A distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking

Abstract

The present invention discloses a distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking. The method comprises: road test facilities collect main road traffic flow information and ramp traffic flow information and publish them to vehicles in the merging area, and calculate the number of vehicles required for main road cooperation; the ramp vehicle obtains the position information of the main road vehicles within the communication radius and whether it cooperates with other ramp vehicles; determines the control mode of the main road cooperative vehicle, calculates the tracking target trajectory, determines the main road cooperative vehicle set, and publishes control instructions to the main road cooperative vehicle; establishes a ramp vehicle longitudinal control kinematic model and an MPC trajectory tracking model; determines the ramp vehicle longitudinal control constraints, determines the ramp vehicle longitudinal control cost function, solves the control acceleration and controls it.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic control, and in particular to a distributed ramp merging longitudinal control method considering main road vehicle coordination and trajectory tracking. Background Art

[0002] The intelligent connected environment is an advanced transportation system that enables real-time information sharing and intelligent interaction through highly interconnected vehicles, infrastructure and cloud services. In this environment, vehicles transmit key information to each other through V2V communication, including location, speed and driving intention, so as to coordinate driving, avoid collisions and improve overall traffic efficiency and safety. This innovative vehicle communication method provides real-time data support for driving decisions and promotes the development of transportation systems in a smarter, more efficient and safer direction. In an intelligent connected environment, fine control of vehicle behavior can be achieved, which allows vehicles to maintain a safe following state at a smaller headway, thereby improving communication efficiency.

[0003] Ramp merging is a key issue in vehicle control in an intelligent connected environment, involving the process of vehicles merging from ramps to main roads. In this scenario, vehicles need to exchange information such as position, speed, and intention in real time through a highly coordinated intelligent communication system to optimize the micro-control of ramp merging. Through advanced control strategies in an intelligent connected environment, vehicles can adjust speed and change lanes more accurately to ensure smooth and efficient integration into the main road flow, minimize traffic congestion, and improve overall road safety. The development of this technology is of great significance for optimizing urban traffic flow, increasing traffic capacity, and reducing traffic accidents.

[0004] In an intelligent connected environment, comprehensive consideration of the coordination between ramp and main road vehicles is the key to ensuring merging smoothness and overall road safety. If a ramp vehicle only optimizes its own trajectory without considering the real-time status of the main road vehicle when merging, it may cause unnecessary deceleration or sudden braking of the main road vehicle when merging, thus affecting the overall traffic smoothness. On the contrary, by comprehensively considering the driving status of the main road vehicle and through intelligent collaborative control, ramp vehicles can choose the right time more accurately, achieve a more efficient and smooth merging process, and ultimately improve overall traffic efficiency.

[0005] The vehicle trajectory tracking algorithm is one of the core technologies in the intelligent driving system, which aims to make the vehicle drive along a predetermined trajectory or a trajectory that adapts to dynamic environmental changes. The algorithm integrates multi-source sensor information, such as lidar, camera, and inertial measurement unit, to establish a vehicle state model, including dynamic parameters such as position, velocity, and acceleration. Common trajectory tracking algorithms include but are not limited to model predictive control (MPC), recursive least squares (RLS), and extended Kalman filter (EKF). During the execution of the algorithm, the vehicle continuously updates its state based on real-time sensor data and uses control inputs to adjust the vehicle's motion trajectory to minimize the deviation from the desired trajectory. This algorithm needs to fully consider the vehicle's dynamic characteristics, environmental factors, and safety constraints to ensure stable and efficient trajectory tracking behavior. With the continuous development of intelligent driving technology, vehicle trajectory tracking algorithms will play a key role in achieving safe, intelligent, and adaptive driving. Summary of the invention

[0006] Purpose of the invention: In order to achieve safe and efficient merging of ramp vehicles, a distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking in an intelligent network environment is proposed. The road test facilities collect the main road traffic flow information and ramp traffic flow information and publish them to the vehicles in the merging area, and calculate the number of vehicles required for main road cooperation; the ramp vehicle obtains the position information of the main road vehicles within the communication radius and whether it cooperates with other ramp vehicles; determines the control mode of the main road cooperative vehicle, calculates the tracking target trajectory, determines the main road cooperative vehicle set, and issues control instructions to the main road cooperative vehicle; establishes the longitudinal control kinematic model of the ramp vehicle and the MPC trajectory tracking model; determines the longitudinal control constraints of the ramp vehicle; and determines the longitudinal control cost function of the ramp vehicle. This method guides microscopic vehicle control through macro traffic flow data to ensure that all ramp vehicles can merge into the main road; provides a control method for the main road cooperative vehicle, determines the main road cooperative vehicle, so that the main road cooperative vehicles can cooperate with each other to produce a merging gap at the merging position, and provides the target trajectory of the ramp vehicle; establishes an MPC-based trajectory tracking model for the ramp vehicle to ensure that the ramp vehicle can smoothly enter the merging gap.

