Multi-bus cooperative centralized control method for bus departure in network connection environment

Abstract

The invention relates to the field of intelligent network connection, in particular to a multi-bus cooperative centralized control method for bus departure in a network connection environment, which comprises the following steps of: (1) after intelligent network connection buses finish passenger getting-on and getting-off tasks at a stop, judging whether the buses can leave the stop at the current moment through a vehicle-mounted calculation unit; (2) when the departure condition is not met, a lane changing request is sent to an intelligent road test unit of a harbor stop station; (3) after the public lane switching request is received, the RSU collects the vehicle driving information of all the networked automatic driving vehicles within the communication control range; (4) the RSU calculates to obtain an optimal cooperation strategy and a driving track of each CAV by taking the minimum total passing cost of all vehicles in the control area as a target; (5) the RSU issues a vehicle decision and path planning control instruction to the CAV; (6) each CAV passes through the road section of the control area according to the control instruction; the driving behavior control of multiple vehicles is realized, so that the vehicles in the region jointly complete the cooperative task of public lane departure.

Description

Technical Field

[0001] The present invention relates to the field of intelligent connected (V2X), and particularly relates to a multi-vehicle collaborative centralized control method for buses leaving stations in a connected environment. Background Art

[0002] With the development of intelligent connected technology, the information interaction function between vehicle and vehicle and between vehicle and infrastructure provides technical support for multi-vehicle collaboration to pass through congested / accident-prone sections. At present, multi-vehicle collaborative control is a hot research issue in intelligent transportation systems and is widely applied to scenarios such as "intelligent highway" and "intelligent park". Multi-vehicle collaborative control methods can be divided into two categories: centralized collaborative control and distributed collaborative control according to whether there is a central node participating. Centralized collaborative control has a central node for system control, and individuals in the system communicate with the central node to complete collaborative tasks. Its advantages are high communication efficiency between the central node and individuals and stable formation effect.

[0003] The lane-changing behavior of a vehicle includes multiple complex elements such as the generation of lane-changing intention, judgment of lane-changing conditions, implementation of lane-changing, and evaluation of lane-changing impacts. The unreasonable lane-changing behavior of a vehicle is the main inducement for road section congestion and traffic accidents. In the scenario where a bus leaves a bay-type bus stop, due to the characteristics of the long bus body and small starting speed, the outbound lane-changing behavior of the bus is prone to conflict with the oncoming straight traffic flow. This conflict relationship will reduce the driving safety of vehicles and the traffic efficiency of road sections, and have a negative external impact on regional traffic. If the bus delays leaving the station to avoid conflicts with oncoming vehicles, it will reduce the immediacy of the bus leaving the station and the punctuality rate of reaching other stations, which is not conducive to improving the bus service level and promoting the "bus priority" policy.

[0004] Regarding the research on lane - changing cooperative control, researchers focus on analyzing the interaction relationship between the lane - changing vehicle and the following vehicle in the target lane to achieve the lane - changing behavior of the lane - changing vehicle. For example, in the patent "Method, Device and Storage Medium for Cooperative Merging Control of Main Line - Ramp Vehicles on Expressway Based on Mixed Traffic Flow", Chinese Patent Application No.: CN202211280796.1, the best cooperative vehicle is selected relying on the detection data of roadside units RSU - 1 and RSU - 2 to achieve cooperative lane - changing between the lane - changing vehicle on the ramp and the cooperative vehicle on the expressway main line. In the article "Two - vehicle Cooperative Lane - Changing Strategy for Intelligent Connected Buses in the Process of Forced Lane - Changing at Station Entrance and Exit" (Ren Hanxiao, Luo Yugong, Guan Shurui, etc. Two - vehicle Cooperative Lane - Changing Strategy for Intelligent Connected Buses in the Process of Forced Lane - Changing at Station Entrance and Exit [J]. Journal of Tsinghua University (Science and Technology), 2024, 64(08): 1456 - 1468. DOI: 10.16511 / j.cnki.qhdxxb.2024.21.019.), a rule - based decision - making method and an early deceleration strategy are adopted to achieve the lane - changing of buses when leaving the station. The above - mentioned patents and literature only consider the interaction relationship between the lane - changing vehicle and the following vehicle in the target lane, ignoring the impact of lane - changing behavior on other vehicles in the area. The centralized cooperative control method can achieve the control of the driving behavior of multiple vehicles, enabling the regional vehicles to jointly complete the cooperative task of bus lane - changing when leaving the station. Therefore, it is necessary to study the rational bus lane - changing behavior when leaving the station on the basis of reducing the impact on regional traffic. Summary of the Invention

