Intersection-oriented intelligent network connection motorcade passing method

Abstract

The invention relates to an intersection-oriented intelligent network connection motorcade passing method. The method comprises the following steps: step 1, obtaining a future green light scheme and a vehicle queuing length of a current lane; 2, estimating queuing dissipation time according to the vehicle queuing length; step 3, obtaining available green light time according to a future green light scheme and queuing dissipation time; 4, obtaining the state of the front vehicle of the motorcade, and calculating the state of the front vehicle in the future time period; and step 5, constructing a model prediction control problem which takes the situation that the motorcade is not split by the signal as a constraint and takes the minimum fuel consumption of the motorcade as a target, solving the control problem to obtain a control instruction of each vehicle in the motorcade, and transmitting the control instruction to each vehicle in the motorcade for execution. Compared with the prior art, the method has the advantages that the motorcade is prevented from being detached by the signal lamps, and energy consumption of the motorcade passing through the intersection is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent network-connected vehicles, and in particular to a method for intelligent network-connected vehicle fleets to pass through intersections. Background Art

[0002] With the development of intelligent transportation systems, intelligent connected vehicles have become an important means to improve traffic efficiency, reduce energy consumption and improve road safety. Compared with a single intelligent connected car, an intelligent connected fleet can effectively reduce the distance between vehicles through vehicle-to-vehicle collaboration, which is more conducive to reducing vehicle energy consumption and improving traffic efficiency.

[0003] However, current research on intelligent networked fleets is limited to highway environments. In highway environments, the fleet is not disturbed by traffic lights. However, in intersection environments, the intelligent networked fleet faces the problem of being dismantled and unable to ensure integrity. At the same time, intersections will also cause the fleet to start and stop frequently, greatly increasing vehicle energy consumption. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent networked vehicle fleet passage method for intersections in order to avoid the vehicle fleet being separated by traffic lights while reducing the energy consumption of the vehicle fleet passing through the intersection.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for passing a smart connected vehicle fleet at an intersection, the method comprising the following steps:

[0007] Step 1: The central controller of the fleet communicates with the roadside equipment at the intersection to obtain the future green light plan and vehicle queue length of the current lane;

[0008] Step 2: Estimate the queue dissipation time based on the length of the vehicle queue;

[0009] Step 3: Obtain the available green light time according to the future green light plan and the queue dissipation time;

[0010] Step 4: Obtain the status of the vehicle in front of the convoy and calculate the status of the vehicle in front in the future time period;

[0011] Step 5: Obtain the status of the convoy. Based on the model predictive control method, combined with the available green light time and the status of the vehicle ahead in the future time period, construct a model predictive control problem with the constraint that the convoy is not split by signals and the goal of minimizing the fuel consumption of the convoy. Solve the control problem to obtain the control instructions for each vehicle in the convoy, and pass the control instructions to each vehicle in the convoy for execution.

[0012] Furthermore, the queue dissipation time is:

[0013]

[0014] Among them, t disspation is the time required for all queued vehicles to pass through the intersection, i.e., the queue dissipation time, l queue is the queue length, l length is the average length of a vehicle, and τ is the average time required for a vehicle to pass through the intersection.

[0015] Furthermore, the available green time is:

[0016]

[0017] in, are the start and end time of the available green light, t start ,t end They are the start and end time of the current green light or the next green light respectively.

[0018] Furthermore, the state of the front vehicle in the future time period is:

[0019]

[0020] Among them, P′ i is the state of the front vehicle in the ith prediction time domain, that is, the longitudinal position of the front vehicle in the ith prediction time domain, i represents the sequence number of the prediction time domain, P′0, v′0 and a′0 represent the initial position, initial instantaneous velocity and initial instantaneous acceleration of the front vehicle in the platoon respectively, Δt represents the time interval, and N represents the prediction time domain.

