Hybrid cooperative driving control method and system considering communication delay

Abstract

The application discloses a hybrid cooperative driving control method and system considering communication delay, and belongs to the technical field of intelligent traffic system control. The method establishes a multi-vehicle-following communication structure; splits a hybrid cooperative driving system into a local hybrid cooperative driving system; models the local hybrid cooperative driving system; establishes a lumped tracking error dynamics model; establishes a delay compensation layer to calculate multi-vehicle information after delay compensation; establishes a model prediction controller in a control layer; a networked automatic driving vehicle applies a first control sequence generated by the model prediction controller to self-control; and the hybrid cooperative driving task is completed. The application can guarantee the stability of the hybrid cooperative driving system under the action of time-varying communication delay, effectively suppress traffic wave oscillation and reduce fuel consumption; can realize the closed-loop stability of the hybrid cooperative driving system under the action of time-varying communication delay, and guarantee the tracking performance and safety of the hybrid cooperative driving system under poor communication conditions.

Description

Technical Field

[0001] This application belongs to the field of intelligent transportation system control technology, specifically relating to a manned / unmanned hybrid cooperative driving control method and system that takes into account communication delay. Background Technology

[0002] Intelligent connected vehicles utilize wireless communication to acquire information about surrounding vehicles and roadside equipment, enabling multi-vehicle cooperative driving, thereby significantly improving traffic safety and efficiency, and enhancing fuel economy. Due to factors such as the long-tail effect of autonomous driving, the coexistence of connected autonomous vehicles and human-driven vehicles will continue for a long time; therefore, hybrid human / unmanned cooperative driving has received widespread attention in recent years.

[0003] Numerous patents exist regarding hybrid cooperative driving. Chinese patent application 202110152533.1 provides a hybrid vehicle cooperative control method considering information from rear-end human vehicles; Chinese patent application 202110958481.7 provides a hybrid vehicle platoon cooperative control method based on model predictive control; Chinese patent application 202310531628.3 provides a data-driven connected vehicle control system in a hybrid traffic environment, effectively solving the problem of unknown vehicle models and the inability to use traditional model-based methods in actual hybrid traffic scenarios; Chinese patent application 202210948622.1 provides a vehicle-road cooperative hybrid vehicle platoon control method considering roadside perception performance; and Chinese patent application 202210620662.3 provides a distributed cooperative control method for hybrid vehicle groups near the signal control zone considering information uncertainty, ensuring that the hybrid vehicle group passes through the signal control zone consistently and stably.

[0004] Existing manned / unmanned hybrid cooperative driving patents mostly assume ideal communication conditions, i.e., the ability to receive information from surrounding vehicles and roadside equipment in real time. However, due to the combined effects of complex driving environments and high-speed vehicle travel, communication is subject to various interferences, inevitably causing communication delays. This results in the hybrid cooperative driving system being unable to respond in real time, affecting system stability and safety. A few Chinese patent applications, such as 202210620662.3, consider the impact of communication delays in cooperative driving; however, because they employ linear feedback control, they cannot account for control input saturation constraints and collision-free safety constraints in controller design, and cannot be extended from near-signal control zone scenarios to general hybrid cooperative driving scenarios.

[0005] To address the aforementioned issues, this invention provides a manned / unmanned hybrid cooperative driving control method and system that takes into account communication latency. Summary of the Invention

[0006] The purpose of this application is to provide a hybrid cooperative driving control method and system that considers communication delay. It transforms the non-convex constraints constructed based on the position, communication distance, and safety distance of unmanned vehicles during formation movement to meet the two major requirements of communication maintenance and collision avoidance into convex constraints. This facilitates the solution of the global optimal solution of the control input that meets these two requirements, thereby solving at least one of the technical problems involved in the background art.