[0005] In view of the above problems, the purpose of the present invention is to provide a centralized control method for multi - vehicle cooperation of buses leaving the station in a networked environment, which is used to achieve the control of the driving behavior of multiple vehicles, enabling the regional vehicles to jointly complete the cooperative task of bus lane - changing when leaving the station, so as to overcome the deficiencies of the above - mentioned prior art.

[0006] A centralized control method for multi - vehicle cooperation of buses leaving the station in a networked environment provided by the present invention includes the following steps: Step 1: After the bus completes the task of picking up and dropping off passengers, judge the outbound conditions. When the front - distance between the bus and the vehicle LV in front of the target lane is greater than the safe pre - lane - changing gap , and the rear - distance between the bus and the vehicle FV behind in the target lane is greater than the braking distance of the vehicle FV behind in the target lane , it is determined that the current time target - lane vehicle gap meets the lane - changing requirements of the bus, and the bus can smoothly leave the station; otherwise, execute Step 2; Step 2: When the outbound conditions are not met, the bus sends a lane - changing request to the roadside control unit RSU; Step 3: After receiving the bus lane - changing request, the roadside control unit RSU collects the vehicle driving information of all CAVs within the communication control range, where the vehicle driving information includes the vehicle body length, vehicle position, and vehicle speed information; Step 4: The calculation unit of the Road Side Unit (RSU) calculates the optimal cooperative strategy and driving trajectory of each CAV with the goal of minimizing the total traffic cost of all vehicles in the control area; Step 5: The Road Side Unit (RSU) issues control instructions for vehicle decision-making and planned paths to the CAV; Step 6: Each CAV passes through the control area section according to the control instructions.

[0007] As a preference of the present invention, the following steps are further included in Step 1: Step 1-1: Calculate ; ; where is the distance between the vehicle in the target lane in front and the bus head; Step 1-2: Calculate ; ; where is the distance between the vehicle in the target lane behind and the bus head, is the bus body length; Step 1-3: Calculate the clearance before safe lane change ; ; In the formula, and respectively represent the vehicle speeds of the bus and the vehicle in the target lane in front at time, represents the predicted lane change time of the bus, represents the steady-state time headway between the bus and the vehicle in the target lane in front after the lane change; Step 1-4: Calculate the braking distance of the FV ; ; In the formula, and respectively represent the speed and braking acceleration of the vehicle in the target lane behind at time, represents the reaction time of the vehicle in the target lane behind.

[0008] As a preference of the present invention, the following steps are further included in Step 4: Step 4-1: Establish a vehicle dynamics model, where the driving characteristics of the CAV in the control area are characterized by a two-degree-of-freedom bicycle model; According to the Ackermann steering principle, a set of dynamic differential equations are obtained from the vehicle dynamics model as shown in the following formula: ; Among them, is the coordinate of the midpoint of the rear axle of the vehicle at moment; 、 、 represents the speed, acceleration and jerk of the vehicle at moment; is the front wheel steering angle of the vehicle at moment; is the heading angle of the vehicle at moment; represents the wheelbase of the vehicle ; and respectively represent the start and end moments when the vehicle receives control; is the angular velocity of the front wheel steering angle of the vehicle ; The state variables of the intelligent connected vehicle are defined as: ; In the formula, is the transpose of the matrix; The control variables are defined as: .

