A multi-scene traffic operation modeling and simulation method for intelligent networking

Abstract

The application relates to the field of intelligent traffic, and discloses a multi-scene traffic operation modeling simulation method for intelligent networking, which comprises the following steps: step one: a ramp exit simulation scene model is built, and speed data and position coordinate data of each vehicle in the lane changing process are collected; step two: road condition simulation is carried out, speed adjustment parameters of the current vehicle are obtained, the speed of the current vehicle is regulated, and a traffic control strategy is realized; and step three: multiple traffic modeling simulation experiments are carried out under the ramp exit traffic scene, and the traffic control strategy or the scene parameters in the simulation model are readjusted. The application can obtain more accurate speed control results by combining the current intelligent networking vehicle and the vehicle conditions of the front and rear vehicles and the lane changing vehicle on both sides in the actual scene, and the verification efficiency of the multi-scene traffic operation modeling simulation technology for the traffic control strategy for intelligent networking is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation and is a multi-scenario traffic operation modeling and simulation method for intelligent networking. Background Art

[0002] Intelligent connected vehicles (ICVs) are vehicles equipped with both autonomous and connected driver assistance systems. Multi-scenario traffic operation modeling and simulation technology can be used to establish motion models for ICVs, build simulation scenarios, and simulate mixed traffic flows in typical congestion scenarios. This allows for the validation of traffic control strategies and improves the efficiency of vehicle behavior decision-making.

[0003] Multi-scenario traffic simulation technology often involves multiple traffic scenarios, such as main roads, ramps, and intersections. This paper focuses on analyzing ramp exit scenarios. By collecting a large amount of ramp vehicle information, a simulation model is built, and the model data can be used to verify traffic control strategies. Near ramp exits, a large number of vehicles may change lanes to exit, resulting in the need to control the speed of following vehicles.

[0004] In traditional ramp lane changes, the speed of intelligent connected vehicles is often controlled by analyzing the distance to the vehicle in front. However, in actual scenarios, the vehicle may be far away from the vehicle in front and close to the vehicle behind. In this case, when the vehicle slows down to avoid the lane-changing vehicle, it needs to consider the approaching performance of the vehicle behind. If the deceleration is too large, the risk of rear-end collision with the vehicle behind will increase. At the same time, vehicle speed control should also consider the performance of the lane-changing vehicle. If the expected lane-changing position of the lane-changing vehicle is relatively close, the risk of rear-end collision will be further increased, resulting in poor verification of traffic control strategies by traffic operation modeling and simulation technology. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above shortcomings and provide a multi-scenario traffic operation modeling and simulation method for intelligent network connection, which includes the following steps: Step 1: Build a ramp exit simulation scene model and collect the speed data and position coordinate data of each vehicle during the lane change process;

[0006] Step 2: Conduct road condition simulation, obtain the speed adjustment parameters of the current vehicle, adjust the current vehicle speed, and implement traffic control strategies;

[0007] Step 3: Conduct multiple traffic modeling simulation experiments under the ramp exit traffic scenario, and verify the results of the traffic control strategy in each experiment. If the vehicle rear-end collision rate in the statistical verification results is lower than the threshold, the traffic control strategy can be applied to the actual connected vehicle traffic control scenario. If the rear-end collision rate in the statistical verification results is higher than the threshold, the traffic control strategy or the scenario parameters in the simulation model need to be readjusted.

[0008] Furthermore, the step one is specifically as follows: a ramp exit simulation scenario model is constructed using the traffic simulation SUMO software, with four lanes set up on the main road and no deceleration lane; actual ramp road conditions that meet the ramp exit scenario are obtained, and the actual ramp exit road condition information is imported into the ramp exit simulation model for simulation; during the ramp exit simulation, when there is a lane change, the adjacent rear vehicle on the target lane of the lane-changing vehicle is regarded as the current vehicle, and a traffic speed control strategy is applied to the speed of the current vehicle during the lane change process; the speed data and position coordinate data of the current vehicle, the leading vehicle, the rear vehicle, and the lane-changing vehicle are read during the lane change process; finally, the read data is uploaded for subsequent analysis.

