A multi-vehicle collaborative centralized control method for buses leaving the station in a connected environment

Abstract

The present invention relates to the field of intelligent networking, and particularly relates to a multi-vehicle collaborative centralized control method for a bus to depart from a station in a networked environment, including: (1) After the intelligent networked bus completes the tasks of boarding and alighting passengers at the stop, it determines whether the bus can depart from the station at the current moment through the on-vehicle computing unit; (2) When the conditions for departing from the station are not met, a lane-changing request is sent to the intelligent roadside unit at the bay stop; (3) After receiving the bus lane-changing request, the RSU collects the vehicle driving information of all networked autonomous vehicles within the communication control range; (4) The RSU calculates the optimal collaborative strategy and driving trajectory of each CAV with the goal of minimizing the total traffic cost of all vehicles within the control area; (5) The RSU issues control instructions for vehicle decision-making and planned paths to the CAVs; (6) Each CAV passes through the control area section according to the control instructions; realizing the control of the driving behaviors of multiple vehicles, and enabling the regional vehicles to jointly complete the collaborative task of the bus changing lanes and departing from the station.

Description

Technical Field

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

[0002] With the development of intelligent network connection 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 popular 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 of the formation.

[0003] The lane-changing behavior of a vehicle includes multiple complex elements such as the generation of lane-changing intention, the judgment of lane-changing conditions, the implementation of lane-changing, and the 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-side stop, due to the characteristics of the long bus body and small starting speed, the outbound lane-changing behavior of the bus is likely to conflict with the oncoming straight traffic flow. This conflict relationship will reduce vehicle driving safety and road section traffic efficiency, and have a negative external impact on regional traffic. If the bus delays leaving the station to avoid conflicts with straight vehicles, this 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 vehicle behind 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 detection data of roadside units RSU-1 and RSU-2 are relied on to select the best cooperative vehicle, realizing cooperative lane-changing between the lane-changing vehicle on the ramp and the cooperative vehicle on the main line of the expressway. 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 realize the lane-changing of buses when leaving the station. The above patents and literatures only consider the interaction relationship between the lane-changing vehicle and the vehicle behind in the target lane, ignoring the impact of lane-changing behavior on other vehicles in the area. The centralized cooperative control method can realize 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 realize 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:

[0007] 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 in 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 inter-vehicle gap in the target lane meets the bus lane-changing requirements, and the bus can leave the station smoothly; otherwise, execute Step 2;

[0008] Step 2: When the departure conditions are not met, the bus sends a lane-changing request to the roadside control unit RSU;

[0009] 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. The vehicle driving information includes the vehicle body length, vehicle position, and vehicle speed information.

[0010] Step 4: The calculation unit of the roadside control unit RSU calculates the optimal cooperation strategy and driving trajectory of each CAV with the goal of minimizing the total passing cost of all vehicles in the control area.

[0011] Step 5: The roadside control unit RSU issues control instructions for vehicle decision-making and planned paths to the CAV.

[0012] Step 6: Each CAV passes through the control area section according to the control instructions.

[0013] As an optimization of the present invention, the following steps are further included in Step 1:

[0014] Step 1-1: Calculate ;

[0015] ;

[0016] where is the distance between the vehicle in front in the target lane and the bus head.

[0017] Step 1-2: Calculate ;

[0018] ;

[0019] where is the distance between the vehicle behind in the target lane and the bus head, is the bus body length;

[0020] Step 1-3: Calculate the clearance before safe lane change ;

[0021] ;

[0022] In the formula, and respectively represent the vehicle speeds of the bus and the vehicle in front in the target lane at moment, represents the expected lane-changing time of the bus, represents the steady-state time headway between the bus and the vehicle in front in the target lane after lane change;

[0023] Step 1-4: Calculate the braking distance of FV ;

[0024] ;

[0025] In the formula, and respectively represent the speed and braking acceleration of the vehicle behind in the target lane at moment, represents the reaction time of the vehicle behind in the target lane.