[0009] As a preference of the present invention, the following steps are further included in step four: Step four-two: Calculate the system cost of all vehicles in the control area; The system cost is the sum of the costs of all participants under each specific strategy, and the expression is as follows: ; Among them, is the total number of vehicles in the control area; represents the single-vehicle cost of vehicle ; Calculate the single-vehicle cost, where the vehicle driving cost is defined by three factors: driving comfort, traffic efficiency and lane-changing cost. The cost expression of vehicle under different strategies in the finite time domain is defined as: ; ; ; ; ; Among them, is the shortest travel time; is the maximum speed limit within the control area; is the vehicle lane-changing cost; is the vehicle at the vertical coordinate change value at the moment; and respectively represent the vertical coordinate values of the vehicle at the moment (current moment) and the initial moment ; is the lane width; , and are different weight coefficients, used to reflect the driving preferences of different CAVs for different strategies, is the differential of time ;

[0010] As a preference of the present invention, the following steps are further included in step four: Step four-three: Set the constraint conditions corresponding to the dynamic model; the dynamic constraints applied in the dynamic model include two parts: state and control variable constraints and bus departure curvature constraints; The state and control variable limits for the applicable scenarios are as follows: Motion space limit: The driving trajectory of the planned CAV is within the control interval, that is, the maximum values of the horizontal and vertical coordinates do not exceed the length and width of the control area; ; Speed limit: There is a maximum speed limit on the control area section of the intelligent connected vehicle, and the minimum speed is set to 0 m / s; ; is the maximum speed limit value of the control area section.

[0011] Acceleration limit: The intelligent connected vehicle travels between the maximum comfortable deceleration and the maximum comfortable acceleration ; ; Heading angle limit: The change range of the heading angle of the intelligent connected vehicle does not exceed the maximum heading angle change value ; ; Front wheel swing angle limit: The change range of the front wheel swing angle of the intelligent connected vehicle does not exceed the maximum swing angle change value ; ; Front wheel steering angle angular velocity limit: The front wheel steering angle angular velocity of the intelligent connected vehicle does not exceed the maximum steering angular velocity ; ; The bus smoothly departs from the station, and a constraint is imposed on the curvature of the departure trajectory. Its expression is as follows: ; ; where, represents the curvature of the bus departure trajectory; represents the longitudinal position of the bus at moment; represents the maximum value of the curvature of the bus departure trajectory; represents the minimum turning radius of the bus, with a value range of 8m - 12m; where, three circles are used to describe the vehicle outer contour. The anti-collision formula between vehicles is as follows. The meaning of the formula is that the outer contour circles of the lane-changing vehicle and the surrounding vehicle NV do not intersect pairwise; ； ; where, and respectively represent the diameters of the outer contour circles of vehicles and NV; represents the safety margin, with a value of 2.5m; and represent the longitudinal and lateral positions of vehicle , and represent the longitudinal and lateral positions of vehicle NV.

[0012] As a preference of the present invention, the following steps are further included in step four three: The collision types between the bus during the departure process and the bay stop include: the head of the vehicle body colliding with the acceleration section, the middle part colliding with the inflection point of the acceleration section, and the tail colliding with the parking section. The specific obstacle avoidance expressions are as follows: ; ; ; ; where , , are the coordinates of the centers of the tail, middle, and head circles describing the outer contour of the bus at moment; and Indicates the longitudinal and lateral positions of the bus at the moment; and respectively represent the longitudinal and lateral positions of the inflection point of the bay acceleration section; and respectively represent the longitudinal and lateral positions of the point closest to the bus in the bay acceleration section at the moment; is the wheelbase of the bus; and respectively represent the lengths of the front overhang and rear overhang of the bus; and respectively represent the longitudinal and lateral positions of the end point of the bay parking section; represents the length of the bus body; Among them, the initial and final states of each vehicle in the scenario are restricted as follows: In the initial state, the vehicles in the same lane are in a stable car-following state and drive at a constant speed, with the same speed and following distance; After the lane-changing vehicle finishes lane-changing, it should maintain the same driving speed as the vehicles in the current lane; The front wheel swing angle and swing angular velocity of the vehicle at the initial and final states are zero; Vehicle The mathematical expressions for the constraints of the initial and final states are as follows: ； ; Among them, and respectively represent the average speeds of the current lane and the lane-changing target lane.