[0009] Furthermore, the speed adjustment parameters of the current vehicle are obtained in step 2 as follows: when a vehicle changes lanes to the current vehicle's lane near the ramp exit, the conditions of the preceding vehicle and the lane-changing vehicle constitute the vehicle condition in front of the current vehicle, and the condition of the following vehicle constitutes the vehicle condition behind the current vehicle. The vehicle condition analysis is performed in front and in rear, and the speed of the current vehicle is adjusted and controlled during the lane change process in combination with the vehicle conditions in front and rear.

[0010] Furthermore, the analysis of the vehicle condition ahead is specifically as follows: analyzing the conditions of the current vehicle and the preceding vehicle to obtain an ideal lane-changing point; analyzing the conditions of the current vehicle and the lane-changing vehicle to obtain a real-time predicted lane-changing point of the lane-changing vehicle; and combining the differences in the points to obtain a deceleration requirement for the preceding condition.

[0011] Furthermore, the analysis of the vehicle conditions of the current vehicle and the preceding vehicle to obtain the ideal lane-changing point is specifically as follows: obtaining the midpoint position between the coordinates of the current vehicle and the preceding vehicle at real-time moments; calculating the speed difference between the preceding vehicle and the current vehicle; simultaneously obtaining the time interval between the t-th moment and the real-time moment; calculating the ideal point offset coefficient of the real-time midpoint position; if the ideal point offset coefficient is greater than 0, the ideal lane-changing point is offset from the real-time midpoint toward the preceding vehicle; otherwise, it is offset toward the current vehicle, thereby determining the offset adjustment direction; taking the absolute value of the ideal point offset coefficient and normalizing it, and determining the normalized value as the offset adjustment amplitude; selecting a preset rated offset of 0.5 m, and then adding the product of the offset adjustment amplitude and the rated offset to the real-time midpoint position in the offset adjustment direction, and the offset position coordinates are determined as the real-time ideal lane-changing point.

[0012] Furthermore, the analysis of the conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane-changing point of the lane-changing vehicle is specifically as follows: the real-time position of the lane-changing vehicle is projected onto the current lane, and the projected position is recorded as the real-time initial predicted lane-changing point before correction; each moment after the lane change is used as the horizontal coordinate, and the initial predicted lane-changing point corresponding to the lane-changing vehicle at each moment is used as the vertical coordinate, each sample point is placed in a two-dimensional coordinate system, and each sample point in the two-dimensional coordinate system is fitted with a least squares straight line to obtain the slope of the fitted straight line; at the same time, the lane-changing vehicle's intelligent network system is used to read the lane-changing speed of the lane-changing vehicle in the direction perpendicular to the traffic flow when changing lanes. The average lane-changing speed of the lane-changing vehicle from the start of the lane change to the real-time moment is calculated; the prediction point correction factor of the real-time initial predicted lane-changing point of the lane-changing vehicle is calculated; if the slope of the fitting straight line obtained at the initial lane-changing point is greater than 0, the real-time initial predicted lane-changing point needs to be corrected in the direction of the preceding vehicle, otherwise it needs to be corrected in the direction of the current vehicle, and the correction direction is then determined; the prediction point correction factor is determined as the correction amplitude; the preset rated correction amount is selected as 0.5m, and the product of the correction amplitude and the rated correction amount is added to the initial predicted lane-changing point in the correction direction, and the corrected position coordinates are determined as the real-time predicted lane-changing point.

[0013] Furthermore, the analysis of the vehicle conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane-changing point of the lane-changing vehicle is as follows: if the predicted lane-changing point in the vehicle condition in front of the current vehicle is closer to the vehicle in front than the theoretical lane-changing point, it means that the predicted lane-changing point is biased towards the vehicle in front compared to the actual trajectory of the lane-changing vehicle. At this time, the speed of the current vehicle is relatively high. At this time, according to the front condition, it is reflected that the current vehicle has a high deceleration requirement. The current vehicle's front condition deceleration requirement is calculated through the real-time predicted lane-changing point and the real-time ideal lane-changing point.

