Mixed traffic flow regulation and control method based on communication delay adaptation

Abstract

The invention discloses a mixed traffic flow regulation and control method based on communication delay adaptation, and aims to overcome the defect that a traffic flow control method aiming at communication delay adaptation is lacked in the prior art. The method comprises the following steps: introducing communication delay to a cooperative adaptive cruise control (CACC) system, and constructing a mixed traffic flow model in combination with a vehicle type and a communication mode; constructing a critical equation, dividing a control compensation dominant area and a communication delay dominant area according to the mixed traffic flow model, and determining critical communication delay; and obtaining a safe time headway function according to the mixed traffic flow model, defining a ratio of a control compensation item to a communication delay item based on the function, performing safe time headway judgment according to the ratio, and realizing communication delay adaptive mixed traffic flow regulation and control. The blank of current research is filled, and a more refined optimization scheme is provided for actual traffic management.

Description

Technical Field

[0001] This application relates to the field of intelligent connected transportation systems, and particularly to a hybrid traffic flow regulation method based on communication delay adaptation. Background Art

[0002] In recent years, with the increasingly wide application of connected and automated vehicles (CAVs) equipped with cooperative adaptive cruise control (CACC), CACC has become a research hotspot in the field of modern transportation technology. Relying on vehicle-to-vehicle (V2V) communication, CACC can achieve closer vehicle distance control and more efficient platoon coordination, thus optimizing road traffic capacity. Although CACC shows great potential in optimizing traffic flow, its actual operation is still significantly affected by communication quality, especially the communication delay problem. In a CACC platoon, the state information such as the speed and acceleration of the leading vehicle needs to be transmitted to the following vehicle in a timely manner to ensure that the following vehicle can respond quickly. However, communication delay may cause information transmission lag, making it impossible for the following vehicle to adjust its driving state at the optimal moment, thereby causing vehicle spacing fluctuations and even leading to traffic flow instability. How to quantify the impact of communication delay on the CACC traffic system and explore optimization strategies is an important topic in current research.

[0003] In existing research, scholars mainly focus on aspects such as stability analysis of CACC platoons, dedicated lane optimization, and adaptive traffic control strategies. Xu Wang et al. studied the stability of CAV platoons under dynamic communication topologies in the article "Stability and Safety Analysis of Connected and Automated Vehicle Platoon Considering Dynamic Communication Topology", and proposed a dynamic weight optimization model to mitigate the impact of communication delay on platoon cooperative control. Lei Yang et al. further analyzed the impact of packet loss and time-varying communication delay on the control stability of CAV platoons in the article "Stability Analysis of Connected Automated Vehicle Platoon Controller Under Unreliable Communication Conditions", and derived the stability constraints of CAV platoons under different communication conditions using the Lyapunov method. At the same time, some research has proposed reducing the mutual interference between CAVs and human-driven vehicles (HDVs) through dedicated lanes, thereby improving the operation efficiency of CAVs in a mixed traffic environment.

[0004] However, existing studies have mainly focused on the stability of CACC vehicles and infrastructure optimization, and the quantitative analysis of how communication delay affects traffic flow and road capacity remains relatively limited. Although it involves the impact of communication delay on platoon cooperative control and stability, it does not comprehensively consider how communication delay affects traffic capacity in a mixed traffic environment. In fact, communication delay may lead to a lag in vehicle control response, making it difficult for CAV platoons to achieve optimal car-following control during actual operation, thus affecting the overall traffic capacity.

[0005] In summary, there is an urgent need for a control gain optimization strategy that can dynamically adapt to changes in communication delay to improve the operating efficiency of CAV platoons in a complex mixed traffic environment. Summary of the Invention

[0006] The object of the present invention is to provide a method for regulating mixed traffic flow based on communication delay adaptation to overcome the deficiency that the prior art lacks a traffic flow control method for communication delay adaptation.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for regulating mixed traffic flow based on communication delay adaptation, comprising:

[0009] Introduce communication delay into the Cooperative Adaptive Cruise Control (CACC) system, and construct a mixed traffic flow model by combining vehicle types and communication modes;

[0010] Construct a critical equation, divide the control compensation dominant region and the communication delay dominant region according to the mixed traffic flow model, and determine the critical communication delay;

[0011] Obtain the safe headway time function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term based on this function, and make a safe headway time judgment according to the ratio size to achieve the regulation of mixed traffic flow adapted to communication delay.

