CAV ecological driving guidance method based on vehicle-road collaboration in mixed traffic environment

Abstract

The present invention discloses a CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment, comprising: 1. collecting vehicle information at time t0 and the real-time status of the current intersection signal light; 2. determining the traffic scene location of the CAV; 3. determining the terminal state of the CAV ecological driving based on different traffic scenarios; 4. determining the optimal speed trajectory curve of the CAV; and 5. dynamically updating and optimizing the trajectory through a triggered trajectory update rule. The present invention can reduce CAV starting and stopping, idling, and acceleration and deceleration at intersections through information exchange between CAVs and roadside facilities, and between CAVs in a vehicle-road collaborative environment, thereby improving traffic efficiency, reducing energy consumption, and achieving sustainable transportation development.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent traffic control, and in particular to the field of speed control of connected automated vehicles (CAVs) at urban signalized intersections. Specifically, it provides an ecological driving guidance method for connected automated vehicles at signalized intersections in a mixed traffic environment. Background Art

[0002] In recent years, with the rapid growth in demand for motorized travel, traffic problems such as energy consumption, pollutant emissions, and congestion have become increasingly prominent. This is particularly true at signalized intersections, where vehicles frequently idle and restart depending on signal conditions, often leading to unnecessary energy consumption. By coordinating vehicle motion information with intersection signal timing, known as eco-driving, this can effectively reduce frequent stops and starts, as well as drastic acceleration and deceleration, at intersections, thereby improving traffic efficiency and reducing energy consumption.

[0003] Thanks to the development of intelligent connected technologies, information exchange between connected autonomous vehicles (CAVs) and road infrastructure has provided strong support for eco-driving, enabling intersection signal optimization and CAV motion control. By acquiring real-time traffic information, control systems can accurately plan optimal vehicle speed trajectories. Research has focused on eco-driving at intersections in a vehicle-road cooperative environment. However, this assumes that all vehicles in the traffic environment are connected autonomous vehicles. However, the new mixed traffic environment, characterized by human-driven vehicles (HVs) and CAVs, is expected to persist for a long time. In real-world traffic scenarios, HV driving behavior can significantly interfere with CAV eco-driving, such as the movement of HVs in front of the CAV and the queuing caused by HVs. Furthermore, the traffic uncertainty introduced by HVs in real traffic conditions requires CAVs to dynamically update their trajectories. Existing research on eco-driving methods in such complex traffic environments is insufficient. Summary of the Invention

[0004] To overcome the shortcomings of the existing technology, the present invention proposes a CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment. This method takes into account vehicle queues in front of intersections or disturbances caused by manually driven vehicles in front, solves the ecological driving problem of networked autonomous vehicles under triggered trajectory update rules, corrects the impact of traffic uncertainty on CAV traffic, thereby reducing vehicle delays and fuel consumption and achieving sustainable transportation development.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0006] The characteristic of the CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment of the present invention is that it is applied to a signal intersection section scene, and in a mixed traffic flow composed of networked autonomous driving vehicles (CAVs) and human-driven vehicles (HVs), a vehicle with a distance of 100 km from the intersection stop line is set. The road section range is used as the length of the guidance area, and the CAV ecological driving guidance method includes the following steps:

[0007] Step 1: Target CAV at the current moment After entering the guidance area, the target CAV is recorded as the target vehicle ,vehicle Use roadside intelligent traffic equipment to obtain the real-time status of the current intersection signal lights and the location information of all vehicles in the guidance area;

[0008] Step 2: Identify the vehicle Traffic scene location within the guidance area:

[0009] If the target vehicle At the current moment If it is the leading vehicle in the guide area, execute step 3; otherwise, determine the target vehicle Whether it is the first CAV in the lane guidance area where the vehicle is traveling. If it is the first CAV, go to step 4; otherwise, go to step 7.

