Automatic driving scene obstacle prediction method, electronic equipment and readable storage medium

Abstract

The invention relates to an automatic driving scene obstacle prediction method, electronic equipment and a readable storage medium, and the method is realized through a trained prediction model, and the trained prediction model comprises a global encoder, a first local encoder and a first decoder, the method comprises the following steps: acquiring state information and environment information of each obstacle in a target scene under a vehicle view angle; inputting the state information and environment information of each obstacle in the target scene into the global encoder to obtain first feature information; obtaining prompt information of a target obstacle; inputting prompt information of the target obstacle into the first local encoder to obtain second feature information; and taking the first feature information as input information of the first decoder, taking the second feature information as prompt information of the first decoder, and inputting the first feature information and the second feature information into the first decoder to obtain prediction information of the target obstacle.

Description

Technical Field

[0001] The present disclosure relates to artificial intelligence technology, and more particularly, to an obstacle prediction method, an electronic device, and a readable storage medium in an autonomous driving scenario. Background Art

[0002] In the prior art, an encoder for trajectory prediction is often an encoder centered on a predicted target obstacle. This encoder models the obstacle information and scene information around each predicted target obstacle once. This modeling process is redundant, with high computational complexity, slow training, and large storage space occupancy.

[0003] In addition, during each modeling process, for example, the same lane will be modeled repeatedly, so that this information cannot be reused in the modeling of different predicted target obstacles. Summary of the Invention

[0004] An object of the present disclosure is to provide a new technical solution for an obstacle prediction method in an autonomous driving scenario.

[0005] According to a first aspect of the present disclosure, there is provided an obstacle prediction method in an autonomous driving scenario, implemented by a trained prediction model. The trained prediction model includes a global encoder, a first local encoder, and a first decoder. The method includes:

[0006] Obtain the state information and environmental information of each obstacle in a target scene from the perspective of an autonomous driving vehicle; wherein the target obstacle is at least one obstacle in the target scene;

[0007] Input the state information and environmental information of each obstacle in the target scene into the global encoder to obtain first feature information;

[0008] Obtain the hint information of the target obstacle;

[0009] Input the hint information of the target obstacle into the first local encoder to obtain second feature information;

[0010] Use the first feature information as the input information of the first decoder, and the second feature information as the hint information of the first decoder, and input them into the first decoder to obtain the prediction information of the target obstacle.

[0011] Optionally, the global encoder is used to construct feature information centered on the autonomous driving vehicle, based on the state information and environmental information of each obstacle in the target scene from the perspective of the autonomous driving vehicle. The first local encoder is used to construct feature information based on the hint information of the target obstacle obtained from the perspective of the autonomous driving vehicle.

[0012] Optionally, the status information of each obstacle includes the position information and motion information of each obstacle, and the environment information includes lane information and traffic light information.

[0013] Optionally, the prompt information of the target obstacle includes the predicted task type information of the target obstacle and the category information of the target obstacle.

[0014] Optionally, the step of inputting the status information and environment information of each obstacle in the target scenario into the global encoder to obtain the first feature information includes:

[0015] Input the status information and environment information of each obstacle in the target scenario into the global encoder to obtain a global feature vector and a position vector; wherein, the position vector includes the position information corresponding to each eigenvalue in the global feature vector.

[0016] Add the global feature vector and the position vector to obtain the first feature information.

[0017] Optionally, the trained prediction model further includes a second local encoder and a second decoder; wherein,

[0018] Obtain the prompt information of the second prediction task; wherein, the first prediction task is one of the prediction tasks in the obstacle prediction, and the first prediction task and the second prediction task are different prediction tasks.

[0019] Input the prompt information of the second prediction task into the second local encoder to obtain the third feature information.

[0020] Use the first feature information as the input information of the second decoder and the third feature information as the prompt information of the second decoder, and input them into the second decoder to obtain the prediction result information of the second prediction task.

[0021] Optionally, the first prediction task and the second prediction task are any one of a trajectory prediction task, an intention understanding task, an intelligent simulation task, and a path planning task.

[0022] Optionally, the method further includes:

[0023] When training the prediction model with the first training sample to obtain the trained global encoder, the trained first local encoder, and the trained first decoder, freeze the trained global encoder.

[0024] Use the second training sample to train only the second local encoder and the second decoder to obtain the trained second local encoder and the trained second decoder.

