A method and system for determining priority of a tunnel vehicle stop point of a single lane

Abstract

The application discloses a tunnel vehicle parking point priority judgment method and system for a single lane, and relates to the technical field of intelligent transportation. In the priority sequence stage, the self state of the parking point and the influence of subsequent vehicles are comprehensively considered, which is beneficial to obtaining a scientific and reasonable parking priority sequence, helping vehicles to select parking points according to the optimized sequence, reducing the time consumption of vehicles in searching for suitable parking points, improving the overall smoothness of tunnel traffic, balancing the use frequency of each parking point, avoiding the overuse of some parking points and the idling of other parking points, calculating the visual risk assessment coefficient in the visual risk assessment process of the parking point, which is beneficial to identifying the parking points with high visual risk in advance, ensuring the visual safety of drivers during parking operation, reasonably avoiding the parking points that do not meet the visual risk requirements, and effectively reducing traffic accidents caused by visual obstacles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, in particular to a single-lane tunnel vehicle parking point priority judgment method and system. BACKGROUND

[0002] With the growth of traffic flow, the contradiction between vehicle parking demand and safety management in single-lane tunnel is highlighted, and the traditional management method is difficult to efficiently allocate parking resources and ensure safety. Therefore, it is necessary to comprehensively evaluate the vehicle parking demand, parking point state, subsequent vehicle risk and visual risk and other factors to realize scientific sorting of parking priority, so as to improve the efficiency and safety of tunnel traffic operation.

[0003] The current single-lane tunnel vehicle parking point priority judgment method and system have the following technical problems: 1. In the aspect of parking demand judgment, if only relying on manual observation or single sensor data, it lacks multi-mode comprehensive information collection and intelligent judgment, which is not conducive to accurately identifying the real parking intention of vehicles, and may lead to waiting or illegal parking of vehicles, seriously affecting the smoothness of tunnel traffic, and even causing traffic accidents.

[0004] 2. In the aspect of vehicle parking point state evaluation, the traditional tunnel vehicle parking point priority judgment method lacks comprehensive consideration, only focuses on individual factors, such as only looking at the remaining parking spaces while ignoring the comprehensive analysis of the expected departure time of parked vehicles and the traffic congestion index, which is not conducive to grasping the overall situation of the parking point, and may cause vehicles to gather in areas with seemingly empty spaces but difficult to disperse, causing local congestion to intensify, and if an emergency occurs, rescue vehicles may not be able to arrive quickly.

[0005] 3. The traditional tunnel vehicle parking point priority judgment method lacks evaluation of the impact of subsequent vehicles, lacks reasonable calculation of risk factors based on vehicle behavior parameters, and lacks quantitative analysis of the complex dynamic relationship between multiple vehicles, which is not conducive to accurately estimating the degree of interference of subsequent vehicles on parking operations, and may lead to frequent threats from rear vehicles during the parking process, increasing the risk of scratches and collisions, and disrupting the order of driving in the tunnel.

[0006] 4. The traditional tunnel vehicle parking point priority judgment method lacks visual risk assessment of each vehicle parking point, lacks evaluation of light change gradient, high-risk period factor and vehicle visual compensation coefficient, which is not conducive to ensuring the visual safety of drivers during parking, and may make it difficult for drivers to accurately operate vehicles at parking points with poor visual conditions, which may lead to parking errors and collisions with surrounding facilities or other vehicles due to visual obstacles, endangering the safety of personnel and vehicles, and also reducing the safety and reliability of the overall operation of the tunnel. SUMMARY

[0007] The purpose of the present application is to provide a single lane tunnel vehicle stopping point priority judgment method and system, which solves the problems in the background art.

[0008] To solve the above technical problems, the present application adopts the following technical scheme: the present application provides a single lane tunnel vehicle stopping point priority judgment method, which comprises the following steps: step one, obtaining the stopping demand of the running vehicle: evaluating whether the specified running vehicle entering the specified tunnel has stopping demand.

[0009] Step two, vehicle stopping prediction evaluation: when the specified running vehicle entering the specified tunnel has stopping demand, the real-time state evaluation index of each vehicle stopping point in the specified tunnel is calculated.

[0010] Step three, subsequent vehicle influence risk evaluation: the vehicle behavior parameters corresponding to each vehicle stopping point in the specified tunnel are collected, and the risk influence factor of each vehicle stopping point in the specified tunnel is calculated.

[0011] Step four, priority sorting: according to the real-time state evaluation index corresponding to each vehicle stopping point and the risk influence factor of the subsequent vehicle in the specified tunnel, the stopping priority of each vehicle stopping point in the specified tunnel is evaluated.

[0012] Step five, stopping point visual risk evaluation: according to the stopping priority of each vehicle stopping point, whether the stopping visual risk of each vehicle stopping point in the specified tunnel meets the requirements is analyzed, so as to complete the arrangement of the vehicle stopping point of the specified running vehicle.

