Intelligent network connection vehicle speed calculation method and system considering passing fairness of bottleneck area

Abstract

The invention provides an intelligent network connection vehicle speed calculation method and system considering the passing fairness of a bottleneck region, and the method comprises the steps: setting a statistical period, carrying out the statistics of the traffic flow of an abnormal event point from a set historical time period to a current moment, and determining the passing capacity of the bottleneck region formed by an abnormal event; setting control areas with different functions at the upstream of the bottleneck area according to the abnormal event scale; calculating the average passing speed and traffic flow density of vehicles in the control area; in the flow limiting area, calculating the current traffic demand, and adjusting the speed of the intelligent network connection vehicle by comparing the traffic demand with the traffic capacity of the bottleneck area; in the speed coordination area, when the number of queuing vehicles in the lane where the abnormal event is located reaches a preset number, the vehicle speed on the lane where the abnormal event is located is compared with the vehicle speed on the adjacent lane, and the intelligent network connection vehicle on the corresponding lane is decelerated. The micro driving behavior of the intelligent network connection vehicle can be controlled, the manual driving vehicle is guided to move, and the passing efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic management, and in particular to a method and system for calculating the speed of an intelligent connected vehicle taking into account traffic fairness in a bottleneck area. Background Art

[0002] With the high-quality development of my country's social economy and the increasing demand for transportation, highways, as an important part of comprehensive transportation hubs, provide the main channels for medium and long-distance transportation. However, the growing demand for travel has made the traffic environment increasingly complex, and traffic flow management faces severe challenges. Especially when temporary road maintenance construction or traffic accidents occur, the number of available lanes on highways is reduced, and it is urgent to design reasonable traffic flow control measures to deal with abnormal events. The development of intelligent connected driving technology has brought opportunities for the formulation of reasonable control measures and provided a new development direction for alleviating traffic congestion and improving traffic efficiency. However, before the realization of full coverage of the road network by intelligent connected driving technology, the mixed flow composed of manually driven vehicles and intelligent connected driving vehicles will exist for a long time, and the uncertainty of the movement behavior of manually driven vehicles will bring new challenges to the formulation of control measures.

[0003] At present, there has been a certain accumulation of theoretical research on traffic flow control methods in bottleneck areas of highways, among which the research on variable speed limit is the most representative. Variable speed limit control dynamically calculates the optimal speed limit value of vehicles in the control area based on the real-time traffic flow status, but the driver's compliance rate will greatly affect the control effect. The development of intelligent connected driving technology provides favorable conditions for the efficient and accurate implementation of variable speed limit control. In a mixed flow environment, how to use the controllability, safety, sensitivity and other characteristics of intelligent connected driving vehicles to indirectly guide the movement behavior of manually driven vehicles, thereby optimizing the implementation effect of variable speed limit control, is the key to improving the traffic efficiency and safety of vehicles in bottleneck areas. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and system for calculating the speed of an intelligent connected vehicle taking into account the fairness of traffic in a bottleneck area, so as to eliminate or improve one or more defects existing in the prior art.

[0005] In one aspect, the present invention provides a method for calculating the speed of an intelligent connected vehicle taking into account the fairness of traffic in a bottleneck area, the method comprising the following steps:

[0006] A statistical cycle is set to count the traffic flow of the abnormal event point from the set historical period to the current moment; the traffic capacity of the bottleneck area formed by the abnormal event is determined according to the traffic flow; according to the scale of the abnormal event, a flow restriction area and a speed coordination area are set upstream of the bottleneck area; wherein one side of the speed coordination area is the bottleneck area, and the other side is the flow restriction area;

[0007] Collecting statistics on vehicle movement information in the flow restriction area and the speed coordination area, calculating the average vehicle speed and vehicle flow density in the flow restriction area, and calculating the average vehicle speed and vehicle flow density in the speed coordination area;

[0008] In the flow restriction area, the current traffic demand is calculated according to the average traffic speed and traffic density of vehicles in the flow restriction area, and the speed of the intelligent networked vehicle is adjusted by comparing the traffic demand with the traffic capacity of the bottleneck area;

[0009] In the speed coordination area, when the number of vehicles queuing in the lane where the abnormal event occurs reaches a preset number, the vehicle speeds in the lane where the abnormal event occurs are compared with the vehicle speeds in the adjacent lanes, and the intelligent connected vehicles in the corresponding lanes are decelerated according to the preset deceleration rules.

