Heterogeneous networked vehicle formation tracking control method based on FlexRay protocol

Abstract

The invention discloses a heterogeneous networked vehicle formation tracking control method based on a FlexRay protocol, and the method comprises the steps: enabling each following vehicle to reconstruct the complete state information of a leading vehicle in a bidirectional communication topology through a distributed adaptive estimator at a formation layer; meanwhile, the time lag device continuously records historical information from front and back adjacent vehicles; in the vehicle layer, the single vehicle state transition matrix performs extrapolation estimation on actual complete states of front and back adjacent vehicles under the action of communication time delay, and performs online updating on real-time changes of a communication time delay scheduling function; and finally, a vehicle formation controller integrates the complete state of an adjacent vehicle obtained by a communication time delay error compensation algorithm, the complete state of the vehicle measured by a sensor and the feedback of a longitudinal dynamic state space model of a single vehicle to suppress the negative influence of an odd-even mode excited by a constant communication time delay on the stability of a vehicle formation system. Therefore, stable control of the vehicle formation under the condition that the FlexRay protocol-based bidirectional communication topology has constant communication time delay is realized.

Description

Technical Field

[0001] This invention belongs to the field of intelligent connected vehicles and autonomous driving technology, and more specifically, relates to a heterogeneous connected vehicle platooning tracking control method based on the FlexRay protocol. Background Technology

[0002] Traffic congestion is widespread, and one of its root causes is that the layered lag in driving responses within a platoon amplifies even minor disturbances, leading to traffic instability. Pacifying and tracking control aims to ensure that each following vehicle tracks the target speed and desired distance in real time, suppressing errors; this is crucial for achieving platoon consistency, safe spacing, and disturbance suppression. While V2V collaboration can suppress disturbances, the bandwidth limitations, latency, and packet loss brought about by the expansion of vehicle networks significantly weaken platoon stability. Even with the introduction of the dual-channel FlexRay protocol, traditional strategies based on offline compensation are insufficient to address the parity-even mode instability in vehicle platooning induced by communication latency, easily leading to control degradation. For scenarios involving heterogeneous vehicles and decentralized bidirectional communication, there is an urgent need for methods that balance communication latency modeling with robust control. Heterogeneous connected vehicle platooning can improve road efficiency by coordinating spacing and speed adjustments, and gradually reduce and ultimately eliminate the negative impact of induced parity-even modes under communication latency conditions, demonstrating significant application potential in addressing the aforementioned pain points.

[0003] For example, patent CN202510280933.9, titled "A Real-Time Automatic Control Method for Vehicle Formation Based on a Nullified Neural Network," uses a distributed dynamic PI observer to estimate communication delay faults encountered by vehicle formation. Then, it iteratively solves the problem using an input-based real-time vehicle formation control model to control the distance, speed, and acceleration of the following vehicles. Patent CN202410994509.6, titled "An Intelligent Scene Vehicle Formation Following Control Method Based on Historical Paths," utilizes a linearized error model obtained by linearizing the nonlinear vehicle kinematics model of the following vehicles. It constructs a linear model predictive controller, solves a predictive optimization problem to obtain the control input for the following vehicles, transmits it to the following vehicles, updates the historical path, and feeds it back to the vehicle formation to achieve vehicle formation control.

[0004] Existing vehicle platooning controller technologies under communication latency still have several shortcomings: First, the communication topology of vehicle platooning is mostly centralized, meaning that following vehicles in the platoon all need to communicate with the lead vehicle, increasing the communication pressure on the information center. Second, most methods focus on homogeneous vehicles, ignoring the impact of vehicle heterogeneity on vehicle platooning control, resulting in insufficient control capability of the vehicle platooning controller under the influence of communication latency. Third, constant communication latency can excite odd and even modes in vehicle platooning, posing a significant challenge to the stability of vehicle platooning control, and there is a lack of relevant tracking control methods. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a heterogeneous connected vehicle platooning tracking control method based on the FlexRay protocol. For heterogeneous vehicles with different dynamic characteristics, under the bidirectional communication topology of the FlexRay protocol, the vehicle platooning controller is used to eliminate the negative impact of parity mode caused by constant communication delay on platooning stability, thereby achieving stable and cooperative vehicle platooning tracking control.

