parameters are selected, a task complexity function of the main vehicle A under the overtaking scene at the current moment is established by utilizing the principle that the covariance of the actual behavior decision probability and the average decision probability of the main vehicle A is larger and the complexity of the overtaking driving task at the current moment is higher, the quantitative evaluation method of the task complexity is popularized to the complex overtaking scene, and the maximum value of the task complexity function is selected to be used for quantitatively evaluating the overall complexity of the overtaking driving task.

Examples

Reading t_pThe time-of-day cut-in scenario, as shown in figure 3,

determining t from a priori knowledge_pAcceleration driving decision probability P of preceding obstacle vehicle B at time_b,1(t_p) 20% constant speed driving decision probability P_b,2(t_p) 80% and a deceleration driving decision probability P_b,3(t_p)＝0％；

Determining t from a priori knowledge_pAcceleration driving decision probability P of vehicle C with conflict of lane change at any moment_c,1(t_p) 10% constant speed driving decision probability P_c,2(t_p) 80% and a deceleration driving decision probability P_c,3(t_p)＝10％；

According to the accelerated driving decision probability, the uniform speed driving decision probability and the decelerated driving decision probability of the front obstacle vehicle B and the lane change conflict vehicle C, t is calculated according to a formula (1)_pDecision joint probability P of time overtaking scene_bc,ij(t_p) I ═ 1,2,3, and j ═ 1,2,3, the results are as follows:

P_bc,11(t_p)＝2％，P_bc,12(t_p)＝16％，P_bc,13(t_p)＝2％，

P_bc,21(t_p)＝8％，P_bc,22(t_p)＝64％，P_bc,23(t_p)＝8％， (8)

P_bc,31(t_p)＝0％，P_bc,32(t_p)＝0％，P_bc,33(t_p)＝0％.

known calculation of t_pConditional probability of the subject vehicle A under each decision joint probability in the time overtaking scene, namely the acceleration lane-unchanging conditional probability P of the subject vehicle_t,1|11(t_p)＝40％、P_t,1|12(t_p)＝50％、P_t,1|13(t_p)＝25％、P_t,1|21(t_p)＝5％、P_t,1|22(t_p)＝4％、P_t,1|23(t_p)＝3％、P_t,1|31(t_p)＝P_t,1|32(t_p)＝P_t,1|33(t_p) 0%, accelerated lane change probability P_t,2|11(t_p)＝10％、P_t,2|12(t_p)＝20％、P_t,2|13(t_p)＝25％、P_t,2|21(t_p)＝5％、P_t,2|22(t_p)＝6％、P_t,2|23(t_p)＝7％、P_t,2|31(t_p)＝P_t,2|32(t_p)＝P_t,2|33(t_p) 0%, constant speed non-changing track probability P_t,3|11(t_p)＝40％、P_t,3|12(t_p)＝30％、P_t,3|13(t_p)＝25％、P_t,3|21(t_p)＝40％、P_t,3|22(t_p)＝32％、P_t,3|23(t_p)＝24％、P_t,3|31(t_p)＝P_t,3|32(t_p)＝P_t,3|33(t_p) 0%, constant speed lane change probability P_t,4|11(t_p)＝10％、P_t,4|12(t_p)＝20％、P_t,4|13(t_p)＝25％、P_t,4|21(t_p)＝40％、P_t,4|22(t_p)＝48％、P_t,4|23(t_p)＝56％、P_t,4|31(t_p)＝P_t,4|32(t_p)＝P_t,4|33(t_p) 0%, deceleration non-lane-change probability P_t,5|11(t_p)＝0％、P_t,5|12(t_p)＝0％、P_t,5|13(t_p)＝0％、P_t,5|21(t_p)＝5％、P_t,5|22(t_p)＝4％、P_t,5|23(t_p)＝3％、P_t,5|31(t_p)＝P_t,5|32(t_p)＝P_t,5|33(t_p) 0% and deceleration lane change probability P_t,6|11(t_p)＝0％、P_t,6|12(t_p)＝0％、P_t,6|13(t_p)＝0％、P_t,6|21(t_p)＝5％、P_t,6|22(t_p)＝6％、P_t,6|23(t_p)＝7％、P_t,6|31(t_p)＝P_t,6|32(t_p)＝P_t,6|33(t_p)＝0％；

