Vehicle driving area detection method, electronic equipment and storage medium

Abstract

The invention relates to a vehicle driving area detection method, electronic equipment and a storage medium. The method comprises the following steps: acquiring image data and point cloud data in a driving area acquired by a target vehicle; lane line detection is carried out on the driving area based on the image data, and obstacle detection is carried out on the driving area based on the point cloud data; and when it is detected that a lane line exists in the driving area, obtaining a detection result of whether an obstacle exists in a target lane corresponding to the lane line. According to the embodiment of the invention, the image data and the point cloud data are combined, lane line detection is carried out by utilizing the image data, and obstacle detection is carried out by utilizing the point cloud data, so that whether an obstacle exists in the target lane or not can be quickly and accurately judged, the computing power can be saved to a great extent, and a better recognition result can be obtained.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to a method for detecting a vehicle driving area, an electronic device, and a storage medium. Background Art

[0002] With the continuous development of technology, vehicles and other means of transportation are becoming more and more intelligent and have become an indispensable part of people's lives to a large extent. However, with the increase in the number of vehicles on the road, traffic pressure has further increased, and traffic accidents have occurred frequently, and traffic accidents mainly involve vehicle collisions. Therefore, during the driving process of the car, the detection of obstacles in the lane has received more and more attention.

[0003] When identifying obstacles on the road, related technologies mainly rely on various image processing and pattern recognition technologies for detection and identification. For example, obstacles on the road are identified through deep learning. However, related technologies require high computing power, and may not achieve good recognition results, especially in complex road conditions or when equipment performance is average. Summary of the invention

[0004] The present disclosure provides a method for detecting a vehicle driving area, an electronic device and a storage medium.

[0005] According to a first aspect of the present disclosure, a method for detecting a vehicle driving area is provided, the method comprising:

[0006] Acquire image data and point cloud data in the driving area collected by the target vehicle;

[0007] Performing lane line detection on the driving area based on the image data, and performing obstacle detection on the driving area based on the point cloud data;

[0008] When it is detected that there is a lane line in the driving area, a detection result of whether there is an obstacle in the target lane corresponding to the lane line is obtained.

[0009] Optionally, the step of acquiring image data and point cloud data in the driving area collected by the target vehicle includes:

[0010] Collecting point cloud data in the driving area by using a laser radar in the target vehicle, and collecting image data in the driving area by using an image acquisition device arranged in the target vehicle;

[0011] The point cloud data and the image data are spatially aligned.

[0012] Optionally, the point cloud data includes three-dimensional point cloud data; and the obstacle detection in the driving area based on the point cloud data includes:

[0013] Projecting the three-dimensional point cloud data on a horizontal plane to obtain two-dimensional point cloud data;

[0014] The two-dimensional point cloud data is clustered to obtain a plurality of clusters, and obstacle detection results are obtained based on the plurality of clusters, wherein each cluster represents an obstacle.

[0015] Optionally, obtaining a detection result of whether an obstacle exists in the target lane corresponding to the lane line includes:

[0016] Obtaining the lane line position information of the target lane and obtaining the position information of each obstacle in the driving area;

[0017] Based on the lane line position information and the position information of each obstacle, it is determined whether there is an obstacle in the target lane to obtain a detection result.

[0018] Optionally, the determining whether there is an obstacle in the target lane includes:

[0019] Generate a target ray using the position of each obstacle as a starting point, wherein the target ray is non-parallel to the lane line;

[0020] The number of intersections between the target ray and the lane line of the target lane is obtained, and whether there is an obstacle in the target lane is determined based on the number of intersections.

[0021] Optionally, the performing lane line detection on the driving area based on the image data includes:

[0022] The image data is input into a lane line detection model, and the lane line in the driving area is detected by the lane line detection model; wherein the lane line detection model is trained based on training samples, and the training samples include lane line annotation information.