[0006] To achieve the above-mentioned objectives, this invention provides a heterogeneous connected vehicle platooning tracking and control method based on the FlexRay protocol, characterized by comprising the following steps:

[0007] (1) Composition and information collection of vehicle formations;

[0008] (2) Construct a longitudinal dynamic state-space model for each following vehicle;

[0009] (3) Construct a communication delay scheduling function based on the Sigmoid function. :

[0010] (4) Based on the current vehicle To neighboring car Forward estimation of state under time delay ;

[0011] (5) Design a suitable distributed adaptive estimator for each following vehicle;

[0012] (6) Establish the odd-even mode model for heterogeneous connected vehicle platooning control, including: the odd-even mode control objective function for vehicle platooning, and the objective functions for vehicle platooning speed and spacing control;

[0013] (7) Design a vehicle platooning controller;

[0014] (8) Based on the current status of each following vehicle Vertical position of time ,speed and acceleration Obtain tracking error information for each following vehicle. , , and the output of the distributed adaptive estimator Then, it is input into the vehicle formation controller with integrated communication delay error compensation algorithm to obtain the output of the vehicle formation controller. Finally, output Substitute the longitudinal dynamic state space model of a single vehicle into step (2) to calculate the vehicle's expected output in real time, thereby realizing the formation control of heterogeneous connected vehicles.

[0015] The objective of this invention is achieved as follows:

[0016] This invention presents a heterogeneous connected vehicle platooning tracking control method based on the FlexRay protocol. At the platooning level, a distributed adaptive estimator enables each following vehicle to reconstruct the complete state information of the lead vehicle within a two-way communication topology. Simultaneously, a time delay device continuously records historical information from neighboring vehicles. At the vehicle level, the single-vehicle state transition matrix extrapolates and estimates the actual complete states of neighboring vehicles under the influence of communication delay, and updates the real-time changes of the communication delay scheduling function online. Finally, the vehicle platooning controller integrates the complete states of neighboring vehicles obtained from the communication delay error compensation algorithm, the complete states of the vehicle itself measured by sensors, and the feedback from the longitudinal dynamic state space model of a single vehicle to suppress the negative impact of odd-even modes excited by constant communication delay on the stability of the vehicle platooning system. This achieves stable vehicle platooning control under constant communication delay conditions based on the FlexRay protocol's two-way communication topology.

[0017] Meanwhile, the heterogeneous connected vehicle platooning tracking and control method based on the FlexRay protocol of this invention also has the following beneficial effects:

[0018] (1) This invention proposes a bidirectional decentralized communication topology based on the FlexRay protocol, that is, each following vehicle in the formation does not need to communicate directly with the lead vehicle, but only interacts with the vehicles in front and behind, which significantly reduces information aggregation and link congestion, and alleviates the system's communication and computing pressure;

[0019] (2) The present invention introduces a non-uniform inertial hysteresis parameter, which enables the vehicle formation controller to be applied to heterogeneous vehicle formations. Compared with homogeneous vehicle formations, it is closer to the road scenario and can effectively improve the adaptability and robustness to heterogeneity when communication delay exists.

[0020] (3) In view of the problem of odd and even modes caused by constant communication delay, this invention proposes a modeling and suppression mechanism for odd and even modes, which weakens and even eliminates the negative impact caused by this mode, thereby improving the stability and controllability of vehicle formation. Attached Figure Description

[0021] Figure 1 This is a flowchart of the heterogeneous connected vehicle platooning tracking and control method based on the FlexRay protocol of this invention;

[0022] Figure 2 It is a diagram of the odd and even mode amplitudes of the vehicle formation;

[0023] Figure 3 It is a speed diagram output by the controller of each following vehicle in the vehicle formation under constant communication delay;

[0024] Figure 4It is a diagram showing the spacing between following vehicles in a vehicle platoon under constant communication latency. Detailed Implementation

[0025] The specific embodiments of the present invention will now be described with reference to the accompanying drawings to enable those skilled in the art to better understand the invention. It should be particularly noted that in the following description, detailed descriptions of known functions and designs that might obscure the main content of the invention will be omitted here.

