A state feedback control method and device for an automatic driving car following system

Abstract

The application provides a state feedback control method and device for an automatic driving following system, and belongs to the technical field of vehicle automatic driving control; the method comprises the following steps: equivalent communication gain is calculated through communication connection, and a following system motion equation is determined through communication-kinetics joint modeling; linearization processing is performed on a steady state working point of the following system, and a following system state space dynamics model taking a vehicle speed deviation and a vehicle distance deviation as state variables is constructed; the equivalent communication gain is taken as an adjusting input, and a vehicle speed feedback coefficient and a vehicle distance feedback coefficient are adjusted based on an adaptive double-state feedback control strategy; when an asymptotic stability condition and a string stability condition are met, an acceleration control amount of a target vehicle is output according to the adaptive double-state feedback control strategy; and the application can realize dynamic matching of communication quality and feedback gain, and improve the stability, response speed and anti-interference ability of the following system in a time-varying communication environment.

Description

Technical Field

[0001] This invention belongs to the field of vehicle autonomous driving control technology, and particularly relates to a state feedback control method and device for autonomous driving following systems. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Current autonomous driving car-following control systems are mostly based on classical control theory, employing compensation strategies such as proportional-derivative, speed feedback, and feedforward control. They only optimize car-following performance at the level of the system's input-output closed-loop transfer function, without deeply analyzing the coupling relationships between internal state variables (vehicle speed and distance between vehicles). The mainstream optimal speed car-following model (OVM) determines the desired speed solely based on the vehicle-to-vehicle headway, failing to incorporate the two core state variables—vehicle speed deviation and distance deviation—into the feedback control closed loop, resulting in a single control dimension.

[0004] In response, some studies have introduced state-space methods to construct two-state feedback control, which adjusts the dynamic characteristics of the system by fixing the feedback coefficients, thus improving car-following stability to some extent. However, the following technical shortcomings still exist: (1) The state feedback under fixed coefficients cannot adapt to the time-varying characteristics of connected communication, resulting in poor system robustness under communication quality fluctuations and problems such as vehicle speed oscillation and vehicle spacing imbalance. At the same time, the communication modeling under the existing autonomous driving follow-up control method is only used for weighted information of the preceding vehicle, which is loosely coupled with state feedback control and cannot achieve coordinated optimization of communication and control.

[0005] (2) Existing technologies lack system stability criteria for communication uncertainties and cannot quantify the feasible domain of feedback coefficients under different communication qualities, which leads to a lack of effective reference data for parameter debugging in practical engineering applications. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention provides a state feedback control method and device for autonomous driving car-following systems, which can achieve dynamic matching of communication quality and feedback gain, and improve the stability, response speed and anti-interference capability of the car-following system under time-varying communication environment.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions: The first aspect of this invention provides a state feedback control method for autonomous driving following systems.

[0008] A state feedback control method for an autonomous driving car-following system includes: The V2V communication quality parameters between the target vehicle and the vehicle in front are obtained, the equivalent communication gain is calculated through the communication connection probability, and the motion equation of the car-following system is determined through joint communication-dynamics modeling. Based on the motion equations and equivalent communication gain of the car-following system, linearization is performed at the steady-state operating point of the car-following system to construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables. Using the equivalent communication gain as the adjustment input, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are dynamically adjusted based on the adaptive dual-state feedback control strategy. Based on the motion equations of the car-following system after parameter adjustment, the asymptotic stability conditions and series stability conditions within the effective communication interval are determined. When the asymptotic stability conditions and series stability conditions are met, the acceleration control quantity of the target vehicle is output according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

[0009] Furthermore, the equivalent communication gain is calculated through the communication connection probability, including: calculating the signal-to-noise ratio at the receiver based on the transmission power of the vehicle communication module, the environmental compensation factor, the noise interference power, and the path loss index, and using the Logistic function to map the signal-to-noise ratio into the point-to-point connection probability.

[0010] Furthermore, when the point-to-point connection probability is not less than a preset effective communication threshold, the communication link is determined to be valid; subsequently, the point-to-point connection probability of the valid link is normalized and weighted to obtain the communication weight between the target vehicle and the vehicle in front; based on the communication weight, the motion information of the vehicle in front is fused to obtain the equivalent communication gain.

