Cooperative control method for dynamic special lane of expressway and vehicle lane changing in networked environment

Abstract

The application discloses a kind of expressway dynamic special lane and vehicle lane changing coordination control method under network connection environment, comprising:1, collection road under the t time interval lane information and vehicle information;2, according to traffic demand, predict the traffic density of each lane in each section under the next time interval and vehicle average speed;3, according to the length of expressway special lane dynamic control model, the length of dynamic special lane under the t time interval is obtained;4, in the t time interval, according to the lane changing rate optimization model of expressway network connection automatic driving vehicle, every t s Time interval updates the CAV lane changing rate of each lane in each section;5, until greater than total control time, end control.The application can select the best road vehicle control scheme according to real-time traffic and vehicle information, improve traffic efficiency under the premise of ensuring safety, reduce vehicle delay, thereby improve the overall traffic capacity of road.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of intelligent network environment expressway traffic control, and specifically relates to a cooperative control method for expressway dynamic special lane and vehicle lane changing in a network environment. BACKGROUND

[0002] With the development of information technology and automatic driving technology, vehicles and infrastructure can exchange information in real time and achieve automatic driving. Setting a special lane for networked automatic driving vehicles on the road can improve the operation efficiency and safety of automatic driving vehicles. However, when there are few networked automatic driving vehicles on the road, it will cause waste of road space resources and reduce the overall traffic operation efficiency. In addition, due to the setting of the special lane, the frequency of lane changing of the networked automatic driving vehicles will be increased, which will increase the traffic operation risk and affect the operation of the road vehicles. SUMMARY

[0003] In order to overcome the deficiencies of the prior art, the present application provides a cooperative control method for expressway dynamic special lane and vehicle lane changing in a network environment, which can dynamically adjust the lane space resource allocation and optimize the lane changing behavior of networked automatic driving vehicles according to the changes of real-time traffic demand, so as to improve the traffic operation efficiency and reduce the safety risk of vehicle lane changing, thereby improving the overall traffic capacity of the road.

[0004] In order to achieve the above-mentioned application purposes, the present application adopts the following technical solutions:

[0005] The cooperative control method for expressway dynamic special lane and vehicle lane changing in a network environment has the characteristics of being applied to a one-way three-lane road with a special lane, and the three lanes are numbered from inside to outside as a first lane, a second lane and a third lane. The first lane is provided with a special lane, and the second lane and the third lane are ordinary lanes. The special lane is only allowed to be driven by networked automatic driving vehicles, and the ordinary lane is allowed to be driven by networked automatic driving vehicles and ordinary vehicles. According to the driving direction of the vehicles, the one-way three-lane road is evenly divided into n road sections, and any one road section is numbered as i, i = 1, 2, … n. The length of each road section is l. Any one lane of any one road section is numbered as j, j = 1, 2, 3. The jth lane of the ith road section is denoted as cell i,j .

[0006] Step 1, collecting road information and vehicle information of each lane on each road section in the tth time interval, including: lane type, vehicle type, vehicle number and vehicle speed;

[0007] Step 2, predicting the traffic density k i,j (t+1) and the average speed v i,j (t+1) of the lane cell i,j in the t+1th time interval.i,j (t+1) and actual traffic

[0008] Step 3: Construct a dynamic control model for the length of dedicated expressway lanes;

[0009] Step 4: Use a genetic algorithm to solve the dynamic control model for the length of the dedicated expressway lane, and obtain the length s of the dedicated lane in the t-th time interval. j (t)·l and the starting segment number i of the dedicated lane s (t) and the termination segment number i e (t), and the length of s j (t)·l and the starting segment number i s (t) and the termination segment number i e (t) is a dedicated lane located on the first lane;

[0010] Step 5: Based on the dedicated lanes set on the first lane during the t-th time interval, construct an optimization model for the lane-changing rate of connected autonomous vehicles on the expressway network;

[0011] Step 6: Use a genetic algorithm to solve the lane-changing rate control model for the connected autonomous vehicles on the expressway network, and obtain the t-th... s Lane change rate of connected autonomous vehicles in all lanes of each road segment within a time interval;

[0012] Step 7, according to the t-th s The lane-changing rate of all lanes of the connected autonomous vehicles on each road segment within a control time interval is calculated. After the connected autonomous vehicles on the road perform lane-changing operations, t will be... s +1 is assigned to t s If t s If M < M, then return to step 5.1 and execute sequentially; otherwise, execute step 8.

