A vehicle longitudinal-lateral coupling decision control method oriented to a passing time window

Abstract

The application provides a kind of vehicle lateral and longitudinal coupling decision control method for passing time window guidance, belongs to the field of intelligent network connected vehicle-road cooperation, comprising the following steps: 1) the roadside equipment transmits the passing time window to the intelligent network connected vehicle;2) the vehicle-mounted device makes driving behavior decision;3) the vehicle-mounted device controls the intelligent network connected vehicle to move.The application relative to prior art, under the guidance framework of passing time window, the lateral and longitudinal coupling decision control of vehicle is completed, the landing application ability of the field of intelligent network connected vehicle-road cooperation is strengthened, the driving efficiency of intelligent network connected vehicle is improved, and the energy consumption and environmental pollution of traffic system are reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of intelligent networked vehicle-road cooperation, and particularly relates to a vehicle transverse and longitudinal coupling decision control method for passing time window guidance. BACKGROUND

[0002] In the vehicle-to-everything (V2X) technology, the roadside unit (RSU) continuously monitors the surrounding traffic conditions and exchanges data with vehicles through sensors, cameras, communication modules and other facilities installed on the road. Vehicles can obtain real-time information from roadside units through the vehicle-to-everything network. Through this technology, roadside units can provide real-time decision guidance for vehicles, making driving behavior more intelligent, accurate and safe. The vehicle-to-everything system not only improves traffic flow, but also provides strong support for the realization and popularization of automatic driving technology.

[0003] Intelligent networked vehicles are a new generation of vehicles with networked and automated features, representing the development trend of future transportation tools. Intelligent networked vehicles can establish a close communication link with roadside units by installing onboard controllers (OBU), and can receive and process real-time data from roadside units. These data include traffic signal light status, road conditions ahead, sudden traffic accident or obstacle warning, pedestrian or non-motor vehicle dynamics, etc. The onboard controller can react to this information within milliseconds, and its reaction speed and decision-making ability are significantly improved compared to traditional human drivers.

[0004] However, traditional methods focus on using vehicle-to-everything technology to guide the longitudinal speed of intelligent networked vehicles, and using the onboard controller to complete speed tracking in the time domain. Since the lateral movement of the vehicle (such as overtaking and lane changing) is not considered, the practicability of the traditional method is poor, which is not conducive to the application of vehicle-to-everything technology. At the same time, the traditional "time domain" approach applied to the lateral movement modeling of vehicles will cause low computational efficiency and dimension explosion problems. In summary, the traditional method has defects and needs to be improved. SUMMARY

[0005] The purpose of the present application is to provide a vehicle transverse and longitudinal coupling decision control method for passing time window guidance, characterized by the following steps:

[0006] S1) The roadside unit transmits the passing time window to the intelligent networked vehicle;

[0007] S2) The onboard device makes a driving behavior decision;

[0008] S3) The onboard device controls the intelligent networked vehicle to move.

[0009] Further, in S1, the transit time window specifies the time range of the vehicle arriving at the road segment feature point, denoted as where and are the lower and upper bounds of the transit time window at the road segment feature point s k , as shown in Figure 2 .

[0010] where the lower and upper bounds of the transit time window are set values or obtained by existing technologies.

[0011] Further, in S2, the driving behavior decision is based on the established rules, as shown in Figure 3 .

[0012] The driving behavior decision is based on the established rules:

[0013] Rule 1: When there is no other vehicle in front of the intelligent connected vehicle, or even if there is, it does not hinder the transit time window guidance, the driving behavior decision result is adaptive cruise control.

[0014] Rule 2: When there is another vehicle in front of the intelligent connected vehicle, and it hinders the transit time window guidance, but the overtaking safety constraint cannot be met, the driving behavior decision result is following the front vehicle.

[0015] Rule 3: When there is another vehicle in front of the intelligent connected vehicle, although it hinders the transit time window guidance, the overtaking safety constraint is met, the driving behavior decision result is overtaking the front vehicle.

