WO2021227502A1

WO2021227502A1 - Method for traffic light and vehicle track control at signalized intersection

Info

Publication number: WO2021227502A1
Application number: PCT/CN2020/138017
Authority: WO
Inventors: 俞春辉; 陈子轩; 马万经; 王玲
Original assignee: 同济大学
Priority date: 2020-05-14
Filing date: 2020-12-21
Publication date: 2021-11-18
Also published as: CN111768637A; CN111768637B

Abstract

A receding horizon optimization method for traffic light and vehicle track control at a signalized intersection, relating to the field of intelligent connected vehicles, comprising the following optimization steps at each time interval: acquiring vehicle information in a target area (S1); solving a mixed-integer linear programming model by using the vehicle information in the target area to obtain traffic light states and vehicle arriving at intersection moments (S2); solving a vehicle queue lead vehicle track optimal control model by using the vehicle arriving at intersection moments to obtain vehicle queue lead vehicle tracks, and solving a vehicle queue following vehicle optimal control model by using the vehicle arriving at intersection moments to obtain vehicle queue following vehicle tracks (S3); and implementing vehicle track control by means of the vehicle queue lead vehicle tracks and the vehicle queue following vehicle tracks, and implementing traffic light control by means of the traffic light states (S4). The method achieves optimization of both vehicle tracks and traffic lights at a signalized intersection, and thus, the traffic lights and the vehicle tracks are controlled more accurately.

Description

Traffic signal lamp and vehicle trajectory control method at signalized intersection

Technical field

The invention relates to the field of intelligent networked automobiles, in particular to a signalized intersection traffic signal lamp and a vehicle trajectory control method.

Background technique

With the increase in traffic demand, traffic congestion has gradually developed into a world-class problem in recent years, causing serious environmental problems and economic losses. In the urban transportation network, intersections are usually regarded as the bottleneck of traffic flow. Therefore, improving traffic signals at intersections can significantly improve the efficiency of urban transportation systems.

In recent years, with the development of intelligent network technology, vehicle-to-vehicle communication (V2V) and vehicle-to-road communication (V2I) provide new data sources for traffic control. At the same time, with the development of autonomous driving technology, vehicle control is for urban traffic. Governance provides new solutions. The current traffic control methods focus on signal light control, and there is less research on the simultaneous optimization of vehicle trajectory and signal light timing.

Summary of the invention

The purpose of the present invention is to provide a signalized intersection traffic signal lamp and a vehicle trajectory control method in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A method for controlling traffic signal lights and vehicle trajectories at signalized intersections. The method includes the following steps:

Step S1: Obtain vehicle information in the target area;

Step S2: Construct a mixed integer linear programming model with the goal of minimizing the intersection delay, and use the vehicle information in the target area to solve the mixed integer linear programming model to obtain the signal status and the time when the vehicle arrives at the intersection

Step S3: Construct an optimal control model for the trajectory of the lead vehicle of the fleet, using the time when the vehicle arrives at the intersection

Solve the optimal control model of the lead vehicle trajectory of the fleet, obtain the trajectory of the lead vehicle of the fleet, construct the optimal control model of the fleet car-following vehicle, and use the time when the vehicle arrives at the intersection

Solve the optimal control model of the car-following fleet and obtain the trajectory of the car-following fleet;

Step S4: Use the trajectory of the leading vehicle of the fleet and the trajectory of the vehicle following the fleet to achieve vehicle trajectory control, and use the state of the signal light to achieve traffic signal light control.

The vehicle information includes the lane number and the distance from the parking line.

