WO2021241834A1

WO2021241834A1 - Virtual lane generation apparatus and method based on traffic flow information perception for autonomous driving in adverse weather conditions

Info

Publication number: WO2021241834A1
Application number: PCT/KR2021/000335
Authority: WO
Inventors: 이경수; 고영일; 이주현
Original assignee: 서울대학교산학협력단; (주)스마트모빌리티랩
Priority date: 2020-05-29
Filing date: 2021-01-11
Publication date: 2021-12-02
Also published as: KR20210148518A; KR102342414B1

Abstract

Disclosed is a method for generating a virtual lane for assisting autonomous driving, the method comprising the steps of: deriving a driving history by accumulating driving data of at least one surrounding vehicle measured by a composite sensor according to the passage of time; classifying the driving history into at least one driving section according to a driving pattern on the basis of self-attention implemented by a convolutional neural network (CNN); deriving a weighted driving history of the at least one surrounding vehicle by assigning a weight to the at least one driving section on the basis of the self-attention; determining a driving trajectory of the at least one surrounding vehicle by performing curve fitting on the weighted driving history through the CNN; and generating a virtual lane on an HD map for assisting autonomous driving on the basis of the driving trajectory.

Description

Apparatus and method for traffic flow information recognition-based virtual lane creation for autonomous driving in bad weather

The present invention relates to a method and apparatus for generating a virtual lane. More particularly, the present invention relates to a method and apparatus for determining a driving trajectory of a surrounding vehicle by utilizing a self-attention mechanism of a convolutional neural network (CNN) and generating a virtual lane therefrom.

Various technologies that provide convenience to drivers, such as driver assistance systems and intelligent vehicle systems, are being developed. In particular, according to the autonomous driving technology, recognition and positioning of the surrounding environment, determination and planning for changes in the surrounding environment, and control of vehicle driving are performed accordingly, so that the vehicle can autonomously drive without driver intervention.

In order for autonomous driving to be smoothly performed, recognition and positioning of the surrounding environment may be required to be precisely performed. To this end, the states of objects constituting the traffic environment, such as surrounding vehicles, pedestrians, and traffic signals, may be detected through sensors such as radar and cameras. Among them, information on lanes displayed on the road surface may be mainly utilized to plan the behavior of the own vehicle and predict the behavior of surrounding vehicles.

In order to acquire information on lanes around the own vehicle, visual information such as an image by a camera may be a main acquisition means. Alternatively, the painted portion of the lane on the road surface may be distinguished from the asphalt by using the difference in reflectivity for laser scanning through LiDAR or the like.

However, since the above methods are based on the premise that the weather environment and road surface condition are good, it becomes difficult to detect lanes in bad weather conditions, such as when there is a lot of precipitation or when lanes are covered by snow, so autonomous driving is performed smoothly. may not be

The technical problem to be solved by the present invention is to provide a virtual environment around the own vehicle so that behavior planning and driving control according to autonomous driving can be performed even when a lane around the own vehicle is not detected due to a weather environment such as heavy snow or heavy rain. It is to provide technology to create lanes.

As a means for solving the above-described technical problem, the method of generating a virtual lane for assisting autonomous driving according to an aspect of the present invention includes driving data of at least one surrounding vehicle measured by a complex sensor in time flow. deriving a driving history by accumulating accordingly; dividing the driving history into at least one driving section according to a driving pattern based on self-attention implemented by a convolutional neural network (CNN); deriving a weighted driving history of the at least one surrounding vehicle by assigning a weight to the at least one driving section based on the self-attention; determining a driving trajectory of the at least one surrounding vehicle by performing curve fitting on the weighted driving history through the CNN; and generating the virtual lane on an HD map for assisting the autonomous driving based on the driving trajectory.

According to another aspect of the present invention, an apparatus for generating a virtual lane for assisting autonomous driving includes: a memory storing at least one program; and a processor for generating the virtual lane by executing the at least one program, wherein the processor is configured to accumulate driving data of at least one surrounding vehicle measured by a complex sensor over time to derive a driving history, , divides the driving history into at least one driving section according to a driving pattern based on self-attention implemented by a convolutional neural network (CNN), and divides the driving history into at least one driving section based on the self-attention. Deriving a weighted driving history of the at least one surrounding vehicle by assigning a weight to a driving section, and performing curve fitting on the weighted driving history through the CNN to determine the driving trajectory of the at least one surrounding vehicle, and The virtual lane is generated on the HD map for assisting the autonomous driving based on the driving trajectory.

