Signal timing and vehicle track collaborative optimization method of intelligent network connection vehicle

Abstract

The invention discloses a signal timing and vehicle trajectory collaborative optimization method for an intelligent networked vehicle, and the method comprises the steps: obtaining a parameter set through employing different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on a road; acquiring state information of vehicles in a communication range through road infrastructures, and transmitting the state information to an upper signal timing optimization layer; according to the parameter set, taking the green time ratio of each phase as a decision variable, taking the state information of the vehicle in the communication range as basic input information, and constructing an upper-layer signal timing optimization model; on the basis of the trigonometric function optimization model, a lower-layer vehicle trajectory optimization model is constructed by judging whether the vehicle meets trajectory optimization conditions or not; and according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, circularly optimizing signal timing and a vehicle trajectory. According to the embodiment of the invention, the traffic optimization capability can be improved, the optimization efficiency is greatly improved, and the method can be widely applied to the technical field of intelligent traffic.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent networked vehicle. Background Art

[0002] Due to the rapid development of vehicle networking and autonomous driving technologies, the penetration rate of intelligent connected vehicles in future road traffic will steadily increase. In mixed traffic flows, despite the rapid growth of the number of cars in society, the penetration rate of intelligent connected vehicles will remain at a low level in the short term. Most existing studies are limited to certain aspects of traffic control methods and cannot effectively improve traffic operation efficiency. For signalized intersections, in this mixed traffic environment, more effective traffic control methods are needed, and trajectory optimization and its coordinated optimization control with traffic signals will be a more promising control method. Summary of the invention

[0003] The main purpose of the embodiments of the present invention is to propose an efficient method for collaboratively optimizing signal timing and vehicle trajectory of intelligent connected vehicles, which can enhance traffic optimization capabilities and significantly improve optimization efficiency.

[0004] To achieve the above objectives, an embodiment of the present invention provides a method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle, comprising the following steps:

[0005] According to different types of following modes corresponding to different types of vehicles on the road, different vehicle following models are used to obtain parameter sets;

[0006] Obtain status information of vehicles within the communication range through road infrastructure and transmit it to the upper signal timing optimization layer;

[0007] According to the parameter set, the green-to-signal ratio of each phase is used as a decision variable, and the status information of vehicles within the communication range is used as basic input information to construct an upper-layer signal timing optimization model;

[0008] Based on the trigonometric function optimization model, the lower-level vehicle trajectory optimization model is constructed by judging whether the vehicle meets the trajectory optimization conditions;

[0009] According to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, signal timing and vehicle trajectory are cyclically optimized.

[0010] In some embodiments, the method of acquiring a parameter set using different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on the road includes the following steps:

[0011] The following modes on the road are divided into three categories, and three different vehicle following models are selected;

[0012] Assuming the acceleration of the vehicle model and the speed difference between the vehicle model and the preceding vehicle in the three following models to be 0, different headway distances of the three following models are calculated;

[0013] Calculate the percentage of the road occupied by the three following models.

[0014] In some embodiments, the three different vehicle following models are IDM, CACC and ACC.

[0015] The expression of the IDM vehicle following model is:

[0016]

[0017] s * (v(t))=s0+v(t)·τ+l car ,

[0018] Where a(t) represents the acceleration function of the vehicle; and is the maximum acceleration and maximum speed limit of the manually driven vehicle during driving; v(t) is the speed of the manually driven vehicle at time t; s*(v(t)) is the expected spacing; Δx is the distance between the manually driven vehicle and the vehicle in front; l caR is the vehicle body length; τ is the vehicle reaction time; s0 is the minimum parking distance;

[0019] The expression of the CACC vehicle following model is:

[0020]

[0021] Where e is the difference between the expected position and the actual position of the intelligent connected vehicle; is the differential of e; tc is the expected headway; k p and k d is the model control parameter; Δt is the model control step length;

[0022] The expression of the ACC vehicle following model is:

[0023] a(T)=k1(Δx-s0-l car -t a ·v(t))+k2·Δv(t)

[0024] Among them, k1 and k2 are model control parameters; t a is the expected headway;

[0025] The calculation formulas for the headway distances of the three vehicle following models are:

[0026]

[0027] h a =v(t)·t a +l car +s0

[0028] h c =v(t)·t c +l car +s0

[0029] Among them, h i ,h a and h c These are the headway distances corresponding to the three vehicle following models: IDM, CACC and ACC.