[0016] Further, in S3, the vehicle-mounted device needs to control the intelligent connected vehicle to perform corresponding movements according to the driving behavior decision result, including the following three movement modes:

[0017] S31: Following the front vehicle;

[0018] S32: Adaptive cruise control;

[0019] S33: Overtaking the front vehicle.

[0020] Further, the mathematical model of S31 following the front vehicle and S32 adaptive cruise control is as follows, which is based on the spatial domain construction, i.e. the variables are functions of road segment feature points, which is different from the traditional time domain, i.e. the variables are functions of time.

[0021]

[0022] s.t.

[0023]

[0024]

[0025]

[0026] wherein,

[0027] System dynamics Cost function Constraints

[0028] wherein, k is the index of the road segment feature point s k ;

[0029] K is the total number of road segment feature points s k ;

[0030] is the control vector;

[0031] is the state vector;

[0032] is the desired state vector;

[0033] is the matrix transpose operator;

[0034] Q f is the state weight matrix; R f is the control weight matrix; Q f and R f are diagonal matrices;

[0035] is the state matrix; is the control matrix;

[0036] is the minimum value of the state vector; is the maximum value of the state vector;

[0037] is the minimum value of the control vector; is the maximum value of the control vector;

[0038] π k is the slowness, defined as

[0039] v k is the velocity; ε is an infinitesimal number greater than 0;

[0040] t k is the time for the ego vehicle to reach s k ; ι k is the lateral position;

[0041] is the heading angle; a k is the acceleration; δ k is the front wheel steering angle;

[0042] Δs k is the distance between adjacent road segment feature points, i.e., Δs k = s k+1 -s k ;

[0043] is the vehicle wheelbase;

[0044] is the time for the front vehicle to reach s k ; t safe is the safety following threshold;

[0045] is the lower bound of the time window for the passage at road segment feature point s k ;

[0046] is the upper bound of the time window for the passage at road segment feature point s k ;

[0047] q time is the time weight; q lat is the lateral position weight; q head is the heading angle weight;

[0048] r acc is the acceleration weight; r whe is the front wheel steering angle weight;

[0049] v max is the road speed limit;

[0050] is the minimum value of t k ;

[0051] ι rl is the minimum value of ι k specified by the road boundary; ι ru is the maximum value of ι k specified by the road boundary;

[0052] -inf represents infinitesimal; inf represents infinity, -inf and inf both mean no constraint;

[0053] represents taking the minimum value in and ;

[0054] a min is the minimum value of acceleration; δmin is the minimum value of front wheel steering angle;

[0055] a max is the maximum value of acceleration; max is the maximum value of front wheel steering angle.

[0056] Further, the mathematical model of overtaking the preceding vehicle in S33 is shown as follows, where the definition of lateral position is shown as Figure 4 The model is based on relative spatial domain, i.e. the variables are functions of relative longitudinal position, which refers to the longitudinal position of the controlled vehicle relative to the overtaken vehicle, which is different from the traditional time domain, i.e. the variables are functions of time.

[0057]

[0058] s.t.

[0059]

[0060]

[0061]

[0062] wherein,

[0063] System dynamics

[0064] Cost function

[0065] Constraints

[0066] wherein, i is the index of relative longitudinal position x i ;

[0067] x i is the relative longitudinal position, i.e.

[0068] κ i is the longitudinal position of the ego vehicle; is the longitudinal position of the preceding vehicle;

[0069] I is the total number of relative longitudinal positions x i ;

[0070] is the control vector; is the state vector; is the desired state vector;

[0071] Q p is the state weight matrix; R p is the control weight matrix; Q p and Rp They are all diagonal arrays;

[0072] It is a state matrix; It is a control matrix; It is an additional matrix;

[0073] It is the minimum value of the state vector; It is the maximum value of the state vector;

[0074] It is the minimum value of the control vector; It is the maximum value of the control vector;

[0075] η i It is the relative longitudinal velocity, defined as

[0076] v i It is the longitudinal speed of the vehicle; It is the longitudinal speed of the vehicle in front;