[0010] In some embodiments of the present invention, counting the vehicle movement information in the flow restriction area and the speed coordination area includes:

[0011] Traffic detectors are used to capture the vehicle movement trajectories in the flow restriction area and the speed coordination area, including the travel speeds and locations of vehicles in different lanes in the flow restriction area and the speed coordination area; and the number of queued vehicles in the lane where the abnormal event is located in the speed coordination area is counted.

[0012] In some embodiments of the present invention, in the flow restriction area, the current traffic demand is calculated according to the average vehicle speed and traffic density in the flow restriction area, wherein the calculation formula of the traffic demand is:

[0013]

[0014] in, represents the traffic demand in the flow restriction area at time t; represents the average speed of vehicles passing through the flow restriction area at time t; Represents the traffic density in the flow restriction area at time t.

[0015] In some embodiments of the present invention, adjusting the speed of the intelligent networked vehicle by comparing the traffic demand with the traffic capacity of the bottleneck area includes:

[0016] If the traffic demand is greater than the traffic capacity of the bottleneck area, reducing the speed of the intelligent network-connected vehicle;

[0017] If the traffic demand is less than the traffic capacity of the bottleneck area, the speed of the intelligent connected vehicle is increased.

[0018] In some embodiments of the present invention, in the flow restriction area, when determining the travel speed of an individual intelligent network-connected vehicle, the process further includes:

[0019] According to the traffic capacity of the bottleneck area and the traffic density of the traffic restriction area, the speed limit value of the intelligent connected vehicle in the traffic restriction area is calculated, and the calculation formula is:

[0020]

[0021] in, Indicates the speed limit value of the intelligent connected vehicle in the traffic restriction area at time t; represents the traffic capacity of the bottleneck area at time t; Represents the traffic density in the flow restriction area at time t.

[0022] In some embodiments of the present invention, the method further comprises:

[0023] A safety constraint condition is set for the speed of the intelligent networked vehicle, wherein the constraint condition includes that the vehicle's own speed change rate is less than the maximum acceleration in the flow restriction area and the vehicle maintains a safe distance from the vehicle in front, and the calculation formula is:

[0024]

[0025] in, represents the speed of vehicle i at time t; a max represents the maximum acceleration; represents the position of vehicle i at time t; h min represents the minimum safe headway between vehicles; L represents the average vehicle length; Δt represents the time step; represents the acceleration of vehicle i at time t.

[0026] In some embodiments of the present invention, when the vehicle maintains a safe distance from the vehicle ahead, the method further includes:

[0027] When the preceding vehicle of the target intelligent connected vehicle is a manually driven vehicle, the manually driven vehicle follows the following formula:

[0028]

[0029] in, represents the acceleration of the manually driven vehicle at time t; a max represents the maximum acceleration; u represents the free flow speed; represents the speed of the manually driven vehicle at time t; Indicates the desired headway; represents the speed difference between the target intelligent connected vehicle and the preceding manually driven vehicle; s0 represents the safe headway; h minrepresents the expected headway; b represents the maximum deceleration.