[0021] Furthermore, step 5 is specifically as follows:

[0022] Step 5.1: Define the state of the fleet;

[0023] Step 5.2: Define the desired state of the fleet;

[0024] Step 5.3: Define the control volume of the fleet;

[0025] Step 5.4: According to the current position and speed of the fleet, the initial value of the state quantity is obtained;

[0026] Step 5.5: Set the update equation of the state quantity;

[0027] Step 5.6: Set the objective function of the model predictive control problem with the goal of minimizing the fuel consumption of the fleet;

[0028] Step 5.7: Set the obstacle avoidance constraints with the front vehicle so that the convoy is not split by the signal;

[0029] Step 5.8: Design green light passing constraints;

[0030] Step 5.9: Solve the control problem to obtain the control instructions for each vehicle in the fleet, and transmit the control instructions to each vehicle in the fleet for execution.

[0031] Furthermore, the state of the fleet is:

[0032]

[0033] Among them, ξ i represents the state quantity in the i-th prediction time domain, M represents the total number of vehicles in the fleet, and the superscript represents the vehicle serial number. represents the state of the jth vehicle, j = 1, 2, ... M, which is defined as Respectively represent the position and speed of the j-th vehicle in the i-th prediction time domain;

[0034] The expected state quantity is:

[0035]

[0036] in, represents the expected state quantity in the i-th prediction time domain, represents the expected state of the jth vehicle, which is defined as They represent the expected position and expected speed of the j-th vehicle in the i-th prediction time domain, which are set to the center position of the intersection and the road speed limit, respectively;

[0037] The control quantity is:

[0038]

[0039] Among them, u i represents the control amount in the i-th prediction time domain, represents the acceleration of the j-th vehicle in the i-th prediction time domain.

[0040] Furthermore, the update equation of the state quantity is:

[0041]

[0042] in, They represent the position and speed of the j-th vehicle in the i+1-th prediction time domain respectively.

[0043] Furthermore, the objective function is:

[0044]

[0045] Among them, a1, a2, a3 are weighting factors, which respectively represent the importance attached to position error, speed error, and comfort, and J is the objective function.

[0046] Furthermore, the obstacle avoidance constraints include the obstacle avoidance between the first vehicle in the convoy and the vehicle in front and the obstacle avoidance between vehicles within the convoy. The obstacle avoidance between the first vehicle in the convoy and the vehicle in front is:

[0047]

[0048] Obstacle avoidance between vehicles within the convoy is:

[0049]

[0050] Furthermore, the green light traffic constraint is:

[0051]

[0052] Among them, p stop Indicates the location of the stop line at the intersection.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention obtains the future green light plan of the current lane and estimates the dissipation time of the vehicles in the queue ahead to obtain the final available green light duration. Based on the model predictive control method, the optimal control instructions of each vehicle in the fleet are planned with the constraint that the fleet is not split by signals and the goal of minimizing the fuel consumption of the fleet. At the same time, it can reduce the energy consumption of the intelligent networked fleet passing through the intersection, avoid the fleet being split by traffic lights, and ensure the integrity of the fleet. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0056] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0057] The present invention proposes a method for intelligent networked vehicle fleet traffic at intersections, the flow chart of which is as follows: Figure 1 As shown, the method comprises the following steps:

[0058] Step 1: The central controller of the fleet communicates with the roadside equipment at the intersection to obtain the future green light plan and vehicle queue length of the current lane;

[0059] Step 2: Estimate the queue dissipation time based on the length of the vehicle queue;

[0060] Step 3: Obtain the available green light time according to the future green light plan and the queue dissipation time;

[0061] Step 4: Obtain the status of the vehicle in front of the convoy and calculate the status of the vehicle in front in the future time period;

[0062] Step 5: Obtain the status of the convoy. Based on the model predictive control method, combined with the available green light time and the status of the vehicle ahead in the future time period, construct a model predictive control problem with the constraint that the convoy is not split by signals and the goal of minimizing the fuel consumption of the convoy. Solve the control problem to obtain the control instructions for each vehicle in the convoy, and pass the control instructions to each vehicle in the convoy for execution.

[0063] In step 1, the future green light solution obtained is the start and end time of the current green light. If the current signal light is red, it is the start and end time of the next green light. The start and end time are denoted as t start ,t end .

[0064] In step 2, the estimation method of queue dissipation time is as follows:

[0065]

[0066] where t disspation It is the time required for all queued vehicles to pass through the intersection, i.e., the queue dissipation time. queue is the queue length. length is the average length of a vehicle. τ is the average time required for a vehicle to pass through the intersection.