[0077] t i At the relative longitudinal position x i The time spent there;

[0078] ι i It refers to the horizontal position; It is the heading angle;

[0079] It is relative acceleration, defined as

[0080] a i It is the vehicle's acceleration; It is the acceleration of the vehicle in front;

[0081] δ i It's the front wheel steering angle;

[0082] Δx i It is the adjacent relative longitudinal position x i The distance between them, i.e., Δx i =x i+1 -x i

[0083] ε is an infinitesimally small number that is greater than 0;

[0084] It refers to the vehicle's wheelbase;

[0085] v max It is a road speed limit;

[0086] It is ι i Expected value;

[0087] ι ov is the desired lateral position in the overtaking process, i.e. the centerline position of the overtaking lane;

[0088] s sl is the minimum value of the safe relative longitudinal position; su is the maximum value of the safe relative longitudinal position;

[0089] q η is the weight of the relative longitudinal speed; lat is the weight of the lateral position; head is the weight of the heading angle;

[0090] is the weight of the relative acceleration; whe is the weight of the front wheel steering angle;

[0091] is the minimum value of i ;

[0092] -inf represents infinitesimal; inf represents infinity, meaning no constraint;

[0093] is the lateral position of the preceding vehicle;

[0094] ι rl is the minimum value of i specified by the road boundary; ru is the maximum value of i specified by the road boundary;

[0095] a min is the minimum value of acceleration; min is the minimum value of the front wheel steering angle;

[0096] a max is the maximum value of acceleration; max is the maximum value of the front wheel steering angle.

[0097] is the acceleration of the preceding vehicle.

[0098] Among the above vehicle-related parameters, the parameters without superscript pre are self-vehicle parameters.

[0099] Further, the mathematical model constructed in S31, S32 and S33 can be solved by calling an open source solver (such as OSQP), and the specific calling method can be found in the official user manual, which is not described here.

[0100] Compared with the prior art, the present application has the following beneficial effects:

[0101] The application innovatively proposes a vehicle transverse and longitudinal coupling decision control method under the traffic time window guidance framework. Compared with the traditional vehicle-road cooperation technology based on longitudinal speed guidance and control, the application has advantages in practicability, road guidance traffic effect, vehicle decision control ability, etc., and promotes the rapid popularization of vehicle-road cooperation technology and automatic driving technology. In addition, the application innovatively applies relative spatial domain modeling, expresses variables as functions of relative longitudinal positions, greatly improves solving efficiency and accuracy, and therefore has a high threshold. BRIEF DESCRIPTION OF DRAWINGS

[0102] Figure 1 A logic block diagram of the vehicle transverse and longitudinal coupling decision control method for traffic time window guidance.

[0103] Figure 2 A schematic diagram of traffic time window guidance.

[0104] Figure 3 A flowchart of driving behavior decision making of a vehicle-mounted device.

[0105] Figure 4 A schematic diagram of transverse position definition in the process of overtaking a preceding vehicle. DETAILED DESCRIPTION

[0106] The vehicle transverse and longitudinal coupling decision control method for traffic time window guidance will be described in more detail below with reference to the accompanying drawings, in which preferred embodiments of the application are shown. It should be understood that those skilled in the art can modify the application described herein while still achieving the advantageous effects of the application, therefore the following description should be understood as extensive knowledge for those skilled in the art and not as a limitation on the application.

[0107] As shown in Figure 1 , the vehicle transverse and longitudinal coupling decision control method for traffic time window guidance specifically includes the following steps:

[0108] Step 1

[0109] The roadside device transmits the traffic time window to the intelligent connected vehicle.

[0110] The traffic time window specifically refers to the time range of the vehicle reaching the road feature point, denoted as wherein and are the lower and upper bounds of the traffic time window at the road feature point s k , as shown in Figure 2 .

