CAV lane changing method and system based on macro and micro traffic flow information

Abstract

The invention belongs to the field of intelligent traffic systems, and relates to a CAV lane changing method and system based on macro and micro traffic flow information, and the method comprises the steps: obtaining the state information of an automobile and the road section information in the CAV advancing direction in real time, and uploading the state information and the road section information to a cloud server; the cloud server preprocesses the uploaded data and calculates a comprehensive macroscopic income index according to the preprocessed data; according to the vehicle state information and the road section information, calculating a microscopic income index of the intelligent network connection vehicle; generating a lane changing decision according to the comprehensive macro-micro income index; the lane changing decision is transmitted to a vehicle-mounted unit of the intelligent networked automobile; the vehicle-mounted unit verifies the lane changing decision, and if verification fails, lane changing operation is not carried out; if the verification is successful, controlling the intelligent networked automobile to change lanes; according to the lane changing decision-making method and system based on the combination of the macroscopic traffic flow information and the microscopic traffic flow information, the global optimization capability and the local safety of lane changing decision-making can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation systems, and in particular relates to a CAV lane changing method and system based on macroscopic and microscopic traffic flow information. Background Art

[0002] With the rapid development of intelligent transportation systems (ITS), autonomous driving technology has shown great potential in improving traffic efficiency, reducing traffic accidents and reducing energy consumption. As an important part of autonomous driving technology, the lane changing decision of connected and autonomous vehicles (CAV) is one of the key issues to ensure vehicle driving efficiency and safety. Lane changing behavior involves comprehensive analysis and decision-making of the vehicle's own state, traffic flow conditions and surrounding vehicle dynamics. Therefore, its complexity requires the vehicle to accurately obtain and process multi-dimensional traffic information.

[0003] Traditional lane-changing decision methods are usually based on fixed rules or simple dynamic models, such as making lane-changing decisions based only on microscopic data such as the distance between the vehicle and the vehicle in front and behind, speed difference, etc. However, these methods often ignore higher-level traffic flow information, such as macroscopic indicators such as the overall density and flow rate of the target lane, as well as comfort, resulting in a lack of globality and optimization in lane-changing decisions. In addition, relying solely on microscopic information for lane-changing decisions may lead to local optimization behaviors, such as frequent lane changes or unnecessary lane changes, which will have a negative impact on the overall traffic flow.

[0004] In recent years, some lane-changing optimization methods based on macroscopic traffic flow characteristics have begun to attract the attention of researchers. These methods preliminarily screen out target lanes that may bring higher traffic efficiency or better comfort by analyzing macroscopic indicators such as lane density and average vehicle speed. However, macroscopic optimization cannot accurately evaluate the potential safety hazards and local dynamic constraints that may exist in the actual lane-changing process, such as the distance to the front and rear vehicles, the relative speed difference, etc. Therefore, the decision-making method based solely on macroscopic information also has certain limitations. Summary of the invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a CAV lane changing method based on macro-micro traffic flow information, the method comprising: obtaining the status information of the intelligent connected vehicle and the surrounding non-connected vehicles in real time; obtaining the road section information in the direction of travel of the intelligent connected vehicle, and uploading the obtained vehicle status information and road section information to a cloud server; at the macro level, the cloud server pre-processes the uploaded data, and calculates a comprehensive macro-benefit index based on the pre-processed data; at the micro level, the lane changing index of the intelligent connected vehicle is calculated based on the vehicle status information and road section information; a lane changing decision is generated based on the comprehensive macro-benefit index and the micro-benefit index; the lane changing decision is transmitted to the on-board unit of the intelligent connected vehicle; the on-board unit verifies the lane changing decision, and if the verification fails, the lane changing operation is not performed; if the verification is successful, the intelligent connected vehicle is controlled to change lanes according to the lane changing decision, and the lane changing operation is uploaded to the cloud server.

