Intelligent network connection commercial vehicle fleet collaborative lane changing system and optimization method

Abstract

The invention relates to the technical field of intelligent traffic and vehicle control, in particular to an intelligent networked commercial vehicle fleet collaborative lane changing system and an optimization method, and provides a control strategy for intelligent networked commercial vehicle fleet collaborative lane changing by combining a V2X technology with a distributed model predictive control (DMPC) algorithm. The method comprises the following steps: introducing a timing interval strategy in a V2X environment to realize real-time information interaction so as to dynamically adjust a vehicle interval, predicting a motorcade lane changing track by using a DMPC algorithm, and converting a track optimization problem into a linear quadratic programming solving problem through a quadratic programming method so as to obtain a motorcade lane changing track with an optimal control sequence, so that the aim of dynamically adjusting the vehicle interval is fulfilled. Therefore, cooperative control over lane changing of the motorcade is achieved, and the overall stability, safety and lane changing efficiency of the motorcade are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation and vehicle control, and particularly to an intelligent connected commercial vehicle fleet cooperative lane-changing system and an optimization method. Background Art

[0002] The intelligent connected commercial vehicle fleet cooperative lane-changing technology can effectively improve the fleet driving efficiency and traffic flow stability in highway scenarios, and has important value for traffic safety and overall traffic efficiency.

[0003] In recent years, with the continuous expansion of the fleet scale and scenario complexity, it is difficult to achieve high-precision and low-latency coordination of longitudinal and lateral motion states in a multi-vehicle system only relying on traditional centralized control methods. Although centralized model predictive control (MPC) can consider multiple constraints of the vehicle itself and the environment while predicting future states and rolling optimizing control variables.

[0004] However, when the number of fleet vehicles increases, the required computing amount increases exponentially, making it difficult to meet the requirements of real-time calculation and iterative update. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent connected commercial vehicle fleet cooperative lane-changing system and an optimization method, aiming to solve the problem of low lane-changing driving efficiency of commercial vehicle fleets.

[0006] To achieve the above purpose, in the first aspect, the present invention provides an intelligent connected commercial vehicle fleet cooperative lane-changing system, including a traffic data acquisition module, a timing distance interval strategy module, a distributed model predictive control module, and a quadratic programming module, which are connected in sequence;

[0007] The traffic data acquisition module obtains the longitudinal and lateral positions, speeds, accelerations, and road environment information of the vehicle through V2X;

[0008] The timing distance interval strategy module dynamically adjusts the inter-vehicle distance according to the fleet speed and traffic environment to optimize the lane-changing space;

[0009] The distributed model predictive control module realizes model prediction and state estimation of the future behavior of the system by discretizing the output states of the vehicle trajectory tracking system in the fleet;

[0010] The quadratic programming module is used to transform the control problem into a linear quadratic programming problem with constraints, and obtain a series of optimal control sequences to achieve precise control of the fleet cooperative lane-changing behavior.

[0011] Among them, the traffic data acquisition module includes a longitudinal position acquisition unit, a lateral position acquisition unit, and a speed acquisition unit;

[0012] The longitudinal position acquisition unit is used to acquire the longitudinal position of the vehicle;

[0013] The lateral position acquisition unit is used to acquire the lateral position of the vehicle;

[0014] The speed acquisition unit is used to acquire the magnitude of the vehicle speed.

[0015] Among them, the distributed model predictive control module includes a control model unit, a feedback correction unit, and a rolling optimization unit, and the control model unit, the feedback correction unit, and the rolling optimization unit are connected in sequence;

[0016] The control model unit predicts the state change of the system in the future finite time based on the current system state information and the future control input variables;

[0017] The feedback correction unit corrects the model-based prediction output according to the actual output of the system and performs optimization at the next moment;

[0018] The rolling optimization unit is used to minimize the deviation between the future predicted output and the desired output of the controlled object.

