Road congestion reason identification method and device, equipment and medium

Abstract

The invention relates to the technical field of road traffic, and discloses a road congestion reason identification method, device and equipment and a medium, and the method comprises the steps: obtaining road image information and track coordinate information collected in the driving process of a vehicle when a road is congested; feature element recognition is carried out on the road image information of the multiple vehicles; a track vacuum area is extracted according to the track coordinate information of the multiple vehicles; and recognizing the congestion reason of the current road according to the recognition result of the feature elements and the extraction result of the track vacuum region. According to the method, the road image information and the track coordinate information of the congested road section are acquired through the vehicle with the crowdsourcing map equipment, and the road congestion reason is comprehensively analyzed according to the feature elements and the track vacuum area, so that the accuracy and the reliability of identifying the road congestion reason can be improved; therefore, more accurate road information is provided for the driver to plan the road, the traffic efficiency is improved, and the probability of further congestion of the road is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of road traffic, and particularly relates to a method, device, equipment and medium for identifying the causes of road congestion. Background Art

[0003] In addition to the congestion caused by heavy traffic, people are also concerned about whether congestion is caused by car accidents or vehicle failures. For example, if congestion caused by a car accident often requires clearing the accident scene before the road can be gradually unblocked. If a driver can know in advance the cause of road congestion, they will make a judgment on whether to re-plan the route according to the actual situation. In current commonly used map software, road congestion information is generally provided based on the average speed of vehicles, and the collection of the causes of congestion is often through users taking pictures and uploading them themselves, which is neither safe nor accurate in actual operation. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, equipment and medium for identifying the causes of road congestion to solve the problem that the prior art lacks accurate identification of the causes of road congestion.

[0005] In a first aspect, the present invention provides a method for identifying the causes of road congestion, which is applied to the cloud, and the method includes:

[0006] When the road is congested, obtain the road image information and trajectory coordinate information collected by the vehicle during driving;

[0007] Identify the characteristic elements of the road image information;

[0008] Extract the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles;

[0009] Identify the cause of congestion on the current road according to the recognition result of the characteristic elements and the extraction result of the trajectory vacuum area.

[0010] The method for identifying the causes of road congestion provided by the embodiments of the present invention, when the road is congested, obtains the road image information and trajectory coordinate information collected by the vehicle during driving, identifies the characteristic elements of the road image information, extracts the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles, and identifies the cause of congestion on the current road according to the identified characteristic elements and the extracted trajectory vacuum area. The present invention collects the road image information and trajectory coordinate information of the congested section through vehicles with crowdsourcing map equipment, comprehensively analyzes the causes of road congestion according to the characteristic elements and the trajectory vacuum area, can improve the accuracy and reliability of the identification of the causes of road congestion, thereby providing more accurate road information for drivers to plan the road, improving the traffic efficiency, and reducing the probability of further road congestion.

[0011] In an alternative embodiment, the process of obtaining road image information and trajectory coordinate information collected during vehicle driving includes: during vehicle driving, the average speed of the vehicle is detected in real time, and road image information and trajectory coordinate information are collected in real time; when the average speed of the vehicle is lower than a preset congestion threshold, the cloud receives the road image information and trajectory coordinate information of the current moment uploaded by the vehicle.

[0012] The present invention can obtain rich road information by having vehicles with crowdsourcing map devices collect relevant information in real time, on the basis of avoiding the safety risks faced by users when taking photos and uploading them by themselves, so as to identify the causes of road congestion more accurately.

[0013] In an alternative embodiment, the process of identifying feature elements from road image information includes: determining feature elements in advance, where the feature elements include at least one of a road construction sign, a traffic accident sign, or a road operation sign; performing image processing and feature element identification on the road image information of multiple vehicles according to the feature elements.

[0014] In an alternative embodiment, after identifying feature elements based on road image information, it further includes: making a preliminary judgment on the cause of congestion of the current road according to the identified feature elements.