[0013] Step 8: Assign t+1 to t. If t≤Z, return to step 1 and execute sequentially. Otherwise, it means that the coordinated control of the dedicated lane and vehicle lane changing within Z time intervals has been completed and the process ends. Here, Z represents the total time interval sequence at which the control ends.

[0014] The method for coordinated control of dynamic dedicated lanes and vehicle lane changing on expressways in a connected environment, as described in this invention, is also characterized in that step 2 includes:

[0015] Step 2.1: Calculate the lane cell in the t-th time interval using equation (1). i,j Traffic density k i,j (t);

[0016]

[0017] In equation (1), m i,j (t) represents the lane cell in the t-th time interval. i,j The number of vehicles;

[0018] Step 2.2: Calculate the lane cell in the t-th time interval using equation (2). i,j Road capacity C i,j (t);

[0019]

[0020] In equation (2), ρ i,j (t) represents the lane cell in the t-th time interval. i,j The proportion of connected autonomous vehicles among all vehicles; v f Indicates the free-flow velocity of the road segment; This represents the lane cell in the t-th time interval. i,j The proportion of connected vehicles is ρ i,j The critical density at (t) is given by:

[0021]

[0022] In equation (3), l c s represents the length of the vehicle body; s0 represents the minimum safe front-to-rear distance; t H t represents the reaction time of a regular vehicle. C Indicates the reaction time of connected autonomous vehicles;

[0023] Step 2.3: Calculate the lane cell in the t-th time interval using equation (4). i,j Downstream transmission capacity S i,j (t);

[0024] S i,j (t)=min{v f k i,j (t),C i,j (t)} (4)

[0025] Step 2.4: Calculate the lane cell at the t-th time interval. i,j Vehicles within the network select the h-th lane of the (i+1)th downstream road segment. i+1,h probability Where h = 1, 2, 3;

[0026] If j≠h, then use equation (5) to calculate.

[0027]

[0028] In equation (5), This represents the lane cell in the t-th time interval. i,j Lane selection for regular vehicles i+1,h The probability is obtained from equation (6). This represents the lane cell in the t-th time interval. i,j Intranet-connected autonomous vehicle lane selection cell i+1,h The probability is obtained from equation (7);

[0029]

[0030] In equation (6), Ω i+1,h (t) represents the lane type of the h-th lane in the (i+1)-th road segment during the t-th time interval. If Ω i+1,h (t) = 1, indicating a dedicated lane; if Ω i+1,h (t) = 0, representing a regular lane; Δt represents the lane-changing time of the vehicle; v i,j (t) represents the lane cell in the t-th time interval. i,j The average speed of the vehicle; v i+1,h (t) represents the lane cell in the t-th time interval. i+1,h The average speed of the vehicle on board; T s Indicates the update time;

[0031]

[0032] If j = h, calculate using equation (8).

[0033]

[0034] In equation (8), and These represent the lane cells in the t-th time interval. i,j Ordinary vehicles and connected autonomous vehicles select the r-th lane of the (i+1)-th road segment. i+1,r The probability of r; and r≠h;

[0035] Step 2.5: Use equation (9) to predict the lane cell in the t-th time interval. i,j In-vehicle lane selection cell i+1,h Traffic

[0036]

[0037] Step 2.6: Use equation (10) to predict the j-th lane cell of the (i+1)-th road segment in the t-th time interval. i+1,j R's ability to accepti+1,j (t);

[0038]

