Obstacle ranging method, apparatus, device, and medium

Abstract

The embodiment of the application discloses a kind of obstacle ranging method, device, equipment and medium, it is related to automatic driving technical field, especially it is related to autonomous parking technical field.The specific implementation scheme is: according to the image to be detected, the projection position information of the obstacle edge in the image to be detected is determined;Based on the projection position information, the distance between the ego vehicle and the obstacle is determined.The embodiment of the application provides a kind of obstacle ranging method, device, equipment and medium to realize the accurate determination of the distance between the obstacle and the ego vehicle.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more particularly to autonomous driving technology. Specifically, this application provides an obstacle ranging method, apparatus, device, and medium. Background Technology

[0002] In the autonomous driving industry, people typically use deep learning to detect obstacles in images in order to adjust the vehicle by sensing the distance between the vehicle and surrounding obstacles in real time.

[0003] The current main method for determining the distance between obstacles and the vehicle is to detect obstacles in a 2D autonomous driving image, obtaining a bounding box that includes the obstacles. The lower left and lower right corners of the bounding box are then used as the ground points for inverse perspective transformation (IPM) projection. IPM is then used to transform the obstacle's spatial position information in a 3D coordinate system.

[0004] However, these two points cannot accurately represent the actual position of the obstacle's edge projected onto the ground, i.e., the grounding point of the projection. Therefore, the distance determined based on these two points cannot accurately represent the true distance between the obstacle and the vehicle. Summary of the Invention

[0005] This application provides an obstacle ranging method, apparatus, device, and medium to accurately determine the distance between an obstacle and a vehicle.

[0006] This application provides an obstacle ranging method, which is executed by an autonomous vehicle, and the method includes:

[0007] Determine the projection position information of the obstacle edge in the image to be detected based on the image to be detected;

[0008] Based on the projected position information, the distance between the vehicle and the obstacle is determined.

[0009] This application embodiment determines the distance between the vehicle and the obstacle based on the projection position information of the obstacle edge in the image to be detected. Since the projection position information of the obstacle edge in the image to be detected can accurately describe the position of the obstacle edge on the ground, this application embodiment can achieve accurate determination of the distance between the vehicle and the obstacle.

[0010] Further, determining the distance between the vehicle and the obstacle based on the projected position information includes:

[0011] The projected position information is converted to a three-dimensional coordinate system to obtain the spatial position information of the obstacle;

[0012] Based on the spatial location information of the obstacle, the distance between the vehicle and the obstacle is calculated.

[0013] Based on this technical feature, the embodiments of this application obtain the spatial position information of the obstacle by converting the projection position information to a three-dimensional coordinate system; based on the spatial position information of the obstacle, the distance between the vehicle and the obstacle is calculated, thereby realizing the calculation of the distance between the vehicle and the obstacle based on the projection position information of the obstacle edge.

[0014] Further, determining the distance between the vehicle and the obstacle based on the projected position information includes:

[0015] Obstacle detection is performed on the image to be detected to obtain the image region of the obstacle;

[0016] Based on the overlap between the image region of the obstacle and the projection position information, the mapping relationship between the obstacle and the projection position information is determined.

[0017] Based on the mapping relationship, the distance between the vehicle and the obstacle is determined.

[0018] Based on this technical feature, embodiments of this application determine the mapping relationship between obstacles and their projected position information by overlapping the image regions and boundaries of the obstacles. Based on this mapping relationship, the distance between the vehicle and the obstacles is determined. This is because the projected position information of at least two adjacent obstacles can be segmented based on this mapping relationship, and the accurate determination of the distance between the vehicle and the obstacles can be achieved based on the segmented projected position information. Therefore, embodiments of this application can further improve the accuracy of the distance between the vehicle and the obstacles.