[0014] Furthermore, the rear vehicle condition analysis is specifically as follows: combining the current vehicle and rear vehicle condition analysis to obtain the rear acceleration requirement of the current vehicle; collaboratively analyzing the front and rear vehicle conditions to obtain the speed-up coefficient, and then analyzing the traffic flow performance between different lane change target units to obtain the current vehicle's speed demand index; and obtaining the speed adjustment parameter based on the speed demand index.

[0015] The present invention proposes a multi-scenario traffic operation modeling and simulation method for intelligent networking. First, a simulation system is built for the ramp exit traffic scenario. Then, when there is a vehicle lane change in the simulation scenario, the adjacent rear vehicle in the target lane change is used as the current analysis vehicle for speed control. The front deceleration requirement is obtained by analyzing the vehicle conditions of the front vehicle and the lane-changing vehicle of the current vehicle. At the same time, the rear acceleration requirement is obtained according to the vehicle condition performance of the rear vehicle of the current vehicle. Then, the front and rear vehicle conditions are collaboratively analyzed to obtain a speed adjustment parameter, and the speed of the current vehicle is regulated according to the speed adjustment parameter, thereby completing the traffic operation simulation model to verify the speed control strategy of the adjacent vehicles of the lane-changing vehicle near the ramp. Compared with the traditional method that only analyzes the distance between vehicles, the present invention can combine the vehicle condition performance of the current intelligent connected vehicle and the front and rear vehicles and the lane-changing vehicle on both sides in the actual scenario to obtain more accurate speed control results, thereby improving the verification efficiency of the traffic control strategy of the multi-scenario traffic operation modeling and simulation technology for intelligent networking. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described below with reference to the accompanying drawings and examples.

[0018] Example: Intelligent connected vehicles (ICVs) are a new generation of vehicles that integrate modern communications and network technologies with advanced onboard devices. They enhance driving safety, comfort, and efficiency by enabling intelligent information exchange between the vehicle and the outside world (people, vehicles, roads, the cloud, etc.). Multi-scenario traffic operation modeling and simulation technology can simulate the conditions of ICVs in multiple traffic scenarios. Traffic control strategies are then input into the simulation model to verify these strategies through traffic simulation. Traffic modeling and simulation involve numerous traffic scenarios, but this invention primarily focuses on ramp exit scenarios. The purpose of traffic simulation is to verify traffic control strategies. Specifically, when a lane-changing vehicle is near a ramp exit, the vehicle behind the lane-changing vehicle on the target road is used as the current vehicle. The traffic control strategy regulates the current vehicle's speed during the lane change process, and the simulation results verify this strategy. When traditional simulation models verify traffic control strategies, the traffic control strategy only adjusts the vehicle speed based on the analysis of the distance between the current vehicle and the preceding vehicle. This speed control strategy has low adaptability (see the technical issues of the present invention for details). Therefore, the present invention combines the vehicle conditions of the current vehicle, the preceding and following vehicles, and the lane-changing vehicle in actual ramp exit lane change scenarios to obtain more accurate speed control strategy results, thereby improving the simulation model's verification effect on traffic control strategies.

[0019] like Figure 1 As shown in FIG, a multi-scenario traffic operation modeling and simulation method for intelligent connected vehicles includes the following steps:

[0020] Step 1: Build a ramp exit simulation scenario model and collect vehicle speed and position coordinate data for each vehicle during the lane change process. First, a ramp exit simulation scenario model is built using the traffic simulation software SUMO. The ramp exit scenario in this invention uses a 4km-long main road with a diversion point 2km down the road. The main road has four lanes and no deceleration lanes. Actual ramp road conditions that match the ramp exit scenario are acquired through a traffic sensing system and imported into the ramp exit simulation model for simulation. During the ramp exit simulation, when a vehicle changes lanes, the adjacent following vehicle on the target lane of the lane-changing vehicle is considered the current vehicle. Traffic speed control strategies are then applied to the current vehicle's speed during the lane change process. In one embodiment of the present invention, the adjacent following vehicle is the vehicle closest to the lane-changing vehicle on the target lane. The simulation parameter storage module reads the speed and position coordinate data for the current vehicle, the preceding vehicle, the following vehicle, and the lane-changing vehicle during the lane change process. Finally, the retrieved data is uploaded to the data acquisition system for subsequent analysis.