[0013] As a preference of the present invention, the following steps are further included in step four: Step four four: The original cooperative control problem is regarded as an optimal control problem of minimizing the traffic cost, that is, finding the optimal control variables and the optimal terminal time to minimize the performance index function, that is, the objective function. First, use the pseudospectral method to transform the continuous optimal control problem into a discrete form of nonlinear programming problem, and then use the interior point method suitable for solving large-scale complex problems for solution; Vehicle The optimal control is represented by the following formula:

[0014] is the state vector of vehicle ; is the control vector of vehicle ; is the vehicle at the initial moment when it is under the control of the RSU. represents the vehicle at the initial moment state vector.

[0015] The PRM pseudo-spectral method can discretize both state variables and control variables simultaneously. Its working principle is to use the collocation method of Legendre-Gauss-Radau (LGR). At the collocation points, the state variables and control variables are approximated by Lagrange interpolation polynomials as basis functions, and the collocation of differential-algebraic equations is realized by Guass integration at the discrete points.

[0016] The interior point method, also known as the interior point penalty function method, its core idea is to transform the constraint conditions into penalty terms. After multiplying the penalty terms by a penalty factor, they are added to the objective function as a penalty function, so that the original constrained problem is transformed into an unconstrained new problem solved within the feasible region. By solving a series of parameterized unconstrained optimization problems, a path leading to the optimal solution can be obtained, which is also called the "central path". When the penalty factor approaches zero, the optimal solution of the new problem objective function also approaches the optimal solution of the original problem.

[0017] The beneficial effects of the present invention are as follows: The bus lane-changing and leaving the station is a bottleneck problem affecting the normal operation of urban road traffic. The cooperative behavior between vehicles can assist buses to leave the station safely and efficiently, which is beneficial to alleviating the congestion of urban roads during peak hours. Different from the previous two-vehicle cooperation methods, the multi-vehicle cooperative centralized control method proposed in this application can effectively achieve multi-vehicle cooperative control, providing a new cooperation method for optimizing the bus lane-changing behavior when leaving the station. The objective function constructed by this application with three elements of travel time, passenger comfort, and lane-changing operation, by minimizing the objective function to solve the driving decisions and trajectory planning of each CAV, can improve the traffic efficiency, vehicle driving safety, and passengers' riding experience in the section of the bay-type bus stop area. Brief Description of the Drawings

[0018] By referring to the following description in conjunction with the drawings, and with a more comprehensive understanding of the present invention, other objects and results of the present invention will become more obvious and easier to understand. In the drawings: Figure 1 is the schematic diagram of the operation process in the present invention; Figure 2 is the schematic diagram of the research scenario in the present invention; Figure 3 is the schematic diagram of the vehicle driving attitude in the present invention; Figure 4 is the description diagram of the vehicle external contour in the present invention; Figure 5 is the shape diagram of the bay bus stop in the present invention; Figure 6 Trajectory planning diagram of the implementation case in the present invention; Figure 7 Evolution diagram of the example bus control variables in the present invention; Figure 8 Schematic diagram of the simulation case parameters in the present invention. Specific implementation manners

[0019] Example 1;

[0020] Refer to Figures 1-7 , this example proposes a centralized control method for multi-vehicle collaborative bus lane-changing and departure at stations in an intelligent connected environment. Among them, the bus stop is a bay-type stop, located in the middle of the road section, and the bus lane-changing behavior when leaving the station is not affected by the traffic lights at the upstream and downstream intersections. This example takes a one-way two-lane urban road as an example, and the lanes are named the first lane and the second lane from the outside to the inside in sequence. All vehicles passing through the research scenario in this example are self-driving vehicles above L3 level, equipped with intelligent perception devices such as sensors and lidar, and configured with in-vehicle computing units, and are driving in an intelligent connected environment. The RSU near the bay-type bus stop has intelligent perception and online computing functions. The transmission of vehicle state information and path decision-making and planning are all realized in real time, and communication delay and packet loss are not considered. The drivers of social vehicles and bus vehicles are all rational participants. The cooperation degree of vehicles in response to the control instructions issued by the RSU is 100%.

