Vehicle transverse and longitudinal coupling decision control method oriented to passing time window guidance

Abstract

The invention provides a vehicle transverse and longitudinal coupling decision control method oriented to passing time window guidance, and belongs to the field of intelligent network connection vehicle-road collaboration, and the method comprises the following steps: 1) a roadside device transmits a passing time window to an intelligent network connection vehicle; 2) the vehicle-mounted equipment makes a driving behavior decision; and (3) the vehicle-mounted equipment controls the intelligent network connection vehicle to move. Compared with the prior art, under a passing time window guiding framework, transverse and longitudinal coupling decision control of the vehicle is completed, the landing application capability of the intelligent network connection vehicle-road cooperation field is enhanced, the driving efficiency of the intelligent network connection vehicle is improved, and energy consumption and environmental pollution of a traffic system are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent connected vehicle-road collaboration, and in particular relates to a vehicle lateral and longitudinal coupling decision control method for travel time window guidance. Background Art

[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 information from roadside equipment in real time through the vehicle-road cooperative network. Through this technology, roadside equipment can provide real-time decision-making guidance for vehicles, making driving behavior more intelligent, accurate and safe. The vehicle-road cooperative system not only improves traffic fluency, but also provides strong support for the realization and popularization of autonomous driving technology.

[0003] Intelligent connected vehicles are a new generation of vehicles with networking and automation features, representing the development trend of future transportation. Intelligent connected vehicles establish a close communication link with roadside equipment through the onboard controller (OBU, Onboard Unit), and can receive and process real-time data from roadside equipment. These data include traffic light status, road conditions ahead, sudden traffic accidents or obstacle warnings, pedestrian or non-motor vehicle dynamics, etc. The onboard controller can respond 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 mostly focus on using vehicle-road cooperative technology to guide the longitudinal speed of intelligent connected vehicles, and using on-board controllers to perform time domain modeling to complete speed tracking. Due to the lack of consideration for the lateral movement of vehicles (such as overtaking and lane changing), the practicality of traditional methods is poor, which is not conducive to the implementation of vehicle-road cooperative technology. At the same time, when the traditional "time domain" approach is applied to the lateral movement modeling of vehicles, it will cause low computational efficiency and the problem of dimensional explosion in solution. In summary, the traditional method has defects and needs to be improved through targeted inventions. Summary of the invention

[0005] The object of the present invention is to provide a vehicle lateral and longitudinal coupling decision control method for passing time window guidance, which is characterized by comprising the following steps:

[0006] S1) The roadside equipment transmits the travel time window to the intelligent connected vehicle;

[0007] S2) The vehicle-mounted equipment makes driving behavior decisions;

[0008] S3) The on-board equipment controls the movement of the intelligent connected vehicle.

[0009] Furthermore, in S1, the travel time window specifically refers to the time range of the vehicle arriving at the characteristic point of the road section, which is recorded as in, and They are the travel time window at the road feature point s k The lower and upper time bounds of Figure 2 shown.

[0010] The lower and upper time limits of the travel time window are set values ​​or obtained through existing technologies.

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

[0012] The driving behavior decisions are based on established rules:

[0013] Rule 1: When there are no other vehicles in front of the intelligent connected vehicle, or even if there are other vehicles in front of it, but they do not hinder the guidance of the travel time window, the driving behavior decision result is adaptive cruise control;

[0014] Rule 2: When there are other vehicles in front of the intelligent connected vehicle and the travel time window guidance is blocked, but the overtaking safety constraint cannot be met, the driving behavior decision result is to follow the vehicle in front;

[0015] Rule 3: When there are other vehicles in front of the intelligent connected vehicle, although they hinder the guidance of the travel time window, the overtaking safety constraints are met, and the driving behavior decision result is to overtake the vehicle in front.

[0016] Furthermore, the on-board equipment in S3 needs to control the intelligent connected vehicle to perform corresponding movements according to the driving behavior decision results, including the following three movement modes:

[0017] S31: Follow the vehicle in front;

[0018] S32: Adaptive cruise control;

[0019] S33: Overtake the vehicle ahead.

[0020] Furthermore, the mathematical model of following the preceding vehicle in S31 and the adaptive cruise control in S32 is as follows. The model is constructed based on the spatial domain, that is, the variables are functions of the characteristic points of the road section. This modeling method is different from the traditional time domain, that is, the variables are functions of time.