[0067] In step 3, the calculation formula for the available green time is as follows:

[0068]

[0069] in They are the start and end time of the available green light respectively.

[0070] Step 4 includes the following steps:

[0071] Step 4.1: Set Δt = 0.1S, set prediction time domain N = 100

[0072] Step 4.2: Get the position P′0, instantaneous speed v′0, and instantaneous acceleration a′0 of the vehicle in front of the convoy, then the longitudinal position of the vehicle in front is, where i represents the prediction time domain:

[0073]

[0074] Step 5 includes the following steps:

[0075] Step 5.1: Define the state of the fleet, as follows:

[0076]

[0077] where ξ i represents the state quantity in the i-th prediction time domain. M represents the total number of vehicles in the fleet. The superscript represents the vehicle serial number. represents the state of the first car, which is defined as in They represent the position and speed of the first vehicle in the i-th prediction time domain respectively.

[0078] Step 5.2: Define the desired state of the fleet, which is defined as follows:

[0079]

[0080] in Represents the expected state quantity in the i-th prediction time domain. represents the desired state of the first car, which is defined as The desired position is usually beyond the stop line, and the desired speed can be set to the road speed limit. Step 5.3: Define the control quantity of the fleet, which is defined as follows

[0081]

[0082] where u i Represents the control amount in the i-th prediction time domain. represents the acceleration of the first vehicle in the i-th prediction time domain.

[0083] Step 5.4: According to the current position and speed of the fleet, the initial value ξ0 of the state variable is obtained.

[0084] Step 5.5: The update equation of the vehicle state in the fleet is designed as follows:

[0085]

[0086] in, They represent the position and speed of the j-th vehicle in the i+1-th prediction time domain respectively.

[0087] Step 5.6: Considering the driving ecology and efficiency of the fleet, set the objective function of the model predictive control problem:

[0088]

[0089] Step 5.7: Let the safety distance be l. For each vehicle in the convoy, it is necessary to set the obstacle avoidance constraint with the vehicle in front. The obstacle avoidance constraint between the first vehicle in the convoy and the vehicle in front is:

[0090]

[0091] Obstacle avoidance between vehicles within the convoy is:

[0092]

[0093] Step 5.8: Design green light traffic constraints. Ensure that the first vehicle cannot cross the intersection stop line before the available green light starts, as shown below:

[0094]

[0095] It is also necessary to ensure that after the available green light ends, the last vehicle has passed the intersection stop line, as shown below:

[0096]

[0097] Step 5.9: Solve the above problem. If the problem has a solution, calculate the optimal control quantity. The control instructions of each vehicle are transmitted to each vehicle for execution. If the problem has no solution, it means that the convoy cannot completely pass the intersection within the available green light time, and the convoy can drive slowly and wait for the next green light cycle.

[0098] The present invention proposes an intelligent networked convoy traffic method for intersections. The method first obtains the future green light plan of the current lane through the roadside equipment, estimates the dissipation time of the queued vehicles in front, and obtains the final available green light duration. Finally, based on the model predictive control method, the optimal control instructions of each vehicle in the convoy are planned with the constraint that the convoy is not split by signals and the goal of minimizing the fuel consumption of the convoy.

[0099] The beneficial effects of the present invention are:

[0100] 1. A method for intersection passage of intelligent connected vehicle fleets is proposed, filling the gap in this research.

[0101] 2. It can reduce the energy consumption of intelligent networked fleets passing through intersections, prevent the fleet from being broken up by traffic lights, and ensure the integrity of the fleet

[0102] 3. The algorithm has the computational efficiency of engineering application level, reducing the computing load.

[0103] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for passing a smart connected vehicle fleet at an intersection, characterized in that: The method comprises the following steps: Step 1: The central controller of the fleet communicates with the roadside equipment at the intersection to obtain the future green light plan and vehicle queue length of the current lane; Step 2: Estimate the queue dissipation time based on the length of the vehicle queue; Step 3: Obtain the available green light time according to the future green light plan and the queue dissipation time; Step 4: Obtain the status of the vehicle in front of the convoy and calculate the status of the vehicle in front in the future time period; Step 5: Obtain the status of the convoy. Based on the model predictive control method, combined with the available green light time and the status of the vehicle ahead in the future time period, construct a model predictive control problem with the constraint that the convoy is not split by signals and the goal of minimizing the fuel consumption of the convoy. Solve the control problem to obtain the control instructions for each vehicle in the convoy, and pass the control instructions to each vehicle in the convoy for execution.