[0006] A CAV lane-changing system based on macro- and micro-traffic flow information, the system comprising: a real-time driving data acquisition unit, a macro-benefit analysis module, a micro-benefit analysis module, a CAV decision recommendation and distribution module, and a collaborative driving decision execution unit;

[0007] The real-time driving data collection unit is used to collect status information of the intelligent network-connected vehicle and surrounding non-network-connected vehicles and road section information in the direction of travel of the intelligent network-connected vehicle;

[0008] The macro-benefit analysis module calculates the macro-benefit of each lane under current conditions based on the collected data information;

[0009] The micro-income analysis module is used to calculate the micro-income under current conditions based on the collected data information;

[0010] The CAV decision recommendation issuing module constructs a lane changing strategy according to the macro benefits and micro benefits of each lane;

[0011] The collaborative driving decision execution unit controls the vehicle to change lanes according to the lane changing strategy.

[0012] Beneficial effects of the present invention:

[0013] The lane-changing decision method and system of the present invention, which is based on the combination of macroscopic and microscopic traffic flow information, can effectively improve the global optimization capability and local safety of lane-changing decisions. By analyzing macroscopic traffic characteristics such as lane density, average lane speed, and comfort, the global optimization selection of the target lane is achieved. At the same time, microscopic information, such as the space with the vehicles in front and behind the target lane, dynamic weight, and the necessity of lane change, is combined to ensure the safety and feasibility of lane changing. The present invention can quickly respond to real-time traffic environments and intelligently generate lane-changing decisions, which not only improves the traffic efficiency and comfort of autonomous driving vehicles, but also significantly enhances the safety and flexibility of decision-making in complex traffic scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the CAV free lane change decision method of the present invention;

[0015] Figure 2 It is a schematic diagram of the structure of the CAV free lane-changing decision system of the present invention;

[0016] Figure 3 A diagram showing simulation results of the CAV free lane-changing decision system of the present invention;

[0017] Figure 4 This is a graph showing the changes of lane changing process indicators of the CAV free lane changing decision system of the present invention over time. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The lane-changing decision method based on the combination of macroscopic and microscopic traffic information has become an important research direction. Macroscopic information is used to screen the optimal lane candidates and provide global optimization guidance, while microscopic information is used to further evaluate the feasibility and safety of lane changing to ensure that the actual operation meets dynamic constraints. Through the combination of macroscopic and microscopic methods, the global optimization capability of lane-changing decisions can be improved, and local safety issues can be effectively avoided.

[0020] Based on macro and micro traffic flow information, the present invention proposes a free lane change decision generation method for CAV. The method includes: by collecting the status information of CAV and surrounding vehicles (including speed, longitude and latitude, acceleration, heading angle, etc.) and road traffic environment data (lane density, lane average speed, etc.) in real time, respectively calculating the macro and micro benefits of the target lane, and constructing a hierarchical lane change decision framework. Finally, the final decision recommendation is sent to the OBU unit of CAV through the 5G communication cloud based on the comprehensive macro and micro benefits. After receiving the decision recommendation, CAV executes the operation to improve operating efficiency and safety.

[0021] A CAV lane-changing method based on macro- and micro-traffic flow information, such as Figure 1As shown, the method includes: obtaining the status information of the intelligent connected vehicle and the surrounding non-connected vehicles in real time; obtaining the road section information in the direction of travel of the intelligent connected vehicle, and uploading the obtained vehicle status information and road section information to the cloud server; at the macro level, the cloud server pre-processes the uploaded data, and calculates the comprehensive macro-benefit index based on the pre-processed data; at the micro level, the lane changing index of the intelligent connected vehicle is calculated according to the vehicle status information and the road section information; a lane changing decision is generated according to the comprehensive macro-benefit index and the lane changing index; the lane changing decision is transmitted to the on-board unit of the intelligent connected vehicle; the on-board unit verifies the lane changing decision, and if the verification fails, the lane changing operation is not performed; if the verification is successful, the intelligent connected vehicle is controlled to change lanes according to the lane changing decision, and the lane changing operation is uploaded to the cloud server.