[0023] Optionally, the method further comprises:

[0024] When no lane line is detected in the driving area, obtaining the body coverage area and the current driving direction of the target vehicle;

[0025] Based on the vehicle body coverage area and the driving direction, a target area where the target vehicle will travel is predicted, and whether there are obstacles in the target area is detected.

[0026] Optionally, predicting a target area where the target vehicle will travel includes:

[0027] Determining body lines on both sides of the target vehicle based on the body coverage area;

[0028] Two body extension lines in the driving direction are generated based on body lines on both sides of the target vehicle, and a target area where the target vehicle is to travel is determined based on the two body extension lines.

[0029] According to a second aspect of the present disclosure, an electronic device is provided. The electronic device comprises: a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0030] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0031] The vehicle driving area detection method, electronic device and storage medium provided in the embodiments of the present disclosure obtain image data and point cloud data in the driving area collected by the target vehicle, perform lane line detection on the driving area based on the image data, and perform obstacle detection on the driving area based on the point cloud data. When the presence of lane lines in the driving area is detected, a detection result of whether there are obstacles in the target lane is obtained. The embodiments combine image data and point cloud data, use image data for lane line detection, and use point cloud data for obstacle detection, so as to quickly and accurately determine whether there are obstacles in the target lane, which can save computing power to a great extent and obtain better recognition results. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0033] Figure 1 A flow chart of a method for detecting a vehicle driving area provided by an exemplary embodiment of the present disclosure;

[0034] Figure 2 The original image provided by the embodiment of the present disclosure;

[0035] Figure 3 A lane line detection and recognition image provided by an exemplary embodiment of the present disclosure;

[0036] Figure 4 The original point cloud data provided by an exemplary embodiment of the present disclosure;

[0037] Figure 5 An obstacle recognition image in point cloud data provided by an exemplary embodiment of the present disclosure;

[0038] Figure 6 A structural block diagram of an electronic device provided for an exemplary embodiment of the present disclosure;

[0039] Figure 7 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0040] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0041] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0042] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0043] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0044] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0045] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0046] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0047] As an optional but non-limiting implementation, in response to receiving an active request from the user, the method of sending a prompt message to the user may be, for example, a pop-up window, in which the prompt message may be presented in text. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0048] With the rapid development of intelligent transportation systems and autonomous driving technologies, the environmental perception capability of vehicles has become the key to achieving safe and efficient driving. In complex traffic environments, vehicles need to accurately identify their drivable areas, which involves not only lane line detection, but also comprehensive perception of the vehicle's surroundings. Therefore, accurate identification of drivable areas is crucial for vehicle path planning, obstacle avoidance, and compliance with traffic rules.

[0049] In order to more accurately identify the drivable area of ​​the vehicle, the disclosed embodiment combines the point cloud data collected by the laser radar with the image data collected by the image acquisition device. The lane lines in the drivable area are detected through the image data, and the obstacles in the lane lines are detected through the point cloud data. When the lane lines are not detected, the drivable area of ​​the vehicle can be predicted based on the relevant information of the vehicle body, and the obstacles in the drivable area can be detected.

[0050] Specifically, the embodiment of the present disclosure first provides a method for detecting a vehicle driving area, such as Figure 1 As shown, the method may include the following steps:

[0051] In step S110, image data and point cloud data in the driving area collected by the target vehicle are obtained.

[0052] In an embodiment, the target vehicle may be provided with a laser radar for collecting point cloud data around the target vehicle. The target vehicle may also be provided with an image acquisition device, such as a camera, for collecting image data in front of the vehicle or in an area related to the target vehicle.

[0053] Therefore, the embodiment can collect point cloud data in the driving area by using the laser radar in the target vehicle, collect image data in the driving area by setting an image acquisition device in the target vehicle, and align the point cloud data and the image data in space.

[0054] Among them, the alignment of point cloud data and image data refers to matching point cloud data and image data in space so that they can accurately correspond in the same coordinate system, thereby achieving a complete description of the scene. Point cloud data records the three-dimensional coordinate data (x, y, z) of the surface of an object and possible color and intensity information. Image data can be a two-dimensional image captured by a camera or camera, which records the visual information of the scene.