[0021] A centralized control method for multi-vehicle collaborative bus departure at stations in a networked environment provided by this example specifically includes the following steps: Step 1: After the bus completes the boarding and alighting tasks, judge the departure conditions. When the front distance between the bus and the vehicle LV in front on the target lane is greater than the safe pre-lane-changing gap , and the rear distance between the bus and the vehicle FV behind on the target lane is greater than the braking distance of the vehicle FV behind on the target lane, it is determined that the current time lane gap on the target lane meets the bus lane-changing requirements, and the bus can leave the station smoothly; otherwise, execute Step 2; Step 1-1: Calculate ; ; Among them, is the distance between the vehicle in front on the target lane and the bus head; Step 1-2: Calculate ;

[0022] Among them, is the distance between the vehicle behind on the target lane and the bus head, is the length of the bus body; Step 1-3: Calculate the gap before safe lane change ;

[0023] In the formula, and respectively represent the vehicle speeds of the bus and the vehicle in front on the target lane at moment, represents the estimated lane change time of the bus, represents the steady-state headway between the bus and the vehicle in front on the target lane after the lane change; Step 1-4: Calculate the braking distance of FV ; ; In the formula, and respectively represent the speed and braking acceleration of the vehicle behind on the target lane at moment, represents the reaction time of the vehicle behind on the target lane.

[0024] Step 2: When the outbound condition is not met, the bus sends a lane change request to the roadside control unit RSU; Step 3: After receiving the bus lane change request, the RSU collects the vehicle driving information of all CAVs (Connected Automous Vehicles) within the communication control range, where the vehicle driving information includes the body length, vehicle position, and vehicle speed information; Step 4: The RSU calculation unit calculates the optimal cooperative strategy and driving trajectory of each CAV with the goal of minimizing the total traffic cost of all vehicles in the control area; Step 4-1: Establish a vehicle dynamics model, where the two-degree-of-freedom bicycle model is used to characterize the driving characteristics of the CAV in the control area; According to the Ackermann steering principle, from the vehicle The vehicle dynamics model gives a set of dynamic differential equations as shown below: ; Among them, is the coordinate of the midpoint of the rear axle of vehicle ; 、 、 represents the speed, acceleration, and variable acceleration of vehicle at moment; is the vehicle at Front wheel steering angle at a moment; For the vehicle At Course angle at a moment; Indicates the vehicle Wheelbase; And Respectively represent the vehicle Start and end moments when receiving control; For the vehicle At Angular velocity of the front wheel steering angle at a moment.

[0025] The state variables of the intelligent connected vehicle are defined as: ; The control variables are defined as: ; Step Four Two: Calculate the system cost of all vehicles in the control area; The system cost is the sum of the costs of all participants under each specific strategy, and the expression is as follows: ; Among them, Is the total number of vehicles in the control area; Calculate the cost of a single vehicle. Among them, the vehicle driving cost is defined by three factors: driving comfort, traffic efficiency, and lane-changing cost. The cost expression of the vehicle Under different strategies within a finite time domain is defined as: ; ; ; ; ; Among them, Is the shortest passing time (ideal state); Is the maximum speed limit in the control area; Is the vehicle lane-changing cost; Is the vehicle Change value of the vertical coordinate; And Respectively represent the vehicle At Moment (current moment) and the initial moment Vertical coordinate values; Is the lane width; 、 And Are different weight coefficients, used to reflect the driving preferences of different CAVs for different strategies, Is for time Differential.

[0026] Step Four Three: Set the constraint conditions corresponding to the model; among them, the dynamic constraints applied in the model include two parts: state and control variable constraints and bus departure curvature constraints. The state and control variable restrictions in the applicable scenarios are as follows: Motion space restriction: The driving trajectory of the planned CAV should be within the control interval, that is, the maximum values of the horizontal and vertical coordinates are less than or equal to the length and width of the control area. And width ; ; Speed limit: There is a maximum speed limit on the control area section of the intelligent connected vehicle. To avoid the appearance of a backward trajectory, the minimum speed is set to 0 m / s; ; is the maximum speed limit value of the control area section.