2. The method for passing an intelligent networked vehicle fleet at an intersection according to claim 1, characterized in that: The queue dissipation time is: Among them, t disspation is the time required for all queued vehicles to pass through the intersection, i.e., the queue dissipation time, l queue is the queue length, l length is the average length of a vehicle, and τ is the average time required for a vehicle to pass through the intersection.

3. The method for passing an intelligent networked vehicle fleet at an intersection according to claim 1, characterized in that: Available green light times are: in, are the start and end time of the available green light, t start ,t end They are the start and end time of the current green light or the next green light respectively.

4. The method for passing an intelligent networked vehicle fleet at an intersection according to claim 1, characterized in that: The state of the vehicle ahead in the future time period is: Among them, P′ i is the state of the front vehicle in the ith prediction time domain, that is, the longitudinal position of the front vehicle in the ith prediction time domain, i represents the sequence number of the prediction time domain, P′0, v′0 and a′0 represent the initial position, initial instantaneous speed and initial instantaneous acceleration of the front vehicle in the convoy respectively, Δt represents the time interval, and N represents the prediction time domain.

5. The method for passing an intelligent networked vehicle fleet at an intersection according to claim 1, characterized in that: Step 5 is as follows: Step 5.1: Define the state of the fleet; Step 5.2: Define the desired state of the fleet; Step 5.3: Define the control volume of the fleet; Step 5.4: According to the current position and speed of the fleet, the initial value of the state quantity is obtained; Step 5.5: Set the update equation of the state quantity; Step 5.6: Set the objective function of the model predictive control problem with the goal of minimizing the fuel consumption of the fleet; Step 5.7: Set the obstacle avoidance constraints with the front vehicle so that the convoy is not split by the signal; Step 5.8: Design green light passing constraints; Step 5.9: Solve the control problem to obtain the control instructions for each vehicle in the fleet, and transmit the control instructions to each vehicle in the fleet for execution.

6. The method for passing an intelligent networked vehicle fleet at an intersection according to claim 5, characterized in that: The state quantity of the fleet is: Among them, ξ i represents the state quantity in the i-th prediction time domain, M represents the total number of vehicles in the fleet, and the superscript represents the vehicle serial number. represents the state of the jth vehicle, j = 1, 2, ... M, which is defined as Respectively represent the position and speed of the j-th vehicle in the i-th prediction time domain; The expected state quantity is: in, represents the expected state quantity in the i-th prediction time domain, represents the expected state of the jth vehicle, which is defined as They represent the expected position and expected speed of the j-th vehicle in the i-th prediction time domain, which are set to the center position of the intersection and the road speed limit, respectively; The control quantity is: Among them, u i represents the control amount in the i-th prediction time domain, represents the acceleration of the j-th vehicle in the i-th prediction time domain.

7. The method for passing a smart connected vehicle fleet at an intersection according to claim 6, characterized in that: The update equation of the state quantity is: in, They represent the position and speed of the j-th vehicle in the i+1-th prediction time domain respectively.

8. The method for passing a smart connected vehicle fleet at an intersection according to claim 7, characterized in that: The objective function is: J=J1+J2 Among them, a1, a2, a3 are weighting factors, which respectively represent the importance attached to position error, speed error, and comfort, and J is the objective function.

9. The method for passing an intelligent networked vehicle fleet at an intersection according to claim 8, characterized in that: The obstacle avoidance constraints include the obstacle avoidance between the first vehicle in the convoy and the vehicle in front and the obstacle avoidance between vehicles within the convoy. The obstacle avoidance between the first vehicle in the convoy and the vehicle in front is: Obstacle avoidance between vehicles within the convoy is:

10. The method for passing a smart connected vehicle fleet at an intersection according to claim 9, characterized in that: Green light traffic constraints are: Among them, p stop Indicates the location of the stop line at the intersection.

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