According to the method and apparatus according to the present invention, even when it is difficult to identify a lane on a road surface due to bad weather, a virtual lane can be created by referring to the driving flow of surrounding vehicles, so that the autonomous driving process based on the virtual lane is can be performed smoothly. In particular, in the process of referring to the driving flow of surrounding vehicles, the driving patterns of surrounding vehicles are classified according to the self-attention mechanism, and behaviors that obscure lane creation, such as lane changes, can be considered with low weight. The generated accuracy can be improved.

1 is a block diagram illustrating elements constituting an apparatus for generating a virtual lane according to some exemplary embodiments.

FIG. 2 is a diagram for explaining how an autonomous driving system including an apparatus for generating a virtual lane according to some exemplary embodiments operates.

3 is a diagram for describing a detailed process of generating a virtual lane according to some exemplary embodiments.

4 is a flowchart illustrating steps of configuring a method for generating a virtual lane according to some embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. The description below is only for specifying the embodiments, and is not intended to limit or limit the scope of rights according to the present invention. What a person of ordinary skill in the art related to the present invention can easily infer from the detailed description and embodiments of the invention should be construed as belonging to the scope of the present invention.

The terms used in the present invention have been described as general terms widely used in the technical field related to the present invention, but the meaning of the terms used in the present invention is the intention of a technician in the relevant field, the emergence of new technology, examination standards or precedents. It may vary depending on Some terms may be arbitrarily selected by the applicant, and in this case, the meaning of the arbitrarily selected terms will be described in detail. Terms used in the present invention should be interpreted as meanings reflecting the overall context of the specification, not just dictionary meanings.

Terms such as 'consisting of' or 'comprising' used in the present invention should not be construed as necessarily including all of the components or steps described in the specification, and if some components or steps are not included, And when additional components or steps are further included, it should also be construed as intended from the term.

Terms including an ordinal number such as 'first' or 'second' used in the present invention may be used to describe various components or steps, but the components or steps should not be limited by the ordinal number. . Terms containing an ordinal number should only be interpreted for the purpose of distinguishing one element or step from other elements or steps.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Detailed descriptions of matters widely known to those of ordinary skill in the art related to the present invention will be omitted.

Referring to FIG. 1 , an apparatus 100 for generating a virtual lane may include a memory 110 and a processor 120 . However, the present invention is not limited thereto, and other general-purpose components other than the components shown in FIG. 1 may be further included in the device 100 .

The apparatus 100 may be a computing device mounted on a vehicle so as to create a virtual lane. The device 100 may include the memory 110 as a means for storing various data, instructions, at least one program or software, and performs processing on various data by executing the instructions or at least one program. The processor 120 may be included as a means for doing so.

For example, the device 100 may refer to an electric component mounted in a vehicle in the form of a smart mobility device. However, the device 100 is not limited to a type mounted on a vehicle, and the device 100 may be implemented as a mobile device that wirelessly exchanges data with the vehicle.

The memory 110 may store various commands related to generation of a virtual lane in the form of at least one program. For example, the memory 110 may store instructions constituting software such as a computer program or mobile application. In addition, the memory 110 may store various data necessary for the execution of at least one program.

The memory 110 includes a read only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable and programmable ROM (EEPROM), a flash memory, a phase-change RAM (PRAM), and an MRAM. (magnetic RAM), RRAM (resistive RAM), FRAM (ferroelectric RAM), etc. may be implemented as a non-volatile memory, or DRAM (dynamic RAM), SRAM (static RAM), SDRAM (synchronous DRAM), PRAM (phase- It may be implemented as a volatile memory such as change RAM), resistive RAM (RRAM), and ferroelectric RAM (FeRAM). In addition, the memory 110 may be implemented as a hard disk drive (HDD), a solid state drive (SSD), a secure digital (SD), a micro secure digital (Micro-SD), or the like.