[0030] In some embodiments of the present invention, comparing the speed of the vehicle in the lane where the abnormal event occurs with the speed of the vehicle in the adjacent lane, and decelerating the intelligent connected vehicle in the corresponding lane according to a preset deceleration rule, includes:

[0031] When the speed of the vehicle in the lane where the abnormal event is located is greater than the speed of the vehicle in its adjacent lane, a first quotient of the speed difference between the two vehicles and the time step is calculated. When the first quotient is greater than the maximum deceleration, it is determined whether the vehicle in the lane where the abnormal event is located can pass safely at the maximum deceleration. If the safety constraint condition cannot be met, the intelligent driving model rule is used to update its speed. When the first quotient is not greater than the maximum deceleration, it is determined whether the vehicle in the lane where the abnormal event is located can pass safely at the first quotient as the deceleration. If the safety constraint condition cannot be met, the intelligent driving model rule is used to update its speed.

[0032] When the speed of the vehicle in the lane where the abnormal event occurs is less than the speed of the vehicle in its adjacent lane, a second quotient of the speed difference between the two vehicles and the time step is calculated; when the second quotient is less than the maximum deceleration, the speed of the vehicle in the adjacent lane is decelerated to the speed of the vehicle in the lane where the abnormal event occurs; otherwise, the speed is updated using the intelligent driving model rule.

[0033] On the other hand, the present invention also provides a speed calculation system for an intelligent connected vehicle taking into account traffic fairness in a bottleneck area, the system comprising:

[0034] The input layer is used to collect statistics on the traffic information of the bottleneck area formed by the abnormal event, and to set a flow restriction area and a speed coordination area upstream of the bottleneck area according to the scale of the abnormal event, and to collect statistics on the traffic information of the flow restriction area and the speed coordination area;

[0035] A model layer, for calculating the speed of a target intelligent networked vehicle according to the traffic information obtained by the statistics of the input layer, using any of the intelligent networked vehicle speed calculation methods mentioned above that considers traffic fairness in the bottleneck area;

[0036] The output layer is used to output the speed of the target intelligent connected vehicle calculated by the model layer.

[0037] On the other hand, the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of any of the methods mentioned above when executed by a processor.

[0038] The present invention provides a method and system for calculating the speed of an intelligent networked vehicle that takes into account the fairness of traffic in bottleneck areas, which can control the micro-driving behavior of intelligent networked vehicles, guide the movement of manually driven vehicles, and improve traffic efficiency and safety. Specifically: by counting the traffic capacity at the bottleneck area, the goal of traffic flow control can be clarified and the control speed can be accurately determined; by integrating the traffic status of the flow restriction area and the speed coordination area, it can ensure that the vehicle passes through the bottleneck area smoothly and safely, and improve the fairness of traffic; the speed calculation method proposed in the present invention is easy to calculate and implement, and only needs to control the movement behavior of the intelligent networked driving vehicle to improve the performance of the entire system, and has strong applicability.

[0039] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.

[0040] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0042] Figure 1 Schematic diagram of the steps of a method for calculating the speed of an intelligent connected vehicle considering traffic fairness in a bottleneck area in one embodiment of the present invention.

[0043] Figure 2 It is a schematic diagram of the structure of the road section where an abnormal event occurs and the control area in one embodiment of the present invention.

[0044] Figure 3 It is the average travel time improvement rate in one embodiment of the present invention.

[0045] Figure 4 is the average GINI coefficient improvement rate in one embodiment of the present invention.

[0046] Figure 5 Schematic diagram of a speed calculation system for intelligent connected vehicles that takes into account fairness in bottleneck area traffic in one embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0048] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0049] It should be emphasized that the term “include / comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0050] It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection involving an intermediate.

[0051] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0052] Since the existing intelligent network driving technology has not yet fully covered the road network, the mixed flow of manually driven vehicles and intelligent networked vehicles will exist for a long time. The uncertainty of the movement behavior of manually driven vehicles will bring new challenges to the formulation of control measures. In order to solve this technical problem and improve the operating efficiency and safety of the entire control system, the present invention provides a speed calculation method for intelligent networked vehicles that considers the fairness of bottleneck area traffic, such as Figure 1 As shown, the method includes the following steps S101 to S104:

[0053] Step S101: Set a statistical cycle, and count the traffic flow of the abnormal event point from the set historical period to the current moment; determine the traffic capacity of the bottleneck area formed by the abnormal event according to the traffic flow; and set a flow restriction area and a speed coordination area upstream of the bottleneck area according to the scale of the abnormal event. One side of the speed coordination area is the bottleneck area, and the other side is the flow restriction area.