[0012] Introduce communication delay into the Cooperative Adaptive Cruise Control (CACC) system, and construct a mixed traffic flow model by combining vehicle types and communication modes, including:

[0013] Introduce communication delay into the Cooperative Adaptive Cruise Control (CACC) system and analyze the impact of communication delay on the Cooperative Adaptive Cruise Control (CACC) system;

[0014] Classify the car-following modes in the mixed traffic environment by combining vehicle types and communication modes. According to the classification results of the car-following modes and the impact of communication delay, respectively derive the headway distance calculation formulas for different car-following mode classifications;

[0015] Introduce the penetration rate of connected automated vehicles (CAVs) in the traffic flow, obtain the average headway in the mixed traffic flow, and construct a mixed traffic flow model.

[0016] After introducing communication delay into the cooperative adaptive cruise control (CACC) system, the control input for the i-th vehicle is:

[0017]

[0018] In the formula, k a is the control gain of the acceleration of the leading vehicle; k v is the speed difference gain, which reflects the adjustment ability of the system to the speed difference between the front and rear vehicles; k p is the spacing error gain, which is used to adjust the influence degree of the vehicle spacing error on the control input; represents the communication delay, that is, the time lag generated when information is transmitted from the leading vehicle to the i-th vehicle; δ i (t) is the spacing error of the i-th vehicle, and its definition is δ i (t): = e i (t) + t c v i (t), where e i (t): = x i (t) - x i-1 (t) + s0, x i (t) and x i-1 (t) are the positions of the i-th vehicle and the leading vehicle at time t respectively, s0 is the minimum safety spacing, and t c is the headway time.

[0019] Classify the car-following modes according to the vehicle type and communication mode in the mixed traffic environment. The classification results of the car-following modes include the cooperative adaptive cruise control (CACC) mode, the adaptive cruise control (ACC) mode, and the intelligent driver model (IDM) mode. The headway calculation formulas corresponding to each car-following mode classification are as follows:

[0020] Cooperative adaptive cruise control (CACC) mode,

[0021]

[0022] In the formula, v represents the vehicle driving speed, is the headway time function related to the communication delay , L is the vehicle's own length, and s0 represents the minimum safety spacing;

[0023] Adaptive Cruise Control ACC mode,

[0024] h A = vt a + L + s0

[0025] In the formula, t a is the safe headway time under ACC;

[0026] Intelligent Driver Model IDM mode,

[0027]

[0028] In the formula, T is the driver's reaction time, and v0 is the free flow speed of the vehicle.

[0029] The proportion of Connected and Autonomous Vehicles CAVs under different following modes is as follows:

[0030]

[0031] In the formula, p is the penetration rate of Connected and Autonomous Vehicles CAVs in the entire traffic flow, and p C represents the proportion of vehicles adopting Cooperative Adaptive Cruise Control CACC mode among all vehicles; p A represents the proportion of vehicles adopting ACC mode; p I is the proportion of vehicles based on the Intelligent Driver Model IDM mode;

[0032] Therefore, for the traffic flow q in the i-th lane, its calculation formula is:

[0033]

[0034] In the formula, k represents the traffic density.

[0035] Construct a critical equation, divide the control compensation dominant area and the communication delay dominant area according to the mixed traffic flow model, and determine the critical communication delay, including:

[0036] Derive the traffic flow calculation formula in the scenario of all Connected and Autonomous Vehicles CAVs and the traffic flow calculation formula in the scenario of all Human-Driven Vehicles HDV, and construct a critical equation based on this;

[0037] Obtain the safe headway time function according to the mixed traffic flow model, and divide the control compensation dominant area and the communication delay dominant area according to this function;

[0038] Derive the critical delay expressions for the control compensation dominant area and the communication delay dominant area respectively according to the critical equation, and obtain the critical communication delays corresponding to the two dominant areas.

[0039] The traffic flow calculation formulas in the scenario of fully connected and autonomous vehicles (CAVs) are as follows:

[0040]

[0041] The traffic flow calculation formulas in the scenario of fully human-driven vehicles (HDVs) are as follows:

[0042]

[0043] Construct the critical equation as follows:

[0044]

[0045] In the formula, q CAV is the traffic flow in the scenario of fully connected and autonomous vehicles (CAVs), q HDV is the traffic flow in the scenario of fully human-driven vehicles (HDVs), and l crit is the critical communication delay.

[0046] The expression of the critical communication delay in the control compensation dominant region is:

[0047]

[0048] The expression of the critical communication delay in the communication delay dominant region is:

[0049]

[0050] Obtain the safe headway function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term based on this function, and judge the safe headway according to the ratio size to achieve the regulation of the mixed traffic flow adapting to the communication delay, including:

[0051] Obtain the safe headway function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term as R based on this function. If R >, the safe headway is dominated by the control compensation term; if R ≤ 1, the safe headway is dominated by the communication delay term.

