Method and device for identifying deviation at fork in road

Abstract

The present disclosure provides a method and device for identifying deviation at a fork in the road, which relates to the field of artificial intelligence technology, specifically to the technical fields of image processing, map navigation, automatic driving, intelligent transportation, etc. The specific implementation scheme is: in response to detecting that a target vehicle has traveled within a preset distance range of a fork in the road, based on multiple vehicle front images of the target vehicle, the moving direction of the guide line area at the fork in the road is determined relative to the target vehicle; based on the moving direction of the guide line area relative to the target vehicle, the target moving path of the target vehicle at the fork in the road is determined; based on the matching result of the target moving path and the planned path, it is determined whether the target vehicle is yawed. This method improves the timeliness and reliability of vehicle deviation identification.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, specifically to the technical fields of image processing, map navigation, automatic driving, intelligent transportation, etc., and more particularly to a method and device for identifying a deviation at a fork in the road. Background Art

[0002] In existing navigation systems and deviation judgment technologies, GPS (Global Positioning System) data is usually relied on to determine whether the vehicle has deviated from the planned route. Specifically, these systems determine whether the vehicle has deviated by comparing the actual GPS coordinates of the vehicle with the distance between the GPS points of the planned route. Summary of the invention

[0003] The embodiments of the present disclosure provide a method, device, equipment and storage medium for identifying a fork in the road.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for identifying a deviation at a fork in the road, the method comprising: in response to detecting that a target vehicle has traveled within a preset distance range of a fork in the road, determining the moving direction of a guide line area at the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle; determining a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; and determining whether the target vehicle has deviated based on a matching result between the target moving path and the planned path.

[0005] In a second aspect, an embodiment of the present disclosure provides a device for identifying a deviation at a fork in the road, the device comprising: a direction determination module, a path determination module and a yaw determination module, wherein the direction determination module is configured to determine the moving direction of a guide line area at the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle in response to detecting that the target vehicle has traveled to within a preset distance range at the fork in the road; the path determination module is configured to determine a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; and the yaw determination module is configured to determine whether the target vehicle has deviated based on a matching result between the target moving path and the planned path.

[0006] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement a method for identifying a fork in the road as in any embodiment of the first aspect.

[0007] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying a fork in the road as in any embodiment of the first aspect.

[0008] In a fifth aspect, an embodiment of the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the method for identifying a fork in the road as in any embodiment of the first aspect.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is an exemplary system architecture diagram in which the present disclosure may be applied;

[0011] Figure 2 is a flow chart of an embodiment of a method for identifying a fork in the road according to the present disclosure;

[0012] Figure 3 is a flow chart of another embodiment of the method for identifying a fork in the road according to the present disclosure;

[0013] Figure 4a is a schematic diagram of an application scenario of the fork in the road deviation identification method disclosed in the present invention;

[0014] Figure 4b is a schematic diagram of another application scenario of the fork in the road deviation identification method disclosed in the present invention;

[0015] Figure 5 is a schematic diagram of an embodiment of a device for identifying a deviation from a fork in a road according to the present disclosure;

[0016] Figure 6 It is a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0018] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] Figure 1An exemplary system architecture 100 is shown to which an embodiment of the fork-in-road deviation identification method of the present disclosure may be applied.

[0020] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0021] Users can use terminal devices 101 , 102 , 103 to interact with server 105 via network 104 to receive or send messages, etc.

[0022] The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are software, they may be installed in the electronic devices listed above. They may be implemented as multiple software or software modules, or as a single software or software module. No specific limitation is made here.

[0023] Server 105 can be a server that provides various services. For example, in response to detecting that the target vehicle is traveling within a preset distance range of a fork in the road, based on multiple vehicle front images of the target vehicle, determining the moving direction of the guide line area at the fork in the road relative to the target vehicle; based on the moving direction of the guide line area relative to the target vehicle, determining the target moving path of the target vehicle at the fork in the road; based on the matching result of the target moving path and the planned path, determining whether the target vehicle has deviated.

[0024] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, for providing fork intersection deviation identification services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0025] It should be noted that the fork in the road deviation identification method provided in the embodiment of the present disclosure can be executed by the server 105, or by the terminal devices 101, 102, 103, or by the server 105 and the terminal devices 101, 102, 103 cooperating with each other. Accordingly, the various parts (such as various units, sub-units, modules, sub-modules) included in the fork in the road deviation identification device can be all set in the server 105, or all set in the terminal devices 101, 102, 103, or respectively set in the server 105 and the terminal devices 101, 102, 103.

[0026] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0027] Figure 2 The flowchart 200 of an embodiment of a method for identifying a course deviation at a fork in the road is shown. The method for identifying a course deviation at a fork in the road may specifically include the following steps:

[0028] Step 201, in response to detecting that a target vehicle is traveling within a preset distance range of a fork in the road, based on a plurality of vehicle front images of the target vehicle, determining a moving direction of a guide line area of ​​the fork in the road relative to the target vehicle.

[0029] In this embodiment, the execution subject (for example, Figure 1 The server 105 or the terminal devices 101, 102, 103) can obtain the current position of the target vehicle in real time or periodically via sensor devices, such as GPS, GNSS (Global Navigation Satellite System), etc.

