Multi-scene traffic operation modeling simulation method for intelligent network connection

Abstract

The invention relates to the field of intelligent traffic, and discloses a multi-scene traffic operation modeling simulation method for intelligent network connection, which comprises the following steps of: 1, building a ramp exit simulation scene model, and collecting speed data and position coordinate data of each vehicle in a lane changing process; 2, road condition simulation is carried out, vehicle speed adjusting parameters of a current vehicle are obtained, the vehicle speed of the current vehicle is regulated and controlled, and a traffic control strategy is achieved; and 3, carrying out multiple traffic modeling simulation experiments in a ramp exit traffic scene, and readjusting scene parameters in a traffic control strategy or a simulation model. According to the method, a more accurate vehicle speed control result can be obtained by combining the vehicle condition performance of the current intelligent network connection vehicle, the front and rear vehicles on the two sides and the lane changing vehicle in the actual scene, and then the verification efficiency of the multi-scene traffic operation modeling simulation technology for intelligent network connection on the traffic control strategy is improved.

Description

Technical Field

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

[0002] An intelligent connected vehicle refers to a vehicle that has both an autonomous driving assistance system and a connected driving assistance system. By using multi-scenario traffic operation modeling and simulation technology to establish a motion model for intelligent connected vehicles, build a simulation scenario, and simulate the operation state of mixed traffic flow under various typical congestion scenarios, the traffic control strategy can be verified to improve the behavior decision-making efficiency of traffic vehicles.

[0003] Multi-scenario traffic simulation technology often involves multiple traffic scenarios such as main roads, ramp intersections, and cross intersections. The present invention mainly analyzes the ramp exit scenario. By collecting a large amount of vehicle information at the ramp intersection, a simulation model is built, and then the traffic control strategy can be verified based on the model data. There may be a large number of vehicles changing lanes to exit the ramp near the ramp intersection, resulting in the need to control the speed of the following vehicle.

[0004] In traditional ramp lane-changing situations, the speed control of intelligent connected vehicles is often obtained by analyzing the distance from the vehicle in front. However, in actual scenarios, there may be a situation where the vehicle is far from the vehicle in front and close to the following vehicle. At this time, when the vehicle decelerates to avoid the lane-changing vehicle, it needs to consider the approaching behavior of the following vehicle. If it decelerates too much, it will increase the rear-end collision risk of the following vehicle. At the same time, the vehicle speed control should consider the vehicle condition of the lane-changing vehicle. If the expected lane-changing position of the lane-changing vehicle is relatively close, it will further increase the rear-end collision risk of the vehicle, resulting in poor verification effect of traffic operation modeling and simulation technology on traffic control strategies. Summary of the Invention

[0005] The purpose of the present invention is to overcome the above deficiencies and provide a multi-scenario traffic operation modeling and simulation method for intelligent connected vehicles, including the following steps: Step 1: Build a ramp exit simulation scenario model, and collect the vehicle speed data and position coordinate data of each vehicle during the lane-changing process; Step 2: Conduct road condition simulation, obtain the vehicle speed adjustment parameters of the current vehicle, and regulate the vehicle speed of the current vehicle to implement the traffic control strategy; Step 3: Conduct multiple traffic modeling and simulation experiments on the ramp exit traffic scenario, and verify the results of the traffic control strategy in each experiment. If the rear-end collision rate of the vehicles 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.

[0006] Further, the specific content of the first step is as follows: Build a ramp exit simulation scenario model through the traffic simulation SUMO software. The main road is set with four lanes and no deceleration lane is set. Obtain the actual ramp road conditions that meet the ramp exit scenario, and import the actual ramp exit road condition information into the ramp exit simulation model for simulation. During the ramp exit simulation process, when there is a vehicle lane change situation, the vehicle following closely behind the lane-changing vehicle on the target lane is regarded as the current vehicle, and during the lane change process, a traffic speed control strategy is applied to the speed of the current vehicle. Read the speed data and position coordinate data of the current vehicle, the vehicle in front, the vehicle behind, and the lane-changing vehicle during the lane change process. Finally, upload the read data for subsequent analysis and use.