The objective function of the mixed integer linear programming model is:

Wherein, α ₁ is the weight of all vehicle retardation, α ₂ is a long period when the weights, i is the index intersection direction, [Omega] _i-oriented sub-optimal initial time t ₀ i is set lane vehicle, [omega] is the vehicle number,

Is a subset of the trajectory variable T,

Is the generation time of the vehicle,

Is the time when the vehicle ω arrives at the intersection, _Li is the length of the target area in the direction i, v _max is the maximum speed of the vehicle, N is the number of signal cycles in the planning time domain, C ⁿ is the cycle duration of the nth signal cycle, and V is the control A collection of variables, S is a subset of the signal sequence of the semaphore;

The constraints of the mixed-integer linear programming model include vehicle trajectory constraints and signal lamp constraints. The vehicle trajectory constraints include allowable lane constraints, target lane-changing lane constraints, lane-changing behavior constraints, inter-vehicle spacing constraints, vehicle arrival time constraints, and immutable lanes. Area constraints, the signal light constraints include lane signal light constraints, green light start time constraints, green light duration constraints, green light end time constraints, cycle duration constraints, clear time constraints, stop line constraints, and other signal light constraints;

The allowable lane occupation constraint is:

Among them, I is the set of intersection directions, K is the set of lanes in each entrance lane, k is the lane index in each entry lane, and when the vehicle ω is on lane k

1, otherwise 0;

The target lane change lane constraints are:

like

Among them, I _A (x) is the indicator function, when _{x ∈ A} , I A (x) = 1, otherwise I _A (x) = 0, K _i is the set of lanes in the direction i, ω'is another vehicle, k ＇Is another lane, Ω _{ω is} the set of vehicles in front of vehicle ω at the initial moment of this optimization,

For this optimization, the distance between the vehicle ω and the parking line at the initial moment, d ^ω is the distance parameter,

For this optimization, the speed of the vehicle ω at the initial moment, τ ^ω is the time parameter, M approaches infinity, and a _L is the maximum deceleration that meets the comfort level. At the initial time of this optimization, if the vehicle ω is on lane k

1, otherwise 0;

The lane change behavior is restricted as:

Among them, K _ω is the set of lanes that the vehicle ω can enter,

Is the last time the vehicle ω changed lanes,

It is the minimum time interval between two lane changes, if the vehicle ω decides to change lanes, μ ^ω is 0, otherwise it is 1;

The workshop spacing constraints are:

Among them, x ^ω (t) is the distance between the vehicle ω and the parking line at time t, if the vehicle ω and the vehicle ω'are in the same lane η ^{ω, ω'is} 0, otherwise it is 1;

The vehicle arrival time constraints are:

Among them, if the vehicle ω keeps the last optimized trajectory λ ^ω is 1, otherwise it is 0,

Is the speed of the vehicle ω passing through the intersection,

For this optimization, the set of vehicles in the immutable lane area at the initial moment, a _U is the maximum acceleration that meets the comfort level, if the vehicle is not affected by the vehicle in front of it, γ ^ω is 0, otherwise it is 1.

To optimize the time when the vehicle ω arrives at the intersection last time,

Is the upper bound of the time required for the vehicle ω to reach the intersection from its current position,

Is the lower bound of the time required for the vehicle ω to reach the intersection from its current position, h ^ω is the time distance between the vehicle ω and the front vehicle, if the vehicle ω is not affected by the vehicle in front of it, ρ ^ω,ω' is 1, otherwise it is 0;

The immutable track area constraints are:

The lane signal light constraints are:

Among them, if lane k in direction i is used by traffic flow (i, j)

Is 1, otherwise it is 0,

Is the green light start time of traffic flow (i, j) in the nth signal period,

Is the green light duration of the traffic flow (i, j) in the nth signal cycle,

Is the start time of the green light for lane k in the intersection direction i,

Is the green light duration of lane k in the intersection direction i, and Ψ is the set of all traffic flows;

The start time of the green light is restricted to:

Among them, Ψ _{0 is} the set of traffic flows that get the green light at the initial moment of this optimization,

Is the green light start time of the active traffic flow (i, j) ∈ Ψ ₀ _{in the current cycle, Ψ p is} the traffic flow that ends the green light before the initial time of this optimization, and t _S is the start time of the signal light planning of the current cycle;