[0030] Furthermore, the execution entity may determine the road network structure of the area where the current location is located in the map data according to the current location.

[0031] The road network structure may include multiple road sections and the connection relationship between each road section.

[0032] Furthermore, the execution entity may determine whether the target vehicle has traveled to within a preset distance range of the fork in the road based on the current position and the road network structure.

[0033] Among them, the preset distance can be 100 meters, 200 meters, etc., and this application does not limit this.

[0034] If the target vehicle travels to within a preset distance range of the fork in the road, the executing entity can obtain multiple vehicle front images of the target vehicle through an image acquisition device installed on the target vehicle, such as a front-view camera, a driving recorder, etc., identify the guide line area in the vehicle front image, and determine the moving direction of the guide line area at the fork in the road relative to the target vehicle over time based on the changes in the relative position relationship between the guide line area and the target vehicle in the multiple vehicle front images.

[0035] Here, the number of guide line areas in an image in front of a vehicle may be one or more, and this application does not impose any limitation on this.

[0036] Among them, the guide line area is used to indicate one or more white V-shaped lines or diagonal line areas set according to the terrain of the intersection. The guide line area is usually set at road intersections, ramp entrances, exits, etc. to guide vehicles to travel along designated routes and reduce traffic conflicts.

[0037] Step 202, based on the moving direction of the guide line area relative to the target vehicle, determine the target moving path of the target vehicle at the fork in the road.

[0038] In this embodiment, the execution entity may determine the target moving path of the target vehicle at the fork in the road according to the identified one or more guide line areas relative to the moving direction of the target vehicle.

[0039] Specifically, the number of guide line areas is one. If the guide line area is to the left rear relative to the moving direction of the target vehicle, it can be determined that the target vehicle enters the right side of the guide line area, that is, the target moving path is the right road of the guide line area.

[0040] If the guide line area is to the right rear relative to the moving direction of the target vehicle, it can be determined that the target vehicle enters the left side of the guide line area, that is, the target moving path is the left road of the guide line area.

[0041] Step 203: Determine whether the target vehicle is yawed based on the matching result between the target moving path and the planned path.

[0042] In this embodiment, after determining the target moving path, the executing entity can match the target moving path with the planned path to obtain a matching result. If the target moving path is not on the current plan of the target vehicle, it is considered that the matching result between the target moving path and the planned path is not matched, and the target vehicle can be further determined to be yawed.

[0043] The method for identifying a deviation at a fork in the road provided by an embodiment of the present disclosure first determines the moving direction of a guide line area at the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle in response to detecting that the target vehicle has traveled within a preset distance range at the fork in the road; then determines the target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; determines whether the target vehicle has deviated based on a matching result between the target moving path and the planned path, thereby realizing the direct identification of whether the vehicle has deviated based on the vehicle front image captured by the on-board image device in scenarios with weak GPS signals such as elevated forks, thereby improving the timeliness and reliability of vehicle deviation identification.

[0044] In some optional implementations of this embodiment, after determining that the target vehicle is yawed, the execution entity may output yaw warning information.

[0045] Among them, the output methods of the alarm information may include multiple methods, such as sound warning, visual warning, vibration warning, etc.

[0046] In some optional methods, a target moving path of the target vehicle at a fork in the road is determined based on the moving direction of the guide line area relative to the target vehicle, including: determining a first moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; determining a second moving path of the target vehicle at the fork in the road based on the positioning information of the target vehicle; determining a third moving path of the target vehicle at the fork in the road based on the inertial measurement information of the target vehicle; and determining the target moving path of the target vehicle at the fork in the road based on the first moving path, the second moving path and the third moving path.

[0047] In this implementation, the execution entity may determine the first moving path of the target vehicle at the fork in the road according to the moving direction of the guide line area relative to the target vehicle.

[0048] Furthermore, the execution entity may determine the positioning information of the target vehicle based on sensor equipment such as GPS and GNSS, and determine the second moving path of the target vehicle at the fork in the road based on the positioning information of the target vehicle.

[0049] Furthermore, the execution entity may determine inertial measurement information of the target vehicle according to an IMU (Inertial measurement unit), and determine a third moving path of the target vehicle at the fork in the road according to the inertial measurement information.

[0050] Furthermore, the executing entity can directly determine the target moving path based on the first moving path, the second moving path and the third moving path (for example, if the first moving path, the second moving path and the third moving path are all the roads on the left side of the guide line area, then the target moving path can be determined to be the road on the left side of the guide line area), or determine the target moving path of the target vehicle at the fork in the road based on the first moving path, the second moving path and the third moving path and the confidence corresponding to each path (for example, if the first moving path and the second moving path are both the road on the left side of the guide line area, and the third moving path is the road on the right side of the guide line area, and the confidence corresponding to the first moving path and the second moving path is higher than the confidence corresponding to the third moving path, then the first moving path and the second moving path can be determined as the target moving paths, that is, the road on the left side of the guide line area is determined as the target moving path), and the present application does not limit this.

[0051] Among them, the confidence level corresponding to each path can be determined based on external environments such as road conditions and weather information.

[0052] This implementation method determines the target moving path by jointly determining the moving path based on GPS, the moving path determined based on IMU, and the moving path determined based on the image, thereby realizing the fusion of multi-source data and further improving the accuracy of the determined target moving path.