[0111] In this example, k∈{1 2 … 300};

[0112]

[0113]

[0114] Step 2

[0115] The in-vehicle device makes driving behavior decision based on the established rules, as shown in the following table, and controls the intelligent connected vehicle to make corresponding movements according to the decision results, including three movement modes: following the front vehicle, adaptive cruise, and overtaking the front vehicle. In this example, for the purpose of full explanation, it is assumed that the decision results are following the front vehicle, adaptive cruise, and overtaking the front vehicle, respectively. Figure 3

[0116] Step 3

[0117] When the in-vehicle device controls the intelligent connected vehicle to make the movement of following the front vehicle, the established mathematical model is shown in the following table, which is based on the spatial domain, i.e., the variables are functions of the characteristic points of the road section. This modeling method is different from the traditional time domain, i.e., the variables are functions of time.

[0118]

[0119] s.t.

[0120]

[0121]

[0122]

[0123] wherein,

[0124] System dynamics

[0125] Cost function

[0126] Constraints

[0127] wherein, k is the index of the characteristic point s k of the road section;

[0128] K is the total number of the characteristic points s k of the road section;

[0129] is the control vector;

[0130] is the state vector;

[0131] is the expected state vector;

[0132] ​ is the matrix transpose operator;

[0133] Q f is the state weight matrix; R f is the control weight matrix; Q f and R f are diagonal matrices;

[0134] is the state matrix; is the control matrix;

[0135] is the minimum value of the state vector; is the maximum value of the state vector;

[0136] is the minimum value of the control vector; is the maximum value of the control vector;

[0137] π k is the slowness, defined as

[0138] v k is the velocity; ε is an infinitesimal number greater than 0;

[0139] t k is the time for the ego vehicle to reach s k ; ι k is the lateral position;

[0140] is the heading angle; a k is the acceleration; δ k is the front wheel steering angle;

[0141] Δs k is the distance between adjacent road segment feature points, i.e., Δs k = s k+1 - s k ;

[0142] is the vehicle wheelbase;

[0143] is the time for the front vehicle to reach s k ; t safe is the safety following threshold;

[0144] is the lower bound of the travel time window at road segment feature point s k ;

[0145] is the upper bound of the travel time window at road segment feature point s k ;

[0146] q time It is the time weight; q lat It is the horizontal position weight; q head It is the heading angle weight;

[0147] r acc It is the acceleration weight; r whe It is the weight of the front wheel steering angle;

[0148] v max It is a road speed limit;

[0149] It is t k The minimum value;

[0150] ι rl It is defined by the road boundary ι k Minimum value; ι ru It is defined by the road boundary ι k The maximum value;

[0151] -inf represents infinitesimal; inf represents infinity.

[0152] Representative at and Take the minimum value from the middle.

[0153] a min It is the minimum acceleration; δ min It is the minimum value of the front wheel steering angle;

[0154] a max It is the maximum acceleration; δ max It is the maximum front wheel steering angle.

[0155] In this example, v0 = 15 meters per second; ε = 10 -6 No unit;

[0156] t0 = 0 seconds; ι0 = 0 meters; radian;

[0157] K = 300 steps; k ∈ {12…300}, unitless;

[0158] Δs k = 1 meter; rice;

[0159] The unit is seconds;

[0160] t safe =0.1 seconds;

[0161] q time =qlat = q head = 10, unitless;

[0162] r acc = r whe = 5, unitless;

[0163] v max = 25 m / s;

[0164] i rl = -1.75 m; i ru = 5.25 m;

[0165] a min = -4 m / s2; a max = 3 m / s2; d min = -0.5 rad; d max = 0.5 rad.

[0166] The optimal acceleration sequence m / s2, the optimal front wheel angle sequence rad.

[0167] Step 3

[0168] When the on-board device controls the intelligent connected vehicle to perform adaptive cruise motion, the mathematical model established is as shown below, which is based on the construction of the spatial domain, that is, the variable is a function of the road segment feature points. This modeling method is different from the traditional time domain, that is, the variable is a function of time.

[0169]

[0170] s.t.