[0054] Step S102: Collecting vehicle movement information in the flow restriction area and the speed coordination area, calculating the average vehicle speed and traffic density in the flow restriction area, and calculating the average vehicle speed and traffic density in the speed coordination area.

[0055] Step S103: In the flow restriction area, the current traffic demand is calculated according to the average vehicle speed and traffic density in the flow restriction area, and the speed of the intelligent connected vehicle is adjusted by comparing the traffic demand with the traffic capacity of the bottleneck area.

[0056] Step S104: In the speed coordination area, when the number of vehicles queuing in the lane where the abnormal event occurs reaches a preset number, the vehicle speeds in the lane where the abnormal event occurs are compared with the vehicle speeds in the adjacent lanes, and the intelligent connected vehicles in the corresponding lanes are decelerated according to the preset deceleration rules.

[0057] The present invention provides a method for calculating the speed of an intelligent connected vehicle taking into account the fairness of traffic in a bottleneck area, so as to match the flow rate flowing into the bottleneck area with its traffic capacity, construct a flow restriction area and a speed coordination area upstream of the bottleneck, and construct a method for solving the speed of an intelligent connected vehicle to directly control its microscopic driving behavior, thereby guiding the movement of manually driven vehicles and improving the operating efficiency and safety of the entire system.

[0058] Specific:

[0059] In step S101, a statistical period is pre-set, such as the past one hour, three hours, etc., and equipment such as traffic detectors are used to count the traffic flow from the abnormal event point from the set historical period to the current moment, and record them in the corresponding traffic flow sets respectively, and analyze the changing characteristics of the traffic flow near the bottleneck point after the incident, so as to determine the traffic capacity of the bottleneck area after the incident.

[0060] Furthermore, according to the scale of the abnormal event, the propagation distance of the congestion wave is estimated, the scope of the control area is determined, and the control area is set upstream of the bottleneck area. The control area includes flow restriction areas and speed coordination areas with different functions, such as Figure 2 As shown, one side of the speed coordination zone is the bottleneck area, and the other side is the flow restriction area, where the rounded rectangle represents a vehicle, the white-filled rounded rectangle represents a manually driven vehicle, and the gray-filled rounded rectangle represents an intelligent connected vehicle.

[0061] In step S102, the vehicle movement information in the control area is firstly counted.

[0062] In some embodiments, devices such as traffic detectors are used to capture the movement trajectory of vehicles in a set control area, including the travel speed and position of vehicles in different lanes in the flow restriction area and the speed coordination area, and the number of vehicles queuing in the lane where the abnormal event occurs in the speed coordination area is counted.

[0063] Based on the statistical vehicle movement information, the average vehicle speed and traffic density in the flow restriction area and speed coordination area are calculated respectively.

[0064] In step S103, the present invention analyzes the traffic demand characteristics based on the traffic status in the flow restriction area after the abnormal event occurs, so as to control the average speed of vehicles in the flow restriction area, and then control the traffic flow flowing into the speed coordination area to match the real-time traffic capacity near the bottleneck point.

[0065] In the traffic restriction area, the current traffic demand is calculated according to the average vehicle speed and traffic density calculated in step S102. The calculation formula can be shown as formula (1):

[0066]

[0067] in, represents the traffic demand in the flow restriction area at time t; represents the average speed of vehicles passing through the flow restriction area at time t; Represents the traffic density in the flow restriction area at time t.