[0010] Step 3: Calculate the target vehicle Time to pass the stop line of the current intersection and the speed at which you pass the stop line ;

[0011] Step 4: Target vehicle The vehicle-mounted sensor equipment identifies the HV in front at the current moment. Location and speed , and judge the target vehicle If the HV in front of the target vehicle does not stop, then go to step 5. Otherwise, the target vehicle The HV ahead of you is lined up before the intersection stop line, and go to step 6;

[0012] Step 5: Assuming that the speed of the HV in front remains unchanged, use formula (4) to predict the time it takes for the HV in front to arrive at the current intersection: , and perform step 8:

[0013] (4)

[0014] In formula (4), is the stop line position of the current intersection;

[0015] Step 6: Target vehicle Obtain the number of vehicles queued before the current intersection from the vehicle-road communication system through short-range wireless communication , used to predict the target vehicle The preceding car, The time it takes for the vehicle to dissipate the queue when the green light starts at the current intersection and The speed at which a vehicle passes the stop line ;

[0016] Step 7: Target vehicle Obtain the current intersection distance from the target vehicle from the vehicle-road communication system through short-range wireless communication The trajectory information of the nearest m-th CAV and the target vehicle The speed and position information of several HVs between the mth CAV and the target vehicle are calculated. Vehicle ahead Time to reach the current intersection and the speed at which you pass the stop line ;

[0017] Step 8: Based on the real-time status of the current intersection signal light and the target vehicle The vehicle information ahead is used to predict the target vehicle j at the moment it passes the current intersection. And the target vehicle j at time Speed ;

[0018] Step 9: Construct the current moment To the future The vehicle trajectory optimization model is solved and the target vehicle is obtained. At the current moment To the future The ecological velocity trajectory;

[0019] Step 10: Assign to Then, return to step 1 to step 8 to obtain the new time when the target vehicle j passes the current intersection. , and determine whether formula (24) is established. If so, execute step 9, otherwise, return to step 10, where For the preset interval, is the time error threshold;

[0020] (twenty four).

[0021] The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment according to the present invention is also characterized in that step 3 includes:

[0022] Step 3.1: Determine the target vehicle Distance to the intersection stop line Is it satisfied , if satisfied, then the target vehicle From the initial speed At maximum acceleration Accelerate to the speed limit Otherwise, the target vehicle below the speed limit The speed of the vehicle passes through the current intersection, and the target vehicle is calculated using formula (1) Earliest arrival time :

[0023] (1)

[0024] Step 3.2: Calculate the target vehicle using formula (2) Stop line time :

[0025] (2)

[0026] In formula (2), For the The time when the green light starts in a signal cycle;

[0027] Step 3.3: Calculate the target vehicle using formula (3) Time at the stop line Speed ​​at the stop line , thereby completing the state prediction of the CAV passing the current intersection and executing step 8;

[0028] (3)

[0029] In formula (3), For the current moment Target vehicle The initial velocity.

[0030] Furthermore, the step 6 includes:

[0031] Step 6.1, starting from the first vehicle closest to the intersection stop line, sequentially move all HVs in the queue from 1 to Index and use formula (5) to calculate the The car at the current moment Distance to the stop line ;

[0032] (5)

[0033] In formula (5), is the minimum safe distance between vehicles, is the length of the vehicle;

[0034] Use formula (6) to predict the first Car in the future Speed and acceleration :

[0035] (6)

[0036] In formula (6), When the traffic light at the current intersection turns green, The moment the car starts moving, It is The average reaction time of the drivers of the vehicles, and ;

[0037] Step 6.2: Record any HV as the current vehicle , use Equation (7)-Equation (8) to model the driving behavior of HV in the traffic scene within the guidance area, and obtain the current vehicle IDM following model;

[0038] (7)

[0039] (8)

[0040] In formula (7)-formula (8), is the maximum deceleration of the vehicle, Is the current vehicle In the future The acceleration of Is the current vehicle In the future Distance requirements from the preceding HV vehicle, Is the current vehicle In the future speed, The safe distance between vehicles. Is the current vehicle In the future The speed difference with the preceding HV, Is the current vehicle In the future The distance difference to the preceding HV;

