Intelligent network connection lane changing hierarchical control method and system based on traffic simulation, computer and storage medium

Abstract

The invention discloses an intelligent network connection lane changing hierarchical control method and system based on traffic simulation, a computer and a storage medium, and the method comprises the steps: collecting road traffic flow information, including vehicle expected speed, vehicle length, maximum acceleration, comfortable deceleration and the like in an intelligent network connection lane and a target lane; and calculating a polite coefficient and a lane changing gain of the target vehicle in the control range, judging whether the target vehicle can change a lane or not, determining a subsequent driving target of the target vehicle, and ensuring safe, efficient and stable driving of the vehicle. According to the method, attribute features and traffic flow features of an intelligent network connection lane of a current lane and a target lane are fully considered, hierarchical control is scientifically and reasonably carried out on lane changing vehicles, the lane changing efficiency of off-ramp vehicles is improved, the influence of the lane changing vehicles on other vehicles is reduced, and the traffic flow passing efficiency and the traffic safety level of off-ramp bottleneck road sections are effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic control, and in particular to a method, system, computer and storage medium for intelligent networked lane change hierarchical control based on traffic simulation. Background Art

[0002] With the development of intelligent transportation technology, intelligent connected lanes, as an emerging technology in the field of transportation, have begun to be applied in some cities. Intelligent connected lanes have independent lane and traffic signal control systems, which can improve vehicle traffic efficiency and road safety. However, when vehicles leave the intelligent connected lanes on the off-ramp, traffic congestion and accident risks will increase due to factors such as large traffic volume and frequent lane changes. Therefore, it is necessary to propose a lane change hierarchical control method, system, computer and storage medium for intelligent connected lanes based on traffic simulation to ensure the safety and smoothness of traffic. Summary of the invention

[0003] Purpose of the invention: In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to propose a smart connected lane changing hierarchical control method, system, computer and storage medium based on traffic simulation. First, road traffic flow information is collected, and the acceleration and position of the vehicle within the control range are used as basic information. The courtesy coefficient and lane changing gain of the target vehicle within the control range are calculated to determine whether the target vehicle can change lanes, and the subsequent driving goal of the target vehicle is determined to ensure safe, efficient and smooth driving of the vehicle.

[0004] Technical solution: To solve the above technical problems, the present invention proposes a lane-changing hierarchical control method for intelligent networked lanes based on traffic simulation, which includes the following steps:

[0005] (1) collecting road traffic flow information of the intelligent network lane and the target lane, wherein the target lane is the lane into which the off-ramp vehicles on the intelligent network lane change lanes;

[0006] (2) Predict the acceleration of vehicle A after completing lane change on the off-ramp in the intelligent connected lane

[0007] (3) Calculate the coefficient p(x) when vehicle A changes lanes;

[0008] (4) Calculate the lane change gain U of vehicle A based on the acceleration in step (2) and the coefficient in step (3) SV , determine the lane change gain U of vehicle A SV Is it greater than a given threshold?

[0009] (5) According to the judgment result in step (4), the driving behavior of vehicle A is controlled; if Then lane changing is allowed, and the lane changing duration is Δt; if Lane changing is not allowed and the vehicle continues to follow the vehicle in front in the current lane.

[0010] Furthermore, in step (1), the road traffic flow information includes: the driving speed v of vehicle A on the ramp down the intelligent network lane SV 、Expected speed v f,SV , the center coordinate of the vehicle's front bumper (x SV ,y SV ), vehicle length L SV 、Maximum vehicle acceleration A SV , comfortable deceleration b SV , vehicle safe lane changing time interval T SV , Vehicle static safety distance s 0,SV , Control area length C l , the lane change graded control critical preset vertical coordinates x1, x2 and x3 within the control area.

[0011] Furthermore, in step (2), the predicted acceleration of vehicle A in the intelligent connected lane is Taking into account all the vehicles following vehicle A in the target lane and the original lane, the calculation formula is as follows:

[0012]

[0013] In the formula, v SV (t), v f,SV 、x SV (t), L SV , A SV , b SV 、T SV 、s 0,SV They represent the speed, expected speed, ordinate, vehicle length, maximum acceleration, comfortable deceleration, safe lane change time interval, and vehicle static safety distance of vehicle A at time t. c is the set of all the following vehicles of vehicle A on the target lane and the original lane, m is the number of all the following vehicles of vehicle A on the target lane, n is the number of all the following vehicles of vehicle A on the original lane, v k (t) represents the set S at time t c The speed of the kth vehicle, x LV (t) represents the ordinate of the vehicle in front of vehicle A in the target lane at time t, δ is the acceleration index, δ>0.

