Tunnel passage time prediction method and device, equipment and storage medium

Abstract

The application provides a tunnel passing time prediction method, device, equipment and storage medium. The method comprises the following steps: when any current vehicle to be driven into a tunnel is detected, determining a congestion state of the tunnel according to an actual passing time of a first type of vehicle in the tunnel; if the tunnel is congested, determining an estimated passing time of the current vehicle in the tunnel according to the actual passing time of the first type of vehicle; if the tunnel is not congested, determining a remaining passing time of any vehicle of the current vehicle and a second type of vehicle in the tunnel according to an untravelled distance and a travel speed change amount of the vehicle in the tunnel; and determining the estimated passing time of the current vehicle in the tunnel according to the remaining passing times of the current vehicle and the second type of vehicle. The application can realize early prediction of the passing time of any vehicle in the tunnel before the vehicle is driven into the tunnel, and improve the passing safety of the vehicle in the tunnel.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, apparatus, device, and storage medium for predicting tunnel travel time. Background Technology

[0002] With the rapid development of road transportation, highway tunnels are often built when roads traverse mountainous areas to replace traditional winding mountain roads, thereby shortening vehicle travel distances and times. However, when vehicles are traveling in tunnels, the navigation and positioning devices installed on the vehicles often experience weak or no signal, making it impossible to accurately predict real-time road conditions within the tunnel, thus posing certain safety hazards when traveling through tunnels.

[0003] Therefore, in order to make it easier to predict the traffic conditions of vehicles in tunnels, there is an urgent need to design a scheme that can determine the estimated travel time of vehicles in tunnels before they enter the tunnel, so that drivers can know the traffic status of their vehicles in the tunnel in advance and minimize traffic accidents in tunnels. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for predicting tunnel travel time, enabling advance prediction of the travel time of any vehicle in a tunnel before it enters the tunnel. Different travel time prediction methods are used under different tunnel congestion conditions to ensure the accuracy of the estimated travel time of vehicles in the tunnel and improve the safety of vehicles traveling in tunnels.

[0005] In a first aspect, embodiments of this application provide a method for predicting tunnel travel time, the method comprising:

[0006] When any vehicle is detected waiting to enter the tunnel, the congestion status of the tunnel is determined based on the actual travel time of a first type of vehicle in the tunnel, where the first type of vehicle is a vehicle that has already exited the tunnel.

[0007] If the tunnel is congested, the estimated travel time of the current vehicle in the tunnel is determined based on the actual travel time of the first type of vehicles.

[0008] If there is no congestion in the tunnel, the remaining travel time of the vehicle in the tunnel is determined based on the untraveled distance and speed change of either the current vehicle or the second type of vehicle in the tunnel. The second type of vehicle refers to vehicles that have entered the tunnel but have not yet exited it.

[0009] Based on the remaining travel time of the current vehicle and the second type of vehicle, the estimated travel time of the current vehicle in the tunnel is determined.

[0010] Secondly, embodiments of this application provide a device for predicting tunnel travel time, the device comprising:

[0011] The tunnel status determination module is used to determine the congestion status of the tunnel based on the actual travel time of a first type of vehicle in the tunnel when any vehicle waiting to enter the tunnel is detected. The first type of vehicle is the vehicle that has already left the tunnel.

[0012] The first-time prediction module is used to determine the estimated travel time of the current vehicle in the tunnel based on the actual travel time of the first type of vehicles if there is congestion in the tunnel.

[0013] The second time prediction module is used to determine the remaining travel time of the vehicle in the tunnel based on the untraveled distance and speed change of either the current vehicle or the second type of vehicle in the tunnel if there is no congestion. The second type of vehicle refers to vehicles that have entered the tunnel but have not yet exited. The module is also used to determine the estimated travel time of the current vehicle in the tunnel based on the remaining travel time of the current vehicle and the second type of vehicle.

[0014] Thirdly, embodiments of this application provide an electronic device, which includes:

[0015] A processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to execute the tunnel travel time prediction method provided in the first aspect of this application.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a computer program that causes a computer to execute the tunnel travel time prediction method provided in the first aspect of this application.

[0017] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the tunnel travel time prediction method provided in the first aspect of this application.

[0018] This application provides a method, apparatus, device, and storage medium for predicting tunnel travel time. When any vehicle is detected about to enter a tunnel, the method first determines whether the tunnel is congested based on the actual travel time of vehicles that have already exited the tunnel (a first-type vehicle). If the tunnel is congested, the estimated travel time of the current vehicle is determined based on the actual travel time of the first-type vehicles. If the tunnel is not congested, the remaining travel time of the current vehicle is determined based on the untraveled distance and speed change of either the current vehicle or any of the second-type vehicles that have entered but not yet exited the tunnel. Then, the estimated travel time of the current vehicle is determined based on the remaining travel times of both vehicles. This allows for advance prediction of the travel time of any vehicle before it enters the tunnel. Different travel time prediction methods are used under different tunnel congestion conditions to ensure the accuracy of the estimated travel time. This allows drivers to have a general understanding of the traffic conditions inside the tunnel before entering, improving vehicle safety in tunnels. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0020] Figure 1 A flowchart illustrating a method for predicting tunnel travel time according to an embodiment of this application;

[0021] Figure 2 This is a schematic diagram illustrating the passage of various vehicles through a tunnel according to an embodiment of this application;

[0022] Figure 3 A flowchart illustrating another method for predicting tunnel travel time according to an embodiment of this application;

[0023] Figure 4 This is a schematic block diagram illustrating a tunnel travel time prediction device according to an embodiment of this application;

[0024] Figure 5 This is a schematic block diagram of an electronic device shown in an embodiment of this application. Detailed Implementation

[0025] 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.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0027] To address the issue of vehicles being unable to predict real-time road conditions in tunnels when navigation and positioning signals are weak or absent, this application proposes deploying corresponding data acquisition devices and roadside units (RSUs) at different locations within the tunnel, and installing corresponding Vehicle to Everything (V2X) communication devices on vehicles. This utilizes V2X technology to collect and process various types of driving data from each vehicle traveling through the tunnel, enabling the analysis of general road conditions before vehicles enter the tunnel.