[0013] In the second aspect, the present application provides a single lane tunnel vehicle stopping point priority judgment system, which comprises: a running vehicle stopping demand acquisition module for evaluating whether the specified running vehicle entering the specified tunnel has stopping demand.

[0014] A vehicle stopping prediction evaluation module is used to calculate the real-time state evaluation index of each vehicle stopping point in the specified tunnel when the specified running vehicle entering the specified tunnel has stopping demand.

[0015] A subsequent vehicle influence risk evaluation module is used to collect the vehicle behavior parameters corresponding to each vehicle stopping point in the specified tunnel, and to calculate the risk influence factor of each vehicle stopping point in the specified tunnel.

[0016] A priority sorting module is used to evaluate the stopping priority of each vehicle stopping point in the specified tunnel according to the real-time state evaluation index corresponding to each vehicle stopping point and the risk influence factor of the subsequent vehicle in the specified tunnel.

[0017] The stop point visual risk assessment module is used for analyzing whether the stop visual risk of each vehicle stop point in the specified tunnel meets the requirements according to the stop priority corresponding to each vehicle stop point, so as to complete the vehicle stop point arrangement of the specified driving vehicle.

[0018] The beneficial effects of the present application are as follows: 1. The single-lane tunnel vehicle stop point priority judgment method and system provided by the present application can comprehensively and accurately determine whether the vehicle has a stop demand by combining multiple ways to evaluate whether the specified driving vehicle entering the specified tunnel has a stop demand in the vehicle stop demand judgment process, thereby avoiding misjudgment caused by the limitation of a single judgment method, effectively improving the accuracy and reliability of the vehicle stop demand judgment of the whole system, ensuring that the subsequent processes can be carried out based on correct demand information, and reducing traffic confusion or resource waste caused by false judgment.

[0019] 2. In the vehicle stop prediction and evaluation link of the embodiment of the present application, the real-time state information such as the number of remaining stopable parking spaces of each vehicle stop point in the specified tunnel, the estimated shortest leaving duration of the stopped vehicle and the traffic congestion index is obtained, and a special calculation formula is used to calculate the real-time state evaluation index, which is beneficial to comprehensively consider the various conditions of the stop point, so that the evaluation result can more truly reflect the actual carrying capacity and operation situation of each stop point.

[0020] 3. In the process of collecting vehicle behavior parameters in the embodiment of the present application, the high-definition camera in the specified tunnel is used to obtain the speed, number and distance from the stop point of the vehicle that has entered the set range of each vehicle stop point, and detailed and accurate data support is helpful to scientifically quantify the risk degree brought by the subsequent vehicle, so that the traffic management system can formulate targeted strategies to reduce the probability of accidents such as collision and interference during the stop of the vehicle, and ensure the safety of vehicle driving and stopping in the tunnel.

[0021] 4. In the priority sorting stage of the embodiment of the present application, the stop priority coefficient is calculated according to the real-time state evaluation index and the risk influence factor of each vehicle stop point, and the stop points are sorted according to the stop priority coefficient, which is beneficial to obtain a scientific and reasonable stop priority sequence by comprehensively considering the state of the stop point itself and the influence of the subsequent vehicle, and is helpful to the vehicle to select a stop point according to the optimized sequence, reduce the time consumption of the vehicle to find a suitable stop point, improve the overall smoothness of the tunnel traffic, balance the use frequency of each stop point, and avoid the situation that some stop points are overused while other stop points are idle.

[0022] 5. In the visual risk assessment process at parking spots, this invention collects visual risk parameters such as light change gradient, high-risk time period factor, and vehicle visual compensation coefficient, and calculates the visual risk assessment coefficient. This helps to identify parking spots with high visual risks in advance, ensures the driver's visual safety during parking operations, and allows for reasonable avoidance of parking spots that do not meet visual risk requirements, effectively reducing traffic accidents caused by visual impairments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the implementation steps of the present invention.

[0025] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1 As shown, the present invention provides a method for determining the priority of vehicle stopping points in a single-lane tunnel. The method includes: Step 1, obtaining the stopping demand of traveling vehicles: assessing whether a specified traveling vehicle entering a specified tunnel has a stopping demand.

[0028] In one specific embodiment, the assessment process for determining whether a designated vehicle entering a designated tunnel has a stopping need is as follows: When the designated vehicle has installed a tunnel driving application in its onboard system and has entered its vehicle information into the application, the vehicle information is identified by a high-definition camera when the vehicle enters the designated tunnel. This determines whether the vehicle has a stopping need. If the vehicle sends a specific identification signal to the traffic management platform within the designated tunnel, it indicates that the vehicle has a stopping need; otherwise, it indicates that the vehicle does not have a stopping need.