[0026] As a preference of the present invention, the following steps are further included in step four:

[0027] Step four-one: 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 Ackerman steering principle, a set of dynamic differential equations are obtained from the vehicle dynamics model as shown in the following formula:

[0028] ;

[0029] Among them, is the coordinate of the midpoint of the rear axle of the vehicle at moment; 、 、 represent the speed, acceleration and variable acceleration of the vehicle at moment; is the front wheel swing 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 front wheel swing angular velocity of the vehicle ;

[0030] The state variables of the intelligent connected vehicle are defined as:

[0031] ;

[0032] In the formula, is the transpose of the matrix;

[0033] The control variables are defined as:

[0034] .

[0035] As a preference of the present invention, the following steps are further included in step four:

[0036] 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:

[0037] ;

[0038] where, is the total number of vehicles in the control area; represents the single-vehicle cost of vehicle ;

[0039] 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 a finite time domain is defined as:

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] where, is the shortest travel time; is the maximum speed limit in the control area; is the vehicle lane-changing cost; is vehicle at the vertical coordinate change value at the moment; and respectively represent the vertical coordinate values of vehicle at the 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 ;

[0046] As a preference of the present invention, the following steps are further included in Step 4:

[0047] Step 43: 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;

[0048] The status and control variable limits in the applicable scenarios are as follows:

[0049] 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 of the control area and the width ;

[0050] ;

[0051] 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;

[0052] ;

[0053] is the maximum speed limit value of the control area section.

[0054] Acceleration limit: The intelligent connected vehicle travels between the maximum comfortable deceleration and the maximum comfortable acceleration ;

[0055] ;

[0056] Heading angle limit: The change range of the heading angle of the intelligent connected vehicle does not exceed the maximum heading angle change value ;

[0057] ;

[0058] 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 ;

[0059] ;

[0060] Front wheel swing angular velocity limit: The front wheel swing angular velocity of the intelligent connected vehicle does not exceed the maximum swing angular velocity ;

[0061] ;

[0062] The bus smoothly departs from the station, and the curvature of the departure trajectory is constrained. Its expression is shown as follows:

[0063] ;

[0064] ;

[0065] Among them, represents the curvature of the bus departure trajectory; represents the longitudinal position of the bus at the 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;

[0066] Among them, three circles are used to describe the vehicle outline. 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;

[0067] ；

[0068] ;

[0069] Among them, and respectively represent the vehicle and the diameter of the outer contour circle of NV; Represents the safety margin, with a value of 2.5m; and represent the vehicle 's longitudinal and lateral positions, and represent the longitudinal and lateral positions of the vehicle NV.

[0070] As a preference of the present invention, the following steps are further included in step four three:

[0071] The collision types between the bus 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:

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] Among them , , are the coordinates of the centers of the tail, middle, and head circles describing the bus outline 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 acceleration section of the moment bay; is the wheelbase of the bus; and represent the front overhang and rear overhang lengths of the bus respectively; and represent the longitudinal and lateral positions of the end point of the bay parking section respectively; represents the body length of the bus;

[0077] Among them, the initial and final states of each vehicle in the scenario are restricted as follows:

[0078] In the initial state, the vehicles in the same lane are in a stable following state and drive at a constant speed, with the same speed and following distance;

[0079] After the lane-changing vehicle finishes lane-changing, it should maintain the same driving speed as the vehicles in the current lane;

[0080] The front wheel swing angle and swing angular velocity of the vehicle at the initial and final states are zero;

[0081] Vehicle The mathematical expressions for the constraints of the initial and final states are as follows:

[0082] ；

[0083] ;

[0084] Among them, and represent the average speeds of the current lane and the lane-changing target lane respectively.

[0085] As a preference of the present invention, the following steps are further included in step four:

[0086] 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 , 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;

[0087] Vehicle The optimal control of is represented by the following formula:

[0088]

[0089] is the state vector of vehicle ; is the vehicle 's control vector; is the vehicle the initial moment when it accepts RSU control. represents the vehicle at the initial moment 's state vector.

[0090] The PRM pseudospectral 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.

[0091] 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.