[0053] In some optional methods, the moving direction of the guide line area at the fork in the road relative to the target vehicle is determined based on multiple vehicle front images of the target vehicle, including: determining the moving direction of the guide line area at the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle captured by a driving recorder.

[0054] In this implementation, the execution entity can collect multiple vehicle front images of the target vehicle via a driving recorder installed on the target vehicle, and determine the moving direction of the guide line area of ​​the fork in the road relative to the target vehicle based on the multiple vehicle front images.

[0055] Among them, a driving recorder is a device installed on a car (usually installed at the top center of the front windshield or on the right side near the rearview mirror) to record video and audio during the vehicle's driving process to record various situations during driving. The image in front of the vehicle captured by the driving recorder can be a color image or a grayscale image.

[0056] This implementation method determines the moving direction of the guide line area at the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle collected by the driving recorder. It utilizes the characteristics of the driving recorder that is installed at a high position and is usually installed in front of the vehicle. The moving direction of the guide line area relative to the target vehicle can be determined in time, effectively improving the timeliness of deviation identification.

[0057] In some optional embodiments, the method further includes: in response to determining that the target vehicle is yawed, generating a new planned path based on the target moving path and the destination.

[0058] In this implementation, after determining that the target vehicle is yawed, the execution entity can correct the current planned path according to the target moving path and destination, generate a new planned path, and use the new planned path to guide the target vehicle into the correct path.

[0059] This implementation helps to correct the planned path in a timely manner by generating a new planned path based on the target movement path and the destination in response to determining the target vehicle yaw.

[0060] Further references Figure 3 , which shows Figure 2 The process 300 of another embodiment of the method for identifying a course deviation at a fork in the road is shown. In this embodiment, the process 300 of the method for identifying a course deviation at a fork in the road may include the following steps:

[0061] Step 301, in response to detecting that a target vehicle is traveling within a preset distance range of a fork in the road, based on a plurality of vehicle front images of the target vehicle, determining a moving direction of a guide line area of ​​the fork in the road relative to the target vehicle.

[0062] In this embodiment, the implementation details and technical effects of step 301 can refer to the description of step 201 and will not be repeated here.

[0063] Step 302: Determine the moving directions of the plurality of guide line areas relative to the target vehicle, and the distances between the plurality of guide line areas and the target vehicle.

[0064] In this embodiment, the number of the multi-streamline areas can be multiple, for example, 2, 3, etc., and the flow diversion directions of the guide line areas can be different. The execution subject can identify the guide line areas in the image in front of the vehicle by using an image recognition algorithm, determine the relative position relationship between the target vehicle and the guide line areas, and then determine the moving directions of the multiple guide line areas relative to the target vehicle and the distances between the multiple guide line areas and the target vehicle according to the changes in the relative position relationship in the images in front of the target multiple vehicles.

[0065] Step 303, determining a target moving path of the target vehicle at the fork in the road based on the moving directions and distances of the plurality of guide line areas.

[0066] In this embodiment, after determining the moving directions and distances of multiple guide line areas, the execution entity can determine the target moving path of the target vehicle at the fork in the road based on the moving directions and distances of multiple, for example, 3, 4, etc., guide line areas.

[0067] The moving directions of the multiple guide line areas relative to the target vehicle may be the same or different, and the distances between the multiple guide line areas and the target vehicle are usually different.

[0068] Specifically, there are three guide line areas, namely the first guide line area, the second guide line area and the third guide line area. If the moving direction of the first guide line area and the second guide line area relative to the target vehicle is the left rear, and the moving direction of the third guide line area relative to the target vehicle is the right rear, and among the first guide line area, the second guide line area and the third guide line area, the distance between the third guide line area and the target vehicle is the shortest, then it can be determined that the target vehicle enters the left lane of the third guide line area, that is, the left lane of the third guide area is the target moving path.

[0069] Step 304 : determining whether the target vehicle is yawed based on the matching result between the target moving path and the planned path.

[0070] In this embodiment, the implementation details and technical effects of step 304 can refer to the description of step 203 and will not be repeated here.

[0071] The above-mentioned embodiments of the present disclosure highlight that when there are multiple guide line areas, the moving directions of the multiple guide line areas relative to the target vehicle and the distances between the multiple guide line areas and the target vehicle are determined; based on the moving directions and distances of the multiple guide line areas, the target moving path of the target vehicle at the fork in the road is determined, and then based on the target moving path and the planned path, whether the target vehicle has yawed is determined, thereby realizing the identification of whether the target vehicle has yawed in the scenario of multiple guide line areas, and improving the timeliness and reliability of yaw identification in the scenario of multiple guide line areas.

[0072] In some optional methods, based on multiple vehicle front images of the target vehicle, the moving direction of the guide line area at the fork in the road relative to the target vehicle is determined, including: obtaining attribute information of the guide line area at the fork in the road from map data; based on the attribute information and multiple vehicle front images, respectively determining the relative position relationship between the target vehicle and the guide line area in the multiple vehicle front images; based on the relative position relationship in the multiple vehicle front images, determining the moving direction of the guide line area at the fork in the road relative to the target vehicle.