[0007] Further, the specific content of obtaining the speed adjustment parameter of the current vehicle in the second step is as follows: When a vehicle changes lanes to the current vehicle lane near the ramp exit, the vehicle conditions of the vehicle in front and the lane-changing vehicle constitute the front vehicle conditions of the current vehicle, and the vehicle condition of the vehicle behind is the rear vehicle condition of the current vehicle. Conduct front vehicle condition analysis and rear vehicle condition analysis, and combine the front and rear vehicle conditions to adjust and control the speed of the current vehicle during the lane change process.

[0008] Further, the specific content of conducting the front vehicle condition analysis is as follows: Analyze the vehicle conditions of the current vehicle and the vehicle in front to obtain the ideal lane change point; Analyze the vehicle conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane change point of the lane-changing vehicle; Combine the point difference situation to obtain the deceleration requirement degree of the front vehicle condition.

[0009] Further, the specific content of analyzing the vehicle conditions of the current vehicle and the vehicle in front to obtain the ideal lane change point is as follows: Obtain the midpoint position between the coordinates of the current vehicle and the vehicle in front at the real-time moment; Calculate the speed difference between the vehicle in front and the current vehicle; At the same time, obtain the time interval between the t-th moment and the real-time moment; Calculate 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 in the direction of the vehicle in front from the real-time midpoint; Otherwise, it is offset in the direction of the current vehicle, and then the offset adjustment direction is determined; 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 amount as 0.5m, then add the product of the offset adjustment amplitude and the rated offset amount to the real-time midpoint position in the offset adjustment direction, and the offset position coordinate is determined as the real-time ideal lane change point.

[0010] Further, the specific method for analyzing 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: project the real-time position of the lane-changing vehicle onto the current lane, and record the projected position as the initial predicted lane-changing point before correction in real time; take each moment after lane change as the abscissa and the corresponding initial predicted lane-changing point of the lane-changing vehicle at each moment as the ordinate, place each sample point in a two-dimensional coordinate system, perform linear fitting on each sample point in the two-dimensional coordinate system by the least squares method to obtain the slope of the fitted line; at the same time, read the lane-changing speed of the lane-changing vehicle in the direction perpendicular to the vehicle flow when changing lanes through the intelligent network connection system of the lane-changing vehicle, and calculate the average lane-changing speed of the lane-changing vehicle from the start of lane change to the real-time moment; calculate the prediction point correction factor of the real-time initial predicted lane-changing point of the lane-changing vehicle; if the slope of the fitted line obtained from 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 vehicle in front, otherwise it needs to be corrected in the direction of the current vehicle, so as to determine the correction direction; determine the prediction point correction factor as the correction amplitude; select the preset rated correction amount as 0.5 m, then add the product of the correction amplitude and the rated correction amount to the initial predicted lane-changing point in the correction direction, and determine the corrected position coordinate as the real-time predicted lane-changing point.

[0011] Further, the specific method for analyzing 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 with the actual trajectory of the lane-changing vehicle. At this time, the speed of the current vehicle is relatively large, and according to the previous situation, the deceleration requirement degree of the current vehicle is relatively high. Then, calculate the deceleration requirement degree of the current vehicle in the previous situation through the real-time predicted lane-changing point and the real-time ideal lane-changing point.

[0012] Further, the analysis of the vehicle condition behind is specifically as follows: combine the vehicle conditions of the current vehicle and the vehicle behind to analyze and obtain the acceleration requirement degree of the current vehicle in the situation behind; synergistically analyze the vehicle conditions in the front and behind to obtain the speed increase coefficient, and then analyze the vehicle flow performance between different lane-changing target units to obtain the vehicle speed requirement index of the current vehicle; obtain the vehicle speed adjustment parameter according to the vehicle speed requirement index.