[0019] Further, determining the distance between the vehicle and the obstacle based on the mapping relationship includes:

[0020] The projection position information associated with the obstacle is determined based on the mapping relationship;

[0021] The distance between the vehicle and the obstacle is determined based on the projected position information associated with the obstacle and the object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

[0022] Based on this technical feature, the embodiments of this application determine the distance between the vehicle and the obstacle by using the projection position information associated with the obstacle and the object information of the obstacle. Since the object information of the obstacle can determine the distance calculation information that is appropriate for the obstacle, the accuracy of the distance between the vehicle and the obstacle can be further improved by calculating the distance between the vehicle and the obstacle based on this information.

[0023] Further, determining the distance between the vehicle and the obstacle based on the projected position information associated with the obstacle and the object information of the obstacle includes:

[0024] The distance calculation logic is determined based on the object information of the obstacle;

[0025] Based on the established distance calculation logic, the distance between the vehicle and the obstacle is determined according to the projection position information associated with the obstacle.

[0026] Based on this technical feature, the embodiments of this application determine the distance between the vehicle and the obstacle by using a determined distance calculation logic and the projection position information associated with the obstacle. Since the distance calculation logic is determined based on the object information of the obstacle, the distance calculation logic is more applicable to the obstacle, thereby improving the accuracy of the distance between the vehicle and the obstacle.

[0027] Further, determining the projection position information of the obstacle edge in the image to be detected based on the image to be detected includes:

[0028] The image to be detected is input into a pre-trained projection position recognition model, which outputs the projection position information of the obstacle edge in the image to be detected.

[0029] Based on this technical feature, the embodiments of this application utilize a pre-trained projection position recognition model to determine the projection position information of the obstacle edge in the image to be detected.

[0030] This application embodiment also provides an obstacle ranging device, which is configured in an autonomous vehicle, and the device includes:

[0031] The position determination module is used to determine the projection position information of the obstacle edge in the image to be detected based on the image to be detected.

[0032] The distance determination module is used to determine the distance between the vehicle and the obstacle based on the projected position information.

[0033] Furthermore, the distance determination module includes:

[0034] The coordinate system transformation unit is used to transform the projected position information to a three-dimensional coordinate system to obtain the spatial position information of the obstacle;

[0035] The distance calculation unit is used to calculate the distance between the vehicle and the obstacle based on the spatial location information of the obstacle.

[0036] Furthermore, the distance determination module includes:

[0037] An obstacle detection unit is used to detect obstacles in the image to be detected and obtain the image region of the obstacle.

[0038] The mapping relationship determination unit is used to determine the mapping relationship between the obstacle and the projection position information based on the overlap relationship between the image region of the obstacle and the projection position information.

[0039] The distance determination unit is used to determine the distance between the vehicle and the obstacle based on the mapping relationship.

[0040] Further, the distance determination unit includes:

[0041] The information determination subunit is used to determine the projection position information associated with the obstacle based on the mapping relationship;

[0042] The distance determination subunit is used to determine the distance between the vehicle and the obstacle based on the projected position information associated with the obstacle and the object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

[0043] Furthermore, the distance determination subunit is specifically used for:

[0044] The distance calculation logic is determined based on the object information of the obstacle;

[0045] Based on the established distance calculation logic, the distance between the vehicle and the obstacle is determined according to the projection position information associated with the obstacle.

[0046] Furthermore, the location determination module includes:

[0047] The position determination unit is used to input the image to be detected into a pre-trained projection position recognition model and output the projection position information of the obstacle edge in the image to be detected.

[0048] This application embodiment also provides an electronic device, the device comprising:

[0049] At least one processor; and

[0050] A memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in any one of the embodiments of this application.