[0025] According to a second aspect of the present disclosure, there is provided an electronic device including a memory and a processor, where the memory stores computer instructions, and the computer instructions, when executed by the processor, implement the obstacle prediction method for an autonomous driving scenario according to any one of the first aspects.

[0026] According to a third aspect of the present disclosure, there is provided a computer-readable storage medium having computer instructions stored thereon, and the computer instructions, when executed by a processor, implement the obstacle prediction method for an autonomous driving scenario according to any one of the first aspects.

[0027] The obstacle prediction method for an autonomous driving scenario provided by the present disclosure uniformly models the information of each obstacle and the environmental information in a target scenario. These information are converted into unbiased features through a global encoder. Here, unbiased means that the global encoder can extract corresponding feature values from each input information. For the prediction of each target obstacle, the global encoder can be shared, eliminating the problem of duplicate modeling of the same information, solving the problem of large space storage occupancy, improving the training speed, and improving the joint modeling ability of the prediction trajectories of multiple target obstacles.

[0028] Through the following detailed description of the exemplary embodiments of the present specification with reference to the accompanying drawings, the features and advantages of the embodiments of the present specification will become clear. Description of the Drawings

[0029] The drawings incorporated in the specification and constituting a part of the specification illustrate the embodiments of the present specification and, together with the description, are used to explain the principles of the embodiments of the present specification.

[0030] Figure 1 is a schematic diagram of the hardware configuration of an electronic device that can be used to implement the embodiments of the present disclosure provided by an embodiment of the present disclosure;

[0031] Figure 2 is a structural block diagram of a trained prediction model according to an embodiment of the present disclosure;

[0032] Figure 3 is a processing flowchart of an obstacle prediction method for an autonomous driving scenario according to an embodiment of the present disclosure;

[0033] Figure 4 is a structural block diagram of a trained prediction model according to an embodiment of the present disclosure;

[0034] Figure 5 is a structural block diagram of a trained prediction model according to an embodiment of the present disclosure;

[0035] Figure 6 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. Detailed Implementation Modes

[0036] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0037] The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0038] The terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such terms can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0039] It should be noted that all actions of obtaining signals, information, or data in the present application are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where it is located and obtaining the authorization given by the owner of the corresponding device.

[0040] <Hardware Configuration>

[0041] Figure 1 is a schematic structural diagram of an electronic device that can be used to implement the embodiments of the present disclosure. This electronic device can be used to implement the obstacle prediction method in the embodiments of the present disclosure for the autonomous driving scenario.

[0042] The electronic device 1000 can be a server, a smart phone, a portable computer, a desktop computer, a tablet computer, etc., which is not limited herein.

[0043] The electronic device 1000 may include, but is not limited to, a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and so on. Among them, the processor 1100 may be a central processing unit CPU, a graphics processing unit GPU, a microprocessor MCU, etc., and is used to execute computer programs / instructions, and the computer programs / instructions may be written using instruction sets such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, ROM (read-only memory), RAM (random access memory), non-volatile memory such as a hard disk, etc. The interface device 1300 includes, for example, a USB interface, a serial interface, a parallel interface, etc. The communication device 1400 can perform wired communication using optical fibers or cables, or perform wireless communication, and specifically may include WiFi communication, Bluetooth communication, 2G / 3G / 4G / 5G communication, etc. The display device 1500 is, for example, a liquid crystal display screen, a touch display screen, etc. The input device 1600 may include, for example, a touch screen, a keyboard, a body sensor input, etc. The speaker 1700 is used to output an audio signal. The microphone 1800 is used to collect an audio signal.

[0044] Applied to the embodiments of the present disclosure, the memory 1200 of the electronic device 1000 is used to store computer programs / instructions, and the computer programs / instructions are used to control the processor 1100 to operate to implement the obstacle prediction method for the autonomous driving scenario according to the embodiments of the present disclosure. Those skilled in the art can design the computer programs / instructions according to the solutions disclosed in the present disclosure. How the computer programs / instructions control the processor to operate is well known in the art, so it will not be described in detail here. The electronic device 1000 may be installed with an intelligent operating system (such as Windows, Linux, Android, IOS, etc.) and application software.

[0045] Those skilled in the art should understand that although multiple devices of the electronic device 1000 are shown in Figure 1 the electronic device 1000 in the embodiments of the present disclosure may only involve some of the devices. For example, only involve the processor 1100 and the memory 1200, etc.

[0046] Next, various embodiments and examples of the present disclosure will be described with reference to the accompanying drawings.