[0007] To solve the above-mentioned technical problems, this application is implemented as follows:

[0008] This application provides a hybrid cooperative driving control method that takes into account communication latency, including the following steps:

[0009] Step S1: Establish a multi-vehicle following communication structure;

[0010] Step S2: The hybrid cooperative driving system is split into multiple interconnected local hybrid cooperative driving systems;

[0011] Step S3: Modeling of the local hybrid cooperative driving system;

[0012] Step S4: Establish a lumped tracking error dynamic model for the design of the model predictive controller;

[0013] Step S5: Establish a delay compensation layer and calculate the multi-vehicle information after delay compensation;

[0014] Step S6: Establish a model predictive controller in the control layer;

[0015] Step S7: The connected autonomous vehicle applies the first control sequence generated by the model prediction controller to the vehicle control.

[0016] Step 8: Repeat steps S5 to S7 until the hybrid cooperative driving mission ends.

[0017] Optionally, in step S1, establishing a multi-vehicle following communication structure specifically includes:

[0018] Each connected autonomous vehicle sends its future trajectory information to vehicles behind it via vehicle-to-vehicle communication, while each human-driven vehicle sends its current status information to vehicles behind it. At the same time, the connected autonomous vehicle receives information from the nearest connected autonomous vehicle ahead, as well as information from all human-driven vehicles between the two connected autonomous vehicles, and stores the received information in a buffer.

[0019] Optionally, in step S2, the hybrid cooperative driving system is split into multiple interconnected local hybrid cooperative driving systems, specifically including:

[0020] The mixed vehicle system in actual traffic flow is divided into multiple interconnected local hybrid cooperative driving systems. Each local hybrid cooperative driving system includes connected autonomous vehicles at the head and tail and human-driven vehicles in the middle.

[0021] Optionally, in step S3, the local hybrid cooperative driving system modeling specifically includes:

[0022] Step S31, establish the dynamic model of the connected autonomous vehicle, expressed by the following formula:

[0023]

[0024]

[0025]

[0026] In the formula, p i v i a i δ i and u i These are the position, velocity, acceleration, actuator delay, and control input of vehicle i, respectively.

[0027] Step S32: Establish a dynamic model of a human-driven vehicle, wherein the car-following behavior of the human-driven vehicle can be represented by the following third-order nonlinear dynamic model:

[0028]

[0029]

[0030]

[0031] In the formula, h i =p i-1 -p i Indicates the current following distance of vehicle i; α i and β i These represent the driver's response gain to following distance and the speed difference between their own vehicle and the vehicle in front, respectively, g(h) i () is the driver's desired speed related to following distance, and its specific form is as follows:

[0032]

[0033] In the formula, h s h g and v max These represent the minimum tracking distance, maximum tracking distance, and maximum speed, respectively; h is defined as follows. * v *Let v be the desired distance and desired speed that each vehicle in the hybrid driving system needs to achieve, and let v be the relationship between them. * =g(h * Define the state variable as x. i =[Δh i Δv i a i ] T , where Δh i =h i -h * Δv i =v i -v * Then the dynamics model of the human-driven vehicle was set at the equilibrium point (h). * v * Linearization at ) yields the following state-space model:

[0034]

[0035] in:

[0036] D1 = 0,

[0037] In the formula, g′(h * ) is g(h i ) in h i =h * The derivative at point;

[0038] Step S33: Establish a local hybrid cooperative driving system model. Based on the above-mentioned connected autonomous driving vehicle dynamics model and human-driven vehicle dynamics model, define the lumped state variable as... The control input is u = u n The dynamic model of the local hybrid cooperative driving system can be obtained as follows:

[0039]

[0040] in:

[0041] B i =0, C i =0, i∈{1,...,n-1}

[0042]

[0043] Optionally, in step S4, a lumped tracking error dynamic model is established for the design of the model predictive controller, specifically including:

[0044] Define the single-vehicle tracking error as e i =xi -[0, 0, a0] T The lumped tracking error is The dynamic model of the lumped tracking error can be obtained as follows:

[0045]

[0046] In the formula, d0=Ea0+Fu0;

[0047] Optionally, in step S5, the delay compensation strategy adopts a constant speed estimation strategy, that is, assuming that the vehicle control input and acceleration are 0 during the time period to be compensated, and the latest state in the buffer is brought into the vehicle dynamics model for calculation.