[0019] Among them, the quadratic programming module includes a constraint condition unit and an optimization solution unit, and the constraint condition unit is connected to the optimization solution unit;

[0020] The constraint condition unit is used to define the physical limit ranges of the vehicle speed, the front wheel steering angle, and their increments; the optimization solution unit calculates the optimal solution of the constrained linear quadratic programming problem in real time by minimizing the control error and the prediction error, and outputs the optimal control sequence.

[0021] In the second aspect, an intelligent connected commercial vehicle fleet cooperative lane-changing optimization method is used for the intelligent connected commercial vehicle fleet cooperative lane-changing system described in the first aspect, and includes the following steps:

[0022] The V2X environment perception module receives in real time the dynamic information such as the speed, position, and acceleration of each vehicle in the fleet, and combines the road environment data to monitor and predict the overall driving state of the fleet;

[0023] In the DMPC optimization module, each vehicle constructs its own local prediction model and objective function based on the local perception information and the V2X shared data, performs rolling optimization within a finite prediction time domain through the quadratic programming method, and periodically exchanges key state variables and intention information with other vehicles;

[0024] The vehicle execution module receives control quantity instructions such as steering and acceleration output from the DMPC optimization module, and completes the lateral and longitudinal motion control of itself through the execution module.

[0025] The intelligent connected commercial vehicle fleet cooperative lane-changing system of the present invention combines vehicle-to-everything (V2X) technology and distributed model predictive control (DMPC) algorithm, and proposes a control strategy for intelligent connected commercial vehicle fleet cooperative lane-changing. By introducing a time-distance spacing strategy in the V2X environment, real-time information interaction is achieved to dynamically adjust the vehicle spacing, and the DMPC algorithm is used to predict the lane-changing trajectory of the fleet. Furthermore, the trajectory optimization problem is transformed into a linear quadratic programming problem by the quadratic programming method, and thus the lane-changing trajectory of the fleet with the optimal control sequence is obtained, so as to realize the cooperative control of the fleet lane-changing, improve the overall stability, safety and lane-changing efficiency of the fleet. Thus, the problem of low lane-changing driving efficiency of commercial vehicle fleets is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0027] Figure 1 It is a schematic diagram of an intelligent connected commercial vehicle fleet in a V2X environment.

[0028] Figure 2 It is a schematic diagram of a commercial vehicle kinematic model.

[0029] Figure 3 It is a schematic diagram of the intelligent connected commercial vehicle fleet cooperative lane-changing system provided by the present invention.

[0030] Figure 4 It is a framework diagram of the intelligent connected commercial vehicle fleet cooperative lane-changing system provided by the present invention.

[0031] Figure 5 It is a schematic diagram of the traffic data acquisition module.

[0032] Figure 6 It is a schematic diagram of the distributed model predictive control module.

[0033] Figure 7 It is a schematic diagram of the quadratic programming module.

[0034] Figure 8 It is a flowchart of the intelligent connected commercial vehicle fleet cooperative lane-changing optimization method provided by the present invention.

[0035] Figure 9 It is a schematic diagram of the intelligent networked commercial vehicle fleet cooperative lane-changing optimization method provided by the present invention.

[0036] In the figure: 1 - Traffic data acquisition module, 2 - Timed distance and spacing strategy module, 3 - Distributed model predictive control module, 4 - Quadratic programming module, 11 - Longitudinal position acquisition unit, 12 - Lateral position acquisition unit, 13 - Speed acquisition unit, 31 - Control model unit, 32 - Feedback correction unit, 33 - Rolling optimization unit, 41 - Constraint condition unit, 42 - Optimization solution unit. Specific implementation manner

[0037] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as a limitation to the present invention.

[0038] Please refer to Figures 1 to 7 , in the first aspect, the present invention provides an intelligent networked commercial vehicle fleet cooperative lane-changing system, including a traffic data acquisition module 1, a timed distance and spacing strategy module 2, a distributed model predictive control module 3, and a quadratic programming module 4. The traffic data acquisition module 1, the timed distance and spacing strategy module 2, the distributed model predictive control module 3, and the quadratic programming module 4 are connected in sequence;

[0039] The traffic data acquisition module 1 obtains the longitudinal and lateral positions, speed, acceleration, and road environment information of the vehicle through V2X;

[0040] The timed distance and spacing strategy module 2 dynamically adjusts the inter-vehicle distance through the fleet speed and traffic environment to optimize the lane-changing space;

[0041] The distributed model predictive control module 3 realizes the model prediction and state estimation of the future behavior of the system by discretizing the output state of the vehicle trajectory tracking system in the fleet;

[0042] The quadratic programming module 4 is used to transform the control problem into a linear quadratic programming problem with constraints, and obtain a series of optimal control sequences to achieve precise control of the fleet cooperative lane-changing behavior.

