CAV ecological driving guiding method based on vehicle-road cooperation in mixed traffic environment

Abstract

The invention discloses a CAV ecological driving guiding method based on vehicle-road cooperation in a mixed traffic environment. The method comprises the following steps: 1, collecting vehicle information at a t0 moment and a real-time state of a signal lamp at a current intersection; 2, judging the position of a traffic scene where the CAV is located; 3, determining a terminal state of CAV ecological driving according to different traffic scenes; 4, determining an optimal speed trajectory curve of the CAV; and 5, dynamically updating and optimizing the track through a trigger type track updating rule. Through information interaction between the CAVs and the roadside facilities and between the CAVs in the vehicle-road coordination environment, the phenomena of starting, stopping, idling, acceleration and deceleration of the CAVs at the intersection can be reduced, so that the traffic efficiency is improved, the energy consumption is reduced, and the sustainable development of traffic is realized.

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 (CAV) at urban signal intersections, and specifically to an ecological driving guidance method for connected automated vehicles at signal intersections in a mixed traffic environment. Background Art

[0002] In recent years, with the rapid growth of motorized travel demand, traffic problems such as energy consumption, pollutant emissions and congestion have become increasingly prominent. Especially at signal-controlled intersections, vehicles frequently idle and restart due to the influence of signal status, often resulting in unnecessary energy consumption. By coordinating the vehicle's motion information with intersection signal timing, i.e., eco-driving, vehicles can effectively reduce the stopping and starting, drastic acceleration and deceleration, and other behaviors at intersections, thereby improving traffic efficiency and reducing energy consumption.

[0003] Thanks to the development of intelligent network technology, the information interaction between connected autonomous vehicles (CAVs) and road infrastructure provides strong support for ecological driving to achieve intersection signal optimization and CAV motion control. By obtaining real-time traffic information, the control system can accurately plan the optimal speed trajectory of the vehicle. At present, some scholars have conducted research on the ecological driving of vehicles at intersections in a vehicle-road cooperative environment. However, it is assumed that all vehicles in the traffic environment are connected autonomous vehicles, but the new mixed traffic environment composed of human-driven vehicles (HVs) and CAVs is expected to continue for a long time. The driving behavior of HVs in real traffic scenarios will cause strong interference to the ecological driving of CAVs, such as the movement of HVs in front of CAVs and the queuing phenomenon caused by HVs. In addition, the traffic uncertainty brought by HVs under actual traffic conditions requires CAVs to dynamically update their trajectories. However, existing research is insufficient on the ecological driving methods in such complex traffic environments. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention proposes a CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment, in order to consider the vehicle queues in front of the intersection or the disturbance of the manually driven vehicle in front, solve the ecological driving problem of the networked autonomous driving vehicle under the triggered trajectory update rule, correct the impact of traffic uncertainty on CAV passage, thereby reducing vehicle delays and fuel consumption, and achieving sustainable development of transportation.

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

[0006] The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment is characterized in that it is applied to a signal intersection section scene, and in a mixed traffic flow composed of a networked self-driving vehicle CAV and a human-driven vehicle HV, a vehicle with a distance of 100 km from the intersection stop line is guided. 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 present 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;

[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 device identifies the HV in front at the current moment. Location and speed , and determine the target vehicle If the HV in front of the target vehicle has not stopped, then execute step 5. Otherwise, the target vehicle The HV ahead of you is lined up before the stop line at the intersection, and step 6 is executed;

[0012] Step 5: Assuming that the speed of the HV ahead remains unchanged, use equation (4) to predict the time it takes for the HV ahead to arrive at the current intersection: , and execute 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 vehicles 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 time when the target vehicle j 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 present 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 comprises:

[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 target 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] Further, the step 6 comprises:

[0031] Step 6.1, starting with the first vehicle closest to the intersection stop line, move all HVs in the queue from 1 to Indexing is performed and formula (5) is used to calculate the The car at the current moment Distance to 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 equations (7)-(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 HV in front, Is the current vehicle In the future speed, The safe distance between the front and rear 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 equation (9)-(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 present moment speed, For the current vehicle At the present moment acceleration; For the current vehicle At the present moment location;

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

[0046] (11)

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

[0048] Further, the step 7 comprises:

[0049] Step 7.1: The mth CAV at the current time The target vehicle is simulated using the IDM following model Several HVs between the mth CAV and the mth 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 HV in the future Speed And as the target vehicle The car in front Speed ​​through the current intersection , proceed to step 8.

[0053] Further, the step 8 comprises:

[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 through 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 execute 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 The 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 time The position and velocity of The target vehicle In the future location, For vehicles In the future location.

[0071] An 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, wherein a computer program is stored on the computer-readable storage medium, and the computer program executes the steps of the CAV ecological trajectory control method when the computer program is executed by a processor.

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

[0074] 1. The ecological driving strategy of the connected autonomous driving vehicle proposed in the present invention takes into account multiple factors such as signal status, queue information, speed of the vehicle in front, and collision avoidance in real traffic scenarios to optimize the ecological trajectory of the CAV under mixed traffic flow, and predicts the trajectory constraints and terminal states of the CAV ecological driving according to different traffic scenarios to determine the optimal speed curve of the CAV, thereby improving the driving efficiency and safety of the CAV under mixed traffic flow, improving the driving stability and driving comfort of the CAV in complex traffic environments, and solving the trajectory update problem of the CAV under dynamic traffic conditions in mixed traffic flow. It has practical engineering application value in the traffic management and control of 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 the ecological driving trajectory and reduce the calculation efficiency. The rule can dynamically update and optimize the trajectory to achieve the ideal ecological driving effect of the vehicle.