[0013] The present invention proposes a multi-scenario traffic operation modeling and simulation method for intelligent connected vehicles. First, a simulation system for the ramp exit traffic scenario is built. Then, when there is a vehicle lane-changing phenomenon in the simulation scenario, the following vehicle adjacent to the target lane change is used as the current analysis vehicle for vehicle speed control. Among them, the deceleration demand degree of the front condition is obtained by analyzing the vehicle conditions of the vehicle in front of the current vehicle and the lane-changing vehicle. At the same time, the acceleration demand degree of the rear condition is obtained according to the vehicle condition performance of the vehicle behind the current vehicle. Then, the vehicle speed adjustment parameters are obtained through collaborative analysis of the front and rear vehicle conditions, and the vehicle speed of the current vehicle is regulated according to the vehicle speed adjustment parameters, thus completing the verification of the vehicle speed control strategy for the vehicle adjacent to the lane-changing vehicle near the ramp by the traffic operation simulation model. Compared with the traditional method that only analyzes based on the distance between vehicles, the present invention can obtain more accurate vehicle speed control results by combining the vehicle condition performances of the current intelligent connected vehicle and the front, rear, and lane-changing vehicles on both sides in the actual scenario, thereby improving the verification efficiency of the multi-scenario traffic operation modeling and simulation technology for intelligent connected vehicles for traffic control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The present invention will be further described below in conjunction with the drawings and embodiments.

[0016] Embodiment: An intelligent connected vehicle is a new generation of vehicle that integrates modern communication, network technology, and advanced in-vehicle devices. By realizing intelligent information interaction between the vehicle and the outside world (people, vehicles, roads, cloud, etc.), the driving safety, comfort, and efficiency are improved. The multi-scenario traffic operation modeling and simulation technology can simulate the vehicle conditions of intelligent connected vehicles in multiple traffic scenarios, and at the same time input traffic control strategies into the simulation model to verify the vehicle traffic control strategies through traffic simulation technology. Traffic modeling and simulation involve many traffic scenarios. In the present invention, it mainly focuses on the ramp exit scenario, and the purpose of traffic simulation is to verify traffic control strategies. Specifically: when there is a lane-changing vehicle near the ramp exit, the vehicle following adjacent to the target lane of the lane-changing vehicle is used as the current vehicle, and the traffic control strategy regulates the vehicle speed of the current vehicle during the lane-changing process, and is verified by the simulation results. When the traditional simulation model verifies traffic control strategies, the traffic control strategy only analyzes based on the distance between the current vehicle and the vehicle in front to regulate the vehicle speed, and the adaptability of this vehicle speed control strategy is not high (see the technical problems of the present invention for details). Therefore, the present invention combines the vehicle condition performances of the current vehicle and the front, rear, and lane-changing vehicles in the actual ramp exit vehicle lane-changing scenario to obtain more accurate vehicle speed control strategy results, thereby improving the verification effect of the simulation model on traffic control strategies.

[0017] As Figure 1 shown, a multi-scenario traffic operation modeling and simulation method for intelligent connected vehicles includes the following steps: Step 1: Build a ramp exit simulation scenario model and collect the speed data and position coordinate data of each vehicle during the lane-changing process. First, build a ramp exit simulation scenario model through the traffic simulation SUMO software. In the present invention, the full length of the main road in the ramp exit scenario is 4 km, the diversion point is 2 km on the main road, the main road has four lanes, and no deceleration lane is set. Obtain the actual ramp road conditions that conform to the ramp exit scenario through the traffic sensing system, and import the actual ramp exit road condition information into the ramp exit simulation model for simulation. During the ramp exit simulation, when there is a vehicle lane-changing situation, the vehicle following closely behind the lane-changing vehicle on the target lane is taken as the current vehicle, and then a traffic speed control strategy is applied to the speed of the current vehicle during the lane-changing process. In an embodiment of the present invention, the vehicle following closely behind is the vehicle closest to the lane-changing vehicle on the target lane. Read the speed data and position coordinate data of the current vehicle, the vehicle in front, the vehicle behind, and the lane-changing vehicle during the lane-changing process through the simulation parameter storage module. Finally, upload the read data to the data acquisition system for subsequent analysis and use.