[0052] Obtain the safe headway function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term based on this function, and judge the safe headway according to the ratio size to achieve the regulation of the mixed traffic flow adapting to the communication delay, including:

[0053] If in the control compensation dominant region, increase the control gain; if in the communication delay dominant region, decrease the control gain to achieve the adaptive regulation of the mixed traffic flow.

[0054] Compared with the prior art, the present invention has the following beneficial technical effects:

[0055] A method for regulating mixed traffic flow based on communication delay adaptation provided by the present invention solves the problem of insufficient communication delay adaptation in the prior art through a systematic method. The solution first introduces communication delay parameters into the CACC system and constructs a mixed traffic flow model containing delay factors. This key improvement makes up for the defect that the traditional method does not fully consider the influence of communication delay. Based on this model, the solution innovatively proposes the concept of critical communication delay, and accurately divides the control compensation dominant area and the communication delay dominant area by constructing a critical equation, providing a scientific judgment basis for subsequent regulation.

[0056] At the level of real-time regulation, the solution designs a complete adaptive control mechanism. Obtain the safe headway time function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term based on this function, and the system can accurately identify the current control area based on the magnitude of this ratio. Dynamically adjust the control gain parameter according to the judgment result to keep the system always in the optimal working state. This closed-loop control structure effectively overcomes the drawback of poor adaptability of the fixed parameter control strategy and realizes intelligent response to different delay conditions.

[0057] The whole set of solutions forms a complete technical chain from theoretical modeling to practical application. The model construction link ensures the accurate quantification of the influence of communication delay, the critical division mechanism provides a reliable regulation benchmark, and the adaptive control part ensures the stable operation of the system under various working conditions. This systematic solution significantly improves the operation efficiency of mixed traffic flow in the presence of communication delay and fills the gap in the prior art. Brief Description of the Drawings

[0058] Figure 1 It is a schematic diagram of a method for regulating mixed traffic flow based on communication delay adaptation in a specific embodiment of the present invention;

[0059] Figure 2 It is a specific implementation flowchart of a method for regulating mixed traffic flow based on communication delay adaptation in a specific embodiment of the present invention;

[0060] Figure 3 It is a schematic diagram of a mixed traffic scenario in a specific embodiment of the present invention;

[0061] Figure 4 It is a curve of the change of traffic capacity with communication delay under different CAVs penetration rates in a specific embodiment of the present invention;

[0062] Figure 5 It is a curve of the change of traffic capacity with communication delay under different control gains k a in a specific embodiment of the present invention. Specific Embodiment

[0063] With the widespread application of Cooperative Adaptive Cruise Control (CACC) in connected autonomous vehicles (CAVs), the vehicle distance control and platoon cooperation achieved through vehicle-to-vehicle (V2V) communication have significantly improved road capacity. However, the lag in the transmission of state information caused by communication delay can lead to fluctuations in vehicle spacing and traffic flow instability, which has become a key factor restricting the effectiveness of CACC.

[0064] Existing research mainly focuses on three directions: the stability analysis of CAV platoons, the optimization of dedicated lanes, and adaptive control strategies. The limitation of current research is that although it pays attention to the impact of communication delay on control stability, it lacks a quantitative analysis of the relationship between communication delay and traffic capacity in a mixed traffic environment. This deficiency makes it difficult for existing methods to effectively solve the suboptimal vehicle distance control problem caused by delay, thereby affecting the overall road traffic efficiency. Currently, there is a lack of a control gain optimization strategy that can dynamically adapt to changes in communication delay to improve the operation efficiency of CAV platoons in complex mixed traffic environments. Therefore, the objective of the present invention is to introduce a mixed traffic flow model considering communication delay and propose an optimization strategy for dynamically adjusting control gain based on communication delay to enhance traffic capacity.

[0065] On the basis of retaining the existing model framework, this scheme introduces communication delay, constructs a mixed traffic flow model considering communication delay, depicts the impact of communication delay on vehicle following behavior, and studies its interaction with control gain through systematic quantitative analysis. In addition, it also quantitatively studies the impact of control gain on road capacity and proposes an optimization strategy for adaptive control gain based on communication delay.

[0066] The present invention fills the gap in current research and provides a more refined optimization scheme for actual traffic management.