[0011] Furthermore, a state-space dynamic model of the car-following system is constructed with vehicle speed deviation and vehicle spacing deviation as state variables, including: performing a Taylor expansion of the optimal speed function at steady-state frontage and steady-state speed to obtain the derivative of the optimal speed function; taking the difference between the actual speed of the target vehicle and the steady-state optimal speed as the vehicle speed deviation, and the difference between the actual frontage of the target vehicle and the steady-state safe frontage as the vehicle spacing deviation; and establishing a state-space equation with the vehicle speed deviation and the vehicle spacing deviation as state variables and the target vehicle acceleration control quantity as input.

[0012] Furthermore, the dynamic adjustment of the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient includes: A first communication quality threshold and a second communication quality threshold are preset, wherein the second communication quality threshold is less than the first communication quality threshold; When the equivalent communication gain is not less than the first communication quality threshold, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are increased according to the first gain adjustment rate. When the equivalent communication gain is not greater than the second communication quality threshold, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are reduced according to the second gain adjustment rate. When the equivalent communication gain is between the first communication quality threshold and the second communication quality threshold, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are determined according to a piecewise linear relationship.

[0013] Furthermore, the asymptotic stability condition is determined in the following manner: The characteristic polynomial of the closed-loop transfer function is obtained based on the car-following state-space dynamics model; the coefficient inequality of the characteristic polynomial is established using the Routh-Hurwitz criterion; by utilizing the characteristic that the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient monotonically increase with the equivalent communication gain, the coefficient inequality is transformed into a constraint at the endpoint of the effective communication interval, thus obtaining the asymptotic stability condition for the entire communication interval.

[0014] Furthermore, the series stability condition is determined as follows: based on the small gain theorem, the infinity norm of the car-following closed-loop transfer function is made less than 1; based on the infinity norm being less than 1, the series stability condition is obtained such that the speed disturbance of the preceding vehicle is not amplified vehicle by vehicle when it is transmitted backward along the convoy.

[0015] A second aspect of the present invention provides a state feedback control device for an autonomous driving following system.

[0016] A state feedback control device for an autonomous driving following system includes: The communication-dynamics joint modeling module is configured to: acquire the V2V communication quality parameters between the target vehicle and the vehicle in front, calculate the equivalent communication gain through the communication connection probability, and determine the motion equation of the car-following system through communication-dynamics joint modeling. The car-following system state-space modeling module is configured to: perform linearization processing at the steady-state operating point of the car-following system based on the motion equations and equivalent communication gain of the car-following system, and construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables; The coefficient adjustment module is configured to: use the equivalent communication gain as the adjustment input, and dynamically adjust the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient based on the adaptive dual-state feedback control strategy; The car-following control module is configured to: determine the asymptotic stability conditions and series stability conditions within the effective communication range based on the motion equations of the car-following system after parameter adjustment; and when the asymptotic stability conditions and series stability conditions are met, output the acceleration control quantity of the target vehicle according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

[0017] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a state feedback control method for an autonomous driving following system as described in the first aspect of the present invention.

[0018] The fourth aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a state feedback control method for an autonomous driving following system as described in the first aspect of the present invention.

[0019] The above one or more technical solutions have the following beneficial effects: This invention calculates the equivalent communication gain, which characterizes the overall reliability of the preceding vehicle's information, based on communication connection probability. Through joint communication-dynamics modeling, communication reliability is directly integrated into the motion equations and state-space dynamics model of the car-following system. Subsequently, the equivalent communication gain is used as the adjustment input for an adaptive dual-state feedback control strategy, dynamically adjusting the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient. This ensures that communication quality is no longer an independent weighting factor of the preceding vehicle's information but directly participates in the feedback control law, forming a closed-loop synergy of "communication perception - feedback adjustment - stable control." Therefore, when communication quality is good, the feedback gain can be increased, improving the system's response speed and disturbance suppression capability; when communication quality is poor, the feedback gain can be reduced, mitigating the impact of information uncertainty on the system and avoiding vehicle speed oscillations and vehicle spacing imbalances caused by gain mismatch due to communication fluctuations under fixed feedback coefficients. This significantly improves the robustness of the autonomous driving car-following system in time-varying communication environments.