[0068] In order to match the real-time traffic demand with the capacity of the bottleneck, the relationship between the traffic demand and the capacity at the bottleneck point is compared. If the traffic demand is greater than the capacity of the bottleneck area, the flow restriction area reduces the traffic flow by reducing the moving speed of the intelligent connected driving vehicles while ensuring safety; if the traffic demand is less than the capacity of the bottleneck area, the flow restriction area needs to reduce the traffic flow by increasing the moving speed of the intelligent connected driving vehicles while ensuring safety; if the traffic demand is equal to the capacity of the bottleneck area, the intelligent driving model (IDM) rule is used to update the vehicle speed.

[0069] In some embodiments, when determining the speed of an individual intelligent networked vehicle in a flow restriction area, the speed limit of the intelligent networked vehicle in the flow restriction area is first calculated based on the traffic capacity of the bottleneck area and the traffic density of the flow restriction area. The calculation formula is shown in formula (2):

[0070]

[0071] in, Indicates the speed limit of the intelligent connected vehicle in the traffic restriction area at time t; represents the capacity of the bottleneck area at time t; Represents the traffic density in the flow restriction area at time t.

[0072] Secondly, determine the speed of the smart connected vehicle while ensuring safety.

[0073] In some embodiments, safety constraints are set for the speed of the intelligent networked vehicle, wherein the constraints include that the vehicle's own speed change rate is less than the maximum acceleration in the flow restriction area and the vehicle maintains a safe distance from the vehicle in front. The calculation method follows formulas (3) to (5):

[0074]

[0075] in, represents the speed of vehicle i at time t; a max Indicates the maximum acceleration; represents the position of vehicle i at time t; h min represents the minimum safe headway between vehicles; L represents the average vehicle length; Δt represents the time step; represents the acceleration of vehicle i at time t.

[0076] In some embodiments, when considering that the target vehicle maintains a safe distance from the preceding vehicle, when the preceding vehicle of the target intelligent connected vehicle is a manually driven vehicle, a microscopic traffic flow model is introduced to determine the acceleration, speed and position of the manually driven vehicle, specifically in accordance with the following formulas (6) and (7):

[0077]

[0078] in, represents the acceleration of the manually driven car at time t; a max represents the maximum acceleration; u represents the free flow speed; represents the speed of the manually driven car at time t; Indicates the desired headway; represents the speed difference between the target intelligent connected vehicle and the preceding manually driven vehicle; s0 represents the safe headway; h min represents the expected headway; b represents the maximum deceleration.

[0079] Based on the above description, it can be seen that for intelligent connected vehicles in traffic restriction areas, the actual travel speed is determined by the speed limit value first. If the safety conditions are not met, the vehicle passes at the critical speed that meets the safety conditions according to the above relationship.

[0080] In step S104, the present invention optimizes the micro-trajectory of the intelligent networked driving vehicle in the speed coordination area on the basis of controlling the vehicle speed in the flow restriction area, so as to enable the vehicle to pass through the bottleneck area smoothly and safely. In particular, for vehicles in the lane where the abnormal event is located, a forced lane change must be completed to pass through the bottleneck area. When a vehicle fails to change lanes, a queue will be formed at the bottleneck point. When vehicles pile up at the bottleneck point and frequently change lanes, it will affect the normal passage of vehicles in other lanes, forming a congestion wave that propagates upstream. Therefore, it is necessary to reasonably optimize the micro-driving behavior of the intelligent networked driving vehicle in the speed coordination area to coordinate the movement between vehicles and improve the overall traffic efficiency and fairness.

[0081] In order to reduce the number of vehicles changing lanes at bottleneck points, the speed of vehicles in the lane where the accident occurred is coordinated with that of vehicles in adjacent lanes to improve the fairness of vehicle traffic in different lanes. In the present invention, when the number of vehicles queuing in the lane where the accident occurred increases cumulatively, the speed of intelligent connected driving vehicles in other lanes is coordinated to reduce the number of vehicles queuing.