[0041] Step 6.3: Use equations (9) and (10) to get the current vehicle At the next moment Speed and location :

[0042] (9)

[0043] (10)

[0044] In formulas (9) and (10), is the time step; For the current vehicle At the current moment speed, For the current vehicle At the current moment acceleration; For the current vehicle At the current moment location;

[0045] Step 6.4: Calculate the first Car in the future The instantaneous queue length , thus obtaining the actual dissipation time of the queue = ,in, for The moment when the queue dissipates;

[0046] (11)

[0047] Step 6.5: Calculate target vehicle The nearest vehicle in front Speed ​​through the current intersection Then, execute step 8; wherein, Indicates the The car dissipates at the moment of queue speed.

[0048] Furthermore, the step 7 includes:

[0049] Step 7.1: The mth CAV at the current moment The target vehicle is simulated using the IDM following model. Several HVs between the mth CAV and the next CAV at the future time speed and position;

[0050] The target vehicle The car in front In the target vehicle The vehicle corresponding to the mth CAV is recorded as HV, then HV in the future The speed is recorded as , the position is recorded as ;

[0051] Step 7.2, according to The time it takes for a HV to arrive at the current intersection Location This is the stop line position of the current intersection , get the target vehicle The car in front Time to reach the current intersection ;

[0052] according to , using the IDM car-following model to obtain the HV in the future Speed and as the target vehicle The car in front Speed ​​through the current intersection , proceed to step 8.

[0053] Furthermore, the step 8 includes:

[0054] Step 8.1: Use formula (12) to get the time when the target vehicle j passes the current intersection :

[0055] (12)

[0056] In formula (12), It is The time when the red light starts in a signal cycle; is the time when the red light starts in the i+1th signal cycle, is the moment when the preceding vehicle of target vehicle j passes the current intersection;

[0057] Step 8.2: Use formula (13) to predict the target vehicle j at the time of passing the current intersection Speed , and perform step 9:

[0058] (13).

[0059] Furthermore, the vehicle trajectory optimization model in step 9 is based on minimizing CAV fuel consumption. With the goal of ensuring driving comfort, the vehicle objective function is constructed using equations (14) to (16): , and use equations (17) to (23) to construct the vehicle kinematic constraints:

[0060] (14)

[0061] (15)

[0062] (16)

[0063] (17)

[0064] (18)

[0065] (19)

[0066] (20)

[0067] (twenty one)

[0068] (twenty two)

[0069] (twenty three)

[0070] In formula (14) to formula (23), is the weight of acceleration, G is the road slope value, is the vehicle mass; For vehicles in the future of torque, 、 、 、 、 、 are the 6 fitting coefficients of the model, For vehicles In the future speed, For vehicles In the future The acceleration of , is the target vehicle j at the current moment The position and velocity of Target vehicle In the future location, For vehicles In the future location.

[0071] The electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the CAV ecological trajectory control method, and the processor is configured to execute the program stored in the memory.

[0072] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps of the CAV ecological trajectory control method when executed by a processor.

[0073] Compared with the existing technology, the beneficial technical effects of the present invention are embodied in:

[0074] 1. The proposed eco-driving strategy for connected autonomous vehicles (CAVs) takes into account multiple factors in real-world traffic scenarios, including signal status, queue information, the speed of the vehicle ahead, and collision avoidance, to optimize the CAV's eco-trajectory in mixed traffic flows. The strategy predicts the trajectory constraints and terminal states of the CAV's eco-driving based on different traffic scenarios to determine the CAV's optimal speed profile. This strategy improves the CAV's driving efficiency and safety in mixed traffic flows, enhances its driving stability and comfort in complex traffic environments, and addresses the issue of CAV trajectory updates under dynamic traffic conditions in mixed traffic flows. The strategy has practical engineering application value in traffic management and control at urban road intersections.