[0020] This invention, based on the motion equations of the car-following system after parameter adjustment, determines the asymptotic stability conditions and series stability conditions within the effective communication range. The asymptotic and series stability conditions jointly define the feasible region or stability parameter constraint range of the feedback coefficients within the effective communication range. This quantifies the acceptable range of feedback coefficients under different communication qualities, providing a clear theoretical basis for tuning engineering parameters such as minimum feedback gain, gain adjustment rate, and sensor sensitivity coefficient. It reduces the blindness of parameter tuning in practical engineering and ensures that the car-following system can simultaneously maintain asymptotic stability for individual vehicles and series stability of the convoy traffic flow even when communication quality fluctuates arbitrarily within the effective range.

[0021] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a flowchart of a state feedback control method for an autonomous driving following system according to Embodiment 1 of the present invention.

[0024] Figure 2 This is a schematic diagram of the queue following system in Embodiment 1 of the present invention.

[0025] Figure 3 This is a schematic diagram of the vehicle communication model in Embodiment 1 of the present invention.

[0026] Figure 4 This is a state variable diagram for an existing intelligent connected car-following system.

[0027] Figure 5 This is a state variable diagram of the optimized intelligent connected car-following system in Embodiment 1 of the present invention. Detailed Implementation

[0028] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0030] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0031] Example 1 This embodiment discloses a state feedback control method for autonomous driving following systems.

[0032] like Figure 1 As shown, a state feedback control method for an autonomous driving following system includes: Step S1: Obtain the V2V communication quality parameters between the target vehicle and the vehicle in front, calculate the equivalent communication gain through the communication connection probability, and determine the motion equation of the car-following system through joint communication-dynamics modeling. Step S2: Based on the motion equations and equivalent communication gain of the car-following system, linearization is performed at the steady-state operating point of the car-following system to construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables. Step S3: Using the equivalent communication gain as the adjustment input, dynamically adjust the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient based on the adaptive dual-state feedback control strategy. Step S4: Based on the motion equations of the car-following system after parameter adjustment, determine the asymptotic stability conditions and series stability conditions within the effective communication range. When the asymptotic stability conditions and series stability conditions are met, output the acceleration control quantity of the target vehicle according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

[0033] Based on the above process, this invention can achieve dynamic matching of communication quality and feedback gain, improving the stability, response speed, and anti-interference capability of the carousel system under time-varying communication environments. To facilitate understanding of the technical solution of this invention, the specific implementation methods of this invention will be further explained and described below.

[0034] In step S1, the V2V communication quality parameters between the target vehicle and the vehicle in front are obtained, the equivalent communication gain is calculated through the communication connection probability, and the motion equation of the car-following system is determined through communication-dynamic joint modeling.

[0035] Assuming all vehicles are in Figure 2 Cars follow each other on the infinitely long single lane shown. Indicates the first n The car is Location at any given moment , For the total number of vehicles, the first The vehicle is the lead vehicle.

[0036] In connected autonomous driving scenarios, vehicles within a queue need to communicate with each other. This invention uses a probabilistic method to model and analyze inter-vehicle communication in a connected autonomous driving following system. If two vehicles exchange information during communication, a valid communication connection is determined, and the probability of this communication connection event is defined as the connection probability. Assuming the vehicle queue is homogeneous and all vehicles have completely identical communication capabilities, the communication model is as follows: Figure 3 As shown, the communication radius is Vehicles in the queuing system and vehicles The communication distance between them is At this point, the signal-to-noise ratio at the receiving end decreases according to a power-law relationship with the communication distance: (1) in, Indicates distance as The signal-to-noise ratio of the vehicle-mounted receiver; Transmit power to the vehicle communication module. As an environmental compensation factor, This refers to noise interference power. The path loss index ranges from 2.7 to 3.5 in highway scenarios and from 3.5 to 4.5 in urban road scenarios.