[0029] When the designated driving vehicle does not install the tunnel driving application in the vehicle-mounted system, when the designated driving vehicle enters the designated tunnel, the vehicle driving state of the designated driving vehicle is monitored by the high-definition camera and the speed sensor at the set monitoring time points, the vehicle driving state includes the vehicle speed and the lateral distance from the tunnel wall on the same side of the parking point, the vehicle speed and the position in the single lane of the designated driving vehicle at each monitoring time point are respectively recorded as and , is the number of each monitoring time point, , is the total number corresponding to the monitoring time point, is a positive integer, the standard vehicle driving state of the corresponding vehicle parking in the designated tunnel is obtained from the database, the standard vehicle driving state includes the standard vehicle speed and the standard lateral distance from the tunnel wall on the same side of the parking point, and is respectively recorded as and The parking demand of the designated driving vehicle is recorded as When is , it indicates that the designated driving vehicle has no parking demand, and when is , it indicates that the designated driving vehicle has parking demand, and the vehicle parking state is determined by the expression: , the parking demand of the designated driving vehicle is obtained , wherein represents the logical and relationship, represents the logical or relationship, represents the logical not.

[0030] It should be noted that when the designated driving vehicle installs the tunnel driving application in the vehicle-mounted system and enters the vehicle information of the designated driving vehicle in the installed tunnel driving application, it means that, for example, the driver clicks the "I need to stop" option on the vehicle-mounted screen, or tells the tunnel vehicle parking point priority judgment system that he has the parking demand through voice instruction, if the driver does not click the "I need to stop" option, the tunnel vehicle parking point priority judgment system does not judge the vehicle parking demand of the vehicle driven by the driver.

[0031] Step two, vehicle parking prediction and evaluation: when the designated driving vehicle in the designated tunnel has parking demand, the real-time state evaluation index of the corresponding vehicle parking point in the designated tunnel is calculated.

[0032] In a specific embodiment, the calculation specifies the real-time state evaluation index of each vehicle parking point in the specified tunnel, and the specific process is as follows: when there is a parking demand for the specified driving vehicle in the specified tunnel, the real-time state information corresponding to each vehicle parking point in the specified tunnel is obtained, the real-time state information includes the number of remaining parking spaces, the estimated minimum leaving time of the parked vehicle, and the traffic congestion index, which are respectively denoted as , and , the maximum number of parked vehicles corresponding to each vehicle parking point is obtained from the database .

[0033] Through the calculation formula: , the real-time state evaluation index of each vehicle parking point in the specified tunnel is obtained , is the number of each vehicle parking point, , is the total number corresponding to the vehicle parking point, is a positive integer, wherein is a natural number, and respectively represent the adjustment coefficient corresponding to the estimated minimum leaving time of the parked vehicle and the adjustment coefficient corresponding to the traffic congestion index.

[0034] It should be noted that the number of remaining parking spaces corresponding to each vehicle parking point is obtained through the camera of each vehicle parking point, the parking type of the parked vehicle corresponding to each vehicle parking point is obtained through telephone communication, the historical parking time corresponding to each parked vehicle of the same parking type is obtained from the database, and the historical parking time corresponding to each parked vehicle is averaged to obtain the estimated minimum leaving time of the parked vehicle corresponding to each vehicle parking point, through the calculation formula: , is the reference vehicle speed corresponding to the specified tunnel obtained from the database, is the reference vehicle density corresponding to each vehicle parking point in the specified tunnel within a set range obtained from the database, , respectively represent the average vehicle speed and vehicle density corresponding to the set range of the th vehicle parking point.

[0035] In the vehicle parking prediction and evaluation link, the number of remaining parking spaces of each vehicle parking point in the specified tunnel, the estimated minimum leaving time of the parked vehicle, and the traffic congestion index and other real-time state information are obtained, and a special calculation formula is used to calculate the real-time state evaluation index, which is beneficial to comprehensively consider the various conditions of the parking point, so that the evaluation result can more truly reflect the actual carrying capacity and operation situation of each parking point.

[0036] Step three, subsequent vehicle impact risk assessment: collecting vehicle behavior parameters corresponding to each vehicle stopping point in the specified tunnel, and then calculating the risk impact factor of each vehicle stopping point in the specified tunnel.

[0037] In a specific embodiment, the collection of vehicle behavior parameters corresponding to each vehicle stopping point in the specified tunnel is specifically as follows: the vehicle behavior parameters include the speed and number of vehicles corresponding to each vehicle in the set range of each vehicle stopping point in the specified tunnel, and the distance between each vehicle stopping point and the corresponding vehicle in the set range. The high-definition camera in the specified tunnel is used to obtain the vehicle behavior parameters corresponding to each vehicle stopping point in the specified tunnel. The speed of each vehicle in the set range of each vehicle stopping point in the specified tunnel and the distance between each vehicle stopping point and the corresponding vehicle in the set range are calculated by the mean value, and the results are the average speed of the corresponding vehicle in the set range of each vehicle stopping point in the specified tunnel and the average distance between each vehicle stopping point and the corresponding vehicle in the set range, respectively, and are denoted as and The number of vehicles corresponding to each vehicle stopping point in the set range of the specified tunnel is denoted as The maximum speed of each vehicle in the set range of each vehicle stopping point in the specified tunnel and the maximum distance between each vehicle stopping point and the corresponding vehicle in the set range are denoted as and The maximum allowed number of vehicles in the corresponding set range of each vehicle stopping point in the specified tunnel is obtained from the database, denoted as .