The processor 120 may create a virtual lane by executing at least one program stored in the memory 110 . The processor 120 may perform a series of processing processes for implementing the creation of the virtual lane. In addition, the processor 120 may perform general functions for controlling the apparatus 100 , and may process various operations inside the apparatus 100 .

The processor 120 may be implemented as an array of a plurality of logic gates or a general-purpose microprocessor. The processor 120 may be configured with a single number or a plurality of processors. The processor 120 may be integrally configured with the memory 110 instead of being separate from the memory 110 for storing at least one program. The processor 120 may be at least one of a central processing unit (CPU), a graphics processing unit (GPU), and an application processor (AP) provided in the device 100 , but this is only an example, and the processor 120 may be It may be implemented in various forms.

The device 100 for generating a virtual lane may be for assisting autonomous driving. The processor 120 of the device 100 may create a virtual lane by performing a series of processing steps.

The processor 120 may derive a driving history by accumulating driving data of at least one surrounding vehicle measured by the complex sensor over time. The processor 120 may acquire driving data such as positions and speeds of surrounding vehicles based on data measured every time unit by the complex sensor. For example, the unit of time for which data is measured by the composite sensor may be 100 ms, or may be set to another suitable value.

The complex sensor may detect data on the surrounding environment of the vehicle by means of a camera, radar, LiDAR, and GPS. In addition, complex sensors can utilize HD maps to access information such as roads, terrain, and buildings. For example, driving data of a surrounding vehicle measured by the complex sensor may be expressed as physical quantities such as a two-dimensional relative position of the surrounding vehicle with respect to the own vehicle, a degree of dependence, and a lateral speed.

The driving data of the surrounding vehicle measured by the complex sensor may be generated for each time unit, and the processor 120 may derive the driving history of the surrounding vehicle by accumulating the driving data generated for each time unit. When two or more surrounding vehicles are detected around the own vehicle, driving histories for each of the surrounding vehicles may be derived.

In the process of deriving the driving history, the processor 120 may derive the driving history by generating driving data by applying Kalman filtering for data purification to data measured by the complex sensor. Kalman filtering is to derive driving history based on more refined data. When driving data is expressed in two-dimensional relative positions and relative speeds of surrounding vehicles, driving data of surrounding vehicles is analyzed through a model that assumes constant speed. It can be estimated and corrected.

The processor 120 may divide the driving history into at least one driving section according to a driving pattern based on self-attention implemented by a convolutional neural network (CNN). The memory 110 may store a CNN including a self-attention layer. The memory 110 may store the learned values of parameters constituting the CNN to store the learned CNN in a completed state. The processor 120 may divide the driving history of the surrounding vehicle into at least one driving section according to the driving pattern by utilizing the CNN stored in the memory 110 .

CNN is one of deep neural networks including a convolutional layer, and can be variously used for machine learning that classifies patterns of inputs. For example, a CNN may include a self-attention layer to classify patterns in driving data of surrounding vehicles measured by complex sensors. That is, the processor 120 may divide the driving history into at least one driving section by classifying driving data by patterns based on CNN.

The CNN stored in the memory 110 and utilized by the processor 120 may include a self-attention layer to utilize the self-attention mechanism. According to the self-attention mechanism, data sections that have high correlation with each other in successive time series data to form a pattern may be classified, and the classified data sections may be expressed as weights.

The self-attention layer may calculate an attention score in order to divide the driving history into at least one driving section according to a driving pattern. The attention score may be calculated in various ways. For example, the self-attention of CNN may calculate an attention score by using a softmax function. The softmax function is a function for estimating the probability of each component of a vector having an arbitrary dimension, and may refer to one of the main activation functions used in the field of machine learning.

The processor 120 may derive a weighted driving history of at least one surrounding vehicle by assigning a weight to at least one driving section based on self-attention. A weight may be given to at least one driving section by the self-attention of the CNN, and the processor 120 considers the corresponding weight, that is, a driving section with a high weight as a higher weight than a driving section with a low weight. A weighted driving history can be derived. That is, the processor 120 may derive the weighted driving history by excluding unnecessary sections from the driving history.

The processor 120 may determine the driving trajectory of at least one surrounding vehicle by performing curve fitting on the weighted driving history through CNN. Curve fitting is a process of determining a trajectory that matches the driving data constituting the weighted driving history. For example, it may refer to a process of deriving a curve or a straight line that is fitted to the weighted driving history on a two-dimensional plane corresponding to the road surface. have. To this end, the CNN may be trained to receive driving data of surrounding vehicles and determine a driving trajectory.