[0073] In this implementation, the execution entity can use image recognition algorithms, such as CNN (Convolutional Neural Networks), FCN (Fully Convolutional Networks), etc., to identify the guide line area in multiple vehicle front images based on the attribute information of the guide line area, and determine the relative position relationship between the target vehicle and the guide line area in the multiple vehicle front images; or the attribute information and multiple vehicle front images can be input into a preset prediction model to determine the relative position relationship between the target vehicle and the guide line area in each of the multiple vehicle front images. This application does not limit this.

[0074] The attribute information of the guide line area may include the number of the guide line areas, the shape of the guide line areas, the number of diagonal lines or V-shaped lines included in the guide line areas, and the like.

[0075] Furthermore, the execution entity may determine the moving direction of the guide line area at the fork in the road relative to the target vehicle according to the change in the relative position relationship in the images in front of the multiple vehicles.

[0076] This implementation method uses the attribute information of the guide line area in the map data to assist in identifying the relative position of the guide line area and the target vehicle in the image in front of the vehicle, and then determines the moving direction of the guide line area relative to the target vehicle, thereby improving the accuracy of the determined moving direction.

[0077] In some optional methods, based on attribute information and multiple images in front of the vehicle, the relative position relationship between the target vehicle and the guide line area in the multiple images in front of the vehicle is determined respectively, including: based on the number of guide line areas, the position information of the boundary lines of the guide line areas, the guide directions of the guide line areas and multiple images in front of the vehicle, the relative position relationship between the target vehicle and each guide line area in the multiple images in front of the vehicle is determined respectively; and based on the relative position relationship in the multiple images in front of the vehicle, the moving direction of the guide line area at the fork in the road relative to the target vehicle is determined, including: based on the relative position relationship between the target vehicle and each guide line area in the multiple images in front of the vehicle, the moving direction of each guide line area at the fork in the road relative to the target vehicle is determined.

[0078] In this implementation, there may be multiple guide line areas, and the attribute information of the guide line areas may include the number of guide line areas, position information of the boundary lines of the guide line areas, and the guide directions of the guide line areas.

[0079] The executing entity can use an image recognition algorithm to identify the guide line areas in multiple vehicle front images based on the number of guide line areas, the position information of the boundary lines of the guide line areas, and the diversion direction of the guide line areas, and determine the relative position relationship between the target vehicle and each guide line area in the multiple vehicle front images; or the number of guide line areas, the position information of the boundary lines of the guide line areas, the diversion direction of the guide line areas and multiple vehicle front images can be input into a preset prediction model to generate the relative position relationship between the target vehicle and each guide line area in the multiple vehicle front images.

[0080] Furthermore, the execution entity may determine the moving direction of each guide line area at the fork in the road relative to the target vehicle according to the change in the relative position relationship between the target vehicle and each guide line area in the plurality of vehicle front images.

[0081] This implementation method utilizes the number of guide line areas in the map data, the position information of the boundary lines of the guide line areas, and the guide directions of the guide line areas to assist in identifying the relative positions of multiple guide line areas and the target vehicle in the image in front of the vehicle, and then determines the moving direction of the guide line areas relative to the target vehicle, thereby further improving the accuracy of the determined moving direction.

[0082] In some optional embodiments, the method also includes: in response to determining that the image in front of the vehicle is a color image, filtering out roads and decorations in multiple images in front of the vehicle through color matrix features to obtain multiple filtered images in front of the vehicle; and based on the attribute information and the multiple images in front of the vehicle, respectively determining the relative position relationship between the target vehicle and the guide line area in the multiple images in front of the vehicle, including: based on the attribute information and the multiple images in front of the vehicle, respectively determining the relative position relationship between the target vehicle and the guide line area in the multiple images in front of the vehicle.

[0083] In this implementation, the execution entity can determine whether the image in front of the vehicle is a color image. If so, the color matrix feature can be used to filter out roads and decorations (for example, street lights, guardrails, flower beds, green belts, etc.) in multiple images in front of the vehicle to obtain multiple filtered images in front of the vehicle.

[0084] Among them, the color matrix feature is a feature used to describe the color distribution in an image. It is usually a matrix in which each element represents the statistical information of a color channel in the image.

[0085] Furthermore, the execution subject may use an image recognition algorithm to identify the guide line area in the filtered multiple vehicle front images based on the attribute information, and determine the relative position relationship between the target vehicle and the guide line area in the multiple vehicle front images.

[0086] This implementation method uses the filtered image in front of the vehicle to determine the relative position relationship between the target vehicle and the guide line area, and then determines the relative movement direction, which effectively improves the recognition efficiency and accuracy of the guide line area, and thus improves the accuracy of the determined relative movement direction.

[0087] In some optional methods, based on the attribute information and the filtered multiple images in front of the vehicle, the relative position relationship between the target vehicle and the guide line area in the multiple images in front of the vehicle is determined respectively, including: based on the attribute information and the filtered multiple images in front of the vehicle, a multi-target tracking algorithm is used to determine the relative position relationship between the target vehicle and the guide line area in the multiple images in front of the vehicle.

[0088] In this implementation, the execution subject can identify the filtered vehicle front image according to the attribute information, and use a multi-target tracking algorithm to determine the relative position relationship between the target vehicle and the guide line area in multiple vehicle front images.