[0038] In a specific embodiment, the calculation of the risk impact factor of each vehicle stopping point in the specified tunnel is specifically as follows: the risk impact factor of each vehicle stopping point in the specified tunnel is obtained by the calculation formula: wherein , , respectively represent the weight factor corresponding to the average speed, the weight factor corresponding to the average distance, and the weight factor corresponding to the number of vehicles.

[0039] It should be noted that , , The values of and ​The setting process of the weight factor is as follows: for example, in a certain urban tunnel, firstly, a large amount of vehicle driving data near each stop point is collected, including average vehicle speed, average distance and vehicle quantity, and corresponding traffic conditions are recorded, such as whether scratching and congestion degree occur, etc., if it is found that when the average vehicle speed is high, the probability of accidents of vehicles near the stop point greatly increases, which indicates that the average vehicle speed has a greater impact on the risk, then the weight factor corresponding to the average vehicle speed is set to 0.5, while when the average distance is in a certain set range, such as too close, although it has an impact, but it is relatively small, the weight factor corresponding to the average distance is set to 0.3, if the vehicle quantity slightly increases the congestion time, but does not significantly increase the accident risk, then the weight factor corresponding to the vehicle quantity is set to 0.2.

[0040] In the process of collecting vehicle behavior parameters in the embodiment of the application, the high-definition camera in the specified tunnel is used to obtain the speed, quantity and distance of the vehicles that have entered the set range of each vehicle stop point, and detailed and accurate data are used to help scientifically quantify the risk degree of subsequent vehicles, so that the traffic management system can develop targeted strategies to reduce the probability of accidents such as collision and interference of vehicles during parking, and ensure the safety of vehicle driving and parking in the tunnel.

[0041] Step four, priority sorting: according to the real-time state evaluation index corresponding to each vehicle stop point and the risk influence factor of the corresponding subsequent vehicle in the specified tunnel, the parking priority of each vehicle stop point in the specified tunnel is evaluated.

[0042] In a specific embodiment, the parking priority of each vehicle stop point in the specified tunnel is evaluated as follows: according to the real-time state evaluation index and the risk influence factor of each vehicle stop point in the specified tunnel, the parking priority coefficient of each vehicle stop point in the specified tunnel is calculated, the parking priority coefficients of each vehicle stop point are sorted in descending order, the vehicle stop point corresponding to the first parking priority coefficient is recorded as the first vehicle stop point, the vehicle stop point corresponding to the second parking priority coefficient is recorded as the second vehicle stop point, and the parking priority of each vehicle stop point in the specified tunnel is obtained.

[0043] In a specific embodiment, the parking priority coefficient of each vehicle stop point in the specified tunnel is calculated as follows: the maximum real-time state evaluation index, the minimum real-time state evaluation index, the maximum risk influence factor and the minimum risk influence factor are selected from the real-time state evaluation index and the risk influence factor of each vehicle stop point in the specified tunnel, and are recorded as , , and .

[0044] Calculation formula: Specify the parking priority coefficient for each vehicle stop within the tunnel. ,in , These represent the weighting factors corresponding to the real-time status assessment index and the risk impact factor, respectively.

[0045] It should be noted that, , The values ​​are all greater than and less than , , The setup process and , , The setup process is the same, so I won't go into too much detail here.

[0046] In the priority ranking stage, this invention calculates a priority coefficient based on the real-time status assessment index and risk impact factor of each vehicle stop, and ranks the stops accordingly. By comprehensively considering the status of the stops themselves and the impact of subsequent vehicles, a scientific and reasonable priority sequence is obtained. This helps vehicles select stops in the optimized order, reduces the time spent by vehicles searching for suitable stops, improves the overall smoothness of tunnel traffic, and balances the usage frequency of each stop, avoiding the situation where some stops are overused while others are idle.

[0047] Step 5: Visual Risk Assessment of Parking Points: Based on the parking priority of each vehicle parking point, analyze whether the visual risk of each vehicle parking point in the designated tunnel meets the requirements, thereby completing the parking point arrangement for the designated vehicles.