[0030] In some embodiments, the method of constructing an upper-layer signal timing optimization model based on the parameter set, taking the green-to-signal ratio of each phase as a decision variable, and taking the status information of vehicles within the communication range as basic input information includes the following steps:

[0031] A phase hypothesis of signal timing is proposed, and the expression of the phase hypothesis is g = [g1, g2, ..., g I ] T , where I is the number of phases in the intersection signal timing scheme; g i is the green light time corresponding to phase i;

[0032] Construct an objective function with the goal of minimizing delay;

[0033] The constraints of the signal timing optimization model are established. The constraints include cycle length constraint and green light time constraint. The cycle length constraint is used to ensure that the sum of the green light time of each phase is equal to the cycle length. The expression of the cycle length constraint C is:

[0034] The green light time constraint is used to control the green light duration of each phase within the maximum green light time and the shortest green light time. The expression of the green light time constraint is:

[0035] Complete the construction of the upper-layer signal timing optimization model.

[0036] In some embodiments, in constructing an objective function with delay minimization as the goal, the expression of the objective function is:

[0037]

[0038] Where D is the total delay at the intersection; C is the signal cycle length; λ i is the green-to-signal ratio, which represents the ratio of the green light time to the signal period in the i-th phase; μ i is the traffic flow ratio in the i-th phase; qi is the traffic volume in the i-th phase; s i is the saturation flow rate in the i-th phase.

[0039] In some embodiments, the trigonometric function optimization model is used to determine whether the vehicle meets the trajectory optimization conditions to construct a lower-level vehicle trajectory optimization model, including the following steps:

[0040] Calculate the local CAV penetration within the intersection;

[0041] Calculate the average headway and saturation flow velocity on the road;

[0042] Determine whether the CAV meets the trajectory optimization condition, specifically: when the current traffic light is green, if the CAV can pass through the intersection within the current green light time, or when the current traffic light is red, if the CAV can pass through the intersection within the next green light time, then the trajectory optimization condition is met, otherwise it is not met;

[0043] Construct a trajectory optimization model based on trigonometric functions. If the initial speed is higher than the average speed, the speed of the CAV will slowly decrease to the average speed v in the period [0, t1]. h , at which point the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at which point the acceleration gradually decreases until it reaches 0; in the period [t2, t arrive ] During the period, the CAV passes through the intersection at a constant speed.

[0044] In some embodiments, the calculation formula of the local CAV permeability is:

[0045]

[0046] Among them, p n is the local CAV penetration rate; d0 is the distance from the vehicle that meets the optimization conditions to the stop line; N means that the CAV is the Nth vehicle from the stop line of the intersection;

[0047] The calculation formula of the average headway is:

[0048] h n =N((p n ) 2 h c +p n (1-p n )h a +(1-p n )h i ),

[0049] The calculation formula of the saturated flow velocity is:

[0050]

[0051] Among them, h n is the average headway between vehicles; is the saturation flow velocity; is the saturation flow;

[0052] The expression of the trajectory optimization model based on trigonometric function is:

[0053]

[0054] Among them, m and n are the key parameters to ensure the smoothness of the trigonometric function model; the calculation formulas of the remaining parameters are as follows:

[0055]

[0056] v d =v h -v0

[0057]

[0058]

[0059] Where v0 is the initial speed of the CAV entering the intersection; v h is the average speed; a max is the maximum acceleration; d max is the maximum deceleration; j max is the maximum first derivative of acceleration.

[0060] Another aspect of the embodiment of the present invention further provides a signal timing and vehicle trajectory coordinated optimization system for intelligent connected vehicles, including:

[0061] The first module is used to obtain a parameter set using different vehicle following models according to different types of following modes corresponding to different types of vehicles on the road;

[0062] The second module is used to obtain the status information of vehicles within the communication range through the road infrastructure and transmit it to the upper signal timing optimization layer;

[0063] The third module is used to construct an upper-layer signal timing optimization model based on the parameter set, taking the green-to-signal ratio of each phase as a decision variable and the state information of vehicles within the communication range as basic input information;

[0064] The fourth module is used to construct a lower-level vehicle trajectory optimization model based on a trigonometric function optimization model by judging whether the vehicle meets the trajectory optimization conditions;

[0065] The fifth module is used to cyclically optimize signal timing and vehicle trajectory according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model.

