A collaborative optimization method for signal timing and vehicle trajectory of intelligent connected vehicles

Abstract

The present invention discloses a method for collaboratively optimizing signal timing and vehicle trajectories for intelligent connected vehicles. The method comprises: obtaining parameter sets using different vehicle following models based on different types of following modes corresponding to different types of vehicles on the road; obtaining status information of vehicles within the communication range through road infrastructure and transmitting it to an upper-layer signal timing optimization layer; constructing an upper-layer signal timing optimization model based on the parameter set, using the green-to-signal ratio of each phase as a decision variable and the status information of vehicles within the communication range as basic input information; constructing a lower-layer vehicle trajectory optimization model based on a trigonometric function optimization model by determining whether the vehicle meets trajectory optimization conditions; and cyclically optimizing signal timing and vehicle trajectories based on the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model. Embodiments of the present invention can enhance traffic optimization capabilities and significantly improve optimization efficiency, and can be widely applied in the field of intelligent transportation technology.

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 intelligent connected vehicles. Background Art

[0002] Due to the rapid development of connected vehicles (IoVs) and autonomous driving technologies, the penetration rate of intelligent connected vehicles (ICVs) in future road traffic will steadily increase. In mixed traffic flows, despite the rapid growth of vehicle ownership, the penetration rate of ICVs will remain low for a short period of time. Existing research is largely limited to a single aspect of traffic control methods, failing to effectively improve traffic efficiency. At signalized intersections, in this mixed traffic environment, more effective traffic control methods are needed, and trajectory optimization and its coordinated optimization with traffic signals are promising control methods. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to propose an efficient method for collaborative optimization of 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 for 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] Based on 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 acquiring of parameter sets using different vehicle following models according to different types of 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 car-following models to be 0, calculate different headway distances for the three car-following models;

[0013] Calculate the percentage of the road occupied by the three car-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 distance; Δx is the distance between the manually driven vehicle and the preceding vehicle; 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] Wherein, 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 formula for the headway distance of the three vehicle following models is:

[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 The headway distances corresponding to the three vehicle following models are IDM, CACC and ACC.

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

[0031] The 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] Establish the constraints of the signal timing optimization model. 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 the goal of minimizing delay, the objective function is expressed as:

[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 rate within the intersection;

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

[0042] Determine whether the CAV meets the trajectory optimization conditions. Specifically, if the CAV can pass through the intersection within the current green light time when the current traffic light is green, or if the CAV can pass through the intersection within the next green light time when the current traffic light is red, then the trajectory optimization conditions are met; otherwise, they are not met.

[0043] Construct a trajectory optimization model based on trigonometric functions. If the initial velocity is higher than the average velocity, the speed of the CAV will slowly decrease to the average speed v in the period [0, t1]. h , at this time the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at this time 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 local CAV permeability is calculated as follows:

[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 represents the CAV that 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 saturation 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 functions 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 present invention provides a system for collaboratively optimizing signal timing and vehicle trajectory for intelligent connected vehicles, including:

[0061] The first module is used to obtain parameter sets 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 build an upper-layer signal timing optimization model based on the parameter set, using the green-to-signal ratio of each phase as a decision variable and the status 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 determining 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 object, another aspect of an embodiment of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program.

[0067] To achieve the above object, 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 method described above is implemented.

[0068] The present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the above method.

[0069] Embodiments of the present invention include at least the following beneficial effects: The present invention provides a method for collaboratively optimizing signal timing and vehicle trajectories for intelligent connected vehicles. This method uses different vehicle following models to obtain parameter sets based on the different types of following modes corresponding to different types of vehicles on the road. Status information of vehicles within the communication range is obtained through road infrastructure and transmitted to an upper-layer signal timing optimization layer. Based on the parameter set, an upper-layer signal timing optimization model is constructed using the green-to-signal ratio of each phase as a decision variable and the status information of vehicles within the communication range as basic input information. A lower-layer vehicle trajectory optimization model is constructed based on a trigonometric function optimization model by determining whether the vehicle meets trajectory optimization conditions. Signal timing and vehicle trajectories are cyclically optimized based on the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model. Embodiments of the present invention can enhance traffic optimization capabilities and significantly improve optimization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0071] Figure 2 It is a flowchart 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 the electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the objects, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. 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 merely examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the appended claims.

[0075] It will be understood that the terms "first," "second," and the like 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 merely used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of," "when," or "in response to a determination."

[0076] The terms "at least one", "plurality", "each", "any", etc. used in the present invention include at least one, two or more, multiple, 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 commonly understood by those skilled in the art to which the present invention pertains. The terms used herein are for the purpose of describing embodiments of the present invention only and are not intended to limit the present invention.