[0171]

[0172]

[0173]

[0174] wherein,

[0175] System dynamics

[0176] Cost function

[0177] Constraints

[0178] wherein, k is the index of the road segment feature point s k ;

[0179] K is the total number of road segment feature points s k ;

[0180] is the control vector;

[0181] is the state vector;

[0182] is the desired state vector;

[0183] is the matrix transpose operator;

[0184] Q f is the state weight matrix; R f is the control weight matrix; Q f and R f are diagonal matrices;

[0185] is the state matrix; is the control matrix;

[0186] is the minimum value of the state vector; is the maximum value of the state vector;

[0187] is the minimum value of the control vector; is the maximum value of the control vector;

[0188] π k is the slowness, defined as

[0189] v k is the velocity; ε is an infinitesimal number greater than 0;

[0190] t k is the time for the ego vehicle to reach s k ; ι k is the lateral position;

[0191] is the heading angle; a k is the acceleration; δ k is the front wheel steering angle;

[0192] Δs k is the distance between adjacent road segment feature points, i.e., Δs k = s k+1 - s k ;

[0193] is the vehicle wheelbase;

[0194] is the time for the front vehicle to reach s k ; tsafe is a safety following threshold;

[0195] is a lower bound of the time window of passage at the road segment feature point s k ;

[0196] is an upper bound of the time window of passage at the road segment feature point s k ;

[0197] q time is a time weight; q lat is a lateral position weight; q head is a heading angle weight;

[0198] r acc is an acceleration weight; r whe is a front wheel steering angle weight;

[0199] v max is a road speed limit;

[0200] is a minimum value of t k ;

[0201] ι rl is a minimum value of ι k defined by road boundaries; ι ru is a maximum value of ι k defined by road boundaries;

[0202] -inf represents infinitesimal; inf represents infinity;

[0203] represents taking the minimum value in and ;

[0204] a min is an acceleration minimum value; δ min is a minimum value of front wheel steering angle;

[0205] a max is an acceleration maximum value; δ max is a maximum value of front wheel steering angle.

[0206] In this example, v0= 15 meters per second; ε = 10 -6 , unitless;

[0207] t0= 0 seconds; ι0= 0 meters; radians;

[0208] K = 300 steps; k e {1 2 … 300}, unitless;

[0209] Δsk = 1 meter;

[0210] in seconds;

[0211] t safe = 0.1 seconds;

[0212] q time = q lat = q head = 10, unitless;

[0213] r acc = r whe = 5, unitless;

[0214] v max = 25 meters per second;

[0215] i rl = -1.75 meters; i ru = 5.25 meters;

[0216] a min = -4 meters per second squared; a max = 3 meters per second squared; d min = -0.5 radians; d max = 0.5 radians.

[0217] Upon computation, the optimal acceleration sequence meters per second squared, and the optimal front wheel angle sequence radians.

[0218] Step 3

[0219] When the on-board device controls the intelligent connected vehicle to overtake the preceding vehicle, the established mathematical model is as shown below, where the definition of the lateral position is as shown in Figure 4 The model is based on the relative spatial domain, i.e., the variable is a function of the relative longitudinal position, which refers to the longitudinal position of the controlled vehicle relative to the overtaken vehicle. This modeling method is different from the traditional time domain, i.e., the variable is a function of time.

[0220]

[0221] s.t.

[0222]

[0223]

[0224]

[0225] wherein,

[0226] System dynamics

[0227] Cost function

[0228] Constraints

[0229] where i is the index of the relative longitudinal position x i ;

[0230] x i is the relative longitudinal position, i.e.