[0007] Technical solution: To solve the above technical problems, the present invention proposes a distributed ramp merging longitudinal control method considering main road vehicle coordination and trajectory tracking, the method comprising the following steps:

[0008] (1) The road test facilities collect traffic flow information on the main road and ramps, calculate the number of vehicles required for coordination on the main road, and publish it to vehicles entering the merging area;

[0009] (2) The ramp vehicle obtains the location information of the main road vehicles within the communication radius and whether it cooperates with other ramp vehicles;

[0010] (3) The ramp vehicle passes through the number of vehicles required for main road coordination and the main road vehicle information within the communication radius, determines the control mode of the main road coordinated vehicle, calculates the tracking target trajectory, determines the main road coordinated vehicle set, and issues control instructions to the main road coordinated vehicles;

[0011] (4) Establish a ramp vehicle longitudinal control kinematic model based on the tracking target trajectory, and establish a discrete MPC trajectory tracking model based on the kinematic model;

[0012] (5) Propose the longitudinal control constraints of the ramp vehicle for the discrete MPC trajectory tracking model;

[0013] (6) Determine the longitudinal control cost function of the ramp vehicle in the tracking model of the discrete MPC trajectory, solve the control acceleration, and control the ramp vehicle by controlling the acceleration.

[0014] Preferably, in step (1), the main road traffic flow information and ramp traffic flow information are:

[0015] The length of the ramp merging control area is D. In the ramp merging area, assuming that the main road traffic flow reaches a balanced state, the main road vehicle flow is Q m , the main road vehicle speed is v0, the main road vehicle headway is h0; assuming that the ramp vehicle arrival satisfies the Poisson distribution, the ramp vehicle flow is Q r , the ramp vehicle speed is The distance between the ramp vehicle and the merging area is The ramp vehicle control starts at t0 and ends at t f ,make The length of the ramp merging control area D is in meters (m), Q m and Q r The unit of is vehicle / hour (veh / h), the unit of h0 is second (s), and the unit of v0 and The unit is meter per second (m / s);

[0016] The calculated headway time h0 of the main road vehicle satisfies the following formula:

[0017] h0=(1 / Q m )×3600

[0018] Assuming that all ramp vehicles merge into the main road and the main road traffic speed remains unchanged after merging, in order to ensure that all ramp vehicles merge into the main road, the headway h1 of the main road vehicles after merging should satisfy the following formula:

[0019] h min ≤h1≤[1 / (Q m +Q r )]×3600

[0020] Among them, h min is the minimum headway allowed for vehicles,

[0021] Figure 2 A schematic diagram of the main road vehicles before and after coordination is given. The vehicle length is l, in meters (m). For a ramp vehicle, there are n1 main road coordinated vehicles before the merging position, and n2 main road coordinated vehicles after the merging position. After the main road coordination, the headway time between the main road coordinated vehicles is reduced from h0 to h1, and a gap that satisfies the merging is provided at the merging position for ramp vehicles to merge in. According to the relationship between the reduction of the headway time and the vehicle length, the number of main road coordinated vehicles needs to satisfy the following formula:

[0022] 2h1+(n1+n2-2)h1+l / v0≤(n1+n2-1)h0

[0023] Considering that the number is an integer, taking the minimum value of n1+n2, the minimum number of cooperative vehicles required on the main road is:

[0024]

[0025] in, Indicates rounding up;

[0026] The road test facilities publish the traffic flow information v0, h0, h1 and the minimum number of cooperative vehicles required on the main road to all vehicles entering the area.

[0027] Preferably, in step (2), the main road vehicle information is:

[0028] Within the communication radius r of the ramp vehicle, there is a main road vehicle train with a length of k. Each car in the train m j Distance from the start of the ramp area and whether it cooperates with other ramp vehicles The position information is the distance between the rear of the vehicle and the beginning of the ramp area, in meters (m). The value is 0 or 1, where 0 means no cooperation with other ramp vehicles and 1 means cooperation with other ramp vehicles.