[0014] Furthermore, in step (3), the coefficient p(x) changes as the distance between vehicle A and the off-ramp exit changes, and its calculation method is as follows:

[0015]

[0016] Where x is the vertical coordinate of vehicle A during its operation.

[0017] Furthermore, in step (5), the lane change gain calculation formula of vehicle A is as follows:

[0018]

[0019] Where U SV is the total lane-changing benefit of vehicle A, a SV are the accelerations of vehicle A before and after changing lanes, FV and PFV are the sets of following vehicles within the control range of the original lane and the target lane, respectively. o is the number of following vehicles within the control range on the original lane, n t is the number of following vehicles within the control range on the target lane, are the accelerations of the i-th vehicle in the FV and PFV sets after and before vehicle A changes lanes, respectively.

[0020] Furthermore, in step (5), if vehicle A can change lanes, a virtual vehicle A' will be generated at the same longitudinal position of the target lane within the lane change duration [0, Δt]. The longitudinal acceleration of the virtual vehicle A' will be calculated based on the preceding vehicle in the target lane to ensure that the longitudinal acceleration of vehicle A is the same as the longitudinal acceleration of the virtual vehicle A'. When the safe lane change time interval T is reached, SV Afterwards, the target vehicle moves directly to the position of the virtual vehicle A'.

[0021] Furthermore, the longitudinal acceleration calculation formula of the virtual vehicle A' is as follows:

[0022]

[0023] In the formula, v SV' (t), v f,SV' 、x SV' (t), L SV' , A SV' 、b SV' , T SV' 、s 0,SV' They represent the speed, expected speed, ordinate, vehicle length, maximum acceleration, comfortable deceleration, safe lane change time interval, and vehicle static safety distance of the virtual vehicle A' at time t, respectively. LV′ (t) represents the ordinate of the preceding vehicle of the virtual vehicle A' at time t, v LV′ (t) represents the driving speed of the preceding vehicle of the virtual vehicle A' at time t, δ is the acceleration index, δ>0.

[0024] In addition, the present invention proposes an intelligent networked lane change hierarchical control system based on traffic simulation, which includes the following modules:

[0025] A collection module collects road traffic flow information of an intelligent network lane and a target lane, wherein the target lane is a lane into which off-ramp vehicles on the intelligent network lane change lanes;

[0026] Acceleration prediction module, predicting the acceleration of vehicle A after changing lanes on the off-ramp in the intelligent connected lane

[0027]

[0028] An information calculation module calculates the coefficient p(x) when vehicle A changes lanes;

[0029] Threshold judgment module, calculates the lane change gain U of vehicle A SV , determine the lane change gain U of vehicle A SV Is it greater than a given threshold?

[0030] Vehicle control module, if Then lane changing is allowed, and the lane changing duration is Δt; if Lane changing is not allowed and the vehicle must continue to follow the vehicle in front in the current lane.

[0031] In addition, the present invention proposes a computer device for intelligent connected lane change hierarchical control based on traffic simulation, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the methods when executing the computer program.

[0032] In addition, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the methods when executed by a processor.

[0033] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0034] The present invention discloses a hierarchical control method, system, computer and storage medium for lane changing of an intelligent networked lane based on traffic simulation, which fully considers the attribute characteristics and traffic flow characteristics of the current lane, the intelligent networked lane and the target lane, scientifically and effectively performs hierarchical control on lane-changing vehicles, improves the lane-changing efficiency of vehicles on the off-ramp, reduces the impact of lane-changing vehicles on other vehicles, and effectively ensures the traffic efficiency and driving safety level of the bottleneck section of the off-ramp. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of the method of the present invention;

[0036] Figure 2It is a scene schematic diagram of the present invention. DETAILED DESCRIPTION

[0037] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] like Figure 1 As shown, the present invention proposes a lane-changing hierarchical control method for intelligent networked lanes based on traffic simulation, and the method comprises the following steps:

[0039] (1) collecting road traffic flow information of the intelligent network lane and the target lane, wherein the target lane is the lane into which the off-ramp vehicles on the intelligent network lane change lanes;

[0040] (2) Predict the acceleration of vehicle A after completing lane change on the off-ramp in the intelligent connected lane

[0041] (3) Calculate the coefficient p(x) when vehicle A changes lanes;

[0042] (4) Calculate the lane change gain U of vehicle A based on the acceleration in step (2) and the coefficient in step (3) SV , determine the lane change gain U of vehicle A SV Is it greater than a given threshold?