[0076] 3. The present invention proposes a method for predicting the dissipation of queues at intersections based on the IDM following model, which can help CAVs accurately predict the dissipation information of queues and changes in queue length, making the CAV ecological trajectory more reasonable, and helping to improve the driving smoothness and driver comfort of CAVs passing 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 It is a schematic diagram of an intersection of the present invention;

[0080] Figure 4 Schematic diagrams of four typical scenarios of CAV eco-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 signal intersection section scene, and in a mixed traffic flow composed of connected automated 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 taken 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 comprises 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 location information of all vehicles in the guidance area; The starting point of entering the guide area is the origin , target vehicle The driving direction 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 guidance area on the road section.

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

[0085] If the target vehicle At the present 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 It 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 target 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 the CAV passing the stop line For 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 The initial velocity;

[0095] Step 4: Target vehicle The vehicle-mounted sensor device identifies the HV in front at the current moment. Location and speed , and determine the target vehicle If the HV in front of the target vehicle has not stopped, then execute step 5. Otherwise, the target vehicle The HV ahead of you lines up before the stop line at the intersection and goes to step 6.

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

[0097] (4)

[0098] In formula (4), is 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 to move when the traffic 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 vehicles 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 with the first vehicle closest to the intersection stop line, move all HVs in the queue from 1 to Indexing is performed and formula (5) is used to calculate the The car at the current moment Distance to 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)-(8) to model the driving behavior of HV in the traffic scene within the guidance area, and obtain the IDM following model;

[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 HV in front, Is the current vehicle In the future speed, The safe distance between the front and rear 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.

[0110] Step 6.3: Use equation (9)-(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 present moment speed, For the current vehicle At the present moment acceleration; For the current vehicle At the present moment location;

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

[0115] (11)

[0116] Step 6.5: Target vehicle Front car 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 moment when the car dissipates in the 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 vehicles 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 time The target vehicle is simulated using the IDM following model Several HVs between the mth CAV and the mth 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 HV in the future Speed 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 time when the target vehicle j 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 through 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 you 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 When passing the current intersection Speed , and execute 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 At this point, the state prediction of the 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) to (23) to construct the vehicle kinematic constraints to construct the current moment To the future The vehicle trajectory optimization model is solved and the target vehicle is obtained. At the present 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 The torque, , , , , , are the 6 fitting coefficients of the model. The target vehicle In the future speed, The target vehicle In the future The acceleration of , is the target vehicle j at the current time The position and velocity of The target vehicle In the future location, For vehicles In the future location.

[0142] Step 10: Assign to After that, return to step 1 to step 8 to get the target vehicle New moment through 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 The target vehicle of step 1 to step 8 is updated every 1 second. 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 to loop in 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 collision. In the PI controller, each time step is based on the target vehicle. The speed error of the preceding vehicle is used as input, and the target vehicle The execution 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 The minimum safe distance from the vehicle ahead, The target vehicle The actual distance to the nearest vehicle ahead, The target vehicle In the future speed, The target vehicle The car ahead of you 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, and 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, and when the computer program is executed by a processor, the steps of the above-mentioned CAV ecological trajectory control method are executed.

[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 all 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 in that: Applied to the signalized intersection section scenario, 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 present 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; 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 4: Target vehicle The vehicle-mounted sensor device identifies the HV in front at the current moment. Location and speed , and determine the target vehicle If the HV in front of the target vehicle has not stopped, then execute step 5. Otherwise, the target vehicle The HV ahead of you is lined up before the stop line at the intersection, and step 6 is executed; Step 5: Assuming that the speed of the HV ahead remains unchanged, use equation (4) to predict the time it takes for the HV ahead to arrive at the current intersection: , and execute 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 vehicles 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 time when the target vehicle j 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 present 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 3 comprises: 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 target 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 The initial velocity.

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 6 comprises: Step 6.1, starting with the first vehicle closest to the intersection stop line, move all HVs in the queue from 1 to Indexing is performed and formula (5) is used to calculate the The car at the current moment Distance to 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 equations (7)-(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 HV in front, Is the current vehicle In the future speed, The safe distance between the front and rear 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; Step 6.3: Use equation (9)-(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 present moment speed, For the current vehicle At the present moment acceleration; For the current vehicle At the present moment location; Step 6.4: Calculate the Car in the future The instantaneous queue length , thus obtaining the actual dissipation time of the queue dissipation = ,in, for The moment when the queue dissipates; (11) Step 6.5: Calculate the target vehicle The nearest vehicle in front Speed ​​through the current intersection Then, execute step 8; wherein, Indicates The moment when the car dissipates in the queue speed.

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 7 comprises: Step 7.1: The mth CAV at the current time The target vehicle is simulated using the IDM following model Several HVs between the mth CAV and the mth 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 HV in the future Speed As the target vehicle The car in front Speed ​​through the current intersection , proceed to step 8.

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 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 through the current intersection; Step 8.2: Use formula (13) to predict the target vehicle j at the time of passing the current intersection Speed , and execute step 9: (13)。 6. The CAV ecological driving guidance method based on vehicle-road collaboration in a mixed traffic environment according to claim 5 is characterized in that: The vehicle trajectory optimization model in step 9 is to minimize the 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 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 The 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 time The position and velocity of The target vehicle In the future location, For vehicles In the future location.

7. 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 trajectory control method described in any one of claims 1-6, and the processor is configured to execute the program stored in the memory.

8. 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 trajectory control method described in any one of claims 1 to 6 are executed.

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