[0026] Example

[0027] In this embodiment, as Figure 1 As shown, the present invention provides a heterogeneous connected vehicle platooning tracking and control method based on the FlexRay protocol, comprising the following steps:

[0028] (1) Composition and information collection of vehicle formations;

[0029] The vehicle platoon includes a virtual navigator and The virtual navigator car, acting as a follower vehicle, can be viewed as an external disturbance imposed on the vehicle formation. The vehicle in question is the following vehicle;

[0030] Each following vehicle is in a one-dimensional driving environment without overtaking in the vehicle formation. Adjacent following vehicles use V2V wireless bidirectional communication under the FlexRay protocol, which can realize the real-time collection and transmission of output information between adjacent vehicles under constant communication latency. This allows each vehicle to receive information from adjacent vehicles in front and behind, and also send its own information to adjacent vehicles in front and behind in real time, thus forming a stable bidirectional communication topology vehicle formation system.

[0031] Use onboard sensors to collect data on each following vehicle in the current situation. Vertical position of time ,speed and acceleration , ;

[0032] Define the distance between two adjacent following vehicles:

[0033]

[0034] in, , Indicates the first , The safe distance between the following vehicle and the lead vehicle. It is the duration of maintaining a constant front-end spacing; , They represent , The first derivative;

[0035] (2) Construct a longitudinal dynamic state-space model for each following vehicle;

[0036] ;

[0037] Where, vector superscript Indicates transpose. For the first The non-uniform inertial lag of the following vehicle, i.e., the heterogeneity of the vehicle; vector ; Indicates the first The desired acceleration control input for the vehicle design; s is the complex frequency variable in the Laplace transform; This is the cutoff angular frequency of the low-pass filter; , for The first derivative;

[0038] (3) Construct a communication delay scheduling function based on the Sigmoid function. :

[0039] ;

[0040] in, For smooth transition time constant, The delay start time, This is the end time of the delay;

[0041] (4) Based on the current vehicle To neighboring car Forward estimation of state under time delay ;

[0042] ;

[0043] in, For the first The state transition matrix of the vehicle, For the first The car was delayed The state after;

[0044] Furthermore, the state transition matrix for:

[0045] ;

[0046] (5) Design a suitable distributed adaptive estimator for each following vehicle;

[0047] (5.1) Define the judgment rules ;

[0048] definition Number the time slots. Duration of a single time slot;

[0049] To facilitate continuous-time modeling, a linear mapping is defined. ;

[0050] The time interval of the static segment channel in the FlexRay protocol is defined as follows: The time interval of the dynamic segment channel is the same as the idle time of the static segment, that is: , All available time slots within a communication cycle;

[0051] Therefore, the following judgment rule is defined. :

[0052] ;

[0053] (5.2) Constructing a distributed adaptive estimator ;

[0054] ;

[0055] ;

[0056] Where, vector , The non-uniform inertial lag of the navigator, i.e., the heterogeneity of the navigator; vector ; It is the state estimation parameter matrix; It is the coupling gain matrix; It is the coupling gain of the distributed adaptive estimator. It receives and calculates the preceding and following vehicle information via the FlexRay protocol. , Indicates the first The following vehicles' estimation of the lead vehicle's status; Indicates the first The following vehicle received the neighboring vehicle Estimation of the navigator's condition; It is the first The following car and the adjacent car Inter-link adaptive gain; It is the first The following car and the adjacent car Damping coefficient between;

[0057] (6) Establish the odd-even mode model for heterogeneous connected vehicle platooning control, including: the odd-even mode control objective function for vehicle platooning, and the objective functions for vehicle platooning speed and spacing control;

[0058] (6.1) Establish the objective function for odd-even mode control of vehicle formation. :

[0059] ;

[0060] in, for time The average speed of the following vehicle;

[0061] (6.2) With vehicles With the temporary vehicle Using speed difference and spacing difference as independent variables, establish the objective function for controlling vehicle formation speed and spacing. and That is, satisfying:

[0062] ;

[0063] (7) Design a vehicle platooning controller;

[0064] (7.1) Constructing a time delay for the static segment channel of the FlexRay protocol :