[0048] Optionally, in step S6, a model predictive controller is established in the control layer, specifically including:

[0049] The cost function is designed as follows Where T is the prediction time domain, and s∈[t] k+1 , t k+1 +T],e p (s|t k ) for t k The lumped tracking error at time s, predicted at time s, is calculated by inputting the multi-ahead vehicle information and the vehicle's measurement information after time delay compensation into the lumped tracking error dynamic model. p (s|t k ) for t k The control input at time s predicted by time s, e p (t k +T|t k ) for t k The lumped tracking error of the terminal is predicted at all times, where Q, R, and P are weight matrices; the control input saturation constraint is designed as follows: in, The set of feasible control inputs; the collision-free safety constraint is e. p (t k +T|t k )∈E, where E is the set of permissible safety errors; the stability constraint is Where 0 < γ < 1 and ε > 0 are design parameters.

[0050] This application also provides a hybrid cooperative driving control system that takes into account communication latency, including a positioning unit installed on the vehicle for acquiring the current vehicle's position, speed, and acceleration information, and a vehicle-to-vehicle communication unit for enabling communication between vehicles, and a control execution unit for performing the method described therein, wherein the control execution unit adjusts the vehicle's acceleration according to control commands to maintain the desired speed and spacing.

[0051] Optionally, it also includes a CAN bus for communicating with the positioning unit, the workshop communication unit, and the control execution unit.

[0052] This application addresses the manned / unmanned hybrid cooperative driving system affected by time-varying communication delays. It designs a two-layer model predictive control method that includes a delay compensation layer and a control layer. The controller design considers the time delay heterogeneity of vehicle actuators, control input saturation constraints, and collision-free safety constraints, thereby ensuring the stability of the hybrid cooperative driving system under the influence of time-varying communication delays, effectively suppressing traffic wave oscillations and reducing fuel consumption, and achieving tracking performance and collision-free safe operation of the manned / unmanned hybrid cooperative driving system under adverse communication conditions.

[0053] This application addresses the manned / unmanned hybrid cooperative driving system affected by time-varying communication delays by designing a two-layer control strategy that includes delay compensation and a control layer. This strategy can achieve closed-loop stability of the hybrid cooperative driving system under the influence of time-varying communication delays, ensuring the tracking performance and safety of the manned / unmanned hybrid cooperative driving system under adverse communication conditions. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0055] Figure 1 A flowchart of a hybrid cooperative driving control method considering communication latency provided in an embodiment of this application;

[0056] Figure 2 A schematic diagram of a hybrid cooperative driving control system considering communication latency provided in an embodiment of this application;

[0057] Figure 3 This is an overall framework diagram of a hybrid cooperative driving control system that takes into account communication latency, provided for an embodiment of this application.

[0058] In the diagram, 1 is the positioning unit; 2 is the workshop communication unit; 3 is the CAN bus; and 4 is the control execution unit. Detailed Implementation

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0061] Please see Figure 1 and 2 As shown in the figure, this application provides a hybrid cooperative driving control method that takes into account communication latency, including the following steps:

[0062] Step S1: Establish a multi-vehicle following communication structure;

[0063] Step S2: The hybrid cooperative driving system is split into multiple interconnected local hybrid cooperative driving systems;

[0064] Step S3: Modeling of the local hybrid cooperative driving system;

[0065] Step S4: Establish a lumped tracking error dynamic model for the design of the model predictive controller;

[0066] Step S5: Establish a delay compensation layer and calculate the multi-vehicle information after delay compensation;

[0067] Step S6: Establish a model predictive controller in the control layer;

[0068] Step S7: The connected autonomous vehicle N applies the first control sequence generated by the model prediction controller to the vehicle control.