[0039] in formula (10), k jam is the congestion density of the road; k i+1,j (t) is the traffic density of the lane cell i+1,j at the tth time interval; C i+1,j (t) is the traffic capacity of the lane cell i+1,j at the tth time interval; is the traffic wave speed of the lane cell i+1,j at the tth time interval, and has:

[0040]

[0041] in formula (11), k is the critical density of the lane cell i+1,j at the tth time interval when the proportion of the connected car is p i+1,j (t);

[0042] Step 2.7, the actual flow q i,j from the lane cell i+1,h at the tth time interval into the lane cell

[0043] Step 2.8, the traffic density k i,j of the lane cell i,j at the t+1th time interval is updated by using formula (13);

[0044]

[0045] in formula (13), q represents the actual flow from the hth lane cell i-1,h of the i-1th road section into the lane cell i,j ;

[0046] Step 2.9, the average speed v i,j of the lane cell i,j at the t+1th time interval is predicted by using formula (14);

[0047]

[0048] The step 3 comprises:

[0049] Step 3.1, taking the minimum total travel time of the road section vehicle and the minimum total lane changing times as the control target, a dynamic regulation model of the length of the expressway special lane is constructed in the target function J of the tth time interval by using formula (15) m (t);

[0050]

[0051] In formula (15), a and β are two parameters;

[0052] Step 3.2, the constraint condition of the dynamic regulation model of the length of the expressway special lane is constructed by using formula (16);

[0053]

[0054] In formula (16), s j (t) represents the number of road sections of the tth time interval in which the lane type is a special lane; s2(t) and s3(t) respectively represent the number of road sections of the tth time interval in which the lane type of the second lane and the third lane is a special lane; s max is the maximum difference of the length of the special lane in adjacent two time intervals; i s (t) and i e (t) are respectively the starting road section number and the ending road section number of the special lane in the tth time interval.

[0055] The step 5 comprises:

[0056] Step 5.1, collecting the road information and vehicle information of each lane on each road section in the t c th control time interval of the t s th time interval, including: lane type, vehicle type, vehicle number and vehicle speed; wherein, and T c = MT e , wherein M is a positive integer; T e is the control time length of the t s th time interval;

[0057] Step 5.2, predicting the traffic density, average speed and actual flow of each lane on each road section in the t s +1th control time interval according to the process of step 2;

[0058] Step 5.3, the target function J of the lane changing rate optimization model of the expressway network automatic driving vehicle is constructed by using formula (17) a (t s );

[0059]

[0060] In formula (17), a and b are two parameters; k i,j (t s ) is the traffic density of lane cell s in the t i,j th control time interval; k i,j (t s +1) is the traffic density of lane cell s in the t i,j +1th control time interval; is the actual flow from lane cell s to lane cell i,j in the t i+1,h th time interval; is the actual flow from lane cell s to lane cell i,j in the t i+1,h +1th time interval;

[0061] Step 5.4, constructing a constraint condition of the expressway network cooperative automatic driving vehicle lane changing rate optimization model by using formula (18);

[0062]

[0063] In formula (18), represents the lane changing rate of the network cooperative automatic driving vehicle on the jth lane cell s of the ith road section in the t i,j th control time interval; represents the lane changing rate of the network cooperative automatic driving vehicle on the jth lane cell s of the ith road section in the t i,j +1th control time interval; λ max represents the maximum difference of the network cooperative automatic driving vehicle lane changing rate between two adjacent control time intervals.

[0064] The electronic device comprises a memory and a processor, and the memory is used for storing a program supporting the processor to execute the dynamic special lane and vehicle lane changing cooperative control method, and the processor is configured to execute the program stored in the memory.

[0065] The computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the dynamic special lane and vehicle lane changing cooperative control method are executed.

[0066] Compared with the prior art, the beneficial technical effects of the present application are embodied in:

[0067] 1. This invention can predict the road traffic density and average vehicle speed in real time based on road traffic volume and the proportion of connected and autonomous vehicles, providing real-time predicted road traffic data for the dynamic lane length control model of expressways, making the dynamic control scheme more real-time and accurate.