[0015] The present invention takes into account that corresponding markers will be placed in special road scenarios to remind drivers. Therefore, by identifying feature elements from the obtained road image information, the preliminary cause of current road congestion can be obtained more directly.

[0016] In an alternative embodiment, the process of extracting a trajectory vacuum area according to the trajectory coordinate information of multiple vehicles includes: obtaining the trajectory lines in the same road area from the trajectory coordinate information of multiple vehicles; determining the trajectory intersection area of the trajectory lines of multiple vehicles; removing the trajectory intersection area from the original drivable area of the road to obtain a trajectory vacuum area.

[0017] The present invention takes into account that if there is congestion at a certain place on the road, the passing vehicles will change lanes and bypass the congested place. Therefore, the area bypassed by the vehicles can be extracted according to the trajectory coordinates of different vehicles, that is, the trajectory vacuum area, so as to determine that there is indeed congestion here and avoid misidentification and other situations.

[0018] In an alternative embodiment, the process of identifying the congestion cause of the current road based on the recognition result of the feature elements and the extraction result of the trajectory vacuum area includes: judging whether the feature elements are recognized according to the recognition result of the feature elements, and judging whether the trajectory vacuum area is extracted according to the extraction result of the trajectory vacuum area; identifying the congestion cause of the current road according to the judgment result; wherein, if the feature elements are recognized and the trajectory vacuum area is extracted, the area where the feature elements are located is compared with the trajectory vacuum area, and if the areas overlap, the current road is marked as the congestion cause corresponding to the feature elements; if the feature elements are not recognized and the trajectory vacuum area is extracted, the current road is marked as temporarily impassable; if the feature elements are recognized and the trajectory vacuum area is not extracted, the current road is marked as having resumed traffic; if the feature elements are not recognized and the trajectory vacuum area is not extracted, the current road is marked as having heavy traffic flow.

[0019] Through the dual judgment of the feature elements and the trajectory vacuum area, the present invention can improve the accuracy and reliability of identifying the congestion cause of the road, and can identify different congestion situations, providing more accurate road information for drivers, so as to judge whether it is necessary to re-plan the route and improve the road traffic efficiency.

[0020] In an alternative embodiment, after identifying the congestion cause of the current road, it further includes: updating the congestion cause to the map and providing a comment channel for users.

[0021] By synchronizing the road congestion cause to the map, the present invention can provide more accurate and reliable road information for drivers for real-time reference by users. At the same time, providing a comment channel allows users to supplement comments on the on-site situation, further providing more detailed on-site information for other drivers.

[0022] In a second aspect, the present invention provides a device for identifying the congestion cause of a road, the device includes:

[0023] An information acquisition module, configured to acquire road image information and trajectory coordinate information collected during the vehicle's driving when the road is congested;

[0024] A feature recognition module, configured to recognize feature elements from the road image information;

[0025] A trajectory processing module, configured to extract a trajectory vacuum area according to the trajectory coordinate information of multiple vehicles;

[0026] A cause recognition module, configured to identify the congestion cause of the current road according to the recognition result of the feature elements and the extraction result of the trajectory vacuum area.

[0027] The road congestion cause identification device provided by the embodiment of the present invention, when the road is congested, obtains the road image information and trajectory coordinate information collected by the vehicle during driving, identifies the feature elements of the road image information, extracts the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles, and identifies the congestion cause of the current road according to the identified feature elements and the extracted trajectory vacuum area. The present invention collects the road image information and trajectory coordinate information of the congested section through the vehicle with the crowdsourcing map device, comprehensively analyzes the road congestion cause according to the feature elements and the trajectory vacuum area, can improve the accuracy and reliability of the road congestion cause identification, so as to provide more accurate road information for the driver to plan the road, improve the traffic efficiency, and reduce the probability of further road congestion.

[0028] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the road congestion cause identification method in the first aspect or any corresponding implementation manner thereof.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the road congestion cause identification method in the first aspect or any corresponding implementation manner thereof.