[0022] In this embodiment, a CAV free lane change decision method based on macro and micro traffic flow information includes: using the SUMO and CARLA joint simulation platform, building a two-lane urban scene based on real roads as a verification platform. The data input source simulates the format collected by the roadside RSU to meet the real-time verification requirements. The specific steps include:

[0023] S1: First, at the micro-information level, CAV (intelligent connected vehicle) uploads its own and surrounding vehicle status information in real time through on-board sensors. At the same time, the roadside unit (RSU) senses and obtains the status information of HDV (non-connected vehicle) in real time. The data includes vehicle speed, longitude and latitude, heading angle, acceleration, vehicle ID and other information. At the macro-information level, the road section information in the direction of CAV travel is obtained through RSU and advanced mobile communications, and finally the vehicle data and road section information are uploaded to the cloud server in real time.

[0024] The simulation data includes vehicle speed, acceleration, longitude and latitude, heading angle, timestamp and ID. At the macro information level, traffic status information related to the road section is further obtained, including the number of lanes, traffic flow distribution of each lane in the section, average lane speed and traffic density, etc. The above information is synchronized and integrated in time to ensure the real-time consistency of micro and macro information.

[0025] In this embodiment, the information is preprocessed, including the following steps: denoising the vehicle status information to remove outliers and invalid data; synchronizing the multi-source data from the vehicle-mounted sensors and roadside units (RSU) according to the timestamps to ensure time consistency; performing multi-dimensional data fusion on the vehicle's real-time latitude and longitude, speed, acceleration and other information to generate a unified data stream; formatting and standardizing the fused data and converting it into a unified data format to facilitate subsequent calculations.

[0026] S2: After receiving the data, the cloud preprocesses the acquired vehicle information data, including data filtering, time synchronization fusion of multi-sensor data, etc. First, in the macro-indicator part, based on the acquired lane information and pre-processed vehicle status data, the evaluation of each lane in the CAV's travel direction is calculated, that is, the normalized quantitative evaluation of the lanes and indicators (efficiency-comfort) decoupled within the road section, in order to support micro-decision recommendations.

[0027] In this embodiment, calculating the comprehensive macro-benefit index includes: calculating the driving comfort benefit of the corresponding vehicle in the lane according to the number of vehicles on the lane, the acceleration of the corresponding vehicle on the lane and the average acceleration of all vehicles on the lane; calculating the density benefit according to the average speed of the vehicles on the lane; obtaining the vehicle speed benefit; assigning weights to the driving comfort benefit, density benefit and speed benefit, and calculating the comprehensive macro-benefit index according to the assigned weights.

[0028] Specifically, the lane change recommendation decision based on the comprehensive evaluation of macro information includes: first, calculating the comfort index benefit, considering that the acceleration and deceleration volatility of each lane can be represented by the acceleration standard deviation of all vehicles in the lane. If acceleration and deceleration occur frequently in the lane, it means that the traffic flow in the lane is not stable and the comfort is poor:

[0029]

[0030] Among them, A std,i is the standard deviation of acceleration on lane i, a j,i is the acceleration of the jth vehicle in lane i, μ a,i is the average acceleration on lane i, N i is the number of vehicles on lane i. Secondly, the macro efficiency of each lane is calculated, mainly considering two key indicators: traffic flow density and average lane speed, which directly reflect the lane's capacity and efficiency performance. The traffic flow density is expressed as:

[0031]

[0032] Among them, N i is the total number of vehicles on lane i, L i is the length of the area covered by lane i on the roadside of the road section. The average speed of the lane is expressed as:

[0033]

[0034] Among them, v j,i is the speed of the jth vehicle on lane i. The higher the speed, the higher the lane traffic efficiency. Due to the different dimensions of each indicator, normalization is required so that the macro-benefit indicator can be comprehensively calculated. The linear normalization method is used:

[0035]

[0036] Among them, X i is the indicator of the current lane, X min , X max is the minimum and maximum value of this indicator in all lanes. Calculate the comprehensive macro return index R of each lane macro,i :

[0037]

[0038]

[0039] in, is the normalized density, is the normalized lane average speed, w s is the comfort index S i The weight, w d is the traffic flow density weight, w v is the lane average speed weight, S i For comfort benefits, is the density gain, For the negative index (D i , A std,i , the smaller the value, the better), after normalization, To transform into a positive indicator. Comprehensive income R macro,i The higher it is, the higher the macro benefit of the lane at the current moment.