[0021]

[0022] st

[0023]

[0024]

[0025]

[0026] in,

[0027] System Dynamics Cost Function constraint

[0028] Among them, k is the road segment feature point s k The index of

[0029] K is the road segment feature point s k The total number of

[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 They are all diagonal arrays;

[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 speed; ε is an infinitely small number but greater than 0;

[0040] t k Is the car arrived at s k time; k is the horizontal position;

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

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

[0043] is the vehicle wheelbase;

[0044] The front car arrives at s k time; t safe is the safe following threshold;

[0045] is the travel time window at the road feature point s k The lower bound of time at

[0046] is the travel time window at the road feature point s k The time upper bound of the location;

[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 weight;

[0049] v max It is the speed limit of the road;

[0050] Yes k The minimum value of

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

[0052] -inf stands for infinitesimal; inf stands for infinity, and both -inf and inf mean no constraints;

[0053] Representatives in and Take the minimum value;

[0054] a min is the minimum acceleration; δ min is the minimum value of the front wheel turning angle;

[0055] a max is the maximum acceleration; δ max is the maximum front wheel turning angle.

[0056] Further, the mathematical model of overtaking the preceding vehicle in S33 is as follows, wherein the definition of the lateral position is as follows: Figure 4 As shown, the model is constructed based on the relative spatial domain, that is, the variables are functions 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, that is, the variables are functions of time.

[0057]

[0058] st

[0059]

[0060]

[0061]

[0062] in,

[0063] System Dynamics

[0064] Cost Function

[0065] constraint

[0066] Where i is the relative longitudinal position x i The index of

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

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

[0069] I is the relative longitudinal position x i The total number of

[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 With R p They are all diagonal arrays;

[0072] is the state matrix; is the control matrix; is the additional matrix;

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

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

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

[0076] v i is the longitudinal velocity of the vehicle; is the longitudinal speed of the leading vehicle;

[0077] t i is at the relative vertical position x i The time of being there;

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

[0079] is the relative acceleration, defined as

[0080] a i is the vehicle acceleration; is the acceleration of the preceding vehicle;

[0081] δ i is the front wheel turning angle;

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

[0083] ε is an infinitely small number but greater than 0;

[0084] is the vehicle wheelbase;

[0085] v max It is the speed limit of the road;

[0086] Yes i Expected value;

[0087] ιov is the desired lateral position during overtaking, i.e. the centerline position of the overtaking lane;

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

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

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

[0091] Yes i The minimum value of

[0092] -inf stands for infinitesimal; inf stands for infinity, which means no constraints;

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

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

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

[0096] a max is the maximum acceleration; δ max is the maximum front wheel turning angle.

[0097] is the acceleration of the vehicle in front.

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

[0099] Furthermore, the mathematical models constructed in S31, S32, and S33 can be solved by calling an open source solver (such as OSQP). The specific calling method can be found in the official website user manual and will not be repeated here.

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

[0101] The present invention innovatively proposes a vehicle's lateral and longitudinal coupling decision control method under the framework of the travel time window guidance. Compared with the traditional vehicle-road cooperative technology based on longitudinal speed guidance and control, the present invention has advantages in practicality, road section guidance effect, vehicle decision control ability, etc., and promotes the rapid popularization of vehicle-road cooperative technology and autonomous driving technology. In addition, since the present invention innovatively applies relative spatial domain modeling and expresses variables as functions of relative longitudinal position, it greatly speeds up the solution efficiency and accuracy, so the present invention also has a high threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] Figure 1 The present invention is a logic block diagram of a vehicle lateral and longitudinal coupling decision control method for passing time window guidance.

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

[0104] Figure 3 Flowchart for making driving behavior decisions for in-vehicle devices.

[0105] Figure 4 This is a schematic diagram of the definition of lateral position during overtaking the vehicle ahead. DETAILED DESCRIPTION

[0106] The following will be described in more detail with reference to the accompanying drawings, in which a vehicle lateral and longitudinal coupling decision control method for passage time window guidance of the present invention is shown, wherein a preferred embodiment of the present invention is shown. It should be understood that those skilled in the art can modify the present invention described herein and still achieve the beneficial effects of the present invention. Therefore, the following description should be understood as being widely known to those skilled in the art and not as a limitation to the present invention.