[0089] Among them, multi-target tracking refers to tracking multiple targets in a video sequence at the same time and maintaining the identity consistency of each target. There are many multi-target tracking algorithms, for example, a center-based multi-target tracking algorithm, a JDE (Joint Detection and Embedding) algorithm, etc.

[0090] This method makes full use of the real-time and high reliability characteristics of the multi-target tracking algorithm, and further improves the efficiency and reliability of the generated relative position relationship.

[0091] In some optional embodiments, a target moving path of a target vehicle at a fork in the road is determined based on the moving directions and distances of multiple guide line areas, including: in response to determining that the moving directions of multiple guide line areas are the same, determining a target area closest to the target vehicle based on the distances of the multiple guide line areas; and determining the target moving path of the target vehicle at the fork in the road based on the moving direction of the target area relative to the target vehicle.

[0092] In this implementation, if there are multiple guide line areas and the moving directions of the multiple guide line areas relative to the target vehicle are the same, the execution entity can determine the target area closest to the target vehicle based on the distances between the multiple guide line areas and the target vehicle.

[0093] Furthermore, the execution entity may determine the target moving path of the target vehicle at the fork in the road according to the moving direction of the target area relative to the target vehicle.

[0094] Specifically, there are three guide line areas, namely the first guide line area, the second guide line area and the third guide line area. If the moving directions of the first guide line area, the second guide line area and the third guide line area relative to the target vehicle are all left rear, and among the first guide line area, the second guide line area and the third guide line area, the second guide line area is closest to the target vehicle, then the second guide line area can be determined as the target area, and the target moving path is the right lane of the second guide line area.

[0095] This implementation method determines the target area closest to the target vehicle based on the distances between the multiple guide line areas in response to determining that the moving directions of multiple guide line areas are the same; determines the target moving path of the target vehicle at the fork in the road based on the moving direction of the target area relative to the target vehicle, and then determines whether the target vehicle has yawed based on the target moving path, thereby realizing the identification of yaw at the fork in the road in the scenario where there are multiple guide areas and the guide line areas have the same moving direction.

[0096] In some optional embodiments, based on the moving direction of the guide line area relative to the target vehicle, the target moving path of the target vehicle at the fork in the road is determined, including: in response to determining that the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both left rear, and the first distance between the first guide line area and the target vehicle is greater than the second distance between the second guide line area and the target vehicle, determining the target moving path to be the right side road of the second guide line area; in response to determining that the moving directions of the first guide line area and the second guide line area are both right rear, and the first distance between the first guide line area and the target vehicle is less than the second distance between the second guide line area and the target vehicle, determining the target moving path to be the left side road of the first guide line area; in response to determining that the moving direction of the first guide line area relative to the target vehicle is left rear and the moving direction of the second guide line area relative to the target vehicle is right rear, determining the target moving path to be the middle road between the first guide line area and the second guide line area.

[0097] In this implementation, there may be multiple guide line areas, namely, a first guide line area and a second guide line area, and the first guide line area and the second guide line area have different guiding directions.

[0098] If the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both left rear, and the first distance between the first guide line area and the target vehicle is greater than the second distance between the second guide line area and the target vehicle, it can be determined that the target vehicle has entered the right side of the second guide line area, that is, the target moving path is the right side road of the second guide line area.

[0099] If the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both right rear, and the first distance between the first guide line area and the target vehicle is less than the second distance between the second guide line area and the target vehicle, it can be determined that the target vehicle has entered the left side of the first guide line area, that is, the target path is the road on the left side of the first guide line area.

[0100] If the first guide line area is moving to the left rear relative to the target vehicle, and the second guide line area is moving to the right rear relative to the target vehicle, it can be determined that the target vehicle enters the middle road between the first guide line area and the second guide line area, that is, the target moving path is the middle road between the first guide line area and the second guide line area.

[0101] This implementation method realizes yaw recognition in a scenario where there are two guide line areas at a fork in the road.

[0102] Continue to see Figure 4a , Figure 4b , Figure 4a , Figure 4bIt is a schematic diagram of an application scenario of the method for identifying a course deviation at a fork in the road according to this embodiment.

[0103] The execution entity can determine the current position of the target vehicle 401 via GPS, and determine the road network structure of the area where the current position is located in the pre-stored map based on the current position, and determine whether the target vehicle has traveled to a preset distance range of a fork in the road (for example, an elevated fork in the road). If so, the front video stream of the target vehicle is obtained via an on-board imaging device, such as a driving recorder, and the front video stream is downsampled to obtain multiple vehicle front images of the target vehicle, and the moving direction of the guide line area of ​​the fork in the road relative to the target vehicle is determined based on the vehicle front image (and the attribute information of the guide line area in the map).

[0104] There are two guide line regions, namely a first guide line region 402 and a second guide line region 403 .

[0105] Furthermore, if the moving directions of the first guide line area 402 and the second guide line area 403 relative to the target vehicle 401 are both right rear, and the first distance between the first guide line area 402 and the target vehicle is greater than the second distance between the second guide line area 403 and the target vehicle 401, it can be determined that the target vehicle 401 has entered the left side of the second guide line area 403, that is, the target moving path is the left road of the second guide line area.

[0106] Further, if the target moving path, ie, the left road in the second guide line area, does not match the planned path, ie, the right road in the second guide line area, it can be determined that the target vehicle is yawed.