[0027] Acceleration limit: To ensure driving comfort, the intelligent connected vehicle should drive between the maximum comfortable deceleration and the maximum comfortable acceleration ; ; Heading angle limit: The change range of the heading angle of the intelligent connected vehicle should not exceed the maximum heading angle change value ; ; Front wheel swing angle limit: The change range of the front wheel swing angle of the intelligent connected vehicle should not exceed the maximum swing angle change value ; ; Front wheel swing angular velocity limit: The front wheel swing angular velocity of the intelligent connected vehicle should not exceed the maximum swing angular velocity ; ; The bus has the characteristics of a long body and a slow starting speed. To ensure the smooth departure of the bus, corresponding constraints on the curvature of the departure trajectory are also required, and its expression is shown as follows: ; ; Among them, represents the curvature of the bus departure trajectory; represents the longitudinal position of the bus at moment; represents the maximum value of the bus departure trajectory curvature; Indicates the minimum turning radius of a bus, usually taken as 8m - 12m; Among them, three circles are used to describe the vehicle's outer contour. The anti-collision formula between vehicles is shown as follows. The meaning of the formula is that the outer contour circles of the lane-changing vehicle and the surrounding vehicle NV do not intersect pairwise; ； ; Among them, and respectively represent the vehicle and the diameter of the outer contour circle of NV; Represents the safety margin, which is taken as 2.5m here; and represent the vehicle longitudinal and lateral positions, and represent the longitudinal and lateral positions of the vehicle NV; The collision types between the bus during the outbound process and the bay stop include: the head of the vehicle body colliding with the acceleration section, the middle part colliding with the inflection point of the acceleration section, and the tail colliding with the parking section. The specific obstacle avoidance expressions are as follows: ; ; ; ; Among them 、 、 are the coordinates of the centers of the tail, middle, and head circles describing the bus's outer contour at time; and represent the longitudinal and lateral positions of the bus at time; and respectively represent the longitudinal and lateral positions of the inflection point of the bay acceleration section; and respectively represent the longitudinal and lateral positions of the point closest to the bus in the bay acceleration section at is the wheelbase of the bus; and respectively represent the lengths of the front overhang and rear overhang of the bus; and respectively represent the longitudinal and lateral positions of the end point of the bay parking section; Represents the length of the bus body; Among them, the initial and final states of each vehicle in this scenario are restricted as follows: In the initial state, vehicles in the same lane are driving at a constant speed in a stable following state, with the same speed and following distance; After the lane-changing vehicle finishes changing lanes, it should maintain the same driving speed as the vehicles in the current lane; The front wheel swing angle and swing angular velocity of the vehicle at the start and end states should be zero; Vehicle The mathematical expressions for the constraints at the start and end states of the vehicle are as follows: ； ; Among them, and respectively represent the average speeds of the current lane and the lane-changing target lane; Step Four: The original cooperative control problem is regarded as an optimal control problem of minimizing the traffic cost, that is, finding the optimal control variables and the optimal terminal time under various constraints, so that the performance index function, that is, the objective function, takes the minimum value. First, use the pseudospectral method to transform the continuous optimal control problem into a discrete form of nonlinear programming problem, and then use the interior point method suitable for solving large-scale complex problems to solve it; Vehicle The optimal control of is represented by the following formula:

[0028] is the state vector of vehicle ; is the control vector of vehicle ; is the initial time when vehicle receives the RSU control. Represents the state vector of vehicle at the initial time ;

[0029] Step Five: The RSU issues control instructions for vehicle decisions and planned paths to the CAV; Step Six: Each CAV passes through the control area section according to the control instructions.

[0030] Embodiment 2;

[0031] For the convenience of those skilled in the art to understand, the following further describes the present invention in combination with a case and referring to Figures 1-8 The content mentioned in the case is not a limitation of the present invention.

[0032] This embodiment provides a centralized control method for multi-vehicle cooperative bus lane-changing and leaving the station in an intelligent networked environment.

[0033] The calculation steps are as follows: Step 1: The bus determines the outbound condition. According to the vehicle position relationship set in the case, , ; . Since , the in-vehicle calculation unit of the bus determines that the outbound condition is not met.

[0034] Step 2: When the outbound condition is not met, the bus sends a lane-changing request to the roadside control unit RSU.

[0035] Step 3: After receiving the bus lane-changing request, the RSU collects the vehicle driving information of all CAVs within the communication control range, including vehicle body length, vehicle position, vehicle speed and other information.

[0036] Step 4: The RSU calculation unit analyzes with the goal of optimizing the total traffic cost of all vehicles in the control area, and calculates the optimal cooperative strategy and driving trajectory of each CAV. This step is implemented based on the MATLAB platform program simulation. According to the simulation analysis results, Vehicle 5 and Vehicle 17 choose to cooperate in lane-changing, and the rest of the social vehicles all maintain following and perform acceleration and deceleration cooperation.