[0043] In this embodiment, starting from the technical characteristics of V2X, the intelligent connected commercial vehicle can obtain the status information of other vehicles in real time; secondly, analyze the safety of the spacing, adopt the fixed-time spacing strategy for the vehicle fleet spacing, and improve the flexibility of the vehicle fleet spacing control; further introduce the MPC algorithm, consider the tracking error when each intelligent connected commercial vehicle in the vehicle fleet changes lanes, design an intelligent connected commercial vehicle fleet cooperative lane-changing control strategy based on DMPC, and solve the model through quadratic programming; the leading vehicle travels according to the planned optimal lane-changing trajectory, and the following vehicle follows the real-time trajectory generated by the state of the preceding vehicle. At the same time, each vehicle maintains the expected spacing of the vehicle fleet during the lane-changing process to meet the requirement that all vehicles in the intelligent connected commercial vehicle fleet change lanes simultaneously, and improve the vehicle fleet driving efficiency. The present invention combines the vehicle networking (V2X) technology and the distributed model predictive control (DMPC) algorithm, and proposes a control strategy for the intelligent connected commercial vehicle fleet cooperative lane-changing. This strategy realizes real-time information interaction to dynamically adjust the vehicle spacing by introducing a fixed-time spacing strategy in the V2X environment, and uses the DMPC algorithm to predict the lane-changing trajectory of the vehicle fleet. Furthermore, the trajectory optimization problem is transformed into solving a linear quadratic programming through the quadratic programming method, and thus the lane-changing trajectory of the vehicle fleet with the optimal control sequence is obtained, thereby realizing the cooperative control of the vehicle fleet lane-changing and improving the overall stability, safety and lane-changing efficiency of the vehicle fleet.

[0044] Furthermore, the traffic data acquisition module 1 includes a longitudinal position acquisition unit 11, a lateral position acquisition unit 12, and a speed acquisition unit 13; the acquisition of the system information is based on the V2X communication technology.

[0045] The longitudinal position acquisition unit 11 is used to acquire the longitudinal position of the vehicle;

[0046] The lateral position acquisition unit 12 is used to acquire the lateral position of the vehicle;

[0047] The speed acquisition unit 13 is used to acquire the magnitude of the vehicle speed.

[0048] In this embodiment, all vehicles in the intelligent connected commercial vehicle fleet have installed vehicle sensors. Based on the V2X technology, the intelligent connected commercial vehicle realizes real-time information interaction with other vehicles and roadside units (RSUs), and obtains the surrounding traffic status information, that is, the lateral positions x m+1 、x m 、x m-1 ,the longitudinal positions y m+1 、y m 、y m-1 ,the speeds v m+1 、v m 、v m-1 and other parameters.

[0049] The Timed Distance Spacing Strategy Module 2: Assume that the intelligent connected commercial vehicle fleet consists of a lead vehicle and two following vehicles, namely lead vehicle m+1, following vehicle m, and following vehicle m-1. The vehicles in the fleet are of the same type. The fleet changes lanes simultaneously on the same lane based on the in-team communication topology of following the leading vehicle. During the entire lane-changing process, the lead vehicle m+1 changes lanes according to the planned lane-changing trajectory. The tracking target of following vehicle m is the vehicle (m+1)', that is, the virtual target (m+1)' after the longitudinal position of the leading vehicle is shifted backward by the expected spacing d des The tracking target of following vehicle m-1 is vehicle m', that is, the virtual target m' after the longitudinal position of the leading vehicle is shifted backward by the expected spacing d des During the entire lane-changing process, the vehicles maintain the expected fleet spacing from each other, thus realizing the synchronous lane-changing of the intelligent connected commercial vehicle fleet, Figure 1 as shown.