[0018] Step 2: Conduct road condition simulation and obtain the speed adjustment parameters of the current vehicle, regulate the speed of the current vehicle, and implement the traffic control strategy. There are many vehicle lane-changing phenomena near the ramp exit so that vehicles can safely drive out of the ramp in advance. The purpose of the present invention is to verify the effect of the vehicle speed control strategy in the vehicle lane-changing scenario near the ramp exit through the traffic modeling and simulation model. Therefore, for the built ramp exit simulation model, when there is a lane-changing vehicle changing lanes in front of the current vehicle, a speed control strategy is applied to the current vehicle, and the speed of the current vehicle is controlled according to the vehicle conditions of the current vehicle, the vehicle in front, the vehicle behind, and the lane-changing vehicle, thereby reducing the risk of rear-end collision and improving the traffic control verification effect of the vehicle speed control strategy by the simulation system.

[0019] Obtain the speed adjustment parameters of the current vehicle. When there is a vehicle changing lanes into the current vehicle's lane near the ramp exit, the speed of the current vehicle at this time should be related to the vehicle in front, the vehicle behind, and the lane-changing vehicle: during the lane-changing process, the vehicle conditions of the vehicle in front and the lane-changing vehicle constitute the front vehicle condition of the current vehicle, and the vehicle condition of the vehicle behind is the rear vehicle condition of the current vehicle. Then, combine the front and rear vehicle conditions to adjust and control the speed of the current vehicle during the lane-changing process.

[0020] The analysis of the front vehicle condition is as follows: Analyze with the vehicle in front to obtain the ideal lane-changing point: The lane-changing vehicle changes from the adjacent lane to the middle position between the current vehicle and the vehicle in front in the current lane. Ideally, in order to ensure that the vehicle distance from the adjacent vehicle after lane-changing is within a safe distance, the ideal lane-changing point should be located at the midpoint position of the current vehicle and the vehicle in front in real time. However, in the actual scenario, the current vehicle and the vehicle in front may have different speed performances. For example, when the current vehicle shows an approaching performance relative to the vehicle in front, it is more prudent to select the ideal lane-changing point after the midpoint at this time. Therefore, analyze the vehicle conditions of the current vehicle and the vehicle in front to obtain the ideal lane-changing point: First, obtain the midpoint position between the coordinates of the current vehicle and the vehicle in front at the real-time moment, and record this real-time midpoint position as ; Calculate the speed difference between the vehicle ahead and the current vehicle , and denote the speed difference at the t-th moment during the process from the lane-changing start time to the real-time moment as ; Meanwhile, obtain the time interval between the t-th moment and the real-time moment , and denote that a total of moments are extracted during the period from the lane-changing start time to the real-time; Calculate the ideal point offset coefficient at the real-time midpoint position : ; In the above formula, the greater the speed difference , it indicates that the vehicle ahead is faster than the current vehicle, then the deviation trend between the two vehicles is greater, and the ideal lane-changing point should deviate more towards the vehicle ahead compared to the real-time midpoint; represents the time-domain confidence level. The smaller this value is, the closer the analysis time is to the real-time moment, and the higher the reference value of the speed difference at this moment; If the ideal point offset coefficient is greater than 0, the ideal lane-changing point deviates from the real-time midpoint towards the vehicle ahead; otherwise, it deviates towards the current vehicle, thereby determining the offset adjustment direction; Take the absolute value of and perform normalization processing, 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 to in the offset adjustment direction, and the offset position coordinate is determined as the real-time ideal lane-changing point .