[0231] κ i is the ego vehicle longitudinal position; is the front vehicle longitudinal position;

[0232] I is the total number of relative longitudinal positions x i ;

[0233] is the control vector; is the state vector; is the desired state vector;

[0234] Q p is the state weight matrix; R p is the control weight matrix; Q p and R p are diagonal matrices;

[0235] is the state matrix; is the control matrix; is the additional matrix;

[0236] is the minimum value of the state vector; is the maximum value of the state vector;

[0237] is the minimum value of the control vector; is the maximum value of the control vector;

[0238] ηi is the relative longitudinal velocity, defined as

[0239] v i is the ego vehicle longitudinal velocity; is the front vehicle longitudinal velocity;

[0240] t i is the time at the relative longitudinal position x i ;

[0241] ι iIt refers to the horizontal position; It is the heading angle;

[0242] It is relative acceleration, defined as

[0243] a i It is the vehicle's acceleration; It is the acceleration of the vehicle in front;

[0244] δ i It's the front wheel steering angle;

[0245] Δx i It is the adjacent relative longitudinal position x i The distance between them, i.e., Δx i =x i+1 -x i

[0246] ε is an infinitesimally small number that is greater than 0;

[0247] It refers to the vehicle's wheelbase;

[0248] v max It is a road speed limit;

[0249] It is ι i Expected value;

[0250] ι ov It is the desired lateral position during overtaking, that is, the position of the center line of the overtaking lane;

[0251] s sl It is the minimum value of the relative longitudinal position of safety; s su It is the maximum value of the safety relative to the longitudinal position;

[0252] q η It is the weight relative to the longitudinal velocity; q lat It is the horizontal position weight; q head It is the heading angle weight;

[0253] It is the weight of relative acceleration; r whe It is the weight of the front wheel steering angle;

[0254] It is ι i The minimum value;

[0255] -inf represents infinitesimal; inf represents infinity.

[0256] It refers to the lateral position of the vehicle in front;

[0257] ιrl is the minimum value of the distance from the road boundary i is the maximum value of the distance from the road boundary ru is the minimum value of the distance from the road boundary i is the maximum value of the distance from the road boundary

[0258] a min is the minimum value of the acceleration min is the minimum value of the front wheel steering angle

[0259] a max is the maximum value of the acceleration max is the maximum value of the front wheel steering angle

[0260] is the acceleration of the vehicle

[0261] In this example, v0 = 15 meters per second;

[0262] meters per second; epsilon = 10 -6 , unitless;

[0263] t0 = 0 seconds; i0 = 0 meters; radians;

[0264] I = 300 steps; i e {12...300}, unitless;

[0265] Delta x i = 1 meter;

[0266] meters;

[0267] i ov = 3.5 meters;

[0268] q η = q lat = q head = 10, unitless;

[0269] unitless;

[0270] s sl = -20 meters;

[0271] s su = 20 meters;

[0272] meters;

[0273] i rl = -1.75 meters; i ru = 5.25 meters;

[0274] a min = -4 meters per second squared; a max= 3 meters per second squared; delta min = -0.5 radians; delta max = 0.5 radians;

[0275] meters per second squared.

[0276] By calculation, the optimal relative acceleration sequence meters per second squared, the optimal front wheel turning angle sequence radians.

[0277] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application in any way. Any person skilled in the art, without departing from the scope of the technical solutions of the present application, can make any form of equivalent replacement or modification of the technical solutions and technical contents disclosed by the present application, and such changes still belong to the protection scope of the present application.