Knowing t_pJoint probability P of each decision in moment overtaking scene_bc,ij(t_p) I ═ 1,2,3, and j ═ 1,2,3, and the conditional probability P of the subject vehicle a_t,k|ij(t_p) And k is {1,2,3,4,5,6}, and the probability P of occurrence of each decision-making behavior of the subject vehicle a is calculated according to the formula (2)_k(t_p) The results are as follows:

knowing t_pThe average decision probability of the time overtaking scene is P_a(t_p) Representing the actual decision number of the main vehicle A by 16.7%, and selecting a task normalization parameter W_c＝1>0, quantitative calculation of t_pThe overtaking task complexity of the main vehicle A in the overtaking scene at any moment is as follows:

J_jb(t_p)＝2.76％ (10)

reading the overtaking scenes at other moments, repeating the steps (1) to (6) to calculate the overtaking task complexity (J) at each moment as shown in figure 4_jb(t_p+1)＝32.48％,J_jb(t_p+1)＝15.74％，J_jb(t_p+1) 3.13%), selecting the maximum value according to the formula (4) as the quantitative evaluation index of the complexity of the overtaking process driving task, and obtaining the following results:

J_jb＝32.48％ (11)

although the invention has been described above with reference to some embodiments, various modifications can be made and equivalents can be substituted for elements thereof without departing from the scope of the invention, the examples merely provide an implementation description for the algorithm of the invention, and therefore, the number of samples and the driving decision prior probability involved in the actual quantitative evaluation result of the complexity of the overtaking task are not in the scope of the discussion of the embodiment case, and therefore, the invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims (2)

1. A running task complexity quantitative evaluation method for a complex overtaking scene is characterized by comprising the following steps: the implementation flow of the running task complexity quantitative evaluation method for the complex overtaking scene is as follows:

1) acquisition of "overtaking" scene prior knowledge

Using SCANER studio to establish overtaking scene, and obstacle vehicle B is in front of main vehicle A by X_abThe longitudinal distance between a conflicted vehicle C in the opposite traffic flow and an obstacle vehicle B is X_bcMeter, setting the initial speed of the obstacle vehicle B to V_bkm/h, initial speed of subject vehicle A V_ckm/h, initial velocity of colliding vehicle C is V_akm/h, with a scene function of S (X)_ab,X_bc,V_a,V_b,V_c) Indicating a passing scene, V, set from the above initial state_a、V_b、V_cThe speed of is selected depending onSelecting x drivers with the driving ages of 3-5 years according to the traffic regulation speed limit value of the corresponding road, performing joint simulation in the built scene by using three driving simulators simultaneously, and designing the x drivers by using a permutation and combination principle to maximally utilize human resources

Group tests, each group test was repeated y times, totaling

The test is performed by recording the running states of the host vehicle A, the obstacle vehicle B and the collision vehicle C in each test, and using N_b1、N_b2、N_b3、N_c1、N_c2、N_c3Respectively represents three driving behaviors of the vehicle B, C, namely acceleration driving, constant speed driving and deceleration driving; m_a1、M_a2、M_a3、M_a4、M_a5、M_a6Respectively representing six driving behaviors of accelerating non-lane change, accelerating lane change, constant-speed non-lane change, constant-speed lane change, decelerating non-lane change and decelerating lane change of the main vehicle A;

let the scene function be S (X)_ab,X_bc,V_a,V_b,V_c) The decision probability P of the acceleration of the vehicle B_b,1Uniform speed running decision probability P_b,2And a deceleration driving decision probability P_b,3Acceleration decision probability P of conflicting vehicle C_c,1Uniform speed running decision probability P_c,2And a deceleration driving decision probability P_c,3Setting the driving behaviors of the obstacle vehicle B and the conflict vehicle C as N respectively_bi、N_cjUnder the scene, the driving behavior of the main vehicle A is M_akHas a decision probability of P_k|ijThe calculation formula is as follows:

changing the scene function S (X)_ab,X_bc,V_a,V_b,V_c) Initial values of variables, S (X) being obtained using the above calculation method_ab,X_bc,V_a,V_b,V_c) And P_b,i、P_c,j、P_k|ijThe mapping table of (1), namely the overtaking scene prior knowledge;