[0048] In a specific embodiment, the analysis of whether the visual risk of each vehicle stop in the designated tunnel meets the requirements is carried out as follows: Visual risk parameters are collected for each vehicle stop, including light change gradient, high-risk time period factor, and vehicle visual compensation coefficient. Then, the visual risk assessment coefficient for each vehicle stop in the designated tunnel is calculated. The standard visual risk assessment coefficient threshold for the corresponding vehicle stop in the designated tunnel is obtained from the database. The visual risk assessment coefficient for each vehicle stop in the designated tunnel is compared with the standard visual risk assessment coefficient threshold. If the visual risk assessment coefficient for each vehicle stop in the designated tunnel is greater than or equal to the standard visual risk assessment coefficient threshold, it indicates that the visual risk of the vehicle stop does not meet the requirements; otherwise, it indicates that the visual risk of the vehicle stop meets the requirements.

[0049] If the first vehicle stopping point corresponding to the stopping visual risk does not meet the requirements, it indicates that the first vehicle stopping point does not meet the stopping requirements of the specified driving vehicle, and the specified driving vehicle is arranged to stop at the second vehicle stopping point, so as to obtain the vehicle stopping point meeting the stopping requirements of the specified driving vehicle.

[0050] It should be noted that the collection process of the visual risk parameters corresponding to each vehicle stopping point is as follows: for example, the light change gradient is obtained by setting light intensity sensors at different positions in the tunnel, installing a light intensity sensor every 50 meters, the light intensity sensor real-time monitors and transmits light data to the tunnel vehicle stopping point priority judgment system, and the light intensity change amount per unit distance is calculated by comparing the data of adjacent light intensity sensors. At the entrance of a certain section of the tunnel, the light intensity of the first light intensity sensor is 500 lux, and the light intensity of the sensor at 50 meters is 300 lux, so the light change gradient is (500-300) ÷ 50 = 4 lux / m; the high-risk period factor is pre-set according to the sunrise and sunset time of the area where the tunnel is located, historical accident-prone period information, etc. The high-risk period factor is set to 1 within 1 hour before and after sunset in a certain tunnel area, and 0 in other periods. The vehicle visual compensation coefficient is detected by vehicle detection equipment to detect the brightness of the vehicle headlight, the light transmittance of the vehicle window, etc. If the brightness of the vehicle headlight meets the standard and the light transmittance of the vehicle window is good, the comprehensive evaluation of the vehicle visual compensation coefficient is 0.8. If the headlight is dim and the window film is too deep to affect the line of sight, the coefficient is as low as 0.3.

[0051] In a specific embodiment, the visual risk evaluation coefficient corresponding to each vehicle stopping point in the specified tunnel is calculated, and the specific process is as follows: by the calculation formula: , the visual risk evaluation coefficient corresponding to each vehicle stopping point in the specified tunnel is obtained , wherein , , respectively represent the light change gradient, the high-risk period factor, and the vehicle visual compensation coefficient corresponding to the first vehicle stopping point, represents the average change amplitude of the light intensity in the high-risk period corresponding to the first vehicle stopping point, , , respectively represent the adjustment coefficient corresponding to the light change gradient, the adjustment coefficient corresponding to the high-risk period factor, and the adjustment coefficient corresponding to the vehicle visual compensation coefficient, , , respectively are the weight factor corresponding to the light change gradient, the weight factor corresponding to the high-risk period factor, and the weight factor corresponding to the vehicle visual compensation coefficient.

[0052] It should be noted that, , , The values of all are greater than and less than , , , The setting process of , , is the same as that of

[0053] It should also be noted that, The value of is or , for example, set the 30 minutes before and after sunrise and sunset as the high-risk period, if the current time is within this range, , otherwise , set the collection time points within the high-risk period, and obtain the illumination intensity of each vehicle stop point corresponding to each collection time point through the sensor, select the maximum and minimum illumination intensity of each vehicle stop point corresponding to each collection time point, and calculate the average change amplitude of the illumination intensity within the high-risk period corresponding to each vehicle stop point through the difference.

[0054] It should also be noted that the adjustment coefficient corresponding to the light change gradient, the adjustment coefficient corresponding to the high-risk period factor, and the adjustment coefficient corresponding to the vehicle visual compensation coefficient are obtained as follows: for example, collect the accident frequency under different light change gradients, and when it is found that the accident rate increases significantly when the light change gradient is above 5 lux / m, set the adjustment coefficient in the gradient range above 5 lux / m to 0.6 through curve fitting, and the adjustment coefficient below 5 lux / m to 0.3, the adjustment coefficient corresponding to the high-risk period factor is determined according to the accident proportion in different time periods, such as statistics found that accidents occurring in the high-risk period account for 60% of all-day accidents, then set the adjustment coefficient corresponding to the high-risk period factor to 0.6, and the adjustment coefficient corresponding to the non-high-risk period to 0.4, and the adjustment coefficient corresponding to the vehicle visual compensation coefficient is obtained through vehicle equipment detection and accident correlation analysis, such as tracking accidents of vehicles with high vehicle visual compensation coefficients, which means vehicles with good lighting and vision, and finding that the proportion of vehicles with high visual compensation coefficients in accidents is low, then the adjustment coefficient is increased accordingly, such as set to 0.7, and the adjustment coefficient corresponding to vehicles with low vehicle visual compensation coefficients is set to 0.3.