[0066] To achieve the above objective, another aspect of an embodiment of the present invention provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0067] To achieve the above objective, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method is implemented.

[0068] The embodiment of the present invention also discloses a computer program product or a computer program, which includes a computer instruction stored in a computer-readable storage medium. A processor of a computer device can read the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the computer device executes the above method.

[0069] The embodiments of the present invention include at least the following beneficial effects: the present invention provides a method for collaboratively optimizing the signal timing and vehicle trajectory of intelligent networked vehicles, which adopts different vehicle following models to obtain parameter sets according to different types of following modes corresponding to different types of vehicles on the road; obtains the status information of vehicles within the communication range through road infrastructure and transmits it to the upper signal timing optimization layer; according to the parameter set, takes the green-to-signal ratio of each phase as the decision variable, takes the status information of vehicles within the communication range as the basic input information, and constructs the upper signal timing optimization model; based on the trigonometric function optimization model, constructs the lower vehicle trajectory optimization model by judging whether the vehicle meets the trajectory optimization conditions; according to the upper signal timing optimization model and the lower vehicle trajectory optimization model, cyclically optimizes the signal timing and vehicle trajectory. The embodiments of the present invention can improve the traffic optimization capability and greatly improve the optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of an implementation environment provided by an embodiment of the present invention;

[0071] Figure 2 is a flow chart of the overall steps provided by an embodiment of the present invention;

[0072] Figure 3 This is a flowchart of the implementation process provided by the embodiment of the present invention

[0073] Figure 4 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

[0075] It is understood that the terms "first", "second", etc. used in the present invention may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0076] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0077] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0078] The signal timing and vehicle trajectory collaborative optimization method of the intelligent networked vehicle provided by the embodiment of the present invention relates to the field of intelligent transportation technology. The signal timing and vehicle trajectory collaborative optimization method of the intelligent networked vehicle provided by the embodiment of the present invention can be applied to the terminal, can also be applied to the server, and can also be software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in the blockchain network; the software can be an application that realizes the signal timing and vehicle trajectory collaborative optimization method of the intelligent networked vehicle, etc., but is not limited to the above forms.

[0079] The present invention can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0080] like Figure 1 FIG. 1 is a schematic diagram of an implementation environment provided by an embodiment of the present invention. Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected to a network wirelessly or wired to complete data transmission and exchange.

[0081] Server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0082] In addition, the server 101 can also be a node server in the blockchain network. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm.

[0083] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc. The terminal 102 may also be a vehicle-mounted terminal of various device types described above, but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in this embodiment of the present invention.

[0084] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle. The following is an example of applying the method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle to a server 101. It can be understood that the method can also be applied to a terminal 102.

[0085] Reference Figure 2 , Figure 2 The flowchart of the method for coordinating the optimization of the signal timing and vehicle trajectory of an intelligent networked vehicle applied to a server provided in an embodiment of the present invention, the execution subject of the method can be any of the aforementioned computer devices (including a server or a terminal). Figure 2 , the method may include the following steps:

[0086] According to different types of following modes corresponding to different types of vehicles on the road, different vehicle following models are used to obtain parameter sets;

[0087] Obtain status information of vehicles within the communication range through road infrastructure and transmit it to the upper signal timing optimization layer;

[0088] According to the parameter set, the green-to-signal ratio of each phase is used as a decision variable, and the status information of vehicles within the communication range is used as basic input information to construct an upper-layer signal timing optimization model;

[0089] Based on the trigonometric function optimization model, the lower-level vehicle trajectory optimization model is constructed by judging whether the vehicle meets the trajectory optimization conditions;

[0090] According to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, signal timing and vehicle trajectory are cyclically optimized.

[0091] In some embodiments, the method of acquiring a parameter set using different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on the road includes the following steps:

[0092] The following modes on the road are divided into three categories, and three different vehicle following models are selected;

[0093] Assuming the acceleration of the vehicle model and the speed difference between the vehicle model and the preceding vehicle in the three following models to be 0, different headway distances of the three following models are calculated;

[0094] Calculate the percentage of the road occupied by the three following models.