The green light duration constraint is:

in,

Is the minimum green light duration of traffic flow (i, j),

Is the green light duration of the inactive traffic flow (i, j) ∈ Ψ _{p in the current cycle;}

The green light end time constraint is:

The cycle length constraint is:

C ⁿ ≥t ₀ -t _s , n=1

Among them, Ψ _ic is the set of conflicting traffic flows. In the nth signal cycle, if the green light start time of the traffic flow (i, j) is after the traffic flow (l, m)

1, otherwise 0, if the green light start time of traffic flow (i, j) is before traffic flow (l, m) in the n-th signal cycle

1, otherwise 0;

The empty time constraint is:

Among them, π _{i, j, l, m} are the clearing time of conflicting traffic flows (i, j) and (l, m);

The parking line constraints are:

Among them, if the vehicle ω passes the intersection in the nth signal cycle

1, otherwise 0;

Other semaphore constraints are:

in,

Is the time difference between the green light activation time of the traffic flow (i, j) and (l, m) in the nth signal cycle,

It is the time difference between the traffic flow (i, j) and the end time of the green light of (l, m) in the nth signal cycle.

The optimal control model for the trajectory of the leader of the fleet is divided into two situations: the leader cannot reach the maximum speed during the driving time and the leader can reach the maximum speed during the driving time. When the leader cannot reach the maximum speed during the driving time, it meets:

Among them, v _max is the maximum speed,

Is the speed of the vehicle ω passing through the intersection,

For this optimization, the distance between the vehicle ω and the parking line at the _{initial time t 0 is} _{optimized, a L} is the maximum deceleration that _{meets the comfort level, and a U} is the maximum acceleration that meets the comfort level.

Optimize the speed of the vehicle ω at the initial time t _{0 for this time;}

When the leader vehicle can reach the maximum speed within the driving time, it meets the following requirements:

When the lead vehicle cannot reach the maximum speed during the driving time, the optimal control model for the trajectory of the lead vehicle in the fleet is:

Among them, i ^ω (t) is the acceleration of the vehicle ω in the control model at time t,

Is the acceleration of the vehicle ω in the control model at time t, v ^ω (t ₀ ) is the speed of the vehicle ω in the control model at the initial time t ₀ ^{of this optimization, v ω} (t) is the speed of the vehicle ω at time t, a ^ω (t) is the acceleration of the vehicle ω at time t, and l ^ω (t ₀ ) is the travel distance of the vehicle ω in the control model at the initial time t _{0 of this optimization,}

In order to control the travel distance of the vehicle ω at the moment of arrival at the intersection in the model,

To control the speed of the vehicle ω at the moment of arrival at the intersection in the model,

To adopt the minimum speed at the minimum acceleration,

In order to use the maximum speed at the maximum acceleration, Δt ^ω is the time interval for the vehicle ω to arrive at the intersection.

When the leader vehicle can reach the maximum speed within the driving time, the optimal control model for the trajectory of the leader vehicle in the fleet is:

in,

It represents the lower bound of the time required for the vehicle ω to reach the intersection from the current position when the leading vehicle can reach the maximum speed.

When the preceding vehicle does not affect the trajectory of the following vehicle within the specified time, the optimal control model of the fleet-following vehicle is:

The distance between the vehicle ω and the parking line at the _{initial time t 0 is} optimized for this time,

Is the speed of the vehicle ω passing through the intersection, v _max is the maximum speed, a _L is the maximum deceleration _{to meet the comfort level, and a U} is the maximum acceleration to meet the comfort level

Is the lower bound of the time required for the vehicle ω to reach the intersection from its current position,

It represents the lower bound of the time required for the vehicle ω to reach the intersection from the current position when the lead vehicle can reach the maximum speed, and Δt ^ω is the time interval for the vehicle ω to reach the intersection.