[0050] d des =t h v h +d a (1)

[0051] d des : The expected spacing value between vehicles in the commercial vehicle fleet;

[0052] t h : The time headway, which is a fixed value to ensure that the expected vehicle spacing is proportional to the vehicle speed;

[0053] v h : The speed of a certain vehicle in the fleet;

[0054] d a : The safety distance between two adjacent commercial vehicles.

[0055] The kinematic model of the commercial vehicle is as Figure 2 shown. (x, y, v) are respectively the abscissa, ordinate, and speed of the rear axle center of the commercial vehicle, is the heading angle of the vehicle body, δ is the front wheel steering angle, and l is the wheelbase length of the commercial vehicle. Assume that the vehicle structures in the intelligent connected commercial vehicle fleet are the same. For any vehicle in the intelligent connected commercial vehicle fleet, its kinematic equation is as shown in formula (2).

[0056]

[0057] In the formula, h1, h2, h3 are the corresponding non-linear differential equations; the state variables are The control variable is u = [v, δ] T ; The mapping relationship is h = [h1, h2, h3] T .

[0058] Formula (2) is rewritten as shown in formula (3).

[0059]

[0060] Equation (3) is the basis for Taylor expansion (linearization), discretization, and subsequent rolling optimization.

[0061] The trajectory tracking point gives the reference point z r , perform Taylor series expansion on Equation (3-4) at (z r , u r ), and ignore the high-order terms as shown in Equation (4).

[0062]

[0063] Subtract from Equation (4) and to obtain Equation (5).

[0064]

[0065] Among them,

[0066] The linearized state-space equation can be obtained from the above A1 and B1 formulas.

[0067] Denote Then Equation (5) can be expressed as shown in Equation (6).

[0068]

[0069] Furthermore, the distributed model predictive control module 3 includes a control model unit 31, a feedback correction unit 32, and a rolling optimization unit 33, and the control model unit 31, the feedback correction unit 32, and the rolling optimization unit 33 are connected in sequence;

[0070] The control model unit 31 predicts the state change of the system in the future finite time based on the current system state information and future control input variables;

[0071] The feedback correction unit 32 corrects the model-based prediction output according to the actual output of the system and performs optimization at the next moment;

[0072] The rolling optimization unit 33 is used to minimize the deviation between the future predicted output and the desired output of the controlled object.

[0073] In this embodiment, in the control system, the continuous state equation cannot be directly used. In order to apply this model to the design of the model predictive controller, forward Euler discretization is performed on (6), as shown in Equation (7).

[0074]

[0075] Where T is the sampling period. Rearranging Equation (7) gives Equation (8). The output equation is shown in Equation (9). The form of Equation (8) is conducive to the rolling optimization in MPC.

[0076]

[0077] Among them,

[0078] I is the identity matrix.

[0079] The model predictive control (MPC) algorithm realizes the model prediction and state estimation of the system's future behavior by discretizing the output state of the vehicle trajectory tracking system in the vehicle platoon. The core of this method lies in its rolling optimization mechanism. That is, at each sampling moment, the MPC algorithm predicts the future behavior based on the current state and optimizes the control input to achieve the desired system performance. In addition to this, the distributed model predictive control can also use the control systems on each vehicle to realize the real-time cooperative control of the entire vehicle platoon, thereby reducing the error of vehicle platoon tracking and improving the stability and safety of vehicle platoon driving.

[0080] Furthermore, the quadratic programming module 4 includes a constraint condition unit 41 and an optimization solution unit 42, and the constraint condition unit 41 is connected to the optimization solution unit 42;

[0081] The constraint condition unit 41 is used to define the physical limit ranges of vehicle speed, front wheel steering angle and their increments; the optimization solution unit 42 calculates the optimal solution of the constrained linear quadratic programming problem in real time by minimizing the control error and prediction error, and outputs the optimal control sequence.