[0078] The method for collaborative optimization of signal timing and vehicle trajectory of intelligent connected vehicles provided by an embodiment of the present invention relates to the field of intelligent transportation technology. The method for collaborative optimization of signal timing and vehicle trajectory of intelligent connected vehicles provided by an embodiment of the present invention can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a 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 an in-vehicle terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or 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 a blockchain network; the software can be an application that implements the method for collaborative optimization of signal timing and vehicle trajectory of intelligent connected vehicles, etc., but is not limited to the above forms.

[0079] The present invention can be used in a wide variety of 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 electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. 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 via a communications 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 the network in a wireless or wired manner 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, server 101 can also be a node server in a 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] Terminal 102 may be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc. Terminal 102 may also be a vehicle-mounted terminal of the various device types described above, but is not limited thereto. Terminal 102 and server 101 may be connected directly or indirectly 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 the server 101. It can be understood that the method can also be applied to the terminal 102.

[0085] Reference Figure 2 , Figure 2 This is a flowchart of a method for collaboratively optimizing the signal timing and vehicle trajectory of an intelligent connected vehicle applied to a server according to an embodiment of the present invention. The execution subject of this method can be any of the aforementioned computer devices (including servers or terminals). 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] Based on 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 acquiring of parameter sets using different vehicle following models according to different types of 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 car-following models to be 0, calculate different headway distances for the three car-following models;

[0094] Calculate the percentage of the road occupied by the three car-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 distance; Δx is the distance between the manually driven vehicle and the preceding vehicle; 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] 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;

[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 formula for the headway distance of the three vehicle following models is:

[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 The headway distances corresponding to the three vehicle following models are IDM, CACC and ACC.

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

[0112] The 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] Establish the constraints of the signal timing optimization model. 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 the goal of minimizing delay, the objective function is expressed as:

[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 rate within the intersection;

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

[0123] Determine whether the CAV meets the trajectory optimization conditions. Specifically, if the CAV can pass through the intersection within the current green light time when the current traffic light is green, or if the CAV can pass through the intersection within the next green light time when the current traffic light is red, then the trajectory optimization conditions are met; otherwise, they are not met.

[0124] Construct a trajectory optimization model based on trigonometric functions. If the initial velocity is higher than the average velocity, the speed of the CAV will slowly decrease to the average speed v in the period [0, t1]. h , at this time the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at this time 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 local CAV permeability is calculated as follows:

[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 represents the CAV that 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 saturation 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 functions 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 with reference to the accompanying drawings:

[0141] like Figure 3 As shown, the signal pairing and vehicle trajectory optimization method for an intelligent connected vehicle according to an 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 the 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. Different vehicle following models are used respectively, namely Intelligence Driver Model (IDM), Cooperative Adaptive Cruise Control (CACC) and Adaptive Cruise Control (ACC). The IDM is represented as:

[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 and the speed difference between the vehicle and the preceding vehicle in the three following models to be 0, calculate different headway distances for the three following models, 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 respectively;

[0158] S13: Calculate the percentages of the three car-following models on the road, 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, the values ​​of some of the above parameters can be: 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 vehicle status information within the communication range, such as vehicle location and speed, through road infrastructure. This information is transmitted to the upper-layer signal timing optimization layer via V2I technology on a per-signal cycle basis. Furthermore, the road infrastructure in S2 includes loop detectors.

[0163] S3: Using 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-layer 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, 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 intersection delay; 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 constraints for the signal timing optimization model. Constraints include two types: cycle length constraints and green light time constraints. The cycle length constraint is used to ensure that the sum of the green light times of each phase is 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. It is specifically expressed as:

[0175]

[0176] Taking a real intersection as an example, the signal cycle length can be set to 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] Furthermore, 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 using the following formula:

[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 represents the CAV that 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, 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 velocity is higher than the average velocity, the speed of the CAV is slowly reduced to the average vehicle speed v in the period [0, t1]. h , at this time the acceleration gradually increases; in the period [t1, t2], the speed of the CAV slowly decreases to a uniform speed, at this time 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. In addition, when performing trajectory optimization, only CAVs that can pass through the intersection within the green light time are selected for optimization, 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 speed changes smoother and acceleration fluctuations smaller, thereby effectively reducing fuel consumption and carbon dioxide emissions.

[0201] 4. The present invention coordinates and optimizes signal timing and vehicle trajectory, further embodying the concept of vehicle-road collaboration and maximizing optimization efficiency.