When the car-following vehicle follows the preceding vehicle within the specified time:

Among them, Δt is the time step, Δx _U is the upper bound of the travel distance, x ^ω (t) is the distance between the vehicle ω and the stop line at time t, and τ ^ω is the time when the car-following vehicle follows the preceding vehicle within the specified time Parameter, d ^ω is the distance parameter when the car following vehicle is following the preceding vehicle within a specified time, and x ^ω' (t) is the distance between the vehicle ω'and the stop line at time t.

The Δx _U is:

Among them, Δt'=(v _max -v ^ω (t))/a _U.

The process of solving the vehicle trajectory includes:

Step S31: If the vehicle's arrival time at the intersection during this optimization is the same as the time at which the vehicle arrived at the intersection during the last optimization, the vehicle trajectory remains unchanged, and step S35 is executed; otherwise, step S32 is executed;

Step S32: Determine whether it is the lead vehicle, if yes, execute step S33, if not, execute step S34;

Step S33: Analyze that the leader cannot reach the maximum speed during the driving time or the leader can reach the maximum speed during the driving time, and the trajectory of the leader of the fleet is solved through the corresponding optimal control model of the trajectory of the leader of the fleet;

Step S34: Analyze the car-following vehicle following the preceding vehicle within the specified time or the preceding vehicle will not affect the trajectory of the following vehicle, and respectively solve the trajectory of the car-following vehicle through the corresponding optimal control model of the car-following vehicle;

Step S35: Obtain the vehicle trajectory.

Compared with the prior art, the present invention has the following advantages:

(1) By establishing a mixed-integer linear programming model and a control model, the vehicle trajectory and traffic lights at signalized intersections can be optimized simultaneously in an intelligent network environment, so that the control of the traffic lights and vehicle trajectories can be more accurate.

(2) It has the ability of real-time control, which can realize real-time control of more than 100 vehicles in the intersection and the signal lights on each lane.

(3) Compared with the existing induction control, the traffic capacity of the intersection can be increased by about 50%, and the delay can be reduced by more than 80%.

Description of the drawings

Figure 1 is a flow chart of the present invention;

Figure 2 is the classification of the leader of the fleet of the invention;

Figure 3 shows the classification of the car-following vehicle fleet of the present invention.

Detailed ways

The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and provides detailed implementation and specific operation procedures, but the protection scope of the present invention is not limited to the following embodiments.

Example

This embodiment provides a method for controlling traffic lights and vehicle trajectories at signalized intersections, as shown in FIG. 1, including the following steps:

Step S1: Obtain vehicle information in the target area;

Solve the optimal control model of the lead vehicle trajectory of the fleet, and obtain the trajectory of the lead vehicle of the fleet; construct the optimal control model of the fleet following vehicles, and use the time when the vehicles arrive at the intersection

in particular:

The vehicle information includes the lane number and the distance from the stop line. The vehicle trajectory is the position, speed and acceleration of the vehicle at each moment. The signal light state includes the signal light phase sequence and phase duration of each lane in the intersection (the driving rules of each lane are subject to each The signal lights above each lane are individually controlled).

The objective function of the mixed integer linear programming model is:

Is a subset of the trajectory variable T,

Is the generation time of the vehicle,

Is the time when the vehicle ω arrives at the intersection, _Li is the length of the target area in the direction i, v _max is the maximum speed of the vehicle, N is the number of signal cycles in the planning time domain, C ⁿ is the cycle duration of the nth signal cycle, and V is the control The set of variables, S is a subset of the signal sequence of the semaphore.

Choose a reasonable α ₁ , the criterion of _{α 2 is:}

in,

In order to make α ₁ =1 and α ₂ =0 to solve the target value of the mixed integer linear programming model, Δd is the minimum unit for the decrease of the target value.

The allowable lane occupation constraints are:

1, otherwise 0;

The target lane change lane constraints are:

like

For this optimization, the distance between the vehicle ω and the parking line at the initial moment, d ^ω is the distance parameter, and τ ^ω is the time parameter.