[0107] like Figure 1 As shown, a vehicle lateral and longitudinal coupling decision control method for passing time window guidance specifically includes the following steps:

[0108] Step 1

[0109] The roadside equipment transmits the travel time window to the intelligent connected vehicles.

[0110] The travel time window refers to the time range when the vehicle arrives at the characteristic point of the road section, which is recorded as in, and They are the travel time window at the road feature point s k The lower and upper time bounds of Figure 2 shown.

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

[0112]

[0113]

[0114] Step 2

[0115] The vehicle-mounted equipment makes driving behavior decisions based on established rules, such as Figure 3 As shown, the intelligent networked vehicle is controlled to perform corresponding movements according to the decision results, including three movement modes: following the vehicle in front, adaptive cruise control, and overtaking the vehicle in front. In this example, for full explanation, it is assumed that the decision results are following the vehicle in front, adaptive cruise control, and overtaking the vehicle in front.

[0116] Step 3

[0117] When the on-board equipment controls the intelligent connected vehicle to follow the vehicle in front, the established mathematical model is as follows. The model is built based on the spatial domain, that is, the variables are functions of the characteristic points of the road section. This modeling method is different from the traditional time domain, that is, the variables are functions of time.

[0118]

[0119] st

[0120]

[0121]

[0122]

[0123] in,

[0124] System Dynamics

[0125] Cost Function

[0126] constraint

[0127] Among them, k is the road segment feature point s k The index of

[0128] K is the road segment feature point s k The total number of

[0129] is the control vector;

[0130] is the state vector;

[0131] is the desired 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 They are all diagonal arrays;

[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 speed; ε is an infinitely small number but greater than 0;

[0139] t k Is the car arrived at s k time; k is the horizontal position;

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

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

[0142] is the vehicle wheelbase;

[0143] The front car arrives at s k time; t safe is the safe following threshold;

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

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

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

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

[0148] v max It is the speed limit of the road;

[0149] Yes k The minimum value of

[0150] ι rl is defined by the road boundary k The minimum value of ru is defined by the road boundary k The maximum value of

[0151] -inf stands for infinitesimal; inf stands for infinity;

[0152] Representatives in and Take the minimum value;

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

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

[0155] In this example, v0 = 15 m / s; 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, no unit;

[0162] r acc =r whe =5, no unit;

[0163] v max =25 m / s;

[0164] ι rl =-1.75 m; l ru =5.25 m;

[0165] a min = -4 meters per second squared; a max =3 meters per second squared; δ min =-0.5 radians; δ max =0.5 radians.

[0166] After calculation, the optimal acceleration sequence is obtained. Meters per second squared, optimal front wheel steering angle sequence radian.

[0167] Step 3

[0168] When the on-board equipment controls the intelligent connected vehicle to perform adaptive cruise control, the established mathematical model is as follows. The model is built based on the spatial domain, that is, the variables are functions of the characteristic points of the road section. This modeling method is different from the traditional time domain, that is, the variables are functions of time.

[0169]

[0170] st

[0171]

[0172]

[0173]

[0174] in,

[0175] System Dynamics

[0176] Cost Function

[0177] constraint

[0178] Among them, k is the road segment feature point s k The index of

[0179] K is the road segment feature point s k The total number of

[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 They are all diagonal arrays;

[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 speed; ε is an infinitely small number but greater than 0;

[0190] t k Is the car arrived at s k time; k is the horizontal position;

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

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

[0193] is the vehicle wheelbase;

[0194] The front car arrives at s k time; tsafe is the safe following threshold;

[0195] is the travel time window at the road feature point s k The lower bound of time at

[0196] is the travel time window at the road feature point s k The time upper bound of the location;

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

[0198] r acc is the acceleration weight; r whe is the front wheel steering weight;

[0199] v max It is the speed limit of the road;

[0200] Yes k The minimum value of

[0201] ι rl is defined by the road boundary k The minimum value of ru is defined by the road boundary k The maximum value of

[0202] -inf stands for infinitesimal; inf stands for infinity;

[0203] Representatives in and Take the minimum value;

[0204] a min is the minimum acceleration; δ min is the minimum value of the front wheel turning angle;

[0205] a max is the maximum acceleration; δ max is the maximum front wheel turning angle.