[0028] Before introducing the specific technical solution of this application, the various devices deployed around the tunnel in this application will be described first:

[0029] 1) Install corresponding high-definition cameras at the tunnel entrance and exit respectively.

[0030] High-definition cameras deployed at the tunnel entrance capture images of every vehicle waiting to enter the tunnel in real time, while high-definition cameras deployed at the tunnel exit capture images of every vehicle that has just exited the tunnel in real time.

[0031] 2) Deploy corresponding roadside units at any location within the tunnel.

[0032] From the moment a vehicle enters the tunnel, its current driving parameters, such as speed and acceleration, are sampled at short intervals (e.g., 2 seconds). The V2X device installed on the vehicle then transmits these parameters to the roadside unit at each sampling interval, enabling V2X communication. The roadside unit then forwards these driving parameters from each vehicle at each sampling interval to the edge computing device.

[0033] Alternatively, a processor installed on the vehicle can process the vehicle's driving parameters at each sampling interval to analyze the distance the vehicle has traveled in the tunnel. Then, a V2X device installed on the vehicle sends the driving parameters acquired at each sampling interval and the distance the vehicle has traveled in the tunnel to the roadside unit to achieve V2X communication. The roadside unit then forwards the driving parameters and distance traveled by each vehicle at each sampling interval to the edge computing device.

[0034] 3) Deploy corresponding edge computing devices at any location within the tunnel.

[0035] High-definition cameras deployed at the tunnel entrance and exit can send images of each vehicle they capture to an edge computing device. The edge computing device then analyzes these images to identify the license plate number of each vehicle. Furthermore, by combining the time of capture for each vehicle image, it can determine whether each vehicle has exited the tunnel, and the times when each vehicle began entering and exiting the tunnel.

[0036] Moreover, when the edge computing device receives only the driving parameters of each vehicle at each sampling interval forwarded by the roadside unit, it can process the driving parameters of each vehicle at each sampling interval to analyze the distance traveled by each vehicle in the tunnel.

[0037] Alternatively, the edge computing device can receive all the driving parameters of each vehicle at each sampling interval and the distance the vehicle has traveled in the tunnel, forwarded by the roadside unit.

[0038] Then, the edge computing device can use the existing information of each vehicle traveling in and out of the tunnel obtained above to execute the tunnel travel time prediction scheme provided in the embodiments of this application, that is, to analyze the estimated travel time of any vehicle waiting to enter the tunnel in the tunnel.

[0039] Furthermore, the edge computing device can send the estimated travel time of a vehicle in the tunnel to the roadside unit. The roadside unit then uses V2X technology to forward the estimated travel time to the V2X device installed on the vehicle. Before the vehicle enters the tunnel, this estimated travel time can be displayed on the vehicle's interface to alert the driver. This allows the driver to have a general understanding of the traffic conditions inside the tunnel before the vehicle enters, thereby improving vehicle safety in the tunnel.

[0040] The specific technical solution of this application will be described below:

[0041] Figure 1 This is a flowchart illustrating a method for predicting tunnel travel time according to an embodiment of this application. (Refer to...) Figure 1 The method may include the following steps:

[0042] S110: When any vehicle waiting to enter the tunnel is detected, determine whether there is congestion in the tunnel based on the actual travel time of the first type of vehicle in the tunnel; if yes, proceed to S120; if no, proceed to S130.

[0043] In this application, high-definition cameras deployed at the tunnel entrance can capture real-time images of each vehicle waiting to enter the tunnel and forward these images to edge computing devices deployed inside the tunnel. The edge computing devices then perform image recognition on the vehicle images forwarded from the high-definition cameras at the tunnel entrance to detect the presence of vehicles waiting to enter the tunnel in real time.

[0044] For any vehicle waiting to enter the tunnel, there are multiple vehicles ahead of it that have already entered the tunnel. At this point, some of the vehicles ahead of the current vehicle have entered the tunnel but have not yet exited, meaning they are still traveling in the tunnel, while others have already exited the tunnel.

[0045] To facilitate a clear description of vehicles in different tunnel driving conditions, this application may classify vehicles that have already exited the tunnel ahead of the current vehicle as Class I vehicles, and vehicles that have entered the tunnel ahead of the current vehicle but have not yet exited as Class II vehicles. Figure 2 As shown, the current vehicle waiting to enter the tunnel can be denoted as N0. Assuming the number of vehicles in the second category is M, then, in order of distance from the current vehicle N0, the second category of vehicles can be denoted as N1, N2, ..., N... M Assuming the number of vehicles in the first category is n, then according to their distance from the current vehicle N0 in ascending order, the vehicles in the first category can be represented as N... M+1 N M+2 ..., NM+n .

[0046] It should be understood that, in order to ensure the reliability and limited number of Class I vehicles as a reference for current vehicles traveling in the tunnel, this application can determine the corresponding current time period based on the current vehicle's entry time into the tunnel, for example, within a 5-minute period preceding that entry time. Then, vehicles exiting the tunnel ahead of the current vehicle within that current time period are considered as Class I vehicles in this application.

[0047] For the first type of vehicle, when it first enters the tunnel, a high-definition camera at the tunnel entrance will capture an image of the vehicle and record the time of its entry into the tunnel. Similarly, when it first exits the tunnel, a high-definition camera at the tunnel exit will also capture an image of the first type of vehicle and record the time of its exit.