In this optimization, the speed of the vehicle ω at the initial moment, M approaches infinity, and a _L is the maximum deceleration that satisfies the comfort level. At the initial moment of this optimization, if the vehicle ω is in lane k

1, otherwise 0;

The lane change behavior is restricted as:

Among them, K _ω is the set of lanes that the vehicle ω can enter,

Is the last time the vehicle ω changed lanes,

The workshop spacing constraints are:

The vehicle arrival time constraints are:

Is the speed of the vehicle ω passing through the intersection,

To optimize the time when the vehicle ω arrives at the intersection last time,

The immutable track area constraints are:

The lane signal light constraints are:

Among them, if lane k in direction i is used by traffic flow (i, j)

Is 1, otherwise it is 0,

Is the green light start time of traffic flow (i, j) in the nth signal period,

Is the green light duration of the traffic flow (i, j) in the nth signal cycle,

The start time of the green light is restricted to:

The green light duration constraint is:

in,

Is the minimum green light duration of traffic flow (i, j),

The green light end time constraint is:

The cycle length constraint is:

C ⁿ ≥t ₀ -t _s , n=1

1, otherwise 0;

The empty time constraint is:

The parking line constraints are:

Among them, if the vehicle ω passes the intersection in the nth signal cycle

1, otherwise 0;

Other semaphore constraints are:

in,

The optimal control model for the trajectory of the team leader and the optimal control model for the following vehicles are collectively referred to as the vehicle trajectory control model. The purpose of the vehicle trajectory control model is to determine the trajectory (position , Speed and acceleration), the judging standard of the fleet is the vehicles passing the intersection in the same signal phase and the same lane.

The optimal control model for the trajectory of the lead vehicle in the fleet is divided into two situations: the lead vehicle cannot reach the maximum speed during the driving time and the lead vehicle can reach the maximum speed during the driving time. As shown in Figure 2, the leader cannot reach the maximum speed during the driving time. Meet the speed:

Among them, v _max is the maximum speed,

Is the speed of the vehicle ω passing through the intersection,

The speed of the vehicle ω at the _{initial time t 0 is} optimized for this time.

When the lead vehicle can reach the maximum speed within the driving time, it meets the following requirements:

To adopt the minimum speed at the minimum acceleration,

in,

The car-following vehicle fleet can be divided into two types. As shown in Figure 3, when the preceding vehicle will not affect the trajectory of the following car within the specified time, the faster the following car drives, the better. The optimal control model of the car-following vehicle fleet is:

Is the speed of the vehicle ω passing through the intersection, v _max is the maximum speed, a _L is the maximum deceleration that _{meets the comfort level, and a U} is the maximum acceleration that meets the comfort level.

When the car-following vehicle follows the preceding vehicle within the specified time, it obeys the Newell first-order linear car-following model, that is, the position at each moment satisfies:

Among them, Δt is the time step, Δx _U is the upper bound of the travel distance, x ^ω (t) is the distance between the vehicle ω and the stop line at time t, and τ ^ω is the time when the car-following vehicle follows the preceding vehicle within the specified time Parameters, d ^ω is the distance parameter when the car-following vehicle follows the preceding vehicle within the specified time, x ^ω' (t) is the distance between the vehicle ω'and the stop line at time t, and Δx _U is:

Among them, Δt′=(v _max -v ^ω (t))/a _U , which ensures that the car-following vehicle satisfies the following inter-vehicle time distance h ^ω and arrival time

Relationship:

The process of solving the vehicle trajectory includes:

Step S35: Obtain the vehicle trajectory.

Some parameters involved are explained in Table 1.

Table 1 Explanation of some parameters

The following is a specific example:

A test case was built in SUMO (a well-known open source micro-simulation software), an intersection with entrance lanes in four directions was set, and the maximum green time for entrances 1, 3 (north-south facing) was set to 30s, and entrances 2, 4 The maximum green light time for the road (east-west facing) is 20s, the minimum green light time is 2s, and the simulation time is set to 1200s. At the same time, the algorithm time interval and the simulation time step are both 1s. Comparing the induction control (commonly used signal light control method for intelligent intersections in reality) with the method of this embodiment, the method of this embodiment can effectively improve the traffic capacity under different traffic flow conditions, of which up to 50%.