[0065] ;

[0066] in, This represents the observation time index of the current time delay in the static segment channel, i.e., from the current time. Counting forward, the Each sampling period is used to characterize the time delay position of the static segment channel on the time axis; This is the upper bound of the historical step size corresponding to the communication delay; Indicates in The first time to obtain Historical error information;

[0067] (7.2) Define the tracking error of adjacent vehicles, including: the position information error of adjacent vehicles. Error in the speed information of vehicles ahead and behind Error in acceleration information of vehicles approaching and behind ;

[0068] ;

[0069] in, Represents the temporary train after the prediction Location, Represents the temporary train after the prediction speed, Represents the temporary train after the prediction The acceleration; To predict the first All of the vehicles Cars gather, To predict the first All of the vehicles Vehicles assembled;

[0070] (7.3) Based on the tracking error information , , and the output of the distributed adaptive estimator Design a communication delay error compensation algorithm based on the communication characteristics of the FlexRay protocol:

[0071] ;

[0072] in, This represents a collection of information on the error between the preceding and following vehicles. , , Parameters representing the error information between the preceding and following vehicles; A collection representing the error information of a distributed adaptive estimator. , , The error information parameter represents the distributed adaptive estimator; This represents information selected in real time based on the communication characteristics of the FlexRay protocol; the present invention introduces a soft-start gate before the compensation algorithm. , Represents the current running time. This represents the duration of the soft start;

[0073] (7.4) Design a vehicle platooning controller with integrated communication delay error compensation algorithm:

[0074] ;

[0075] In the formula, , , , , The control gain is to be determined;

[0076] (8) Based on the current status of each following vehicle Vertical position of time ,speed and acceleration Obtain tracking error information for each following vehicle. , , and the output of the distributed adaptive estimator Then, it is input into the vehicle formation controller with integrated communication delay error compensation algorithm to obtain the output of the vehicle formation controller. Finally, output Substitute the longitudinal dynamic state space model of a single vehicle into step (2) to calculate the vehicle's expected output in real time, thereby realizing the formation control of heterogeneous connected vehicles.

[0077] In this embodiment, a bidirectional communication topology with constant communication delay is constructed to describe the objective function of decentralized vehicle platooning tracking control. Its odd-even mode decomposition characteristics are then used to characterize the dynamic evolution of the vehicle platooning system. For odd and even mode amplitudes. Figure 2 This demonstrates the excitation of parity-even modes under constant communication delay for each vehicle in a convoy of 5 following vehicles, i.e., the parity-even mode amplitudes of the entire convoy. Specifically, acceleration occurs between 0 and 10 seconds. The input disturbance; there is acceleration at 60-70 seconds. The input disturbance; there is a continuous constant communication delay effect in the 30-50 second range. Figures (a) and (c) are the odd-even mode amplitude diagrams without the proposed vehicle formation tracking control method; Figures (b) and (d) are the odd-even mode amplitude diagrams with the proposed vehicle formation tracking control method. Figures (a) and (b) show the overall odd-even mode amplitude of the vehicle formation when the communication delay of each following vehicle is different. It can be seen that the odd-even mode amplitude in Figure (a) changes more drastically than that in Figure (b). Both are affected by external disturbances of acceleration input in the 0-10 second and 60-70 second ranges. The highest odd-even mode amplitude affected by communication delay in Figure (a) is 0.221 and the lowest is -0.126; the highest odd-even mode amplitude affected by communication delay in Figure (b) is only 0.0517 and the lowest is only -0.0408. Therefore, the odd-even mode amplitude variation in Figure (b) is smaller. After the 50-second constant communication delay disappeared, the odd-even mode amplitude in Figure (a) returned to -0.068, and the odd-even mode amplitude in Figure (b) quickly returned to -0.0054. Compared with Figure (a), the overall odd-even mode of the vehicle formation in Figure (b) quickly returned to near 0. Compared with the effect of Figure (a), the overall odd-even mode influence of the vehicle formation in Figure (b) was significantly suppressed.