[0048] Then, high-definition cameras deployed at the tunnel entrance send vehicle images and entry timestamps of the first type of vehicles to the edge computing device, and high-definition cameras deployed at the tunnel exit also send vehicle images and exit timestamps of the first type of vehicles to the edge computing device. The edge computing device can then identify the various vehicle images forwarded by the high-definition cameras at the tunnel entrance to obtain the license plate number of the first type of vehicle when it entered the tunnel. Furthermore, the edge computing device can identify the various vehicle images forwarded by the high-definition cameras at the tunnel exit to obtain the license plate number of the first type of vehicle when it exited the tunnel.

[0049] By comparing the license plate numbers at the tunnel entrance and exit, the entry and exit times of each Class I vehicle can be matched. Then, based on the time difference between the entry and exit times, the actual travel time of each Class I vehicle within the tunnel can be determined.

[0050] Considering that the actual traffic conditions of any vehicle in a tunnel are related to the congestion of that tunnel, when analyzing the traffic conditions of a vehicle in a tunnel before it enters the tunnel, it is first necessary to determine whether there is congestion in the tunnel.

[0051] As an optional implementation in this application, considering that the first type of vehicles have just exited the tunnel, their actual travel time in the tunnel will also be affected by tunnel congestion. Therefore, after determining the actual travel time of each first type of vehicle in the tunnel, this application can determine the congestion status of the tunnel by comprehensively judging the length of the actual travel time of each first type of vehicle in the tunnel.

[0052] In some implementations, this application can calculate the average travel time of each type of vehicle in the tunnel. Furthermore, based on the normal travel time of a vehicle traveling at a normal, uniform speed in the tunnel when there is no congestion, a normal travel time under non-congestion conditions can be preset for the tunnel. Then, the average travel time is compared with the preset normal travel time. If the average travel time is greater than the normal travel time, it indicates that the type of vehicle may be taking a long time to travel in the tunnel, thus confirming that the tunnel is congested. Conversely, if the average travel time is less than or equal to the normal travel time, it indicates that the type of vehicle is not taking a long time to travel in the tunnel, thus confirming that the tunnel is not congested.

[0053] In other feasible methods, a normal tunnel speed for vehicles under non-congested conditions can be pre-set for the tunnel. Then, by comparing the travel speed of the first type of vehicles in the tunnel with the normal tunnel speed, it can be determined whether the tunnel is congested. Specifically, based on the actual travel time of the first type of vehicles in the tunnel and the total length of the tunnel, the average travel speed of the first type of vehicles in the tunnel is determined; if the average travel speed is less than the preset normal tunnel speed, the tunnel is determined to be congested; if the average travel speed is greater than or equal to the normal tunnel speed, the tunnel is determined not to be congested.

[0054] In other words, after determining the actual travel time of each type of vehicle in the tunnel, the travel speed of each type of vehicle in the tunnel can be calculated based on the total length of the tunnel. Then, by calculating the average travel speed of each type of vehicle in the tunnel, the average travel speed of the type of vehicle in the tunnel can be obtained.

[0055] Furthermore, if the average traffic speed is less than the tunnel's preset normal speed, it indicates that the first type of vehicles are traveling too slowly in the tunnel, thus confirming that the tunnel is congested. Conversely, if the average traffic speed is greater than or equal to the tunnel's preset normal speed, it indicates that the first type of vehicles will travel at the normal speed allowed under non-congested conditions, thus confirming that the tunnel is not congested.

[0056] For example, the first type of vehicle N M+1 N M+2 ..., N M+n The actual travel time can be expressed as T. M+1 T M+2 ... T M+n So, the first type of vehicle N M+1 N M+2 ..., N M+n The speed of travel in a tunnel can be expressed as V. M+1 VM+2 V M+n Therefore, the average travel speed of the first type of vehicle in the tunnel can be...

[0057] S120, based on the actual travel time of the first type of vehicle, determine the estimated travel time of the current vehicle in the tunnel.

[0058] When tunnel congestion is identified, considering that tunnel congestion will affect the passage of Category II vehicles within the tunnel, making it difficult to accurately predict their actual passage status, this application, in order to ensure the accuracy of predicting the current vehicle travel time within the tunnel, will not rely on the passage status of Category II vehicles, but rather on the actual travel time of each Category I vehicle within the tunnel when congestion is present.

[0059] In this application, based on the actual travel time of each type of vehicle in the tunnel, the average travel time of the type of vehicle in the tunnel can be calculated. This average travel time can represent the approximate impact of tunnel congestion on vehicle traffic. Therefore, this application can use the average travel time of the type of vehicle in the tunnel as the estimated travel time of the current vehicle in the tunnel.

[0060] S130, determine the remaining travel time of the vehicle in the tunnel based on the untraveled distance and speed change of either the current vehicle or any of the second-class vehicles in the tunnel.

[0061] If it is determined that there is no congestion in the tunnel, it means that the current vehicle and the second type of vehicle that has not yet exited the tunnel will not be affected by congestion while traveling in the tunnel, but will proceed according to normal driving conditions. Therefore, the real-time driving speed of the current vehicle and each second type of vehicle in the tunnel may change randomly.

[0062] In this application, considering that overtaking is generally not allowed in tunnels to ensure traffic safety, any vehicle traveling in a tunnel will be affected by the traffic of the vehicle in front. Therefore, the travel time of the current vehicle in the tunnel will be affected by the traffic of each of the second-class vehicles. Therefore, to ensure the accuracy of the predicted travel time of the current vehicle in the tunnel, this application, in addition to analyzing the current vehicle's traffic status in the tunnel, also needs to analyze the traffic status of each of the second-class vehicles in the tunnel.

[0063] For each Type II vehicle, from the moment it enters the tunnel, it will continuously collect data on its current speed change at pre-set sampling intervals (e.g., 2 seconds). The speed change collected at each sampling interval can include the current speed and current longitudinal acceleration.