[0055] Point cloud data and image data can be aligned by matching timestamps. Since the acquisition frequencies of point cloud and image may be different, it is necessary to match them by timestamps to find the closest time point for alignment. Alternatively, alignment can be performed by spatial transformation, for example, by calculating the spatial transformation relationship (such as rotation and translation) between point cloud and image, so that the two can accurately correspond in space.

[0056] In the embodiments provided by the present disclosure, the image data collected by the camera and the point cloud data collected by the laser radar can also be calibrated by calibration to obtain the internal and external parameters of the camera and the laser radar. The camera's internal parameter calibration can use Zhang's calibration method to calculate the camera's internal parameters through multiple checkerboard images. By collecting image data and point cloud data in the calibration room, and projecting the point cloud into the image according to the manual parameter adjustment parameters, until the point cloud and the image coincide, the final external parameter parameters can be obtained. In this way, the point cloud data collected by the laser radar and the image data collected by the camera can be aligned.

[0057] In step S120, lane line detection is performed on the driving area based on the image data, and obstacle detection is performed on the driving area based on the point cloud data.

[0058] In an embodiment, after obtaining the image data, the lane lines in the image data may be detected. Specifically, the lane lines may be detected by a trained model or by an image-related algorithm, such as an image edge detection operator.

[0059] In the embodiment, Figure 2 As stated, Figure 2 The original image provided by the embodiment of the present disclosure, Figure 3 The lane line detection recognition image provided by the embodiment of the present disclosure. By performing lane line detection on the original image, the following can be obtained: Figure 3 The lane line detection and recognition results are shown.

[0060] When detecting obstacles in the driving area through point cloud data, the point cloud data can be processed by clustering, so that multiple clusters can be obtained, each cluster can represent an obstacle. The category of the obstacle can be determined based on the size or shape of each cluster. The embodiment can further detect the state of the obstacle, such as a stationary state or a moving state.

[0061] In step S130, when it is detected that there is a lane line in the driving area, a detection result of whether there is an obstacle in the target lane corresponding to the lane line is obtained.

[0062] In the embodiment, by performing lane line detection in the driving area, multiple lanes in the driving area can be detected. For example, the lane where the target vehicle is currently located can be used as the target lane, and by detecting obstacles in the target lane, instructions can be given for vehicle control, such as whether to change lanes, accelerate or decelerate, etc.

[0063] like Figure 4 what Figure 5 As shown, Figure 4 The original point cloud data provided by the embodiment of the present disclosure, Figure 5 Obstacle recognition images in point cloud data provided by the embodiment of the present disclosure. Figure 4 The original point cloud is processed and projected onto the ground to detect obstacles within the lane line of the target lane. Figure 5 The red color in the figure indicates obstacles within the lane line of the target lane.

[0064] In the embodiment, the adjacent lanes in the driving area can also be used as target vehicles. For example, when lane change is required, the obstacle situation in the target lane can be detected to generate indication information on whether lane change is possible. Furthermore, when lane change is possible, indication information on accelerating lane change or decelerating lane change can be generated according to the obstacle situation in the target lane.

[0065] The method for detecting a vehicle driving area provided in the embodiment of the present disclosure obtains image data and point cloud data in the driving area collected by the target vehicle, performs lane line detection on the driving area based on the image data, and performs obstacle detection on the driving area based on the point cloud data. When the presence of lane lines in the driving area is detected, a detection result of whether there are obstacles in the target lane is obtained. The embodiment combines image data and point cloud data, uses image data for lane line detection, and uses point cloud data for obstacle detection, thereby being able to quickly and accurately determine whether there are obstacles in the target lane, which can save computing power to a great extent and obtain better recognition results.

[0066] Based on the above embodiment, in another embodiment provided by the present disclosure, the above point cloud data may specifically include three-dimensional point cloud data, and the above step S120 may specifically include the following steps:

[0067] In step S121, the three-dimensional point cloud data is projected on a horizontal plane to obtain two-dimensional point cloud data.