[0021] Step 2: Perform a road condition simulation to obtain the current vehicle's speed adjustment parameters, adjust the current vehicle's speed, and implement a traffic control strategy. Many vehicles change lanes near ramp exits, allowing for safe and early exit. The purpose of this invention is to verify the effectiveness of the vehicle speed control strategy in lane-changing scenarios near ramp exits using a traffic modeling simulation model. Therefore, for the constructed ramp exit simulation model, when a lane-changing vehicle changes lanes in front of the current vehicle, a speed control strategy is applied to the current vehicle. The speed of the current vehicle is controlled based on its performance relative to the vehicles ahead, behind, and changing lanes, thereby reducing the risk of rear-end collisions and improving the simulation system's effectiveness in verifying the vehicle speed control strategy.

[0022] Obtain the speed adjustment parameters for the current vehicle. When a vehicle changes lanes to the current vehicle's lane near the ramp exit, the current vehicle's speed should be related to the preceding vehicle, the following vehicle, and the lane-changing vehicle. During the lane change, the conditions of the preceding vehicle and the lane-changing vehicle constitute the current vehicle's front condition, while the condition of the following vehicle constitutes the current vehicle's rear condition. The current vehicle's speed is adjusted and controlled based on the conditions of the preceding and following vehicles.

[0023] The vehicle situation ahead is analyzed as follows:

[0024] Analyze with the preceding vehicle to obtain the ideal lane-changing point: The lane-changing vehicle changes lanes from the adjacent lane to the middle position between the current vehicle and the preceding vehicle in the current lane. Ideally, in order to ensure a safe distance from the adjacent vehicle after the lane change, the ideal lane-changing point should be located at the real-time midpoint between the current vehicle and the preceding vehicle. However, in actual scenarios, the current vehicle and the preceding vehicle may have different speed performances. For example, when the current vehicle is approaching the preceding vehicle, it is safer to select the ideal lane-changing point after the midpoint. Therefore, the ideal lane-changing point is obtained by analyzing the conditions of the current vehicle and the preceding vehicle: First, obtain the midpoint position between the real-time coordinates of the current vehicle and the preceding vehicle, and record the real-time midpoint position as ; Calculate the speed difference between the front vehicle and the current vehicle , the speed difference at the tth moment from the start of lane change to the real time is ; At the same time, get the time interval between the tth moment and the real time , record the total number of times the lane change starts and ends moment; calculate the ideal point offset coefficient of the real-time midpoint position :

[0025] ;

[0026] The speed difference in the above formula is The larger the value, the faster the preceding vehicle is compared to the current vehicle, the greater the divergence between the two vehicles, and the further the ideal lane change point should be offset from the preceding vehicle compared to the real-time midpoint. Indicates the time domain confidence. The smaller the value, the closer the analysis time is to the real time, and the higher the reference value of the speed difference at that time. If the ideal point offset coefficient If it is greater than 0, the ideal lane change point will be offset from the real-time midpoint to the direction of the preceding vehicle; otherwise, it will be offset to the direction of the current vehicle, and then the offset adjustment direction will be determined; Take the absolute value and normalize it, and determine the normalized value as the offset adjustment amplitude; finally, select the preset rated offset as 0.5m, then Add the product of the offset adjustment amplitude and the rated offset in the offset adjustment direction, and the offset position coordinates are determined as the real-time ideal lane change point. .