[0069] Step 8: Repeat steps S5 to S7 until the hybrid cooperative driving mission ends.

[0070] In step S1, a multi-vehicle following communication structure is established, specifically including:

[0071] Each connected autonomous vehicle sends its future trajectory information to vehicles behind it via vehicle-to-vehicle communication, while each human-driven vehicle sends its current status information to vehicles behind it. At the same time, the connected autonomous vehicle receives information from the nearest connected autonomous vehicle ahead, as well as information from all human-driven vehicles between the two connected autonomous vehicles, and stores the received information in a buffer.

[0072] In step S2, during actual operation, connected autonomous vehicles and human-driven vehicles in the hybrid cooperative driving system are randomly mixed. To facilitate controller deployment, the mixed vehicle system in the actual traffic flow is divided into multiple interconnected local hybrid cooperative driving systems. Each local hybrid cooperative driving system includes connected autonomous vehicles located at the head and tail, and human-driven vehicles in the middle.

[0073] It should be noted that the interrelationship of the partial hybrid cooperative driving systems is reflected in the fact that the rear connected autonomous vehicle of the front partial hybrid cooperative driving system is also the head connected autonomous vehicle of the rear partial hybrid cooperative driving system.

[0074] Optionally, in step S3, the vehicles in the local hybrid cooperative driving system are numbered from 0 to N from front to back, where the 0th vehicle is the head connected autonomous driving vehicle, the Nth vehicle is the tail connected autonomous driving vehicle, and the remaining vehicles are human-driven vehicles.

[0075] Modeling a partial hybrid cooperative driving system specifically includes the following steps:

[0076] Step S31, establish the dynamic model of the connected autonomous vehicle, expressed by the following formula:

[0077]

[0078]

[0079]

[0080] In the formula, p i v i a i δ i and u i These are the position, velocity, acceleration, actuator delay, and control input of vehicle i, respectively.

[0081] As can be seen from step S31 above, the actuator time delay of different connected autonomous vehicles can be different, which is more in line with the actual situation.

[0082] Step S32: Establish a dynamic model of a human-driven vehicle, wherein the car-following behavior of the human-driven vehicle can be represented by the following third-order nonlinear dynamic model:

[0083]

[0084]

[0085]

[0086] In the formula, h i =p i-1 -p i Indicates the current following distance of vehicle i; α i and β i These represent the driver's response gain to following distance and the speed difference between their own vehicle and the vehicle in front, respectively, g(h) i () is the driver's desired speed related to following distance, and its specific form is as follows:

[0087]

[0088] In the formula, h s h g and v max These represent the minimum tracking distance, maximum tracking distance, and maximum speed, respectively; h is defined as follows. * v * Let v be the desired distance and desired speed that each vehicle in the hybrid driving system needs to achieve, and let v be the relationship between them. * =g(h * Define the state variable as x. i =[Δh i Δv i a i ] T , where Δh i =h i -h * Δv i =v i -v * Then the dynamics model of the human-driven vehicle was set at the equilibrium point (h). * v * Linearization at ) yields the following state-space model:

[0089]

[0090] in:

[0091] D1 = 0,

[0092] In the formula, g′(h * ) is g(h i ) in h i =h * The derivative at point;

[0093] Step S33: Establish a local hybrid cooperative driving system model. Based on the above-mentioned connected autonomous driving vehicle dynamics model and human-driven vehicle dynamics model, define the lumped state variable as... The control input is u = u n The dynamic model of the local hybrid cooperative driving system can be obtained as follows:

[0094]

[0095] in:

[0096] B i =0, C i =0, i∈{1,...,n-1}

[0097]

[0098] Optionally, in step S4, a lumped tracking error dynamic model is established for the design of the model predictive controller, specifically including:

[0099] Define the single-vehicle tracking error as e i =x i -[0, 0, a0] T The lumped tracking error is The dynamic model of the lumped tracking error can be obtained as follows:

[0100]

[0101] In the formula, d0=Ea0+Fu0;

[0102] In step S5, for the connected autonomous vehicle N, the information received from the vehicle ahead via vehicle-to-vehicle communication has a communication delay, while its own measurement information does not, resulting in inconsistent timestamps between the two types of information. In the delay compensation layer, prediction is used to complete the information from multiple preceding vehicles, aligning the timestamps of the two types of information and thus mitigating the adverse effects of communication delay.