[0206] In this example, v0 = 15 m / s; ε = 10 -6 , no unit;

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

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

[0209] Δsk = 1 meter;

[0210] The unit is seconds;

[0211] t safe = 0.1 seconds;

[0212] q time =q lat =q head =10, no unit;

[0213] r acc =r whe =5, no unit;

[0214] v max =25 m / s;

[0215] ι rl =-1.75 m; ru =5.25 m;

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

[0217] After calculation, the optimal acceleration sequence is obtained. Meters per second squared, optimal front wheel steering angle sequence radian.

[0218] Step 3

[0219] When the on-board equipment controls the intelligent connected vehicle to overtake the vehicle ahead, the established mathematical model is as follows, where the definition of the lateral position is as follows: Figure 4 As shown, the model is constructed based on the relative spatial domain, that is, the variables are functions 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, that is, the variables are functions of time.

[0220]

[0221] st

[0222]

[0223]

[0224]

[0225] in,

[0226] System Dynamics

[0227] Cost Function

[0228] constraint

[0229] Where i is the relative longitudinal position x i The index of

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

[0231] κ i is the longitudinal position of the vehicle; is the longitudinal position of the preceding vehicle;

[0232] I is the relative longitudinal position x i The total number of

[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 With R p They are all diagonal arrays;

[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 longitudinal velocity of the vehicle; is the longitudinal speed of the leading vehicle;

[0240] t i is at the relative vertical position x i The time of being there;

[0241] ι iis the horizontal position; is the heading angle;

[0242] is the relative acceleration, defined as

[0243] a i is the vehicle acceleration; is the acceleration of the preceding vehicle;

[0244] δ i is the front wheel turning angle;

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

[0246] ε is an infinitely small number but greater than 0;

[0247] is the vehicle wheelbase;

[0248] v max It is the speed limit of the road;

[0249] Yes i Expected value;

[0250] ι ov is the desired lateral position during overtaking, i.e. the centerline position of the overtaking lane;

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

[0252] q η is the weight of the relative longitudinal velocity; q lat is the lateral position weight; q head is the heading angle weight;

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

[0254] Yes i The minimum value of

[0255] -inf stands for infinitesimal; inf stands for infinity;

[0256] is the lateral position of the preceding vehicle;

[0257] ιrl is defined by the road boundary i The minimum value of ru is defined by the road boundary i The maximum value of

[0258] a min is the minimum acceleration; δ min is the minimum value of the front wheel turning angle;

[0259] a max is the maximum acceleration; δ max is the maximum front wheel turning angle.

[0260] is the acceleration of the vehicle in front.

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

[0262] Meters per second; ε = 10 -6 , no unit;

[0263] t0 = 0 seconds; ι0 = 0 meters; radian;

[0264] I = 300 steps; i∈{12…300}, unitless;

[0265] Δx i = 1 meter;

[0266] rice;

[0267] ι ov =3.5 m;

[0268] q η =q lat =q head =10, no unit;

[0269] No units;

[0270] s sl =-20m;

[0271] s su =20 m;

[0272] rice;

[0273] ι rl =-1.75 m; ru =5.25 m;

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

[0275] Meters per second squared.

[0276] After calculation, the optimal relative acceleration sequence is obtained. Meters per second squared, optimal front wheel steering angle sequence radian.

[0277] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A vehicle lateral and longitudinal coupling decision control method for passing time window guidance, characterized in that: The steps include: S1) The roadside equipment transmits the travel time window to the intelligent connected vehicle; S2) The vehicle-mounted equipment makes driving behavior decisions; S3) The on-board equipment controls the movement of the intelligent connected vehicle.

2. The vehicle lateral and longitudinal coupling decision control method for passing time window guidance according to claim 1 is characterized in that: In S1, the travel time window specifically refers to the time range when the vehicle arrives at the characteristic point of the road section, which is recorded as in, and They are the travel time window at the road feature point s k The lower and upper time bounds of the .