[0082] In this embodiment, by constructing a system model and defining operation constraints, the quadratic programming method transforms the control problem into a constrained linear quadratic programming problem, and then obtains a series of optimal control sequences to achieve the precise control of the cooperative lane-changing behavior of the vehicle platoon. This method not only improves the driving safety and efficiency of the vehicle platoon, but also enhances the adaptability to complex traffic environments. Therefore, this section optimizes and solves the above model based on quadratic programming.

[0083] Equation (9) is a state variable linear error model. The control variable can mutate within a certain range in the actual physical sense, and there is no need to design a new state variable to represent the future predicted output. Set the prediction horizon to N P , and the control horizon to N C (N P > N C ), after derivation, the prediction model of the system is shown in Equation (10).

[0084]

[0085] Among them,

[0086]

[0087] The objective function is designed based on the prediction model as shown in formula (11).

[0088]

[0089] In the formula, Q, F, and R are the system state error weight, control quantity error weight, and terminal error weight respectively.

[0090] At the same time, considering the actual driving situation of intelligent connected commercial vehicles, there are certain range limitations for speed and front wheel steering angle. The rotation of the front wheel is restricted by transmission and cannot have large mutations. At the same time, in order to make the steering smoother, the front wheel steering angle increment constraint is added. The constraint conditions are as shown in formulas (12) and (13).

[0091] u min ≤u(k + i)≤u max , i = 0, 1, 2,..., N C -1(12)

[0092] Δδ min ≤δ(k + i)-δ(k + i - 1)≤Δδ max , i = 0, 1, 2,..., N C -1(13)

[0093] Combining formulas (10)-(13), the control problem is transformed into a linear quadratic programming problem with constraints, and the optimal sequence is solved through linear quadratic programming with constraints, as shown in formula (14).

[0094] J = 2ΔU T E + ΔU T HΔU(14)

[0095] Among them, is the weight matrix.

[0096] During the cooperative lane change process of intelligent connected commercial vehicle fleets, the following assumptions are made for the fleets:

[0097] Assumption 1: In the intelligent connected commercial vehicle fleet system, the structures of commercial vehicles are the same.

[0098] Assumption 2: During the fleet's travel, the fleet communication is not restricted.

[0099] Assumption 3: Each intelligent connected commercial vehicle in the fleet can communicate with surrounding vehicles through the communication topology and transmit status information.

[0100] Based on the above assumptions, in the local coordinate system with the following vehicle m as the reference, the desired position of the following vehicle m is z mr , and the position of its leading vehicle m+1 is z m+1 . The following vehicle m and its leading vehicle m+1 maintain a desired spacing d longitudinally des . Combining the platoon information, the desired position z of the following vehicle m is obtained mr as shown in Equation (15).

[0101]

[0102] Among them,

[0103] The cooperative lane-changing control objective of the intelligent connected commercial vehicle platoon is that each following vehicle in the platoon tracks its desired position in real time. Based on Equation (15), the cooperative lane-changing control objective of the platoon under local information conditions is obtained, that is, the following vehicle m tracks its desired position z mr as shown in Equation (16).

[0104]

[0105] Combining Equation (16), based on the cooperative lane-changing control objective of the intelligent connected commercial vehicle platoon under local information conditions, in order to ensure that the following vehicle m quickly and smoothly tracks its desired position, the objective function of the controller of the following vehicle m is designed as shown in Equation (17).

[0106]

[0107] Similarly, the following vehicle m-1 and its leading vehicle m maintain a desired spacing d longitudinally des . In order to meet the cooperative lane-changing control objective, that is, the following vehicle m-1 tracks its desired position z (m-1)r , the objective function of the controller of the following vehicle m-1 is designed as shown in Equation (18).