2) "overtaking" decision probability extraction

Reading in a specific overtaking driving case aiming at a 'overtaking' driving task in a bidirectional two-lane conflict scene, respectively calculating an accelerated driving decision probability, a constant speed driving decision probability and a decelerated driving decision probability of a front obstacle vehicle B and a lane change conflict vehicle C at the current moment through priori knowledge, establishing a mixed decision joint probability of the overtaking scene of a main vehicle A at the current moment based on a statistical analysis theory, extracting a conditional probability of each decision of the main vehicle A by utilizing the priori knowledge, and calculating an accelerated non-lane-changing decision probability, an accelerated lane-changing decision probability, a constant speed non-lane-changing decision probability, a constant speed lane-changing decision probability, a decelerated non-lane-changing decision probability and a decelerated lane-changing decision probability of the main vehicle A under each decision of the overtaking scene at the current moment;

3) quantitative evaluation of 'overtaking' driving task complexity

The method comprises the steps of counting the occurrence probability of each behavior decision of a main vehicle A in a overtaking scene, calculating the average decision probability of overtaking, selecting a reasonable task complexity normalization parameter, establishing a task complexity function of the main vehicle A in the overtaking scene at the current time by utilizing the principle that the larger the covariance of the actual behavior decision probability and the average decision probability of the main vehicle A is, the higher the complexity of the overtaking driving task at the current time is, popularizing the task complexity quantitative evaluation method to the complex overtaking scene, and selecting the maximum value of the task complexity function for quantitatively evaluating the overall complexity of the overtaking driving task.

2. The complex overtaking scene oriented running task complexity quantitative evaluation method as recited in claim 1, characterized in that: the realization formula of the driving task complexity quantitative evaluation method facing the complex overtaking scene is as follows:

reading t_pScene of overtaking at any moment

(1) Determining t from a priori knowledge_pAcceleration driving decision probability P of preceding obstacle vehicle B at time_b,1(t_p) Uniform speed running decision probability P_b,2(t_p) And a deceleration driving decision probability P_b,3(t_p)；

(2) Determining t from a priori knowledge_pAcceleration driving decision probability P of vehicle C with conflict of lane change at any moment_c,1(t_p) Uniform speed running decision probability P_c,2(t_p) And a deceleration driving decision probability P_c,3(t_p)；

(3) Calculating t according to the accelerated driving decision probability, the uniform speed driving decision probability and the decelerated driving decision probability of the front obstacle vehicle B and the lane change conflict vehicle C_pDecision joint probability P of time overtaking scene_bc,ij(t_p) Where i is {1,2,3}, and j is {1,2,3}, the following formula:

P_bc,ij(t_p)＝P_c,i(t_p)×P_c,j(t_p) (4)；

(4) known calculation of t_pConditional probability of the subject vehicle A under each decision joint probability in the time overtaking scene, namely the acceleration lane-unchanging conditional probability P of the subject vehicle_t,1|ij(t_p) Accelerated lane change probability P_t,2|ij(t_p) Constant velocity non-changing track probability P_t,3|ij(t_p) Uniform speed lane changing probability P_t,4|ij(t_p) Deceleration non-lane-changing probability P_t,5|ij(t_p) And deceleration lane change probability P_t,6|ij(t_p)；

(5) According to t_pJoint probability P of each decision in moment overtaking scene_bc,ij(t_p)，i＝{1,2,3}，j＝{1,2,3} and conditional probability P of subject vehicle A_t,k|ij(t_p) And k is {1,2,3,4,5,6}, and the probability P of occurrence of each decision-making behavior of the subject vehicle A is calculated_k(t_p) The formula is as follows:

(6) knowing t_pThe average decision probability of the time overtaking scene is P_a(t_p) 1/n, n-6 represents the actual decision number of the main vehicle A, and a task normalization parameter W is selected_c>0, quantitative calculation of t_pThe overtaking task complexity of the main vehicle A in the overtaking scene at any moment is shown in the following formula;

(7) reading the overtaking scene at the next moment, wherein p + tau, tau is 1, …, N, repeating the steps (1) - (6) to calculate the overtaking task complexity (formula (3)) at each moment, and selecting the maximum value as a quantitative evaluation index of the overtaking task complexity in the overtaking process, wherein the formula is as follows:

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