[0067] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0068] Refer to Figures 1 to 2 As shown in a specific implementation manner of the mixed traffic flow regulation method based on communication delay adaptation provided by the present invention, it includes:

[0069] Introduce communication delay into the Cooperative Adaptive Cruise Control (CACC) system, and construct a mixed traffic flow model in combination with vehicle types and communication modes;

[0070] Construct a critical equation, divide the control compensation-dominated region and the communication delay-dominated region according to the mixed traffic flow model, and determine the critical communication delay;

[0071] Obtain the safe headway time function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term based on this function, and judge the safe headway according to the ratio size to achieve the regulation of the mixed traffic flow adapting to the communication delay.

[0072] Specifically, the steps introduce communication delay into the cooperative adaptive cruise control (CACC) system, and construct a mixed traffic flow model in combination with vehicle types and communication modes, including:

[0073] Introduce communication delay into the cooperative adaptive cruise control (CACC) system and analyze the impact of communication delay on the cooperative adaptive cruise control (CACC) system;

[0074] Classify the following vehicle patterns in the mixed traffic environment in combination with vehicle types and communication modes. According to the classification results of the following vehicle patterns and in combination with the impact of communication delay, respectively deduce the calculation formulas for the headway between vehicles under different following vehicle pattern classifications;

[0075] Introduce the penetration rate of connected and autonomous vehicles (CAVs) in the traffic flow, obtain the average headway in the mixed traffic flow, and construct a mixed traffic flow model.

[0076] First, introduce the communication delay factor into the CACC system. For the i-th vehicle, the control input after introducing the communication delay is:

[0077]

[0078] In the formula, k a is the control gain of the acceleration of the vehicle in front, reflecting the response intensity of the system to the change in the acceleration of the vehicle in front; k v is the speed difference gain, reflecting the adjustment ability of the system to the speed difference between the front and rear vehicles; K p is the spacing error gain, used to adjust the influence degree of the vehicle spacing error on the control input; represents the communication delay, that is, the time lag generated when the information is transmitted from the vehicle in front to the i-th vehicle; δ i (t) is the spacing error of the i-th vehicle, and its definition is δ i (t): = e i (t) + t c v i (t), where e i (t): = x i (t) - x i-1 (t) + s0, x i (t) and x i-1(t) are the positions of the i-th vehicle and the preceding vehicle at time t, s0 is the minimum safe distance, t c is the headway time.

[0079] Based on the above control input, i By performing Laplace transform on (t), the spacing error propagation equation can be derived:

[0080] δ i (s)=H1(s)δ i-1 (s)

[0081] Where H1(s) is the transfer function, N1(s)=k a s 2 e -es +k v s+k p , D1(s)=τs 3 +s 2 +(k v +t c k p )s+k p τ represents the parasitic drive hysteresis, which ranges from τ∈(0, τ0], reflecting the inherent delay characteristics of the vehicle actuator in responding to the control signal.

[0082] To ensure the stable operation of the entire CACC system, the system must have internal stability. This means that the characteristic equation D1(s) = 0 must satisfy the Hurwitz stability condition. According to the Hurwitz stability criterion, an important criterion for the internal stability of the system is γ-τ0k p >0, where γ = k v +t c k p . This condition ensures that the poles of the system are all located in the left half of the complex plane, so that the system can converge to a stable state when subjected to external interference. Substituting s = jω into H1(s), the expression of H1(jω) can be obtained. String stability requires that the error will not be amplified when propagating in the vehicle queue, that is, the following conditions are met:

[0083]

[0084] This condition ensures that the propagation of errors in the vehicle queue will not continue to accumulate as the number of vehicles increases, thereby maintaining the stable operation of the queue. In the frequency domain, it becomes It has periodicity. (k is an integer), H1(jω) is derived to obtain the headway time t c The lower limit of must satisfy the following conditions:

[0085]

[0086] This condition comprehensively considers the influences of factors such as control gain, communication delay, and parasitic driving lag on the headway, providing an important theoretical basis for the design and optimization of the CACC system.

[0087] Next, a mixed traffic flow model is constructed. Referring to Figure 3 As shown, in a traffic environment mixed with CAVs and HDVs, the interaction modes of vehicles can be divided into three categories: CACC, ACC, and IDM. Next, the calculation of the headway for these three following modes will be described separately.

[0088] Cooperative Adaptive Cruise Control (CACC): In this mode, the headway of the vehicle is determined by means of V2V communication technology. At the same time, the factor of communication delay is also taken into account, and the calculation expression of the headway is as follows:

[0089]

[0090] In the formula, v represents the vehicle driving speed, is the headway function related to the communication delay , L is the vehicle's own length, and s0 represents the minimum safety distance.