[0021] Analyze and obtain the predicted lane-changing point for the lane-changing vehicle: The ideal lane-changing point is determined by the current vehicle and the vehicle ahead. Considering the influence of factors such as the driver's driving habits and vehicle performance on the lane-changing vehicle in the actual scenario, there may be a deviation between the actual lane-changing point of the lane-changing vehicle and the ideal lane-changing point. Therefore, in this step, the vehicle conditions of the current vehicle and the lane-changing vehicle are analyzed to obtain the real-time predicted lane-changing point of the lane-changing vehicle. First, project the real-time position of the lane-changing vehicle onto the current lane, and denote this projected position as the initial predicted lane-changing point before correction in real time ; Take the moments after lane-changing as the abscissa and the corresponding initial predicted lane-changing points of the lane-changing vehicle at each moment as the ordinate (it should be noted here that the initial predicted lane-changing 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 a two-dimensional coordinate system, and perform linear fitting on each sample point in the two-dimensional coordinate system by the least squares method to obtain the fitting line slope ; Meanwhile, read the lane-changing speed of the lane-changing vehicle in the direction perpendicular to the traffic flow when changing lanes through the intelligent network connection system of the lane-changing vehicle, and calculate the average lane-changing speed of the lane-changing vehicle from the start of lane-changing to the real-time moment ; Calculate the prediction point correction factor of the real-time initial predicted lane-changing point of the lane-changing vehicle : ; In the above formula, the slope of the fitted straight line obtained from the initial lane-changing point is a positive number, and the larger it is, the more the predicted lane-changing point predicted according to the trajectory of the lane-changing vehicle tends to approach the vehicle in front. At this time, the predicted lane-changing point should be corrected more towards the vehicle in front, and vice versa, more towards the current vehicle. At the same time, the average lane-changing speed of the lane-changing vehicle from the start of lane-changing to the real-time moment is faster, which further indicates that the lane-changing vehicle is more likely to complete the lane change quickly within a short time. At this time, to avoid excessive deviation, the correction amplitude of the predicted lane-changing point needs to be adjusted downwards. If the slope of the fitted straight line obtained from the initial lane-changing point is greater than 0, the real-time initial predicted lane-changing point needs to be corrected towards the vehicle in front, and vice versa, towards the current vehicle, thereby determining the correction direction; the predicted point correction factor is determined as the correction amplitude; finally, the preset rated correction amount is selected as 0.5 m, then add the product of the correction amplitude and the rated correction amount in the correction direction, and the corrected position coordinate is determined as the real-time predicted lane-changing point .

[0022] Combined with the point difference situation, the deceleration demand degree of the front situation is obtained: If the predicted lane-changing point in the vehicle condition in front of the current vehicle is closer to the vehicle in front compared to the theoretical lane-changing point, it indicates that the predicted lane-changing point is biased towards the vehicle in front compared to the actual trajectory of the lane-changing vehicle, which means that the speed of the current vehicle is relatively large at this time. Then, according to the above logic, there is a deceleration demand for the current vehicle, so the deceleration demand degree of the front situation of the current vehicle is calculated :

[0023] Furthermore, the deceleration demand degree of the front situation of the current vehicle is obtained. The larger this value is, the higher the deceleration demand of the current vehicle reflected by the vehicle condition in front of the current vehicle.

[0024] The analysis of the vehicle condition behind is as follows: During the vehicle lane-changing process, the current vehicle may decelerate to give way to the vehicle in front for lane-changing. If the following vehicle is following closely at this time, the deceleration amplitude of the current vehicle should not be too large, that is, the acceleration demand increases; on the contrary, when the following vehicle shows a deviating trend from the current vehicle, the current vehicle can decelerate with confidence at this time, and the acceleration demand decreases. Therefore, it is necessary to analyze and obtain the acceleration demand degree of the rear situation of the current vehicle by combining the vehicle conditions of the current vehicle and the following vehicle.

[0025] First, obtain the real-time speed of the following vehicle and the real-time speed of the current vehicle ; obtain the real-time distance between the current vehicle and the following vehicle ; calculate the deviating trend degree of the following vehicle of the current vehicle in real time : ; In the above formula, the greater the distance between the current vehicle and the following vehicle, and the faster the speed of the current vehicle compared to the following vehicle, the greater the deviation trend of the following vehicle compared to the current vehicle. At the same time, with the time after lane change as the abscissa and the distance between the current vehicle and the following vehicle as the ordinate, place each distance sample point in a two-dimensional coordinate system, perform function fitting on each distance sample point, and calculate the standard deviation of the fitting function during the period from the start of lane change to the real-time moment. ; The standard deviation of the fitting function corresponding to the vehicle distance is smaller, indicating that the distance between the front and rear vehicles is more stable during the period from the start of lane change to the real-time moment, further indicating that the credibility of the deviation trend degree of the following vehicle of the real-time current vehicle is higher. Furthermore, the higher the deviation trend degree of the following vehicle of the current vehicle in real time, the greater the deceleration reserve space behind the current vehicle, that is, the higher the deceleration demand. On the contrary, the acceleration demand is higher. At the same time, is used as the confidence parameter of the deviation trend degree of the following vehicle, and calculate the acceleration demand degree of the following situation of the current vehicle : ; Finally, the acceleration demand degree of the following situation reflecting the acceleration demand of the current vehicle is calculated from the vehicle condition behind the current vehicle.