[0055] It should also be noted that, The parameter for adjusting the light change gradient function shape changes the function curve trend of the light change gradient affecting the overall visual risk degree by changing its own value, avoiding unreasonable overestimation or underestimation of the influence of the light change gradient on the visual risk degree due to the light change gradient being too large or too small, representing the fluctuation range of the illumination intensity in the high-risk period, used to measure the instability of the illumination environment in the high-risk period, The numerical value directly reflects the amplitude of the change of the illumination condition in the high-risk period, The adjustment is nonlinear, for example, when is small, The proportion in the denominator is relatively large, and at this time The change of caused by the slight change of , is gentle, for example, if , if , then , is doubled, but The growth rate of

[0056] In the process of visual risk assessment of the parking point, the light change gradient, the high-risk period factor and the vehicle visual compensation coefficient and other visual risk parameters are collected and the visual risk assessment coefficient is calculated, which is beneficial to identifying the parking point with high visual risk in advance, ensuring the visual safety of the driver during parking operation, reasonably avoiding the parking point that does not meet the visual risk requirement, and effectively reducing the traffic accidents caused by visual obstacles.

[0057] Referring to Figure 2 , a single-lane tunnel vehicle parking point priority judgment system, comprising the following modules: a driving vehicle parking demand acquisition module, a vehicle parking prediction and evaluation module, a subsequent vehicle influence risk assessment module, a priority sorting module, a parking point visual risk assessment module and a database.

[0058] The driving vehicle parking demand acquisition module is connected with the vehicle parking prediction and evaluation module and the database, the vehicle parking prediction and evaluation module is connected with the subsequent vehicle influence risk assessment module and the database, the subsequent vehicle influence risk assessment module is connected with the priority sorting module and the database, the priority sorting module is connected with the parking point visual risk assessment module, and the parking point visual risk assessment module is connected with the database.

[0059] The driving vehicle parking demand acquisition module is used to evaluate whether the specified driving vehicle entering the specified tunnel has parking demand.

[0060] The vehicle parking prediction evaluation module is used for calculating the real-time state evaluation index of each vehicle parking point in the specified tunnel when the specified driving vehicle entering the specified tunnel has parking demand.

[0061] The subsequent vehicle influence risk evaluation module is used for collecting the vehicle behavior parameters of each vehicle parking point in the specified tunnel, and calculating the risk influence factor of each vehicle parking point in the specified tunnel.

[0062] The priority sorting module is used for evaluating the parking priority of each vehicle parking point in the specified tunnel according to the real-time state evaluation index of each vehicle parking point and the risk influence factor of the subsequent vehicle in the specified tunnel.

[0063] The parking point visual risk evaluation module is used for analyzing whether the parking visual risk of each vehicle parking point in the specified tunnel meets the requirements according to the parking priority of each vehicle parking point, so as to arrange the vehicle parking point of the specified driving vehicle.

[0064] The database is used for storing the standard vehicle driving state of the vehicle parking in the specified tunnel, the standard vehicle driving state including the standard vehicle speed and the standard lateral distance between the vehicle and the tunnel wall on the same side, storing the maximum number of vehicles that can be parked at each vehicle parking point, storing the maximum number of driving vehicles allowed in the specified range corresponding to each vehicle parking point in the specified tunnel, storing the standard visual risk evaluation coefficient threshold of each vehicle parking point in the specified tunnel, and storing the reference vehicle speed in the specified tunnel and the reference vehicle density in the specified range of each vehicle parking point in the specified tunnel.

[0065] The tunnel vehicle parking point priority judgment method and system for single lane provided by the application can evaluate whether the specified driving vehicle entering the specified tunnel has parking demand by combining multiple ways in the vehicle parking demand judgment process, which is beneficial to comprehensively and accurately determine whether the vehicle has parking demand, avoid misjudgment caused by the limitation of single judgment method, effectively improve the accuracy and reliability of the whole system for vehicle parking demand judgment, ensure that the subsequent process can be carried out based on correct demand information, and reduce traffic confusion or resource waste caused by false judgment.

[0066] The above content is only an example and description of the concept of the application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the concept of the application or exceed the scope defined in the specification, which should belong to the protection scope of the application.