[0082] For vehicles in different lanes, assuming that it is time t-1, the vehicle in the lane where the abnormal event occurs is i, and its speed is The vehicle in the adjacent lane is j, and its speed is Then, at the next moment, the update of the target vehicle speed follows:

[0083] Update rules for the target vehicle speed in the lane where the abnormal event occurs:

[0084] When the speed of vehicle i in the lane where the abnormal event occurs is greater than the speed of vehicle j in its adjacent lane, vehicle i needs to decelerate to smoothly complete the mandatory lane change in order to find the right time to complete the lane change. The deceleration rule follows the following algorithm:

[0085] Calculate the first quotient of the speed difference between the two vehicles and the time step, and get The maximum deceleration is denoted by b. When , according to the safety constraints set above, it is determined whether vehicle i can pass safely at the maximum deceleration b. If the safety constraints cannot be met, the intelligent driving model (IDM) rule is used to update its speed; when When , according to the safety constraints set above, it is determined whether vehicle i can The vehicle decelerates to pass safely. If the safety constraints cannot be met, the intelligent driving model (IDM) rules are used to update its speed.

[0086] Update rule for the target vehicle speed in the adjacent lane:

[0087] When the speed of vehicle i in the lane where the abnormal event occurs is lower than that of vehicle j in its adjacent lane, and there is a queue of vehicles that fail to change lanes in the bottleneck area of ​​the lane where the abnormal event occurs, vehicle j needs to decelerate to improve the success rate of lane changes in the lane where the abnormal event occurs. Similarly, the deceleration rule follows the following algorithm:

[0088] Calculate the first quotient of the speed difference between the two vehicles and the time step, and get The maximum deceleration is denoted by b. When , the speed of vehicle j is updated to the speed of vehicle i Otherwise, the intelligent driving model (IDM) rule is used to update its speed.

[0089] When the speed of the vehicle in the lane where the abnormal event occurs is equal to the speed of the vehicle in its adjacent lane, the intelligent driving model (IDM) rule is used to update its speed.

[0090] The present invention will be further described below in conjunction with a specific embodiment.

[0091] In order to verify the effectiveness of the intelligent connected vehicle speed calculation method proposed in this paper, the mobile characteristics of the intelligent connected vehicle and the manually driven vehicle are integrated to build a micro-driving simulation system. In the scenario where no control is applied, the vehicle following behavior of the intelligent connected vehicle and the manually driven vehicle is characterized by the IDM model, and the lane changing behavior is characterized by the MOBIL vehicle lane changing model.

[0092] The MOBIL algorithm is shown in formula (8):

[0093]

[0094] in, Indicates the acceleration change of the target vehicle after changing lanes; Indicates the acceleration change of the adjacent vehicle after the target vehicle changes lanes; represents the acceleration change of the adjacent vehicle before the target vehicle changes lanes; p is the yield degree; Δa th An acceleration threshold is set to allow a lane change to be performed. When the acceleration increment satisfies the lane change threshold, the lane change is performed.

[0095] Simulation scene setting: Taking the highway scene as an example, you can refer to Figure 2 , an abnormal event occurred in the outermost lane of the three lanes of the highway. The accident area was set to be 1 km long, the flow restriction area (zone 1) in the control area was 500 meters long, and the speed coordination area (zone 2) was 1000 meters long. The traffic demand was to simulate the movement of vehicles at 3000 vehicles / hour in the first 5 minutes, and then simulate at a constant 4800 vehicles / hour. Each simulation experiment ran for a total of 55 minutes, among which the data collected from 5 to 50 minutes of simulation were taken for comparative analysis, and each simulation scenario was run independently 5 times.

[0096] This embodiment also sets different intelligent connected driving vehicle ratios (Market Penetration Rates, MPR), and the MPR values ​​are 0.6, 0.7, 0.8, and 0.9.

[0097] This embodiment analyzes the efficiency and fairness of vehicle traffic, wherein the efficiency is measured by the average travel time of vehicles passing through a designated area length upstream of the bottleneck, and the fairness is quantified by the classic indicator GINI coefficient.