[0030] Advantages of the present invention:

[0031] (1) The present invention collects the road image information of the congested road through the vehicle with the crowdsourcing map device, can identify the feature elements causing congestion, and preliminarily judge the current congestion cause according to the feature elements. The judgment method is direct and has high accuracy;

[0032] (2) The present invention extracts the trajectory vacuum areas of multiple vehicles according to the trajectory coordinate information of the vehicle with the crowdsourcing map device, and can determine whether the congestion area of the congested section is a fixed position according to the trajectory vacuum area, so as to master the current congestion situation;

[0033] (3) The present invention combines the feature elements and the trajectory vacuum area to identify the congestion cause of the congested section, can cover various congestion situations, and provides a more accurate and reliable road state for the driver;

[0034] (4) The present invention updates the obtained congestion cause to the map in time, can provide accurate and reliable road congestion conditions for the driver, so as to decide whether to re-plan the route, improve the road traffic efficiency, and avoid further congestion aggravation. Description of the Drawings

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 is a schematic flowchart of a method for identifying the causes of road congestion according to an embodiment of the present invention;

[0037] Figure 2 is a schematic flowchart of another method for identifying the causes of road congestion according to an embodiment of the present invention;

[0038] Figure 3 is a schematic flowchart of yet another method for identifying the causes of road congestion according to an embodiment of the present invention;

[0039] Figure 4 is a structural block diagram of a device for identifying the causes of road congestion according to an embodiment of the present invention;

[0040] Figure 5 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Specific Embodiments

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0042] The embodiments of the present invention are applicable to scenarios for judging the causes of road congestion. The embodiments of the present invention provide a method for identifying the causes of road congestion, which achieves the effect of identifying the causes of congestion by making judgments based on the road image information and trajectory coordinate information collected by vehicles with crowdsourcing map devices. It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0043] In this embodiment, a method for identifying the causes of road congestion is provided, which can be deployed in the cloud in the above-mentioned computer. Figure 1 is a flowchart of a method for identifying the causes of road congestion according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0044] Step S101: When the road is congested, obtain the road image information and trajectory coordinate information collected by the vehicle during driving.

[0045] Specifically, in the embodiment of the present invention, the vehicle is a vehicle equipped with a crowdsourcing map device, and the vehicle is installed with a front-view camera for taking pictures of the road ahead to collect road image information, and a coordinate information collection device for collecting and measuring real-time trajectory coordinate information, and the vehicle can communicate with the cloud. The vehicle in the embodiment of the present invention travels normally on the road, detects the average speed in real time during driving, and collects road image information and trajectory coordinate information in real time. When the vehicle passes through a congested section of the road, the vehicle speed decreases. When the average speed of the vehicle is lower than a preset congestion threshold, the road image information and trajectory coordinate information collected at the current moment are uploaded to the cloud. The cloud then receives the road image information and trajectory coordinate information of the vehicle at the current moment. In actual situations, there is at least one vehicle passing through this congested section.

[0046] Step S102: Identify the feature elements of the road image information.

[0047] Specifically, in the embodiment of the present invention, the cloud has relevant processing procedures for image processing and feature recognition, and can perform image processing and feature element recognition on the road images of the vehicle according to the pre-determined feature elements. Among them, the feature elements include at least one of a road construction sign, a traffic accident sign, or a road operation sign. For example, a tripod is generally placed on the road to alert other drivers when a vehicle breaks down or there is a traffic accident, so it is a traffic accident sign; construction signs and conical barrels are generally placed on the road to remind drivers when a certain section of the road is under construction, so they are road construction signs; detour signs are generally set on work vehicles, such as road sweepers, so they are road operation signs. The above are only examples and are not limited thereto. In the embodiment of the present invention, the cloud can specifically perform feature element recognition on the road image information collected by the vehicle according to a tripod, a construction sign, a conical barrel, or a detour sign, etc. The specific image processing process and feature recognition process are conventional technical means in the art and will not be elaborated here.