[0107] Here, the execution entity may also directly input the downsampled multiple vehicle front images and the road network data determined based on GPS (such as lane data, guide line area attribute information, etc.) into the yaw recognition model to generate a yaw recognition result.

[0108] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for identifying a deviation at a fork in the road. Figure 2 The method embodiments shown correspond.

[0109] like Figure 5 As shown, the device 500 for identifying a deviation at a fork in the road of this embodiment includes: a direction determination module 501 , a path determination module 502 and a deviation determination module 503 .

[0110] The direction determination module 501 is configured to determine the moving direction of the guide line area of ​​the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle in response to detecting that the target vehicle is traveling within a preset distance range of the fork in the road.

[0111] The path determination module 502 is configured to determine a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle.

[0112] The yaw determination module 503 is configured to determine whether the target vehicle yaws based on the matching result between the target moving path and the planned path.

[0113] In some optional embodiments of this embodiment, the path determination module further includes: a distance determination unit, configured to determine the moving directions of multiple guide line areas relative to the target vehicle, and the distances between the multiple guide line areas and the target vehicle; a path determination unit, configured to determine the target moving path of the target vehicle at the fork in the road based on the moving directions of the multiple guide line areas and the distances.

[0114] In some optional modes of this embodiment, the path determination unit is further configured to: in response to determining that the moving directions of multiple guide line areas are the same, determine the target area closest to the target vehicle based on the distances of the multiple guide line areas; and determine the target moving path of the target vehicle at the fork in the road based on the moving direction of the target area relative to the target vehicle.

[0115] In some optional modes of the present embodiment, the path determination module is further configured to: in response to determining that the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both left rear, and the first distance between the first guide line area and the target vehicle is greater than the second distance between the second guide line area and the target vehicle, determine the target moving path as the right side road of the second guide line area; in response to determining that the moving directions of the first guide line area and the second guide line area are both right rear, and the first distance between the first guide line area and the target vehicle is less than the second distance between the second guide line area and the target vehicle, determine the target moving path as the left side road of the first guide line area; in response to determining that the moving direction of the first guide line area relative to the target vehicle is left rear and the moving direction of the second guide line area relative to the target vehicle is right rear, determine the target moving path as the middle road between the first guide line area and the second guide line area.

[0116] In some optional embodiments of this embodiment, the path determination module further includes: a first determination unit, configured to determine a first moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; a second determination unit, configured to determine a second moving path of the target vehicle at the fork in the road based on the positioning information of the target vehicle; a third determination unit, configured to determine a third moving path of the target vehicle at the fork in the road based on the inertial measurement information of the target vehicle; a target determination unit, configured to determine the target moving path of the target vehicle at the fork in the road based on the first moving path, the second moving path and the third moving path.

[0117] In some optional modes of this embodiment, the direction determination module further includes: an attribute determination unit, configured to obtain attribute information of the guide line area at the fork in the road from map data; a position determination unit, configured to determine the relative position relationship between the target vehicle and the guide line area in multiple vehicle front images based on the attribute information and multiple vehicle front images; and a direction determination unit, configured to determine the moving direction of the guide line area at the fork in the road relative to the target vehicle based on the relative position relationship in multiple vehicle front images.

[0118] In some optional embodiments of this embodiment, the device also includes: a color filtering module, configured to, in response to determining that the image in front of the vehicle is a color image, filter out roads and decorations in multiple images in front of the vehicle through color matrix features to obtain multiple filtered images in front of the vehicle; and the position determination unit is further configured to: based on the attribute information and the multiple filtered images in front of the vehicle, respectively determine the relative position relationship between the target vehicle and the guide line area in the multiple images in front of the vehicle.

[0119] In some optional modes of this embodiment, the position determination unit is further configured to: based on the attribute information and the filtered multiple vehicle front images, use a multi-target tracking algorithm to respectively determine the relative position relationship between the target vehicle and the guide line area in the multiple vehicle front images.

[0120] In some optional embodiments of the present embodiment, the position determination unit is further configured to: determine the relative position relationship between the target vehicle and each guide line area in the multiple vehicle front images based on the number of guide line areas, the position information of the boundary lines of the guide line areas, the guide directions of the guide line areas and the multiple vehicle front images; and the direction determination unit is further configured to: determine the moving direction of each guide line area at the fork in the road relative to the target vehicle based on the relative position relationship between the target vehicle and each guide line area in the multiple vehicle front images.

[0121] In some optional aspects of this embodiment, the direction determination module is further configured to determine the moving direction of the guide line area at the fork in the road relative to the target vehicle based on multiple vehicle front images of the target vehicle collected by the driving recorder.

[0122] In some optional aspects of this embodiment, the device further includes: a path planning module configured to generate a new planned path based on the target moving path and the destination in response to determining that the target vehicle is yawed.

[0123] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0124] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0125] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0126] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0127] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0128] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a method for fork in the road deviation identification. For example, in some embodiments, the method for fork in the road deviation identification may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for fork in the road deviation identification described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the method for fork-in-road deviation identification in any other appropriate manner (for example, by means of firmware).

[0129] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0131] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0134] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services. The server can also be divided into a server of a distributed system, or a server combined with a blockchain.