[0029] Preferably, in step (3), the method is:

[0030] 4.1 Parameter setting

[0031] Consider the maximum comfortable acceleration of the main road vehicle as a max , the maximum comfortable deceleration is b max , the unit is meter per second squared (m / s 2 ), take a max =b max=a0, a0 is the acceleration constant, the maximum speed of the vehicle on the main road is v max , the minimum allowable speed is v min , the unit is meter / second (m / s);

[0032] like Figure 2 As shown, for the car before the merging position, the distance between the n'th car on the main road before the merging position and the n1th car before the merging position is S n' :

[0033] S n' =(h0-h1)(n1-n')v0

[0034] like Figure 2 As shown in the figure, for the car after the merge position, the distance between the nth car on the main road after the merge position and the n2th car after the merge position is S n” :

[0035] S n” =(h0-h1)(n1-n”)v0

[0036] 4.2 Main road vehicle control method

[0037] The main road cooperative vehicle control mode is divided into the following four situations:

[0038] Case 1: If the main road cooperative vehicle is before the merging position and satisfies (v max -v0) 2 / a0≥S n'

[0039] First, a uniform acceleration motion with a time of t and an acceleration of a0 is performed; then a uniform deceleration motion with a time of t and a deceleration of a0 is performed, where the time t is calculated as:

[0040]

[0041] At this time, the total control time is t total =2t;

[0042] Case 2: If the main road cooperative vehicle is before the merging position and satisfies (v max -v0) 2 / a0<S n' :

[0043] First, a uniform acceleration motion is performed with a time of t and an acceleration of a0; then a uniform acceleration motion is performed with a time of t1 and a speed of v max uniform linear motion with a time of t and a deceleration of a0, where the time t and t1 are calculated as follows:

[0044]

[0045] At this time, the total control time is t total =2t+t1;

[0046] Case 3: If the main road cooperative vehicle is at the merging position and satisfies (v0-v min ) 2 / a0≥S n”

[0047] First, a uniform deceleration motion with a time of t and a deceleration of a0 is performed; then a uniform acceleration motion with a time of t and an acceleration of a0 is performed, where the time t is calculated as:

[0048]

[0049] At this time, the total control time is t total =2t;

[0050] Case 4: If the main road cooperative vehicle is at the merging position and satisfies (v0-v min ) 2 / a0<S n”

[0051] First, a uniform deceleration motion is performed at a time of t and a deceleration of a0; then a uniform deceleration motion is performed at a time of t2 and a speed of v min uniform linear motion with time t and acceleration a0, where the time t and t2 are calculated as follows:

[0052]

[0053] At this time, the total control time is t total =2t+t2;

[0054] After the control method is determined, the control time required for the vehicle in front of the merging position and the vehicle behind the merging position is the longest, that is, S n' =(h0-h1)(n1-1)v0orS n ”=(h0-h1)(n2-1)v0, t total Take the maximum value;

[0055] 4.3. Determine the main road control train

[0056] Step 1: Based on the position of the ramp vehicle Determine the i-th vehicle in the main road train such that:

[0057]

[0058] At this time, the train Assign values ​​to Index = 0;

[0059] Step 2: Verify the train Do the following three conditions apply?

[0060] Condition 1: The main road cooperative vehicle is within the communication range of the ramp vehicle, that is,

[0061] Condition 2: The main road cooperative vehicle has not yet cooperated with the ramp vehicle, that is, the sequence All are 0;

[0062] Condition 3: The cooperative vehicle on the main road can create a merging gap before the ramp vehicle reaches the merging area, and n1 is Middle position ratio The larger the number of vehicles, the value of n2 is Middle position ratio The number of vehicles is small. At this time, the trajectory function x of the merging position starting from the control initial time t0 is calculated. ref (t):

[0063]

[0064] Solving the equation for x ref (t f )=D to get t f ,verify Is it true? mc1 is the first vehicle of the coordinated vehicle on the main road, mc n The tail vehicle of the cooperative vehicle on the main road, and are the distances of the first and last vehicles from the ramp merging start area;

[0065] If the above three conditions are met, then That is, the set of cooperative vehicles on the main road. Otherwise, let Index = Index + 1, and then let the vehicle train Repeat step 2;

[0066] 4.4. Release control information to vehicles on the main road

[0067] Determine the set of cooperative vehicles on the main road After that, the control mode is released to the vehicles in the collection, and the indicators of the corresponding main road cooperative vehicles are Change to 1.