[0048] In some optional embodiments, the embodiment of the present invention can perform feature element recognition on the road image information of multiple vehicles, and determine the feature elements of the current congested section by comparison, so as to improve the accuracy of feature element recognition and further improve the accuracy of identifying the cause of road congestion.

[0049] Step S103: Extract the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles.

[0050] Specifically, in the embodiments of the present invention, when a certain area of a road is congested, a vehicle will change lanes to avoid the congested area and continue to move forward. For example, when a traffic accident occurs in the rightmost lane of a road, it takes a long time because the traffic police need to come to handle it. During this period, the vehicles driving in the rightmost lane need to change lanes to the left to bypass this area, that is, there are no vehicles passing through this area temporarily, thus creating a trajectory vacuum area. The embodiments of the present invention extract the trajectory vacuum area based on the trajectory coordinate information of different vehicles to determine that congestion actually occurs here.

[0051] Step S104, identify the congestion cause of the current road according to the recognition result of the characteristic elements and the extraction result of the trajectory vacuum area.

[0052] Specifically, in the embodiments of the present invention, considering that in the actual vehicle driving process, congestion may occur for various reasons, in order to improve the recognition accuracy of the road congestion cause, a comprehensive determination is made on the characteristic elements and the trajectory vacuum area to prevent misrecognition or non-recognition.

[0053] The road congestion cause recognition method provided by the embodiments of the present invention, when the road is congested, obtains the road image information and trajectory coordinate information collected by the vehicle during driving, performs characteristic element recognition on the road image information, extracts the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles, and identifies the congestion cause of the current road according to the recognized characteristic elements and the extracted trajectory vacuum area. The present invention collects the road image information and trajectory coordinate information of the congested section through vehicles with crowdsourcing map devices, and comprehensively analyzes the road congestion cause according to the characteristic elements and the trajectory vacuum area, which can improve the accuracy and reliability of the recognition of the road congestion cause, thereby providing more accurate road information for drivers to plan the road, improving the traffic efficiency, and reducing the probability of further road congestion.

[0054] In this embodiment, a road congestion cause recognition method is provided, which can be deployed in the cloud in the above-mentioned computer. Figure 2 It is a flowchart of the road congestion cause recognition method according to the embodiments of the present invention, as Figure 2 shown, and this process includes the following steps:

[0055] Step S201, when the road is congested, obtain the road image information and trajectory coordinate information collected by the vehicle during driving. For details, please refer to Figure 1 Step S101 of the embodiment shown, which will not be elaborated here.

[0056] Step S202, perform characteristic element recognition on the road image information. For details, please refer to Figure 1 Step S102 of the embodiment shown, which will not be elaborated here.

[0057] Step S203: Based on the identified feature elements, make a preliminary judgment on the congestion cause of the current road.

[0058] Specifically, in the embodiment of the present invention, when the cloud identifies feature elements such as tripods, construction signs, cone barrels, or detour indicator signs from the road image information, it preliminarily judges the congestion cause of the current road according to the feature elements. Specifically, tripods correspond to congestion caused by vehicle failures or traffic accidents; construction signs and cone barrels correspond to congestion caused by road construction; detour indicator signs correspond to congestion caused by work vehicles. This is only an example and not limited thereto.

[0059] Step S204: Extract the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles. For details, please refer to Figure 1 Step S103 of the illustrated embodiment, which will not be elaborated here.

[0060] Step S205: Identify the congestion cause of the current road according to the recognition result of the feature elements and the extraction result of the trajectory vacuum area. For details, please refer to Figure 1 Step S104 of the illustrated embodiment, which will not be elaborated here.

[0061] Step S206: Update the congestion cause to the map and provide a comment channel for users.

[0062] Specifically, in the embodiment of the present invention, when the cloud determines the congestion cause of the current congested road, it updates the congestion cause to the map for users to refer to in real time. Users can decide whether to re-plan the route according to the specific congestion cause, thereby improving the road traffic efficiency. At the same time, a comment channel is provided, and all drivers can supplement comments on the on-site situation through the comment channel to provide more detailed on-site information for other drivers.