[0026] Through collaborative analysis of the vehicle conditions in the front and rear, obtain the speed-up coefficient, and then analyze the traffic flow performance between different lane-changing target units to obtain the vehicle speed demand index of the current vehicle: In this step, obtain the speed-up coefficient of the current vehicle according to the vehicle conditions in the front and rear. At the same time, regard the lane-changing vehicle, the current vehicle, the vehicle in front, and the vehicle behind with lane-changing behavior as a lane-changing target unit, and analyze the influence of the ramp traffic flow between the current lane-changing target unit and the previous lane-changing target unit on the vehicle control of the current lane-changing target unit. For example, if the intermittent starting and braking performance of the middle ramp traffic flow is strong and the distance between the vehicles in the traffic flow is large, then lane-changing is more likely to occur in the ramp traffic flow at this time, which will further cause a braking demand for the speed of the vehicle in front in the current lane-changing target unit, and further reduce the vehicle speed demand degree of the current vehicle. On the contrary, it will increase. Therefore, based on the speed-up coefficient, predict the lane-changing phenomenon in the ramp according to the performance of the ramp traffic flow, that is, predict the braking performance of the vehicle in front in the current lane-changing target unit, so as to obtain the vehicle speed demand index of the current vehicle.

[0027] First of all, if the deceleration demand degree of the front situation of the current vehicle in real time is smaller, and at the same time the acceleration demand degree of the following situation in real time is higher, it indicates that the speed-up demand of the current vehicle is higher according to the vehicle conditions in the front and rear. Therefore, calculate the speed-up coefficient of the current vehicle in real time : ; Furthermore, taking the lane-changing vehicle, the current vehicle, the vehicle ahead, and the vehicle behind with a lane-changing impact relationship 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; calculate the standard deviation of the vehicle speeds of the x-th vehicle in the intermediate ramp traffic flow within the past 8s , and obtain the minimum value of the standard deviation of the vehicle speeds of all vehicles in the intermediate ramp traffic flow within the past three months ; at the same time, obtain the distance between the x-th vehicle in the intermediate ramp traffic flow and the vehicle ahead , and obtain the average distance of all vehicles in the intermediate ramp traffic flow within the past three months ; record that a total of vehicles are extracted in the intermediate ramp traffic flow in real time; calculate the degree of influence of the intermediate ramp traffic flow on the acceleration of the current vehicle : ; In the above formula, the larger the standard deviation of the vehicle speeds of the vehicles in the intermediate ramp traffic flow in the short term in the past in front of the current lane-changing target unit, the greater the possibility of starting and braking of the vehicles in the short term in the past. At the same time, if the traffic flow distance in the intermediate ramp traffic flow is large, it is more likely to have a lane-changing phenomenon in the short term in the future at the intermediate ramp traffic flow; finally, if the degree of influence of the intermediate ramp traffic flow on the acceleration of the current vehicle is higher, that is, it is more likely to have a lane-changing phenomenon in the short term in the future at the intermediate ramp traffic flow, then there will be a demand for braking of the vehicle ahead in the current lane-changing target unit, and then the acceleration demand of the current vehicle will be reduced. Therefore, the acceleration coefficient is corrected according to the degree of influence of acceleration, and the vehicle speed demand index of the current vehicle is obtained : ; Obtain the vehicle speed adjustment parameter according to the vehicle speed demand index: obtain the vehicle speed control direction and amplitude of the current vehicle based on the principle of negative feedback regulation. First, calculate the real-time vehicle speed demand index of the current vehicle and the vehicle speed demand index at the previous moment . If the real-time vehicle speed demand index is larger than that at the previous moment, the vehicle speed of the current vehicle needs to be increased in the next moment, otherwise it needs to be decreased. Therefore, calculate the vehicle speed control direction of the current vehicle in the next moment : ; At the same time, if the difference between the real-time vehicle speed demand index of the current vehicle and the vehicle speed demand index at the previous moment is larger, it means that the vehicle speed control amplitude in the next moment is larger. Therefore, calculate the vehicle speed control amplitude of the current vehicle in the next moment : ; Finally, obtain the vehicle speed control direction and amplitude of the current vehicle in the next moment