[0068] In step S122, clustering is performed on the two-dimensional point cloud data to obtain a plurality of clusters, and obstacle detection results are obtained based on the plurality of clusters, wherein each cluster represents an obstacle.

[0069] In an embodiment, in order to determine whether an obstacle is located within the lane line of the target lane, the three-dimensional point cloud data may be projected onto a horizontal plane, and specifically, the three-dimensional point cloud data may be projected onto the ground.

[0070] Specifically, the point cloud data can be projected onto the ground by calculating the shortest distance from each point to the ground and projecting the point onto the ground. For example, assuming the point (x0, y0, z0), the plane equation is mx+ny+sz+d=0, and the equation of the line passing through the point (x0, y0, z0) and perpendicular to the plane is x-x0m=y-y0n=z-z0s. The projection of the point to the plane is the intersection of the above line and the plane. The normal vector (m, n, s) and the constant term d of the plane equation can be obtained, and for each point (x, y, z) in the point cloud, the shortest distance t to the plane is calculated. Substitute the calculated t into the plane equation to obtain the coordinates of the projection point, that is, obtain the two-dimensional point cloud data.

[0071] In the embodiment, in the process of clustering the two-dimensional point cloud data to obtain multiple clusters, and obtaining obstacle detection results based on the multiple clusters, the following method can be used for processing:

[0072] (1) The point cloud can be divided into several clusters by calculating the distance from each point in the point cloud to the cluster center, and each cluster represents an obstacle. The specific steps include selecting the initial cluster center, calculating the distance and assigning samples, recalculating the cluster center, and repeating the operation until the termination condition is met. In this way, multiple cluster centers are finally obtained, and each cluster center can represent a cluster.

[0073] (2) The point cloud can be projected onto the depth map, the angle between two adjacent rows of points on the depth map can be calculated, and obstacle detection can be performed based on the angle, so as to facilitate the determination of whether there are obstacles, etc. For example, a binocular camera can be set on the target vehicle, so that the image data collected is depth image data, and the point cloud data can be projected onto the depth image data, and the angle between two adjacent rows of points on the depth map can be calculated, and obstacle detection can be performed based on the angle.

[0074] (3) The embodiment can also use a supervoxel approach to segment the point cloud into small areas with similar features, called supervoxels. The supervoxel segmentation process will not cross the object boundary, which can avoid over-segmentation of the point cloud. The main steps include point cloud voxelization, generating voxel adjacency, gridding screening, supervoxel seed voxel selection, feature and distance measurement, and supervoxel clustering segmentation based on flow constraints.

[0075] Based on the above embodiment, in another embodiment provided by the present disclosure, the above step S130 may specifically include the following steps:

[0076] In step S131, the lane line position information of the target lane is obtained, and the position information of each obstacle in the driving area is obtained.

[0077] In step S132, based on the lane line position information and the position information of each obstacle, it is determined whether there is an obstacle in the target lane to obtain a detection result.

[0078] In an embodiment, by obtaining the lane line position information of the target lane and the position information of each obstacle in the driving area, the relative position of each obstacle to the lane line of the target lane is determined through the lane line position information and the position information of each obstacle, so that it can be determined whether there is an obstacle in the target lane.

[0079] Specifically, in an embodiment, a target ray may be generated with the position of each obstacle as a starting point. The number of intersections between the target ray and the lane line of the target lane is obtained, and whether there is an obstacle in the target lane is determined based on the number of intersections. The target ray and the lane line are in a non-parallel relationship.

[0080] For a closed curve in a plane, when a ray crosses the polygon boundary, there are only two cases: entering the polygon or passing through the polygon. It is impossible for a straight line to enter the polygon again from the inside or pass through the polygon again from the outside. Therefore, if the number of times the ray crosses the polygon boundary is odd, the point is inside the polygon; if it is even, the point is outside the polygon.