[0063] The road congestion cause recognition method provided by the embodiment of the present invention, when there is road congestion, obtains the road image information and trajectory coordinate information collected by vehicles during driving, recognizes the feature elements of the road image information, extracts the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles, recognizes the congestion cause of the current road according to the recognized feature elements and the extracted trajectory vacuum area, and updates the congestion cause to the map. The present invention collects the road image information and trajectory coordinate information of the congested section through vehicles with crowdsourcing map devices, comprehensively analyzes the road congestion cause according to the feature elements and the trajectory vacuum area, can provide more accurate and reliable road congestion causes for drivers, thereby facilitating drivers to plan roads, improving traffic efficiency, and reducing the probability of further road congestion.

[0064] In this embodiment, a road congestion cause recognition method is provided, which can be used in the cloud deployed in the above computer. Figure 3is a flowchart of a method for identifying the causes of road congestion according to an embodiment of the present invention. As Figure 3 shown, the process includes the following steps:

[0065] Step S301, when the road is congested, obtain the road image information and trajectory coordinate information collected by the vehicle during driving. For details, please refer to Figure 1 step S101 of the embodiment shown herein, which will not be elaborated herein.

[0066] Step S302, perform feature element recognition on the road image information. For details, please refer to Figure 1 step S102 of the embodiment shown herein, which will not be elaborated herein.

[0067] Step S303, extract the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles.

[0068] Specifically, the above step S303 includes:

[0069] Step S3031, obtain the trajectory lines in the same road area from the trajectory coordinate information of multiple vehicles.

[0070] Step S3032, determine the trajectory intersection area of the trajectory lines of multiple vehicles.

[0071] Step S3033, remove the trajectory intersection area from the original road drivable area to obtain the trajectory vacuum area.

[0072] Specifically, in the embodiment of the present invention, when a congested section appears, not all vehicles need to change lanes to avoid, but all vehicles will not drive in the congested area uniformly. Therefore, by obtaining the trajectory lines in the same road area from the trajectory coordinate information of different vehicles, that is, the trajectory lines of the congested section, the trajectory intersection area of each trajectory line can be determined, and then the trajectory intersection area is removed from the original lane area to obtain the trajectory vacuum area, which corresponds to the specific congested area.

[0073] Step S304, identify the congestion cause of the current road according to the recognition result of the feature element and the extraction result of the trajectory vacuum area.

[0074] Specifically, the above step S304 includes:

[0075] Step S3041, judge whether a feature element is recognized according to the recognition result of the feature element, and judge whether a trajectory vacuum area is extracted according to the extraction result of the trajectory vacuum area.

[0076] Specifically, in the embodiments of the present invention, since there are multiple reasons for road congestion, it is not certain that feature elements or trajectory vacuum regions can be identified. Therefore, when identifying the reasons for road congestion, comprehensive analysis is performed based on the identified feature elements and the extracted trajectory vacuum regions.

[0077] Step S3042: Identify the reason for the congestion of the current road according to the judgment result.

[0078] Specifically, in the embodiments of the present invention, different judgment results of feature elements and trajectory vacuum regions correspond to different congestion reasons. Among them, if a feature element is identified and a trajectory vacuum region is extracted, the region where the feature element is located is compared with the trajectory vacuum region. If the regions overlap, the current road is marked with the congestion reason corresponding to the feature element, such as road construction, traffic accident or road operation; if no feature element is identified and a trajectory vacuum region is extracted, the current road is marked as temporarily impassable, and a reminder will be reported during the user's driving, such as a certain lane xx meters ahead is temporarily impassable; if a feature element is identified and no trajectory vacuum region is extracted, the current road is marked as having resumed traffic. For example, the detour sign of a sprinkler truck is identified, but since the sprinkler truck will keep moving forward, there will be no trajectory vacuum region in this case, so no processing is done; if no feature element is identified and no trajectory vacuum region is extracted, the current road is marked as having heavy traffic flow, that is, slow traffic caused by heavy traffic flow.

