Method, device and system for detecting objects around a vehicle and traveling

Abstract

To disclose a method for predicting a future trajectory of a current target vehicle using movement information on one or more past surrounding vehicles.SOLUTION: A method includes the steps of: receiving, by a server, movement information on one or more past surrounding vehicles at a reference location of a past vehicle; acquiring, by the server, from the movement information, first state information on the one or more past surrounding vehicles at a first time point which corresponds to a past time point prior to a reference time point; acquiring, by the server, from the movement information, movement positions on the one or more past surrounding vehicles at a second time point after a predetermined time elapsed from the first time point; and probabilistically calculating, by the server, a distribution of the movement positions using a clustering technique. The method is characterized in that the distribution of movement positions is used to predict a future trajectory of a current target vehicle.SELECTED DRAWING: Figure 16

Description

[Technical field]

[0001] The present disclosure relates to a method, apparatus and system for detecting objects around a vehicle and navigating the vehicle. [Background technology]

[0002] The convergence of information and communication technology and the vehicle industry is rapidly making vehicles smarter. As vehicles become smarter, they are evolving from simple mechanical devices to smart cars, and autonomous driving in particular is attracting attention as a core technology for smart cars. Autonomous driving is a technology that allows a vehicle to reach its destination by itself without the driver having to operate the steering wheel, accelerator pedal, brakes, etc.

[0003] As various additional functions related to autonomous driving continue to be developed, there is a continuing demand for research into methods for providing a safe autonomous driving experience for both passengers and pedestrians by using various data to recognize and assess the driving environment and control the vehicle.

[0004] In particular, autonomous vehicles may need to predict the future trajectories or movements of surrounding vehicles for safe operation. Thus, there is a need for inventions relating to methods for predicting the trajectories of surrounding vehicles.

[0005] However, other information is also needed that can be used to predict the movements of surrounding vehicles.

[0006] The above-mentioned background art is technical information possessed by the inventor for the purpose of deriving the present invention or acquired in the process of deriving the present invention, and is not necessarily publicly known art that was disclosed to the general public prior to the filing of the present application. Summary of the Invention [Problem to be solved by the invention]

[0007] Some embodiments of the present disclosure provide a method, device, and system for detecting objects around a vehicle and traveling. The problem to be solved by the present invention is not limited to the problem described above, and other problems and advantages of the present invention not described will be understood from the following description and will be more clearly understood from the embodiments of the present invention. It will also be understood that the problems and advantages to be solved by the present invention can be realized by the means and combinations thereof shown in the claims. [Means for solving the problem]

[0008] As a technical means for solving the above-mentioned technical problem, a first aspect of the present disclosure can provide a method for probabilistically calculating a distribution of target positions for predicting a future trajectory of a current target vehicle using past movement information of one or more past surrounding vehicles, the method including a step of a server receiving the movement information regarding the one or more past surrounding vehicles at a reference position of a past traveling vehicle, a step of the server acquiring first state information regarding the one or more past surrounding vehicles at a first time point - the first time point corresponding to a past time point before the reference time point - from the movement information, a step of the server acquiring movement positions of the one or more past surrounding vehicles at a second time point a predetermined time after the first time point, and a step of the server probabilistically calculating the distribution of the movement positions using a clustering method, characterized in that the distribution of the movement positions is used to predict the future trajectory of the current target vehicle.

[0009] Here, the method may further include a step of receiving second status information regarding the current target vehicle at the reference position and the reference time, and a step of selecting a distribution of movement positions that matches the second status information regarding the current target vehicle from the distribution of movement positions regarding the one or more past surrounding vehicles.

[0010] Here, the distribution of the selected moving positions is used to estimate a distribution of target positions for the current target vehicle at a third time point - the third time point corresponding to a future time point at which the predetermined time has elapsed from the reference time point - and the distribution of target positions can be used to predict a future trajectory of the current target vehicle.

[0011] Here, the first state information regarding the past surrounding vehicle and the second state information regarding the current target vehicle may include a position value and a speed value of the vehicle.

[0012] Here, the step of selecting the distribution of movement positions to be matched with the second status information may include the steps of receiving the position value and the speed value of the current target vehicle, extracting from the first status information position values ​​of the one or more past surrounding vehicles that fall within a predetermined range from the position value, selecting one or more past surrounding vehicles having speed values ​​that fall within a predetermined range from the speed value from among the one or more past surrounding vehicles having the position values, and selecting a distribution of movement positions for the selected one or more past surrounding vehicles at the second time point.

[0013] Here, the movement position may be a position specified by at least one of a road on which the one or more previous surrounding vehicles were traveling at the second time point and a lane included in the road.

[0014] Here, the step of probabilistically calculating the distribution of the movement positions may include a step of generating one or more clusters for the movement positions by using a clustering method, a step of approximating a Gaussian distribution for each cluster, and a step of acquiring a Gaussian mixture distribution for the movement positions by applying a Gaussian Mixture Model (GMM) to the one or more clusters approximated to the Gaussian distribution.

[0015] Here, the clustering method may be a Density Based Spatial Clustering of Applications with Noise (DBSCAN) method.

[0016] A second aspect of the present disclosure provides a server that predicts a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles, the server including a memory having at least one program stored therein and at least one processor that executes the at least one program, wherein the at least one processor receives the movement information regarding the one or more past surrounding vehicles at a reference position of a past traveling vehicle, obtains from the movement information first state information regarding the one or more past surrounding vehicles at a first time point - the first time point corresponding to a past time point before the reference time point -, obtains from the movement information movement positions of the one or more past surrounding vehicles at a second time point a predetermined time after the first time point, probabilistically calculates a distribution of the movement positions using a clustering method, and the distribution of the movement positions is used to predict the future trajectory of the current target vehicle.

[0017] A third aspect of the present disclosure provides a system for predicting a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles, the system including: a device in a past traveling vehicle that acquires movement information regarding one or more past surrounding vehicles at a reference position of the past traveling vehicle; a server that probabilistically calculates a distribution of movement positions regarding the one or more past surrounding vehicles using the movement information; and a device in a currently traveling vehicle that predicts a future trajectory of the currently target vehicle using the distribution of movement positions, wherein the device in the currently traveling vehicle acquires second state information regarding the current target vehicle at the reference position and a reference time point, receives from the server the distribution of movement positions regarding the past surrounding vehicles that are matched with the second state information regarding the currently target vehicle, estimates a distribution of target positions regarding the current target vehicle at a third time point - the third time point corresponding to a future time point at which a predetermined time has elapsed from the reference time point - using the distribution of movement positions, and predicts the future trajectory of the currently target vehicle using the distribution of target positions.

[0018] Here, the device in the currently traveling vehicle may further include obtaining a Gaussian mixture distribution for the target position, and calculating an average point of the Gaussian mixture distribution and a probability for each cluster included in the Gaussian mixture distribution.

[0019] A fourth aspect of the present disclosure can provide a method for determining the behavior of an autonomous vehicle planning to turn right to merge onto a main lane, the method including the steps of acquiring a collision lane from among lanes included in the main lane that may overlap with a driving path of the autonomous vehicle, classifying one or more candidate vehicles that may collide with the autonomous vehicle from among vehicles traveling in the collision lane, predicting whether the candidate vehicle will occupy a predetermined collision area within a specified time by predicting a path for the candidate vehicle, and determining the behavior of the autonomous vehicle planning to turn right based on the prediction result regarding the occupancy and the current driving state of the autonomous vehicle.

[0020] Here, the method may further include a step of setting the collision area based on the driving path of the autonomous vehicle and the collision lane.

[0021] Here, the step of determining the behavior of the autonomous vehicle may include a step of determining the behavior of the autonomous vehicle to slow down in the behavior decision area when the current driving state is a first state in which the autonomous vehicle is entering a behavior decision area, which is an area within a predetermined distance from the collision area.

[0022] Here, the step of determining the behavior of the autonomous vehicle may include a step of determining the behavior of the autonomous vehicle to slow down at or below a first threshold speed in the behavior decision area when the current driving state is a first state and no pedestrian is identified.

[0023] Here, the step of determining the behavior of the autonomous vehicle includes a step of determining the behavior of the autonomous vehicle such that, when the current driving state is a first state and a pedestrian is identified, the autonomous vehicle slows down at or below a second threshold speed in the behavior decision area, and the value of the first threshold speed may be greater than the value of the second threshold speed.

[0024] Here, the step of determining the behavior of the autonomous vehicle may include, when the current driving state is a second state in which the autonomous vehicle is moving slowly in the behavior decision area and the candidate vehicle is predicted to occupy the collision area, determining the behavior of the autonomous vehicle so that the autonomous vehicle stops in the behavior decision area and does not pass through the collision area for a predetermined time.

[0025] Here, the step of determining the behavior of the autonomous vehicle may include a step of determining the behavior of the autonomous vehicle such that, when the current driving state is a second state in which the autonomous vehicle is moving slowly in the behavior decision area and it is predicted that the candidate vehicle will not occupy the collision area, the autonomous vehicle passes through the collision area by turning right onto the main line.

[0026] Here, the step of determining the behavior of the autonomous vehicle may include a step of determining the behavior of the autonomous vehicle to slow down in the behavior decision area when the current driving state is a third state in which the autonomous vehicle is stopped in the behavior decision area and it is predicted that the candidate vehicle will not occupy the collision area.