[0092] 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 realize 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 area of the bay - type bus stop. Brief Description of the Drawings

[0093] 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:

[0094] Figure 1 is the schematic diagram of the operation process in the present invention;

[0095] Figure 2 is the schematic diagram of the research scenario in the present invention;

[0096] Figure 3 is the schematic diagram of the vehicle driving attitude in the present invention;

[0097] Figure 4 Description diagram of the vehicle's external contour in the present invention;

[0098] Figure 5 Shape diagram of the harbor stop in the present invention;

[0099] Figure 6 Trajectory planning diagram of the implementation case in the present invention;

[0100] Figure 7 Evolution diagram of the example bus control variables in the present invention;

[0101] Figure 8 Schematic diagram of the simulation case parameters in the present invention. Detailed implementation method

[0102] Example 1;

[0103] Refer to Figures 1-7 In this embodiment, a centralized control method for multi-vehicle collaborative bus lane-changing and leaving the station in an intelligent networked environment is proposed. Among them, the bus stop is a harbor-style stop, located in the middle reaches 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. In this embodiment, a one-way two-lane urban road is taken 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 in the research scenario of this embodiment are self-driving vehicles above L3 level. The vehicles are equipped with intelligent perception devices such as sensors and lidar and are configured with in-vehicle computing units, and are driving in an intelligent networked environment. The RSU near the harbor-style bus stop has intelligent perception and online computing functions. The transmission of vehicle status 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 with respect to the control instructions issued by the RSU is 100%.

[0104] A centralized control method for multi-vehicle collaborative bus leaving the station in a networked environment provided by this embodiment specifically includes the following steps:

[0105] Step 1: After the bus completes the boarding and alighting tasks, judge the outbound conditions. When the front distance between the bus and the vehicle LV in front in 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 moment's vehicle gap in the target lane meets the bus lane-changing requirements, and the bus can leave the station smoothly; otherwise, execute Step 2;

[0106] Step 11: Calculate ;

[0107] ;

[0108] Among them, is the distance between the vehicle in front of the bus in the target lane and the front of the bus;

[0109] Step 1-2: Calculate ;

[0110]

[0111] Among them, is the distance between the vehicle behind the bus in the target lane and the front of the bus, is the length of the bus body;

[0112] Step 1-3: Calculate the clearance before safe lane change ;

[0113]

[0114] In the formula, and respectively represent the vehicle speeds of the bus and the vehicle in front of it in the target lane at moment, represents the estimated lane change time of the bus, represents the steady-state time headway between the bus and the vehicle in front of it in the target lane after the lane change;

[0115] Step 1-4: Calculate the braking distance of FV ;

[0116] ;

[0117] In the formula, and respectively represent the speed and braking acceleration of the vehicle behind the bus in the target lane at moment, represents the reaction time of the vehicle behind the bus in the target lane.

[0118] Step 2: When the departure condition is not met, the bus sends a lane change request to the roadside control unit RSU;

[0119] 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. Among them, the vehicle driving information includes the vehicle body length, vehicle position, and vehicle speed information;

[0120] Step 4: The RSU calculation unit calculates the optimal cooperative strategy and driving trajectory of each CAV with the goal of minimizing the total cost of all vehicle passages in the control area;

[0121] 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 Ackerman steering principle, from the vehicle dynamics model, a set of dynamic differential equations are obtained as shown in the following formula:

[0122] ;

[0123] Where, is the coordinate of the midpoint of the rear axle of the vehicle ; 、 、 represents the speed, acceleration and jerk of the vehicle at time; is the front wheel steering angle of the vehicle at time; is the heading angle of the vehicle at time; represents the wheelbase of the vehicle ; and respectively represent the start and end times when the vehicle receives control; is the angular velocity of the front wheel steering angle of the vehicle at time.