[0040] In this embodiment, the weight value w s Set to 0.2, w d Set to 0.4, w v Set to 0.4.

[0041] S3: In the micro-indicator part, the pre-processed vehicle status information and road section information are used to evaluate the micro-benefit of CAV. The calculation is based on two main dimensions: spatial benefit and lane change necessity. Spatial benefit evaluates whether the target lane has enough safe lane change space to meet safety requirements, and the necessity of lane change is determined by the distance and speed of the preceding vehicle to see whether the minimum threshold requirements are met. Finally, the cloud combines macro and micro indicators for comprehensive analysis; if the micro and macro benefits of the target lane are significantly higher than those of the current lane, a lane change decision is generated to prepare for sending the recommended decision in the next step.

[0042] In this embodiment, a micro lane change recommendation decision is generated based on a comprehensive evaluation of the collected micro vehicle status information. The necessity and spatial benefit index of lane change behavior are dynamically calculated based on the real-time status information of CAV and surrounding vehicles (including speed, longitude and latitude, heading angle, lane number, acceleration, etc.) and the traffic environment. First, the spatial benefit of the target lane is calculated, and the lane information is combined to determine whether there are front and rear vehicles in the target lane. If so, their positions are obtained in real time through longitude and latitude, and the actual gap is calculated using the Haversine formula combined with the azimuth:

[0043]

[0044] θ front =arctan2(sin(λ front -λ cav )·cos(φ front ),cos(φ cav )·sin(φ front )-sin(φ cav )·cos(φ front )·cos(λ front -λ cav ))

[0045] θ rear =arctan2(sin(λ rear -λ host )·cos(φ rear ),cos(φ host )·sin(φ rear )-sin(φ host )·cos(φ rear )·cos(λ rear -λ host ))

[0046] Among them, d front is the distance to the front vehicle, θ front is the relative azimuth angle of the preceding vehicle, d rear is the distance to the following vehicle, θ rear is the relative azimuth of the following vehicle, r is the radius of the earth 6371km, φ cav ,φ front and φ rear are the latitudes of CAV, the front vehicle and the rear vehicle, respectively, cav , front and λ rear are the longitudes of the CAV, the front vehicle, and the rear vehicle respectively. The relative positions of the front and rear vehicles are determined as follows:

[0047] Δθ front =(θ front -θ host +360)mod360

[0048] Δθ rear =(θ rear -θ host +360)mod360

[0049] The vehicle in front of the target lane must satisfy 0≤Δθ front ≤90 or 270<Δθ front Only vehicles with a radius of ≤360° can be used as the front reference vehicle of the CAV, and vehicles behind the target lane must satisfy 90°<Δθ rear Only vehicles with a radius of ≤270 can be used as the rear reference vehicle of the CAV. Then the dynamic weights of the front and rear vehicles are calculated separately:

[0050]

[0051] Among them, F front and F rear are the influence weights of the front and rear vehicles on the lane change of CAV, v front and v rear are the front and rear vehicle speeds respectively. In this embodiment, T human is the driver's reaction time, set to 1.5s, a maxbrake is the maximum braking capacity of the vehicle, set to 8m / s 2 Therefore, the formula for judging the adequacy of spatial benefits can be expressed as:

[0052]

[0053] Among them, gap sufficient When it is 1, it means that the space gain is sufficient, and when it is 0, it is insufficient.