[0027] Here, the step of determining the behavior of the autonomous vehicle may include a step of determining the behavior of the autonomous vehicle so as to maintain the autonomous vehicle stopped in the behavior decision area when the current driving state is a third state in which the autonomous vehicle is stopped in the behavior decision area and the candidate vehicle is predicted to occupy the collision area.

[0028] A fifth aspect of the present disclosure can provide an apparatus for determining the behavior of an autonomous vehicle planning to turn right to merge onto a main lane, the apparatus including a memory having at least one program stored therein, and at least one processor that executes the at least one program, wherein the at least one processor obtains a collision lane from among lanes included in the main lane that may overlap with a driving path of the autonomous vehicle, classifies one or more candidate vehicles that may collide with the autonomous vehicle from among vehicles traveling in the collision lane, predicts a path for the candidate vehicles, thereby predicting whether the candidate vehicles will occupy a predetermined collision area within a specified time, and determines the behavior of the autonomous vehicle planning to turn right based on the prediction result regarding the occupancy and the current driving state of the autonomous vehicle.

[0029] A sixth aspect of the present disclosure may provide a computer-readable recording medium having recorded thereon a program for causing a computer to execute the method according to the first aspect or the method according to the fourth aspect.

[0030] In addition, other methods and systems for implementing the present invention, and computer-readable recording media having computer programs for executing the methods can also be provided.

[0031] Further aspects, features, and advantages will become apparent from the accompanying drawings, the claims, and the following detailed description of the invention. Effect of the Invention

[0032] According to the problem-solving means of the present disclosure described above, by probabilistically calculating the distribution of movement positions using movement information regarding past surrounding vehicles, it is possible to generate information used to predict the trajectory of surrounding vehicles.

[0033] According to the problem-solving means of the present disclosure described above, a more accurate future trajectory of the surrounding vehicles can be predicted by estimating the distribution of target positions of the surrounding vehicles using the distribution of movement positions of the surrounding vehicles in the past.

[0034] According to the problem-solving means of the present disclosure described above, by observing the movements of vehicles surrounding the autonomous vehicle, it is possible to determine safe and efficient actions for an autonomous vehicle planning to turn right to merge onto a main lane. [Brief description of the drawings]

[0035] [Figure 1] FIG. 1 is a diagram for explaining an example of an autonomous driving system according to an embodiment. [Figure 2A] FIG. 2 is a block diagram for explaining the internal configuration of an apparatus according to an embodiment. [Figure 2B] FIG. 2 is a block diagram illustrating an internal configuration of a server according to an embodiment. [Diagram 3] FIG. 2 is a diagram for explaining an autonomous driving method according to an embodiment. [Figure 4] FIG. 2 is a diagram for explaining an autonomous driving method according to an embodiment. [Diagram 5] FIG. 2 is a diagram for explaining an autonomous driving method according to an embodiment. [Figure 6] 1 is a diagram illustrating an example of a plurality of objects included in video data according to an embodiment. [Figure 7] FIG. 2 is a diagram illustrating a method for receiving movement information regarding one or more past surrounding vehicles according to an embodiment. [Figure 8] 4 is a diagram for explaining a method for acquiring first state information on previously surrounding vehicles from movement information according to an embodiment. FIG. [Figure 9] 11 is a diagram for explaining a method for acquiring a past movement position of a nearby vehicle from movement information according to an embodiment. FIG. [Figure 10] 11 is a diagram for explaining a method for acquiring a past movement position of a nearby vehicle from movement information according to an embodiment. FIG. [Figure 11] FIG. 11 is a diagram for explaining a method for probabilistically calculating a distribution of movement positions using a clustering method according to an embodiment. [Figure 12] FIG. 11 is a diagram for explaining a method for probabilistically calculating a distribution of movement positions using a clustering method according to an embodiment. [Figure 13] 11 is a diagram illustrating a method for estimating a distribution of target positions for a current target vehicle using a distribution of moving positions according to an embodiment; [Figure 14] 10 is a diagram illustrating a method for predicting a future trajectory of a current target vehicle using a distribution of target positions according to an embodiment; [Figure 15] 4 is a flowchart illustrating a method for probabilistically calculating a distribution of movement positions using past movement information of surrounding vehicles according to an embodiment of the present invention. [Figure 16] 4 is a flowchart illustrating a method for predicting a future trajectory of a current target vehicle using movement information of past surrounding vehicles according to an embodiment of the present invention. [Figure 17] FIG. 13 is a diagram for explaining a method for acquiring a collision lane according to an embodiment of the present disclosure. [Figure 18] 1 is a diagram for explaining a method for classifying candidate vehicles from among vehicles traveling in a collision lane according to an embodiment of the present disclosure. FIG. [Figure 19] 1 is a diagram for explaining a method for classifying candidate vehicles from among vehicles traveling in a collision lane according to an embodiment of the present disclosure. FIG. [Figure 20] 1 is a diagram for explaining a method for predicting whether a candidate vehicle occupies a collision area according to an embodiment of the present disclosure. FIG. [Figure 21] FIG. 2 is a diagram for explaining a method for determining the behavior of an autonomous vehicle in a behavior decision area according to one embodiment of the present disclosure. [Figure 22] 1 is a flowchart illustrating a method for determining an action of an autonomous vehicle in an action decision region according to one embodiment of the present disclosure. [Diagram 23] 1 is a flowchart illustrating a method for determining an action of an autonomous vehicle according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0036] The advantages and features of the present invention, as well as the methods for achieving them, will become apparent from the embodiments described in detail with the accompanying drawings. However, the present invention is not limited to the embodiments presented below, and can be realized in various different forms, and should be understood to include all modifications, equivalents, and alternatives within the spirit and technical scope of the present invention. The embodiments presented below are provided to complete the disclosure of the present invention and to allow those skilled in the art to fully understand the scope of the invention. In describing the present invention, if a detailed description of related publicly known technology is deemed to obscure the gist of the present invention, the detailed description will be omitted.

[0037] The terms used in this application are merely used to describe certain embodiments and are not intended to limit the present invention. A singular expression includes a plural expression unless the context clearly indicates otherwise. In this application, the terms "include" and "have" are used to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and are not to be understood as precluding the presence or additional possibility of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0038] Some embodiments of the present disclosure may be illustrated in terms of functional block configurations and various processing steps. Some or all of such functional blocks may be implemented in any number of hardware and / or software configurations for performing specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors or by circuit configurations for a given function. Also, for example, the functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks may be implemented by algorithms executed by one or more processors. Furthermore, the present disclosure may employ conventional techniques for electronic configuration, signal processing, and / or data processing, etc. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical and physical configurations.

[0039] It should be noted that the connecting lines or connecting members between components shown in the drawings are merely exemplary of functional and / or physical connections or circuit connections, and that in an actual device, connections between components may be represented by various alternative or additional functional connections, physical connections or circuit connections.

[0040] Hereinafter, the term "vehicle" may refer to any type of transportation means having a locomotive and used to move people or goods, such as a car, a bus, a motorcycle, a scooter, or a truck.

[0041] The present disclosure will now be described in detail with reference to the accompanying drawings.

[0042] FIG. 1 is a diagram for explaining an example of an autonomous driving system according to an embodiment.

[0043] 1, the system 10 may include a server 20 and one or more in-vehicle devices 30. For example, the server 20 and the in-vehicle devices 30 are connected to each other via a wired or wireless communication method, and can transmit and receive data (e.g., video data, etc.) to and from each other.

[0044] 1 illustrates system 10 including, but not limited to, server 20 and in-vehicle device 30. For example, system 10 may include other external devices (not shown), and the operations of server 20 and in-vehicle device 30 described below may be implemented by a single device (e.g., server 20 or in-vehicle device 30) or more devices.

[0045] The server 20 may be a device that communicates with external devices (not shown) including the in-vehicle device 30. As an example, the server 20 may be a device that stores various data including video data acquired by the in-vehicle device 30. Alternatively, the server 20 may be a computing device including a memory and a processor and having its own computing power. When the server 20 is a computing device, the server 20 may perform at least a part of the operation of the in-vehicle device 30 described below. For example, the server 20 may be, but is not limited to, a cloud server.

[0046] Meanwhile, as an example, the in-vehicle device 30 may be an electronic device built into a vehicle. For example, the in-vehicle device 30 may be an electronic device inserted into a vehicle by tuning after the production process. For example, the in-vehicle device 30 may be an autonomous driving device that is installed in an autonomous driving vehicle to realize the autonomous driving vehicle.

[0047] As another example, the in-vehicle device 30 may be a mobile electronic device. For example, the in-vehicle device 30 may be a smartphone, a tablet PC, a PC, a smart TV, a PDA (personal digital assistant), a laptop, a media player, a navigation system, a camera-equipped device, and other mobile electronic devices. The in-vehicle device 30 may also be a wearable device, such as a watch, glasses, a headband, or a ring, that has a communication function and a data processing function.

[0048] FIG. 2A is a block diagram for explaining the internal configuration of an apparatus according to an embodiment.

[0049] Referring to Fig. 2A, the device 100 in the vehicle may include a processor 110, a memory 120, an input / output interface 130, and a communication module 140. For convenience of explanation, Fig. 2A shows only components related to the present invention. Therefore, in addition to the components shown in Fig. 2A, other general-purpose components may be further included in the device 100 in the vehicle. It is obvious to a person skilled in the art related to the present invention that the processor 110, the memory 120, the input / output interface 130, and the communication module 140 shown in Fig. 2A may be realized as independent devices.