[0108]

[0109] The collaborative lane-changing control objective of intelligent connected commercial vehicle fleets is for each following vehicle to track its desired position in real time. Based on the DMPC algorithm, a separate MPC controller is designed for each vehicle in the fleet, which can split the collaborative lane-changing control problem of the fleet into the problem of a single vehicle tracking its desired trajectory, that is, the vehicle can track its desired position throughout the lane-changing process. And an objective function is designed for each following vehicle. Each vehicle independently solves its local optimal control problem at each sampling period, and then uses the first item of the optimal control sequence as the actual control input to drive the vehicle. In the next control time domain, the system re-predicts the system output according to the current state information and updates the optimal control sequence through the optimization solution process. This cycle repeats, and the control algorithm iterates continuously to ensure that each intelligent connected commercial vehicle tracks its desired position, and thus can effectively achieve the synchronous lane-changing of intelligent connected commercial vehicle fleets. The present invention adopts the DMPC strategy, combines the fixed-time headway spacing strategy with dynamic adjustment of vehicle speed and vehicle-to-everything (V2X) technology, and realizes the efficient collaborative control of intelligent connected commercial vehicle fleets during lane-changing through quadratic programming optimization, improving the flexibility and safety of lane-changing.

[0110] Please refer to Figures 8 to 9 In a second aspect, an optimization method for collaborative lane-changing of intelligent connected commercial vehicle fleets, which is used for the collaborative lane-changing system of intelligent connected commercial vehicle fleets described in the first aspect, includes the following steps:

[0111] S1: The V2X environment perception module receives in real time the dynamic information such as the speed, position, and acceleration of each vehicle in the fleet, and combines the road environment data to monitor and predict the overall driving state of the fleet;

[0112] Specifically, first, the V2X environment perception module receives in real time the dynamic information such as the speed, position, and acceleration of each vehicle in the fleet, and combines the road environment data to monitor and predict the overall driving state of the fleet;

[0113] S2: In the DMPC optimization module, each vehicle constructs its own local prediction model and objective function based on local perception information and V2X shared data, performs rolling optimization within a finite prediction time domain through quadratic programming, and periodically exchanges key state variables and intention information with other vehicles;

[0114] Specifically, secondly, in the DMPC optimization module, each vehicle constructs its own local prediction model and objective function based on local perception information and V2X shared data, performs rolling optimization within a finite prediction time domain through quadratic programming, and periodically exchanges key state variables and intention information with other vehicles, so as to independently solve the optimal control input respectively on the premise of ensuring overall stability;

[0115] S3: The vehicle execution module receives the control quantity instructions such as steering and acceleration output from the DMPC optimization module, and completes the lateral and longitudinal motion control of itself through the execution module.

[0116] Specifically, finally, the vehicle execution module receives the control quantity instructions such as steering and acceleration output from the DMPC optimization module, and completes the lateral and longitudinal motion control of itself through the execution system, ensuring that the convoy driving path and the desired vehicle distance can still be synchronously tracked under complex lane-changing conditions. Through the above method, this system can not only significantly improve the lane-changing efficiency and safety of the convoy in congested or high-throughput sections, but also timely respond to external disturbances and uncertainties, make dynamic collaborative planning for the lateral and longitudinal motion of vehicles in the future finite time domain, and finally achieve highly reliable and more energy-efficient convoy collaborative lane-changing control.

[0117] Beneficial effects:

[0118] First, the present invention realizes the dynamic adjustment of the vehicle distance by introducing a timed-distance spacing strategy, ensuring that the convoy can maintain an appropriate following distance under different vehicle speeds and turning conditions, thereby improving the convoy driving efficiency and enhancing the road space utilization rate.

[0119] Second, the present invention proposes a rolling optimization algorithm based on DMPC, which real-time corrects the vehicle tracking error and optimizes the control quantity, significantly improving the unity and system control performance during the convoy lane-changing process.

[0120] Third, the present invention uses the quadratic programming method to transform the control problem into an optimization model with constraints, and calculates and outputs the optimal control sequence in real time by minimizing the control error and the prediction error, thereby realizing the continuous optimization and effective guarantee of the system performance.