[0027] Analyze the lane-changing vehicle to obtain the predicted lane-changing point: The ideal lane-changing point is determined by the current vehicle and the preceding vehicle. However, considering that the lane-changing vehicle in the actual scene is affected by factors such as the driver's driving habits and vehicle performance, the actual lane-changing point of the lane-changing vehicle may deviate from the ideal lane-changing point. Therefore, this step analyzes the conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane-changing point of the lane-changing vehicle. First, the real-time position of the lane-changing vehicle is projected onto the current lane, and this projected position is recorded as the real-time initial predicted lane-changing point before correction. ; Take the time after the lane change as the horizontal coordinate, and the initial predicted lane change point corresponding to the lane-changing vehicle at each time as the vertical coordinate (it should be noted here that the initial predicted lane change point also represents the relative displacement difference between the current vehicle and the lane-changing vehicle in the current lane direction), place each sample point in the two-dimensional coordinate system, and use the least squares method to fit the sample points in the two-dimensional coordinate system to obtain the slope of the fitting line At the same time, the lane-changing vehicle's intelligent network system reads the lane-changing vehicle's lane-changing speed in the direction perpendicular to the traffic flow when changing lanes, and calculates the average lane-changing speed of the lane-changing vehicle from the beginning of the lane change to the real-time moment. ; Calculate the prediction point correction factor of the real-time initial prediction lane change point of the lane-changing vehicle :

[0028] ;

[0029] The slope of the fitted line obtained from the initial lane change point in the above formula is The larger the positive number is, the more the predicted lane change point based on the trajectory of the lane-changing vehicle has a tendency to approach the vehicle in front. In this case, the predicted lane change point should be corrected more towards the vehicle in front, and vice versa. At the same time, the average lane change speed of the lane-changing vehicle from the beginning of the lane change to the real-time moment is The faster it is, the more likely it is that the lane-changing vehicle will complete the lane change quickly in a short period of time. In this case, to avoid excessive deviation, the correction amplitude of the predicted lane-changing point needs to be reduced. If the slope of the fitting line obtained at the initial lane-changing point is If it is greater than 0, the real-time initial predicted lane change point needs to be corrected in the direction of the preceding vehicle, otherwise it needs to be corrected in the direction of the current vehicle, and then the correction direction is determined; the prediction point correction factor Finally, the preset rated correction amount is selected as 0.5m, then Add the product of the correction amplitude and the rated correction amount in the correction direction, and the corrected position coordinates are determined as the real-time predicted lane change point. .

[0030] Combined with the difference in the points, the previous deceleration requirement is obtained: if the lane change point is predicted in the vehicle ahead of the current vehicle Compared with the theoretical lane change point The closer it is to the vehicle in front, the more the predicted lane change point is closer to the vehicle in front than the actual trajectory of the lane-changing vehicle, which means that the current vehicle's speed is too high. At this time, the current vehicle needs to slow down according to the previous situation. Therefore, the current vehicle's previous situation deceleration requirement is calculated based on the above logic. :

[0031]

[0032] Then, the deceleration requirement of the current vehicle's front condition is obtained. The larger the value, the higher the deceleration requirement of the current vehicle reflected by the vehicle condition in front of the current vehicle.

[0033] The rear vehicle condition analysis is as follows:

[0034] During a lane change, the current vehicle may slow down to give way to the vehicle changing lanes in front. If the vehicle behind is following closely, the current vehicle's deceleration should not be too large, which means that the acceleration demand increases. On the contrary, when the vehicle behind is diverging from the current vehicle, the current vehicle can safely slow down and the acceleration demand decreases. Therefore, it is necessary to analyze the conditions of the current vehicle and the vehicle behind to obtain the acceleration demand of the current vehicle.

[0035] First, get the real-time speed of the following vehicle and real-time current vehicle speed ; Get the real-time distance between the current vehicle and the following vehicle ; Calculate the real-time deviation trend of the car behind the current car :

[0036] ;

[0037] In the above formula, the larger the distance between the current car and the following car, and the faster the current car's speed is compared to the following car's speed, the greater the divergence trend of the following car compared to the current car. At the same time, with the time after the lane change as the horizontal coordinate and the distance between the current car and the following car as the vertical coordinate, each distance sample point is placed in a two-dimensional coordinate system, and a function is fitted for each distance sample point. The standard deviation of the fitting function from the start of the lane change to the real time is calculated. ; The standard of the fitting function corresponding to the vehicle spacing The smaller the difference, the more stable the distance between the front and rear vehicles is from the start of lane change to the real-time moment, which further indicates that the credibility of the real-time vehicle divergence trend of the current vehicle is higher; furthermore, the higher the real-time vehicle divergence trend of the current vehicle is, the larger the deceleration reserve space behind the current vehicle is, that is, the higher the deceleration demand is, and vice versa, the higher the acceleration demand is. As the confidence parameter of the following car's deviation from the trend, calculate the acceleration requirement of the current car :

[0038] ;

[0039] Finally, the rear acceleration demand degree reflecting the acceleration demand of the current vehicle is calculated based on the vehicle condition behind the current vehicle.