[0037] Based on this, a sigmoid (logistic) function is used to characterize the probability of communication connections. This reflects the nonlinear mapping relationship between signal-to-noise ratio and communication success rate. (2) in, The signal-to-noise ratio threshold for successful communication. This is the channel fading shape factor.

[0038] Set effective communication threshold When the probability of a point-to-point connection (i.e., the probability of a communication connection between two points) is greater than or equal to the effective communication threshold, the communication link is deemed valid, and the communication coefficient is defined as follows: (3) in, Indicates vehicle and vehicles Communication coefficients between them; For the Heaviside function. When hour, This indicates the vehicle The information can be successfully transmitted to the vehicle. n Conversely, when hour, If the error message is 0, it means that the information could not be transmitted successfully.

[0039] Definition of the first The set of vehicles that can communicate is Based on the point-to-point connection probability, the effective links are normalized and weighted to obtain the first... Vehicle and the Vehicle communication weighting coefficient: (4) in, Indicates the first Vehicle and the The communication weight coefficient of the vehicle.

[0040] The communication weight coefficient satisfies the normalization characteristic (the sum of all weights is 1), and its physical meaning is: the better the communication quality of the preceding vehicle, the higher the proportion of its motion information in the car-following control calculation; vehicles with substandard communication quality are directly excluded and do not participate in the control calculation.

[0041] Next, the intelligent connected vehicle platooning system in a V2V communication environment is modeled using traffic flow theory. Based on the existing optimal speed model, this invention combines inter-vehicle communication and the vehicle's autonomous driving function, and the motion equations of the constructed intelligent connected car-following system are shown below: (5) (6) in, This represents the sensitivity coefficient of sensors in autonomous vehicles. and Representing the first The car is Acceleration and velocity at any moment; vehicle Indicates that the vehicle queue contains vehicles other than vehicles. Any car other than that; Indicates vehicle and vehicles At any moment The average vehicle spacing, Indicates the first The optimal speed equation for the vehicle. For maximum speed, To maintain a safe distance; This represents the hyperbolic tangent function.

[0042] In step S2, based on the motion equations of the car-following system and the equivalent communication gain, linearization is performed at the steady-state operating point of the car-following system to construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables.

[0043] Assuming all vehicles maintain a constant front-end spacing If traffic continues forward, the traffic flow will reach the following steady state: (7) in, and They represent the first The speed and headway of the vehicle when it is in a steady state are respectively... and .

[0044] By linearizing equation (6) and performing a Taylor expansion on equation (6) based on the steady state of equation (7), the linearized form of the optimal velocity function can be obtained: (8) make To obtain the first derivative of the optimal speed function at the steady-state headway, an equivalent communication gain is introduced to scale the communication-dependent deviation term while keeping the steady-state term unchanged. Therefore, the equivalent expected speed is expressed as: (9) in, This represents the first derivative of the optimal speed function at the steady-state headway; The equivalent communication gain coefficient reflects the overall reliability of the vehicle (i.e., the target vehicle) in acquiring information from the vehicle ahead, and its value ranges from [value range missing]. .

[0045] Substituting equation (9) into equation (5), we can obtain the linearized form of the queue following system in this V2V communication environment: (10) (11) in, and These represent the distance error between vehicles and the speed error, respectively.

[0046] Based on this, the transfer function of the car-following system can be obtained by performing a Laplace transform on equation (10). : (12) in, and They represent and The Laplace transform form.

[0047] Furthermore, the dynamic equations of the car-following system can be expressed as: (13) The above equation can be rewritten in matrix form as shown below: (14) in, Represents the state variables of the queue system. . and These represent the system's input and output variables, respectively. Therefore, these three variables can be used to describe the dynamic characteristics of the traffic flow system: (15) In step S3, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are dynamically adjusted based on the adaptive dual-state feedback control strategy, using the equivalent communication gain as the adjustment input.

[0048] The state variable diagram of the method of this invention differs from the state variable diagram of a car-following system based on classical control theory in the prior art (e.g., Figure 4 As shown in the figure, the difference is that the state variable diagram under the method of the present invention can reflect the functional relationship between variables in the car-following system.