[0098] Figure 3 and Figure 4 Compared with the scenario without control, the improvement rates of the average vehicle travel time and the average GINI coefficient after applying the method proposed in the present invention are shown in the figure. It can be seen that the method provided by the present application can effectively improve the efficiency and fairness of vehicle travel in the bottleneck area under the specified scenario.

[0099] The present invention also provides a speed calculation system for intelligent networked vehicles that takes into account bottleneck area traffic fairness. Figure 5 As shown, the system includes:

[0100] The input layer is used to collect statistics on the traffic information of the bottleneck area formed by the abnormal event, and to set the flow restriction area and speed coordination area upstream of the bottleneck area according to the scale of the abnormal event, and collect statistics on their traffic information. Exemplarily, the traffic information of the bottleneck area includes the traffic flow set, and the traffic information of the flow restriction area and the speed coordination area includes the average speed, traffic density and queue length of each area.

[0101] The model layer is used to calculate the speed of the target intelligent networked vehicle according to the traffic information of each area obtained by statistics of the input layer, using the intelligent networked vehicle speed calculation method considering the fairness of bottleneck area traffic provided by the present invention.

[0102] The output layer is used to output the speed of the target intelligent connected vehicle calculated by the model layer.

[0103] Corresponding to the above method, the present invention also provides an electronic device, which includes a computer device, the computer device includes a processor and a memory, the memory stores computer instructions, the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the electronic device implements the steps of the method described above.

[0104] The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the aforementioned edge computing server deployment method are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0105] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0106] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0107] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for calculating the speed of an intelligent connected vehicle considering traffic fairness in a bottleneck area, characterized in that: The method comprises the following steps: A statistical cycle is set to count the traffic flow of the abnormal event point from the set historical period to the current moment; the traffic capacity of the bottleneck area formed by the abnormal event is determined according to the traffic flow; according to the scale of the abnormal event, a flow restriction area and a speed coordination area are set upstream of the bottleneck area; wherein one side of the speed coordination area is the bottleneck area, and the other side is the flow restriction area; Collecting statistics on vehicle movement information in the flow restriction area and the speed coordination area, calculating the average vehicle speed and vehicle flow density in the flow restriction area, and calculating the average vehicle speed and vehicle flow density in the speed coordination area; In the flow restriction area, the current traffic demand is calculated according to the average traffic speed and traffic density of vehicles in the flow restriction area, and the speed of the intelligent networked vehicle is adjusted by comparing the traffic demand with the traffic capacity of the bottleneck area; In the speed coordination area, when the number of vehicles queuing in the lane where the abnormal event occurs reaches a preset number, the vehicle speeds in the lane where the abnormal event occurs are compared with the vehicle speeds in the adjacent lanes, and the intelligent connected vehicles in the corresponding lanes are decelerated according to the preset deceleration rules.

2. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 1 is characterized in that: The vehicle movement information in the flow restriction area and the speed coordination area is collected, including: Traffic detectors are used to capture the vehicle movement trajectories in the flow restriction area and the speed coordination area, including the travel speeds and locations of vehicles in different lanes in the flow restriction area and the speed coordination area; and the number of queued vehicles in the lane where the abnormal event is located in the speed coordination area is counted.

3. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 1 is characterized in that: In the flow restriction area, the current traffic demand is calculated according to the average vehicle speed and traffic density in the flow restriction area, wherein the calculation formula of the traffic demand is: in, represents the traffic demand in the flow restriction area at time t; represents the average speed of vehicles passing through the flow restriction area at time t; Represents the traffic density in the flow restriction area at time t.

4. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 1 is characterized in that: By comparing the traffic demand and the traffic capacity of the bottleneck area, adjusting the speed of the intelligent networked vehicle includes: If the traffic demand is greater than the traffic capacity of the bottleneck area, reducing the speed of the intelligent network-connected vehicle; If the traffic demand is less than the traffic capacity of the bottleneck area, the speed of the intelligent connected vehicle is increased.

5. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 1 is characterized in that: In the flow restriction area, when determining the speed of individual intelligent network-connected vehicles, it also includes: According to the traffic capacity of the bottleneck area and the traffic density of the traffic restriction area, the speed limit value of the intelligent connected vehicle in the traffic restriction area is calculated, and the calculation formula is: in, Indicates the speed limit value of the intelligent connected vehicle in the traffic restriction area at time t; represents the traffic capacity of the bottleneck area at time t; Represents the traffic density in the flow restriction area at time t.

6. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 5 is characterized in that: The method further comprises: A safety constraint condition is set for the speed of the intelligent networked vehicle, wherein the constraint condition includes that the vehicle's own speed change rate is less than the maximum acceleration in the flow restriction area and the vehicle maintains a safe distance from the vehicle in front, and the calculation formula is: in, represents the speed of vehicle i at time t; a max represents the maximum acceleration; represents the position of vehicle i at time t; h min represents the minimum safe headway between vehicles; L represents the average vehicle length; Δt represents the time step; represents the acceleration of vehicle i at time t.

7. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 6 is characterized in that: When the vehicle maintains a safe distance from the vehicle ahead, the method further includes: When the preceding vehicle of the target intelligent connected vehicle is a manually driven vehicle, the manually driven vehicle follows the following formula: in, represents the acceleration of the manually driven vehicle at time t; a max represents the maximum acceleration; u represents the free flow speed; represents the speed of the manually driven vehicle at time t; Indicates the desired headway; represents the speed difference between the target intelligent connected vehicle and the preceding manually driven vehicle; s0 represents the safe headway; h min represents the expected headway; b represents the maximum deceleration.

8. The intelligent connected vehicle speed calculation method considering bottleneck area traffic fairness according to claim 6 is characterized in that: Comparing the speed of the vehicle in the lane where the abnormal event occurs with the speed of the vehicle in the adjacent lane, and decelerating the intelligent connected vehicle in the corresponding lane according to the preset deceleration rule, including: When the speed of the vehicle in the lane where the abnormal event is located is greater than the speed of the vehicle in its adjacent lane, a first quotient of the speed difference between the two vehicles and the time step is calculated. When the first quotient is greater than the maximum deceleration, it is determined whether the vehicle in the lane where the abnormal event is located can pass safely at the maximum deceleration. If the safety constraint condition cannot be met, the intelligent driving model rule is used to update its speed. When the first quotient is not greater than the maximum deceleration, it is determined whether the vehicle in the lane where the abnormal event is located can pass safely at the first quotient as the deceleration. If the safety constraint condition cannot be met, the intelligent driving model rule is used to update its speed. When the speed of the vehicle in the lane where the abnormal event occurs is less than the speed of the vehicle in its adjacent lane, a second quotient of the speed difference between the two vehicles and the time step is calculated; when the second quotient is less than the maximum deceleration, the speed of the vehicle in the adjacent lane is decelerated to the speed of the vehicle in the lane where the abnormal event occurs; otherwise, the speed is updated using the intelligent driving model rule.

9. A speed calculation system for intelligent connected vehicles considering traffic fairness in bottleneck areas, characterized in that: The system comprises: The input layer is used to collect statistics on the traffic information of the bottleneck area formed by the abnormal event, and to set a flow restriction area and a speed coordination area upstream of the bottleneck area according to the scale of the abnormal event, and to collect statistics on the traffic information of the flow restriction area and the speed coordination area; A model layer, for calculating the speed of a target intelligent networked vehicle according to the traffic information obtained by statistics of the input layer, using the intelligent networked vehicle speed calculation method considering traffic fairness in bottleneck areas as described in any one of claims 1 to 8; The output layer is used to output the speed of the target intelligent connected vehicle calculated by the model layer.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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