[0064] Then, based on the speed changes of the second-type vehicles collected in each sampling interval, the distance traveled by the second-type vehicles in each sampling interval can be analyzed, thereby determining the tunnel position of each second-type vehicle when the current vehicle is preparing to enter the tunnel. Then, based on the tunnel position of each second-type vehicle when the current vehicle is preparing to enter the tunnel, the untraveled distance of each second-type vehicle in the tunnel can be determined.

[0065] Furthermore, considering that vehicles will continue to travel normally within the tunnel, their normal travel patterns can be predicted based on their current speed and longitudinal acceleration upon entering the tunnel. In other words, when analyzing the untraveled distance of each type of vehicle within the tunnel, the untraveled distance of the current vehicle within the tunnel can also be analyzed.

[0066] As an optional implementation of this application, the untraveled distance in the tunnel for either the current vehicle or the second type of vehicle can be determined by the following steps:

[0067] The first step is to determine the distance traveled by the second type of vehicle in each sampling interval based on the change in its speed from the moment it enters the tunnel, so as to obtain the distance traveled by the second type of vehicle in the tunnel.

[0068] From the moment each Type II vehicle enters the tunnel, it will continuously collect driving parameters such as current speed and current longitudinal acceleration within each preset sampling interval, thereby obtaining the change in driving speed of the Type II vehicle within each sampling interval.

[0069] Then, by using the current driving speed, current longitudinal acceleration of the second type of vehicle in each sampling interval and the fixed value of that sampling interval, the driving distance of the second type of vehicle in each sampling interval can be calculated.

[0070] For example, for the second type of vehicles N1, N2, ..., N M Any of the second category vehicles N J Where 1 ≤ J ≤ M. Assume the second type of vehicle N J The current driving speed within any sampling interval is v k The current longitudinal acceleration is a k The sampling interval is fixed at τ. Then, the second type of vehicle N... J The driving distance within this sampling interval can be Following the same method described above, the second type of vehicle N can be calculated. J The distance traveled during each sampling interval before the current vehicle enters the tunnel.

[0071] Then, after determining the travel distance of each second-class vehicle in each sampling interval, the travel distance of each second-class vehicle in each sampling interval can be summed by using discrete integration to obtain the travel distance of each second-class vehicle in the tunnel.

[0072] The second step is to determine the untraveled distance of the second category of vehicles in the tunnel based on the total length of the tunnel and the distance already traveled by the second category of vehicles.

[0073] For each Class II vehicle, the distance traveled in the tunnel can be calculated by subtracting the difference between the total length of the tunnel and the distance traveled by each Class II vehicle, thus obtaining the untraveled distance of each Class II vehicle in the tunnel.

[0074] For example, suppose the second type of vehicles are N1, N2, ..., N M The distance traveled in the tunnel can be represented sequentially as L1, L2, ..., L... M Let L be the total length of the tunnel. Then, the untraveled distance of each type II vehicle in the tunnel can be represented as L-L1, L-L2, ..., LL. M .

[0075] The third step is to use the total length of the tunnel as the untraveled distance of the current vehicle in the tunnel.

[0076] Since the vehicle has not yet entered the tunnel, the total length of the tunnel can be taken as the untraveled distance of the vehicle in the tunnel, denoted as L-L0, where L0 is 0.

[0077] It should be noted that the specific calculation process for the untraveled distance of either the current vehicle or the second type of vehicle in the tunnel can be executed by the processor on that vehicle, and after the calculation is completed, the untraveled distances of the current vehicle and the second type of vehicle in the tunnel can be forwarded to the edge computing device. Alternatively, the specific calculation process for the untraveled distance of either the current vehicle or the second type of vehicle in the tunnel can also be calculated by the edge computing device based on the changes in driving speed collected in each sampling interval by the current vehicle and the second type of vehicle, and this application does not limit this.

[0078] After determining the untraveled distance of the current vehicle and any of the second-class vehicles in the tunnel, this application first obtains the change in the vehicle's speed within the current sampling interval when the current vehicle entered the tunnel, i.e., the vehicle's current speed and current longitudinal acceleration. Then, by setting the vehicle to travel the untraveled distance in the tunnel according to its current speed and current longitudinal acceleration, the remaining travel time required for the vehicle to continue in the tunnel can be determined.

[0079] By following the above method, the remaining travel time of the current vehicle and each second-class vehicle in the tunnel can be obtained.

[0080] S140, determine the estimated travel time of the current vehicle in the tunnel based on the remaining travel time of the current vehicle and the second category of vehicles.

[0081] To ensure tunnel safety, overtaking is generally not permitted, meaning that every vehicle traveling in a tunnel is affected by the traffic ahead. Therefore, if a vehicle enters the tunnel after a vehicle in the second category, its travel time in the tunnel will be greater than the remaining travel time of all vehicles in the second category.

[0082] Therefore, this application can select the largest remaining travel time from the remaining travel time of the current vehicle and each second-class vehicle in the tunnel as the estimated travel time of the current vehicle in the tunnel in this application.

[0083] The technical solution provided in this application, upon detecting any vehicle about to enter a tunnel, first determines whether the tunnel is congested based on the actual travel time of vehicles of the first category that have already exited the tunnel. If the tunnel is congested, the estimated travel time of the current vehicle in the tunnel is determined based on the actual travel time of the first category vehicles. If the tunnel is not congested, the remaining travel time of the current vehicle in the tunnel is determined based on the untraveled distance and speed change of any vehicle of the second category that has entered but not yet exited the tunnel. Then, the estimated travel time of the current vehicle in the tunnel is determined based on the remaining travel time of the current vehicle and the second category vehicles. This allows for advance prediction of the travel time of any vehicle in the tunnel before it enters. Different travel time prediction methods are used under different tunnel congestion conditions to ensure the accuracy of the estimated travel time of vehicles in the tunnel. This allows drivers to have a general understanding of the traffic conditions in the tunnel before entering, improving the safety of vehicles traveling in tunnels.