[0124] The state variables of the intelligent connected vehicle are defined as:

[0125] ;

[0126] The control variables are defined as:

[0127] ;

[0128] Step 4-2: 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:

[0129] ;

[0130] Where, is the total number of vehicles in the control area;

[0131] Calculate the cost of a single vehicle, where 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 in a finite time domain is defined as:

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] Among them, is the shortest travel 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 time (current time) and the vertical coordinate value at the initial time ; 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 ;

[0138] 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;

[0139] The restrictions on state and control variables in the applicable scenarios are as follows:

[0140] Motion space limit: 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 control area length and width ;

[0141] ;

[0142] Speed limit: There is a maximum speed limit for the intelligent connected vehicle control area section, and the minimum speed is set to 0 m / s to avoid the appearance of backward trajectories;

[0143] ;

[0144] is the maximum speed limit value of the control area section;

[0145] Acceleration limit: To ensure driving comfort, the intelligent connected vehicle should drive between the maximum comfortable deceleration and the maximum comfortable acceleration ;

[0146] ;

[0147] Heading angle limit: The change range of the heading angle of the intelligent connected vehicle should not exceed the maximum heading angle change value ;

[0148] ;

[0149] 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 ;

[0150] ;

[0151] 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 ;

[0152] ;

[0153] Buses are characterized by their long body length and slow starting speed. To ensure the smooth departure of buses, corresponding constraints on the curvature of the departure trajectory are also required. The expression is as follows:

[0154] ;

[0155] ;

[0156] where, represents the curvature of the bus departure trajectory; represents the longitudinal position of the bus at time; represents the maximum value of the curvature of the bus departure trajectory; represents the minimum turning radius of the bus, usually taken as 8m - 12m;

[0157] where, three circles are used to describe the vehicle outer contour. The formula for anti-collision 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;

[0158] ；

[0159] ;

[0160] where, and respectively represent the vehicle and the diameter of the NV outer contour circle; represents the safety margin, which is taken as 2.5 m here; and represent the vehicle 's longitudinal and lateral positions, and represent the longitudinal and lateral positions of the vehicle NV;

[0161] The collision types between the bus during the process of leaving the station and the bay stop include: collision of the head of the vehicle body with the acceleration section, the middle part with the inflection point of the acceleration section, and the tail with the parking section. The specific obstacle avoidance expressions are as follows:

[0162] ;

[0163] ;

[0164] ;

[0165] ;

[0166] where , , are the coordinates of the centers of the circles at the tail, middle, and head of 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;

[0167] Among them, the initial and final states of each vehicle in this scenario are restricted as follows:

[0168] In the initial state, the vehicles in the same lane are in a stable following state and drive at a constant speed, with the same speed and following distance;

[0169] After the lane-changing vehicle finishes lane-changing, it should maintain the same driving speed as the vehicle in the current lane;

[0170] The front wheel steering angle and steering angular velocity of the vehicle at the start and end states should be zero;

[0171] Vehicle The mathematical expressions for the constraints at the start and end states are as follows:

[0172] ；

[0173] ;

[0174] Among them, and represent the average speeds of the current lane and the lane-changing target lane respectively;

[0175] 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;

[0176] Vehicle The optimal control of is represented by the following formula:

[0177]

[0178] 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 ;

[0179] Step Five: The RSU issues control instructions for vehicle decisions and planned paths to the CAV;

[0180] Step Six: Each CAV passes through the control area section according to the control instructions.

[0181] Embodiment 2;

[0182] For the convenience of those skilled in the art to understand, the present invention will be further described below in combination with a case and with reference to Figures 1-8 The content mentioned in the case is not a limitation of the present invention.

[0183] This embodiment provides a centralized control method for multi-vehicle cooperative bus lane-changing and departure in an intelligent connected environment.

[0184] The calculation steps are as follows:

[0185] Step 1: The bus determines the departure conditions. According to the vehicle position relationship set in the case, , ; . Since , the on-vehicle computing unit of the bus determines that the departure conditions are not met.

[0186] Step 2: When the departure conditions are not met, the bus sends a lane-changing request to the roadside control unit RSU.

[0187] 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, etc.

[0188] Step 4: The RSU calculation unit analyzes with the goal of optimizing the total passing cost of all vehicles in the control area, and calculates the optimal cooperative strategy and driving trajectory of each CAV. This step is realized based on the MATLAB platform program simulation. According to the simulation analysis results, Vehicle 5 and Vehicle 17 selected lane-changing cooperation, and the rest of the social vehicles maintained following and carried out acceleration and deceleration cooperation.