[0078] Figures (c) and (d) show the odd-even mode amplitudes of the vehicle formation as a whole when the communication delays of each following vehicle are different. It can be seen that the odd-even mode amplitudes in Figure (c) change more drastically than those in Figure (d). Both are affected by external disturbances of acceleration input in the 0-10 second and 60-70 second intervals. The odd-even mode amplitudes affected by communication delays in Figure (c) range from a maximum of 0.0006 to a minimum of -0.0099. In Figure (d), the odd-even mode amplitudes affected by communication delays range from a maximum of 0.0081 to a minimum of -0.0089. The odd-even mode amplitude changes in Figure (d) are 0.0065 smaller than those in Figure (c). Therefore, the odd-even mode amplitude changes in Figure (d) are smaller. Compared to Figure (c), after the 50-second constant communication delay disappears, the overall odd-even mode of the vehicle formation in Figure (d) quickly returns to near 0. Compared to the effect of Figure (c), the overall odd-even mode effect of the vehicle formation in Figure (d) is significantly suppressed.

[0079] Finally, the odd and even modes of the vehicle formations in Figures (b) and (d) quickly return to 0, representing the objective function that satisfies formula (12), that is, the odd and even modes in the vehicle formation excited by constant communication delay gradually decrease until they are eliminated.

[0080] Figure 3 The speed responses of all following vehicles in a platoon are presented. Figures (a), (c), and (e) show the speed outputs of the controllers for each following vehicle in the platoon under different communication delays, while Figures (b), (d), and (f) show the speed outputs of the controllers for each following vehicle in the platoon under the same communication delay. It can be observed that regardless of whether the communication delays of each vehicle are consistent, the speed trajectories remain stable and the interference of odd and even modes is minimized. The platoon operates stably even under the influence of time delay; under low latency (<100 ms), the performance indicators are almost unaffected.

[0081] Figure 4 The diagram illustrates the spacing changes in a vehicle platoon consisting of 5 following vehicles. Figures (a), (c), and (e) show the spacing between the following vehicles in the platoon under different communication delays, while Figures (b), (d), and (f) show the spacing between the following vehicles in the platoon under the same communication delay. When there is a communication delay in the platoon and the delay is large, the vehicle spacing is significantly affected by the delay, but it eventually converges quickly to the same spacing. When there is a communication delay in the platoon and the delay is small, the spacing of the platoon is basically insensitive to the delay, showing rapid and almost complete convergence, and stably maintaining the target spacing. Therefore, the overall spacing of the vehicle platoon is stable and shows a convergent trend: the impact on performance is negligible at low delays (<100 ms); at larger delays (approximately 100–800 ms), individual spacings show short-term deviations, but the system can quickly correct and return to the expected value, avoiding error accumulation and "logic collisions," demonstrating good robustness and self-recovery capability.

[0082] In summary, Figure 3 The speed trajectories of each following vehicle are shown to be asymptotically consistent, eventually converging to the same speed curve, thus achieving speed consistency. Figure 4 This indicates that the spacing between all following vehicles dynamically adjusts with speed and eventually converges uniformly. The above results satisfy the defined objective function requirements, indicating that the formation ultimately achieves the desired formation.

[0083] Although the illustrative specific embodiments of the present invention have been described above to enable those skilled in the art to understand the invention, it should be understood that the invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the invention as defined and determined by the appended claims, and all inventions utilizing the concept of the present invention are protected.