[0040] Collaborative analysis of the preceding and following vehicle conditions yields an acceleration coefficient, and further analysis of the traffic flow performance between different lane change target units yields the current vehicle's speed requirement index: This step yields the current vehicle's acceleration coefficient based on the preceding and following vehicle conditions. The lane-changing vehicle, the current vehicle, the preceding vehicle, and the following vehicle are considered as a lane change target unit. The impact of the ramp traffic flow between the current lane change target unit and the previous lane change target unit on vehicle control within the current lane change target unit is analyzed. For example, if the intermediate ramp traffic flow exhibits strong intermittent starting and braking performance and the spacing between vehicles is large, lane changes are more likely to occur within the ramp traffic flow. This, in turn, creates a braking demand for the preceding vehicle within the current lane change target unit, further reducing the current vehicle's speed requirement, and vice versa. Therefore, based on the acceleration coefficient, a prediction of lane changes within the ramp is made based on the ramp traffic flow performance. This prediction is then used to predict the braking performance of the preceding vehicle within the current lane change target unit, thereby obtaining the current vehicle's speed requirement index.

[0041] First, if the current vehicle's real-time deceleration requirement The smaller the size, the faster the demand for real-time post-situation acceleration. The higher the value, the higher the speed increase requirement of the current vehicle, which is reflected in the vehicle conditions before and after. Therefore, the real-time speed increase coefficient of the current vehicle is calculated. :

[0042] ;

[0043] Then, the lane-changing vehicle, the current vehicle, the preceding vehicle, and the following vehicle, which have a lane-changing influence, are taken as a single lane-changing target unit. The traffic flow between the current lane-changing target unit and the previous lane-changing target unit in the same lane is extracted and determined as the intermediate ramp traffic flow corresponding to the current vehicle. The standard deviation of the vehicle speed of the xth vehicle in the intermediate ramp traffic flow within the historical 8 seconds is calculated. , and obtain the minimum standard deviation of the speed of all vehicles in the intermediate ramp traffic flow in the past three months ; At the same time, get the distance between the xth vehicle and the preceding vehicle in the intermediate ramp traffic flow , and obtain the average distance between all vehicles in the intermediate ramp traffic flow in the past three months ; Record the number of vehicles extracted from the middle ramp in real time vehicles; calculate the impact of the intermediate ramp traffic on the current vehicle's speed increase :

[0044] ;

[0045] In the above formula, the larger the standard deviation of the vehicle speed in the intermediate ramp traffic flow in front of the current lane change target unit in the short term, the greater the possibility of the vehicle starting to brake in the short term in the history. At the same time, if the traffic spacing in the intermediate ramp traffic flow is large, it is more likely for the intermediate ramp traffic flow to change lanes in the short term in the future. Finally, if the intermediate ramp traffic flow affects the speed increase of the current vehicle The higher the value, the more likely the intermediate ramp traffic flow is to change lanes in the short term in the future, which will create a demand for braking of the vehicle in front of the current lane change target unit, thereby reducing the speed increase demand of the current vehicle. Therefore, the speed increase coefficient is corrected according to the degree of speed increase impact to obtain the speed demand index of the current vehicle. :

[0046] ;

[0047] Get the speed adjustment parameters based on the speed demand index: Get the speed control direction and amplitude of the current vehicle based on the negative feedback regulation principle. First, calculate the real-time speed demand index of the current vehicle. and the speed demand index at the previous moment If the real-time speed demand index is greater than that of the previous moment, the speed of the current vehicle needs to be increased at the next moment, otherwise it needs to be decreased. Therefore, the speed control direction of the current vehicle at the next moment is calculated. :

[0048] ;

[0049] At the same time, if the difference between the current vehicle's real-time speed demand index and the previous moment's speed demand index is greater, it means that the speed control range at the next moment is greater, so the speed control range of the current vehicle at the next moment is calculated. :

[0050] ;

[0051] Finally, the speed control direction and amplitude of the current vehicle at the next moment are obtained.