[0050] The processor 110 may process instructions of a computer program by performing basic arithmetic, logic, and input / output operations, where the instructions may be provided from the memory 120 or from an external device (such as the server 20), and may generally control the operation of other components included in the device 100 within the vehicle.

[0051] The processor 110 may be implemented with an array of logic gates or may be implemented with a general-purpose microprocessor and a memory in which a program executable by the microprocessor is stored. For example, the processor 110 may include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the processor 110 may also include an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. For example, the processor 110 may represent a combination of processing devices, such as a combination of a digital signal processor (DSP) and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors coupled to a digital signal processor (DSP) core, or any other such configuration.

[0052] The memory 120 may include any non-transitory computer-readable recording medium. As an example, the memory 120 may include a permanent mass storage device such as a random access memory (RAM), a read only memory (ROM), a disk drive, a solid state drive (SSD), or a flash memory. As another example, the permanent mass storage device such as a ROM, an SSD, a flash memory, or a disk drive may be a separate permanent storage device separate from the memory. The memory 120 may also store an operating system (OS) and at least one program code (e.g., code for the processor 110 to execute the operation described below as the operation of the device 100 in the vehicle).

[0053] Such software components may be loaded from a computer-readable recording medium separate from the memory 120. Such a separate computer-readable recording medium may be a recording medium that can be directly connected to the device 100 in the vehicle, and may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD / CD-ROM drive, or a memory card. Alternatively, the software components may be loaded into the memory 120 by the communication module 140, rather than a computer-readable recording medium. For example, at least one program may be loaded into the memory 120 based on a computer program (e.g., a computer program for the processor 110 to execute an operation described below as an operation of the device 100 in the vehicle) that is installed by a file provided by the communication module 140 from a developer or a file distribution system that distributes an installation file of an application.

[0054] The I / O interface 130 may be a means for interfacing with devices for input or output (e.g., a keyboard, a mouse, etc.) connected to or included in the in-vehicle device 100. Although the I / O interface 130 is shown in FIG. 2A as an element configured separately from the processor 110, this is not limiting and the I / O interface 130 may be configured to be included in the processor 110.

[0055] The communication module 140 can provide a configuration or function for an external device (e.g., the server 20) and the in-vehicle device 100 to communicate with each other via a network. The communication module 140 can also provide a configuration or function for the in-vehicle device 100 to communicate with other external devices. For example, control signals, instructions, data, etc. provided under the control of the processor 110 can be transmitted to the server 20 and / or the external device via the communication module 140 and the network.

[0056] Meanwhile, although not shown in Fig. 2A, the in-vehicle device 100 may further include a display device, or the in-vehicle device 100 may be connected to an independent display device via a wired or wireless communication method, and may transmit and receive data to and from the independent display device.

[0057] FIG. 2B is a block diagram for explaining the internal configuration of the server according to an embodiment.

[0058] Referring to Fig. 2B, the server 200 includes a processor 210, a memory 220, and a communication module 230. For convenience of explanation, Fig. 2B shows only components related to the present invention. Therefore, in addition to the components shown in Fig. 2B, other general-purpose components may be further included in the server 200. It is obvious to a person skilled in the art related to the present invention that the processor 210, the memory 220, and the communication module 230 shown in Fig. 2B may be realized as independent devices.

[0059] According to one embodiment, at least one of the operations of the processor 110 of the in-vehicle device 100 described below can be performed by the processor 210 of the server 200. In that case, the in-vehicle device 100 can output information transmitted from the server 200 by a display device.

[0060] Meanwhile, the implementation of the processor 210 is similar to the implementation of the processor 110 described above with reference to FIG. 2A, so a detailed description thereof will be omitted.

[0061] The memory 220 can store various data, such as video data acquired by the device 100 in the vehicle. The memory 220 can also store an operating system (OS) and at least one program (e.g., a program required for the processor 210 to operate).

[0062] Meanwhile, the implementation of the memory 220 is similar to the implementation of the memory 120 described above with reference to FIG. 2A, so a detailed description thereof will be omitted.

[0063] The communication module 230 may provide a configuration or function for the server 200 and one or more in-vehicle devices 100 to communicate with each other via a network. The communication module 230 may also provide a configuration or function for the server 200 to communicate with other external devices. For example, control signals, instructions, data, etc. provided under the control of the processor 210 may be transmitted to the in-vehicle devices 100 and / or external devices via the communication module 230 and the network.

[0064] FIG. 3 is a diagram for explaining an autonomous driving method according to an embodiment.

[0065] Referring to FIG. 3, the in-vehicle device 30 according to an embodiment of the present invention may be mounted on a vehicle to realize an autonomous vehicle 31. For example, the in-vehicle device 30 mounted on the autonomous vehicle 31 may be an autonomous driving device. The autonomous driving device mounted on the autonomous vehicle 31 may include various sensors for collecting surrounding situation information. As an example, the autonomous driving device may detect the movement of a leading vehicle 32 traveling ahead using an image sensor and / or an event sensor mounted on the front of the autonomous vehicle 31. The autonomous driving device may further include sensors for detecting other vehicles 33 traveling on adjacent roads as well as pedestrians around the autonomous vehicle 31.

[0066] At least one of the sensors for collecting situational information around the autonomous vehicle can have a predetermined angle of view (FoV) as shown in Fig. 3. As an example, when a sensor mounted on the front of the autonomous vehicle 31 has an angle of view (FoV) as shown in Fig. 3, information detected at the center of the sensor can have a relatively high importance. This is because most of the information corresponding to the movement of the leading vehicle 32 is included in the information detected at the center of the sensor.

[0067] The autonomous driving device processes information collected by sensors of the autonomous vehicle 31 in real time to control the movement of the autonomous vehicle 31, while at least a portion of the information collected by the sensors can be stored in a memory device.

[0068] FIG. 4 is a block diagram illustrating hardware included in an in-vehicle device according to one embodiment.

[0069] 4, the in-vehicle device 40 may include a sensor unit 41, a processor 46, a memory system 47, a vehicle body control module 48, etc. The sensor unit 41 includes a plurality of sensors 42-45, which may include an image sensor, an event sensor, an illuminance sensor, a GPS device, an acceleration sensor, etc.

[0070] The data collected by the sensors 42-45 can be transmitted to the processor 46. The processor 46 can store the data collected by the sensors 42-45 in the memory system 47 and control the body control module 48 to determine the movement of the vehicle based on the data collected by the sensors 42-45. The memory system 47 can include two or more memory devices and a system controller for controlling the memory devices. Each of the memory devices can be provided as a single semiconductor chip.

[0071] In addition to the system controller of memory system 47, each of the memory devices included in memory system 47 may include a memory controller, and the memory controller may include an artificial intelligence (AI) calculation circuit such as a neural network. The memory controller can generate calculation data by applying a predetermined weight to data received from sensors 42 to 45 or processor 46, and store the calculation data in a memory chip.

[0072] FIG. 5 is a diagram showing an example of video data acquired by a sensor (including a camera) of an autonomous vehicle equipped with an autonomous driving device according to an embodiment.

[0073] 5, the video data 50 may be data acquired by a sensor attached to the front of the autonomous vehicle. Thus, the video data 50 may include a front portion 51 of the autonomous vehicle, a preceding vehicle 52 on the same road as the autonomous vehicle, vehicles 53 traveling around the autonomous vehicle, and a background 54.

[0074] 5, data of the area in which the front part 51 and background 54 of the autonomous vehicle are displayed may be data that is unlikely to affect the operation of the autonomous vehicle. In other words, the front part 51 and background 54 of the autonomous vehicle may be considered to be data having a relatively low importance.

[0075] In contrast, the distance to the preceding vehicle 52, lane-changing movements of the moving vehicle 53, and the like can be very important factors in the safe operation of the autonomous vehicle. Therefore, in the video data 50, data of an area including the preceding vehicle 52 and the moving vehicle 53 can have a relatively high degree of importance in the operation of the autonomous vehicle.

[0076] The memory device of the autonomous driving device can store the image data 50 received from the sensor by assigning different weights to each area. For example, a high weight can be assigned to data of an area including a leading vehicle 52 and a moving vehicle 53, and a low weight can be assigned to data of an area including a front part 51 of the autonomous driving vehicle and a background 54.

[0077] FIG. 6 is a diagram for explaining an example of a plurality of objects included in video data according to an embodiment.

[0078] Video data collected by one or more monocular cameras can be used to train a deep neural network model for depth estimation. The collected video data may contain multiple objects.

[0079] Information about an object includes object type information and object attribute information. Here, object type information is index information indicating the type of object and is composed of a group, which is a broad range, and a class, which is a finer range. Also, object attribute information indicates attribute information related to the current state of an object and includes motion information, rotation information, traffic information, color information, and visibility information.

[0080] In one embodiment, the groups and classes included in the object type information are as shown in Table 1 below, but are not limited thereto. [Table 1] Furthermore, the information included in the object attribute information may include Action information, Rotate information, Traffic information, Color information, and Visibility information.

[0081] Action information indicates the motion information of an object, and can be defined as stopping, parking, moving, etc. In the case of a vehicle, stopping, parking, and moving can be determined as object attribute information, and in the case of a stationary object such as a traffic light, the default value of stillness can be determined as object attribute information.