[0068] Preferably, in step (4), the kinematic model of ramp vehicle longitudinal control is proposed as follows:

[0069] The kinematic model of ramp vehicle longitudinal control is proposed as follows:

[0070]

[0071] Where x is the position of the ramp vehicle, v is the speed of the ramp vehicle, a is the acceleration of the ramp vehicle, and u is the control acceleration of the ramp vehicle. The units are meter (m), meter / second (m / s), and meter / second squared (m / s 2 ), τ is the response parameter of the system;

[0072] Let z = [x, v, a] T ,but Thus:

[0073]

[0074]

[0075] Based on the kinematic model of the longitudinal control of the ramp vehicle, let the control start time be t0 and the control end time be t f , discretize the time with Δt as the control interval, and ensure that the total number of control steps λ=(t f -t0) / Δt is an integer, and a discretized MPC trajectory tracking model is established. The model equation is as follows:

[0076] z i+1 =Az i +Bu i +C

[0077] Among them, A, B, and C are:

[0078]

[0079]

[0080]

[0081] Among them, i represents the i-th time step, z i+1 represents the state vector at the i+1th time step, z i represents the state vector at the i-th time step, u i represents the control acceleration at the i-th time step, I 3×3 is a third-order unit diagonal matrix.

[0082] Preferably, in step (5), there are three constraints on the model in (4):

[0083] Constraint 1, initial value condition:

[0084]

[0085] Constraint 2, terminal value constraint:

[0086]

[0087] Constraint 3, vehicle performance constraint:

[0088]

[0089] Among them, x ref (·) is the trajectory function of the confluence position.

[0090] Preferably, in step (6), a cost function for tracking the trajectory of the model in (4) is established:

[0091] Establish the cost function for trajectory tracking:

[0092]

[0093] Among them, Q1 and Q2 are both positive definite diagonal matrices, r is a positive real number, Calculated by the following formula:

[0094]

[0095] The discrete MPC trajectory tracking model is solved according to the constraints and cost functions to obtain the control acceleration, and the ramp vehicle is controlled by controlling the acceleration.

[0096] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0097] The present invention proposes a distributed ramp merging longitudinal control method that considers the main road vehicle cooperation and trajectory tracking in an intelligent network environment. This method guides microscopic vehicle control through macroscopic traffic flow data to ensure that all ramp vehicles can merge into the main road; a control method for the main road cooperative vehicle is given, the main road cooperative vehicle is determined, so that the main road cooperative vehicles can cooperate with each other to generate a merging gap at the merging position, and the target trajectory of the ramp vehicle is given; an MPC-based trajectory tracking model is established for the ramp vehicle to ensure that the ramp vehicle can smoothly enter the merging gap. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 It is a flow chart of a method according to an embodiment of the present invention.

[0099] Figure 2 It is a schematic diagram of the main road vehicle cooperation of the present invention. DETAILED DESCRIPTION

[0100] like Figure 1 As shown, the present invention proposes a distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking, the method comprising the following steps:

[0101] (1) The road test facilities collect traffic flow information on the main road and ramps, calculate the number of vehicles required for coordination on the main road, and publish it to vehicles entering the merging area;

[0102] (2) The ramp vehicle obtains the location information of the main road vehicles within the communication radius and whether it cooperates with other ramp vehicles;

[0103] (3) The ramp vehicle passes through the number of vehicles required for main road coordination and the main road vehicle information within the communication radius, determines the control mode of the main road coordinated vehicle, calculates the tracking target trajectory, determines the main road coordinated vehicle set, and issues control instructions to the main road coordinated vehicles;

[0104] (4) Establish a ramp vehicle longitudinal control kinematic model based on the tracking target trajectory, and establish a discrete MPC trajectory tracking model based on the kinematic model;

[0105] (5) Propose the longitudinal control constraints of the ramp vehicle for the discrete MPC trajectory tracking model;

[0106] (6) Determine the longitudinal control cost function of the ramp vehicle in the tracking model of the discrete MPC trajectory, solve the control acceleration, and control the ramp vehicle by controlling the acceleration.