Claims

1. A heterogeneous connected vehicle platooning tracking and control method based on the FlexRay protocol, characterized in that, Includes the following steps: (1) Composition and information collection of vehicle formations; Assume that the vehicle platoon includes one virtual navigator and N follower vehicles. Each follower vehicle is in a one-dimensional driving environment without overtaking in the vehicle platoon. Adjacent follower vehicles communicate wirelessly in two directions via V2V under the FlexRay protocol. Collect data on each following vehicle in the current situation. Vertical position of time ,speed and acceleration , ; Define the distance between two adjacent following vehicles: ； in, , Indicates the first , The safe distance between the following vehicle and the lead vehicle. It is the duration of maintaining a constant front-end spacing; , They represent , The first derivative; (2) Construct a longitudinal dynamic state-space model for each following vehicle; ； Where, vector superscript Indicates transpose. For the first The non-uniform inertial lag of the following vehicle, i.e., the heterogeneity of the vehicle; vector ; Indicates the first The desired acceleration control input for the vehicle design; s is the complex frequency variable in the Laplace transform; This is the cutoff angular frequency of the low-pass filter; , for The first derivative; (3) Construct a communication delay scheduling function based on the Sigmoid function. : ； in, For smooth transition time constant, The delay start time, This is the end time of the delay; (4) Based on the current vehicle To neighboring car Forward estimation of state under time delay ; ； in, For the first The state transition matrix of the vehicle, For the first The car was delayed The state after; Furthermore, the state transition matrix for: ； (5) Design a suitable distributed adaptive estimator for each following vehicle; (5.1) Define the judgment rules ; definition Number the time slots. Duration of a single time slot; To facilitate continuous-time modeling, a linear mapping is defined. ; The time interval of the static segment channel in the FlexRay protocol is defined as follows: The time interval of the dynamic segment channel is the same as the idle time of the static segment, that is: , All available time slots within a communication cycle; Therefore, the following judgment rule is defined. : ； (5.2) Constructing a distributed adaptive estimator ; ； ； Where, vector , The non-uniform inertial lag of the navigator, i.e., the heterogeneity of the navigator; vector ; It is the state estimation parameter matrix; It is the coupling gain matrix; It is the coupling gain of the distributed adaptive estimator. It receives and calculates the preceding and following vehicle information via the FlexRay protocol. , Indicates the first The following vehicles' estimation of the lead vehicle's status; Indicates the first The following vehicle received the neighboring vehicle Estimation of the navigator's condition; It is the first The following car and the adjacent car Inter-link adaptive gain; It is the first The following car and the adjacent car Damping coefficient between; (6) Establish the odd-even mode model for heterogeneous connected vehicle platooning control, including: the odd-even mode control objective function for vehicle platooning, and the objective functions for vehicle platooning speed and spacing control; (6.1) Establish the objective function for odd-even mode control of vehicle formation. : ； in, for time The average speed of the following vehicle; (6.2) With vehicles With the temporary vehicle Using speed difference and spacing difference as independent variables, establish the objective function for controlling vehicle formation speed and spacing. and That is, satisfying: ； (7) Design a vehicle platooning controller; (7.1) Constructing a time delay for the static segment channel of the FlexRay protocol : ； in, This represents the observation time index of the current time delay in the static segment channel, i.e., from the current time. Counting forward, the Each sampling period is used to characterize the time delay position of the static segment channel on the time axis; This is the upper bound of the historical step size corresponding to the communication delay; Indicates in The first time to obtain Historical error information; (7.2) Define the tracking error of adjacent vehicles, including: the position information error of adjacent vehicles. Error in the speed information of vehicles ahead and behind Error in acceleration information of vehicles approaching and behind ; ； in, Represents the temporary train after the prediction Location, Represents the temporary train after the prediction speed, Represents the temporary train after the prediction The acceleration; To predict the first All of the vehicles Cars gather, To predict the first All of the vehicles Vehicles assembled; (7.3) Based on the tracking error information , , and the output of the distributed adaptive estimator Design a communication delay error compensation algorithm based on the communication characteristics of the FlexRay protocol: ； in, This represents a collection of information on the error between the preceding and following vehicles. , , Parameters representing the error information between the preceding and following vehicles; A collection representing the error information of a distributed adaptive estimator. , , The error information parameter represents the distributed adaptive estimator; This represents information selected in real time based on the communication characteristics of the FlexRay protocol; soft start gate. , Represents the current running time. This represents the duration of the soft start; (7.4) Design a vehicle platooning controller with integrated communication delay error compensation algorithm: ； In the formula, , , , , The control gain is to be determined; (8) Based on the current status of each following vehicle Vertical position of time ,speed and acceleration Obtain tracking error information for each following vehicle. , , and the output of the distributed adaptive estimator Then, it is input into the vehicle formation controller with integrated communication delay error compensation algorithm to obtain the output of the vehicle formation controller. Finally, output Substitute the longitudinal dynamic state space model of a single vehicle into step (2) to calculate the vehicle's expected output in real time, thereby realizing the formation control of heterogeneous connected vehicles.

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