[0043] (5) According to the judgment result in step (4), the driving behavior of vehicle A is controlled; if Then lane changing is allowed, and the lane changing duration is Δt; if Lane changing is not allowed and the vehicle continues to follow the vehicle in front in the current lane.

[0044] Furthermore, in step (1), the road traffic flow information includes: the driving speed v of vehicle A on the ramp down the intelligent network lane SV 、Expected speed v f,SV , the center coordinate of the vehicle's front bumper (x SV ,y SV ), vehicle length L SV 、Maximum vehicle acceleration A SV , comfortable deceleration b SV , vehicle safe lane changing time interval T SV , Vehicle static safety distance s 0,SV , Control area length C l , the lane change graded control critical preset vertical coordinates x1, x2 and x3 within the control area.

[0045] Furthermore, in step (2), the predicted acceleration of vehicle A in the intelligent connected lane is Taking into account all the vehicles following vehicle A in the target lane and the original lane, the calculation formula is as follows:

[0046]

[0047] In the formula, v SV (t), v f,SV 、x SV (t), L SV , A SV 、b SV , T SV 、s 0,SV They represent the speed, expected speed, ordinate, vehicle length, maximum acceleration, comfortable deceleration, safe lane change time interval, and vehicle static safety distance of vehicle A at time t. c is the set of all the following vehicles of vehicle A on the target lane and the original lane, m is the number of all the following vehicles of vehicle A on the target lane, n is the number of all the following vehicles of vehicle A on the original lane, v k (t) represents the set S at time t c The speed of the kth vehicle, x LV (t) represents the ordinate of the vehicle in front of vehicle A in the target lane at time t, δ is the acceleration index, δ>0.

[0048] Furthermore, in step (3), the coefficient p(x) changes as the distance between vehicle A and the off-ramp exit changes, and its calculation method is as follows:

[0049]

[0050] Where x is the vertical coordinate of vehicle A during its operation.

[0051] Furthermore, in step (5), the lane change gain calculation formula of vehicle A is as follows:

[0052]

[0053] Where U SV is the total lane-changing benefit of vehicle A, a SV are the accelerations of vehicle A before and after changing lanes, FV and PFV are the sets of following vehicles within the control range of the original lane and the target lane, respectively. o is the number of following vehicles within the control range on the original lane, n t is the number of following vehicles within the control range on the target lane, are the accelerations of the i-th vehicle in the FV and PFV sets after and before vehicle A changes lanes, respectively.

[0054] Furthermore, in step (5), if vehicle A can change lanes, a virtual vehicle A' will be generated at the same longitudinal position of the target lane within the lane change duration [0, Δt]. The longitudinal acceleration of the virtual vehicle A' will be calculated based on the preceding vehicle in the target lane to ensure that the longitudinal acceleration of vehicle A is the same as the longitudinal acceleration of the virtual vehicle A'. When the safe lane change time interval T is reached, SV Afterwards, the target vehicle moves directly to the position of the virtual vehicle A'.

[0055] Furthermore, the longitudinal acceleration calculation formula of the virtual vehicle A' is as follows:

[0056]

[0057] In the formula, v SV' (t), v f,SV' 、x SV' (t), L SV' , A SV' , b SV' 、T SV' 、s 0,SV' They represent the speed, expected speed, ordinate, vehicle length, maximum acceleration, comfortable deceleration, safe lane change time interval, and vehicle static safety distance of the virtual vehicle A' at time t, respectively. LV′ (t) represents the ordinate of the preceding vehicle of the virtual vehicle A' at time t, v LV′ (t) represents the driving speed of the preceding vehicle of the virtual vehicle A' at time t, δ is the acceleration index, δ>0.