[0084] As an optional implementation in this application, considering the speed limit in the tunnel, the remaining travel time of the second type of vehicle in the tunnel, based on its current speed and longitudinal acceleration, will be affected. Therefore, this application needs to utilize the tunnel's speed limit to further accurately analyze the remaining travel time of the current vehicle and any vehicle in the second type of vehicle in the tunnel.

[0085] Furthermore, for any given vehicle, there will be a certain margin of error between its actual travel time and its estimated travel time in the tunnel. Therefore, this application can also utilize the error between the actual travel time and the estimated travel time of each type of vehicle to further correct the estimated travel time of the current vehicle in the tunnel, thereby ensuring the accuracy of the estimated travel time of the current vehicle in the tunnel.

[0086] Next, this application will provide a detailed explanation of the specific process for determining the estimated travel time of a vehicle in a tunnel when there is no congestion.

[0087] Figure 3 This is a flowchart illustrating another method for predicting tunnel travel time, as shown in an embodiment of this application. Figure 3 As shown, the method may include the following steps:

[0088] S310: When any vehicle waiting to enter the tunnel is detected, determine whether there is congestion in the tunnel based on the actual travel time of the first type of vehicle in the tunnel; if yes, proceed to S320; if no, proceed to S330.

[0089] S320 determines the estimated travel time of the current vehicle in the tunnel based on the actual travel time of the first type of vehicle.

[0090] S330: For any vehicle in the current vehicle category and the second category of vehicles, obtain the current driving speed, current longitudinal acceleration, and the speed limit of the tunnel within the current sampling interval.

[0091] For the current vehicle and any of the various Class II vehicles, this application can first determine the untraveled distance of the vehicle in the tunnel. Moreover, as the current vehicle and any of the various Class II vehicles continue to travel within the untraveled distance of the vehicle in the tunnel, the real-time speed of the vehicle in the untraveled distance will be continuously changed based on the current speed and current longitudinal acceleration of the vehicle in the current sampling interval, and the real-time speed is required not to exceed the speed limit of the tunnel.

[0092] Therefore, this application needs to obtain the current driving speed and current longitudinal acceleration of the current vehicle and any vehicle in the second category within the current sampling interval, as well as the speed limit of the tunnel.

[0093] S340, based on the vehicle's untraveled distance in the tunnel, current speed, current longitudinal acceleration, and speed limit, determine the vehicle's remaining travel time in the tunnel.

[0094] For the current vehicle and any of the vehicles in the second category, as the vehicle continues to travel within the untraveled portion of the tunnel, its real-time speed will continuously increase based on its current speed and longitudinal acceleration within the current sampling interval. Therefore, when the vehicle continuously increases its speed within the untraveled portion, two scenarios may occur: 1) the vehicle increases its speed from its current speed to the tunnel's speed limit before exiting the tunnel; 2) the vehicle exits the tunnel before its current speed increases to the tunnel's speed limit.

[0095] Considering that the speed of any vehicle in the tunnel cannot exceed the tunnel's speed limit, when the vehicle increases from its current speed to the tunnel's speed limit before exiting the tunnel, it is required that the vehicle can accelerate during the early part of the journey before exiting the tunnel. After increasing from its current speed to the tunnel's speed limit, it can use that speed limit to maintain a constant speed during the later part of the journey before exiting the tunnel.

[0096] Therefore, based on the specific movements of the current vehicle and any of the vehicles in the second category, specifically the initial acceleration and the subsequent constant speed driving phases performed by the vehicle during the untraveled distance, the remaining travel time of the vehicle in the tunnel can be calculated.

[0097] If the vehicle has already exited the tunnel before its current speed increases to the tunnel's speed limit, then the vehicle is required to continue accelerating for the remaining distance traveled until it exits the tunnel. Therefore, based on the specific movements of the current vehicle and any of the other vehicles in the second category during the acceleration phase that the vehicle continues to travel for the remaining distance, the remaining travel time of the vehicle in the tunnel can be calculated.

[0098] For example, the current vehicle N0 and each of the second-class vehicles N1, N2, ..., N M The untraveled distances can be L-L0, L-L1, L-L2, ..., LL in sequence. M Where L0 is 0. Assume the current vehicle N0 and each of the second-class vehicles N1, N2, ..., N... M The remaining travel time in the tunnel can be represented as T′0, T′1, T′2, ..., T′. M .

[0099] Then, for the current vehicle N0 and each of the second-class vehicles N1, N2, ..., N M Any vehicle N in i Where 0≤i≤M, the number of vehicles N can be set. i The current driving speed is v i The current longitudinal acceleration is a iThe maximum speed limit in the tunnel is V, and the remaining travel time in the tunnel is T′. i The untraveled distance in the tunnel is LL i .

[0100] On the one hand, in vehicle N i From the current driving speed v i When the speed is increased to the tunnel's maximum speed limit V, and the vehicle has not yet exited the tunnel, the following formula can be satisfied:

[0101]

[0102] Among them, v i +a i T′ i V can represent vehicle N. i If a vehicle continues to accelerate and exits the tunnel at a speed exceeding the tunnel's speed limit V, then the vehicle's speed will be greater than the tunnel's speed limit V, which is equivalent to the speed of vehicle N. i Vehicle N will be in the untraveled section of the tunnel. i When increasing from the current driving speed to the tunnel's speed limit, before exiting the tunnel.

[0103] and, Vehicle N can be represented i The distance traveled during the acceleration phase, which is performed in the early stage of the untraveled journey within the tunnel. Vehicle N can be represented i The distance traveled during the constant speed travel phase performed at the upper speed limit V in the later part of the untraveled distance in the tunnel.