[0135] According to the technical solution of the embodiment of the present disclosure, the timeliness and reliability of vehicle deviation identification are effectively improved.

[0136] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions provided by this disclosure can be achieved, and this document does not limit this.

[0137] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for identifying a fork in the road, comprising: In response to detecting that a target vehicle is traveling within a preset distance range of a fork in the road, based on a plurality of vehicle front images of the target vehicle, determining a moving direction of a guide line area of ​​the fork in the road relative to the target vehicle; Determining a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; Based on the matching result between the target moving path and the planned path, it is determined whether the target vehicle is yawed.

2. The method according to claim 1, wherein: There are multiple guide line areas, each of which has a different guide direction. Determining a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle includes: Determining the moving directions of the plurality of guide line areas relative to the target vehicle and the distances between the plurality of guide line areas and the target vehicle; Based on the moving directions and the distances of the plurality of guide line areas, a target moving path of the target vehicle at the fork in the road is determined.

3. The method according to claim 2, wherein: Determining a target moving path of the target vehicle at the fork in the road based on the moving directions and distances of the plurality of guide line areas comprises: In response to determining that the moving directions of the plurality of guide line areas are the same, determining a target area closest to the target vehicle based on the distances of the plurality of guide line areas; Based on the moving direction of the target area relative to the target vehicle, a target moving path of the target vehicle at the fork in the road is determined.

4. The method according to claim 1, wherein: There are multiple guide line areas, the multiple guide line areas include a first guide line area and a second guide line area, and the first guide line area and the second guide line area have different guiding directions; The step of determining a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle includes: In response to determining that the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both left rear, and a first distance between the first guide line area and the target vehicle is greater than a second distance between the second guide line area and the target vehicle, determining that the target moving path is a right side road of the second guide line area; In response to determining that the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both right rearward, and a first distance between the first guide line area and the target vehicle is less than a second distance between the second guide line area and the target vehicle, determining that the target moving path is a left road of the first guide line area; In response to determining that the first guide line area moves to the left rear relative to the target vehicle and the second guide line area moves to the right rear relative to the target vehicle, the target moving path is determined to be a middle road between the first guide line area and the second guide line area.

5. The method according to claim 1, wherein: The step of determining a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle includes: Determining a first moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; Determining a second moving path of the target vehicle at the fork in the road based on the positioning information of the target vehicle; Determining a third moving path of the target vehicle at the fork in the road based on the inertial measurement information of the target vehicle; A target moving path of the target vehicle at the fork in the road is determined based on the first moving path, the second moving path, and the third moving path.

6. The method according to claim 1, wherein: The determining, based on the plurality of vehicle front images of the target vehicle, the moving direction of the guide line area of ​​the fork in the road relative to the target vehicle comprises: Acquire attribute information of the guide line area of ​​the fork in the road from map data; Based on the attribute information and the plurality of vehicle front images, respectively determining a relative position relationship between the target vehicle and the guide line area in the plurality of vehicle front images; Based on the relative position relationship in the plurality of vehicle front images, a moving direction of the guide line area of ​​the fork in the road relative to the target vehicle is determined.

7. The method according to claim 6, further comprising: In response to determining that the image in front of the vehicle is a color image, filtering out roads and decorations in the plurality of images in front of the vehicle by using a color matrix feature to obtain a plurality of filtered images in front of the vehicle; as well as The step of determining the relative position relationship between the target vehicle and the guide line area in the plurality of vehicle front images based on the attribute information and the plurality of vehicle front images respectively includes: Based on the attribute information and the filtered plurality of vehicle front images, the relative position relationship between the target vehicle and the guide line area in the plurality of vehicle front images is determined respectively.

8. The method according to claim 7, wherein: The method of determining the relative position relationship between the target vehicle and the guide line area in the plurality of vehicle front images based on the attribute information and the plurality of vehicle front images after filtering, comprises: Based on the attribute information and the filtered multiple vehicle front images, a multi-target tracking algorithm is used to respectively determine the relative position relationship between the target vehicle and the guide line area in the multiple vehicle front images.

9. The method according to claim 6, wherein: The number of the guide line areas is multiple, the attribute information includes: the number of the guide line areas, the position information of the boundary lines of the guide line areas and the guide direction of the guide line areas, and the relative position relationship between the target vehicle and the guide line areas in the multiple vehicle front images is determined based on the attribute information and the multiple vehicle front images, including: Based on the number of the guide line areas, the position information of the boundary lines of the guide line areas, the guide directions of the guide line areas and the multiple vehicle front images, respectively determine the relative position relationship between the target vehicle and each guide line area in the multiple vehicle front images; The determining, based on the relative position relationship in the plurality of vehicle front images, the moving direction of the guide line area at the fork in the road relative to the target vehicle comprises: Based on the relative positional relationship between the target vehicle and each guide line area in a plurality of vehicle front images, the moving direction of each guide line area at the fork in the road relative to the target vehicle is determined.

10. The method according to claim 1, wherein: The determining, based on the plurality of vehicle front images of the target vehicle, the moving direction of the guide line area of ​​the fork in the road relative to the target vehicle comprises: Based on a plurality of vehicle front images of the target vehicle collected by the driving recorder, a moving direction of the guide line area of ​​the fork in the road relative to the target vehicle is determined.