[0121] Fourth, this method realizes the real-time information exchange between vehicles through the V2X communication technology, considers the tracking error of each commercial vehicle in the convoy when changing lanes by using the model predictive control algorithm, and designs a distributed control strategy. This strategy enables the leading vehicle to drive along the predetermined optimal lane-changing trajectory, while the following vehicles track the trajectory generated by the state of the preceding vehicle in real time and maintain the desired convoy spacing during the lane-changing process, realizing the synchronous lane-changing of all vehicles in the convoy. In addition, by introducing the timed-distance convoy spacing strategy, the system can dynamically adjust the vehicle distance according to the vehicle speed, improve the flexibility and efficiency of convoy driving, reduce energy consumption and enhance road capacity, effectively avoid traffic congestion and reduce the collision risk.

[0122] The above-disclosed is only the preferred embodiment of the intelligent networked commercial vehicle fleet cooperative lane-changing system and optimization method of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Those of ordinary skill in the art can understand all or part of the processes of implementing the above embodiments, and the equivalent changes made according to the claims of the present invention still fall within the scope covered by the invention.

Claims

1. Intelligent networked commercial vehicle fleet cooperative lane changing system, characterized by: It includes a traffic data acquisition module, a timed distance spacing strategy module, a distributed model prediction control module and a secondary planning module, wherein the traffic data acquisition module, the timed distance spacing strategy module, the distributed model prediction control module and the secondary planning module are connected in sequence; The traffic data acquisition module acquires the vehicle's lateral and longitudinal position, speed, acceleration and road environment information through V2X; The timed interval strategy module dynamically adjusts the distance between vehicles according to the convoy speed and traffic environment to optimize the lane change space; The distributed model predictive control module realizes model prediction and state estimation of the future behavior of the system by discretizing the output state of the vehicle trajectory tracking system in the fleet; The quadratic programming module is used to transform the control problem into a linear quadratic programming problem with constraints, and obtain a series of optimal control sequences to achieve precise control of the coordinated lane-changing behavior of the fleet.

2. The intelligent networked commercial vehicle fleet cooperative lane changing system according to claim 1, characterized in that: The traffic data acquisition module includes a longitudinal position acquisition unit, a transverse position acquisition unit and a speed acquisition unit; The longitudinal position acquisition unit is used to acquire the longitudinal position of the vehicle; The lateral position acquisition unit is used to acquire the lateral position of the vehicle; The speed collection unit is used to collect the speed of the vehicle.

3. The intelligent networked commercial vehicle fleet cooperative lane changing system as claimed in claim 2, characterized in that: The distributed model predictive control module includes a control model unit, a feedback correction unit and a rolling optimization unit, and the control model unit, the feedback correction unit and the rolling optimization unit are connected in sequence; The control model unit predicts the state change of the system within a limited time in the future based on the current system state information and the future control input variables; The feedback correction unit corrects the predicted output based on the model according to the actual output of the system and performs optimization at the next moment; The rolling optimization unit is used to minimize the deviation between the future predicted output and the expected output of the controlled object.

4. The intelligent networked commercial vehicle fleet cooperative lane changing system according to claim 1, characterized in that: The quadratic programming module includes a constraint condition unit and an optimization solution unit, and the constraint condition unit is connected to the optimization solution unit; The constraint condition unit is used to define the physical limit range of vehicle speed, front wheel deflection angle and its increment; the optimization solution unit calculates the optimal solution of the constrained linear quadratic programming problem in real time by minimizing the control error and prediction error, and outputs the optimal control sequence.

5. A method for optimizing the coordinated lane-changing of a fleet of intelligent networked commercial vehicles, used in the coordinated lane-changing system of a fleet of intelligent networked commercial vehicles according to any one of claims 1 to 4, characterized in that: The following steps are involved: The V2X environmental perception module receives real-time dynamic information such as the speed, position, acceleration, etc. of each vehicle in the fleet, and combines it with road environment data to monitor and predict the overall driving status of the fleet; In the DMPC optimization module, each vehicle builds its own local prediction model and objective function based on local perception information and V2X shared data, performs rolling optimization within a limited prediction time domain through the quadratic programming method, and periodically exchanges key state quantities and intention information with other vehicles; The vehicle execution module receives steering, acceleration and other control quantity instructions output by the DMPC optimization module, and completes its own lateral and longitudinal motion control through the execution module.

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