[0107] Preferably, in step (1), the main road traffic flow information and ramp traffic flow information are:

[0108] The length of the ramp merging control area is D. In the ramp merging area, assuming that the main road traffic flow reaches a balanced state, the main road vehicle flow is Q m , the main road vehicle speed is v0, the main road vehicle headway is h0; assuming that the ramp vehicle arrival satisfies the Poisson distribution, the ramp vehicle flow is Q r , the ramp vehicle speed is The distance between the ramp vehicle and the merging area is The ramp vehicle control starts at t0 and ends at t f ,make The length of the ramp merging control area D is in meters (m), Q m and Q r The unit of is vehicle / hour (veh / h), the unit of h0 is second (s), and the unit of v0 and The unit is meter per second (m / s);

[0109] The calculated headway time h0 of the main road vehicle satisfies the following formula:

[0110] h0=(1 / Q m )×3600

[0111] Assuming that all ramp vehicles merge into the main road and the main road traffic speed remains unchanged after merging, in order to ensure that all ramp vehicles merge into the main road, the headway h1 of the main road vehicles after merging should satisfy the following formula:

[0112] h min ≤h1≤[1 / (Q m +Q r )]×3600

[0113] Among them, h min is the minimum headway allowed for vehicles,

[0114] Figure 2 A schematic diagram of the main road vehicles before and after coordination is given. The vehicle length is l, in meters (m). For a ramp vehicle, there are n1 main road coordinated vehicles before the merging position, and n2 main road coordinated vehicles after the merging position. After the main road coordination, the headway time between the main road coordinated vehicles is reduced from h0 to h1, and a gap that satisfies the merging is provided at the merging position for ramp vehicles to merge in. According to the relationship between the reduction of the headway time and the vehicle length, the number of main road coordinated vehicles needs to satisfy the following formula:

[0115] 2h1+(n1+n2-2)h1+l / v0≤(n1+n2-1)h0

[0116] Considering that the number is an integer, taking the minimum value of n1+n2, the minimum number of cooperative vehicles required on the main road is:

[0117]

[0118] in, Indicates rounding up;

[0119] The road test facilities publish the traffic flow information v0, h0, h1 and the minimum number of cooperative vehicles required on the main road to all vehicles entering the area.

[0120] Preferably, in step (2), the main road vehicle information is:

[0121] Within the communication radius r of the ramp vehicle, there is a main road vehicle train with a length of k. Each car in the train m j Distance from the start of the ramp area and whether it cooperates with other ramp vehicles The position information is the distance between the rear of the vehicle and the beginning of the ramp area, in meters (m). The value is 0 or 1, where 0 means no cooperation with other ramp vehicles and 1 means cooperation with other ramp vehicles.

[0122] Preferably, in step (3), the method is:

[0123] 4.1 Parameter setting

[0124] Consider the maximum comfortable acceleration of the main road vehicle as amax , the maximum comfortable deceleration is b max , the unit is meter per second squared (m / s 2 ), take a max =b max =a0, a0 is the acceleration constant, the maximum speed of the vehicle on the main road is v max , the minimum allowable speed is v min , the unit is meter / second (m / s);

[0125] like Figure 2 As shown, for the car before the merging position, the distance between the n'th car on the main road before the merging position and the n1th car before the merging position is S n' :

[0126] S n' =(h0-h1)(n1-n')v0

[0127] like Figure 2 As shown in the figure, for the car after the merge position, the distance between the nth car on the main road after the merge position and the n2th car after the merge position is S n” :

[0128] S n” =(h0-h1)(n1-n”)v0

[0129] 4.2 Main road vehicle control method

[0130] The main road cooperative vehicle control mode is divided into the following four situations:

[0131] Case 1: If the main road cooperative vehicle is before the merging position and satisfies (v max -v0) 2 / a0≥S n'

[0132] First, a uniform acceleration motion with a time of t and an acceleration of a0 is performed; then a uniform deceleration motion with a time of t and a deceleration of a0 is performed, where the time t is calculated as:

[0133]

[0134] At this time, the total control time is t total =2t;

[0135] Case 2: If the main road cooperative vehicle is before the merging position and satisfies (v max -v0) 2 / a0<S n' :

[0136] First, a uniform acceleration motion is performed with a time of t and an acceleration of a0; then a uniform acceleration motion is performed with a time of t1 and a speed of vmax uniform linear motion with a time of t and a deceleration of a0, where the time t and t1 are calculated as follows:

[0137]

[0138] At this time, the total control time is t total =2t+t1;

[0139] Case 3: If the main road cooperative vehicle is at the merging position and satisfies (v0-v min ) 2 / a0≥S n”

[0140] First, a uniform deceleration motion with a time of t and a deceleration of a0 is performed; then a uniform acceleration motion with a time of t and an acceleration of a0 is performed, where the time t is calculated as:

[0141]

[0142] At this time, the total control time is t total =2t;

[0143] Case 4: If the main road cooperative vehicle is at the merging position and satisfies (v0-v min ) 2 / a0<S n”