[0047] <Method Embodiment>

[0048] In this embodiment, an obstacle prediction method for an autonomous driving scenario is provided. This method is implemented through a trained prediction model.

[0049] The architecture of the trained prediction model is shown in Figure 2 According to Figure 2As shown, the trained prediction model includes a global encoder, a first local encoder, and a first decoder.

[0050] The global encoder is used to construct feature information centered on the autonomous driving vehicle, based on the state information and environmental information of each obstacle in the target scene from the perspective of the autonomous driving vehicle. The first local encoder is used to construct feature information based on the hint information of the target obstacle obtained from the perspective of the autonomous driving vehicle.

[0051] According to Figure 3 As shown, the method for predicting obstacles in the autonomous driving scene of this embodiment may include the following steps S310 to S350.

[0052] Step S310, obtain the state information and environmental information of each obstacle in the target scene from the perspective of the autonomous driving vehicle; wherein, the target obstacle is at least one obstacle in the target scene.

[0053] The state information and environmental information of each obstacle can be collected by devices installed on the vehicle.

[0054] In one embodiment, the state information of each obstacle includes the position information and motion information of each obstacle. The position information of each obstacle is the position information in the coordinate system established with the autonomous driving vehicle as the origin. The position information of each obstacle includes the current position information and historical position information of each obstacle. The historical position information is the position information of the obstacle at the previous moment of the current moment, or can also be the position information of the obstacle at multiple historical moments within a previous time period of the current moment. The motion information of each obstacle includes the distance between each obstacle and the autonomous driving vehicle, the speed of each obstacle, and the acceleration of each obstacle. The motion information of each obstacle includes the current motion information and historical motion information. The historical motion information is the motion information of the obstacle at the previous moment of the current moment, or can also be the motion information of the obstacle at multiple historical moments within a previous time period of the current moment.

[0055] Obstacles include movable obstacles and stationary obstacles.

[0056] The environmental information includes lane information and traffic light information. The lane information includes the lane information where the autonomous driving vehicle is currently traveling, the isolation belt information, and the sidewalk information.

[0057] Step S320, input the state information and environmental information of each obstacle in the target scene into the global encoder to obtain the first feature information.

[0058] In one embodiment, step S320 includes: inputting the status information and environmental information of each obstacle in the target scenario into a global encoder to obtain a global feature vector and a position vector; wherein, the position vector includes the position information corresponding to each eigenvalue in the global feature vector; adding the global feature vector and the position vector to obtain first feature information.

[0059] Step S330, obtain the hint information of the target obstacle.

[0060] The hint information of the target obstacle can be collected by a device installed on the vehicle.

[0061] In one embodiment, the hint information of the target obstacle includes the predicted task type information of the target obstacle and the category information of the target obstacle. The predicted task type information of the target obstacle can be any one of trajectory prediction, intention prediction, intelligent simulation, and path planning. Trajectory prediction is the movement trajectory information of a movable obstacle at the next moment. Intention prediction is the movement intention of a stationary obstacle at the next moment. Here, the movement intention can be stationary, or any one of moving forward, moving backward, turning left, and turning right. The category information of the target obstacle can be any one of motor vehicles, non-motor vehicles, pedestrians, and stationary obstacles.

[0062] The target obstacle can be one obstacle in the target scenario or multiple obstacles in the target scenario.

[0063] Step S340, input the hint information of the target obstacle into a first local encoder to obtain second feature information.

[0064] Step S350, input the first feature information as the input information of a first decoder and the second feature information as the hint information of the first decoder into the first decoder to obtain the prediction information of the target obstacle.

[0065] The first feature information characterizes the status information and environmental information of each obstacle around the target obstacle. The second feature information characterizes the information of the target obstacle. Using the attention mechanism, obtain the feature information related to the prediction information of the target obstacle from the first feature information and the second feature information. According to the feature information related to the prediction information of the target obstacle, obtain the prediction information of the target obstacle.

[0066] In one embodiment, the prediction of the obstacle in the autonomous driving scenario can be trajectory prediction, so that the prediction information of the target obstacle obtained based on the autonomous driving scenario obstacle prediction method is trajectory prediction information. The predicted trajectory information of each target obstacle can be one predicted trajectory information or multiple predicted trajectory information.

[0067] The obstacle prediction method for the autonomous driving scenario provided by the present disclosure uniformly models the information of each obstacle and the environmental information in the target scenario. These information are transformed into unbiased features through the global encoder. Here, "unbiased" means that the global encoder can extract corresponding feature values from each input information. For the prediction of each target obstacle, the global encoder can be shared, eliminating the problem of repeated modeling of the same information, solving the problem of large space storage occupation, improving the training speed, and enhancing the joint modeling ability of the prediction trajectories of multiple target obstacles.