11. The method according to any one of claims 1 to 10, further comprising: In response to determining that the target vehicle is yawed, a new planned path is generated based on the target movement path and a destination.

12. A device for identifying a deviation at a fork in the road, comprising: a direction determination module, configured to determine, in response to detecting that a target vehicle is traveling within a preset distance range of a fork in the road, a moving direction of a guide line area of ​​the fork in the road relative to the target vehicle based on a plurality of vehicle front images of the target vehicle; a path determination module, configured to determine a target moving path of the target vehicle at the fork in the road based on the moving direction of the guide line area relative to the target vehicle; The yaw determination module is configured to determine whether the target vehicle yaws based on a matching result between the target moving path and the planned path.

13. The device according to claim 12, wherein: The number of the guide line areas is multiple, and the guide line areas have different guide directions, and the path determination module further includes: a distance determination unit configured to determine a moving direction of the plurality of guide line areas relative to the target vehicle and a distance between the plurality of guide line areas and the target vehicle; The path determination unit is configured to determine a target movement path of the target vehicle at the fork in the road based on the movement directions and the distances of the plurality of guide line areas.

14. The device according to claim 13, wherein: The path determination unit is further configured to: In response to determining that the moving directions of the plurality of guide line areas are the same, determining a target area closest to the target vehicle based on the distances of the plurality of guide line areas; Based on the moving direction of the target area relative to the target vehicle, a target moving path of the target vehicle at the fork in the road is determined.

15. The device according to claim 12, wherein: The number of the guide line areas is multiple, the multiple guide line areas include a first guide line area and a second guide line area, and the first guide line area and the second guide line area have different guide directions; and the path determination module is further configured to: In response to determining that the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both left rear, and a first distance between the first guide line area and the target vehicle is greater than a second distance between the second guide line area and the target vehicle, determining that the target moving path is a right side road of the second guide line area; In response to determining that the moving directions of the first guide line area and the second guide line area relative to the target vehicle are both right rearward, and a first distance between the first guide line area and the target vehicle is less than a second distance between the second guide line area and the target vehicle, determining that the target moving path is a left road of the first guide line area; In response to determining that the first guide line area moves to the left rear relative to the target vehicle and the second guide line area moves to the right rear relative to the target vehicle, the target moving path is determined to be a middle road between the first guide line area and the second guide line area.

16. The device according to claim 12, wherein: The path determination module further comprises: A first determining unit is configured to determine a first moving path of the target vehicle at the fork in the road based on a moving direction of the guide line area relative to the target vehicle; A second determining unit is configured to determine a second moving path of the target vehicle at the fork in the road based on the positioning information of the target vehicle; a third determining unit configured to determine a third moving path of the target vehicle at the fork in the road based on the inertial measurement information of the target vehicle; A target determination unit is configured to determine a target movement path of the target vehicle at the fork in the road based on the first movement path, the second movement path, and the third movement path.

17. The device according to claim 12, wherein: The direction determination module further comprises: an attribute determination unit configured to obtain attribute information of the guide line area of ​​the fork in the road from map data; a position determination unit configured to determine the relative position relationship between the target vehicle and the guide line area in the plurality of vehicle front images, respectively, based on the attribute information and the plurality of vehicle front images; The direction determination unit is configured to determine the moving direction of the guide line area of ​​the fork intersection relative to the target vehicle based on the relative position relationship in the plurality of vehicle front images.

18. The apparatus according to claim 17, further comprising: A color filtering module is configured to, in response to determining that the image in front of the vehicle is a color image, filter out roads and decorations in the plurality of images in front of the vehicle by using a color matrix feature to obtain a plurality of filtered images in front of the vehicle; and The position determination unit is further configured to: Based on the attribute information and the filtered plurality of vehicle front images, the relative position relationship between the target vehicle and the guide line area in the plurality of vehicle front images is determined respectively.

19. The device according to claim 18, wherein: The position determination unit is further configured to: Based on the attribute information and the filtered multiple vehicle front images, a multi-target tracking algorithm is used to respectively determine the relative position relationship between the target vehicle and the guide line area in the multiple vehicle front images.

20. The device according to claim 12, wherein: The number of the guide line areas is multiple, the attribute information includes: the number of the guide line areas, the position information of the boundary lines of the guide line areas, and the guide direction of the guide line areas, and the position determination unit is further configured to: Based on the number of the guide line areas, the position information of the boundary lines of the guide line areas, the guide directions of the guide line areas and the multiple vehicle front images, respectively determine the relative position relationship between the target vehicle and each guide line area in the multiple vehicle front images; And the direction determination unit is further configured to: Based on the relative positional relationship between the target vehicle and each guide line area in a plurality of vehicle front images, the moving direction of each guide line area at the fork in the road relative to the target vehicle is determined.

21. The device according to claim 12, wherein: The direction determination module is further configured to: Based on a plurality of vehicle front images of the target vehicle collected by the driving recorder, a moving direction of the guide line area of ​​the fork in the road relative to the target vehicle is determined.

22. The device according to any one of claims 12 to 21, further comprising: The path planning module is configured to generate a new planned path based on the target moving path and a destination in response to determining that the target vehicle is off course.

23. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

24. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 11.

25. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 11.

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