[0079] The method for identifying the reason for road congestion provided by the embodiments of the present invention, when the road is congested, obtains the road image information and trajectory coordinate information collected by the vehicle during driving, identifies the feature elements of the road image information, extracts the trajectory vacuum region according to the trajectory coordinate information of multiple vehicles, identifies the reason for the congestion of the current road according to the identified feature elements and the extracted trajectory vacuum region, and updates the congestion reason to the map. The present invention collects the road image information and trajectory coordinate information of the congested section through vehicles with crowdsourcing map devices, and comprehensively analyzes the reason for road congestion according to the feature elements and the trajectory vacuum region, which can provide more accurate and reliable reasons for road congestion for drivers, thereby facilitating drivers to plan the road, improving the traffic efficiency, and reducing the probability of further road congestion.

[0080] In this embodiment, a device for identifying the reason for road congestion is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0081] This embodiment provides a device for identifying the causes of road congestion, as follows Figure 4 shown, including:

[0082] An information acquisition module 401, configured to obtain road image information and trajectory coordinate information collected by a vehicle during driving when the road is congested;

[0083] A feature recognition module 402, configured to perform feature element recognition on the road image information;

[0084] A trajectory processing module 403, configured to extract a trajectory vacuum area according to the trajectory coordinate information of multiple vehicles;

[0085] A cause identification module 404, configured to identify the congestion cause of the current road according to the recognition result of the feature element and the extraction result of the trajectory vacuum area.

[0086] In some alternative embodiments, the information acquisition module 401 includes:

[0087] An information collection unit, configured to detect the average speed of the vehicle in real time during driving, and collect road image information collection and trajectory coordinate information in real time.

[0088] An information receiving unit, configured to, when the average speed of the vehicle is lower than a preset congestion threshold, receive the road image information and trajectory coordinate information of the current moment uploaded by the vehicle from the cloud.

[0089] In some alternative embodiments, the feature recognition module 402 includes:

[0090] A feature element determination unit, configured to pre-determine feature elements, where the feature elements include at least one of a road construction sign, a traffic accident sign, or a road operation sign.

[0091] A feature element recognition unit, configured to perform image processing and feature element recognition on the road image information of multiple vehicles according to the feature elements.

[0092] A preliminary cause judgment unit, configured to perform a preliminary congestion cause judgment on the current road according to the recognized feature elements.

[0093] In some alternative embodiments, the trajectory processing module 403 includes:

[0094] A trajectory line acquisition unit, configured to obtain a trajectory line in the same road area from the trajectory coordinate information of multiple vehicles.

[0095] A trajectory intersection area determination unit, configured to determine a trajectory intersection area of the trajectory lines of multiple vehicles.

[0096] A trajectory vacuum area acquisition unit, configured to remove the trajectory intersection area within the drivable area of the original road to obtain a trajectory vacuum area.

[0097] In some alternative embodiments, the cause identification module 404 includes:

[0098] A judgment unit, configured to judge whether a characteristic element is recognized and whether a trajectory vacuum area is extracted.

[0099] An identification unit, configured to identify the congestion cause of the current road according to the judgment result. Specifically, if a characteristic element is recognized and a trajectory vacuum area is extracted, the area where the characteristic element is located is compared with the trajectory vacuum area. If the areas overlap, the current road is marked as the congestion cause corresponding to the characteristic element; if no characteristic element is recognized and a trajectory vacuum area is extracted, the current road is marked as temporarily impassable; if a characteristic element is recognized and no trajectory vacuum area is extracted, the current road is marked as having resumed traffic; if no characteristic element is recognized and no trajectory vacuum area is extracted, the current road is marked as having heavy traffic flow.