[0075] 2. Taking into account the uncertainty of real traffic scenarios, the present invention proposes a triggered trajectory update rule to ensure the feasibility of ecological driving trajectories and reduce computational efficiency. This rule can dynamically update and optimize trajectories to achieve the ideal ecological driving effect for vehicles.

[0076] 3. Based on the IDM car-following model, the present invention proposes a method for predicting the dissipation of intersection queues, which can help CAVs accurately predict queue dissipation information and queue length changes, making the CAV ecological trajectory more reasonable and helping to improve the driving smoothness and driver comfort of CAVs through intersections. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is the overall flow chart of the present invention;

[0078] Figure 2 is a CAV decision flow chart of the present invention;

[0079] Figure 3 A schematic diagram of an intersection of the present invention;

[0080] Figure 4 Schematic diagrams of four typical scenarios of CAV ecological driving of the present invention. DETAILED DESCRIPTION

[0081] In this embodiment, a CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment is applied to a signalized intersection section scenario, and in a mixed traffic flow consisting of connected automated vehicles (CAVs) and human-driven vehicles (HVs), the distance from the intersection stop line is The road section range is used as the length of the guidance area. The present invention only considers the longitudinal trajectory of the vehicle. Please refer to Figure 1 , the CAV ecological driving guidance method includes the following steps:

[0082] Step 1, please refer to Figure 3 Schematic diagram of the intersection, the target CAV is at the current moment After entering the guidance area, the target CAV is recorded as the target vehicle ,vehicle Use roadside intelligent traffic equipment to obtain the real-time status of the current intersection signal lights and the position information of all vehicles in the guidance area; The starting point for entering the guide area is the origin , target vehicle The direction of travel is the positive direction of the x-axis, and the direction perpendicular to the x-axis is the y-axis, and a road coordinate system is established;

[0083] The target vehicle The real-time status of the current intersection traffic lights is obtained through short-range wireless communication technology and vehicle-road communication systems; the intersection vehicle-road cooperative system uses intelligent roadside equipment installed on the road to obtain the position and speed of all vehicles in the guide area on the road section.

[0084] Step 2: Identify the vehicle For the traffic scene location within the guidance area, please refer to Figure 2 CAV decision flow chart,

[0085] If the target vehicle At the current moment If it is the leading vehicle in the guide area, execute step 3; otherwise, determine the target vehicle Whether it is the first CAV in the lane guidance area where the vehicle is traveling. If it is the first CAV, execute step 4; otherwise, execute step 7.

[0086] Step 3: Calculate the target vehicle Time to pass the stop line of the current intersection and the speed at which you pass the stop line ; At this time, the target vehicle Will not be affected by any queue parking, the traffic scenario is Figure 4 Scenario A, target vehicle The time of passing the stop line is the earliest arrival time The time closest to the green light stage The maximum value of .

[0087] Step 3.1: When the vehicle is free-running, according to the vehicle kinematics formula, when the target vehicle When the distance to the intersection is far, assuming that the vehicle always wants to reach the current intersection at the fastest speed, the target vehicle is judged Distance to the intersection stop line Is it satisfied , if satisfied, then the target vehicle From the initial speed At maximum acceleration Accelerate to the speed limit Otherwise, the target vehicle below the speed limit The speed of the vehicle passes through the current intersection, and the target vehicle is calculated using formula (1) Earliest arrival time :

[0088] (1)

[0089] Step 3.2: Calculate the target vehicle using formula (2) Stop line time :

[0090] (2)

[0091] In formula (2), For the The time when the green light starts in a signal cycle.

[0092] Step 3.3: Calculate the target vehicle using formula (2) Speed ​​at the stop line , the speed of CAV passing the stop line Speed ​​limit and the minimum value of the free travel speed; thereby completing the state prediction of the CAV passing the current intersection and executing step 8;

[0093] (3)

[0094] In formula (3), For the current moment Target vehicle Initial velocity;

[0095] Step 4: Target vehicle The vehicle-mounted sensor equipment identifies the HV in front at the current moment. Location and speed , and judge the target vehicle If the HV in front of the target vehicle does not stop, then go to step 5. Otherwise, the target vehicle The HV in front of you is lined up before the stop line at the intersection and go to step 6.