[0068] 2. This invention utilizes a dynamic dedicated lane length control model to calculate the dynamic dedicated lane length for connected autonomous vehicles at different times, which not only ensures the priority right-of-way and operational efficiency of connected autonomous vehicles, but also improves the overall traffic efficiency of roads.

[0069] 3. Based on the dynamic adjustment of dedicated lane length, this invention further utilizes the lane-changing rate optimization model of connected autonomous vehicles on expressways to adjust the lane-changing behavior of connected autonomous vehicles in real time, ensuring lane-changing safety and reducing the overall traffic operation risk of the road. Attached Figure Description

[0070] Figure 1 This is the overall flowchart of the present invention;

[0071] Figure 2 This is a flowchart illustrating the specific control steps of the present invention;

[0072] Figure 3 This is a schematic diagram illustrating the setting of a dynamic three-lane dedicated lane for one-way expressways according to the present invention. Detailed Implementation

[0073] In this embodiment, a collaborative control method for dynamic dedicated lanes on expressways and vehicle lane changing in a connected environment is described, such as... Figure 3 The diagram illustrates a three-lane one-way road with a dedicated lane. The three lanes are numbered sequentially from the inside out as lane 1, lane 2, and lane 3. Lane 1 is a dedicated lane, while lanes 2 and 3 are regular lanes. The dedicated lane is for connected autonomous vehicles only, while the regular lanes allow both connected autonomous vehicles and regular vehicles to travel together. The three-lane one-way road is evenly divided into n segments according to the direction of travel. Each segment is numbered i, i = 1, 2, ..., n, and each segment has a length of l. Each lane within a segment is numbered j, j = 1, 2, 3. The j-th lane in the i-th segment is denoted as cell. i,j ;

[0074] Step 1, as follows Figure 1 As shown, road and vehicle information for each lane on each road segment during the t-th time interval is collected, including: lane type, vehicle type, number of vehicles, and vehicle speed.

[0075] Step 2, as follows Figure 2 As shown, the lane cell is predicted at the (t+1)th time interval.i,j Traffic density, average speed, and actual flow:

[0076] Step 2.1, as follows Figure 2 As shown, the lane cell in the t-th time interval is calculated using equation (1). i,j Traffic density k i,j (t);

[0077]

[0078] In equation (1), m i,j (t) represents the lane cell in the t-th time interval. i,j The number of vehicles;

[0079] Step 2.2, as follows Figure 2 As shown, the lane cell in the t-th time interval is calculated using equation (2). i,j Road capacity C i,j (t);

[0080]

[0081] In equation (2), ρ i,j (t) represents the lane cell in the t-th time interval. i,j The proportion of connected autonomous vehicles among all vehicles; v f This represents the free-flow velocity of the road segment, which is a constant and can be selected based on the actual road design conditions. This represents the lane cell in the t-th time interval. i,j The proportion of connected vehicles is ρ i,j The critical density at (t) is given by:

[0082]

[0083] In equation (3), l c The length of the vehicle body is represented by s0, which is usually taken as 5m; s0 represents the minimum safe front-to-rear distance, which is usually taken as 2m; t H This represents the reaction time of a typical vehicle, usually taken as 1.5 seconds; t C This represents the reaction time of a connected autonomous vehicle, typically taken as 0.6 seconds.

[0084] Step 2.3, as follows Figure 2 As shown, the lane cell in the t-th time interval is calculated using equation (4). i,j Downstream transmission capacity S i,j (t);

[0085] S i,j (t)=min{v f k i,j(t),C i,j (t)} (4)

[0086] Step 2.4, as follows Figure 2 As shown, the lane cell is calculated at the t-th time interval. i,j Vehicles within the network select the h-th lane of the (i+1)th downstream road segment. i+1,h probability Where h = 1, 2, 3;

[0087] If j≠h, then use equation (5) to calculate.