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

[0203] The first module is used to obtain parameter sets 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 build an upper-layer signal timing optimization model based on the parameter set, using the green-to-signal ratio of each phase as a decision variable and the status 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 determining 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] An embodiment of the present invention further provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, implements the aforementioned method for collaboratively optimizing signal timing and vehicle trajectory for an intelligent connected vehicle. The electronic device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0210] It can be understood that the contents of the above method embodiments are 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 according to another embodiment is shown. The electronic device includes:

[0212] The processor 401 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided by 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 application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and is called by the processor 401 to execute the signal timing and vehicle trajectory collaborative optimization method for intelligent connected vehicles according to the embodiments of the present invention.

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

[0215] Communication interface 404, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (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 a 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 intelligent connected vehicles.

[0219] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment 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 arranged 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 the user's 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 such 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. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present invention will be obtained.

[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 will 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 understand that the technical solutions shown in the figures do not constitute a limitation on 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. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

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

[0226] The terms "first," "second," "third," "fourth," and the like (if any) in the description of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or apparatus.

[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 merely illustrative. For example, the division of the above units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, 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 separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0230] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or 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, 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 and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other 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 invention is not limited thereby. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the invention should be within the scope of the invention.

Claims

1. A method for collaboratively optimizing signal timing and vehicle trajectory for 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; Based on 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; cyclically optimizing signal timing and vehicle trajectory according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model; The method of acquiring parameter sets 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 car-following models to be 0, calculate different headway distances for the three car-following models; Calculate the percentage of the three car-following models on the road; The three different car-following models are IDM, CACC and ACC; The method comprises the following steps: 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: The phase hypothesis of signal timing is proposed, and the expression of the phase hypothesis is: ,in is the number of phases in the intersection signal timing scheme; Phase Corresponding green light time; Construct an objective function with the goal of minimizing delay; Establish the constraints of the signal timing optimization model, which 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 should be equal to the cycle length, and the cycle length constraint The expression 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; 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 rate within the intersection; Calculate the average headway and saturation flow velocity on the road; Determine whether the CAV meets the trajectory optimization conditions. Specifically, if the CAV can pass through the intersection within the current green light time when the current traffic light is green, or if the CAV can pass through the intersection within the next green light time when the current traffic light is red, then the trajectory optimization conditions are met; otherwise, they are not met. Construct a trajectory optimization model based on trigonometric functions. If the initial velocity is higher than the average velocity, During the time period, the speed of the CAV slowly decreases to the average speed , at this time the acceleration gradually increases; During the period, the speed of the CAV slowly decreases to a constant speed, at which time the acceleration gradually decreases until it reaches 0; During the time period, the CAV passes through the intersection at a constant speed.

2. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 1, characterized in that: The expression of the IDM vehicle following model is: ， ， in, represents the acceleration function of the vehicle; and The maximum acceleration and maximum speed limit of a manually driven vehicle during driving; is the speed of the manually driven vehicle at time t; is the expected spacing; is the distance between the manually driven vehicle and the vehicle ahead; is the vehicle body length; is the vehicle reaction time; is the minimum parking distance; The expression of the CACC vehicle following model is: in, is the difference between the expected position and the actual position of the intelligent connected vehicle; for The differential of is the expected headway; and Control parameters for the model; Control the step size for the model; The expression of the ACC vehicle following model is: in, and Control parameters for the model; is the expected headway; The calculation formula for the headway distance of the three vehicle following models is: in, , and The headway distances corresponding to the three vehicle following models: IDM, CACC and ACC.

3. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 1, characterized in that: In constructing the objective function with the goal of minimizing delay, the expression of the objective function is: in, is the total delay at the intersection; is the signal cycle length; Green letter ratio, representing the The ratio of the green light time to the signal period in the phase; For the Traffic flow ratio in phase; For the Traffic volume in the phase; For the Saturation flow rate in the phase.

4. The method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to claim 1, characterized in that: The calculation formula of the local CAV permeability is: in, is the local CAV penetration rate; The distance from the vehicle to the stop line that meets the optimization conditions; Indicates that the CAV is the first vehicle from the intersection stop line. a car; The calculation formula of the average headway is: ， The calculation formula of the saturation flow velocity is: ， in, 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 functions 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: in, is the initial speed of the CAV entering the intersection; is the average speed; is the maximum acceleration; is the maximum deceleration; is the maximum first derivative of acceleration.

5. A system for implementing the method for collaboratively optimizing signal timing and vehicle trajectory of an intelligent connected vehicle according to any one of claims 1 to 4, characterized in that: include: The first module is used to obtain parameter sets 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 build an upper-layer signal timing optimization model based on the parameter set, using the green-to-signal ratio of each phase as a decision variable and the status 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 determining 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.

6. 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 4.

7. 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 4.

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