[0082] Rotate information indicates rotation information of an object and can be defined as front, rear, horizontal, vertical, side, etc. In the case of a vehicle, the front, rear, and side can be determined as object attribute information, and for a horizontal or vertical traffic light, the horizontal or vertical can be determined as object attribute information, respectively.

[0083] Traffic information means traffic information of an object, and can be defined as traffic sign instructions, cautions, regulations, auxiliary signs, etc. Color information means color information of an object, and can indicate the color of an object, traffic lights, and traffic signs.

[0084] Referring to FIG. 6, objects included in the collected video data may be, but are not limited to, traffic lights, road signs, the current lane, road markings, crosswalks, speed bumps, intersections, etc.

[0085] On the other hand, as described above, an autonomous vehicle may need to predict the movements or trajectories of a preceding vehicle 51, a traveling vehicle 53, etc., for safe operation.

[0086] In order to predict the movement or trajectory of surrounding vehicles operating around the autonomous vehicle, accumulated past driving information and map information regarding the operation of the surrounding vehicles can be used.

[0087] Below, in Figure 7 and subsequent figures, we will explain a method of probabilistically calculating the distribution of target positions for the current target vehicle using movement information of one or more past surrounding vehicles, and predicting the trajectory of the current target vehicle using the distribution of target positions.

[0088] FIG. 7 is a diagram illustrating a method for receiving movement information regarding one or more previous surrounding vehicles according to an embodiment.

[0089] Referring to FIG. 7, the server 20 can receive movement information regarding one or more past surrounding vehicles at a reference position of a past traveling vehicle.

[0090] The previously traveling vehicle 710 may refer to an autonomous vehicle that has traveled on a road at a past time. As described above, the previously traveling vehicle 710 may be equipped with an in-vehicle device 20 (e.g., an autonomous vehicle) to acquire image data using a sensor.

[0091] As described above, the video data may include driving data related to vehicles driving in the vicinity. That is, the video data may include movement information related to the surrounding vehicles based on the position of the autonomous vehicle. For example, the video data may include data related to where a surrounding vehicle driving in front of the autonomous vehicle at a certain time has moved to at another time.

[0092] According to one embodiment, a device in the autonomous vehicle may acquire image data while repeatedly driving a specific section. That is, the device in the previously driven vehicle 710 may accumulate and store image data acquired while repeatedly driving a specific section. For example, the accumulated image data may include movement information regarding one or more previously driven nearby vehicles.

[0093] According to an embodiment, the server can receive movement information regarding one or more previously surrounding vehicles at a reference position of the previously traveling vehicle from a device in the previously traveling vehicle.

[0094] 7, movement information 730 regarding past surrounding vehicles is shown based on the position of a past traveling vehicle 710. The movement information 730 may include information regarding where four past surrounding vehicles traveling ahead of the past traveling vehicle 710 at a certain point in time have moved thereafter.

[0095] Meanwhile, as described above, the movement information may include log data related to the past movements of the surrounding vehicles. That is, the log data may refer to movement information related to the surrounding vehicles that is repeatedly obtained in a specific section.

[0096] According to one embodiment, the server can record the log data in a space obtained by dividing the above-mentioned specific section into lattices. For example, a position value or a speed value of a past surrounding vehicle at a certain time point can be recorded in one lattice of the lattice space, and a position value or a speed value of the above-mentioned past surrounding vehicle at another certain time point can be recorded in another lattice of the lattice space.

[0097] According to another embodiment, the server can use the log data to obtain a categorical distribution, for example, to determine which locations a vehicle with a certain speed at a certain location statistically moves to more or less frequently.

[0098] FIG. 8 is a diagram for explaining a method for acquiring first state information on previously surrounding vehicles from movement information according to an embodiment.

[0099] 8, the server may obtain first state information on one or more past surrounding vehicles at a first time point from the movement information. Here, the first time point may correspond to a past time point before a reference time point. For example, the first time point may refer to a specific time in the past or a continuous time interval in the past.

[0100] As described above, the movement information may include information regarding the traveling of the previous surrounding vehicle that has traveled around the previous traveling vehicle. The traveling information may include the position, speed, lane, relative distance, steering direction, etc. of the previous surrounding vehicle. Therefore, the first state information may include the position value and speed value of the previous surrounding vehicle.

[0101] According to an embodiment, the position value of the past surrounding vehicle may be determined based on the position of the past traveling vehicle. As an example, the position value of the past surrounding vehicle may be determined by a coordinate system with the position of the past traveling vehicle as an origin. As another example, the position value of the past surrounding vehicle may be determined as a relative position based on the position of the past traveling vehicle.

[0102] Meanwhile, the speed value of the past surrounding vehicle may refer to the speed value at the moment when the past surrounding vehicle has the above-mentioned position value.

[0103] Referring to FIG. 8, first state information on a past surrounding vehicle 830 at a reference position of a past traveling vehicle 810 is acquired. Here, since the server acquires state information on the past surrounding vehicles from accumulated movement information of the past surrounding vehicles, a plurality of past surrounding vehicles having the same state information may exist in the movement information. For example, in FIG. 8, one past surrounding vehicle 850 is shown, but there may be a plurality of vehicles having the same position value and speed value. That is, as described above, there may be a plurality of past surrounding vehicles having the same position value and speed value at a first time point, which is a certain time point in the past, but at other times after the first time point, the plurality of vehicles may have different position values ​​and speed values.

[0104] 9 and 10, the moving positions of a plurality of previous surrounding vehicles 850 having the same position value in FIG. 8 will be described below.

[0105] 9 and 10 are diagrams for explaining a method for acquiring past movement positions of nearby vehicles from movement information according to an embodiment.

[0106] 9 and 10, the server may acquire, from the movement information, movement positions of one or more previous nearby vehicles at a second time point that is a predetermined time after the first time point. Here, the second time point may correspond to a past time point before the reference time point, similar to the first time point.

[0107] On the other hand, as described above, the movement information may include information regarding the travel of previously surrounding vehicles that have traveled around the previously traveling vehicle, so the server can obtain information regarding to which position the previously surrounding vehicles have moved from the movement information.

[0108] Here, the moved position can be understood to mean the moved position of all the previously surrounding vehicles included in the movement information, rather than the moved position of a specific previously surrounding vehicle.

[0109] However, in Figures 9 and 10, for the sake of consistency with the content described in Figure 8 and for ease of explanation, we will explain obtaining the movement positions of multiple past surrounding vehicles 930, 1030 that have specific position values ​​at a first time point.

[0110] 9, vehicles 930 traveling ahead of a previously traveled vehicle 910 in the right lane at the reference position of the previously traveled vehicle 910 may be one or more previously nearby vehicles 930 having first status information at a first time point. As an example, the vehicles 930 having the first status information at a first time point are set to be four previously nearby vehicles.

[0111] Then, at a second time point when a predetermined time has elapsed from the first time point, the above-mentioned four previous surrounding vehicles may move to different positions. For example, referring to FIG. 9, it is shown that the moving positions 950 of the respective previous surrounding vehicles at the second time point are acquired.

[0112] As another example, referring to Fig. 10, a vehicle 1030 traveling ahead of a previously traveling vehicle 1010 in the right lane at the reference position of the vehicle may be one or more previously surrounding vehicles 1030 having first status information at a first time point, similar to Fig. 9. As in Fig. 9, the vehicles 1030 having first status information at a first time point are set to be four previously surrounding vehicles.

[0113] Then, at a second time point when a predetermined time has elapsed from the first time point, the above-mentioned four previous surrounding vehicles may move to different positions. For example, referring to FIG. 10, it is shown that the moving positions 1050 and 1070 of the respective previous surrounding vehicles at the second time point are obtained.

[0114] It can be seen that the movement positions 950 of the four previous surrounding vehicles in Fig. 9 are relatively close to each other. In contrast, it can be seen that the movement positions 1050 and 1070 of the four previous surrounding vehicles in Fig. 10 are separated into three movement positions 1050 and one movement position 1070 on different roads.

[0115] 9 and 10, it can be seen that different moving positions can be obtained as moving positions of one or more previous nearby vehicles at the second time point depending on the set predetermined time. For example, it can be estimated that the predetermined time in FIG. 9 is shorter than the predetermined time in FIG.

[0116] Here, the predetermined time may be related to the time at which the distribution of target positions for the current target vehicle is probabilistically calculated. For example, the predetermined time may be related to the time from the reference time t x If we calculate the distribution of target positions at time points after time, the time interval between the first and second time points is also t x It can be time, which is explained in more detail in Figures 13 and 14.

[0117] Meanwhile, according to one embodiment, the moving positions of one or more past surrounding vehicles may be specified by information on the road and lane they are traveling in. For example, the moving positions of the past surrounding vehicles may be positions specified by at least one of the road and the lane included in the road on which the past surrounding vehicles were traveling at the second time point.

[0118] 11 and 12 are diagrams for explaining a method for probabilistically calculating the distribution of movement positions using a clustering method according to an embodiment.

[0119] 11 and 12, the server can probabilistically calculate the distribution of movement positions using a clustering method.

[0120] As described above, the movement positions of one or more previous surrounding vehicles may be relatively close to each other as shown in Fig. 9. Alternatively, the positions may be scattered as shown in Fig. 10.

[0121] According to an embodiment of the present disclosure, in order to predict the future trajectory of the current target vehicle using the movement positions of one or more past surrounding vehicles, a clustering method can be used to probabilistically calculate the distribution of the movement positions. In other words, by calculating the distribution of the movement positions using the clustering method, data that can be easily used for predicting the future trajectory can be generated.