[0052] Control the speed of the current vehicle to implement traffic control strategies: According to the speed control parameters of the current vehicle and the real-time speed, adaptively obtain the speed of the current vehicle at the next moment :

[0053] ;

[0054] In the above formula Indicates the current real-time speed of the vehicle; represents the vehicle speed control parameter. This allows for speed control of adjacent vehicles in the target lane when lane changes occur in the ramp exit simulation system, thus enabling the implementation of traffic control strategies in the simulation system.

[0055] Step 3: Conduct multiple traffic modeling simulation experiments under the ramp exit traffic scenario, and verify the results of the traffic control strategy in each experiment. If the vehicle rear-end collision rate in the statistical verification results is lower than the threshold, the traffic control strategy can be applied to the actual connected vehicle traffic control scenario. If the rear-end collision rate in the statistical verification results is higher than the threshold, the traffic control strategy or the scenario parameters in the simulation model need to be readjusted.

[0056] Specifically: First, multiple traffic modeling and simulation experiments are conducted under the ramp exit traffic scenario, and the traffic control strategy in each experiment is verified. The number of simulation experiments is preset to 2000 times; if the vehicle rear-end collision rate in the statistical verification results is lower than 0.1%, it means that the traffic modeling and simulation model has a good verification effect on the traffic control strategy, and the traffic control strategy can be applied to the actual connected vehicle traffic control scenario. It should be noted here that the adjustment of vehicle speed in the actual scenario cannot directly control the vehicle speed, but the connected vehicle's intelligent network system will give voice reminders to the driver; if the rear-end collision rate in the statistical verification results is higher than 0.1%, the verification effect is not good, and the traffic control strategy or the scenario parameters in the simulation model need to be readjusted; at the same time, modeling and simulation experiments are designed synchronously according to multiple traffic scenarios such as ramp entrances, intersections, and main roads, and the application effect of the traffic control strategy in multiple traffic scenarios is simulated and verified; finally, a more accurate multi-scenario traffic operation modeling and simulation process for intelligent networking is realized.

Claims

1. A multi-scenario traffic operation modeling and simulation method for intelligent network connection, characterized by The following steps are involved: Step 1: Build a ramp exit simulation scenario model and collect the speed data and position coordinate data of each vehicle during the lane change process; Step 2: Conduct road condition simulation, obtain the speed adjustment parameters of the current vehicle based on the speed demand index, and adjust the speed control direction and amplitude of the current vehicle at the next moment to implement the traffic control strategy; Step 3: Conduct multiple traffic modeling simulation experiments under the ramp exit traffic scenario, and verify the results of the traffic control strategy in each experiment. If the vehicle rear-end collision rate in the statistical verification results is lower than the threshold, the traffic control strategy can be applied to the actual connected vehicle traffic control scenario. If the rear-end collision rate in the statistical verification results is higher than the threshold, the traffic control strategy or the scenario parameters in the simulation model need to be readjusted.

2. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 1 is characterized in that The first step specifically includes: building a ramp exit simulation scenario model using traffic simulation software SUMO, with a four-lane main road and no deceleration lane; obtaining actual ramp road conditions that meet the ramp exit scenario, and importing the actual ramp exit road condition information into the ramp exit simulation model for simulation; during the ramp exit simulation, if a vehicle changes lanes, the adjacent following vehicle in the target lane of the lane-changing vehicle is used as the current vehicle, and a traffic speed control strategy is applied to the speed of the current vehicle during the lane change process; The speed and position coordinate data of the current vehicle, the preceding vehicle, the following vehicle, and the lane-changing vehicle are read during the lane-changing process; finally, the read data is uploaded for subsequent analysis.

3. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 1 or 2 is characterized in that The speed adjustment parameters of the current vehicle are obtained in step 2 as follows: when a vehicle changes lanes to the current vehicle's lane near the ramp exit, the conditions of the preceding vehicle and the lane-changing vehicle constitute the vehicle condition in front of the current vehicle, and the condition of the following vehicle constitutes the vehicle condition behind the current vehicle. The vehicle condition analysis is performed in front and in rear, and the speed of the current vehicle during the lane change is adjusted and controlled in combination with the vehicle conditions in front and in rear.

4. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 3 is characterized in that The analysis of the vehicle condition ahead is specifically performed as follows: analyzing the conditions of the current vehicle and the preceding vehicle to obtain an ideal lane-changing point; analyzing the conditions of the current vehicle and the lane-changing vehicle to obtain a real-time predicted lane-changing point; and combining the differences in the points to obtain a deceleration requirement for the preceding condition.

5. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 4 is characterized in that The analysis of the conditions of the current vehicle and the preceding vehicle to obtain the ideal lane change point specifically comprises: obtaining the midpoint position between the coordinates of the current vehicle and the preceding vehicle at real time; calculating the speed difference between the preceding vehicle and the current vehicle; simultaneously obtaining the time interval between the t-th moment and the real time; calculating the ideal point offset coefficient of the real-time midpoint position; if the ideal point offset coefficient is greater than 0, the ideal lane change point is offset from the real-time midpoint toward the preceding vehicle; otherwise, the ideal lane change point is offset toward the current vehicle, thereby determining the offset adjustment direction; Take the absolute value of the ideal point offset coefficient and normalize it, and determine the normalized value as the offset adjustment amplitude. Select the preset rated offset as 0.5m, then add the product of the offset adjustment amplitude and the rated offset to the real-time midpoint position in the offset adjustment direction, and the offset position coordinates are determined as the real-time ideal lane change point.

6. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 5 is characterized in that The analysis of the conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane-changing point of the lane-changing vehicle is specifically as follows: the real-time position of the lane-changing vehicle is projected onto the current lane, and the projected position is recorded as the real-time initial predicted lane-changing point before correction; each time after the lane change is used as the horizontal coordinate, and the initial predicted lane-changing point corresponding to the lane-changing vehicle at each time is used as the vertical coordinate, each sample point is placed in a two-dimensional coordinate system, and each sample point in the two-dimensional coordinate system is fitted with a least squares straight line to obtain the slope of the fitted straight line; at the same time, the lane-changing speed of the lane-changing vehicle in the direction perpendicular to the traffic flow when changing lanes is read by the lane-changing vehicle's intelligent network system, and the average lane-changing speed of the lane-changing vehicle from the start of the lane change to the real-time moment is calculated; Calculate the prediction point correction factor of the real-time initial prediction lane change point of the lane-changing vehicle; If the slope of the fitted line obtained from the initial lane change point is greater than 0, the real-time initial predicted lane change point needs to be corrected in the direction of the preceding vehicle; otherwise, it needs to be corrected in the direction of the current vehicle, and the correction direction is then determined. The forecast point correction factor is determined as the correction amplitude; If the preset rated correction amount is 0.5m, the initial predicted lane change point is added with the product of the correction amplitude and the rated correction amount in the correction direction, and the corrected position coordinates are determined as the real-time predicted lane change point.

7. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 6 is characterized in that The analysis of the vehicle conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane-changing point of the lane-changing vehicle is specifically as follows: if the predicted lane-changing point in the vehicle condition in front of the current vehicle is closer to the vehicle in front than the theoretical lane-changing point, it means that the predicted lane-changing point is biased towards the vehicle in front compared to the actual trajectory of the lane-changing vehicle. At this time, the speed of the current vehicle is relatively high. At this time, the front condition reflects that there is a deceleration need for the current vehicle. The front condition deceleration need degree of the current vehicle is calculated through the real-time predicted lane-changing point and the real-time ideal lane-changing point.

8. The multi-scenario traffic operation modeling and simulation method for intelligent network connection according to claim 3 is characterized in that The rear vehicle condition analysis is specifically as follows: combining the current vehicle and rear vehicle condition analysis to obtain the rear acceleration requirement of the current vehicle; collaboratively analyzing the front and rear vehicle conditions to obtain the speed-up coefficient, and then analyzing the traffic flow performance between different lane change target units to obtain the current vehicle's speed demand index; and obtaining the speed adjustment parameter based on the speed demand index.

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