[0088]

[0089] In equation (5), This represents the lane cell in the t-th time interval. i,j Lane selection for regular vehicles i+1,h The probability is obtained from equation (6). This represents the lane cell in the t-th time interval. i,j Intranet-connected autonomous vehicle lane selection cell i+1,h The probability is obtained from equation (7);

[0090]

[0091] In formula (6), such as Figure 3 As shown, Ω i+1,h (t) represents the lane type of the h-th lane in the (i+1)-th road segment during the t-th time interval. If Ω i+1,h (t) = 1, indicating a dedicated lane; if Ω i+1,h (t) = 0, representing a regular lane; Δt represents the lane-changing time of the vehicle; v i,j (t) represents the lane cell in the t-th time interval. i,j The average speed of the vehicle; v i+1,h (t) represents the lane cell in the t-th time interval. i+1,h The average speed of the vehicle on board; T s Indicates the update time, which is a constant;

[0092]

[0093] If j = h, calculate using equation (8).

[0094]

[0095] In equation (8), and These represent the lane cells in the t-th time interval. i,j Ordinary vehicles and connected autonomous vehicles select the r-th lane of the (i+1)-th road segment. i+1,r The probability of r is r; and r ≠ h.

[0096] Step 2.5, as follows Figure 2 As shown, Equation (9) is used to predict the lane cell in the t-th time interval. i,j In-vehicle lane selection cell i+1,h Traffic

[0097]

[0098] Step 2.6, as follows Figure 2 As shown, Equation (10) is used to predict the j-th lane cell of the (i+1)-th road segment in the t-th time interval. i+1,j R's ability to accept i+1,j (t);

[0099]

[0100] In equation (10), k jam Let k be the road congestion density, and k be a constant. i+1,j (t) represents the lane cell during the t-th time interval. i+1,j Traffic density; C i+1,j (t) represents the lane cell during the t-th time interval. i+1,j Traffic capacity; For lane cell in the t-th time interval i+1,j The traffic wave velocity, and has:

[0101]

[0102] In equation (11), For lane cell in the t-th time interval i+1,j The proportion of connected vehicles is ρ i+1,j Critical density at (t).

[0103] Step 2.7, as follows Figure 2 As shown, the t-th time interval from lane cell is calculated using equation (12). i,j Enter the lane cell i+1,h Actual traffic

[0104]

[0105] Step 2.8, as follows Figure 2 As shown, the lane cell is updated using equation (13).i,j Traffic density k in the (t+1)th time interval i,j (t+1);

[0106]

[0107] In equation (13), Cell represents the h-th lane from the (i-1)-th road segment. i-1,h Flowing into the lane cell i,j The actual traffic.

[0108] Step 2.9, as follows Figure 2 As shown, Equation (14) is used to predict the lane cell in the (t+1)th time interval. i,j The average speed of the vehicle, v i,j (t+1);

[0109]

[0110] Step 3, as follows Figure 1 As shown, a dynamic control model for the length of dedicated expressway lanes is constructed:

[0111] Step 3.1: Taking the minimum total travel time and the minimum number of lane changes for vehicles on the road segment as the control objectives, the objective function J of the dynamic control model for the length of the expressway dedicated lane is constructed using equation (15) at the t-th time interval. m (t);

[0112]

[0113] In equation (15), α and β are two parameters.

[0114] Step 3.2: Construct the constraints of the dynamic control model for the length of the expressway dedicated lane using equation (16);

[0115]

[0116] In equation (16), s j s(t) represents the number of road segments with lane type dedicated lane in the t-th time interval; s2(t) and s3(t) represent the number of road segments with lane type dedicated lane in the second and third lanes, respectively, in the t-th time interval; s max This is the maximum difference in the length of the dedicated lane between two adjacent time intervals, preventing excessive changes in the length of the dedicated lane over time from affecting traffic flow stability; s (t) and i e (t) represents the starting and ending segments of the dedicated lane within the t-th time interval.