[0095] In some embodiments, the three different vehicle following models are IDM, CACC and ACC.

[0096] The expression of the IDM vehicle following model is:

[0097]

[0098] s * (v(t))=s0+v(t)·τ+l car ,

[0099] Where a(t) represents the acceleration function of the vehicle; and is the maximum acceleration and maximum speed limit of the manually driven vehicle during driving; v(t) is the speed of the manually driven vehicle at time t; s*(v(t)) is the expected spacing; Δx is the distance between the manually driven vehicle and the vehicle in front; l car is the vehicle body length; τ is the vehicle reaction time; s0 is the minimum parking distance;

[0100] The expression of the CACC vehicle following model is:

[0101]

[0102] Where e is the difference between the expected position and the actual position of the intelligent connected vehicle; is the differential of e; t c is the expected headway; k p and k d is the model control parameter; Δt is the model control step length;

[0103] The expression of the ACC vehicle following model is:

[0104] a(t)=k1(Δx-s0-l car -t a ·v(t))+k2·Δv(t)

[0105] Among them, k1 and k2 are model control parameters; t a is the expected headway;

[0106] The calculation formulas for the headway distances of the three vehicle following models are as follows:

[0107]

[0108] h a =v(t)·t a +l car +s0

[0109] h c =v(t)·t c +l car +s0

[0110] Among them, h i ,h a and h c These are the headway distances corresponding to the three vehicle following models: IDM, CACC and ACC.

[0111] In some embodiments, the method of constructing an upper-layer signal timing optimization model based on the parameter set, taking the green-to-signal ratio of each phase as a decision variable, and taking the status information of vehicles within the communication range as basic input information includes the following steps:

[0112] A phase hypothesis of signal timing is proposed, and the expression of the phase hypothesis is g = [g1, g2, ..., g I ] T , where I is the number of phases in the intersection signal timing scheme; g i is the green light time corresponding to phase i;

[0113] Construct an objective function with the goal of minimizing delay;

[0114] The constraints of the signal timing optimization model are established. The constraints include cycle length constraint and green light time constraint. The cycle length constraint is used to ensure that the sum of the green light time of each phase is equal to the cycle length. The expression of the cycle length constraint C is:

[0115] The green light time constraint is used to control the green light duration of each phase within the maximum green light time and the shortest green light time. The expression of the green light time constraint is:

[0116] Complete the construction of the upper-layer signal timing optimization model.

[0117] In some embodiments, in constructing an objective function with delay minimization as the goal, the expression of the objective function is:

[0118]

[0119] Where D is the total delay at the intersection; C is the signal cycle length; λ i is the green-to-signal ratio, which represents the ratio of the green light time to the signal period in the i-th phase; μ i is the traffic flow ratio in the i-th phase; q i is the traffic volume in the i-th phase; s i is the saturation flow rate in the i-th phase.

[0120] In some embodiments, the trigonometric function optimization model is used to determine whether the vehicle meets the trajectory optimization conditions to construct a lower-level vehicle trajectory optimization model, including the following steps:

[0121] Calculate the local CAV penetration within the intersection;

[0122] Calculate the average headway and saturation flow velocity on the road;

[0123] Determine whether the CAV meets the trajectory optimization condition, specifically: when the current traffic light is green, if the CAV can pass through the intersection within the current green light time, or when the current traffic light is red, if the CAV can pass through the intersection within the next green light time, then the trajectory optimization condition is met, otherwise it is not met;

[0124] Construct a trajectory optimization model based on trigonometric functions. If the initial speed is higher than the average speed, the speed of the CAV will slowly decrease to the average speed v in the period [0, t1]. h , at which point the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at which point the acceleration gradually decreases until it reaches 0; in the period [t2, t arrive ] During the period, the CAV passes through the intersection at a constant speed.