[0104] According to the above formula, it is possible to find the vehicle N i From the current driving speed v i When the speed is increased to the tunnel's maximum speed limit V, and the vehicle N has not yet exited the tunnel, calculate the following: i The remaining travel time T in the tunnel i '.

[0105] On the other hand, in vehicle N i From the current driving speed v i If the vehicle has already exited the tunnel before reaching the tunnel's speed limit V, the following formula can be satisfied:

[0106]

[0107] Among them, v i +a i T′ i ≤V can represent vehicle N i If a vehicle continues to accelerate and exits the tunnel at a speed that does not exceed the tunnel's speed limit V, then the vehicle's speed N...i Vehicle N will be in the untraveled section of the tunnel. i If you have already exited the tunnel before your current speed increases to the tunnel's speed limit.

[0108] and, Vehicle N can be represented i The vehicle continues to accelerate throughout the untraveled distance within the tunnel until it exits the tunnel.

[0109] According to the above formula, the vehicle Ni can travel from its current speed v... i Calculate the remaining travel time T of vehicle Ni in the tunnel before it reaches the tunnel's speed limit V, assuming it has already exited the tunnel. i '.

[0110] Using the above method, the current vehicle N0 and each of the second-class vehicles N1, N2, ..., N can be calculated. M The remaining travel time in the tunnel is T′0, T′1, T′2, ..., T′. M .

[0111] S350: The maximum of the remaining travel time of the current vehicle and the second category of vehicles is taken as the initial travel time of the current vehicle in the tunnel.

[0112] Considering that there is usually a certain error between the actual travel time and the estimated travel time of any vehicle in the tunnel, after determining the remaining travel time of the current vehicle and each second-class vehicle in the tunnel, this application can take the maximum value of the remaining travel time of the current vehicle and the second-class vehicle as the initial travel time of the current vehicle in the tunnel. Subsequently, the initial travel time can be corrected by analyzing the prediction error of the travel time of each first-class vehicle.

[0113] S360 corrects the initial travel time based on the travel time error between the actual travel time and the estimated travel time of the first type of vehicle, and obtains the estimated travel time of the current vehicle in the tunnel.

[0114] For each Category I vehicle that has already exited the tunnel, its estimated travel time within the tunnel is predicted in advance when it first enters. Therefore, its actual travel time within the tunnel is also determined when it exits.

[0115] Therefore, this application can calculate the travel time error of each Class I vehicle based on its actual and estimated travel time in the tunnel. Then, by using the average travel time error of each Class I vehicle, the initial travel time of the current vehicle in the tunnel can be corrected, thus obtaining the estimated travel time of the current vehicle in the tunnel.

[0116] As an optional implementation of this application, in order to further ensure the accuracy of the estimated travel time of the current vehicle in the tunnel, this application will also exclude some data that are far different from the current vehicle from the first type of vehicles, so as to improve the correction accuracy of the estimated travel time of the current vehicle.

[0117] Therefore, the specific process for correcting the estimated travel time of the current vehicle in the tunnel may include: determining the associated first-class vehicle of the current vehicle based on the difference between the estimated travel time of the first-class vehicle and the initial travel time of the current vehicle; correcting the initial travel time based on the preset association coefficient of the associated first-class vehicle and the travel time error between the actual travel time and the estimated travel time of the associated first-class vehicle, to obtain the estimated travel time of the current vehicle in the tunnel.

[0118] In other words, by calculating the difference between the estimated travel time of the first-class vehicles and the initial travel time of the current vehicle, k first-class vehicles with smaller differences can be selected as the associated first-class vehicles of the current vehicle in this application. The value of k can be adaptively adjusted according to the number of first-class vehicles. For example, assuming the number of first-class vehicles is n, a pre-defined value Q is used to represent the selection ratio of associated first-class vehicles. At this time, [] indicates rounding up.

[0119] Then, since the closer the associated first-category vehicle is to the current vehicle, the closer it will be to the current vehicle's entry time, and the higher the reliability of the estimated travel time of the current vehicle in the tunnel. Therefore, this application can pre-set a correlation coefficient for each associated first-category vehicle according to the order in which they exit the tunnel. Moreover, the closer the associated first-category vehicle is to the current vehicle, the higher its pre-set correlation coefficient, so that the estimated travel time of the current vehicle is more reliable in relation to the travel time of associated first-category vehicles that exit the tunnel later.

[0120] Furthermore, this application can calculate the travel time error between the actual travel time and the estimated travel time for each associated first-category vehicle. Then, the ratio between the preset correlation coefficient of each associated first-category vehicle and the sum of the preset correlation coefficients of all associated first-category vehicles is used as the weight of that associated first-category vehicle. Then, using the weight of each associated first-category vehicle, the travel time errors of all associated first-category vehicles are weighted and summed to obtain the corresponding correction value. This correction value is then used to correct the initial travel time of the current vehicle in the tunnel, thereby obtaining the estimated travel time of the current vehicle in the tunnel.

[0121] For example, assuming the number of each associated first-class vehicle is k, the estimated travel time of each associated first-class vehicle can be represented as G1(N1), G2(N2), ..., G1(N1), in order of distance from the current vehicle. k (N k The actual travel times of each associated Class I vehicle can be represented as G1, G2, ..., G... k The preset correlation coefficients for each associated Class I vehicle can be represented as λ1, λ2, ..., λ3, respectively. k And λ1>λ2>…>λ k =1.

[0122] Therefore, the estimated travel time of a vehicle in the tunnel can be determined by the following formula:

[0123]

[0124] Where T0(N0) is the initial travel time of the current vehicle in the tunnel.