[0144] First, a uniform deceleration motion is performed at a time of t and a deceleration of a0; then a uniform deceleration motion is performed at a time of t2 and a speed of v min uniform linear motion; then uniform accelerated motion with time t and acceleration a0, where the time t and t2 are calculated as:

[0145]

[0146] At this time, the total control time is t total =2t+t2;

[0147] After the control method is determined, the control time required for the vehicle in front of the merging position and the vehicle behind the merging position is the longest, that is, S n' =(h0-h1)(n1-1)v0orS n” =(h0-h1)(n2-1)v0, t total Take the maximum value;

[0148] 4.3. Determine the main road control train

[0149] Step 1: Based on the position of the ramp vehicle Determine the i-th vehicle in the main road train such that:

[0150]

[0151] At this time, the train Assign values ​​to Index = 0;

[0152] Step 2: Verify the train Do the following three conditions apply?

[0153] Condition 1: The main road cooperative vehicle is within the communication range of the ramp vehicle, that is,

[0154] Condition 2: The main road cooperative vehicle has not yet cooperated with the ramp vehicle, that is, the sequence All are 0;

[0155] Condition 3: The cooperative vehicle on the main road can create a merging gap before the ramp vehicle reaches the merging area, and n1 is Middle position ratio The larger the number of vehicles, the value of n2 is Middle position ratio The number of vehicles is small. At this time, the trajectory function x of the merging position starting from the control initial time t0 is calculated. ref (t):

[0156]

[0157] Solving the equation for x ref (t f )=D to get t f ,verify Is it true? mc1 is the first vehicle of the coordinated vehicle on the main road, mc n The tail vehicle of the cooperative vehicle on the main road, and are the distances of the first and last vehicles from the ramp merging start area;

[0158] If the above three conditions are met, then That is, the set of cooperative vehicles on the main road. Otherwise, let Index = Index + 1, and then let the vehicle train for Repeat step 2;

[0159] 4.4. Release control information to vehicles on the main road

[0160] Determine the set of cooperative vehicles on the main road After that, the control mode is released to the vehicles in the collection, and the indicators of the corresponding main road cooperative vehicles are Change to 1.

[0161] Preferably, in step (4), the kinematic model of ramp vehicle longitudinal control is proposed as follows:

[0162] The kinematic model of ramp vehicle longitudinal control is proposed as follows:

[0163]

[0164] Where x is the position of the ramp vehicle, v is the speed of the ramp vehicle, a is the acceleration of the ramp vehicle, and u is the control acceleration of the ramp vehicle. The units are meter (m), meter / second (m / s), and meter / second squared (m / s 2 ), τ is the response parameter of the system;

[0165] Let z = [x, v, a] T ,but Thus:

[0166]

[0167]

[0168] Based on the kinematic model of the longitudinal control of the ramp vehicle, let the control start time be t0 and the control end time be t f , discretize the time with Δt as the control interval, and ensure that the total number of control steps λ=(t f -t0) / Δt is an integer, and a discretized MPC trajectory tracking model is established. The model equation is as follows:

[0169] z i+1 =Az i +Bu i +C

[0170] Among them, A, B, and C are:

[0171]

[0172]

[0173]

[0174] Among them, i represents the i-th time step, z i+1 represents the state vector at the i+1th time step, z i represents the state vector at the i-th time step, u i represents the control acceleration at the i-th time step, I 3×3 is a third-order unit diagonal matrix.

[0175] Preferably, in step (5), there are three constraints on the model in (4):

[0176] Constraint 1, initial value condition:

[0177]

[0178] Constraint 2, terminal value constraint:

[0179]

[0180] Constraint 3, vehicle performance constraint:

[0181]

[0182] Among them, x ref (·) is the trajectory function of the confluence position.

[0183] Preferably, in step (6), a cost function for tracking the trajectory of the model in (4) is established:

[0184] Establish the cost function for trajectory tracking:

[0185]

[0186] Among them, Q1 and Q2 are both positive definite diagonal matrices, r is a positive real number, Calculated by the following formula:

[0187]

[0188] The discrete MPC trajectory tracking model is solved according to the constraints and cost functions to obtain the control acceleration, and the ramp vehicle is controlled by controlling the acceleration.