[0091] Adaptive Cruise Control (ACC): In this mode, CAVs mainly rely on on-vehicle sensors to detect the running state of the vehicle in front in real time and adopt a fixed headway strategy for following. In the ACC mode, the calculation formula of the headway is:

[0092] h A = vt a + L + s0

[0093] where t a is the safe headway under ACC.

[0094] Intelligent Driver Model (IDM): Based on the classical intelligent driving model, fully considering the reaction time T of human drivers during driving, the headway in the IDM mode is calculated by the following formula:

[0095]

[0096] In the formula, v0 is the free flow speed of the vehicle.

[0097] In a real mixed traffic flow scenario, vehicles in different following modes are intertwined and coexist. Assuming that the penetration rate of CAVs in the entire traffic flow is p, then the proportions of vehicles in different following modes are as follows:

[0098]

[0099] Among them, p C represents the proportion of vehicles adopting the CACC mode among all vehicles; p A represents the proportion of vehicles adopting the ACC mode; p I is the proportion of vehicles based on the IDM mode. Based on the proportion distribution of vehicles in the above different following - vehicle modes, the average headway in the mixed traffic flow can be calculated by the following formula:

[0100]

[0101] This formula weights and sums the headways in different modes according to the proportion of each type of vehicle, so as to obtain the average headway of the entire mixed traffic flow. Further, for the traffic flow q in the i - th lane, its calculation formula is:

[0102]

[0103] In the formula, k represents the traffic density. This formula provides a key quantitative basis for in - depth analysis of the characteristics of mixed traffic flow and is the core of the mixed traffic flow model necessary for the mixed traffic flow regulation method based on communication - delay adaptation.

[0104] Specifically, the steps of constructing the critical equation, dividing the control - compensation - dominant region and the communication - delay - dominant region according to the mixed traffic flow model, and determining the critical communication delay include:

[0105] Derive the traffic flow calculation formula in the all - CAV scenario and the traffic flow calculation formula in the all - HDV scenario, and construct the critical equation based on this;

[0106] Obtain the safe headway time - distance function according to the mixed traffic flow model, and divide the control - compensation - dominant region and the communication - delay - dominant region according to this function;

[0107] Derive the critical - delay expressions for the control - compensation - dominant region and the communication - delay - dominant region respectively according to the critical equation, and obtain the critical communication delays corresponding to the two dominant regions.

[0108] Define the critical communication delay as the delay when the advantages of CAVs completely disappear, that is, the traffic flow when all vehicles are CAVs (P = 1) is equal to the traffic flow when all vehicles are HDVs (P = 0). The concept of critical communication delay characterizes a threshold. Once the actual communication delay exceeds this threshold, the advantages shown by CAVs in improving traffic flow efficiency and other aspects will be completely offset by the negative impacts brought by communication delay, and further lead to the inability to effectively improve the performance of the traffic system due to the vehicle intelligence.

[0109] Let the traffic flow in the all - CAV scenario be qCAV , and its calculation formula is as follows:

[0110]

[0111] Similarly, let the traffic flow in the all-HDV scenario be q HDV , and its calculation formula is:

[0112]

[0113] Based on this, a critical equation is constructed:

[0114]

[0115] By solving this equation, the critical communication delay under different conditions can be determined. The safe headway function plays a crucial role in capturing the interaction between the control gain k a and the delay . Since is a piecewise function, it is divided into two different cases for discussion here: the control compensation dominant region and the communication delay dominant region.

[0116] In the control compensation dominant region, the compensation ability of the controller partially alleviates the impact of communication delay, making the safe headway mainly determined by the control gain. In contrast, in the communication delay dominant region, the impact of communication delay exceeds the compensation ability of the controller and becomes the dominant factor affecting system stability, resulting in the safe headway being directly determined by the delay. For the hybrid traffic flow regulation method of the present invention to be more easily understood and to facilitate a deeper understanding of how different factors affect the critical communication delay, the following will analyze these two cases in detail.

[0117] In the control compensation dominant region, when the communication delay is relatively small, the compensation ability of the controller comes into play. The controller can actively adjust the behavior of the vehicle according to the received information to offset the negative impact brought by the delay. For example, it can finely adjust the acceleration or deceleration of the vehicle to maintain an appropriate following distance.