[0052] This application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method described in any one of the embodiments of this application. Attached Figure Description

[0053] The accompanying drawings are provided for a better understanding of this solution and do not constitute a limitation of this application. Wherein:

[0054] Figure 1 This is a flowchart of an obstacle ranging method provided in the first embodiment of this application;

[0055] Figure 2 This is a flowchart of an obstacle ranging method provided in the second embodiment of this application;

[0056] Figure 3 This is a flowchart of an obstacle ranging method provided in the third embodiment of this application;

[0057] Figure 4 This is a schematic diagram of a target detection effect provided in the third embodiment of this application;

[0058] Figure 5 This is a schematic diagram of the structure of an obstacle ranging device provided in the fourth embodiment of this application;

[0059] Figure 6 This is a block diagram of an electronic device used to implement the obstacle ranging method of the embodiments of this application. Detailed Implementation

[0060] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0061] First Embodiment

[0062] Figure 1 This is a flowchart of an obstacle ranging method provided in the first embodiment of this application. This embodiment is applicable to situations where an autonomous vehicle perceives the distance between itself and surrounding obstacles in real time during autonomous driving and controls the vehicle based on the perceived distance. This method can be executed by an obstacle ranging device, which can be implemented in software and / or hardware. See also... Figure 1 An obstacle ranging method provided in this application includes:

[0063] S110. Determine the projection position information of the obstacle edge in the image to be detected based on the image to be detected.

[0064] The image to be detected is the image from which the distance between the vehicle and the obstacle is to be detected. Specifically, this image can be acquired in real time by the vehicle's image acquisition device.

[0065] Typically, the projection position information of an obstacle edge in the image to be detected refers to the position information of the obstacle edge vertically projected onto the ground in the image to be detected.

[0066] Specifically, determining the projection position information of obstacle edges in the image to be detected based on the image to be detected includes:

[0067] The image to be detected is input into the projection position recognition model, which outputs the projection position information of the obstacle edge in the image to be detected.

[0068] The projection position recognition model can be pre-trained based on sample images labeled with the projection position information of obstacle edges.

[0069] Optionally, determining the projection position information of the obstacle edge in the image to be detected based on the image to be detected includes:

[0070] Based on the set projection position recognition logic, projection position information is identified from the image to be detected.

[0071] S120. Based on the projected position information, determine the distance between the vehicle and the obstacle.

[0072] Specifically, determining the distance between the vehicle and the obstacle based on the projected position information includes:

[0073] Based on the projection position information, determine the image distance between the vehicle and the obstacle in the two-dimensional image coordinate system;

[0074] Based on the image scale and the aforementioned image distances, determine the actual distance between the vehicle and the obstacle.

[0075] Typically, determining the distance between the vehicle and the obstacle based on the projected position information includes:

[0076] The projected position information is converted to a three-dimensional coordinate system to obtain the spatial position information of the obstacle;

[0077] Based on the spatial location information of the obstacle, the distance between the vehicle and the obstacle is calculated.

[0078] Specifically, based on inverse perspective transformation, the projected position information is converted to a three-dimensional coordinate system to obtain the spatial position information of the obstacle.

[0079] The technical solution of this application embodiment determines the distance between the vehicle and the obstacle based on the projection position information of the obstacle edge in the image to be detected. Since the projection position information of the obstacle edge in the image to be detected can accurately describe the position of the obstacle edge on the ground, this application embodiment can achieve accurate determination of the distance between the vehicle and the obstacle.

[0080] Second Embodiment

[0081] Figure 2 This is a flowchart of an obstacle ranging method provided in the second embodiment of this application. This embodiment is an optional solution proposed based on the above embodiments. See also Figure 2 The obstacle ranging method provided in this application includes:

[0082] S210. Determine the projection position information of the obstacle edge in the image to be detected based on the image to be detected.

[0083] S220. Perform obstacle detection on the image to be detected to obtain the image region of the obstacle.

[0084] The image region of an obstacle refers to the region in the image to be detected that contains the obstacle.

[0085] Specifically, the image region of an obstacle refers to a coarse image region that includes the obstacle. The image region of an obstacle can be implemented based on obstacle detection logic or obtained from an obstacle detection model.

[0086] Typically, the image regions of obstacles can be labeled using bounding boxes.

[0087] S230. Based on the overlap between the image region of the obstacle and the projection position information, determine the mapping relationship between the obstacle and the projection position information.