[0122] The clustering method may refer to a data mining method for classifying individuals having similar characteristics into clusters based on similarity or distance. For example, the clustering method may include at least one of the K-means method, the mean shift method, the DBSCAN (Density Based Spatial Clustering of Applications with Noise) method, and the GMM (Gaussian Mixture Model).

[0123] According to one embodiment, the server can generate one or more clusters for the moving positions by using a clustering method. For example, the server can generate one cluster by using a DBSCAN method when a threshold number or more of the moving positions of one or more previous surrounding vehicles are included within a radius having a predetermined value.

[0124] According to the embodiment, the server can approximate each generated cluster to a Gaussian distribution. Then, the server can obtain a Gaussian mixture distribution for the moving positions by applying the GMM method to one or more clusters that have been approximated to the Gaussian distribution. According to the embodiment, the distribution of the moving positions can be approximated to one Gaussian distribution, and the distribution of the moving positions can be easily calculated.

[0125] 11 and 12, it is shown that the distribution of moving positions of one or more past surrounding vehicles 1130, 1230 set to have the same position value as described above is calculated probabilistically.

[0126] 11, a position value 1130 at a first time point and a distribution of moving positions at a second time point 1150 for one or more past surrounding vehicles are shown ahead of a past traveling vehicle 1110. That is, four past surrounding vehicles having the same position value at a first time point move to different positions at a second time point, and the distribution of moving positions is relatively concentrated, so that one cluster 1150 can be formed.

[0127] 12, in front of the past traveling vehicle 1210, a position value 1230 at a first time point and distributions 1250, 1270 of moving positions at a second time point for one or more past surrounding vehicles are shown. That is, in the case of the example of FIG. 12, four past surrounding vehicles having the same position value at a first time point move to different positions at a second time point, and the distribution of moving positions can form two clusters 1250, 1270.

[0128] 11 and 12, for convenience of explanation, the reference positions of the previously traveling vehicles 1110 and 1210 are set to be the same, and the position values ​​included in the state information of one or more previously traveling nearby vehicles at a first time point are also set to be the same. That is, from the examples of FIG. 11 and FIG. 12, it can be seen that when the predetermined time interval between the first time point and the second time point is different, the distribution of moving positions is calculated to be different.

[0129] 9 to 12, the moving positions of past surrounding vehicles having a specific position value as first status information at a first time point are acquired, and the distribution of the moving positions is calculated. However, it can be understood that according to an embodiment of the present disclosure, the server can acquire first status information at a first time point for each of one or more past surrounding vehicles included in the movement information, and can probabilistically calculate the distribution of the moving positions at a second time point.

[0130] Meanwhile, the distribution of moving positions calculated as described above can be used to predict the future trajectory of the current target vehicle.

[0131] For example, the server may receive information about the current target vehicle from a device in the currently moving vehicle and provide a distribution of matching movement positions to the device in the currently moving vehicle to predict the future trajectory.

[0132] Specifically, the server may receive second state information on the current target vehicle at a reference position and a reference time from a device in the currently traveling vehicle. As described above, the currently traveling vehicle may refer to an autonomous vehicle currently traveling on a road. In addition, the currently traveling vehicle may include an autonomous traveling device and may acquire image data using a sensor, and the device in the currently traveling vehicle may predict a future trajectory of the currently traveling target vehicle.

[0133] Here, the current target vehicle may refer to a vehicle whose future trajectory is to be predicted among surrounding vehicles currently traveling around the autonomous vehicle, that is, the current target vehicle may refer to a plurality of surrounding vehicles.

[0134] In addition, the reference time may refer to a time point at which the future trajectory of the current target vehicle is predicted. The reference position may refer to a position value of the currently traveling vehicle at the above-mentioned reference time. Here, according to an embodiment of the present disclosure, in order to predict the trajectory of the target vehicle traveling around the currently traveling vehicle using image data acquired by the previously traveling vehicle, the reference positions of the previously traveling vehicle and the currently traveling vehicle may refer to the same position value.

[0135] Meanwhile, the second state information regarding the current target vehicle may include the position value and the speed value of the current target vehicle as described above.

[0136] The server can then select a distribution of travel positions that matches the second state information regarding the currently moving target vehicle from the calculated distribution of travel positions, and provide the same to a device in the currently moving vehicle.

[0137] According to one embodiment, the server can receive a position value and a speed value of the current target vehicle, and extract from the first state information the position values ​​of one or more past surrounding vehicles that are within a predetermined range from the received position value of the current target vehicle.

[0138] Here, the predetermined range may vary depending on the accuracy required for calculating the distribution of target positions for the current target vehicle. For example, the distribution of target positions may be calculated so accurately that there is no difference between the position value of the current target vehicle and the position values ​​of past surrounding vehicles. However, if the amount of movement information is limited, the predetermined range may be extended to a range in which a position value similar to the position value of the current target vehicle can be extracted.

[0139] The server can then select one or more past surrounding vehicles having speed values ​​that fall within a predetermined range from the received speed value of the current target vehicle from among the one or more past surrounding vehicles having the extracted position values, and can select a distribution of movement positions at a second time point for the selected one or more past surrounding vehicles as a distribution of movement positions to be matched with the second status information for the current target vehicle.

[0140] The server then transmits the distribution of the selected travel positions to the device in the currently moving vehicle, thereby allowing the device in the currently moving vehicle to provide the distribution for predicting the future trajectory.

[0141] That is, the distribution of the selected moving positions can be used to estimate a distribution of target positions for the current target vehicle at the third time point. Also, the estimated distribution of target positions for the current target vehicle can be used to predict a future trajectory of the current target vehicle.

[0142] Here, the distribution of the target position of the current target vehicle may refer to information regarding the target location to which the target vehicle is traveling, that is, the distribution of the target position may refer to an estimate of the distribution of the location to which the target vehicle will arrive at a certain time in the future after a predetermined time has elapsed from a reference time point.

[0143] As described above, by using information on the target positions of the surrounding vehicles in predicting the trajectories of the surrounding vehicles traveling around the autonomous vehicle, the trajectories can be predicted more accurately. Furthermore, the behavior of the autonomous vehicle can be determined or the operation can be controlled based on the prediction of the trajectories of the surrounding vehicles.

[0144] Meanwhile, the third time point may correspond to a future time point after a predetermined time has elapsed from the reference time point. As described above, the predetermined time may mean the same time interval as the time interval between the first time point and the second time point. For example, when estimating the distribution of target positions of a current target vehicle after 5 seconds, the information between the first time point and the second time point of the past surrounding vehicles included in the movement information may be information about a time interval of 5 seconds.

[0145] Hereinafter, an example in which the distribution of target positions for the current target vehicle is estimated using the distribution of movement positions and an example in which the future trajectory is predicted will be described with reference to FIGS.

[0146] FIG. 13 is a diagram for explaining a method for estimating a distribution of target positions for a current target vehicle using a distribution of movement positions according to an embodiment.

[0147] Referring to FIG. 13, a device 1310 in a currently moving vehicle can estimate distributions 1350, 1370 of target positions for a currently target vehicle 1330 at a third time point using distributions of moving positions for past surrounding vehicles.

[0148] Specifically, the device 1310 in the currently traveling vehicle may obtain second state information regarding the currently target vehicle at a reference position and a reference time and transmit the second state information to the server. For example, the device 1310 in the currently traveling vehicle may obtain position values ​​and speed values ​​regarding the currently target vehicle 1330 based on the obtained image data and transmit the second state information to the server.

[0149] Thereafter, the device 1310 in the currently traveling vehicle may receive from the server a distribution of movement positions of past surrounding vehicles that are matched with the second state information of the currently target vehicle. For example, if the second state information of the currently target vehicle includes a position value A and a speed value B, the server may search for information on one or more past surrounding vehicles having a position value A and a speed value B at a first time point from the movement information, select movement positions at a second time point and a distribution of the movement positions, and provide them to the device in the currently traveling vehicle.

[0150] Thereafter, the device 1310 in the currently moving vehicle can estimate a distribution of target positions for the current target vehicle at the third time point using the distribution of the moving positions. For example, referring to Fig. 13, it is shown that two clusters 1350 and 1370 are estimated as target positions at the third time point for the current target vehicle 1330 at the reference time point. It can be seen that the probability that the current target vehicle 1330 reaches the first target position (1350) and the probability that the current target vehicle 1330 reaches the second target position (1370) at the third time point are estimated to be 0.75 and 0.25, respectively.

[0151] According to one embodiment, the device 1310 in the current moving vehicle can obtain a Gaussian mixture distribution for the target position, i.e., a Gaussian distribution can be obtained for the distribution of the target position for the current target vehicle, like the distribution of the moving position described above.

[0152] According to an embodiment, the device 1310 in the currently moving vehicle may calculate the mean point of the Gaussian mixture distribution and the probability of each cluster included in the Gaussian mixture distribution. For example, referring to FIG. 13, the device 1310 in the currently moving vehicle may obtain the distribution 1350 of the first target position and the distribution 1370 of the second target position, as well as the mean points 1351 and 1371 of each distribution.

[0153] FIG. 14 is a diagram illustrating a method for predicting a future trajectory of a current target vehicle using a distribution of target positions according to an embodiment.