[0096] Step 5: The traffic scene is Figure 4 In scenario B, assuming that the speed of the HV in front remains unchanged, the time for the HV in front to arrive at the current intersection is predicted using formula (4): , and perform step 8:

[0097] (4)

[0098] In formula (4), The stop line position of the current intersection.

[0099] Step 6: The traffic scene is Figure 4 Scenario C: There is a queue of vehicles at the intersection downstream of the CAV. The last vehicle in front stops at the traffic light. The first vehicle starts moving when the light turns green. The target vehicle Obtain the number of vehicles queued before the current intersection from the vehicle-road communication system through short-range wireless communication , used to predict the target vehicle The preceding car, The time it takes for the vehicle to dissipate the queue when the green light starts at the current intersection and The speed at which a vehicle passes the stop line ;;

[0100] Step 6.1, starting from the first vehicle closest to the intersection stop line, sequentially move all HVs in the queue from 1 to Index and use formula (5) to calculate the The car at the current moment Distance to the stop line ;

[0101] (5)

[0102] In formula (5), is the minimum safe distance between vehicles, is the length of the vehicle.

[0103] Use formula (6) to predict the first Car in the future Speed and acceleration :

[0104] (6)

[0105] In formula (6), When the traffic light at the current intersection turns green, The moment the car starts moving, It is The average reaction time of the drivers of the vehicles, and .

[0106] Step 6.2: Record any HV as the current vehicle , using Equations (7) and (8) to model the driving behavior of HVs in the traffic scene within the guidance area, the IDM following model is obtained;

[0107] (7)

[0108] (8)

[0109] In formula (7)-formula (8), is the maximum deceleration of the vehicle, Is the current vehicle In the future The acceleration of Is the current vehicle In the future Distance requirements from the preceding HV vehicle, Is the current vehicle In the future speed, The safe distance between vehicles. Is the current vehicle In the future The speed difference with the preceding HV, Is the current vehicle In the future The distance difference to the preceding HV vehicle.

[0110] Step 6.3: Use equations (9) and (10) to get the current vehicle At the next moment Speed and location :

[0111] (9)

[0112] (10)

[0113] In formulas (9) and (10), is the time step; For the current vehicle At the current moment speed, For the current vehicle At the current moment acceleration; For the current vehicle At the current moment location;

[0114] Step 6.4: Calculate the first Car in the future The instantaneous queue length , thus obtaining the actual dissipation time of the queue = ,in, for The moment when the queue dissipates;

[0115] (11)

[0116] Step 6.5: Target vehicle Car in front The speed of the target vehicle passing through the current intersection can be calculated by the following model. The nearest vehicle in front Speed ​​through the current intersection Then, execute step 8; wherein, Indicates the The car dissipates at the moment of queue speed.

[0117] Step 7: The traffic scene is Figure 4 Scene D, target vehicle Obtain the current intersection distance from the target vehicle from the vehicle-road communication system through short-range wireless communication Recent CAVs Trajectory information and target vehicle With CAV The speed and position information of the HV between them; thus calculating the target vehicle Vehicle ahead Time to reach the current intersection and the speed at which you pass the stop line .

[0118] Step 7.1: The mth CAV at the current moment The target vehicle is simulated using the IDM following model. Several HVs between the mth CAV and the next CAV at the future time speed and position;

[0119] The target vehicle The car in front In the target vehicle The vehicle corresponding to the mth CAV is recorded as HV, then HV in the future The speed is recorded as , the position is recorded as ;

[0120] Step 7.2, according to The time it takes for a HV to arrive at the current intersection Location This is the stop line position of the current intersection , get the target vehicle The car in front Time to reach the current intersection ;

[0121] according to , using the IDM car-following model to obtain the HV in the future Speed and as the target vehicle The car in front Speed ​​through the current intersection , proceed to step 8.