[0125] The technical solution provided in this application, upon detecting any vehicle about to enter a tunnel, first determines whether the tunnel is congested based on the actual travel time of vehicles of the first category that have already exited the tunnel. If the tunnel is congested, the estimated travel time of the current vehicle in the tunnel is determined based on the actual travel time of the first category vehicles. If the tunnel is not congested, the remaining travel time of the current vehicle in the tunnel is determined based on the untraveled distance and speed change of any vehicle of the second category that has entered but not yet exited the tunnel. Then, the estimated travel time of the current vehicle in the tunnel is determined based on the remaining travel time of the current vehicle and the second category vehicles. This allows for advance prediction of the travel time of any vehicle in the tunnel before it enters. Different travel time prediction methods are used under different tunnel congestion conditions to ensure the accuracy of the estimated travel time of vehicles in the tunnel. This allows drivers to have a general understanding of the traffic conditions in the tunnel before entering, improving the safety of vehicles traveling in tunnels.

[0126] Figure 4 This is a schematic block diagram illustrating a tunnel travel time prediction device according to an embodiment of this application.

[0127] like Figure 4 As shown, the device 400 may include:

[0128] The tunnel status determination module 410 is used to determine the congestion status of the tunnel based on the actual travel time of a first type of vehicle in the tunnel when any vehicle waiting to enter the tunnel is detected. The first type of vehicle is a vehicle that has already left the tunnel.

[0129] The first-time prediction module 420 is used to determine the estimated travel time of the current vehicle in the tunnel based on the actual travel time of the first type of vehicles if there is congestion in the tunnel.

[0130] The second time prediction module 430 is used to determine the remaining travel time of the vehicle in the tunnel based on the untraveled distance and speed change of either the current vehicle or the second type of vehicle in the tunnel if there is no congestion in the tunnel. The second type of vehicle refers to vehicles that have entered the tunnel but have not yet exited. The module is also used to determine the estimated travel time of the current vehicle in the tunnel based on the remaining travel time of the current vehicle and the second type of vehicle.

[0131] In some implementations, the untraveled distance of either the current vehicle or the second type of vehicle in the tunnel can be determined by a distance determination module. This distance determination module can be used to:

[0132] Based on the change in speed of the second type of vehicle within each sampling interval from the time it enters the tunnel, the distance traveled by the second type of vehicle within each sampling interval is determined, so as to obtain the distance traveled by the second type of vehicle in the tunnel.

[0133] Based on the total length of the tunnel and the distance already traveled by the second type of vehicle, determine the distance not yet traveled by the second type of vehicle in the tunnel;

[0134] The total length of the tunnel is taken as the untraveled distance of the current vehicle in the tunnel.

[0135] In some implementations, the second time prediction module 430 may include:

[0136] The driving speed acquisition unit is used to acquire, for any vehicle in the current vehicle and the second type of vehicle, the current driving speed and current longitudinal acceleration of the vehicle in the current sampling interval, as well as the speed limit of the tunnel;

[0137] The remaining travel time determination unit is used to determine the remaining travel time of the vehicle in the tunnel based on the untraveled distance of the vehicle in the tunnel, the current travel speed, the current longitudinal acceleration, and the speed limit.

[0138] In some implementations, the second time prediction module 430 may further include:

[0139] The initial travel time prediction unit is used to take the maximum value of the remaining travel time of the current vehicle and the second type of vehicle as the initial travel time of the current vehicle in the tunnel;

[0140] The passage time correction unit is used to correct the initial passage time based on the passage time error between the actual passage time and the expected passage time of the first type of vehicle, so as to obtain the expected passage time of the current vehicle in the tunnel.

[0141] In some implementations, the passage time correction unit can be specifically used for:

[0142] The associated first type of vehicle is determined based on the difference between the estimated passage time of the first type of vehicle and the initial passage time of the current vehicle;

[0143] Based on the preset correlation coefficient of the associated first-class vehicles and the travel time error between the actual travel time and the expected travel time of the associated first-class vehicles, the initial travel time is corrected to obtain the expected travel time of the current vehicle in the tunnel.

[0144] In some implementations, the tunnel state determination module 410 can be specifically used for:

[0145] The average travel speed of the first type of vehicle in the tunnel is determined based on the actual travel time of the first type of vehicle in the tunnel and the total length of the tunnel.

[0146] If the average passage speed is less than the preset normal tunnel speed, then the tunnel is determined to be congested.

[0147] If the average traffic speed is greater than or equal to the normal tunnel speed, then the tunnel is determined to be free of congestion.

[0148] In this embodiment, when any vehicle is detected waiting to enter the tunnel, the system first determines whether the tunnel is congested based on the actual travel time of vehicles of the first category that have already exited the tunnel. If the tunnel is congested, the estimated travel time of the current vehicle in the tunnel is determined based on the actual travel time of the first category vehicles. If the tunnel is not congested, the remaining travel time of the current vehicle in the tunnel is determined based on the untraveled distance and speed change of any vehicle of the second category that has entered but not yet exited the tunnel. Then, the estimated travel time of the current vehicle in the tunnel is determined based on the remaining travel time of the current vehicle and the second category vehicles. This allows for advance prediction of the travel time of any vehicle in the tunnel before it enters. Different travel time prediction methods are used under different tunnel congestion conditions to ensure the accuracy of the estimated travel time of vehicles in the tunnel. This allows drivers to have a general understanding of the traffic conditions in the tunnel before entering, improving the safety of vehicles traveling in the tunnel.

[0149] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, further details will not be provided here. Specifically, Figure 4 The apparatus 400 shown can execute any of the method embodiments in this application, and the foregoing and other operations and / or functions of each module in the apparatus 400 are respectively for implementing the corresponding processes in the various methods in the embodiments of this application. For the sake of brevity, they will not be described in detail here.

[0150] The apparatus 400 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0151] Figure 5 This is a schematic block diagram of an electronic device shown in an embodiment of this application.