[0058] In addition, the present invention proposes an intelligent networked lane change hierarchical control system based on traffic simulation, which includes the following modules:

[0059] A collection module collects road traffic flow information of an intelligent network lane and a target lane, wherein the target lane is a lane into which off-ramp vehicles on the intelligent network lane change lanes;

[0060] Acceleration prediction module, predicting the acceleration of vehicle A after changing lanes on the off-ramp in the intelligent connected lane

[0061]

[0062] An information calculation module calculates the coefficient p(x) when vehicle A changes lanes;

[0063] Threshold judgment module, calculates the lane change gain U of vehicle A SV , determine the lane change gain U of vehicle A SV Is it greater than a given threshold?

[0064] Vehicle control module, if Then lane changing is allowed, and the lane changing duration is Δt; if Lane changing is not allowed and the vehicle must continue to follow the vehicle in front in the current lane.

[0065] In addition, the present invention proposes a computer device for intelligent connected lane change hierarchical control based on traffic simulation, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the methods when executing the computer program.

[0066] In addition, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the methods when executed by a processor.

[0067] Example:

[0068] An embodiment of the present invention discloses a method for controlling lane change in an intelligent network-connected vehicle based on traffic simulation, comprising the following steps:

[0069] (1) Collect road traffic flow information of the intelligent connected lane and the target lane. Specifically, the road traffic flow information is shown in the following table:

[0070]

[0071]

[0072] Among them, v f , L, A, b, T, and s0 represent the vehicle's expected speed, vehicle length, maximum acceleration, comfortable deceleration, safe lane-changing time interval, and vehicle stationary safety distance, respectively.

[0073] (2) Predict the acceleration of vehicle A on the off-ramp in the intelligent connected lane after completing the lane change;

[0074]

[0075]

[0076] Among them, δ=10.

[0077] (3) Calculate the number of vehicles A and C exiting the ramp in the intelligent connected lane l The longitudinal distance x of the upstream section;

[0078]

[0079] Among them, x can be the front bumper of vehicle A and C l The spacing of the upstream section is indicated.

[0080] (4) Based on the distance in step (3), calculate the politeness coefficient p(x) of vehicle A when changing lanes;

[0081]

[0082]

[0083] (5) According to the acceleration in step (2) and the politeness coefficient in step (4), determine the lane change gain U of the lane-changing vehicle A SV Is it greater than a given threshold?

[0084] in,

[0085]

[0086]

[0087] (6) According to the judgment result in step (5), the driving behavior of the lane-changing vehicle A is controlled. When t = 25s, the lane-changing gain is greater than the given threshold. At this moment, vehicle A changes lanes.

[0088] It should be emphasized that the examples described in the present invention are illustrative rather than restrictive, and therefore the present invention is not limited to the examples described in the specific embodiments. Any other embodiments derived by those skilled in the art based on the technical solution of the present invention that do not depart from the purpose and scope of the present invention, whether modified or replaced, also fall within the scope of protection of the present invention.

Claims

1. A hierarchical control method for lane change in intelligent networked vehicles based on traffic simulation, characterized in that: The method comprises the following steps: (1) collecting road traffic flow information of the intelligent network lane and the target lane, wherein the target lane is the lane into which the off-ramp vehicles on the intelligent network lane change lanes; (2) Predict the acceleration of vehicle A after completing lane change on the off-ramp in the intelligent connected lane (3) Calculate the coefficient p(x) when vehicle A changes lanes; (4) Calculate the lane change gain U of vehicle A based on the acceleration in step (2) and the coefficient in step (3) SV , determine the lane change gain U of vehicle A SV Is it greater than a given threshold? (5) According to the judgment result in step (4), the driving behavior of vehicle A is controlled; if Then lane changing is allowed, and the lane changing duration is Δt; if Lane changing is not allowed and the vehicle continues to follow the vehicle in front in the current lane.

2. According to the intelligent network lane changing hierarchical control method based on traffic simulation according to claim 1, it is characterized in that: In step (1), the road traffic flow information includes: the driving speed v of vehicle A on the ramp down the intelligent network lane SV 、Expected speed v f,SV , the center coordinate of the vehicle's front bumper (x SV ,y SV ), vehicle length L SV 、Maximum vehicle acceleration A SV , comfortable deceleration b SV , vehicle safe lane changing time interval T SV , Vehicle static safety distance s 0,SV , Control area length C l , the lane change graded control critical preset vertical coordinates x1, x2 and x3 within the control area.