[0154] Referring to FIG. 14, a device 1410 in a currently moving vehicle can predict the future trajectory of a currently target vehicle 1430 using the distribution of target positions estimated as described above.

[0155] For example, a device 1410 in a currently moving vehicle can estimate a distribution 1450 of target positions for a current target vehicle 1430 at a third time point. The distribution 1450 of target positions at the third time point is relatively clustered and may be shown as a cluster.

[0156] According to the embodiment described above, the device 1410 in the current moving vehicle can use the distribution of travel positions to estimate a distribution 1470 of target positions for the current target vehicle at a fourth time point.

[0157] Here, the fourth time point may correspond to a future time point after a predetermined time has elapsed from the reference time point, or may correspond to a future time point after the third time point. The distribution 1470 of the target positions at the fourth time point may be represented as two clusters each having a different average point.

[0158] Thereafter, the device 1410 in the currently traveling vehicle can repeatedly estimate the distribution of the target position at the fifth and sixth time points after the fourth time point. That is, the device 1410 in the currently traveling vehicle can predict the future trajectory of the currently target vehicle by using the distribution of the target position repeatedly estimated for the currently target vehicle. For example, as shown in FIG. 14, the future trajectory of the currently target vehicle can be predicted by connecting the average points of each cluster.

[0159] FIG. 15 is a flowchart illustrating a method for probabilistically calculating a distribution of movement positions using past movement information of surrounding vehicles according to an embodiment of the present invention.

[0160] 15, the method for probabilistically calculating a distribution of movement positions of past surrounding vehicles using movement information of one or more past surrounding vehicles may include steps 1510 to 1540. However, without being limited thereto, other general-purpose steps may be further included in the method for probabilistically calculating a distribution of movement positions of past surrounding vehicles using movement information of one or more past surrounding vehicles in addition to the steps shown in FIG.

[0161] First, in step 1510, the server can receive movement information regarding one or more previously traveling surrounding vehicles at a reference position of a previously traveling vehicle.

[0162] Thereafter, in step 1520, the server may obtain first state information regarding one or more past surrounding vehicles at a first time point from the movement information, where the first time point may correspond to a past time point before the reference time point.

[0163] According to one embodiment, the first state information may include past position and speed values ​​of the surrounding vehicles.

[0164] Then, in step 1530, the server can obtain, from the movement information, movement positions of one or more past surrounding vehicles at a second time point when a predetermined time has elapsed from the first time point.

[0165] According to one embodiment, the movement position of the previously nearby vehicle may be a position specified by at least one of a road on which the previously nearby vehicle was traveling at the second time point and a lane included in the road.

[0166] Then, in step 1540, the server can probabilistically calculate the distribution of the movement locations using a clustering method.

[0167] According to one embodiment, the clustering technique may include at least one of a K-means technique, a mean shift technique, a Density Based Spatial Clustering of Applications with Noise (DBSCAN) technique, and a Gaussian Mixture Model (GMM).

[0168] According to one embodiment, the server can generate one or more clusters for the moving positions by using a clustering method. According to the embodiment, the server can approximate a Gaussian distribution for each of the generated clusters. Then, the server can obtain a Gaussian mixture distribution for the moving positions by applying a GMM method to one or more clusters approximated to the Gaussian distribution.

[0169] Meanwhile, the distribution of the movement positions can be used to predict the future trajectory of the current target vehicle. The distribution of the selected movement positions can be used to estimate the distribution of the target positions for the current target vehicle at the third time point. Also, the estimated distribution of the target positions for the current target vehicle can be used to predict the future trajectory of the current target vehicle.

[0170] FIG. 16 is a flowchart illustrating a method for predicting a future trajectory of a current target vehicle using past movement information of surrounding vehicles according to an embodiment of the present invention.

[0171] 16, the method for predicting a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles may include steps 1601 to 1608. However, without being limited thereto, other general steps may be further included in the method for predicting a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles in addition to the steps shown in FIG.

[0172] First, in step 1601 , a device 1610 in a previously traveled vehicle can obtain movement information regarding one or more previously surrounding vehicles at a reference position of the previously traveled vehicle, and transmit the movement information to a server 1630 .

[0173] Thereafter, in step 1602, the server 1630 may obtain first state information about one or more past surrounding vehicles at a first time point from the movement information, where the first time point may correspond to a past time point before the reference time point.

[0174] Then, in step 1603, the server 1630 can obtain, from the movement information, movement positions of one or more past surrounding vehicles at a second time point when a predetermined time has elapsed from the first time point.

[0175] Then, in step 1604, the server 1630 can probabilistically calculate the distribution of the movement locations using a clustering method.

[0176] Thereafter, in step 1605 , a device 1650 in the currently moving vehicle can obtain and transmit to the server 1630 second status information regarding the current target vehicle at the reference position and reference time.

[0177] According to one embodiment, the second state information regarding the current target vehicle may include position and velocity values ​​of the current target vehicle, as discussed above.

[0178] Thereafter, in step 1606, a device 1650 in the currently moving vehicle may receive from the server a distribution of moving positions for past surrounding vehicles that are matched with the second state information for the currently target vehicle.

[0179] According to one embodiment, the server 1630 may receive second state information regarding the current target vehicle at a reference position and a reference time from the device 1650 in the currently moving vehicle. The server 1630 may then select a distribution of movement positions that matches the second state information regarding the currently target vehicle from the distribution of calculated movement positions, and provide the distribution to the device in the currently moving vehicle.

[0180] According to an embodiment, the server 1630 may receive a position value and a speed value of the current target vehicle, and extract from the first state information position values ​​of one or more past surrounding vehicles that are within a predetermined range from the received position value of the current target vehicle. The server 1630 may then select one or more past surrounding vehicles that have a speed value that is within a predetermined range from the received speed value of the current target vehicle from among the one or more past surrounding vehicles that have the extracted position values, and may select a distribution of movement positions at a second time point for the selected one or more past surrounding vehicles as a distribution of movement positions to be matched with the second state information for the current target vehicle.

[0181] Thereafter, in step 1607, the device 1650 in the currently moving vehicle may estimate a distribution of target positions for the currently moving target vehicle at a third time point using the distribution of the moving positions, where the third time point may correspond to a future time point at which a predetermined time has elapsed from the reference time point.

[0182] According to one embodiment, the device 1650 in the currently moving vehicle can obtain a Gaussian mixture distribution for the target position. The device 1650 in the currently moving vehicle can also calculate the mean point of the Gaussian mixture distribution and the probability of each cluster included in the Gaussian mixture distribution.

[0183] Then, in step 1608, a device 1650 in the currently moving vehicle can use the distribution of target positions to predict the future trajectory of the currently target vehicle.

[0184] Meanwhile, hereinafter, with reference to Figs. 17 to 23, a method and device for determining the behavior of an autonomous vehicle based on video data acquired by the autonomous vehicle described above with reference to Figs. 2 to 5 will be described.

[0185] FIG. 17 is a diagram for explaining a method for acquiring a collision lane according to an embodiment of the present disclosure.

[0186] 17, an in-vehicle device can obtain a collision lane. According to one embodiment, an in-vehicle device can determine a course of action for a right turn for an autonomous vehicle planning to turn right to merge onto a main lane.

[0187] A collision lane may refer to a lane included in the main lane that may overlap with the driving path of an autonomous vehicle.

[0188] 17, a main line may include at least two lanes, including a lane in one direction and a lane in the other direction. For example, a 10 o'clock-4 o'clock main line 1701 and a 1 o'clock-7 o'clock main line 1702 each include four lanes.

[0189] Meanwhile, the driving route of the autonomous vehicle may refer to a route set for the autonomous vehicle to turn right. The driving route may refer to a route set by the autonomous driving device described above. Alternatively, the driving route may refer to a route set by an external server and received by the autonomous driving device, but is not limited thereto.

[0190] 17, for example, a driving route 1730 can be set in which an autonomous vehicle 1720 turns right to merge onto a main lane. Here, some lanes 1711, 1712 included in the main lanes 1701, 1702 may overlap with the autonomous vehicle's driving route 1730. The above-mentioned some lanes 1711, 1712 can be set as collision lanes.

[0191] According to one embodiment, the in-vehicle device can set the collision lane from video data captured by the sensor of the autonomous vehicle described above, or according to another embodiment, the in-vehicle device can obtain the collision lane by receiving information about the lane from an external device or server.

[0192] 18 and 19 are diagrams for explaining a method for classifying candidate vehicles from among vehicles traveling in a collision lane according to an embodiment of the present disclosure.

[0193] Referring to FIG. 18 as an example of the present disclosure, a device in an autonomous vehicle 1820 can acquire collision lanes 1811-1813 that may overlap with the driving path of the autonomous vehicle 1820 from among lanes included in the main lane.

[0194] For example, one or more vehicles may be traveling in the collision lanes 1811-1813. Here, the device in the vehicle may focus on collecting information about a vehicle that may be related to the right turn of the autonomous vehicle 1820, among the one or more vehicles, in order to determine the behavior of the autonomous vehicle 1820.

[0195] Referring to FIG. 19, a device in the vehicle can classify one or more candidate vehicles that may be in collision with the autonomous vehicle 1920 from among the vehicles traveling in the collision lane.

[0196] For example, two vehicles 1911, 1912 may be traveling in the collision lane 1811 in Fig. 18 described above. Here, the two vehicles 1911, 1912 may be classified as candidate vehicles because they may collide with the autonomous vehicle 1920 on the main lane.