[0189] Step 5: The RSU issues control instructions for vehicle decisions and planned paths to the CAVs.

[0190] Step 6: Each CAV passes through the control area section according to the control instructions.

[0191] 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 multi-vehicle collaborative centralized control method for buses leaving the station in a connected environment, characterized in that It includes the following steps: Step 1: After the bus completes the task of picking up and dropping off passengers, judge the outbound condition. When the front distance Δy1 between the bus and the vehicle LV in front in the target lane is greater than the safe lane-changing front gap Gap (Bus,LV) , and the rear distance Δy2 between the bus and the vehicle FV behind in the target lane is greater than the braking distance d of the vehicle FV behind in the target lane (Bus,FV) , it is determined that the current lane gap in the target lane 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 condition is 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. Among them, the vehicle driving information includes the vehicle body length, vehicle position, and vehicle speed information; Step 4: The calculation unit of the roadside control unit RSU calculates the optimal cooperative strategy and driving trajectory of each CAV with the goal of minimizing the total passing cost of all vehicles in the control area; Step 4-2: 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: where N is the total number of vehicles in the control area; J i represents the single-vehicle cost of vehicle i; Calculate the single-vehicle cost, where the vehicle driving cost is defined by three factors: driving comfort, passing efficiency, and lane-changing cost. The cost expression of vehicle i under different strategies in a finite time domain is defined as: Δy i (t) = y i (t) - y i (t0); κ1 + κ2 + κ3 = 1; where t desire is the shortest travel time; v max is the maximum speed limit in the control area; C i is the vehicle lane-changing cost; Δy i (t) is the vertical coordinate change value of vehicle i at time t; y i (t) and y i (t0) represent the vertical coordinate values of vehicle i at time t and the initial time t0 respectively; R W is the lane width; κ1, κ2, and κ3 are different weight coefficients used to reflect the driving preferences of different CAVs for different strategies, and dt is the differential of time t; Step 5: The roadside control unit RSU issues control instructions for vehicle decisions and planned paths to the CAV; Step 6: Each CAV passes through the control area section according to the control instructions.

2. The centralized control method for multi-vehicle cooperation when a bus departs from a station in a networked environment according to claim 1, wherein The following steps are also included in Step 1: Step 1-1: Calculate Δy1; Δy1 = |y LV |; Among them, y LV is the distance between the vehicle in front on the target lane and the front of the bus; Step 1-2: Calculate Δy2; Δy2 = |y FV -l Bus |; Among them, y FV is the distance between the vehicle behind on the target lane and the front of the bus, and l Bus is the length of the bus body; Step 1-3: Calculate the gap Gap before safe lane change (Bus,LV) ; where, v Bus (t) and v LV (t) respectively represent the vehicle speeds of the bus and the vehicle in front on the target lane at time t, T1 represents the predicted lane-changing time of the bus, and τ represents the steady-state time headway between the bus and the vehicle in front on the target lane after the lane change is completed; Step 14: Calculate the braking distance d of FV (Bus,FV) ; where, v FV (t) and a FV (t) respectively represent the speed and braking acceleration of the vehicle behind in the target lane at time t, and r represents the reaction time of the vehicle behind in the target lane.