[0117] Step 4: Use a genetic algorithm to solve the dynamic control model for the length of the dedicated expressway lane, and obtain the length s of the dedicated lane in the t-th time interval. j (t)·l and the starting segment number i of the dedicated lane s (t) and the termination segment number i e (t), and the length of s j (t)·l and the starting segment number i s (t) and the termination segment number i e The dedicated lane for (t) is located on the first lane.

[0118] Step 5, as follows Figure 1 As shown, based on the dedicated lane set on the first lane in the t-th time interval, an optimization model for the lane-changing rate of autonomous vehicles connected to the expressway network is constructed.

[0119] Step 5.1, as follows Figure 2 As shown, the control duration T for the t-th time interval is collected. c The tth s The data includes road and vehicle information for each lane on each road segment within a control time interval, including lane type, vehicle type, number of vehicles, and vehicle speed; where T... c =MT e Where M is a positive integer; T e For the tth s The control duration of each time interval is a constant; the t-th time interval represents a time series, for example, t = 1, 2, 3... Z, and the control duration of each time series is T. c ; the tth s Each time interval also represents a time series, for example, t = 1, 2, 3... M, and the control duration for each time series is T. e .

[0120] Step 5.2: Predict the t-th step according to the process in Step 2. s Traffic density, average speed, and actual flow rate of each lane on each road segment within +1 control time interval;

[0121] Step 5.3: Construct the objective function J of the lane-changing rate optimization model for connected autonomous vehicles on expressways using equation (17). a (t s );

[0122]

[0123] In equation (17), a and b are two parameters; k i,j (t s ) is the t-th s Lane cell within a control time interval i,jtraffic density of the lane cell i,j (t s +1) is the traffic density of the lane cell s in the t i,j +1 control time interval. is the actual flow from the lane cell s to the lane cell i,j in the t i+1,h time interval. is the actual flow from the lane cell s to the lane cell i,j in the t i+1,h +1 time interval.

[0124] Step 5.4, constructing the constraint condition of the expressway network connected autonomous vehicle lane changing rate optimization model by using formula (18);

[0125]

[0126] In formula (18), represents the lane changing rate of the network connected autonomous vehicle on the jth lane cell s of the ith road section in the t i,j control time interval. represents the lane changing rate of the network connected autonomous vehicle on the jth lane cell s of the ith road section in the t i,j +1 control time interval. max represents the maximum difference of the network connected autonomous vehicle lane changing rate between two adjacent control time intervals, which avoids the lane changing rate difference being too large to cause traffic disorder.

[0127] Step 6, solving the expressway network connected autonomous vehicle lane changing rate control model by using genetic algorithm, and obtaining the lane changing rate of the network connected autonomous vehicle on all lanes of each road section in the t s time interval.

[0128] Step 7, as shown in formula (19), according to the lane changing rate of the network connected autonomous vehicle on all lanes of each road section in the t s control time interval, the network connected autonomous vehicle on the road is controlled to perform lane changing operation, and then t s +1 is assigned to t s . If t s M, then return to step 5.1 for sequential execution; otherwise, execute step 8.

[0129] Step 8, as shown in formula (20), the lane changing rate of the network connected autonomous vehicle on all lanes of each road section in the t s +1 control time interval is obtained, and then t s +2 is assigned to t s . If t M, then return to step 5.1 for sequential execution; otherwise, execute step 9. Figure 2If t≤Z, the process returns to step 1 for sequential execution, otherwise, it indicates that the cooperative control of the exclusive lane and the vehicle lane changing in Z time intervals is completed, and the flow ends; wherein, Z represents the total time interval sequence of the control end. In this embodiment, the method idea of the present application is not limited to three lanes on the one-way expressway, and other embodiments obtained by those skilled in the art without creative changes are within the protection scope of the present application.

[0130] In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.

[0131] In this embodiment, a computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to perform the steps of the above method.