[0049] like Figure 5 As shown, under the method of the present invention, if the state variable and The differential remains unchanged, that is and When the value equals zero, the system is stable; among which, Indicates system input, This represents the system output. In classical control theory, feedback is typically used to stabilize a system, and the essence of feedback control is to utilize deviations for control. Among these, the state variable... and These are the deviations, i.e. and Therefore, based on the deviation of state variables, a state feedback control strategy can be used to control the stability of the system. In the optimal velocity model, the internal state... Differential Typically used to reflect the car-following status of a system, state feedback can be used here to feed back the two state variables. .

[0050] Furthermore, considering the time-varying characteristics of V2V communication, an adaptive dual-state feedback control strategy is designed, using the real-time communication gain as the adjustment input for the feedback coefficient to dynamically adjust the vehicle speed feedback coefficient. Feedback coefficient of vehicle distance To achieve dynamic matching between communication quality and control gain, based on the above theory, a new state equation can be obtained: (16) Furthermore, a new coefficient matrix can be obtained: (17) Two adaptive feedback coefficients (i.e., vehicle speed feedback coefficients) Feedback coefficient of vehicle distance A piecewise linear adjustment rule is adopted. When the communication quality is good ( When the communication quality is poor, increase the feedback gain to improve the system response speed and disturbance suppression capability; when the communication quality is poor ( When this happens, reducing the feedback gain mitigates the impact of information uncertainty on the system and ensures basic stability. The specific expression is as follows: (18) in, , These are the state feedback coefficients. and The minimum feedback gain, , They are respectively and Gain adjustment rate.

[0051] In step S4, the asymptotic stability condition and series stability condition within the effective communication range are determined based on the motion equation of the car-following system after parameter adjustment. When the asymptotic stability condition and series stability condition are met, the acceleration control quantity of the target vehicle is output according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

[0052] Based on the coefficient matrix of the improved car-following system, the closed-loop transfer function of the improved system can be obtained. : (19) Correspondingly, the characteristic polynomial of the transfer function of the improved car-following system is expressed as: (20) If the optimized transfer function By performing an inverse Laplace transform, we can obtain the optimized dynamic model of the intelligent connected car-following system: (twenty one) After obtaining the optimized dynamic model of the intelligent connected car-following system, a system stability analysis is performed under time-varying communication conditions, specifically: V2V communication is affected by factors such as vehicle travel distance, environmental noise, and channel fading. The communication gain will... Internal dynamic fluctuations mean that analyzing only the fixed-point stability under a single communication quality condition is insufficient to guarantee reliable operation in real-world scenarios. Therefore, this invention derives a robust stability criterion across the entire effective communication range, ensuring that the car-following system can simultaneously maintain asymptotic stability of individual vehicles and traffic flow stability of the convoy when communication quality fluctuates arbitrarily within the effective range. This theoretically quantifies the system's resistance to communication disturbances.

[0053] First, there is the asymptotic stability condition based on the Routh criterion. The physical meaning of asymptotic stability is that after the system is disturbed and deviates from the steady state, the internal state (vehicle speed deviation, vehicle spacing deviation) can converge back to the steady state value over time, without divergent oscillations or continuous constant amplitude oscillations.

[0054] In specific implementation, according to the Routh-Hurwitz stability condition, if the characteristic polynomial If the following inequality is satisfied, it indicates that the control system is stable: (twenty two) Therefore, the asymptotic stability condition can be obtained from the above inequality as follows: (twenty three) Equation (22) above must apply to all If the condition holds true for all, perform extreme value analysis for each of the two conditions: 1) Regarding the first condition .

[0055] Adaptive feedback coefficient Follow Monotonically increasing, therefore Also follow Monotonically increasing, its minimum value appears At that point, the minimum value is Due to the sensor sensitivity coefficient Minimum feedback gain ,therefore Heng was established.

[0056] 2) Regarding the second condition .