[0088] Specifically, based on the overlap between the image region of the obstacle and the projected position information, the mapping relationship between the obstacle and the projected position information is determined, including:

[0089] Determine the overlapping area of ​​the image region and the associated region of the projected position information of the obstacle;

[0090] The mapping relationship between obstacles and projection location information is determined based on the proportion of the overlapping area in the associated region of the projection location information.

[0091] S240. Based on the mapping relationship, determine the distance between the vehicle and the obstacle.

[0092] Specifically, determining the distance between the vehicle and the obstacle based on the mapping relationship includes:

[0093] Based on this mapping relationship, the projected positions of at least two obstacles that are stuck together are segmented;

[0094] Based on the segmented projection position, the distance between the vehicle and the obstacle is determined.

[0095] Typically, determining the distance between the vehicle and the obstacle based on the mapping relationship includes:

[0096] The projection position information associated with the obstacle is determined based on the mapping relationship;

[0097] The distance between the vehicle and the obstacle is determined based on the projected position information associated with the obstacle and the object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

[0098] Based on this technical feature, embodiments of this application determine the distance between the vehicle and the obstacle by using the projected position information associated with the obstacle and the object information of the obstacle. Because distance calculation information appropriate to the obstacle can be determined based on the object information of the obstacle, calculating the distance between the vehicle and the obstacle based on this information can further improve the accuracy of the distance calculation between the vehicle and the obstacle. Therefore, embodiments of this application can further improve the accuracy of the distance calculation between the vehicle and the obstacle.

[0099] The distance calculation information mentioned above can be any information used to calculate distances, and this embodiment does not limit it.

[0100] Specifically, the aforementioned distance calculation information can be distance calculation parameters or distance calculation logic.

[0101] The technical solution of this application embodiment determines the mapping relationship between the obstacle and the projected position information based on the overlap of the image region of the obstacle and the obstacle boundary; based on this mapping relationship, the distance between the vehicle and the obstacle is determined. Because this mapping relationship allows for the segmentation of the projected position information of at least two obstacles that are connected together, the accurate determination of the distance between the vehicle and the obstacle can be achieved based on the segmented projected position information. Therefore, this application embodiment can further improve the accuracy of the distance between the vehicle and the obstacle.

[0102] Third Embodiment

[0103] Figure 3 This is a flowchart of an obstacle ranging method provided in the third embodiment of this application. This embodiment is an optional solution proposed based on the above embodiments. See also Figure 3 The obstacle ranging method provided in this application includes:

[0104] S310. Input the real-time acquired image to be detected into the pre-trained target detection model, and output the projection position information of the obstacle edge in the image to be detected, as well as the bounding box of the obstacle.

[0105] For specific output results, please refer to [link / details]. Figure 4 .

[0106] S320. Based on the overlap between the bounding box of the obstacle and the above-mentioned projection position information, determine the mapping relationship between the obstacle and the projection position information.

[0107] S330. Determine the projection position information associated with the obstacle based on the mapping relationship.

[0108] S340. Based on the transformation relationship between the two-dimensional coordinate system and the three-dimensional coordinate system, the projected position information associated with the obstacle is transformed to the three-dimensional coordinate system to obtain the spatial position information of the obstacle.

[0109] S350. Determine the distance between the vehicle and the obstacle based on the spatial location information and object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

[0110] This application embodiment determines the distance between the vehicle and the obstacle based on the projection position information of the obstacle edge in the image to be detected. Since the projection position information of the obstacle edge in the image to be detected can accurately describe the position of the obstacle edge on the ground, this application embodiment can achieve accurate determination of the distance between the vehicle and the obstacle.

[0111] Fourth embodiment

[0112] Figure 5 This is a schematic diagram of an obstacle ranging device according to the fourth embodiment of this application. Typically, the obstacle ranging device provided in this embodiment can be configured in an autonomous vehicle. See also... Figure 5 The obstacle ranging device 500 provided in this application embodiment includes: a position determination module 501 and a distance determination module 502.