[0189] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking, characterized in that: The method comprises the following steps: (1) The road test facilities collect traffic flow information on the main road and ramps, calculate the number of vehicles required for coordination on the main road, and publish it to vehicles entering the merging area; (2) The ramp vehicle obtains the location information of the main road vehicles within the communication radius and whether it cooperates with other ramp vehicles; (3) The ramp vehicle passes through the number of vehicles required for main road coordination and the main road vehicle information within the communication radius, determines the control mode of the main road coordinated vehicle, calculates the tracking target trajectory, determines the main road coordinated vehicle set, and issues control instructions to the main road coordinated vehicles; (4) Establish a ramp vehicle longitudinal control kinematic model based on the tracking target trajectory, and establish a discrete MPC trajectory tracking model based on the kinematic model; (5) Propose the longitudinal control constraints of the ramp vehicle for the discrete MPC trajectory tracking model; (6) Determine the ramp vehicle longitudinal control cost function of the discrete MPC trajectory tracking model, solve the control acceleration, and control the ramp vehicle by controlling the acceleration; The specific method of step (1) is as follows: The length of the ramp merging control area is D. In the ramp merging area, assuming that the main road traffic flow reaches a balanced state, the main road vehicle flow is Q m , the main road vehicle speed is v0, the main road vehicle headway is h0; assuming that the ramp vehicle arrival satisfies the Poisson distribution, the ramp vehicle flow is Q r , the ramp vehicle speed is v t0 , the distance between the ramp vehicle and the merging area is x t0 , the ramp vehicle control start time is t0, and the control end time is t f , let v t0 <v0, the length of the ramp merging control area D is in meters (m), Q m and Q r The unit of h0 is vehicle / hour (veh / h), the unit of v0 is second (s), and the unit of v t0 The unit is meter per second (m / s); The calculated headway time h0 of the main road vehicle satisfies the following formula: h0=(1 / Q m )×3600 Assuming that all ramp vehicles merge into the main road and the main road traffic speed remains unchanged after merging, in order to ensure that all ramp vehicles merge into the main road, the headway h1 of the main road vehicles after merging should satisfy the following formula: h min ≤h1≤[1 / (Q m +Q r )]×3600 Among them, h min is the minimum headway time allowed for the vehicle; the vehicle length is l, in meters (m). For a ramp vehicle, there are n1 main road cooperative vehicles before the merging position, and n2 main road cooperative vehicles after the merging position. After the main road cooperation, the headway time between the main road cooperative vehicles is reduced from h0 to h1, and a gap that satisfies the merging is provided at the merging position for ramp vehicles to merge. According to the relationship between the reduction of the headway time and the vehicle length, the number of main road cooperative vehicles is calculated to satisfy the following formula: 2h1+(n1+n2-2)h1+l / v0≤(n1+n2-1)h0 Considering that the number is an integer, taking the minimum value of n1+n2, the minimum number of cooperative vehicles required on the main road is: in, Indicates rounding up; The road test facility publishes the traffic flow information v0, h0, h1 and the minimum number of cooperative vehicles required on the main road to all vehicles entering the area; The specific method of step (2) is as follows: Within the communication radius r of the ramp vehicle, there is a main road vehicle train with a length of k. Each car in the train m j Distance from the start of the ramp area The indicator of whether it cooperates with other ramp vehicles is The position information is the distance between the rear of the vehicle and the beginning of the ramp area, in meters (m). The value is 0 or 1, where 0 means no cooperation with other ramp vehicles and 1 means cooperation with other ramp vehicles. The specific method of step (3) is as follows: 4.1 Parameter setting Consider the maximum comfortable acceleration of the main road vehicle as a max , the maximum comfortable deceleration is b max , the unit is meter per second squared (m / s 2 ), take a max =b max =a0, a0 is the acceleration constant, the maximum speed of the vehicle on the main road is v max , the minimum allowable speed is v min , the unit is meter / second (m / s); For the car before the merging position, the distance between the n'th car on the main road before the merging position and the n1th car before the merging position is S n' : WITH n' =(h0-h1)(n1-n')v0 For the car after the merge position, the distance between the nth car on the main road after the merge position and the n2th car after the merge position is S n” : WITH n” =(h0-h1)(n1-n”)v0 4.2 Main road vehicle control method The main road cooperative vehicle control mode is divided into the following four situations: Case 1: If the main road cooperative vehicle is before the merging position and satisfies (v max -v0) 2 / a0≥S n' First, a uniform acceleration motion with a time of t and an acceleration of a0 is performed; then a uniform deceleration motion with a time of t and a deceleration of a0 is performed, where the time t is calculated as: At this time, the total control time is t total =2t; Case 2: If the main road cooperative vehicle is before the merging position and satisfies (v max -v0) 2 / a0<S n' : First, a uniform acceleration motion is performed with a time of t and an acceleration of a0; then a uniform acceleration motion is performed with a time of t1 and a speed of v max uniform linear motion with a time of t and a deceleration of a0, where the time t and t1 are calculated as follows: At this time, the total control time is t total =2t+t1; Case 3: If the main road cooperative vehicle is at the merging position and satisfies (v0-v min ) 2 / a0≥S n” First, a uniform deceleration motion with a time of t and a deceleration of a0 is performed; then a uniform acceleration motion with a time of t and an acceleration of a0 is performed, where the time t is calculated as: At this time, the total control time is t total =2t; Case 4: If the main road cooperative vehicle is at the merging position and satisfies (v0-v min ) 2 / a0<S n” First, a uniform deceleration motion is performed at a time of t and a deceleration of a0; then a uniform deceleration motion is performed at a time of t2 and a speed of v min uniform linear motion with time t and acceleration a0, where the time t and t2 are calculated as follows: At this time, the total control time is t total =2t+t2; After the control method is determined, the control time required for the vehicle in front of the merging position and the vehicle behind the merging position is the longest, that is, S n' =(h0-h1)(n1-1)v0orS n” =(h0-h1)(n2-1)v0, t total Take the maximum value; 4.