[0118] When the following conditions are met, it is in this region:

[0119]

[0120] After organizing and solving this inequality, we get:

[0121]

[0122] At this time, the critical delay is determined by the following equation:

[0123]

[0124] By simplifying the equation, the expression for the critical communication delay in this region is derived:

[0125]

[0126] In the communication delay-dominated region, when the communication delay further increases, the compensation ability of the controller will gradually be lost, and the stability of the system will start to be dominated by the delay itself. At this time, due to the large lag in obtaining the state information of the vehicle in front, the effectiveness of control adjustment decreases, which may lead to an increase in the vehicle following deviation, and further cause the instability of the traffic flow.

[0127] The boundary of this region is given by the following inequality:

[0128]

[0129] Solving this inequality gives:

[0130]

[0131] In this case, the critical communication delay satisfies the following equation:

[0132]

[0133] Solve The result is:

[0134]

[0135] Thus, the expressions for the critical communication delay in the two corresponding regions of the control compensation-dominated region and the communication delay-dominated region are obtained respectively.

[0136] Specifically, the steps are as follows: Obtain the safe headway function according to the mixed traffic flow model, define the ratio of the control compensation term to the communication delay term based on this function, and make a safe headway judgment according to the ratio size to achieve the regulation of the mixed traffic flow adapting to the communication delay. It also includes the following:

[0137] Obtain the safe headway function according to the mixed traffic flow model, and define the ratio of the control compensation term to the communication delay term as R based on this function. If R > 1, the safe headway is dominated by the control compensation term; if R ≤ 1, the safe headway is dominated by the communication delay term.

[0138] If in the control compensation-dominated region, increase the control gain; if in the communication delay-dominated region, decrease the control gain to achieve the adaptive regulation of the mixed traffic flow.

[0139] First, define the ratio of the control compensation term to the communication delay term. The ratio R of the control compensation term to the communication delay term is defined as follows:

[0140]

[0141] This ratio is an important basis for judging the dominant factor of the safe headway. The specific judgment criteria are as follows:

[0142] When R > 1, the safe headway is dominated by the control compensation term.

[0143] When R ≤, the safe headway is dominated by the communication delay term.

[0144] When ,

[0145] Therefore, the safe headway t c is determined by the control compensation term, and the expression is:

[0146]

[0147] To understand the influence of k a on t c , calculate the derivative of t c with respect to k a :

[0148]

[0149] Since the derivative This means the following relationship: when k a increases, it will cause t c to become smaller. According to the formula h C = vt C + L + s0, it can be seen that the decrease of t c will cause h C to also decrease. Since the traffic density k is inversely proportional to h C , the decrease of h C will cause k to increase. Further, according to the traffic flow formula q = vk, when k increases, the traffic flow q will also increase accordingly.

[0150] Based on this situation, the adaptive control gain optimization strategy formulated by the present invention is: when , a high-gain mode is adopted. In the high-gain mode, the controller will effectively amplify the acceleration signal of the vehicle in front, enabling the vehicle behind to anticipate and adjust its own speed in advance, thereby compensating for the information lag caused by communication delay. This early speed adjustment helps to maintain an appropriate headway and density, and thus improve the traffic flow efficiency.

[0151] When , according to the equation When the denominator is 0, Based on this, in the present invention, a case-by-case discussion is carried out for k a as follows.

[0152] When for any it holds that R > 1. The safe headway is always dominated by the control compensation term. The derivative is:

[0153]

[0154] Since the derivative is positive: This positive derivative means that as k a increases, t c increases. Consequently, the headway h C increases, the density k decreases, and the traffic flow decreases.

[0155] When and is large enough, R ≤ 1. In this case, the safe headway t c is determined by the communication delay term, and the formula is It should be noted that in this case, the value of t c is independent of k a . Therefore, regardless of how k a changes, the traffic flow remains unchanged.

[0156] Therefore, the adaptive control gain optimization strategy formulated in the present invention is: when , a low-gain mode is adopted. Because at this time, the influence of communication delay on the safe headway is more significant, and reducing the controller gain can ensure the stability of the traffic flow and avoid unnecessary interference caused by excessive compensation or improper calibration of control parameters.

[0157] In summary, by analyzing the dominant factors of the safe headway and its influence on the traffic flow under different communication delays and control gain k a , the present invention formulates an adaptive control gain optimization strategy of adopting a high-gain mode when and a low-gain mode when to achieve dynamic optimization of traffic flow performance.