Claims

1. A vehicle lateral and longitudinal coupling decision control method for time window oriented guidance, characterized in that, The method comprises the following steps: S1) a roadside device transmits a passing time window to an intelligent connected vehicle; S2) a vehicle-mounted device makes a driving behavior decision; S3) the vehicle-mounted device controls the intelligent connected vehicle to move; In S3, the vehicle-mounted device needs to control the intelligent connected vehicle to move according to the driving behavior decision result, including three movement modes: adaptive cruise, following a front vehicle, and overtaking a front vehicle; The mathematical model of the adaptive cruise and the following front vehicle is: ； s.t. ； ； ； Wherein, ； ； ； wherein, is an index of a road segment feature point ; is the total number of road segment feature points of the road segment is a control vector; is the state vector; is the desired state vector; is the matrix transpose operator; is a state weight matrix; is a control weight matrix; and are diagonal matrices; is a state matrix; is a control matrix; is the minimum value of the state vector; is the maximum value of the state vector; is the minimum value of the control vector; is the maximum value of the control vector; is the slowness, defined as ; is the velocity; is an infinitesimal number greater than 0; is the time when the ego vehicle arrives at ; is the lateral position; is the heading angle; is the acceleration; is the front wheel steering angle; is the distance between the feature points of adjacent road segments, i.e. ; is the wheelbase of the vehicle; is the time of arrival of the preceding vehicle ; is a safety following threshold; is the lower bound of the time window at the feature point of the road segment ; is the upper bound of the time window at the feature point of the road segment ; is a time weight; is a lateral position weight; is a heading angle weight; is an acceleration weight; is a front wheel steering angle weight; is the road speed limit; is a minimum value; is a minimum value of the distance from the road boundary is a maximum value of the distance from the road boundary is a minimum value of the distance from the road boundary is a maximum value of the distance from the road boundary represents an infinitesimal; represents an infinite; representing in with taking the minimum value; is a minimum value of acceleration; is a minimum value of the front wheel steering angle; is the maximum value of acceleration; is the maximum value of front wheel steering angle. 2.The vehicle longitudinal and lateral coupling decision control method for time window oriented guidance according to claim 1, wherein, In the S1, the passage time window specifically refers to a time range of a vehicle arriving at a feature point of a road section, denoted as wherein, and are respectively a lower bound and an upper bound of the passage time window at the feature point of the road section. 3.The vehicle longitudinal and lateral coupling decision control method for time window oriented guidance according to claim 1, wherein, In S2, the driving behavior decision is based on the following rules: Rule 1: when there is no other vehicle in front of the intelligent connected vehicle, or even if there is, it does not hinder the passing time window guidance, the driving behavior decision result is adaptive cruise; Rule 2: when there is other vehicle in front of the intelligent connected vehicle, and it hinders the passing time window guidance, but the overtaking safety constraint cannot be met, the driving behavior decision result is following a front vehicle; Rule 3: when there is other vehicle in front of the intelligent connected vehicle, although it hinders the passing time window guidance, the overtaking safety constraint is met, the driving behavior decision result is overtaking a front vehicle.

4. The vehicle longitudinal and lateral coupling decision control method oriented to the time window of passing according to claim 1, characterized in that, The mathematical model of overtaking a front vehicle is: ； s.t. ； ； ； Wherein, ； ； ； wherein is an index of the relative longitudinal position ; is the relative longitudinal position, i.e. ; is the longitudinal position of the ego vehicle; is the longitudinal position of the front vehicle; is a relative longitudinal position total number of is a control vector; is a state vector; is a desired state vector; is a state weight matrix; is a control weight matrix; and are diagonal matrices; is a state matrix; is a control matrix; is an additional matrix; is the minimum value of the state vector; is the maximum value of the state vector; is the minimum value of the control vector; is the maximum value of the control vector; is the relative longitudinal velocity, defined as ; is the longitudinal speed of the ego vehicle; is the longitudinal speed of the preceding vehicle; is the time at the relative longitudinal position ; is a lateral position; is a heading angle; is the relative acceleration, defined as ; is the host vehicle acceleration; is the preceding vehicle acceleration; is the front wheel steering angle; is the distance between the adjacent opposite longitudinal positions ; and ; is an infinitesimal number greater than 0; is the wheelbase of the vehicle; is the road speed limit; is the expected value; is the lateral position desired during the overtaking process, i.e. the center line position of the overtaking lane; is a minimum value of the safe relative longitudinal position; is a maximum value of the safe relative longitudinal position; is a weight for the relative longitudinal speed; is a lateral position weight; is a heading angle weight; is a weight of the relative acceleration; is a front wheel steering angle weight; is the minimum value of represents an infinitesimal; represents an infinite; is the lateral position of the preceding vehicle; is a minimum value of the distance from the road boundary is a maximum value of the distance from the road boundary is a minimum value of the distance from the road boundary is a maximum value of the distance from the road boundary is a minimum value of acceleration; is a minimum value of the front wheel steering angle; is the maximum value of acceleration; is the maximum value of front wheel steering angle; is the front vehicle acceleration.

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