[0081] Therefore, in the embodiments provided by the present disclosure, a ray can be drawn from a given obstacle as a starting point, and the number of intersections between the ray and the lane line boundary can be calculated. If the number of intersections is an odd number, then the point is inside the lane line; if the number of intersections is an even number, then the point is outside the lane line.

[0082] Among them, special situations that exist in practice can be handled in the following ways.

[0083] (1) The point is on the edge of the polygon. In this case, whether the ray is considered to be a crossing depends on the specific situation. If it is considered a crossing, the opposite result may be obtained, so special processing is required.

[0084] (2) The point and the vertex of the polygon coincide. This situation requires special processing to ensure the accuracy of the judgment.

[0085] (3) The ray passes through the vertex of the polygon. When the ray passes through the vertex of the polygon, it can be counted as one traversal, not two.

[0086] Through the embodiments provided in the present disclosure, it is possible to effectively determine whether an obstacle is within the lane line of the target lane, thereby providing important location information for the autonomous driving and navigation systems.

[0087] Based on the above embodiment, in another embodiment provided by the present disclosure, when lane line detection is performed on a driving area based on image data, the image data can also be input into a lane line detection model, and the lane line in the driving area is detected by the lane line detection model. The lane line detection model is trained based on training samples, and the training samples include lane line annotation information.

[0088] Based on the above embodiment, in another embodiment provided by the present disclosure, the method may further include the following steps:

[0089] In step S140, when no lane line is detected in the driving area, the body coverage area and the current driving direction of the target vehicle are obtained.

[0090] In the embodiment, when the target vehicle is traveling in the target lane, it is mainly necessary to detect obstacles in the target lane, or to detect obstacles in lanes adjacent to the current lane, so as to take emergency avoidance measures.

[0091] When no lane line is detected in the driving area, the body coverage area and current driving direction of the target vehicle can be obtained to predict the area to be driven, so as to facilitate the detection of obstacles in the driving area.

[0092] For example, in an embodiment, when no lane line is detected visually, a fixed range of vision can be intercepted on the left and right sides of the vehicle body to ensure that obstacles ahead can be detected normally when no lane line is detected.

[0093] In step S150, based on the vehicle body coverage area and the driving direction, the target area where the target vehicle will travel is predicted, and whether there are obstacles in the target area is detected.

[0094] In an embodiment, for example, the coverage area of ​​the target vehicle may be determined based on the width of the vehicle body, so that the straight lines on both sides of the vehicle body may be used as lane lines according to the driving direction.

[0095] Specifically, the body lines on both sides of the target vehicle can be determined based on the body coverage area, two body extension lines in the driving direction can be generated based on the body lines on both sides of the target vehicle, and the target area where the target vehicle will travel can be determined based on the two body extension lines. In this way, by predicting the target area where the target vehicle will travel, obstacles in the target area can be detected in time, so that effective measures can be taken to ensure driving safety.

[0096] An embodiment of the present disclosure also provides an electronic device, comprising: at least one processor; and a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0097] Figure 6 The structure diagram of an electronic device provided by an exemplary embodiment of the present disclosure is shown in FIG. Figure 6 As shown, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801 , and the processor 1801 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0098] The processor 1801 may also be referred to as a central processing unit (CPU), which may be an integrated circuit chip having signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure may be completed by an integrated logic circuit of hardware in the processor 1801 or by instructions in the form of software. The processor 1801 may be a general-purpose processor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present disclosure may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage media in the art. The processor 1801 reads the information in the memory 1802 and completes the steps of the method in combination with its hardware.

[0099] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Figure 7 The computer system 1900 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 7 A structural block diagram of a computer system provided for an exemplary embodiment of the present disclosure.

[0100] Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices 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 claimed herein.