[0037] Step 5: The RSU issues control instructions for vehicle decision-making and planned paths to the CAVs.

[0038] Step 6: Each CAV passes through the control area section according to the control instructions.

[0039] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for coordinated centralized control of multiple buses leaving a bus station in a networked environment, characterized in that: The following steps are involved: Step 1: After the bus completes the task of picking up and dropping off passengers, the exit condition is determined. When the distance between the bus and the front vehicle LV in the target lane is Greater than the clearance before safe lane change , and the distance between the bus and the vehicle behind the target lane FV Greater than the braking distance of the vehicle behind in the target lane FV , then it is judged that the gap between vehicles in the target lane at the current moment meets the requirements for bus lane change, and the bus can exit the station smoothly; Otherwise, go to step 2; Step 2: When the exit conditions are not met, the bus sends a lane change request to the road test control unit RSU; Step 3: After receiving the lane change request, the road test control unit RSU collects the vehicle driving information of all CAVs within the communication control range, where the vehicle driving information includes vehicle length, vehicle position, and vehicle speed information; Step 4: The calculation unit of the road test control unit RSU calculates the optimal coordination strategy and driving trajectory of each CAV with the goal of minimizing the total travel cost of all vehicles in the control area; Step 5: The road test control unit RSU issues control instructions for vehicle decision-making and path planning to the CAV; Step 6: Each CAV passes through the controlled area section according to the control instruction.

2. According to the method of claim 1, the method is characterized in that: The first step also includes the following steps: Step 1: Calculation ; ； in, is the distance between the front vehicle in the target lane and the front of the bus; Step 1 and 2: Calculation ; ； in, is the distance between the rear vehicle in the target lane and the front of the bus, is the length of the bus body; Step 13: Calculate the clearance before safe lane change ; ； In the formula, and Respectively represent the bus and the front vehicle in the target lane The speed of the car at the moment, Indicates the estimated time for the bus to change lanes. It represents the steady-state headway between the bus and the vehicle ahead in the target lane after the lane change is completed; Step 14: Calculate the braking distance of FV ; ； In the formula, and Respectively represent the vehicle behind the target lane in The speed and braking acceleration at the moment, Indicates the reaction time of the vehicle behind in the target lane.

3. According to the method of claim 1, the method is characterized in that: Step 4 also includes the following steps: Step 41: Establish a vehicle dynamics model, in which a two-degree-of-freedom bicycle model is used to characterize the driving characteristics of the CAV in the control area; according to the Ackerman steering principle, the vehicle The kinetic model obtains a set of kinetic differential equations as shown below: ； in, For vehicles exist The coordinates of the midpoint of the rear axis at the moment; 、 、 Indicates vehicle exist Speed, acceleration and variable acceleration at each moment; For vehicles exist The front wheel swing angle at the moment; For vehicles exist The heading angle at the moment; Indicates vehicle Wheelbase; and Respectively represent vehicles The beginning and end moments of accepting control; For vehicles exist The front wheel swing angle velocity at the moment; The state variables of the intelligent connected vehicle are defined as: ； In the formula, is the transpose of the matrix; The control variables are defined as: 。 4. According to the method of claim 1, the method is characterized in that: Step 4 also includes the following steps: Step 42: Calculate the system cost of all vehicles in the control area; the system cost is the sum of the costs of all participants under each specific strategy, and the expression is as follows: ； in, is the total number of vehicles in the control area; Indicates vehicle The cost of a bicycle; Calculate the cost of a single vehicle, where the vehicle driving cost is defined by three factors: driving comfort, traffic efficiency and lane change cost. The cost expressions of different strategies in a finite time domain are defined as: ； ； ； ； ； in, is the shortest travel time; The maximum speed limit in the control area; is the vehicle lane-changing cost; It is a vehicle exist The vertical coordinate change value at the moment; and Respectively represent vehicles exist Time and initial time The vertical coordinate value of is the lane width; , and are different weight coefficients used to reflect the driving preferences of different CAVs for different strategies. It's about time The differential of .