[0158] The present invention focuses on the mixed traffic environment where CAVs and HDVs coexist. By introducing a communication delay factor to optimize the mixed traffic flow model, the concept of critical communication delay is innovatively proposed, and the impact of communication delay on traffic capacity is deeply analyzed. Based on this, the present invention proposes an optimization strategy for dynamically adjusting the control gain, which can effectively reduce the negative impact brought by communication delay, solve the traffic bottleneck problem, improve the operation efficiency of CAV platoons in the mixed traffic flow, and thus increase traffic capacity and road throughput.

[0159] Compared with the prior art, the present invention introduces a quantitative analysis of communication delay, presents a mixed traffic flow model that comprehensively considers communication delay, and reveals how communication delay affects vehicle following behavior and traffic capacity through quantitative analysis. By setting representative values for each system parameter, as shown in Table 1.

[0160] Table 1 Simulation key parameters

[0161]

[0162] And according to Table 1, the curve of traffic capacity varying with communication delay under different CAVs penetration rates is plotted, as Figure 4 shown. As the communication delay increases, the traffic capacity curves under different penetration rates all show a downward trend, indicating that communication delay has a negative impact on traffic capacity in the mixed traffic environment. In addition, the critical communication delay is determined to be 0.77 seconds. In the low-delay region for example, when and P > 80%, the traffic flow can be increased by more than 20%. However, when the communication delay exceeds , the safety headway time of CAVs exceeds that of HDVs, resulting in more conservative following behavior of CAVs than HDVs, thus eliminating the advantages of CAVs in the traffic flow. In this case, the presence of CAVs may instead exacerbate traffic congestion. Through this quantitative analysis, the present invention reveals the specific impact mechanism of communication delay on traffic capacity changes, providing a clearer theoretical basis for understanding and solving traffic flow problems in the mixed traffic environment.

[0163] The present invention formulates a traffic capacity optimization strategy for dynamically adjusting the control gain according to communication delay. In the prior art, although some studies focus on the impact of communication delay on the stability of CAVs, there is a lack of an optimization strategy for dynamically adjusting the control gain in response to changes in communication delay. By adaptively adjusting the control gain, the present invention can effectively cope with the challenges under different communication delay conditions and improve the overall efficiency of CAV platoons in the mixed traffic flow. The simulation results are as Figure 5 shown. It can be seen from the figure that there is an intersection point for each curve, and the communication delay value corresponding to this intersection point is When the communication delay is at this value, the traffic flow is not affected by the controller gain. Additionally, when , k a is larger, the traffic capacity is larger. When , for the case of , as k a increases, the traffic capacity gradually decreases instead; for the case of , when is large enough, in the simulation results, it can be observed that the traffic capacity curves corresponding to k a = 0.2 and k a = 0.3 tend to coincide. This phenomenon strongly verifies the theoretical conclusion.

[0164] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 flow or multiple flows and / or one Figure 1 block or multiple blocks.

[0166] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means for implementing the functions specified in one Figure 1 flow or multiple flows and / or one Figure 1 block or multiple blocks.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0168] The foregoing has shown and described the basic principles, main features and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that without departing from the spirit or essential characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Accordingly, all changes that fall within the meaning and scope of the equivalent elements of the claims are intended to be embraced by the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0169] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for explaining the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A hybrid traffic flow control method based on communication delay adaptation, characterized in that: include: Introducing communication delay into the cooperative adaptive cruise control (CACC) system, and building a mixed traffic flow model by combining vehicle types and communication modes; Construct a critical equation, divide the control compensation dominant area and the communication delay dominant area according to the mixed traffic flow model, and determine the critical communication delay; The safe headway function is obtained according to the mixed traffic flow model. The ratio of the control compensation term to the communication delay term is defined based on the function. The safe headway judgment is made according to the ratio to achieve mixed traffic flow control with communication delay adaptation.

2. A hybrid traffic flow control method based on communication delay adaptation according to claim 1, characterized in that: The introduction of communication delay into the collaborative adaptive cruise control CACC system and the construction of a mixed traffic flow model in combination with vehicle types and communication modes include: Introducing communication delay into the CACC system, analyzing the impact of communication delay on the CACC system; The following modes in mixed traffic environments are classified based on vehicle type and communication mode. Based on the classification results and the influence of communication delay, the headway calculation formulas under different following modes are derived. The penetration rate of networked autonomous vehicles (CAVs) in traffic flow is introduced to obtain the average headway in mixed traffic flow and to construct a mixed traffic flow model.