[0125] In some embodiments, the calculation formula of the local CAV permeability is:

[0126]

[0127] Among them, p n is the local CAV penetration rate; d0 is the distance from the vehicle that meets the optimization conditions to the stop line; N means that the CAV is the Nth vehicle from the stop line of the intersection;

[0128] The calculation formula of the average headway is:

[0129] h n =N((p n ) 2 h c +p n (1-p n )h a +(1-p n )h i ),

[0130] The calculation formula of the saturated flow velocity is:

[0131]

[0132] Among them, h n is the average headway between vehicles; is the saturation flow velocity; is the saturation flow;

[0133] The expression of the trajectory optimization model based on trigonometric function is:

[0134]

[0135] Among them, m and n are the key parameters to ensure the smoothness of the trigonometric function model; the calculation formulas of the remaining parameters are as follows:

[0136]

[0137] v d =v h -v0

[0138]

[0139] Where v0 is the initial speed of the CAV entering the intersection; v h is the average speed; a max is the maximum acceleration; d max is the maximum deceleration; j max is the maximum first derivative of acceleration.

[0140] The specific implementation process of the present invention is described in detail below in conjunction with the accompanying drawings:

[0141] like Figure 3 As shown, the signal pairing and vehicle trajectory optimization method of the intelligent connected vehicle according to the embodiment of the present invention may include the following steps:

[0142] S1: According to different types of following modes corresponding to different types of vehicles on the road, different vehicle following models are used to obtain basic parameters required by the algorithm, which specifically includes the following steps:

[0143] S11: The following modes on the road are divided into three categories, namely HDV-HDV (CAV), CAV-CAV and CAV-HDV, and different vehicle following models are used respectively, namely Intelligence Driver Model (IDM), Cooperative Adaptive Cruise Control (CACC) and Adaptive Cruise Control (ACC), where IDM is represented by:

[0144]

[0145] s * (v(t))=s0+v(t)·τ+l car

[0146] in, and is the maximum acceleration and maximum speed limit of the RV during driving; v(t) is the driving speed of the RV at time t; s*(v(t)) is the expected distance; Δx is the distance between the RV and the preceding vehicle; l car is the vehicle body length; τ is the vehicle reaction time; s0 is the minimum parking distance;

[0147] CACC is expressed as:

[0148]

[0149] Where, e is the difference between the expected position and the actual position of the CAV; is the differential of e; t c is the expected headway; k p and k d is the model control parameter; Δt is the model control step length;

[0150] ACC is expressed as:

[0151] a(t)=k1(Δx-s0-l car -t a ·v(t))+k2·△v(t)

[0152] Among them, k1 and k2 are model control parameters; t a is the expected headway;

[0153] S12: Assuming the acceleration of the vehicle in the three following models and the speed difference between the vehicle and the preceding vehicle to be 0, different headway distances of the three following models are calculated, which are expressed as:

[0154]

[0155] h a =v(t)·t a +l car +s0

[0156] h c =v(t)·t c +l car +s0

[0157] Among them, h i ,h a and h c They are the headway distances corresponding to the IDM, CACC and ACC vehicle following models;

[0158] S13: Calculate the percentages of the three car-following models on the road, which are specifically expressed as:

[0159] p i =1-p,p c =p 2 ,p a =p(1-p)

[0160] Among them, p i ,p c and p a are the proportions of vehicles using IDM, CACC and ACC vehicle following models, respectively, and p is the CAV penetration rate.

[0161] Taking a real intersection as an example, some of the above parameters can be taken as follows: l car =4.37m; τ=1.6s; s0=2m; Δt=0.01s; t c =0.6s; k d =0.25; k p =0.45;k1=0.23;k2=0.07;t a =1.1s.

[0162] S2: Detect the status information of vehicles within the communication range, such as the position and speed of the vehicles, through the road infrastructure, and transmit the information to the upper signal timing optimization layer through V2I technology based on each signal cycle. Further, the road infrastructure in S2 includes a loop coil detector.

[0163] S3: Taking the green-to-signal ratio of each phase as the decision variable and the status of vehicles within the communication range as the basic input information, an upper-level signal timing optimization model is constructed.