[0152] like Figure 5 As shown, the electronic device 500 may include:

[0153] The system includes a memory 510 and a processor 520. The memory 510 stores computer programs and transfers the program code to the processor 520. In other words, the processor 520 can retrieve and run the computer program from the memory 510 to implement the methods described in the embodiments of this application.

[0154] For example, the processor 520 can be used to execute the above-described method embodiments according to instructions in the computer program.

[0155] In some embodiments of this application, the processor 520 may include, but is not limited to:

[0156] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0157] In some embodiments of this application, the memory 510 includes, but is not limited to:

[0158] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0159] In some embodiments of this application, the computer program may be divided into one or more modules, which are stored in the memory 510 and executed by the processor 520 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0160] like Figure 5 As shown, the electronic device may also include:

[0161] Transceiver 530, which can be connected to processor 520 or memory 510.

[0162] The processor 520 can control the transceiver 530 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 530 may include a transmitter and a receiver. The transceiver 530 may further include antennas, and the number of antennas may be one or more.

[0163] It should be understood that the various components in the electronic device are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0164] This application also provides a computer storage medium storing a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0165] When implemented using software, it can be implemented entirely or partially as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0166] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0167] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0168] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0169] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting tunnel travel time, characterized in that, include: When any vehicle is detected waiting to enter the tunnel, the congestion status of the tunnel is determined based on the actual travel time of a first type of vehicle in the tunnel, where the first type of vehicle is a vehicle that has already exited the tunnel. If the tunnel is congested, the estimated travel time of the current vehicle in the tunnel is determined based on the actual travel time of the first type of vehicles. If there is no congestion in the tunnel, the remaining travel time of the vehicle in the tunnel is determined based on the untraveled distance and speed change of either the current vehicle or the second type of vehicle in the tunnel. The second type of vehicle refers to vehicles that have entered the tunnel but have not yet exited it. The estimated travel time of the current vehicle in the tunnel is determined based on the maximum of the remaining travel times of the current vehicle and the second type of vehicle.

2. The method according to claim 1, characterized in that, The untraveled distance of either the current vehicle or the second type of vehicle in the tunnel is determined by the following steps: Based on the change in speed of the second type of vehicle within each sampling interval from the time it enters the tunnel, the distance traveled by the second type of vehicle within each sampling interval is determined, so as to obtain the distance traveled by the second type of vehicle in the tunnel. Based on the total length of the tunnel and the distance already traveled by the second type of vehicle, determine the distance the second type of vehicle has not traveled in the tunnel; The total length of the tunnel is taken as the untraveled distance of the current vehicle in the tunnel.

3. The method according to claim 1, characterized in that, The step of determining the remaining travel time of a vehicle in the tunnel based on the untraveled distance and speed change of either the current vehicle or the second type of vehicle in the tunnel includes: For any vehicle in the current vehicle and the second type of vehicle, obtain the current driving speed and current longitudinal acceleration of the vehicle in the current sampling interval, as well as the speed limit of the tunnel; The remaining travel time of the vehicle in the tunnel is determined based on the untraveled distance of the vehicle in the tunnel, the current travel speed, the current longitudinal acceleration, and the speed limit.

4. The method according to claim 1, characterized in that, The estimated travel time of the current vehicle in the tunnel is determined based on the maximum of the remaining travel times of the current vehicle and the second type of vehicle, including: The maximum value of the remaining travel time of the current vehicle and the second type of vehicle shall be used as the initial travel time of the current vehicle in the tunnel; Based on the travel time error between the actual travel time and the estimated travel time of the first type of vehicle, the initial travel time is corrected to obtain the estimated travel time of the current vehicle in the tunnel.

5. The method according to claim 4, characterized in that, The step of correcting the initial travel time based on the travel time error between the actual travel time and the estimated travel time of the first type of vehicle to obtain the estimated travel time of the current vehicle in the tunnel includes: The associated first type of vehicle is determined based on the difference between the estimated passage time of the first type of vehicle and the initial passage time of the current vehicle; Based on the preset correlation coefficient of the associated first-class vehicles and the travel time error between the actual travel time and the expected travel time of the associated first-class vehicles, the initial travel time is corrected to obtain the expected travel time of the current vehicle in the tunnel.

6. The method according to claim 1, characterized in that, Determining the congestion status of the tunnel based on the actual travel time of the first type of vehicles in the tunnel includes: The average travel speed of the first type of vehicle in the tunnel is determined based on the actual travel time of the first type of vehicle in the tunnel and the total length of the tunnel. If the average passage speed is less than the preset normal tunnel speed, then the tunnel is determined to be congested. If the average traffic speed is greater than or equal to the normal tunnel speed, then the tunnel is determined to be free of congestion.

7. A device for predicting tunnel travel time, characterized in that, include: The tunnel status determination module is used to determine the congestion status of the tunnel based on the actual travel time of a first type of vehicle in the tunnel when any vehicle waiting to enter the tunnel is detected. The first type of vehicle is the vehicle that has already left the tunnel. The first-time prediction module is used to determine the estimated travel time of the current vehicle in the tunnel based on the actual travel time of the first type of vehicles if there is congestion in the tunnel. The second time prediction module is used to determine the remaining travel time of a vehicle in the tunnel based on the untraveled distance and speed change of any vehicle in the tunnel, if there is no congestion in the tunnel. The second type of vehicle refers to vehicles that have entered the tunnel but have not yet exited it. The estimated travel time of the current vehicle in the tunnel is determined based on the maximum of the remaining travel times of the current vehicle and the second type of vehicle.

8. An electronic device, characterized in that, include: A processor and a memory, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory to perform the method for predicting tunnel travel time according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the method for predicting tunnel travel time as described in any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for predicting tunnel travel time as described in any one of claims 1-6.

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