[0103] In one specific implementation, the delay compensation strategy adopts a constant speed estimation strategy, that is, assuming that the vehicle control input and acceleration are 0 during the time period to be compensated, the latest state in the buffer is brought into the vehicle dynamics model for calculation.

[0104] In step S6, a model predictive controller is established in the control layer, specifically including:

[0105] The cost function is designed as follows Where T is the prediction time domain, and s∈[t]k+1 , t k+1 +T],e p (s|t k ) for t k The lumped tracking error at time s, predicted at time s, is calculated by inputting the multi-ahead vehicle information and the vehicle's measurement information after time delay compensation into the lumped tracking error dynamic model. p (s|t k ) for t k The control input at time s predicted by time s, e p (t k +T|t k ) for t k The lumped tracking error of the terminal is predicted at all times, where Q, R, and P are weight matrices; the control input saturation constraint is designed as follows: in, The set of feasible control inputs; the collision-free safety constraint is e. p (t k +T|t k )∈E, where E is the set of permissible safety errors; the stability constraint is Where 0 < γ < 1l and ε > 0 are design parameters.

[0106] Combined Figure 3 As shown, this application also provides a hybrid cooperative driving control system that considers communication latency, including a positioning unit 1 installed on the vehicle for acquiring the current vehicle's position, speed, and acceleration information, a vehicle-to-vehicle communication unit 2 for enabling communication between vehicles, and a control execution unit 4 for performing the above-described method. The control execution unit 4 adjusts the vehicle's acceleration according to control commands to maintain the desired speed and spacing. The system also includes a CAN bus 3 for communicationally connecting the positioning unit 1, the vehicle-to-vehicle communication unit 2, and the control execution unit 4.

[0107] This application addresses the manned / unmanned hybrid cooperative driving system affected by time-varying communication delays. It designs a two-layer model predictive control method that includes a delay compensation layer and a control layer. The controller design considers the time delay heterogeneity of vehicle actuators, control input saturation constraints, and collision-free safety constraints, thereby ensuring the stability of the hybrid cooperative driving system under the influence of time-varying communication delays, effectively suppressing traffic wave oscillations and reducing fuel consumption, and achieving tracking performance and collision-free safe operation of the manned / unmanned hybrid cooperative driving system under adverse communication conditions.

[0108] This application addresses the manned / unmanned hybrid cooperative driving system affected by time-varying communication delays by designing a two-layer control strategy that includes delay compensation and a control layer. This strategy can achieve closed-loop stability of the hybrid cooperative driving system under the influence of time-varying communication delays, ensuring the tracking performance and safety of the manned / unmanned hybrid cooperative driving system under adverse communication conditions.