The processor 120 may determine the driving trajectory by performing curve fitting by inferring coefficients of a polynomial function for representing a trajectory fitted to the weighted driving history. That is, the driving trajectory may be determined in the form of a polynomial function on a two-dimensional plane corresponding to the road surface. For example, the processor 120 may determine the coefficients of the quadratic function or the linear function by setting the driving trajectory as a curve of the quadratic function or a straight line of the linear function so that the driving trajectory is fitted to the weighted driving history.

The processor 120 may generate a virtual lane on the HD map for assisting autonomous driving based on the driving trajectory. The HD map may display objects, such as surrounding vehicles or pedestrians, which are the basis for determination in the planning and determination stage of autonomous driving. The processor 120 may generate virtual lanes based on driving trajectories of surrounding vehicles on the HD map, for example, assuming that each driving trajectory is formed between the virtual lanes.

The memory 110 may store the CNN, and the CNN divides the driving history into at least one driving section including a self-attention layer, derives a weighted driving history through weighting, and performs curve fitting to perform a driving trajectory can be learned to determine, and the processor 120 can generate a virtual lane based on the CNN, so even in bad weather conditions, that is, even when the actual lane on the road surface is difficult to identify by visual information such as a camera, the complex sensor Since a virtual lane can be created only with driving data measured by , robustness to the weather environment and road surface condition of autonomous driving can be secured.

Referring to FIG. 2 , the autonomous driving system 200 may be configured with various modules. As an example, the autonomous driving system 200 may include a complex sensor 210 , a localization module 220 , a virtual lane creation module 230 , a vehicle control module 240 , and a vehicle driving module 250 .

The apparatus 100 described with reference to FIG. 1 may be for implementing the function of the virtual lane generating module 230 . Memory 110 and processor 120 of device 100 generate virtual lanes from inputs by composite sensor 210 and localization module 220, to plan and control the behavior of the vehicle. Information on the virtual lane may be provided to the vehicle control module 240 requiring the information.

The complex sensor 210 may detect data on the surrounding environment of the vehicle using a camera, radar, LiDAR, GPS, or the like. In addition, complex sensors can utilize HD maps to access information such as roads, terrain, and buildings. In particular, since the composite sensor 210 may include a lidar (LiDAR) that detects surrounding objects by utilizing the reflection of laser light, even when it is difficult to visually identify the actual lane on the road surface due to bad weather or road conditions through the lidar , driving data of surrounding vehicles, which is a basis for generating a virtual lane, may be obtained and provided to the virtual lane generating module 230 .

The localization module 220 may match the location of the own vehicle on the HD map. In addition, when the relative position of the surrounding vehicle is detected by the complex sensor 210 , the approximate absolute positions of the surrounding vehicle and the host vehicle may be matched on the HD map. Meanwhile, the approximate absolute position of the own vehicle and lane information on the HD map by the localization module 220 may be utilized in the process of determining the driving trajectory of the surrounding vehicle.

The virtual lane generating module 230 may include a driving trajectory determining module 231 , a virtual lane generating module 232 , and a target selection module 233 . The driving trajectory determining module 231 and the virtual lane generating module 232 may be understood to perform respective processing steps for generating a virtual lane performed in the device 100 .

The target selection module 233 may select a target vehicle that is a basis for speed control and inter-vehicle distance control of the host vehicle based on the virtual lane. That is, the autonomous driving system 200 may control the speed of the own vehicle so that an appropriate inter-vehicle distance with the target vehicle is maintained. , selecting a target vehicle used for speed control and inter-vehicle distance control according to autonomous driving based on the virtual lane.

The vehicle control module 240 may perform steering control, speed control, and inter-vehicle distance control of the own vehicle. The vehicle control module 240 may control a variable such as a yaw rate that determines the steering of the own vehicle, that is, the lateral behavior of the vehicle, based on the virtual lane provided from the virtual lane generating module 230, and , the speed of the own vehicle and the inter-vehicle distance to the target vehicle may be controlled based on the target vehicle provided from the virtual lane generating module 230 .