Claims

1. A method for determining priority of a tunnel vehicle stop point of a single lane, characterized by, The application relates to a tunnel parking arrangement method and device. whether the specified driving vehicle entering the specified tunnel has a parking demand; When there is a parking demand for a specified traveling vehicle in a specified tunnel, real-time state information corresponding to each vehicle parking point in the specified tunnel is obtained, the real-time state information including a remaining number of parking spaces, a predicted minimum leaving time of a parked vehicle and a traffic congestion index, respectively denoted as , , , and a maximum number of parking vehicles corresponding to each vehicle parking point is obtained from a database . By the calculation formula: , the real-time state evaluation index corresponding to each vehicle stop point in the designated tunnel is obtained , is the number of each vehicle stop point, , is the total number corresponding to the vehicle stop point, is a positive integer, wherein is a natural number, and respectively represent the adjustment coefficient corresponding to the estimated minimum departure duration of the parked vehicle and the adjustment coefficient corresponding to the traffic congestion index. collecting vehicle behavior parameters corresponding to each vehicle parking point in the specified tunnel, and then calculating a risk influence factor of each vehicle parking point in the specified tunnel; By calculating formula: , the risk influence factor corresponding to each vehicle stop point in the designated tunnel is obtained , wherein , , respectively represent the weight factor corresponding to the average vehicle speed, the weight factor corresponding to the average distance, and the weight factor corresponding to the number of vehicles. wherein, represents the average vehicle speed of the corresponding vehicle within the set range of the each vehicle stop point having entered the specified tunnel, represents the average distance of the corresponding vehicle within the set range of the each vehicle stop point having entered the specified tunnel, represents the number of the corresponding vehicle within the set range of the each vehicle stop point having entered the specified tunnel, represents the maximum vehicle speed of the corresponding vehicle within the set range of the each vehicle stop point having entered the specified tunnel, represents the maximum distance of the corresponding vehicle within the set range of the each vehicle stop point having entered the specified tunnel, represents the maximum allowed number of vehicles traveling within the set range corresponding to the each vehicle stop point having entered the specified tunnel; The maximum real-time state evaluation index, the minimum real-time state evaluation index, the maximum risk influence factor and the minimum risk influence factor are selected from the real-time state evaluation indexes and the risk influence factors corresponding to each vehicle stopping point in the designated tunnel respectively, and are denoted as , , and respectively. calculating a parking priority coefficient of each vehicle parking point in the specified tunnel according to a real-time state evaluation index and the risk influence factor of each vehicle parking point in the specified tunnel; The stop priority coefficient corresponding to each vehicle stop point in the designated tunnel is obtained by a calculation formula: , wherein , , respectively represent the weight factor corresponding to the real-time state evaluation index and the weight factor corresponding to the risk influence factor. ranking the parking priority coefficients of the vehicle parking points in descending order, taking the vehicle parking point corresponding to the first parking priority coefficient as a first vehicle parking point, taking the vehicle parking point corresponding to the second parking priority coefficient as a second vehicle parking point, and obtaining a parking priority of each vehicle parking point in the specified tunnel; analyzing whether the parking visual risk of each vehicle parking point in the specified tunnel meets the requirements according to the parking priority of each vehicle parking point, so as to arrange a vehicle parking point for the specified driving vehicle.

2. The method of claim 1, wherein the priority of the tunnel vehicle stop point of the single lane is determined based on the number of vehicles in the tunnel. The specific evaluation process is as follows: ​ when the specified driving vehicle is installed with a tunnel driving application program in a vehicle-mounted system and the vehicle information of the specified driving vehicle is input in the installation of the tunnel driving application program, the vehicle information of the specified driving vehicle is recognized through a high-definition camera when the specified driving vehicle enters the specified tunnel, and then it is determined whether the specified driving vehicle has a parking demand; if the specified driving vehicle sends a specific identification signal to a traffic management platform in the specified tunnel, it indicates that the specified driving vehicle has a parking demand; otherwise, it indicates that the specified driving vehicle does not have a parking demand. When the specified driving vehicle does not install the tunnel driving application in the vehicle-mounted system, then when the specified driving vehicle enters the specified tunnel, the vehicle driving state of the specified driving vehicle is monitored by the high-definition camera and the speed sensor at the set monitoring time points, the vehicle driving state includes the vehicle speed and the lateral distance from the tunnel wall on the same side of the parking point, the vehicle speed and the position in the single lane of the specified driving vehicle at each monitoring time point are respectively recorded as and , is the number of each monitoring time point, , is the total number corresponding to the monitoring time point, is a positive integer, the standard vehicle driving state corresponding to the vehicle parking in the specified tunnel is obtained from the database, the standard vehicle driving state includes the standard vehicle speed and the standard lateral distance from the tunnel wall on the same side of the parking point, and is respectively recorded as and , the parking demand of the specified driving vehicle is recorded as When is , it indicates that the specified driving vehicle has no parking demand, when is , it indicates that the specified driving vehicle has parking demand, and the vehicle parking state discrimination expression is: , the parking demand of the specified driving vehicle is obtained , wherein represents the logical and relationship, represents the logical or relationship, represents the logical not.