3. The hybrid traffic flow control method based on communication delay adaptation according to claim 2 is characterized in that: After introducing the communication delay into the cooperative adaptive cruise control CACC system, the control input for the i-th vehicle is: In the formula, k a is the control gain of the front vehicle acceleration; k v is the speed difference gain, which reflects the system's ability to adjust the speed difference between the front and rear vehicles; k p is the spacing error gain, which is used to adjust the influence of the vehicle spacing error on the control input; represents the communication delay, i.e., the time lag caused by the information being transmitted from the preceding vehicle to the i-th vehicle; δ i (t) is the spacing error of the i-th vehicle, which is defined as δ i (t): = e i (t)+t c v i (t), where e i (t): = x i (t)-x i-1 (t)+s0,x i (t) and x i-1 (t) are the positions of the i-th vehicle and the preceding vehicle at time t, s0 is the minimum safe distance, t c is the headway time.

4. The hybrid traffic flow control method based on communication delay adaptation according to claim 2 is characterized in that: The following modes are classified in mixed traffic environments based on vehicle type and communication mode. The following mode classification results include collaborative adaptive cruise control CACC mode, adaptive cruise control ACC mode, and intelligent driver model IDM mode. The calculation formula for the headway distance corresponding to each following mode classification is as follows: Cooperative Adaptive Cruise Control CACC mode, In the formula, v represents the vehicle speed, Is related to communication delay The related headway function, L is the length of the vehicle itself, s0 represents the minimum safe distance; Adaptive cruise control ACC mode, h A =vt a +L+s0 Where, t a It is the safe headway distance under ACC; Intelligent Driver Model IDM mode, Where T is the driver's reaction time and v0 is the free flow speed of the vehicle.

5. The hybrid traffic flow control method based on communication delay adaptation according to claim 2 is characterized in that: The proportion of connected autonomous vehicles (CAVs) in different following modes is as follows: Where p is the penetration rate of CAV in the entire traffic flow, p C represents the proportion of vehicles using the cooperative adaptive cruise control (CACC) mode among all vehicles; p A represents the proportion of vehicles using ACC mode; p I The proportion of vehicles based on the intelligent driver model (IDM) mode; Therefore, for the traffic flow q of the i-th lane, the calculation formula is: Where k represents the traffic density.

6. The hybrid traffic flow control method based on communication delay adaptation according to claim 1 is characterized in that: The critical equation is constructed, and the control compensation dominant area and the communication delay dominant area are divided according to the mixed traffic flow model, and the critical communication delay is determined, including: Derive the traffic flow calculation formula for the scenario with all connected autonomous vehicles (CAVs) and the traffic flow calculation formula for the scenario with all human-driven vehicles (HDVs), and construct the critical equation based on this; The safe headway function is obtained according to the mixed traffic flow model, and the control compensation dominant area and the communication delay dominant area are divided according to the function; According to the critical equation, the critical delay expressions of the control compensation dominant region and the communication delay dominant region are derived respectively, and the critical communication delays corresponding to the two dominant regions are obtained.

7. The hybrid traffic flow control method based on communication delay adaptation according to claim 6 is characterized in that: The traffic flow calculation formula for the CAV scenario is as follows: The traffic flow calculation formula in the HDV scenario where all vehicles are driven by humans is as follows: The critical equation is constructed as follows: In the formula, q CAN For the traffic flow in the scenario where all connected autonomous vehicles (CAVs) are connected, q HDV The traffic flow in the HDV scene with all human-driven vehicles is shown in Figure 1. crit is the critical communication delay.

8. The hybrid traffic flow control method based on communication delay adaptation according to claim 6 is characterized in that: The critical communication delay expression of the control compensation dominant region is: The critical communication delay expression in the communication delay-dominated region is:

9. The hybrid traffic flow control method based on communication delay adaptation according to claim 1 is characterized in that: The method obtains a safe headway function according to the mixed traffic flow model, defines a ratio of a control compensation term to a communication delay term based on the function, makes a safe headway judgment according to the ratio, and realizes mixed traffic flow control with communication delay adaptation, including: The safe headway function is obtained according to the mixed traffic flow model. Based on the function, the ratio of the control compensation term to the communication delay term is defined as R. If R>1, the safe headway is dominated by the control compensation term. If R≤1, the safe headway is dominated by the communication delay term.

10. The hybrid traffic flow control method based on communication delay adaptation according to claim 1, characterized in that: The method obtains a safe headway function according to the mixed traffic flow model, defines a ratio of a control compensation term to a communication delay term based on the function, makes a safe headway judgment according to the ratio, and realizes mixed traffic flow control with communication delay adaptation, including: If it is in the control compensation dominant area, the control gain is increased; if it is in the communication delay dominant area, the control gain is reduced to achieve adaptive mixed traffic flow control.

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