[0068] The feature information constructed by the trained global encoder is unbiased features, which are not only applicable to the trajectory prediction of obstacles, but also applicable to the prediction of other tasks, such as intention understanding, intelligent simulation, path planning, etc. In this way, when performing the prediction of different tasks, the global encoder can be shared, and the corresponding local encoder and decoder can be used to complete the tasks.

[0069] In one embodiment, refer to Figure 4 , the trained prediction model further includes a second local encoder and a second decoder. The method further includes: obtaining the hint information of the second prediction task, inputting the hint information of the second prediction task into the second local encoder to obtain the third feature information, using the first feature information as the input information of the second decoder, and using the third feature information as the hint information of the second decoder, and inputting them into the second decoder to obtain the prediction result information of the second prediction task. The first prediction task is a prediction task in obstacle prediction, and the first prediction task and the second prediction task are different prediction tasks.

[0070] The second local encoder is used to construct feature information based on the hint information of the second prediction task obtained from the perspective of the autonomous driving vehicle.

[0071] In one embodiment, the method further includes: when training the prediction model with the first training sample to obtain the trained global encoder, the trained first local encoder, and the trained first decoder, freezing the trained global encoder, and using the second training sample to train only the second local encoder and the second decoder to obtain the trained second local encoder and the trained second decoder.

[0072] Freezing the trained global encoder is to freeze the parameters involved in the optimization during the training of the global encoder. When training the local encoder and the second decoder with the second training sample, the global encoder no longer participates in the training.

[0073] Since the global encoder transforms the input information into unbiased features, the output information of the global encoder can be shared with different tasks to share the global encoder.

[0074] In one embodiment, in combination with Figure 5 , the prediction method for the first prediction task and the prediction method for the second prediction task are described. The first prediction task is the trajectory prediction of obstacles. The second prediction task is the path planning task.

[0075] First, obtain the state information of movable obstacles, the state information of stationary obstacles, lane information, and traffic light information in the target scene from the vehicle's perspective.

[0076] Then, input the state information of movable obstacles, the state information of stationary obstacles, lane information, and traffic light information in the target scene into the global encoder to obtain a global feature vector and a position vector. The position vector includes the position information corresponding to each eigenvalue in the global feature vector. Add the global feature vector and the position vector to obtain the first feature information.

[0077] Obtain the hint information of multiple target obstacles. The hint information of each target obstacle includes the prediction task type information of the target obstacle and the category information of the target obstacle. Input the hint information of each target obstacle into the first local encoder to obtain multiple second feature information. Figure 5 The hints of the three target obstacles shown are only taken as an example and do not impose any limitation on the present invention.

[0078] Use the first feature information as the input information of the first decoder, and each second feature information as the hint information of the first decoder, and input them into the first decoder to obtain the predicted trajectory information of each target obstacle.

[0079] Obtain the hint information of the path planning task, input the hint information of the path planning task into the second local encoder to obtain the third feature information, use the first feature information as the input information of the second decoder, and the third feature information as the hint information of the second decoder, and input them into the second decoder to obtain the predicted result information of the path planning task.

[0080] The above-mentioned trajectory prediction task of obstacles and the path planning task of the vehicle can be carried out simultaneously or separately according to actual needs, and no limitation is imposed here.

[0081] <Device Embodiment>

[0082] Figure 6 FIG. 600 is a schematic diagram of an electronic device 600 provided by an embodiment of the present disclosure. The electronic device 600 includes a processor 610 and a memory 620. The memory 620 stores computer instructions, and when the computer instructions are executed by the processor 610, the obstacle prediction method for the autonomous driving scenario disclosed in any of the foregoing embodiments is implemented.

[0083] An embodiment of the present disclosure also provides a computer-readable storage medium, on which computer instructions are stored. When the computer instructions are executed by a processor, the obstacle prediction method for an autonomous driving scenario disclosed in any of the foregoing embodiments is implemented.

[0084] <Vehicle Embodiment>

[0085] An embodiment of the present disclosure also provides a vehicle, which includes the electronic device disclosed in any of the foregoing embodiments.

[0086] The various embodiments in the present disclosure are described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the apparatus and device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0087] The above describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0088] The embodiments of the present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium, on which computer-readable program instructions for causing a processor to implement various aspects of the embodiments of the present disclosure are loaded.