[0100] In some alternative embodiments, it further includes: a map update module, configured to update the congestion cause to the map and provide a comment channel for users.

[0101] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0102] The road congestion cause identification device in this embodiment is presented in the form of functional units. Here, the unit refers to an FPGA (Field Programmable Gate Array) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0103] The embodiment of the present invention further provides a computer device having the above-mentioned Figure 4 shown road congestion cause identification device.

[0104] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if needed, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 5 Taking one processor 10 as an example in

[0105] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0106] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0107] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0108] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0109] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0110] Embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented by downloading over a network from an original storage in a remote storage medium or a non-transitory machine-readable storage medium and will be stored in a local storage medium, so that the methods described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0111] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for identifying the causes of road congestion, characterized in that, Applied to the cloud, the method includes: When the road is congested, obtain the road image information and trajectory coordinate information collected by the vehicle during driving; Perform feature element recognition on the road image information; Extract the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles; Identify the congestion cause of the current road according to the recognition result of the feature element and the extraction result of the trajectory vacuum area.

2. The method according to claim 1, characterized in that, The process of obtaining the road image information and trajectory coordinate information collected by the vehicle during driving includes: The vehicle detects the average speed of the vehicle in real time during driving, and collects road image information and trajectory coordinate information in real time; When the average speed of the vehicle is lower than the preset congestion threshold, the cloud receives the road image information and trajectory coordinate information of the current moment uploaded by the vehicle.

3. The method according to claim 1, characterized in that, The process of performing feature element recognition on the road image information includes: Pre-determine the feature elements, and the feature elements include at least one of a road construction sign, a traffic accident sign or a road operation sign; Perform image processing and feature element recognition on the road image information of multiple vehicles according to the feature elements.

4. The method according to claim 3, characterized in that, After performing feature element recognition on the road image information, it further includes: Make a preliminary judgment on the congestion cause of the current road according to the recognized feature elements.

5. The method according to claim 1, characterized in that, The process of extracting the trajectory vacuum area according to the trajectory coordinate information of multiple vehicles includes: Obtain the trajectory lines in the same road area in the trajectory coordinate information of the multiple vehicles; Determine the trajectory intersection area of the trajectory lines of the multiple vehicles; Remove the trajectory intersection area from the original drivable area of the road to obtain the trajectory vacuum area.

6. The method according to any one of claims 4 or 5, characterized in that, The process of identifying the congestion cause of the current road according to the recognition result of the feature element and the extraction result of the trajectory vacuum area includes: Judge whether the feature element is recognized according to the recognition result of the feature element, and judge whether the trajectory vacuum area is extracted according to the extraction result of the trajectory vacuum area; Identify the congestion cause of the current road according to the judgment result; Among them, if the feature element is recognized and the trajectory vacuum area is extracted, compare the area where the feature element is located with the trajectory vacuum area. If the areas overlap, mark the current road as the congestion cause corresponding to the feature element; If the feature element is not recognized and the trajectory vacuum area is extracted, mark the current road as temporarily impassable; If the feature element is recognized and the trajectory vacuum area is not extracted, mark the current road as having resumed traffic; If the feature element is not recognized and the trajectory vacuum area is not extracted, mark the current road as having heavy traffic.

7. The method according to claim 6, characterized in that, After identifying the congestion cause of the current road, it further includes: Update the congestion cause to the map and provide a comment channel for users.

8. A device for identifying the causes of road congestion, characterized in that, The device includes: An information acquisition module for obtaining the road image information and trajectory coordinate information collected by the vehicle during driving when the road is congested; A feature recognition module for performing feature element recognition on the road image information; A trajectory processing module, configured to extract a trajectory vacuum area according to the trajectory coordinate information of multiple vehicles; A cause identification module, configured to identify the congestion cause of the current road according to the identification result of the feature elements and the extraction result of the trajectory vacuum area.

9. A computer device, characterized in that,Including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the road congestion cause identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the road congestion cause identification method according to any one of claims 1 to 7.

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