[0109] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0110] Furthermore, it should be noted that the scope of the methods and systems in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0111] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A hybrid cooperative driving control method considering communication latency, characterized in that, Includes the following steps: Step S1: Establish a multi-vehicle following communication structure; Step S2: The hybrid cooperative driving system is split into multiple interconnected local hybrid cooperative driving systems; Step S3, local hybrid cooperative driving system modeling; specifically including: Step S31, establish the dynamic model of the connected autonomous vehicle, expressed by the following formula: In the formula, and vehicles Position, velocity, acceleration, actuator delay, and control input; Step S32: Establish a dynamic model of a human-driven vehicle, wherein the car-following behavior of the human-driven vehicle can be represented by the following third-order nonlinear dynamic model: In the formula, Indicates vehicle Current following distance; and These represent the driver's response gain to following distance and the speed difference between their vehicle and the vehicle in front, respectively. This is the driver's desired speed related to following distance, and its specific form is as follows: In the formula, , and These are the minimum tracking distance, maximum tracking distance, and maximum speed, respectively; (Definition) The desired distance and desired speed to be achieved by each vehicle in the hybrid driving system are related by the following condition: Define the state variable as ,in Then the dynamics model of the human-driven vehicle was placed at the equilibrium point ( Linearization at ) yields the following state-space model: in: , , In the formula, , for exist The derivative at point; Step S33: Establish a local hybrid cooperative driving system model. Based on the above-mentioned connected autonomous driving vehicle dynamics model and human-driven vehicle dynamics model, define the lumped state variable as... The control input is The dynamic model of the local hybrid cooperative driving system can be obtained as follows: in: ; Step S4, establish a lumped tracking error dynamic model for model predictive controller design; specifically including: Define single-vehicle tracking error as The lumped tracking error is The dynamic model of the lumped tracking error can be obtained as follows: In the formula, ; , , , , , , ; Step S5: Establish a delay compensation layer and calculate the multi-vehicle information after delay compensation; Step S6: Establish a model predictive controller in the control layer; Step S7: The connected autonomous vehicle applies the first control sequence generated by the model prediction controller to the vehicle control. Step S8: Repeat steps S5 to S7 until the hybrid cooperative driving mission ends.

2. The method according to claim 1, characterized in that, In step S1, a multi-vehicle following communication structure is established, specifically including: Each connected autonomous vehicle sends its future trajectory information to vehicles behind it via vehicle-to-vehicle communication, while each human-driven vehicle sends its current status information to vehicles behind it. At the same time, the connected autonomous vehicle receives information from the nearest connected autonomous vehicle ahead, as well as information from all human-driven vehicles between the two connected autonomous vehicles, and stores the received information in a buffer.

3. The method according to claim 2, characterized in that, In step S2, the hybrid cooperative driving system is split into multiple interconnected local hybrid cooperative driving systems, specifically including: The mixed vehicle system in actual traffic flow is divided into multiple interconnected local hybrid cooperative driving systems. Each local hybrid cooperative driving system includes connected autonomous vehicles at the head and tail and human-driven vehicles in the middle.

4. The method according to claim 1, characterized in that, In step S5, the time delay compensation adopts a constant speed estimation strategy, that is, assuming that the vehicle control input and acceleration are 0 during the time period to be compensated, the latest state in the buffer is brought into the vehicle dynamics model for calculation.

5. The method according to claim 1, characterized in that, In step S6, a model predictive controller is established in the control layer, specifically including: The cost function is designed as follows ,in To predict the time domain, , for Time prediction The lumped tracking error at any given time is calculated by inputting the time-delay-compensated information from multiple preceding vehicles and the vehicle's own measurement information into the lumped tracking error dynamics model. for Time prediction Time-based control input, for Predict the lumped tracking error of the terminal at all times. The weight matrix is ​​used; the control input saturation constraint is designed as follows: ,in, The set of feasible control inputs; the collision-free safety constraint is ,in The set of allowable safety errors; the stability constraint is... ,in, , These are design parameters.

6. A hybrid cooperative driving control system considering communication latency, comprising a positioning unit installed on a vehicle for acquiring the current vehicle's position, speed, and acceleration information, and a vehicle-to-vehicle communication unit for enabling communication between vehicles, characterized in that, It also includes a control execution unit for performing the method of any one of claims 1-5, the control execution unit adjusting the vehicle's acceleration according to a control command to maintain a desired speed and spacing.

7. The system according to claim 6, characterized in that, It also includes a CAN bus for communication connection of the positioning unit, the workshop communication unit and the control execution unit.

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