[0054] In this embodiment, Δ safe The safety constant is set to 5m. In this way, the dynamic relationship between the CAV and the front and rear vehicles in the target lane can be fully described to determine whether the space gain of lane change is sufficient. Secondly, the necessity of CAV lane change is calculated to reflect whether CAV needs to change lanes. The safe vehicle distance s safe It is expressed as:

[0055] s safe =v pv ·t thw

[0056] Among them, v pv is the speed of the vehicle ahead in the current lane, t thw The time interval threshold is set to 1.5s. By comparing the distance between the CAV and the vehicle ahead with the safe vehicle distance s safe , the necessity of lane change related to vehicle distance is calculated as:

[0057]

[0058] Among them, s pv is the distance between the CAV and the vehicle ahead in the current lane. pv <s safe Calculate the necessity of lane change related to speed difference:

[0059] Δv desired =v desired -v pv

[0060] Where Δv desired represents the speed of the preceding vehicle and the expected speed of the CAV v desired In this embodiment, desired Assuming it is 50km / h, the speed difference necessity assessment is:

[0061]

[0062] According to Δv desired size, to determine whether the front vehicle is limiting the driving efficiency of the CAV. desired >v pv Finally, according to the current traffic conditions, weights are assigned to the vehicle-to-vehicle distance necessity and speed difference necessity:

[0063] w distance +w speed =1

[0064] f necessity =w distance ·distance necessity +w speed speed necessity

[0065] When f necessity >Threshold, a lane-changing decision is initiated, where Threshold is the threshold for lane-changing necessity, which is set to 0.5 in this embodiment.

[0066] S4: When comprehensively evaluating macro and micro indicators, the cloud gives priority to the macro-level traffic conditions, and then makes the final decision based on the micro benefits and feasibility. When both the macro and micro indicators meet the lane changing conditions, the cloud generates a lane changing recommendation decision and sends the lane changing recommendation to the CAV's onboard unit (OBU) in real time through 5G communication to guide the CAV to complete the optimal lane changing operation.

[0067] S5: After receiving the decision suggestion, the CAV verifies the rationality and feasibility of the cloud-based recommendation by combining its real-time status (speed, acceleration, lane position) and surrounding environment information (such as the speed and distance of surrounding vehicles). After verification, the CAV performs lane change operations based on the cloud-based decision suggestions to maximize driving efficiency at the macro and micro levels, and transmits the actual operation data back to the cloud to help the cloud optimize the subsequent lane change recommendation algorithm and improve the accuracy and response speed of the lane change strategy.

[0068] In this embodiment, CAV analyzes the received decision and evaluates the current driving status of the vehicle, including speed, position, surrounding traffic environment, etc. Based on the received lane change decision recommendation and the vehicle's own status, after confirming that the decision is correct and the risk is controllable, it executes the corresponding driving behavior and performs lane change operations.

[0069] In this embodiment, a corresponding feedback mechanism is formulated, and the CAV (OBU) sends feedback to the cloud based on the received recommended decision information and actual driving conditions for continuous optimization of the algorithm.

[0070] A CAV lane-changing system based on macro- and micro-traffic flow information, whose core modules are as follows Figure 2 As shown, the system includes: a real-time driving data acquisition unit, a macro-benefit analysis module, a micro-benefit analysis module, a CAV decision recommendation and distribution module, and a collaborative driving decision execution unit;

[0071] The real-time driving data collection unit is used to collect status information of the intelligent network-connected vehicle and surrounding non-network-connected vehicles and road section information in the direction of travel of the intelligent network-connected vehicle;

[0072] The macro-benefit analysis module calculates the macro-benefit of each lane under current conditions based on the collected data information;

[0073] The micro-income analysis module is used to calculate the micro-income under current conditions based on the collected data information;

[0074] The CAV decision recommendation issuing module constructs a lane changing strategy according to the macro benefits and micro benefits of each lane;

[0075] The collaborative driving decision execution unit controls the vehicle to change lanes according to the lane changing strategy.

[0076] In this embodiment, the system includes at least: a roadside sensing device, CAV-mounted on-board sensors for collecting vehicle status data and macro information; a 5G communication network for achieving high-speed data transmission between vehicle, road and cloud; a cloud server equipped with high-performance equipment for processing the transmitted data and meeting the real-time calculation of macro and micro lane change benefits; an OBU installed on the CAV for receiving cloud-recommended decisions and responding.

[0077] In this embodiment, lane change decisions are divided into three types: turn left (0), keep going straight (1), and turn right (2).