Claims

1. A cooperative control method for expressway dynamic special lane and vehicle lane changing in a networked environment, The application is characterized in that the three lanes are numbered from inside to outside as a first lane, a second lane and a third lane, the first lane is provided with a special lane, and the second lane and the third lane are common lanes. The special lane only allows the networked automatic driving vehicle to travel, and the ordinary lane allows the networked automatic driving vehicle and the ordinary vehicle to travel together; according to the traveling direction of the vehicle, the three one-way traffic lanes are evenly divided into n road sections, and the number of any one road section is i, , and the length of each road section is . Let the number of lanes of any road segment be j, ; the number of lanes of the first road segment be j ; and the number of lanes of the first road segment be j Step 1, collecting road information and vehicle information of each lane on each road section in the tth time interval, including lane type, vehicle type, vehicle number and vehicle speed; Step 2, predicting the traffic density , average speed , and actual flow of the lane for the next time interval ; Step 3, constructing a dynamic control model of the length of the special lane of the expressway; Step 3.1, taking the minimum total travel time of the vehicle on the section and the minimum total number of lane changes as control targets, a dynamic control model of the length of the expressway shoulder is constructed using formula (15) to construct the objective function of the model in the tth time interval ; (15) In equation (15), and It has 2 parameters; For the t-th time interval, the lane Traffic density; For the (t+1)th time interval, the lane is lowered. Traffic density; For the first Lane at each time interval The actual traffic, Indicates the update time; Step 3.2, constructing a constraint condition of the dynamic control model of the length of the special lane of the expressway by using formula (16); (16) In formula (16), denotes the number of road segments with the lane type as exclusive lane in the tth time interval, denotes the number of road segments with the lane type as exclusive lane in the t+1th time interval, and denote the number of road segments with the lane type as exclusive lane in the tth time interval on the second lane and the third lane, respectively; is the maximum difference of the length of exclusive lane in adjacent two time intervals; and denote the number of road segments with the lane type as exclusive lane in the tth time interval on the second lane and the third lane, respectively; Step 4, solving the length dynamic regulation model of the expressway special lane by using genetic algorithm to obtain the length of the special lane in the tth time interval and the starting section number of the special lane and the ending section number of the special lane , and setting the special lane with the length of and the starting section number of the special lane and the ending section number of the special lane on the first lane; Step 5, constructing an optimization model of the lane-changing rate of the network-connected autonomous vehicle of the expressway according to the special lane provided on the first lane in the tth time interval; Step 6, solving the lane-changing rate optimization model of the expressway network connected autonomous vehicle by using a genetic algorithm, and obtaining the lane-changing rate of the network connected autonomous vehicle on all lanes of each road section in the first time interval; the first time interval; Step 7, according to the lane changing rate of the connected and automatic driving vehicle on each road section of all lanes in each control time interval, control the connected and automatic driving vehicle on the road to perform the lane changing operation, and then assign the value to, if, return to step 5.1 for sequential execution; otherwise, execute step 8. Step 8, according to the lane changing rate of the connected and automatic driving vehicle on each road section of all lanes in each control time interval, control the connected and automatic driving vehicle on the road to perform the lane changing operation, and then assign the value to, if, return to step 5.1 for sequential execution; otherwise, execute step 9. Step 9, according to the lane changing rate of the connected and automatic driving vehicle on each road Step 8, set to , if , return to step 1 for sequential execution, otherwise, indicate completion of coordinated control of the exclusive lane and vehicle lane changing within the time interval, and end the process; wherein indicates the total time interval sequence of control completion.