3. The method of claim 1, wherein the priority of the tunnel vehicle stop point is determined based on the number of vehicles in the single lane. The specific process of collecting the vehicle behavior parameters corresponding to each vehicle parking point in the specified tunnel is as follows: The vehicle behavior parameters include the speed and the number of vehicles in the set range of each vehicle stop point corresponding to each vehicle that has entered the specified tunnel, and the distance between each vehicle stop point and each vehicle in the set range. The vehicle behavior parameters of each vehicle stop point corresponding to each vehicle that has entered the specified tunnel are obtained by a high-definition camera in the specified tunnel. The speed of each vehicle in the set range of each vehicle stop point corresponding to each vehicle that has entered the specified tunnel and the distance between each vehicle stop point and each vehicle in the set range are calculated by the mean value, respectively. The results are the average speed of the corresponding vehicle in the set range of each vehicle stop point corresponding to each vehicle that has entered the specified tunnel and the average distance between each vehicle stop point and each vehicle in the set range, respectively, and are denoted as and The number of vehicles in the set range of each vehicle stop point corresponding to each vehicle that has entered the specified tunnel is denoted as The maximum speed of each vehicle in the set range of each vehicle stop point corresponding to each vehicle that has entered the specified tunnel and the maximum distance between each vehicle stop point and each vehicle in the set range are denoted as and The maximum allowed number of vehicles in the set range corresponding to each vehicle stop point in the specified tunnel is obtained from the database, and is denoted as .

4. The single-lane tunnel vehicle stop point priority determination method according to claim 1, characterized by, The specific analysis process of whether the parking visual risk of each vehicle parking point in the specified tunnel meets the requirements is as follows: collecting visual risk parameters corresponding to each vehicle parking point, the visual risk parameters including a light change gradient, a high-risk period factor and a vehicle visual compensation coefficient, and then calculating a visual risk evaluation coefficient of each vehicle parking point in the specified tunnel; obtaining a standard visual risk evaluation coefficient threshold of the vehicle parking point in the specified tunnel from a database, comparing the visual risk evaluation coefficient of each vehicle parking point in the specified tunnel with the standard visual risk evaluation coefficient threshold, and if the visual risk evaluation coefficient of each vehicle parking point in the specified tunnel is greater than or equal to the standard visual risk evaluation coefficient threshold, it indicates that the parking visual risk of the vehicle parking point does not meet the requirements; otherwise, it indicates that the parking visual risk of the vehicle parking point meets the requirements; if the parking visual risk of the first vehicle parking point does not meet the requirements, it indicates that the first vehicle parking point does not meet the parking requirements of the specified driving vehicle, and then the specified driving vehicle is arranged to the second vehicle parking point for parking, so as to obtain a vehicle parking point meeting the parking requirements of the specified driving vehicle.

5. The single-lane tunnel vehicle stop point priority determination method according to claim 4, characterized by, The specific process of calculating the visual risk evaluation coefficient of each vehicle parking point in the specified tunnel is as follows: The visual risk assessment coefficient corresponding to each vehicle stop point in the designated tunnel is obtained by the calculation formula: , wherein , , , respectively represent the light change gradient, the high-risk period factor and the vehicle visual compensation coefficient corresponding to the first vehicle stop point, respectively represent the average change amplitude of the light intensity in the high-risk period corresponding to the first vehicle stop point, , , respectively represent the adjustment coefficient corresponding to the light change gradient, the adjustment coefficient corresponding to the high-risk period factor and the adjustment coefficient corresponding to the vehicle visual compensation coefficient, , , respectively are the weight factor corresponding to the set light change gradient, the weight factor corresponding to the high-risk period factor and the weight factor corresponding to the vehicle visual compensation coefficient.

6. A system for performing the method of determining the priority of a tunnel vehicle stop point for a single lane road according to any one of claims 1 to 5, characterized in that, The application comprises the following modules: The driving vehicle parking demand acquisition module is configured to evaluate whether the specified driving vehicle entering the specified tunnel has a parking demand; The vehicle parking prediction evaluation module is configured to calculate a real-time state evaluation index of each vehicle parking point in the specified tunnel when the specified driving vehicle entering the specified tunnel has a parking demand; The subsequent vehicle influence risk evaluation module is configured to collect vehicle behavior parameters corresponding to each vehicle parking point in the specified tunnel, and calculate a risk influence factor of each vehicle parking point in the specified tunnel; The priority sorting module is configured to evaluate a parking priority of each vehicle parking point in the specified tunnel according to the real-time state evaluation index of each vehicle parking point and the risk influence factor of the subsequent vehicle in the specified tunnel; The parking point visual risk evaluation module is configured to analyze whether a parking visual risk of each vehicle parking point in the specified tunnel meets a requirement according to the parking priority of each vehicle parking point, so as to complete the arrangement of the vehicle parking point of the specified driving vehicle.

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