5. According to the method of claim 1, the method is characterized in that: Step 4 also includes the following steps: Step 43: Set the constraints corresponding to the dynamic model; the dynamic constraints applied in the dynamic model include state and control variable constraints and bus exit curvature constraints; The state and control variables in the applicable scenarios are restricted as follows: Movement space restriction: The planned CAV driving trajectory is within the control range, that is, the maximum value of the horizontal and vertical coordinates does not exceed the length of the control area With width ; ； Speed ​​limit: There is a maximum speed limit on the road section in the intelligent connected vehicle control area, and the minimum speed is set to 0m / s; ； is the maximum speed limit of the road section in the control area; Acceleration limit: Intelligent connected vehicles at maximum comfortable deceleration With maximum comfort acceleration Travel between; ； Heading angle limit: The heading angle change range of the intelligent connected vehicle does not exceed the maximum heading angle change value ; ； Front wheel swing angle limit: The front wheel swing angle change range of the intelligent connected vehicle shall not exceed the maximum swing angle change value ; ； Front wheel swing angle speed limit: The front wheel swing angle speed of the intelligent connected vehicle shall not exceed the maximum swing angle speed ; ； The bus leaves the station smoothly, and the curvature of the exit trajectory is constrained. The expression is as follows: ； ； in, represents the curvature of the bus exit trajectory; Indicates that the bus is The vertical position at the moment; Indicates the maximum curvature of the bus exit trajectory; Indicates the minimum turning radius of a bus, ranging from 8m to 12m; Among them, three circles are used to describe the vehicle's outline. The anti-collision formula between vehicles is shown as follows. The formula means that the lane-changing vehicle The outer contour circles of the two vehicles NV do not intersect with each other; ； ； in, and Respectively represent vehicles and the diameter of the NV outer contour circle; Indicates the safety margin, the value is 2.5m; and Indicates vehicle The vertical and horizontal positions, and Indicates the longitudinal and lateral positions of the vehicle NV.

6. According to the method of claim 5, a method for coordinated centralized control of multiple buses leaving a bus station in a networked environment is characterized in that: Step 43 also includes the following steps: The types of collisions between the bus and the port stop during the bus exit process include: collision between the front of the bus and the acceleration section, collision between the middle of the bus and the turning point of the acceleration section, and collision between the tail of the bus and the parking section. The specific obstacle avoidance expressions are as follows: ； ； ； ； in , , It is the tail, middle and head circles that describe the bus's outline. The coordinates of the moment; and Indicates that the bus is Vertical and horizontal position at the moment; and They respectively represent the longitudinal and transverse positions of the turning point of the harbor acceleration section; and Respectively The longitudinal and lateral position of the point closest to the bus in the acceleration section of the harbor at the moment; is the wheelbase of the bus; and Respectively represent the front and rear overhang lengths of the bus; and Respectively indicate the longitudinal and transverse positions of the end point of the harbor parking section; Indicates the length of the bus body; The following restrictions are imposed on the initial and final states of each vehicle in the applicable scenario: In the initial state, vehicles in the same lane are in a stable following state and travel at a constant speed, with the same speed and following distance; After the lane-changing vehicle completes the lane change, it will travel at the same speed as the vehicle in the current lane; The front wheel swing angle and swing angle angular velocity of the vehicle at the initial and final states are zero; vehicle The mathematical expressions of the initial and final state constraints are as follows: ； ； in, and Respectively represent the average speed of the current lane and the lane change target lane.

7. According to the method of claim 1, the method is characterized in that: Step 4 also includes the following steps: Step 44: The original collaborative control problem is regarded as an optimal control problem to minimize the travel cost, that is, to find the optimal control variables under multiple constraints. and optimal terminal time , so that the performance index function, i.e. the objective function, takes the minimum value, wherein the continuous optimal control problem is first transformed into a discrete nonlinear programming problem using the pseudo-spectral method, and then the interior point method of large-scale complex problems is used to solve it; vehicle Optimal control of It is expressed by the following formula: ； It is a vehicle The state vector of It is a vehicle The control vector of It is a vehicle The initial moment of accepting RSU control, Indicates vehicle At the initial moment The state vector of .

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