2. The method according to claim 1, wherein the method is characterized in that, The step 2 comprises: Step 2.1, calculating the traffic density of the lane at the tth time interval using formula (1) ;​ (1) In formula (1), denotes the number of vehicles in the lane at the tth time interval. Step 2.2, calculating the road capacity of the lane at the tth time interval using formula (2) ;​ (2) In formula (2), the lane at the tth time interval the proportion of the connected autonomous vehicles in the total vehicles; the free flow speed of the road segment; the lane at the tth time interval the critical density when the proportion of the connected vehicles is . (3) In formula (3), denotes the length of the vehicle body; denotes the minimum safe headway; denotes the reaction time of a normal vehicle; denotes the reaction time of a connected and automated vehicle; Step 2.3, calculating the lane for the tth time interval using formula (4) Downstream traffic capacity ; (4) Step 2.4, compute the probability of the tth time interval under the lane downstream of the inner vehicle +1 segments of the tth lane of the inner vehicle where, ; If then the calculation is made using equation (5) ; (5) In formula (5), denotes the probability of the lane selection by the ordinary vehicle within the lane at the tth time interval, and is obtained by formula (6), denotes the probability of the lane selection by the connected automatic driving vehicle within the lane at the tth time interval, and is obtained by formula (7); (6) In formula (6), represents the lane type of the hth lane of the i+1th road section in the tth time interval, if represents a special lane, if represents a common lane; represents the lane changing time of the vehicle; represents the average speed of the vehicle on the lane in the tth time interval; represents the average speed of the vehicle on the lane in the tth time interval; (7) If time, the value of the parameter is calculated using equation (8) ; (8) In equation (8), and These represent the lanes at the t-th time interval. Choose between conventional vehicles and connected autonomous vehicles +1 road segment lane The probability of; and ; Step 2.5, predicting the lane of the tth time interval using the formula (9) The inner vehicle is expected to select the lane of the traffic flow ; (9) Step 2.6, predict the acceptance capacity of the tthlane of the +1thlink in the tthtime interval using the formula (10) ;​​​ (10) In equation (10), The density of traffic congestion on the road; For the t-th time interval, the lane Traffic density; For the t-th time interval, the lane Traffic capacity; For the t-th time interval, the lane The traffic wave velocity, and has: (11) In formula (11), is the critical density when the proportion of connected vehicles is is the critical density when the proportion of connected vehicles is is the critical density when the proportion of connected vehicles is Step 2.7, calculate the actual flow into the lane from the carriageway at the tth time interval using formula (12) ;​​ (12) Step 2.8, updating the lane with formula (13) Traffic density at the t+1 time interval ; (13) In formula (13), represents the number of vehicles that have entered the road segment from the -1th road segment th lane of the actual flow of vehicles that have entered the lane from the preceding road segment Step 2.9, predicting the average speed of vehicles in the lane of the t+1th time interval using the formula (14) ;​ (14)。 3. The method according to claim 2, wherein the method is characterized by, The step 5 comprises: Step 5.1: Collect the control duration of the t-th time interval. The The road and vehicle information for each lane on each road segment within each control time interval includes: lane type, vehicle type, number of vehicles, and vehicle speed; where, and Where M is a positive integer; For the first The control duration of each time interval; Step 5.2, predict the traffic density, average speed and actual flow on each lane of each link in the nth control time interval according to the procedure of Step 2; Step 5.2, predict the traffic density, average speed and actual flow on each lane of each link in the nth control time interval according to the procedure of Step 2; Step 5.3, constructing a target function of the lane-changing rate optimization model of the expressway network connected autonomous vehicle by using formula (17) ; (17) In formula (17), and are 2 parameters; is the traffic density in the lane for the control time interval; is the traffic density in the lane for the control time interval; is the actual flow from the lane to the lane for the time interval; is the actual flow from the lane to the lane for the time interval; Step 5.4, constructing a constraint condition of the optimization model of the lane-changing rate of the network-connected autonomous vehicle of the expressway by using formula (18); (18) In equation (18), Indicates the first The first control time interval The first section of the road lane Lane-changing rate of connected autonomous vehicles; Indicates the first The first control time interval The first section of the road lane Lane-changing rate of connected autonomous vehicles; This represents the maximum difference in lane-changing rates between two adjacent control time intervals for connected autonomous vehicles.

4. An electronic device comprising a memory and a processor, characterized in that The memory is used for storing a program supporting the processor to execute the dynamic special lane and vehicle lane-changing cooperative control method in any one of claims 1-3, and the processor is configured to execute the program stored in the memory.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to execute the steps of the dynamic special lane and vehicle lane-changing cooperative control method in any one of claims 1-3.

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