[0113] Among them, the position determination module 501 is used to determine the projection position information of the obstacle edge in the image to be detected based on the image to be detected;

[0114] The distance determination module 502 is used to determine the distance between the vehicle and the obstacle based on the projected position information.

[0115] The technical solution of this application embodiment determines the distance between the vehicle and the obstacle based on the projection position information of the obstacle edge in the image to be detected. Since the projection position information of the obstacle edge in the image to be detected can accurately describe the position of the obstacle edge on the ground, this application embodiment can achieve accurate determination of the distance between the vehicle and the obstacle.

[0116] Furthermore, the distance determination module includes:

[0117] The coordinate system transformation unit is used to transform the projected position information to a three-dimensional coordinate system to obtain the spatial position information of the obstacle;

[0118] The distance calculation unit is used to calculate the distance between the vehicle and the obstacle based on the spatial location information of the obstacle.

[0119] Furthermore, the distance determination module includes:

[0120] An obstacle detection unit is used to detect obstacles in the image to be detected and obtain the image region of the obstacle.

[0121] The mapping relationship determination unit is used to determine the mapping relationship between the obstacle and the projection position information based on the overlap relationship between the image region of the obstacle and the projection position information.

[0122] The distance determination unit is used to determine the distance between the vehicle and the obstacle based on the mapping relationship.

[0123] Further, the distance determination unit includes:

[0124] The information determination subunit is used to determine the projection position information associated with the obstacle based on the mapping relationship;

[0125] The distance determination subunit is used to determine the distance between the vehicle and the obstacle based on the projected position information associated with the obstacle and the object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

[0126] Furthermore, the distance determination subunit is specifically used for:

[0127] The distance calculation logic is determined based on the object information of the obstacle;

[0128] Based on the established distance calculation logic, the distance between the vehicle and the obstacle is determined according to the projection position information associated with the obstacle.

[0129] Furthermore, the location determination module includes:

[0130] The position determination unit is used to input the image to be detected into a pre-trained projection position recognition model and output the projection position information of the obstacle edge in the image to be detected.

[0131] Fifth Embodiment

[0132] According to embodiments of this application, this application also provides an electronic device and a readable storage medium.

[0133] like Figure 6The diagram shown is a block diagram of an electronic device for an obstacle ranging method according to an embodiment of this application. 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0134] like Figure 6 As shown, the electronic device includes one or more processors 601, a memory 602, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take the 601 processor as an example.

[0135] The memory 602 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the obstacle ranging method provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the obstacle ranging method provided in this application.

[0136] Memory 602, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the obstacle ranging method in the embodiments of this application (e.g., attached...). Figure 5 (The position determination module 501 and distance determination module 502 are shown). The processor 601 executes various server functions and data processing by running non-transient software programs, instructions, and modules stored in the memory 602, thereby implementing the obstacle ranging method in the above method embodiments.

[0137] Memory 602 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the obstacle ranging electronic device. Furthermore, memory 602 may include high-speed random access memory and may also include non-transient memory, such as at least one disk storage device, flash memory device, or other non-transient solid-state storage device. In some embodiments, memory 602 may optionally include memory remotely located relative to processor 601, and this remote memory may be connected to the obstacle ranging electronic device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, blockchain networks, local area networks, mobile communication networks, and combinations thereof.