[0197] Similarly, a vehicle 1914 may be traveling in the collision lane 1813 of Figure 18 described above. Vehicle 1914 is expected to make a left turn at the intersection and may be classified as a candidate vehicle because it is likely to collide on the main lane with autonomous vehicle 1920 planning to make a right turn.

[0198] As another example, a vehicle 1913 may be traveling in the collision lane 1812 in Fig. 18 described above. However, the vehicle 1913 is expected to be turning left at the intersection and is therefore unlikely to collide with the autonomous vehicle 1920 on the main road, and therefore may not be classified as a candidate vehicle.

[0199] As yet another example, a vehicle traveling on the main line but not traveling in the collision lane may not be classified as a candidate vehicle because there is no possibility of a collision with the autonomous vehicle 1920 on the main line.

[0200] Meanwhile, below we explain how information about the colliding vehicle is used in determining the behavior of the autonomous vehicle 1920.

[0201] FIG. 20 is a diagram for explaining a method for predicting whether a candidate vehicle occupies a collision area according to an embodiment of the present disclosure.

[0202] Referring to FIG. 20, a device in a vehicle can predict paths for candidate vehicles classified in the manner described above, thereby predicting whether the candidate vehicles will occupy a pre-defined collision area within a given time period.

[0203] For example, the in-vehicle device can predict the future route of the candidate vehicle using image data acquired by a sensor provided in the autonomous vehicle. For example, the in-vehicle device can receive map information and past driving information of the candidate vehicle, and can predict the future route of the candidate vehicle using the map information and the past driving information.

[0204] According to one embodiment, the device in the vehicle can estimate a distribution of target positions for the candidate vehicles and predict the future route of the candidate vehicles using the estimated distribution of target positions. Here, the target position of the candidate vehicle may refer to a position where the candidate vehicle intends to reach after a predetermined time. According to an embodiment, the future trajectory of the vehicle can be predicted more accurately by using both the past driving information and the distribution of target positions.

[0205] The device in the vehicle can estimate the distribution of target positions for the candidate vehicles using accumulated past driving information for the surrounding vehicles driving around the autonomous vehicle. For example, the autonomous vehicle can estimate the distribution of target positions for the candidate vehicles by using a clustering method on the accumulated past driving information. As an example, the clustering method can include a density based spatial clustering of applications with noise (DBSCAN) method and a Gaussian mixture model (GMM) method.

[0206] Meanwhile, the device in the vehicle can predict whether the candidate vehicle will occupy the collision area within a predetermined time based on the current time. As an example, the predetermined time may be set based on how many seconds into the future the trajectory predicted for the candidate vehicle will be. As another example, the predetermined time may be set based on how many seconds it will take for the autonomous vehicle to reach the collision area. For example, the predetermined time may mean 5 seconds into the future.

[0207] Here, the collision area may refer to an area where the autonomous vehicle and the candidate vehicle may collide after a predetermined time, i.e., the collision area may refer to an area corresponding to a lane included in a main lane where a collision with a candidate vehicle may occur if the autonomous vehicle turns right and merges into the main lane.

[0208] According to one embodiment, the device in the vehicle can set a collision area based on the collision lane and the driving path of the autonomous vehicle. The device in the vehicle can set an area where the collision lane and the driving path of the autonomous vehicle overlap as the collision area.

[0209] For example, referring to Fig. 20, the device in the vehicle can predict, as described above, paths for vehicles 2011-2013 classified as candidate vehicles from among vehicles traveling in the collision lane, thereby predicting whether the candidate vehicles will occupy a preset collision area 2020 within a predetermined time. The device in the vehicle can determine the behavior of the autonomous vehicle 2030 based on the prediction results regarding the occupancy of the collision area 2020 by the candidate vehicles 2011-2013.

[0210] FIG. 21 is a diagram for explaining a method for determining the behavior of an autonomous vehicle in a behavior decision area according to one embodiment of the present disclosure.

[0211] Referring to FIG. 21, an in-vehicle device can determine the behavior of an autonomous vehicle 2120 planning to make a right turn based on the prediction results regarding occupancy from the above-described method and the current driving state of the autonomous vehicle 2120.

[0212] Here, the prediction results regarding the candidate vehicle's occupancy of the collision area may include a prediction that the candidate vehicle will occupy a part of the collision area within a specified time, and a prediction that the candidate vehicle will not occupy any area of ​​the collision area within a specified time.

[0213] In addition, the current driving state of the autonomous vehicle 2120 may be set based on a behavior decision area. For example, referring to Fig. 21, the behavior decision area 2110 refers to an area located within a predetermined distance from the collision area, and may refer to an area in which the behavior of the autonomous vehicle 2120 is determined before the autonomous vehicle 2120 enters the collision area.

[0214] As one example, the current driving state of the autonomous vehicle 2120 may include a state of entering the action decision area. As another example, the current driving state of the autonomous vehicle 2120 may include a state of slowly moving in the action decision area.

[0215] As yet another example, the current driving state of the autonomous vehicle 2120 may include being stopped in an action decision area.

[0216] Meanwhile, a specific embodiment for determining the behavior of the autonomous vehicle 2120 based on the occupancy prediction results and the current driving state of the autonomous vehicle 2120 will be described below in conjunction with FIG. 22.

[0217] FIG. 22 is a flow chart illustrating a method for determining an action of an autonomous vehicle in an action decision region according to one embodiment of the present disclosure.

[0218] As described above in FIG. 21, the device in the vehicle can determine the behavior of the autonomous vehicle based on the prediction results regarding the occupancy of the collision area of ​​the candidate vehicles and the current driving state of the autonomous vehicle.

[0219] According to one embodiment, if the current driving state of the autonomous vehicle is a first state in which the autonomous vehicle is entering an action decision area (2210), a device in the vehicle can determine (2220) an action of the autonomous vehicle to slow down in the action decision area.

[0220] Meanwhile, according to one embodiment, the device in the vehicle can determine the speed at which the autonomous vehicle should slow down. For example, the device in the vehicle can identify pedestrians from the acquired video data as described above, and can determine the speed at which the autonomous vehicle should slow down based on the presence or absence of pedestrians.

[0221] For example, a pedestrian may refer to a person walking on a crosswalk included in the behavior decision area or the collision area or in the vicinity of the crosswalk described above. The device in the vehicle may determine a behavior of the autonomous vehicle such that, if a pedestrian is not identified, the autonomous vehicle slows down in the behavior decision area at or below a first threshold speed.

[0222] Alternatively, the in-vehicle device may determine a behavior of the autonomous vehicle if a pedestrian is identified, such that the autonomous vehicle slows down in the behavior decision region at or below a second threshold speed, where the first threshold speed value may be greater than the second threshold speed value.

[0223] According to another embodiment, if the current driving state of the autonomous vehicle is a second state (2220) in which the autonomous vehicle is moving slowly in the action decision area, a device in the vehicle can predict (2230) the occupancy of the collision area by a candidate vehicle and determine the action of the autonomous vehicle.

[0224] For example, if a candidate vehicle is predicted to occupy a collision area, a device in the vehicle can determine an action for the autonomous vehicle to avoid passing through the collision area for a predetermined period of time by stopping (2250) in the action decision area.

[0225] As another example, if the candidate vehicle is predicted not to occupy the collision area, the device in the vehicle may determine an action for the autonomous vehicle to pass through the collision area by turning right onto the main lane (2240).

[0226] According to another embodiment, if the current driving state of the autonomous vehicle is a third state (2250) in which the autonomous vehicle is stopped in an action decision area, a device in the vehicle can predict (2260) the occupancy of the collision area by a candidate vehicle and determine the action of the autonomous vehicle.

[0227] For example, if the candidate vehicle is predicted not to occupy the collision area, the device in the vehicle may determine the behavior of the autonomous vehicle such that the autonomous vehicle slows down (2220) in the behavior decision area.

[0228] Alternatively, if the candidate vehicle is predicted to occupy the collision area, the device in the vehicle can determine the behavior of the autonomous vehicle to keep the autonomous vehicle stopped (2250) in the behavior decision area.

[0229] FIG. 23 is a flowchart illustrating a method for determining an action of an autonomous vehicle according to one embodiment of the present disclosure.

[0230] 23, the method for determining a behavior of an autonomous vehicle planning to make a right turn to merge onto a main lane may include steps 2310 to 2340. However, the method is not limited thereto, and other general-purpose steps may be further included in the method for determining a behavior of an autonomous vehicle planning to make a right turn to merge onto a main lane in addition to the steps shown in FIG.

[0231] First, in step 2310, the device in the vehicle can obtain collision lanes that may overlap with the driving path of the autonomous vehicle from among the lanes included in the main line.

[0232] Thereafter, in step 2320, the in-vehicle device may classify one or more candidate vehicles from among the vehicles traveling in the collision lane that may have a collision potential with the autonomous vehicle.

[0233] Then, in step 2330, the device in the vehicle can predict whether the candidate vehicle will occupy the pre-defined collision area within a given time by predicting a path for the candidate vehicle.

[0234] According to one embodiment, an in-vehicle device can estimate a distribution of target positions for a candidate vehicle and predict a future route of the candidate vehicle using the estimated distribution of target positions. The in-vehicle device can estimate a distribution of target positions for the candidate vehicle using accumulated past driving information for surrounding vehicles driving around the autonomous vehicle. For example, the autonomous vehicle can estimate a distribution of target positions for the candidate vehicle by using a clustering method on the accumulated past driving information. As an example, the clustering method can include a density based spatial clustering of applications with noise (DBSCAN) method and a Gaussian mixture model (GMM) method.