[0078] In this embodiment, Figure 3 This is a display diagram of the simulation results of the CAV free lane-changing decision system of the present invention. The cloud calculates macro and micro benefits, analyzes that the comprehensive index of the right lane at the current moment is higher than that of the lane itself, and meets the feasibility, and sends the decision to the CAV. The CAV verifies the rationality of the decision at the current moment and executes lane-changing decision 2 (right lane change).

[0079] In this embodiment, Figure 4 The graph of the macro and micro indicators of the lane changing process over time shows that the CAV is driving in lane 1. The comprehensive macro benefit of lane 2 gradually exceeds that of lane 1 over time. At this time, the micro benefit and feasibility are verified. While the space meets (1), the necessity of lane changing is also greater than the threshold of 0.5. The macro and micro benefits are satisfied at the same time to trigger the lane changing recommendation decision. It can be seen that after the lane changing, the necessity of lane changing sometimes exceeds the threshold, but at this time the macro indicator lane 2 is always greater than lane 1, and the lane changing recommendation decision will not be triggered.

[0080] This embodiment uses the simulation parameters shown in Table 1 and Table 2:

[0081] Table 1 Macro parameter weight value settings

[0082]

[0083] Table 2 Microscopic parameter weight value settings

[0084]

[0085] In the simulation experiment, the present invention simulated the information interaction process of the real vehicle-road-cloud three terminals, realized the full process calculation and delivery time of each lane change decision within 60ms (including data upload, macro-micro indicator calculation, comprehensive analysis, decision generation and delivery), and sent the decision information to the CAV every 0.1 seconds. The system's efficiency and real-time performance were proven to meet the real-time lane change requirements of autonomous vehicles in complex and dynamic traffic environments.

[0086] The system implementation of the present invention is the same as the method implementation.

[0087] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, which can include: ROM, RAM, disk or CD, etc.

[0088] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A CAV lane changing method based on macro and micro traffic flow information, characterized in that: include: Obtain status information of smart connected vehicles and surrounding non-connected vehicles in real time; Obtain the road section information in the direction of travel of the intelligent networked vehicle, and upload the obtained vehicle status information and road section information to the cloud server; At the macro level, the cloud server pre-processes the uploaded data and calculates the comprehensive macro benefit index based on the pre-processed data; at the micro level, the micro benefit index of the intelligent connected vehicle is calculated based on the vehicle status information and the road section information; the lane change decision is generated based on the comprehensive macro benefit index and the micro benefit index; the lane change decision is transmitted to the on-board unit of the intelligent connected vehicle; the on-board unit verifies the lane change decision, and if the verification fails, the lane change operation is not performed; If the verification is successful, the intelligent connected vehicle is controlled to change lanes according to the lane changing decision, and the lane changing operation is uploaded to the cloud server.

2. A CAV lane changing method based on macro and micro traffic flow information according to claim 1, characterized in that: The status information of intelligent connected vehicles and surrounding non-connected vehicles includes: vehicle speed, acceleration, longitude and latitude, heading angle, timestamp and ID; road section information includes the number of lanes, road section flow distribution, lane average speed and traffic density.

3. The CAV lane changing method based on macro and micro traffic flow information according to claim 1, characterized in that: Preprocessing of uploaded data includes: denoising the vehicle status information, deleting abnormal and invalid data from the denoised data; synchronizing multi-source data with invalid data removed based on timestamps; fusing the vehicle's real-time latitude and longitude, speed, and acceleration in multiple dimensions to generate a unified data stream; and formatting and standardizing the fused data to convert it into a unified data format.

4. The CAV lane changing method based on macro and micro traffic flow information according to claim 1, characterized in that: Calculation of the comprehensive macro-benefit index includes: calculating the driving comfort benefit of the corresponding vehicle in the lane based on the number of vehicles on the lane, the acceleration of the corresponding vehicle on the lane and the average acceleration of all vehicles on the lane; calculating the density benefit based on the average speed of vehicles on the lane; obtaining the vehicle speed benefit; assigning weights to the driving comfort benefit, density benefit and speed benefit, and calculating the comprehensive macro-benefit index based on the assigned weights.