[0122] Step 8: Based on the real-time status of the current intersection signal light and the target vehicle The vehicle information ahead is used to predict the target vehicle j at the moment it passes the current intersection. And the target vehicle j at time Speed ;

[0123] Step 8.1: Use formula (12) to get the time when the target vehicle j passes the current intersection :

[0124] (12)

[0125] In formula (12), It is The time when the red light starts in a signal cycle; is the time when the red light starts in the i+1th signal cycle, is the moment when the preceding vehicle of target vehicle j passes the current intersection;

[0126] The first branch of formula (12) is the front vehicle and the target vehicle The second branch of formula (12) is that the front vehicle can pass the stop line before the red phase, while the target vehicle If the vehicle cannot pass through in time, the CAV can only pass when the next green light starts. The third branch of formula (12) is that the vehicle in front cannot pass the stop line before the red light. At this time, the target vehicle The time it takes to pass the intersection is the time when the green light starts Plus the queue dissipation time Plus safe headway .

[0127] Step 8.2: Use formula (13) to predict the target vehicle At the moment of passing the current intersection Speed , and perform step 9:

[0128] (13)

[0129] Target vehicle The speed at the stop line is the speed limit , free travel speed and the speed of the vehicle ahead The minimum value among ; at this time, the state prediction of CAV passing through the intersection is completed.

[0130] Step 9: Minimize CAV fuel consumption With the goal of ensuring driving comfort, the vehicle objective function is constructed using equations (14) to (16): , and use equations (17)-(23) to construct the vehicle kinematic constraints, and construct the current moment To the future The vehicle trajectory optimization model is solved and the target vehicle is obtained. At the current moment To the future Eco-speed trajectory:

[0131] (14)

[0132] (15)

[0133] (16)

[0134] (17)

[0135] (18)

[0136] (19)

[0137] (20)

[0138] (twenty one)

[0139] (twenty two)

[0140] (twenty three)

[0141] In formula (14) to formula (23), is the weight of acceleration, G is the road slope value, is the vehicle mass; For vehicles in the future of torque, 、 、 、 、 、 are the 6 fitting coefficients of the model, Target vehicle In the future speed, Target vehicle In the future The acceleration of , is the target vehicle j at the current moment The position and velocity of Target vehicle In the future location, For vehicles In the future location.

[0142] Step 10: Assign to Then, return to step 1 to step 8 to get the target vehicle New moment of passing the current intersection , and determine whether formula (24) is established. If so, execute step 9, otherwise, return to step 10, where For the preset interval, is the time error threshold;

[0143] (twenty four)

[0144] As the current moment , assuming the terminal time error The preset duration is 1.5s 1s, that is, the target vehicle of steps 1 to 8 is updated every 1s Through the state prediction of the intersection, if the predicted terminal time of the current ecological trajectory is for( )s, in Re-state prediction is performed at all times, and the predicted arrival time for( )s, If not, continue the cycle Re-state prediction is performed at all times.

[0145] Step 11: Target vehicle The ecological trajectory speed is implemented, but considering the inevitable prediction errors such as network delays, roadside smart device sensor failures, etc., the target vehicle A PI-based speed tracker is designed to prevent rear-end collisions. In the PI controller, each time step is based on the target vehicle. The speed error of the target vehicle is taken as input. The acceleration is output to achieve accurate tracking of the target velocity, and the collision avoidance strategy is implemented at each time step. Will make a judgment, if it meets , then execute the ecological trajectory speed; otherwise, let the target vehicle Speed , the strategy is calculated by the following formula (25) and formula (26):

[0146] (25)

[0147] (26)

[0148] In formula (25) and formula (26), In the future Target vehicles that ensure driving comfort Minimum safe distance from the vehicle in front, Target vehicle The actual distance to the nearest vehicle ahead, Target vehicle In the future speed, Target vehicle The preceding car at the next moment actual speed.