[0164] Furthermore, S3 is specifically as follows: with the goal of minimizing vehicle delays at signalized intersections, an upper-level signal timing optimization model is constructed, and corresponding constraints are proposed, which specifically includes the following steps:

[0165] S31: Propose a phase hypothesis for signal timing, which is specifically expressed as:

[0166] g=[g1,g2,...,g I ] T

[0167] Where I is the number of phases in the intersection signal timing scheme; g i is the green light time corresponding to phase i;

[0168] S32: Construct an objective function with the goal of minimizing delay, which is specifically expressed as:

[0169]

[0170]

[0171] Where D is the total delay at the intersection; C is the signal cycle length, calculated using the Webster formula; λ i is the green-to-signal ratio, i.e., the ratio of the green light time to the signal period in the i-th phase; μ i is the traffic flow ratio in the i-th phase; q i is the traffic volume in the i-th phase; s i is the saturation flow rate in the i-th phase;

[0172] S33: Establishing constraint conditions for the signal timing optimization model. The constraint conditions include two types, namely, cycle length constraint and green light time constraint. The cycle length constraint is used to ensure that the sum of the green light time of each phase should be equal to the cycle length, which is specifically expressed as:

[0173]

[0174] The green light time constraint is used to control the green light duration of each phase within the maximum green light time and the shortest green light time, which is specifically expressed as:

[0175]

[0176] Taking a real intersection as an example, the signal cycle length may be 82 seconds.

[0177] S4: Based on the trigonometric function optimization model, a lower-level vehicle trajectory optimization model is constructed by judging whether the vehicle meets the trajectory optimization conditions.

[0178] Further, S4 is specifically: determining whether the CAV meets the trajectory optimization conditions, and applying a speed optimization model based on trigonometric functions to the vehicle that meets the conditions, which specifically includes the following steps:

[0179] S41: Calculate the local CAV penetration rate within the intersection range, and the calculation formula is:

[0180]

[0181] Among them, p n is the local CAV penetration rate; d0 is the distance from the vehicle that meets the optimization conditions to the stop line; N means that the CAV is the Nth vehicle from the stop line of the intersection;

[0182] S42: Calculate the average headway distance and saturation flow velocity on the road, which is specifically expressed as:

[0183] h n =N((p n ) 2 h c +p n (1-p n )h a +(1-p n )h i )

[0184]

[0185] Among them, h n is the average headway between vehicles; is the saturation flow velocity; is the saturation flow;

[0186] S43: Determine whether the CAV meets the trajectory optimization condition. The determination method is: when the current traffic light is green, if the CAV can pass through the intersection within the current green light time, or when the current traffic light is red, if the CAV can pass through the intersection within the next green light time, then the trajectory optimization condition is met, otherwise it is not met. The specific determination algorithm is as follows:

[0187]

[0188] Among them, S is the current traffic light status information; T r The duration of the red light;

[0189] S44: Construct a trajectory optimization model based on trigonometric functions. If the initial speed is higher than the average speed, the speed of the CAV is slowly reduced to the average speed v in the period [0, t1]. h , at which point the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at which point the acceleration gradually decreases until it reaches 0; in the period [t2, tarrive ] period, the CAV passes through the intersection at a constant speed. The specific algorithm is expressed as:

[0190]

[0191] Among them, m and n are the key parameters to ensure the smoothness of the trigonometric function model; the calculation formulas of the remaining parameters are as follows:

[0192]

[0193] v d =v h -v0

[0194]

[0195] Where v0 is the initial speed of the CAV entering the intersection; v h is the average speed; a max is the maximum acceleration; d max is the maximum deceleration; j max is the maximum first derivative of acceleration.

[0196] S5: Repeat S2-S4 to cyclically optimize signal timing and vehicle trajectory.

[0197] In summary, compared with the prior art, the present invention has the following advantages:

[0198] 1. The present invention can still demonstrate good traffic optimization capabilities in mixed traffic flows with low CAV penetration, which is in line with current road conditions.

[0199] 2. The present invention performs trajectory optimization based on vehicle groups, which is more efficient than single-vehicle optimization. When performing trajectory optimization, only CAVs that can pass through intersections within the green light time are selected for optimization, thus avoiding unnecessary waste of resources.

[0200] 3. The trajectory optimization algorithm adopted by the present invention is based on a trigonometric function model, which can make the speed change smoother and the acceleration fluctuation smaller, thereby effectively reducing fuel consumption and carbon dioxide emissions.

[0201] 4. The present invention coordinates and optimizes signal timing and vehicle trajectory, further embodies the idea of ​​vehicle-road collaboration, and maximizes optimization efficiency.