3. Determine the main road control train Step 1: Based on the position of the ramp vehicle Determine the i-th vehicle in the main road train such that: At this time, the train Assign values ​​to Index = 0; Step 2: Verify the train Whether the following three conditions are met: Condition 1: The main road cooperative vehicle is within the communication range of the ramp vehicle, that is, Condition 2: The main road cooperative vehicle has not yet cooperated with the ramp vehicle, that is, the sequence All are 0; Condition 3: The cooperative vehicle on the main road can create a merging gap before the ramp vehicle reaches the merging area, and n1 is Middle position ratio The larger the number of vehicles, the value of n2 is Middle position ratio The number of vehicles is small. At this time, the trajectory function x of the merging position starting from the control initial time t0 is calculated. ref (t): Solving the equation for x ref (t f )=D to get t f ; verify Is it true? mc1 is the first vehicle of the coordinated vehicle on the main road, mc n The tail vehicle of the cooperative vehicle on the main road, and are the distances of the first and last vehicles from the ramp merging start area; If the above three conditions are met, then That is, the set of cooperative vehicles on the main road. Otherwise, let Index = Index + 1, and then let the vehicle train for Repeat step 2; 4.

4. Release control information to vehicles on the main road Determine the set of cooperative vehicles on the main road After that, the control mode is released to the vehicles in the collection, and the indicators of the corresponding main road cooperative vehicles are Modified to 1; The method of step (4) is as follows: The kinematic model of ramp vehicle longitudinal control is proposed as follows: Where x is the position of the ramp vehicle, v is the speed of the ramp vehicle, a is the acceleration of the ramp vehicle, and u is the control acceleration of the ramp vehicle. The units are meter (m), meter / second (m / s), and meter / second squared (m / s 2 ), τ is the response parameter of the system; Let z = [x, v, a] T ,but Thus: Based on the kinematic model of the longitudinal control of the ramp vehicle, let the control start time be t0 and the control end time be t f , discretize the time with Δt as the control interval, and ensure that the total number of control steps λ=(t f -t0) / Δt is an integer, and a discretized MPC trajectory tracking model is established. The model equation is as follows: z i+1 =The i +Bu i +C Among them, A, B, and C are: Among them, i represents the i-th time step, z i+1 represents the state vector at the i+1th time step, z i represents the state vector at the i-th time step, u i represents the control acceleration at the i-th time step, I 3×3 is a third-order unit diagonal matrix.

2. A distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking according to claim 1, characterized in that: The constraints in step (5) are as follows: Constraint 1, initial value condition: Constraint 2, terminal value constraint: Constraint 3, vehicle performance constraint: Among them, x ref (·) is the trajectory function of the confluence position.

3. A distributed ramp merging longitudinal control method considering main road vehicle cooperation and trajectory tracking according to claim 2, characterized in that: The method of step (6) is as follows: Establish the cost function for trajectory tracking: Among them, Q1 and Q2 are both positive definite diagonal matrices, r is a positive real number, Calculated by the following formula: The discrete MPC trajectory tracking model is solved according to the constraints and cost functions to obtain the control acceleration, and the ramp vehicle is controlled by controlling the acceleration.

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