The vehicle driving module 250 may drive the own vehicle according to a control command determined by the vehicle control module 240 , for example, according to a steering command or an acceleration/deceleration command.

Referring to FIG. 3 , it is shown that lane recognition may be possible in the case of good weather 301 , but lane recognition may not be possible in the case of bad weather 302 , and in the case of bad weather 302 in which lane recognition is impossible A first example 310 and a second example 320 of the creation of a virtual lane are shown. The first example 310 is to illustrate a case in which the CNN and self-attention of the present invention are not utilized, and the second example 320 is to illustrate a case in which the CNN and self-attention of the present invention are utilized. .

The first example 310 may represent a case in which the surrounding vehicle 312 and the surrounding vehicle 313 are driven together with the own vehicle 311 , and the second example 320 is also the same as the first example 310 . A case in which the vehicle 321 , the surrounding vehicle 322 , and the surrounding vehicle 323 travel may be represented.

In the first example 310, the surrounding vehicle 312 may drive while following the lane without changing the lane, but the surrounding vehicle 313 may change from before the change 313A through the lane change 313B to after the change ( 313C) to change the driving lane. In contrast, when the autonomous driving system of the own vehicle 311 cannot interpret the behavior of the surrounding vehicle 313 as a lane change, the virtual lane 314 may be generated according to the driving trajectory of the surrounding vehicle 313, Accuracy of virtual lane generation may be deteriorated.

In order to solve the problem that the accuracy of generating a virtual lane is reduced as in the first example 310, the device 100 divides the driving history of the surrounding vehicle into at least one driving section based on CNN and self-attention, and , a method of deriving a weighted driving history by assigning weights to each driving section, it is possible to consider the influence of a lane change of a surrounding vehicle.

Specifically, the processor 120 may divide the driving history into at least one driving section by dividing the driving history into a lane following section and a lane change section according to a driving pattern. For example, in the second example 320 , the processor 120 lane-follows driving data corresponding to the before-change 323A and after-change 323C of the surrounding vehicle 323 based on the CNN and self-attention. As a section, driving data corresponding to the lane change 323B may be divided into a lane change section.

Also, the processor 120 may derive the weighted driving history by setting the weight of the lane following section to be higher than the weight of the lane change section. For example, in the second example 320 , the processor 120 may give a high weight to the lane following section before the change 323A and after the change 323C, and the lane change of the lane change 323B A low weight can be given to the section.

Accordingly, since driving data related to the lane change 323B may be reflected in the weighted driving history with a low weight, the virtual lane 324 that more accurately matches the lane on the actual road surface that is not identified by visual information such as a camera is provided. can be generated, so that the accuracy of virtual lane generation can be prevented from being deteriorated.

Referring to FIG. 4 , the method of generating a virtual lane may include steps 410 to 450 . However, the present invention is not limited thereto, and general steps other than the steps shown in FIG. 4 may be further included in the method of generating a virtual lane.

The method of generating a virtual lane for assisting autonomous driving of FIG. 4 may include steps processed in time series by the apparatus 100 for generating a virtual lane described with reference to FIGS. 1 to 3 . Accordingly, even if the contents of the method of FIG. 4 are omitted below, the contents described above for the apparatus 100 may be equally applied to the method of FIG. 4 .

In operation 410 , the device 100 may derive a driving history by accumulating driving data of at least one surrounding vehicle measured by the complex sensor over time.

The apparatus 100 may derive the driving history by generating driving data by applying Kalman filtering for data purification to data measured by the complex sensor.

In operation 420 , the device 100 may divide the driving history into at least one driving section according to a driving pattern based on self-attention implemented by a convolutional neural network (CNN).

The apparatus 100 may divide the driving history into at least one driving section by dividing the driving history into a lane following section and a lane change section according to the driving pattern.

The apparatus 100 may derive the weighted driving history by setting the weight of the lane following section to be higher than the weight of the lane change section.

In operation 430 , the apparatus 100 may derive a weighted driving history of at least one surrounding vehicle by giving weights to at least one driving section based on self-attention.

In operation 440 , the device 100 may determine the driving trajectory of at least one surrounding vehicle by performing curve fitting on the weighted driving history through CNN.