[0028] Adjust the speed of the current vehicle to implement the traffic control strategy: adaptively obtain the speed of the current vehicle at the next moment according to the speed regulation parameters and the real-time speed of the current vehicle : ; In the above formula represents the real-time speed of the current vehicle; represents the speed regulation parameter. Furthermore, complete the speed regulation of the adjacent vehicle in the target lane when there is a lane change in the ramp exit simulation system, and implement the traffic control strategy in the simulation system.

[0029] Step 3: Conduct multiple traffic modeling and simulation experiments for the ramp exit traffic scenario, and verify the results of the traffic control strategy in each experiment. If the rear-end collision rate of the vehicles 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.

[0030] Specifically: First, conduct multiple traffic modeling and simulation experiments for the ramp exit traffic scenario, and verify the results of the traffic control strategy in each experiment. The number of simulation experiments is preset to 2000 times; if the rear-end collision rate of the vehicles 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 in the actual scenario, the speed cannot be directly regulated, but the connected vehicle intelligent network system gives a voice reminder 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, design traffic modeling and simulation experiments synchronously according to multiple traffic scenarios such as ramp entrances, intersections, and main roads, and conduct simulation verification on the application effect of the traffic control strategy in multiple traffic scenarios; finally, implement a multi-scenario traffic operation modeling and simulation process with higher accuracy for intelligent connected vehicles.

Claims

1. A multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles, characterized in that It includes the following steps: Step 1: Build a ramp exit simulation scenario model, and collect the vehicle speed data and position coordinate data of each vehicle during the lane change process; Step 2: Conduct road condition simulation, obtain the vehicle speed adjustment parameters of the current vehicle, regulate the vehicle speed of the current vehicle, and implement traffic control strategies; Step 3: Conduct multiple traffic modeling and simulation experiments on the ramp exit traffic scenario, verify the results of the traffic control strategies in each experiment. If the rear-end collision rate of the vehicles 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, it is necessary to readjust the traffic control strategy or the scenario parameters in the simulation model.

2. The multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles according to claim 1, characterized in that The specific content of Step 1 is as follows: Build a ramp exit simulation scenario model through the traffic simulation SUMO software. Set four lanes on the main road and do not set deceleration lanes; Obtain the actual ramp road conditions that meet the ramp exit scenario, and import the actual ramp exit road condition information into the ramp exit simulation model for simulation; During the ramp exit simulation process, when there is a vehicle lane change situation, take the vehicle behind the lane-changing vehicle on the target lane as the current vehicle, and apply traffic vehicle speed control strategies to the vehicle speed of the current vehicle during the lane change process; Read the vehicle speed data and position coordinate data of the current vehicle, the vehicle in front, the vehicle behind, and the lane-changing vehicle during the lane change process; Finally, upload the read data for subsequent analysis.

3. The multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles according to claim 1 or 2, characterized in that The specific content of obtaining the vehicle speed adjustment parameters of the current vehicle in Step 2 is as follows: When a vehicle changes lanes to the current vehicle's lane near the ramp exit, the vehicle conditions of the vehicle in front and the lane-changing vehicle constitute the front vehicle conditions of the current vehicle, and the vehicle condition of the vehicle behind is the rear vehicle condition of the current vehicle. Conduct front vehicle condition analysis and rear vehicle condition analysis, and adjust and control the vehicle speed of the current vehicle during the lane change process in combination with the front and rear vehicle conditions.