[0149] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the above-mentioned CAV ecological trajectory control method. The processor is configured to execute the program stored in the memory.

[0150] In this embodiment, a computer-readable storage medium stores a computer program, which executes the steps of the above-mentioned CAV ecological trajectory control method when executed by a processor.

[0151] In this embodiment, the method concept of the present invention is not limited to a two-way six-lane road. Other embodiments obtained by ordinary technicians in this field without creative changes are within the scope of protection of the present invention.

Claims

1. A CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment, characterized by: Applied to the scene of signalized intersection section, and in the mixed traffic flow composed of networked autonomous driving vehicles CAV and human-driven vehicles HV, the distance from the intersection stop line is The road section range is used as the length of the guidance area, and the CAV ecological driving guidance method includes the following steps: Step 1: Target CAV at the current moment After entering the guidance area, the target CAV is recorded as the target vehicle ,vehicle Use roadside intelligent traffic equipment to obtain the real-time status of the current intersection signal lights and the location information of all vehicles in the guidance area; Step 2: Identify the vehicle Traffic scene location within the guidance area: If the target vehicle At the current moment If it is the leading vehicle in the guide area, execute step 3; otherwise, determine the target vehicle Whether it is the first CAV in the lane guidance area where the vehicle is traveling. If it is the first CAV, go to step 4; otherwise, go to step 7. Step 3: Calculate the target vehicle Time to pass the stop line of the current intersection and the speed at which you pass the stop line ; Step 3.1: Determine the target vehicle Distance to the intersection stop line Is it satisfied , if satisfied, then the target vehicle From the initial speed At maximum acceleration Accelerate to the speed limit Otherwise, the target vehicle below the speed limit The speed of the vehicle passes through the current intersection, and the target vehicle is calculated using formula (1) Earliest arrival time : (1) Step 3.2: Calculate the target vehicle using formula (2) Stop line time : (2) In formula (2), For the The time when the green light starts in a signal cycle; Step 3.3: Calculate the target vehicle using formula (3) Time at the stop line Speed ​​at the stop line , thereby completing the state prediction of the CAV passing the current intersection and executing step 8; (3) In formula (3), For the current moment Target vehicle Initial velocity; Step 4: Target vehicle The vehicle-mounted sensor equipment identifies the HV in front at the current moment. Location and speed , and judge the target vehicle If the HV in front of the target vehicle does not stop, then go to step 5. Otherwise, the target vehicle The HV ahead of you is lined up before the intersection stop line, and go to step 6; Step 5: Assuming that the speed of the HV in front remains unchanged, use formula (4) to predict the time it takes for the HV in front to arrive at the current intersection: , and perform step 8: (4) In formula (4), is the stop line position of the current intersection; Step 6: Target vehicle Obtain the number of vehicles queued before the current intersection from the vehicle-road communication system through short-range wireless communication , used to predict the target vehicle The preceding car, The time it takes for the vehicle to dissipate the queue when the green light starts at the current intersection and The speed at which a vehicle passes the stop line ; Step 7: Target vehicle Obtain the current intersection distance from the target vehicle from the vehicle-road communication system through short-range wireless communication The trajectory information of the nearest m-th CAV and the target vehicle The speed and position information of several HVs between the mth CAV and the target vehicle are calculated. Vehicle ahead Time to reach the current intersection and the speed at which you pass the stop line ; Step 8: Based on the real-time status of the current intersection signal light and the target vehicle The vehicle information ahead is used to predict the target vehicle j at the moment it passes the current intersection. And the target vehicle j at time Speed ; Step 9: Construct the current moment To the future The vehicle trajectory optimization model is solved and the target vehicle is obtained. At the current moment To the future The ecological velocity trajectory; Step 10: Assign to Then, return to step 1 to step 8 to obtain the new time when the target vehicle j passes the current intersection. , and determine whether formula (24) is established. If so, execute step 9, otherwise, return to step 10, where For the preset interval, is the time error threshold; (24)。 2. The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment according to claim 1 is characterized in that: The step 6 comprises: Step 6.1, starting from the first vehicle closest to the intersection stop line, sequentially move all HVs in the queue from 1 to Index and use formula (5) to calculate the The car at the current moment Distance to the stop line ; (5) In formula (5), is the minimum safe distance between vehicles, is the length of the vehicle; Use formula (6) to predict the first Car in the future Speed and acceleration : (6) In formula (6), When the traffic light at the current intersection turns green, The moment the car starts moving, It is The average reaction time of the drivers of the vehicles, and ; Step 6.2: Record any HV as the current vehicle , use Equation (7)-Equation (8) to model the driving behavior of HV in the traffic scene within the guidance area, and obtain the current vehicle IDM following model; (7) (8) In formula (7)-formula (8), is the maximum deceleration of the vehicle, Is the current vehicle In the future The acceleration of Is the current vehicle In the future Distance requirements from the preceding HV vehicle, Is the current vehicle In the future speed, The safe distance between vehicles. Is the current vehicle In the future Speed ​​difference with the preceding HV, Is the current vehicle In the future The distance difference to the preceding HV; Step 6.3: Use equations (9) and (10) to get the current vehicle At the next moment Speed and location : (9) (10) In formulas (9) and (10), is the time step; For the current vehicle At the current moment speed, For the current vehicle At the current moment acceleration; For the current vehicle At the current moment location; Step 6.4: Calculate the first Car in the future The instantaneous queue length , thus obtaining the actual dissipation time of the queue = ,in, for The moment when the queue dissipates; (11) Step 6.5: Calculate target vehicle The nearest vehicle in front Speed ​​through the current intersection Then, execute step 8; wherein, Indicates the The car dissipates at the moment of queue speed.