[0235] Thereafter, in step 2340, the in-vehicle device may determine a behavior for the autonomous vehicle planning to make a right turn based on the predicted occupancy outcome and the current driving state of the autonomous vehicle.

[0236] According to one embodiment, if the current driving state of the autonomous vehicle is a first state in which the autonomous vehicle is entering an action decision area (2210), a device in the vehicle can determine (2220) an action of the autonomous vehicle to slow down in the action decision area.

[0237] According to one embodiment, if the current driving state of the autonomous vehicle is a second state (2220) of slowly moving in an action decision area, a device in the vehicle can predict (2230) the occupancy of the collision area by a candidate vehicle and determine the action of the autonomous vehicle.

[0238] For example, if a candidate vehicle is predicted to occupy a collision area, a device in the vehicle can determine an action for the autonomous vehicle to avoid passing through the collision area for a predetermined period of time by stopping (2250) in the action decision area.

[0239] As another example, if the candidate vehicle is predicted not to occupy the collision area, the device in the vehicle may determine an action for the autonomous vehicle to pass through the collision area by turning right onto the main lane (2240).

[0240] According to another embodiment, if the current driving state of the autonomous vehicle is a third state (2250) in which the autonomous vehicle is stopped in an action decision area, a device in the vehicle can predict (2260) the occupancy of the collision area by a candidate vehicle and determine the action of the autonomous vehicle.

[0241] For example, if the candidate vehicle is predicted not to occupy the collision area, the device in the vehicle may determine the behavior of the autonomous vehicle such that the autonomous vehicle slows down (2220) in the behavior decision area.

[0242] Alternatively, if a candidate vehicle is predicted to occupy a collision area, a device within the vehicle can determine the behavior of the autonomous vehicle to keep the autonomous vehicle stopped in the behavior decision area.

[0243] The embodiments according to the present invention may be realized in the form of a computer program executable by various components on a computer, and such a computer program may be recorded on a computer readable medium, including magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program instructions, such as ROMs, RAMs, flash memories, and the like.

[0244] On the other hand, the computer program may be one specially designed and constructed for the present invention, or it may be one that is well known and available to those skilled in the art of computer software. Examples of computer programs include not only machine code, such as that produced by a compiler, but also high-level language code that is executed by a computer using an interpreter or the like.

[0245] According to one embodiment, the methods according to various embodiments of the present disclosure can be provided in a computer program product. The computer program product can be traded between sellers and buyers as a commodity. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or can be distributed in the form of an application store (e.g., the Play Store). TM ) or directly between two user devices. In the case of online distribution, at least a part of the computer program product may be at least temporarily stored or temporarily generated on a machine-readable storage medium, such as a memory of a manufacturer's server, an application store's server, or an intermediary server.

[0246] Unless there is an explicit order description or a contrary description regarding the steps constituting the method according to the present invention, the steps can be performed in any suitable order. The present invention is not necessarily limited to the order of the steps described above. The use of all examples or exemplary terms (such as, for example, etc.) in the present invention is merely for the purpose of explaining the present invention in detail, and the scope of the present invention is not limited by the examples or exemplary terms unless limited by the claims. In addition, those skilled in the art will understand that various modifications, combinations and changes can be made within the scope of the claims or their equivalents according to design conditions and factors.

[0247] Therefore, the concept of the present invention should not be limited to the above-described embodiments, and all scopes equivalent to or modified equivalently to the scope of the attached claims, as well as the scope of the present invention, fall within the scope of the concept of the present invention.

Claims

1. A method for predicting a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles, comprising: A server receives the movement information regarding the one or more past surrounding vehicles at a reference position of a past traveling vehicle; The server acquires, from the movement information, first state information regarding the one or more past surrounding vehicles at a first time point, the first time point corresponding to a past time point before a reference time point; The server acquires, from the movement information, movement positions of the one or more previous surrounding vehicles at a second time point when a predetermined time has elapsed since the first time point; The server probabilistically calculates the distribution of the movement positions by using a clustering method, The distribution of the moving positions is used to predict a future trajectory of the current target vehicle; The method comprises: receiving second status information regarding the current target vehicle at the reference position and the reference time; selecting a distribution of movement positions that matches the second state information of the current target vehicle from the distributions of the movement positions of the one or more past surrounding vehicles; The step of selecting a distribution of the movement positions to be matched with the second state information includes: receiving position and velocity values ​​of the current target vehicle; extracting, from the first state information, position values ​​of the one or more previous surrounding vehicles that are within a predetermined range from the position value; selecting one or more past surrounding vehicles having a speed value within a predetermined range from the speed value from among the one or more past surrounding vehicles having the position value; and selecting a distribution of movement positions for the selected one or more past surrounding vehicles at the second time point.

2. The distribution of the selected movement positions is used to estimate a distribution of target positions for the current target vehicle at a third time point, the third time point corresponding to a future time point that is a predetermined amount of time from the reference time point; The distribution of the target positions is The method of claim 1, further comprising predicting a future trajectory of the current target vehicle.

3. The method of claim 1 , wherein the first state information about the past surrounding vehicles and the second state information about the current target vehicle include vehicle position and velocity values.

4. The moving position is: The method according to claim 1 , wherein the location is specified by at least one of a road on which the one or more previous surrounding vehicles were traveling at the second time point and a lane included in the road.

5. The step of probabilistically calculating the distribution of the movement positions includes: generating one or more clusters for the movement locations by using a clustering technique; approximating a Gaussian distribution for each cluster; The method of claim 1 , further comprising: obtaining a Gaussian mixture distribution for the movement position by applying a Gaussian Mixture Model (GMM) to the one or more clusters that approximate the Gaussian distribution.

6. The method of claim 5 , wherein the clustering technique is a Density Based Spatial Clustering of Applications with Noise (DBSCAN) technique.

7. A server predicts a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles, The server, a memory having at least one program stored therein; at least one processor for executing said at least one program; The at least one processor receiving the movement information regarding the one or more past surrounding vehicles at a reference position of a past traveling vehicle; obtaining first state information about the one or more previous surrounding vehicles at a first time point from the movement information, the first time point corresponding to a past time point before a reference time point; acquiring, from the movement information, movement positions of the one or more previous surrounding vehicles at a second time point that is a predetermined time after the first time point; Probabilistically calculating the distribution of the movement positions using a clustering method; The distribution of the moving positions is used to predict a future trajectory of the current target vehicle; The at least one processor further comprises: receiving second status information regarding the current target vehicle at the reference position and the reference time; selecting a distribution of movement positions that matches the second state information regarding the current target vehicle from the distributions of the movement positions regarding the one or more past surrounding vehicles; The at least one processor, in selecting a distribution of movement positions to match the second state information, receiving a position value and a velocity value of the current target vehicle; extracting from the first state information the position values ​​of the one or more previous surrounding vehicles that are within a predetermined range from the position value; selecting one or more past surrounding vehicles having a speed value within a predetermined range from the speed value from among the one or more past surrounding vehicles having the position value; A server selects a distribution of movement positions for the selected one or more past surrounding vehicles at the second time point.

8. A computer-readable recording medium having recorded thereon a program for executing the method according to claim 1 on a computer.

9. A system for predicting a future trajectory of a current target vehicle using movement information of one or more past surrounding vehicles, comprising: A device in a previously traveled vehicle that acquires movement information regarding one or more previously traveled surrounding vehicles at a reference position of the previously traveled vehicle; A server that uses the movement information to probabilistically calculate a distribution of movement positions of the one or more past surrounding vehicles; a device in the currently moving vehicle that predicts a future trajectory of the currently moving target vehicle using the distribution of the moving positions; The device in the currently traveling vehicle includes: obtaining second status information regarding the current target vehicle at the reference position and the reference time; receiving from the server a distribution of the movement positions of the past surrounding vehicles that are matched with the second state information of the current target vehicle; estimating a distribution of target positions for the current target vehicle at a third time point using the distribution of movement positions, the third time point corresponding to a future time point at which a predetermined time has elapsed from the reference time point; predicting a future trajectory of the current target vehicle using the distribution of target positions; The server further comprises: obtaining first state information about the one or more previous surrounding vehicles at a first time point from the movement information, the first time point corresponding to a past time point before a reference time point; acquiring, from the movement information, movement positions of the one or more previous surrounding vehicles at a second time point that is a predetermined time after the first time point; receiving the second status information from a device in the currently traveling vehicle; selecting a distribution of movement positions that matches the second state information regarding the current target vehicle from the distributions of the movement positions regarding the one or more past surrounding vehicles; The server, in selecting a distribution of moving positions to be matched with the second state information, receiving a position value and a velocity value of the current target vehicle; extracting from the first state information the position values ​​of the one or more previous surrounding vehicles that are within a predetermined range from the position value; selecting one or more past surrounding vehicles having a speed value within a predetermined range from the speed value from among the one or more past surrounding vehicles having the position value; The system selects a distribution of movement positions for the selected one or more past surrounding vehicles at the second time point.

10. The device in the currently traveling vehicle includes: Obtaining a Gaussian mixture distribution for the target position; The system of claim 9 , further comprising: calculating a mean point of the Gaussian mixture distribution and a probability for each cluster included in the Gaussian mixture distribution.

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