5. The CAV lane changing method based on macro and micro traffic flow information according to claim 1, characterized in that: Calculation of micro-benefit indicators of intelligent connected vehicles includes: Micro-benefit indicators include space benefit indicators and lane change necessity indicators; In the space benefit indicators, Haversine formula is used in combination with the longitude and latitude of the vehicle to calculate the actual gap between vehicles; The relative azimuth of the front vehicle and the relative azimuth of the rear vehicle are calculated according to the longitude and latitude of the front vehicle and the rear vehicle in the target lane; The relative positions of the front and rear vehicles are calculated according to the relative azimuth of the front vehicle and the relative azimuth of the rear vehicle; The vehicles are screened according to the relative positions of the front and rear vehicles, and the dynamic weights of the front and rear vehicles are calculated according to the screened vehicles; The spatial benefit of vehicle lane change is calculated based on the dynamic weight and the actual gap between vehicles. When the spatial benefit is 0, no lane change is performed. In the lane change necessity index, the safety distance between the CAV and the vehicle in front is set. The distance between the CAV and the vehicle in front in the current lane is obtained, and the necessity of lane change related to vehicle distance is calculated based on the distance and the safety distance. The necessity of lane change related to speed difference is calculated and the necessity of speed difference is evaluated. Weights are assigned to the necessity of lane change related to vehicle distance and the necessity of lane change related to speed difference. The lane change index of the intelligent connected vehicle is calculated based on the assigned weights.

6. A CAV lane changing method based on macro and micro traffic flow information according to claim 5, characterized in that: The lane change indicators are: f necessity =w distance ·distance necessity +w speed ·speed necessity In distance +in speed =1 Among them, f necessity To get the necessity index, w distance is the weight of the necessity of lane change related to vehicle distance, distance necessity is the necessity of lane change related to vehicle distance, w speed The weight for evaluating the necessity of speed difference, speed necessity The results are necessary for speed difference evaluation.

7. The CAV lane changing method based on macro and micro traffic flow information according to claim 1, characterized in that: The lane-changing decision generated based on the comprehensive macro-micro benefit indicators includes: between the current lane and the target lane, judging whether the lane is the optimal target lane based on the macro-benefit indicator; if no lane is better than the current lane, the current lane remains unchanged; otherwise, it is the CAV recommended target lane; in the recommended target lane, the lane-changing process is evaluated through micro-benefit to see whether it meets the safety conditions; if so, the lane-changing decision is executed; otherwise, the current lane is maintained and continuously re-evaluated until the conditions are met.

8. The CAV lane changing method based on macro and micro traffic flow information according to claim 7, characterized in that: The lane-changing decision is transmitted to the on-board unit of the intelligent connected vehicle via the 5G communication network.

9. The CAV lane changing method based on macro and micro traffic flow information according to claim 1, characterized in that: The on-board unit verifies the lane-changing decision, including: after receiving the recommendation, the CAV verifies the rationality and feasibility of the recommendation plan based on its own real-time status and surrounding vehicle environment information, and executes the lane-changing operation according to the recommendation plan after the verification; after the lane change is completed, the CAV transmits the actual operation data back to the cloud.

10. A CAV lane changing system based on macro and micro traffic flow information, the system being used to execute a CAV lane changing method based on macro and micro traffic flow information as claimed in any one of claims 1 to 9, the system comprising: Real-time driving data collection unit, macro-benefit analysis module, micro-benefit analysis module, CAV decision recommendation and distribution module, collaborative driving decision execution unit; The real-time driving data collection unit is used to collect status information of the intelligent networked vehicle and surrounding non-networked vehicles and road section information in the direction of travel of the intelligent networked vehicle; The macro benefit analysis module calculates the macro benefit of each lane under current conditions based on the collected data information; The micro-income analysis module is used to calculate the micro-income under current conditions based on the collected data information; The CAV decision recommendation issuing module constructs a lane changing strategy according to the macro benefits and micro benefits of each lane; The cooperative driving decision execution unit controls the vehicle to change lanes according to the lane changing strategy.

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