[0202] Another aspect of the embodiment of the present invention further provides a signal timing and vehicle trajectory coordinated optimization system for intelligent connected vehicles, including:

[0203] The first module is used to obtain a parameter set using different vehicle following models according to different types of following modes corresponding to different types of vehicles on the road;

[0204] The second module is used to obtain the status information of vehicles within the communication range through the road infrastructure and transmit it to the upper signal timing optimization layer;

[0205] The third module is used to construct an upper-layer signal timing optimization model based on the parameter set, taking the green-to-signal ratio of each phase as a decision variable and the state information of vehicles within the communication range as basic input information;

[0206] The fourth module is used to construct a lower-level vehicle trajectory optimization model based on a trigonometric function optimization model by judging whether the vehicle meets the trajectory optimization conditions;

[0207] The fifth module is used to cyclically optimize signal timing and vehicle trajectory according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model.

[0208] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0209] The embodiment of the present invention further provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned intelligent networked vehicle signal timing and vehicle trajectory coordinated optimization method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0210] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0211] See also Figure 4 , Figure 4 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0212] The processor 401 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0213] The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other applications. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 402, and the processor 401 calls and executes the signal timing and vehicle trajectory collaborative optimization method of the intelligent networked vehicle in the embodiment of the present invention;

[0214] Input / output interface 403, used to implement information input and output;

[0215] Communication interface 404, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0216] Bus 405 , which transmits information between various components of the device (e.g., processor 401 , memory 402 , input / output interface 403 , and communication interface 404 );

[0217] The processor 401 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via the bus 405 .

[0218] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle.

[0219] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0220] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0221] It should be noted that in various specific embodiments of the present invention, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present invention needs to obtain the user's sensitive personal information, it will obtain the user's separate permission or consent through a pop-up window or jump to a confirmation page, and after clearly obtaining the user's separate permission or consent, it will obtain the necessary user-related data for the normal operation of the embodiment of the present invention.

[0222] The embodiments described in the embodiments of the present invention are intended to more clearly illustrate the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art can appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0223] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present invention and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0224] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0225] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0226] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0227] It should be understood that in the present invention, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can represent: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0228] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0229] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0230] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0231] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store programs.

[0232] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the embodiments of the present invention is not limited thereby. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present invention shall be within the scope of the rights of the embodiments of the present invention.

Claims

1. A method for collaboratively optimizing signal timing and vehicle trajectory of intelligent connected vehicles, characterized in that: The following steps are involved: According to different types of following modes corresponding to different types of vehicles on the road, different vehicle following models are used to obtain parameter sets; Obtain status information of vehicles within the communication range through road infrastructure and transmit it to the upper signal timing optimization layer; According to the parameter set, the green-to-signal ratio of each phase is used as a decision variable, and the status information of vehicles within the communication range is used as basic input information to construct an upper-layer signal timing optimization model; Based on the trigonometric function optimization model, the lower-level vehicle trajectory optimization model is constructed by judging whether the vehicle meets the trajectory optimization conditions; According to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, signal timing and vehicle trajectory are cyclically optimized.

2. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 1, characterized in that: The method of acquiring a parameter set using different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on the road includes the following steps: The following modes on the road are divided into three categories, and three different vehicle following models are selected; Assuming the acceleration of the vehicle model and the speed difference between the vehicle model and the preceding vehicle in the three following models to be 0, different headway distances of the three following models are calculated; Calculate the percentage of the road occupied by the three following models.

3. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 2, characterized in that: The three different vehicle following models are IDM, CACC and ACC; The expression of the IDM vehicle following model is: s * (v(t))=s0+v(t)·τ+l car , Where a(t) represents the acceleration function of the vehicle; and is the maximum acceleration and maximum speed limit of the manually driven vehicle during driving; v(t) is the speed of the manually driven vehicle at time t; s*(v(t)) is the expected spacing; Δx is the distance between the manually driven vehicle and the vehicle in front; l car is the vehicle body length; τ is the vehicle reaction time; s0 is the minimum parking distance; The expression of the CACC vehicle following model is: Wherein, e is the difference between the expected position and the actual position of the intelligent connected vehicle; is the differential of e; t c is the expected headway; k p and k d is the model control parameter; Δt is the model control step length; The expression of the ACC vehicle following model is: a(t)=k1(Δx-s0-l car -t a ·v(t))+k2·Δv(t) Among them, k1 and k2 are model control parameters; t a is the expected headway; The calculation formulas for the headway distances of the three vehicle following models are as follows: h a =v(t)·t a +l car +s0 h c =v(t)·t c +l car +s0 Among them, h i ,h a and h c These are the headway distances corresponding to the three vehicle following models: IDM, CACC and ACC.

4. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 1, characterized in that: The method of constructing an upper-layer signal timing optimization model based on the parameter set, taking the green-to-signal ratio of each phase as a decision variable, and taking the status information of vehicles within the communication range as basic input information, comprises the following steps: A phase hypothesis of signal timing is proposed, and the expression of the phase hypothesis is g = [g1, g2, ..., g I ] T , where I is the number of phases in the intersection signal timing scheme; g i is the green light time corresponding to phase i; Construct an objective function with the goal of minimizing delay; The constraints of the signal timing optimization model are established. The constraints include cycle length constraint and green light time constraint. The cycle length constraint is used to ensure that the sum of the green light time of each phase is equal to the cycle length. The expression of the cycle length constraint C is: The green light time constraint is used to control the green light duration of each phase within the maximum green light time and the shortest green light time. The expression of the green light time constraint is: Complete the construction of the upper-layer signal timing optimization model.

5. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 4, characterized in that: In the objective function constructed with the goal of minimizing delay, the expression of the objective function is: Where D is the total delay at the intersection; C is the signal cycle length; λ i is the green-to-signal ratio, which represents the ratio of the green light time to the signal period in the i-th phase; μ i is the traffic flow ratio in the i-th phase; q i is the traffic volume in the i-th phase; s i is the saturation flow rate in the i-th phase.

6. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 5, characterized in that: The trigonometric function optimization model is based on determining whether the vehicle meets the trajectory optimization conditions to construct a lower-level vehicle trajectory optimization model, including the following steps: Calculate the local CAV penetration within the intersection; Calculate the average headway and saturation flow velocity on the road; Determine whether the CAV meets the trajectory optimization condition, specifically: when the current traffic light is green, if the CAV can pass through the intersection within the current green light time, or when the current traffic light is red, if the CAV can pass through the intersection within the next green light time, then the trajectory optimization condition is met, otherwise it is not met; Construct a trajectory optimization model based on trigonometric functions. If the initial speed is higher than the average speed, the speed of the CAV will slowly decrease to the average speed v in the period [0, t1]. h , at which point the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at which point the acceleration gradually decreases until it reaches 0; in the period [t2, t arrive ] During the period, the CAV passes through the intersection at a constant speed.

7. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 6, characterized in that: The calculation formula of the local CAV permeability is: Among them, p n is the local CAV penetration rate; d0 is the distance from the vehicle that meets the optimization conditions to the stop line; N means that the CAV is the Nth vehicle from the stop line of the intersection; The calculation formula of the average headway is: h n =N((p n ) 2 h c +p n (1-p n )h a +(1-p n )h i ), The calculation formula of the saturated flow velocity is: Among them, h n is the average headway between vehicles; is the saturation flow velocity; is the saturation flow; The expression of the trajectory optimization model based on trigonometric function is: Among them, m and n are the key parameters to ensure the smoothness of the trigonometric function model; the calculation formulas of the remaining parameters are as follows: v d =v h -v0 Where v0 is the initial speed of the CAV entering the intersection; v h is the average speed; a max is the maximum acceleration; d max is the maximum deceleration; j max is the maximum first derivative of acceleration.

8. A signal timing and vehicle trajectory collaborative optimization system for intelligent connected vehicles, characterized in that: include: The first module is used to obtain a parameter set using different vehicle following models according to different types of following modes corresponding to different types of vehicles on the road; The second module is used to obtain the status information of vehicles within the communication range through the road infrastructure and transmit it to the upper signal timing optimization layer; The third module is used to construct an upper-layer signal timing optimization model based on the parameter set, taking the green-to-signal ratio of each phase as a decision variable and the state information of vehicles within the communication range as basic input information; The fourth module is used to construct a lower-level vehicle trajectory optimization model based on a trigonometric function optimization model by judging whether the vehicle meets the trajectory optimization conditions; The fifth module is used to cyclically optimize signal timing and vehicle trajectory according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 7.

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