Claims

A method for controlling traffic signal lights and vehicle trajectories at signalized intersections, characterized in that the method includes the following steps:

Step S1: Obtain vehicle information in the target area;

Step S2: Construct a mixed integer linear programming model with the goal of minimizing the intersection delay, and use the vehicle information in the target area to solve the mixed integer linear programming model to obtain the signal status and the time when the vehicle arrives at the intersection

Step S3: Construct an optimal control model for the trajectory of the lead vehicle of the fleet, using the time when the vehicle arrives at the intersection
Solve the optimal control model of the lead vehicle trajectory of the fleet, obtain the trajectory of the lead vehicle of the fleet, construct the optimal control model of the fleet car-following vehicle, and use the time when the vehicle arrives at the intersection
Solve the optimal control model of the car-following fleet and obtain the trajectory of the car-following fleet;

Step S4: Use the trajectory of the leading vehicle of the fleet and the trajectory of the vehicle following the fleet to achieve vehicle trajectory control, and use the state of the signal light to achieve traffic signal light control.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 1, wherein the vehicle information includes a lane number and a distance from a stop line.
The method for controlling traffic lights and vehicle trajectories at signalized intersections according to claim 1, wherein the objective function of the mixed integer linear programming model is:

Wherein, α 1 is the weight of all vehicle retardation, α 2 is a long period when the weights, i is the index intersection direction, [Omega] i-oriented sub-optimal initial time t 0 i is set lane vehicle, [omega] is the vehicle number,
Is a subset of the trajectory variable T,
Is the generation time of the vehicle,
Is the time when the vehicle ω arrives at the intersection, Li is the length of the target area in the direction i, v max is the maximum speed of the vehicle, N is the number of signal cycles in the planning time domain, C n is the cycle duration of the nth signal cycle, and V is the control A collection of variables, S is a subset of the signal sequence of the semaphore;

The constraints of the mixed-integer linear programming model include vehicle trajectory constraints and signal lamp constraints. The vehicle trajectory constraints include allowable lane constraints, target lane-changing lane constraints, lane-changing behavior constraints, inter-vehicle spacing constraints, vehicle arrival time constraints, and immutable lanes. Area constraints, the signal light constraints include lane signal light constraints, green light start time constraints, green light duration constraints, green light end time constraints, cycle duration constraints, clear time constraints, stop line constraints, and other signal light constraints;

The allowable lane occupation constraint is:

Among them, I is the set of intersection directions, K is the set of lanes in each entrance lane, k is the lane index in each entry lane, and when the vehicle ω is on lane k
1, otherwise 0;

The target lane change lane constraints are:

like

like

Among them, I A (x) is the indicator function, when x ∈ A , I A (x) = 1, otherwise I A (x) = 0, K i is the set of lanes in the direction i, ω'is another vehicle, k ＇Is another lane, Ω ω is the set of vehicles in front of vehicle ω at the initial moment of this optimization,
For this optimization, the distance between the vehicle ω and the parking line at the initial moment, d ω is the distance parameter,
For this optimization, the speed of the vehicle ω at the initial moment, τ ω is the time parameter, M approaches infinity, and a L is the maximum deceleration that meets the comfort level. At the initial time of this optimization, if the vehicle ω is on lane k
1, otherwise 0;

The lane change behavior is restricted as:

Among them, K ω is the set of lanes that the vehicle ω can enter,
Is the last time the vehicle ω changed lanes,
It is the minimum time interval between two lane changes, if the vehicle ω decides to change lanes, μ ω is 0, otherwise it is 1;

The workshop spacing constraints are:

Among them, x ω (t) is the distance between the vehicle ω and the parking line at time t, if the vehicle ω and the vehicle ω'are in the same lane η ω, ω'is 0, otherwise it is 1;

The vehicle arrival time constraints are:

Among them, if the vehicle ω keeps the last optimized trajectory λ ω is 1, otherwise it is 0,
Is the speed of the vehicle ω passing through the intersection,
For this optimization, the set of vehicles in the immutable lane area at the initial moment, a U is the maximum acceleration that meets the comfort level, if the vehicle is not affected by the vehicle in front of it, γ ω is 0, otherwise it is 1.
To optimize the time when the vehicle ω arrives at the intersection last time,
Is the upper bound of the time required for the vehicle ω to reach the intersection from its current position,
Is the lower bound of the time required for the vehicle ω to reach the intersection from its current position, h ω is the time distance between the vehicle ω and the front vehicle, if the vehicle ω is not affected by the vehicle in front of it, ρ ω,ω' is 1, otherwise it is 0;

The immutable track area constraints are:

The lane signal light constraints are:

Among them, if lane k in direction i is used by traffic flow (i, j)
Is 1, otherwise it is 0,
Is the green light start time of traffic flow (i, j) in the nth signal period,
Is the green light duration of the traffic flow (i, j) in the nth signal cycle,
Is the start time of the green light for lane k in the intersection direction i,
Is the green light duration of lane k in the intersection direction i, and Ψ is the set of all traffic flows;

The start time of the green light is restricted to:

Among them, Ψ 0 is the set of traffic flows that get the green light at the initial moment of this optimization,
Is the green light start time of the active traffic flow (i, j) ∈ Ψ 0 in the current cycle, Ψ p is the traffic flow that ends the green light before the initial time of this optimization, and t S is the start time of the signal light planning of the current cycle;

The green light duration constraint is:

in,
Is the minimum green light duration of traffic flow (i, j),
Is the green light duration of the inactive traffic flow (i, j) ∈ Ψ p in the current cycle;

The green light end time constraint is:

The cycle length constraint is:

C n ≥t 0 -t s , n=1

Among them, Ψ ic is the set of conflicting traffic flows. In the nth signal cycle, if the green light start time of the traffic flow (i, j) is after the traffic flow (l, m)
1, otherwise 0, if the green light start time of traffic flow (i, j) is before traffic flow (l, m) in the n-th signal cycle
1, otherwise 0;

The empty time constraint is:

Among them, π i, j, λ, m are the clearing time of conflicting traffic flows (i, j) and (l, m);

The parking line constraints are:

Among them, if the vehicle ω passes the intersection in the nth signal cycle
1, otherwise 0;

Other semaphore constraints are:

in,
Is the time difference between the green light activation time of the traffic flow (i, j) and (l, m) in the nth signal cycle,
It is the time difference between the traffic flow (i, j) and the end time of the green light of (l, m) in the nth signal cycle.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 1, wherein the optimal control model for the trajectory of the lead vehicle of the fleet is divided into the leader vehicle cannot reach the maximum speed during the traveling time and the leader vehicle during the traveling time There are two situations in which the maximum speed can be reached, and when the leader cannot reach the maximum speed within the driving time, it meets:

Among them, v max is the maximum speed,
Is the speed of the vehicle ω passing through the intersection,
For this optimization, the distance between the vehicle ω and the parking line at the initial time t 0 is optimized, a L is the maximum deceleration that meets the comfort level, and a U is the maximum acceleration that meets the comfort level.
Optimize the speed of the vehicle ω at the initial time t 0 for this time;

When the leader vehicle can reach the maximum speed within the driving time, it meets the following requirements:
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 4, characterized in that, when the lead vehicle cannot reach the maximum speed during the driving time, the optimal control model for the trajectory of the lead vehicle of the fleet is:

Among them, i ω (t) is the acceleration of the vehicle ω in the control model at time t,
Is the acceleration of the vehicle ω in the control model at time t, v ω (t 0 ) is the speed of the vehicle ω in the control model at the initial time t 0 of this optimization, v ω (t) is the speed of the vehicle ω at time t, a ω (t) is the acceleration of the vehicle ω at time t, and l ω (t 0 ) is the travel distance of the vehicle ω in the control model at the initial time t 0 of this optimization,
In order to control the travel distance of the vehicle ω at the moment of arrival at the intersection in the model,
To control the speed of the vehicle ω at the moment of arrival at the intersection in the model,
To adopt the minimum speed at the minimum acceleration,
In order to use the maximum speed at the maximum acceleration, Δt ω is the time interval for the vehicle ω to arrive at the intersection.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 5, characterized in that, when the leading vehicle can reach the highest speed within the driving time, the optimal control model for the trajectory of the lead vehicle of the fleet is:

in,
It represents the lower bound of the time required for the vehicle ω to reach the intersection from the current position when the leading vehicle can reach the maximum speed.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 1, wherein the optimal control model of the fleet-following vehicle is when the preceding vehicle does not affect the trajectory of the following vehicle within a specified time for:

Among them, i ω (t) is the acceleration of the vehicle ω in the control model at time t,
Is the acceleration of the vehicle ω in the control model at time t, v ω (t 0 ) is the speed of the vehicle ω in the control model at the initial time t 0 of this optimization, v ω (t) is the speed of the vehicle ω at time t, a ω (t) is the acceleration of the vehicle ω at time t, and l ω (t 0 ) is the travel distance of the vehicle ω in the control model at the initial time t 0 of this optimization,
In order to control the travel distance of the vehicle ω at the moment of arrival at the intersection in the model,
To control the speed of the vehicle ω at the moment of arrival at the intersection in the model,
The distance between the vehicle ω and the parking line at the initial time t 0 is optimized for this time,
Is the speed of the vehicle ω passing through the intersection, v max is the maximum speed, a L is the maximum deceleration to meet the comfort level, and a U is the maximum acceleration to meet the comfort level
Is the upper bound of the time required for the vehicle ω to reach the intersection from its current position,
Is the lower bound of the time required for the vehicle ω to reach the intersection from its current position,
It represents the lower bound of the time required for the vehicle ω to reach the intersection from the current position when the lead vehicle can reach the maximum speed, and Δt ω is the time interval for the vehicle ω to reach the intersection.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 7, wherein the following vehicle meets the following requirements when following the preceding vehicle within a specified time:

Among them, Δt is the time step, Δx U is the upper bound of the travel distance, x ω (t) is the distance between the vehicle ω and the stop line at time t, and τ ω is the time when the car-following vehicle follows the preceding vehicle within the specified time Parameters, d ω is the distance parameter when the car following vehicle follows the preceding vehicle within a specified time, and x ω′ (t) is the distance between the vehicle ω′ and the stop line at time t.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 8, wherein the Δx U is:

Among them, Δt'=(v max -v ω (t))/a U.
A signalized intersection traffic signal light and vehicle trajectory control method according to claim 1, wherein the process of solving the vehicle trajectory includes:

Step S31: If the vehicle's arrival time at the intersection during this optimization is the same as the time at which the vehicle arrived at the intersection during the last optimization, the vehicle trajectory remains unchanged, and step S35 is executed; otherwise, step S32 is executed;

Step S32: Determine whether it is the lead vehicle, if yes, execute step S33, if not, execute step S34;

Step S33: Analyze that the leader cannot reach the maximum speed during the driving time or the leader can reach the maximum speed during the driving time, and the trajectory of the leader of the fleet is solved through the corresponding optimal control model of the trajectory of the leader of the fleet;

Step S34: Analyze the car-following vehicle following the preceding vehicle within a specified time or the preceding vehicle will not affect the trajectory of the following vehicle, and respectively solve the trajectory of the car-following vehicle through the corresponding optimal control model of the car-following vehicle;

Step S35: Obtain the vehicle trajectory.