3. The vehicle lateral and longitudinal coupling decision control method for passing time window guidance according to claim 1 is characterized in that: In S2, the driving behavior decision is based on established rules: Rule 1: When there are no other vehicles in front of the intelligent connected vehicle, or even if there are other vehicles in front of it, but they do not hinder the guidance of the travel time window, the driving behavior decision result is adaptive cruise control; Rule 2: When there are other vehicles in front of the intelligent connected vehicle and they hinder the time window guidance, However, if the overtaking safety constraint cannot be met, the driving behavior decision result is to follow the vehicle in front; Rule 3: When there are other vehicles in front of the intelligent connected vehicle, although it hinders the time window guidance, However, if the overtaking safety constraint is met, the driving behavior decision result is to overtake the vehicle in front.

4. The vehicle lateral and longitudinal coupling decision control method for passing time window guidance according to claim 1 is characterized in that: In S3, the on-board equipment needs to control the intelligent connected vehicle to perform corresponding movements according to the driving behavior decision results, including three movement modes: adaptive cruise, following the vehicle in front, and overtaking the vehicle in front.

5. The vehicle lateral and longitudinal coupling decision control method for passing time window guidance according to claim 4 is characterized in that: The mathematical model of the adaptive cruise control and the front vehicle following is: st in, System Dynamics Cost Function constraint Among them, k is the road segment feature point s k The index of K is the road segment feature point s k The total number of is the control vector; is the state vector; is the desired state vector; is the matrix transpose operator; Q f is the state weight matrix; R f is the control weight matrix; Q f and R f They are all diagonal arrays; is the state matrix; is the 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; π k is the slowness, defined as v k is the speed; ε is an infinitely small number but greater than 0; t k Is the car arrived at s k time; k is the horizontal position; is the heading angle; a k is the acceleration; δ k is the front wheel turning angle; Δs k is the distance between feature points of adjacent road sections, i.e. Δs k =s k+1 -s k ; is the vehicle wheelbase; The front car arrives at s k time; t safe is the safe following threshold; is the travel time window at the road feature point s k The lower bound of time at is the travel time window at the road feature point s k The time upper bound of the location; q time is the time weight; q lat is the lateral position weight; q head is the heading angle weight; r acc is the acceleration weight; r whe is the front wheel steering weight; v max It is the speed limit of the road; Yes k The minimum value of l rl is defined by the road boundary k The minimum value of ru is defined by the road boundary k The maximum value of -inf stands for infinitesimal; inf stands for infinity; Representatives in and Take the minimum value; a min is the minimum acceleration; δ min is the minimum value of the front wheel turning angle; a max is the maximum acceleration; δ max is the maximum front wheel turning angle.

6. The vehicle lateral and longitudinal coupling decision control method for passing time window guidance according to claim 4 is characterized in that: The mathematical model of overtaking the preceding vehicle is: st in, System Dynamics Cost Function constraint Where i is the relative longitudinal position x i The index of x i is the relative vertical position, i.e. κ i is the longitudinal position of the vehicle; is the longitudinal position of the preceding vehicle; I is the relative longitudinal position x i The total number of is the control vector; is the state vector; is the desired state vector; Q p is the state weight matrix; R p is the control weight matrix; Q p With R p They are all diagonal arrays; is the state matrix; is the control matrix; is the 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; η i is the relative longitudinal velocity, defined as v i is the longitudinal velocity of the vehicle; is the longitudinal speed of the leading vehicle; t i is at the relative vertical position x i The time of being there; ι i is the horizontal position; is the heading angle; is the relative acceleration, defined as a i is the vehicle acceleration; is the acceleration of the preceding vehicle; δ i is the front wheel turning angle; Δx i is the adjacent relative longitudinal position x i The distance between them, namely Δx i =x i+1 -x i ε is an infinitely small number but greater than 0; is the vehicle wheelbase; v max It is the speed limit of the road; Yes i Expected value; ι ov is the desired lateral position during overtaking, i.e. the centerline position of the overtaking lane; s sl is the minimum value of the safe relative longitudinal position; s su is the maximum value of the safe relative longitudinal position; q η is the weight of the relative longitudinal velocity; q lat is the lateral position weight; q head is the heading angle weight; is the weight of relative acceleration; r whe is the front wheel steering weight; Yes i The minimum value of -inf stands for infinitesimal; inf stands for infinity; is the lateral position of the preceding vehicle; ι rl is defined by the road boundary i The minimum value of ru is defined by the road boundary i The maximum value of a min is the minimum acceleration; δ min is the minimum value of the front wheel turning angle; a max is the maximum acceleration; δ max is the maximum front wheel turning angle. is the acceleration of the vehicle in front.

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