The apparatus 100 may determine the driving trajectory by performing curve fitting by inferring coefficients of a polynomial function for representing the trajectory fitted to the weighted driving history.

In operation 450 , the device 100 may generate a virtual lane on the HD map for assisting autonomous driving based on the driving trajectory.

The self-attention may calculate an attention score by using a softmax function.

The composite sensor may include a lidar (LiDAR) that detects surrounding objects by utilizing the reflection of laser light.

In addition to steps 410 to 450 , the device 100 may select a target vehicle used for speed control and inter-vehicle distance control according to autonomous driving based on the virtual lane.

Meanwhile, the method of generating a virtual lane for assisting autonomous driving of FIG. 4 may be recorded in a computer-readable recording medium in which at least one program or software including instructions for executing the method is recorded.

Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and floppy disks. Magneto-optical media and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like may be included. Examples of program instructions may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

Although the embodiments of the present invention have been described in detail above, the scope of the rights according to the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention described in the following claims are also provided. It should be interpreted as being included in the scope of rights according to the

Claims

A method of generating a virtual lane for assisting autonomous driving, the method comprising:

deriving a driving history by accumulating driving data of at least one surrounding vehicle measured by a complex sensor over time;

dividing the driving history into at least one driving section according to a driving pattern based on self-attention implemented by a convolutional neural network (CNN);

deriving a weighted driving history of the at least one surrounding vehicle by assigning a weight to the at least one driving section based on the self-attention;

determining a driving trajectory of the at least one surrounding vehicle by performing curve fitting on the weighted driving history through the CNN; and

and generating the virtual lane on an HD map for assisting the autonomous driving based on the driving trajectory.
The method of claim 1,

The dividing into the at least one driving section includes dividing the driving history into a lane following section and a lane change section according to the driving pattern.
3. The method of claim 2,

The deriving the weighted driving history may include setting a weight of the lane following section to be higher than a weight of the lane change section.
The method of claim 1,

The self-attention is a method of calculating an attention score by using a softmax function.
The method of claim 1,

The determining of the driving trajectory may include performing the curve fitting by inferring coefficients of a polynomial function for representing a trajectory fitted to the weighted driving history.
The method of claim 1,

The deriving of the driving history may include generating the driving data by applying Kalman filtering for data purification to data measured by the composite sensor.
The method of claim 1,

The composite sensor includes a lidar (LiDAR) that detects a surrounding object by utilizing the reflection of laser light.
The method of claim 1,

The method may further include selecting a target vehicle used for speed control and inter-vehicle distance control according to the autonomous driving based on the virtual lane.
An apparatus for generating a virtual lane for assisting autonomous driving, comprising:

a memory storing at least one program; and

a processor for generating the virtual lane by executing the at least one program;

The processor is

Deriving a driving history by accumulating driving data of at least one surrounding vehicle measured by a complex sensor over time,

Classifying the driving history into at least one driving section according to a driving pattern based on self-attention implemented by a convolutional neural network (CNN),

Deriving a weighted driving history of the at least one surrounding vehicle by assigning a weight to the at least one driving section based on the self-attention,

determining the driving trajectory of the at least one surrounding vehicle by performing curve fitting on the weighted driving history through the CNN;

An apparatus for generating the virtual lane on an HD map for assisting the autonomous driving based on the driving trajectory.
10. The method of claim 9,

The processor divides the driving history into the at least one driving section by dividing the driving history into a lane following section and a lane change section according to the driving pattern.
11. The method of claim 10,

The processor is configured to set a weight of the lane following section to be higher than a weight of the lane change section to derive the weighted driving history.
10. The method of claim 9,

The self-attention is an apparatus for calculating an attention score by using a softmax function.
10. The method of claim 9,

and the processor determines the driving trajectory by performing the curve fitting by inferring coefficients of a polynomial function for representing a trajectory fitted to the weighted driving history.
10. The method of claim 9,

The processor is configured to generate the driving data by applying Kalman filtering for data purification to data measured by the composite sensor to derive the driving history.
10. The method of claim 9,

The composite sensor is a device including a lidar (LiDAR) for detecting a surrounding object by utilizing the reflection of laser light.
10. The method of claim 9,

The processor is an apparatus for selecting a target vehicle used for speed control and inter-vehicle distance control according to the autonomous driving based on the virtual lane.