[0101] like Figure 7As shown, the computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 to a random access memory (RAM) 1903. In the RAM 1903, various programs and data required for the operation of the computer system 1900 can also be stored. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other via a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0102] A plurality of components in the computer system 1900 are connected to the I / O interface 1905, including: an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909. The input unit 1906 may be any type of device capable of inputting information to the computer system 1900, and the input unit 1906 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 1907 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1908 may include, but is not limited to, a disk, an optical disk. The communication unit 1909 allows the computer system 1900 to exchange information / data with other devices over a network such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0103] The computing unit 1901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 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 1901 performs the various methods and processes described above. For example, in some embodiments, the above methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 1902 and / or communication unit 1909. In some embodiments, the computing unit 1901 may be configured to perform the above methods disclosed in the embodiments of the present disclosure in any other appropriate manner (e.g., by means of firmware).

[0104] The embodiment of the present disclosure further provides a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0105] The computer-readable storage medium in the disclosed embodiments 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. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specifically, the computer-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.

[0106] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0107] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the above method disclosed in the embodiments of the present disclosure is implemented.

[0108] In embodiments of the present disclosure, computer program codes for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0109] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0110] The modules, components or units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the names of the modules, components or units do not, in some cases, limit the modules, components or units themselves.

[0111] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0112] The above descriptions are only some embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, a technical solution formed by replacing the above features with the technical features with similar functions disclosed in the present disclosure (but not limited to).

[0113] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for detecting a vehicle driving area, characterized in that: The method comprises: Acquire image data and point cloud data in the driving area collected by the target vehicle; Performing lane line detection on the driving area based on the image data, and performing obstacle detection on the driving area based on the point cloud data; When it is detected that there is a lane line in the driving area, a detection result of whether there is an obstacle in the target lane corresponding to the lane line is obtained.

2. The method according to claim 1, characterized in that The step of acquiring image data and point cloud data in the driving area collected by the target vehicle includes: Collecting point cloud data in the driving area by using a laser radar in the target vehicle, and collecting image data in the driving area by using an image acquisition device arranged in the target vehicle; The point cloud data and the image data are spatially aligned.

3. The method according to claim 1, characterized in that The point cloud data includes three-dimensional point cloud data; and the obstacle detection in the driving area based on the point cloud data includes: Projecting the three-dimensional point cloud data on a horizontal plane to obtain two-dimensional point cloud data; The two-dimensional point cloud data is clustered to obtain a plurality of clusters, and obstacle detection results are obtained based on the plurality of clusters, wherein each cluster represents an obstacle.

4. The method according to claim 3, characterized in that The obtaining of a detection result of whether there is an obstacle in the target lane corresponding to the lane line includes: Obtaining the lane line position information of the target lane and obtaining the position information of each obstacle in the driving area; Based on the lane line position information and the position information of each obstacle, it is determined whether there is an obstacle in the target lane to obtain a detection result.

5. The method according to claim 4, characterized in that The determining whether there is an obstacle in the target lane includes: Generate a target ray using the position of each obstacle as a starting point, wherein the target ray is non-parallel to the lane line; The number of intersections between the target ray and the lane line of the target lane is obtained, and whether there is an obstacle in the target lane is determined based on the number of intersections.

6. The method according to claim 1, characterized in that The performing lane line detection on the driving area based on the image data includes: The image data is input into a lane line detection model, and the lane line in the driving area is detected by the lane line detection model; wherein the lane line detection model is trained based on training samples, and the training samples include lane line annotation information.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: When no lane line is detected in the driving area, obtaining the body coverage area and the current driving direction of the target vehicle; Based on the vehicle body coverage area and the driving direction, a target area where the target vehicle will travel is predicted, and whether there are obstacles in the target area is detected.

8. The method according to claim 7, characterized in that The predicting of the target area where the target vehicle will travel includes: Determining body lines on both sides of the target vehicle based on the body coverage area; Two body extension lines in the driving direction are generated based on body lines on both sides of the target vehicle, and a target area where the target vehicle is to travel is determined based on the two body extension lines.

9. An electronic device, characterized in that: include: at least one processor; a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 8.

Images