[0138] The electronic device for obstacle ranging methods may further include an input device 603 and an output device 604. The processor 601, memory 602, input device 603, and output device 604 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0139] Input device 603 can receive input digital or character information, and generate key signal inputs related to user settings and function control of the obstacle ranging electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, indicator, one or more mouse buttons, trackball, joystick, etc. Output device 604 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0140] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] These computational programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0142] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0143] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0144] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0145] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0146] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for measuring distances from obstacles, characterized in that, Performed by an autonomous vehicle, the method includes: The projection position information of the obstacle edge in the image to be detected is determined based on the image to be detected; wherein, the projection position information is the position information of the obstacle edge vertically projected onto the ground in the image to be detected; the image to be detected is an image acquired in real time by the image acquisition device of the vehicle; Based on the projected position information, the distance between the vehicle and the obstacle is determined; The step of determining the distance between the vehicle and the obstacle based on the projected position information includes: Obstacle detection is performed on the image to be detected to obtain the image region of the obstacle; Based on the overlap between the image region of the obstacle and the projection position information, the mapping relationship between the obstacle and the projection position information is determined. Based on this mapping relationship, the projected positions of at least two obstacles that are stuck together are segmented; Based on the segmented projection position, the distance between the vehicle and the obstacle is determined.

2. The method according to claim 1, characterized in that, Determining the distance between the vehicle and the obstacle based on the projected position information includes: The projected position information is converted to a three-dimensional coordinate system to obtain the spatial position information of the obstacle; Based on the spatial location information of the obstacle, the distance between the vehicle and the obstacle is calculated.

3. The method according to claim 2, characterized in that, Determining the distance between the vehicle and the obstacle based on the mapping relationship includes: The projection position information associated with the obstacle is determined based on the mapping relationship; The distance between the vehicle and the obstacle is determined based on the projected position information associated with the obstacle and the object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

4. The method according to claim 3, characterized in that, The step of determining the distance between the vehicle and the obstacle based on the projected position information associated with the obstacle and the object information of the obstacle includes: The distance calculation logic is determined based on the object information of the obstacle; Based on the established distance calculation logic, the distance between the vehicle and the obstacle is determined according to the projection position information associated with the obstacle.

5. The method according to claim 1, characterized in that, The step of determining the projection position information of the obstacle edge in the image to be detected based on the image to be detected includes: The image to be detected is input into a pre-trained projection position recognition model, which outputs the projection position information of the obstacle edge in the image to be detected.

6. An obstacle ranging device, characterized in that, The device, configured for use in autonomous vehicles, includes: The position determination module is used to determine the projection position information of the obstacle edge in the image to be detected based on the image to be detected; wherein, the projection position information is the position information of the obstacle edge vertically projected onto the ground in the image to be detected; the image to be detected is an image acquired in real time by the image acquisition device of the vehicle; The distance determination module is used to determine the distance between the vehicle and the obstacle based on the projected position information; The distance determination module includes: An obstacle detection unit is used to detect obstacles in the image to be detected and obtain the image region of the obstacle. The mapping relationship determination unit is used to determine the mapping relationship between the obstacle and the projection position information based on the overlap relationship between the image region of the obstacle and the projection position information. The distance determination unit is used to segment the projected positions of at least two obstacles that are stuck together based on the mapping relationship; and to determine the distance between the vehicle and the obstacles based on the segmented projected positions.

7. The apparatus according to claim 6, characterized in that, The distance determination module includes: The coordinate system transformation unit is used to transform the projected position information to a three-dimensional coordinate system to obtain the spatial position information of the obstacle; The distance calculation unit is used to calculate the distance between the vehicle and the obstacle based on the spatial location information of the obstacle.

8. The apparatus according to claim 7, characterized in that, The distance determination unit includes: The information determination subunit is used to determine the projection position information associated with the obstacle based on the mapping relationship; The distance determination subunit is used to determine the distance between the vehicle and the obstacle based on the projected position information associated with the obstacle and the object information of the obstacle. The object information is obtained by identifying the image region of the obstacle.

9. The apparatus according to claim 8, characterized in that, The distance determination subunit is specifically used for: The distance calculation logic is determined based on the object information of the obstacle; Based on the established distance calculation logic, the distance between the vehicle and the obstacle is determined according to the projection position information associated with the obstacle.

10. The apparatus according to claim 6, characterized in that, The location determination module includes: The position determination unit is used to input the image to be detected into a pre-trained projection position recognition model and output the projection position information of the obstacle edge in the image to be detected.

11. 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 to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

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