[0057] Adaptive feedback coefficient Substituting, we get: (twenty four) The monotonicity of this function is determined by its coefficient. The decision, whether monotonically increasing or decreasing, is made on a closed interval. The minimum value appears at both endpoints. Therefore, as long as the inequality is satisfied at both endpoints, the constraint holds true for the entire interval, i.e., the constraint for asymptotic stability is: (25) In summary, the necessary and sufficient condition for the system to maintain asymptotic stability across the entire communication range is the two sets of endpoint constraints mentioned above. This condition ensures that even if the communication quality fluctuates drastically within the effective range, the deviations in vehicle speed and spacing will always converge back to a steady state, without instability or divergence.

[0058] Secondly, there is the string stability condition based on the small gain theorem. The physical meaning of string stability is that when the speed disturbance of the preceding vehicle is propagated along the convoy, the disturbance amplitude will not amplify with each subsequent vehicle. If string stability is not satisfied, the disturbance of the preceding vehicle will amplify along the convoy.

[0059] This invention uses the small gain theorem to analyze the stability condition of a train. For a car-following platoon system, if the closed-loop transfer function of the vehicle... If the infinity norm is less than 1, then the disturbance will not be amplified during transmission, and the convoy satisfies string stability, i.e.: In other words: (26) Therefore, we can conclude that: (27) In summary, by simultaneously applying the asymptotic stability condition and the series stability condition, we can obtain the parameter constraint interval for system stability within the effective communication interval: (28) Based on the above method, compared with the prior art, the present invention can achieve the following significant improvements: a) Significantly improved stability: state feedback can greatly reduce the oscillation amplitude of vehicle speed and distance, and disturbances converge quickly under closed boundaries; b) Enhanced anti-interference capability: the system responds faster and recovers more smoothly in the face of sudden changes in the speed of the vehicle in front; c) Higher robustness: the stable range is clear, and the parameters are adapted to different road conditions and vehicle types; d) Adaptable to mixed traffic flow: it can effectively suppress the transmission of traffic flow disturbances and reduce the risk of congestion and rear-end collisions; e) Easy to implement in engineering: the state deviation can be calculated based on existing sensor data without the need for additional hardware.

[0060] Example 2 This embodiment discloses a state feedback control device for an autonomous driving following system.

[0061] A state feedback control device for an autonomous driving following system includes: The communication-dynamics joint modeling module is configured to: acquire the V2V communication quality parameters between the target vehicle and the vehicle in front, calculate the equivalent communication gain through the communication connection probability, and determine the motion equation of the car-following system through communication-dynamics joint modeling. The car-following system state-space modeling module is configured to: perform linearization processing at the steady-state operating point of the car-following system based on the motion equations and equivalent communication gain of the car-following system, and construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables; The coefficient adjustment module is configured to: use the equivalent communication gain as the adjustment input, and dynamically adjust the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient based on the adaptive dual-state feedback control strategy; The car-following control module is configured to: determine the asymptotic stability conditions and series stability conditions within the effective communication range based on the motion equations of the car-following system after parameter adjustment; and when the asymptotic stability conditions and series stability conditions are met, output the acceleration control quantity of the target vehicle according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

[0062] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0063] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a state feedback control method for an autonomous driving following system as described in Embodiment 1 of this disclosure.

[0064] Example 4 The purpose of this embodiment is to provide an electronic device.

[0065] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in a state feedback control method for an autonomous driving following system as described in Embodiment 1 of this disclosure.

[0066] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0067] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0068] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A state feedback control method for an autonomous driving following system, characterized in that, include: The V2V communication quality parameters between the target vehicle and the vehicle in front are obtained, the equivalent communication gain is calculated through the communication connection probability, and the motion equation of the car-following system is determined through joint communication-dynamics modeling. Based on the motion equations and equivalent communication gain of the car-following system, linearization is performed at the steady-state operating point of the car-following system to construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables. Using the equivalent communication gain as the adjustment input, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are dynamically adjusted based on the adaptive dual-state feedback control strategy. Based on the motion equations of the car-following system after parameter adjustment, the asymptotic stability conditions and series stability conditions within the effective communication interval are determined. When the asymptotic stability conditions and series stability conditions are met, the acceleration control quantity of the target vehicle is output according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

2. The state feedback control method for an autonomous driving following system as described in claim 1, characterized in that, The equivalent communication gain is calculated by the communication connection probability, including: calculating the signal-to-noise ratio at the receiver based on the transmission power of the vehicle communication module, the environmental compensation factor, the noise interference power and the path loss index, and using the Logistic function to map the signal-to-noise ratio to the point-to-point connection probability.