4. The multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles according to claim 3, wherein The specific content of conducting the front vehicle condition analysis is as follows: Analyze the vehicle conditions of the current vehicle and the vehicle in front to obtain the ideal lane change point; Analyze the vehicle conditions of the current vehicle and the lane-changing vehicle to obtain the real-time predicted lane change point of the lane-changing vehicle; Combine the point difference situation to obtain the front condition deceleration requirement.

5. The multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles according to claim 4, characterized in that The specific content of analyzing the vehicle conditions of the current vehicle and the vehicle in front to obtain the ideal lane change point is as follows: Obtain the midpoint position between the coordinates of the current vehicle and the vehicle in front at the real-time moment; Calculate the vehicle speed difference between the vehicle in front and the current vehicle; At the same time, obtain the time interval between the t-th moment and the real-time moment; Calculate 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 forward from the real-time midpoint towards the vehicle in front; Otherwise, it is offset towards the current vehicle, thereby determining the offset adjustment direction; Take the absolute value of the ideal point offset coefficient and perform normalization processing, and determine the normalized value as the offset adjustment amplitude; Select the preset rated offset amount as 0.5 m, then add the product of the offset adjustment amplitude and the rated offset amount to the real-time midpoint position in the offset adjustment direction, and the offset position coordinate is determined as the real-time ideal lane change point.

6. The multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles according to claim 5, characterized in that The analysis of the current vehicle and the lane-changing vehicle conditions to obtain the real-time predicted lane-changing point of the lane-changing vehicle is specifically as follows: project the real-time position of the lane-changing vehicle onto the current lane, and record the projected position as the initial predicted lane-changing point before correction in real time; take each moment after lane-changing as the abscissa and the corresponding initial predicted lane-changing point of the lane-changing vehicle at each moment as the ordinate, place each sample point in a two-dimensional coordinate system, and perform linear fitting on each sample point in the two-dimensional coordinate system by the least squares method to obtain the slope of the fitted line; at the same time, read the lane-changing speed of the lane-changing vehicle in the direction perpendicular to the traffic flow when the lane-changing vehicle changes lanes through the intelligent network connection system of the lane-changing vehicle, and calculate the average lane-changing speed of the lane-changing vehicle from the start of lane-changing to the real-time moment. Calculate the prediction point correction factor of the real-time initial predicted lane-changing point of the lane-changing vehicle. If the slope of the fitted line obtained from 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 vehicle in front, otherwise it needs to be corrected in the direction of the current vehicle, and then the correction direction is determined. Determine the prediction point correction factor as the correction amplitude. Select the preset rated correction amount to be 0.5 m, then add the product of the correction amplitude and the rated correction amount to the initial predicted lane-changing point in the correction direction, and determine the corrected position coordinate as the real-time predicted lane-changing point.

7. The multi-scenario traffic operation modeling and simulation method for intelligent networked vehicles according to claim 6, characterized in that The analysis of the current vehicle and the lane-changing vehicle conditions 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 front vehicle condition 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 with the actual trajectory of the lane-changing vehicle. At this time, the speed of the current vehicle is relatively large. At this time, according to the front condition, it is reflected that there is a deceleration requirement for the current vehicle. Then, calculate the deceleration requirement degree of the current vehicle according to the real-time predicted lane-changing point and the real-time ideal lane-changing point.

8. The method for modeling and simulating multi-scenario traffic operation for intelligent networked vehicles according to claim 3, wherein The rear vehicle condition analysis is specifically as follows: combine the current vehicle and the rear vehicle conditions to analyze and obtain the acceleration requirement degree of the current vehicle in the rear condition; jointly analyze the front and rear vehicle conditions to obtain the acceleration coefficient, and then analyze the traffic flow performance between different lane-changing target units to obtain the vehicle speed requirement index of the current vehicle; obtain the vehicle speed adjustment parameter according to the vehicle speed requirement index.

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