3. The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment according to claim 2 is characterized in that: The step 7 comprises: Step 7.1: The mth CAV at the current moment The target vehicle is simulated using the IDM following model. Several HVs between the mth CAV and the next CAV at the future time speed and position; The target vehicle The car in front In the target vehicle The vehicle corresponding to the mth CAV is recorded as HV, then HV in the future The speed is recorded as , the position is recorded as ; Step 7.2, according to The time it takes for a HV to arrive at the current intersection Location This is the stop line position of the current intersection , get the target vehicle The car in front Time to reach the current intersection ; according to , using the IDM car-following model to obtain the HV in the future Speed and as the target vehicle The car in front Speed ​​through the current intersection , proceed to step 8.

4. The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment according to claim 3 is characterized in that: The step 8 comprises: Step 8.1: Use formula (12) to get the time when the target vehicle j passes the current intersection : (12) In formula (12), It is The time when the red light starts in a signal cycle; is the time when the red light starts in the i+1th signal cycle, is the moment when the preceding vehicle of target vehicle j passes the current intersection; Indicates safe headway; Step 8.2: Use formula (13) to predict the target vehicle j at the time of passing the current intersection Speed , and perform step 9: (13)。 5. The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment according to claim 4 is characterized in that: The vehicle trajectory optimization model in step 9 is to minimize the fuel consumption of the CAV With the goal of ensuring driving comfort, the vehicle objective function is constructed using equations (14) to (16): , and use equations (17) to (23) to construct the vehicle kinematic constraints composition: (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) In formula (14) to formula (23), is the weight of acceleration, G is the road slope value, is the vehicle mass; For vehicles in the future of torque, 、 、 、 、 、 are the 6 fitting coefficients of the model, For vehicles In the future speed, For vehicles In the future The acceleration of , is the target vehicle j at the current moment The position and velocity of Target vehicle In the future location, For vehicles In the future location.

6. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the CAV ecological driving guidance method according to any one of claims 1 to 5, and the processor is configured to execute the program stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the CAV ecological driving guidance method according to any one of claims 1 to 5 are executed.

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