3. The intelligent networked lane changing hierarchical control method based on traffic simulation according to claim 1 is characterized in that: In step (2), the predicted acceleration of vehicle A in the intelligent connected lane is Taking into account all the vehicles following vehicle A in the target lane and the original lane, the calculation formula is as follows: In the formula, v SV (t), v f,SV 、x SV (t), L SV , A SV , b SV , T SV 、s 0,SV They represent the speed, expected speed, ordinate, vehicle length, maximum acceleration, comfortable deceleration, safe lane change time interval, and vehicle static safety distance of vehicle A at time t. c is the set of all the following vehicles of vehicle A on the target lane and the original lane, m is the number of all the following vehicles of vehicle A on the target lane, n is the number of all the following vehicles of vehicle A on the original lane, v k (t) represents the set S at time t c The speed of the kth vehicle, x LV (t) represents the ordinate of the vehicle in front of vehicle A in the target lane at time t, δ is the acceleration index, δ>0.

4. The intelligent networked lane changing hierarchical control method based on traffic simulation according to claim 3 is characterized in that: In step (3), the coefficient p(x) changes as the distance between vehicle A and the off-ramp exit changes, and its calculation method is as follows: Where x is the vertical coordinate of vehicle A during its operation.

5. The intelligent networked lane changing hierarchical control method based on traffic simulation according to claim 4 is characterized in that: In step (5), the lane change gain calculation formula of vehicle A is as follows: Where U SV is the total lane-changing benefit of vehicle A, a SV are the accelerations of vehicle A before and after changing lanes, FV and PFV are the sets of following vehicles within the control range of the original lane and the target lane, respectively. o is the number of following vehicles within the control range on the original lane, n t is the number of following vehicles within the control range on the target lane, are the accelerations of the i-th vehicle in the FV and PFV sets after and before vehicle A changes lanes, respectively.

6. The intelligent networked lane changing hierarchical control method based on traffic simulation according to claim 5 is characterized in that: In step (5), if vehicle A can change lanes, a virtual vehicle A' will be generated at the same longitudinal position in the target lane within the lane change duration [0, Δt]. The longitudinal acceleration of the virtual vehicle A' will be calculated based on the preceding vehicle in the target lane to ensure that the longitudinal acceleration of vehicle A is the same as that of the virtual vehicle A'. SV Afterwards, the target vehicle moves directly to the position of the virtual vehicle A'.

7. The intelligent networked lane changing hierarchical control method based on traffic simulation according to claim 6 is characterized in that: The longitudinal acceleration calculation formula of the virtual vehicle A' is as follows: In the formula, v SV' (t), v f,SV' 、x SV' (t), L SV' , A SV' , b SV' , T SV' 、s 0,SV' They represent the speed, expected speed, ordinate, vehicle length, maximum acceleration, comfortable deceleration, safe lane change time interval, and vehicle static safety distance of the virtual vehicle A' at time t, respectively. LV′ (t) represents the ordinate of the preceding vehicle of the virtual vehicle A' at time t, v LV′ (t) represents the driving speed of the preceding vehicle of the virtual vehicle A' at time t, δ is the acceleration index, δ>0.

8. An intelligent networked lane change hierarchical control system based on traffic simulation, characterized by: include: A collection module collects road traffic flow information of an intelligent network lane and a target lane, wherein the target lane is a lane into which off-ramp vehicles on the intelligent network lane change lanes; Acceleration prediction module, predicting the acceleration of vehicle A after changing lanes on the off-ramp in the intelligent connected lane An information calculation module calculates the coefficient p(x) when vehicle A changes lanes; Threshold judgment module, calculates the lane change gain U of vehicle A SV , determine the lane change gain U of vehicle A SV Is it greater than a given threshold? Vehicle control module, if Then lane changing is allowed, and the lane changing duration is Δt; if Lane changing is not allowed and the vehicle must continue to follow the vehicle in front in the current lane.

9. A computer device for intelligent networked lane change hierarchical control based on traffic simulation, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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