3. The centralized control method for multi-vehicle cooperation when a bus departs from a station in a networked environment according to claim 1, wherein The following steps are also 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 dynamics model of vehicle i as shown in the following formula: Among them, (x i (t), y i (t)) is the coordinate of the midpoint of the rear axle of vehicle i at time t; v i (t), a i (t), u i (t) represent the speed, acceleration, and jerk of vehicle i at time t; δ i (t) is the front wheel steering angle of vehicle i at time t; is the heading angle of vehicle i at time t; L W represents the wheelbase of vehicle i; and respectively represent the start and end times when vehicle i receives control; ω i (t) is the angular velocity of the front wheel steering angle of vehicle i at time t; The state variables of the intelligent connected vehicle are defined as: Where, T is the transpose of the matrix; The control variables are defined as: U i (t) = [a i (t), ω i (t)] T 。 4. A multi-vehicle collaborative centralized control method for buses leaving the station in a connected environment according to claim 1, characterized in that, The following steps are also included in Step 4: Step 4-3: Set the constraint conditions corresponding to the dynamics model; Among them, the dynamic constraints applied in the dynamics model include two parts: state and control variable constraints and bus outbound curvature constraints; The restrictions on state and control variables in the applicable scenario 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 L and width W of the control area; Speed limit: There is a maximum speed limit on the section of the intelligent connected vehicle control area, and the minimum speed is set to 0 m / s; v ∈ [0, v max ; v max is the maximum speed limit value of the control section of the road; Acceleration limit: The intelligent connected vehicle travels between the maximum comfortable deceleration a min and the maximum comfortable acceleration a max ; a ∈ [a min , a max ; Heading angle limit: The variation range of the heading angle of the intelligent connected vehicle does not exceed the maximum heading angle variation value Front wheel steering angle limit: The change range of the front wheel steering angle of the intelligent connected vehicle does not exceed the maximum steering angle change value Δδ max ; 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 Ω max ; ω ∈ [0, Ω max ; The bus smoothly exits the station, and the curvature of the outbound trajectory is constrained. Its expression is as shown in the following formula: Among them, K represents the curvature of the bus departure trajectory; y b (t) represents the longitudinal position of the bus at time t; K max represents the maximum value of the curvature of the bus departure trajectory; R min represents the minimum turning radius of the bus, with a value range of 8m - 12m; Among them, 3 circles are used to describe the vehicle outer contour. The anti-collision formula between vehicles is as shown in the following formula. The meaning of the formula is that the outer contour circles of the lane-changing vehicle i and the surrounding vehicles NV do not intersect pairwise; Among them, and d NV respectively represent the outer contour circle diameter of vehicle C i and NV; ε represents the safety margin, with a value of 2.5 m; and represent the longitudinal and lateral positions of vehicle C i where x NV and y NV represent the longitudinal and lateral positions of vehicle NV.

5. A multi-vehicle collaborative centralized control method for buses leaving the station in a networked environment according to claim 4, characterized in that The following steps are also included in Step 4-3: The collision types between the bus outbound process and the bay stop include: the head of the vehicle body collides with the acceleration section, the middle part collides with the inflection point of the acceleration section, and the tail collides with the parking section. The specific obstacle avoidance expressions are as follows: Among them are the coordinates of the centers of the circles at the tail, middle, and head of the bus's outer contour at time t; x B (t) and y B (t) represent the longitudinal and lateral positions of the bus at time t; and represent the longitudinal and lateral positions of the inflection point of the bay acceleration section respectively; x C (t) and y C (t) represent the longitudinal and lateral positions of the point closest to the bus in the bay acceleration section at time t respectively; L B W is the wheelbase of the bus; L B F and L B R represent the lengths of the front overhang and rear overhang of the bus respectively; and represent the longitudinal and lateral positions of the end point of the bay parking section respectively; L b represents the length of the bus body; Among them, the initial and final states of each vehicle in the applicable scenario are restricted as follows: At the initial state, the vehicles in the same lane are in a stable following state and drive at a constant speed, with the same speed and following distance; After the lane-changing vehicle finishes lane-changing, it has the same driving speed as the vehicle in the current lane; The front wheel swing angle and swing angular velocity of the vehicle initial and final states are zero; The mathematical expression of the constraints on the initial and final states of vehicle i is as follows: Among them, and respectively represent the average speeds of the current lane and the lane-changing target lane.

6. The multi-vehicle collaborative centralized control method for buses leaving the station in a networked environment according to claim 1, wherein The following steps are also included in Step 4: 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 under various constraints and the optimal terminal time t f , so that the performance index function, that is, the objective function, takes the minimum value. First, the pseudospectral method is used to transform the continuous optimal control problem into a discrete-form nonlinear programming problem, and then the interior point method for large-scale complex problems is used for solution; Optimal control of vehicle i is expressed by the following formula: X i is the state vector of vehicle i; U i is the control vector of vehicle i; is the initial moment when vehicle i receives RSU control, indicating the state vector of vehicle i at the initial moment .

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