3. The state feedback control method for an autonomous driving following system as described in claim 2, characterized in that, When the point-to-point connection probability is not less than a preset effective communication threshold, the communication link is determined to be valid; then, the point-to-point connection probability of the valid link is normalized and weighted to obtain the communication weight between the target vehicle and the vehicle in front; based on the communication weight, the motion information of the vehicle in front is fused to obtain the equivalent communication gain.

4. The state feedback control method for an autonomous driving following system as described in claim 1, characterized in that, A state-space dynamic model of the car-following system is constructed with vehicle speed deviation and vehicle spacing deviation as state variables. This includes: performing a Taylor expansion of the optimal velocity function at steady-state frontage and steady-state speed to obtain the derivative of the optimal velocity function; taking the difference between the actual speed of the target vehicle and the steady-state optimal speed as the vehicle speed deviation, and the difference between the actual frontage of the target vehicle and the steady-state safe frontage as the vehicle spacing deviation; and establishing a state-space equation with the vehicle speed deviation and the vehicle spacing deviation as state variables and the target vehicle acceleration control quantity as input.

5. The state feedback control method for an autonomous driving following system as described in claim 1, characterized in that, The dynamic adjustment of the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient includes: A first communication quality threshold and a second communication quality threshold are preset, wherein the second communication quality threshold is less than the first communication quality threshold; When the equivalent communication gain is not less than the first communication quality threshold, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are increased according to the first gain adjustment rate. When the equivalent communication gain is not greater than the second communication quality threshold, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are reduced according to the second gain adjustment rate. When the equivalent communication gain is between the first communication quality threshold and the second communication quality threshold, the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient are determined according to a piecewise linear relationship.

6. The state feedback control method for an autonomous driving following system as described in claim 1, characterized in that, The asymptotic stability condition is determined in the following manner: The characteristic polynomial of the closed-loop transfer function is obtained based on the car-following state-space dynamics model; the coefficient inequality of the characteristic polynomial is established using the Routh-Hurwitz criterion; by utilizing the characteristic that the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient monotonically increase with the equivalent communication gain, the coefficient inequality is transformed into a constraint at the endpoint of the effective communication interval, thus obtaining the asymptotic stability condition for the entire communication interval.

7. The state feedback control method for an autonomous driving following system as described in claim 1, characterized in that, The series stability condition is determined as follows: based on the small gain theorem, the infinity norm of the car-following closed-loop transfer function is made less than 1; based on the infinity norm being less than 1, the series stability condition is obtained so that the speed disturbance of the preceding vehicle is not amplified vehicle by vehicle when it is transmitted backward along the convoy.

8. A state feedback control device for an autonomous driving following system, characterized in that, include: The communication-dynamics joint modeling module is configured to: acquire the V2V communication quality parameters between the target vehicle and the vehicle in front, calculate the equivalent communication gain through the communication connection probability, and determine the motion equation of the car-following system through communication-dynamics joint modeling. The car-following system state-space modeling module is configured to: perform linearization processing at the steady-state operating point of the car-following system based on the motion equations and equivalent communication gain of the car-following system, and construct a state-space dynamic model of the car-following system with vehicle speed deviation and vehicle spacing deviation as state variables; The coefficient adjustment module is configured to: use the equivalent communication gain as the adjustment input, and dynamically adjust the vehicle speed feedback coefficient and the vehicle spacing feedback coefficient based on the adaptive dual-state feedback control strategy; The car-following control module is configured to: determine the asymptotic stability conditions and series stability conditions within the effective communication range based on the motion equations of the car-following system after parameter adjustment; and when the asymptotic stability conditions and series stability conditions are met, output the acceleration control quantity of the target vehicle according to the adaptive dual-state feedback control strategy to realize autonomous driving car-following control.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the state feedback control method for an autonomous driving following system as described in any one of claims 1-7.

10. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the state feedback control method for an autonomous driving following system as described in any one of claims 1-7.

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