[0089] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: 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), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not construed to be a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0090] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or can be downloaded to an external computer or an external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0091] The computer program instructions for performing the operations of the embodiments of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine - related instructions, microcode, firmware instructions, state - setting data, or source code or object code written in any combination of one or more programming languages, including object - oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer - readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand - alone software package, partially on the user's computer and 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 through any type of network, including a local area network (LAN) or a wide area network (WAN), or, alternatively, may be connected to an external computer (e.g., via an Internet service provider through the Internet). In some embodiments, by using the state information of the computer - readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field - programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer - readable program instructions to implement various aspects of the embodiments of the present disclosure.

[0092] Aspects of the embodiments of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer - readable program instructions.

[0093] These computer - readable program instructions can be provided to a processor of a general - purpose computer, a special - purpose computer, or other programmable data - processing apparatus to produce a machine such that the instructions, when executed by the processor of the computer or other programmable data - processing apparatus, create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer - readable program instructions can also be stored in a computer - readable storage medium, which causes a computer, a programmable data - processing apparatus, and / or other devices to operate in a particular manner, so that the computer - readable medium storing the instructions includes a manufacture, which includes instructions for implementing various aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0094] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0095] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions. As is well known to those skilled in the art, implementation through hardware, implementation through software, and implementation through a combination of software and hardware are equivalent.

[0096] The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of technologies in the market, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting obstacles in an autonomous driving scenario, characterized in that: This is achieved by a trained prediction model, wherein the trained prediction model includes a global encoder, a first local encoder, and a first decoder, and the method includes: Acquire state information and environment information of each obstacle in a target scene from the perspective of the autonomous driving vehicle; wherein the target obstacle is at least one obstacle in the target scene; Inputting state information and environment information of each obstacle in the target scene into the global encoder to obtain first feature information; Get prompt information of target obstacles; Inputting the prompt information of the target obstacle into the first local encoder to obtain second feature information; The first feature information is used as input information of the first decoder, and the second feature information is used as prompt information of the first decoder, and is input into the first decoder to obtain prediction information of the target obstacle.

2. The method according to claim 1, characterized in that The global encoder is used to construct feature information based on the state information and environmental information of each obstacle in the target scene from the perspective of the autonomous driving vehicle, with the autonomous driving vehicle as the center. The first local encoder is used to construct feature information based on the prompt information of the target obstacle obtained from the perspective of the autonomous driving vehicle.

3. The method according to claim 1, characterized in that The state information of each obstacle includes the position information and movement information of each obstacle, and the environmental information includes lane information and traffic light information.

4. The method according to claim 1, characterized in that: The prompt information of the target obstacle includes prediction task type information of the target obstacle and category information of the target obstacle.

5. The method according to claim 1, characterized in that The step of inputting the state information and environment information of each obstacle in the target scene into the global encoder to obtain the first feature information includes: Inputting the state information and environment information of each obstacle in the target scene into the global encoder to obtain a global feature vector and a position vector; wherein the position vector includes the position information corresponding to each eigenvalue in the global feature vector; The global feature vector and the position vector are added to obtain the first feature information.

6. The method according to claim 1, characterized in that The trained prediction model also includes a second local encoder and a second decoder; wherein, Obtain prompt information of a second prediction task; wherein the first prediction task is a prediction task in the obstacle prediction, and the first prediction task and the second prediction task are different prediction tasks; Inputting the prompt information of the second prediction task into the second local encoder to obtain third feature information; The first feature information is used as input information of the second decoder, and the third feature information is used as prompt information of the second decoder, and is input into the second decoder to obtain prediction result information of the second prediction task.

7. The method according to claim 6, characterized in that The first prediction task and the second prediction task are any two of a trajectory prediction task, an intention understanding task, an intelligent simulation task, and a path planning task.

8. The method according to claim 6, characterized in that The method further comprises: When the prediction model is trained by using the first training sample to obtain a trained global encoder, a trained first local encoder, and a trained first decoder, freezing the trained global encoder; Only the second local encoder and the second decoder are trained using the second training sample to obtain a trained second local encoder and a trained second decoder.

9. An electronic device, characterized in that: It includes a memory and a processor, the memory stores computer instructions, and the computer instructions, when executed by the processor, implement the automatic driving scene obstacle prediction method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the method for predicting obstacles in an autonomous driving scene according to any one of claims 1-8 is implemented.

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