KR20220100128A - Autonomous vehicle driving control method and system using infrastructure information, driving information of nearby vehicles and pre-learning information - Google Patents

Abstract

The present invention relates to a method for controlling driving of an autonomous vehicle using infrastructure information, operation information of surrounding vehicles, and prior learning information, and a system. According to one embodiment of the present invention, operation design area correction information is generated by using the operation information and road infrastructure information of specific surrounding vehicles operating within a predetermined area range based on a location of the autonomous vehicle and prior learning information of a learning DB. Therefore, the operation design area correction information is transmitted to the autonomous vehicle. As a result, driving safety of the autonomous vehicle may be improved. The method for controlling the driving of the autonomous vehicle of the present invention comprises: a first receiving step; a second receiving step; a third receiving step; a selection step; an inquiry step; a judgment step; a creation step; and a transmission step.

Description

Autonomous vehicle driving control method and system using infrastructure information, driving information of nearby vehicles and pre-learning information

The present invention relates to a method and system for controlling the driving of an autonomous driving vehicle using infrastructure information, driving information of surrounding driving vehicles, and prior learning information.

Self-driving cars are ultimately vehicles that can drive automatically without the intervention of a human driver.

Currently, the R&D stage before the commercialization of fully autonomous vehicles without human intervention is divided into several stages according to the level of autonomous driving (automation level). As a standard suggested by Engineers; SAE, autonomous vehicles are divided into five levels (SAE presents autonomous driving technology grades from 0 to 5, of which level 0 does not apply any technology related to autonomous driving). Since it refers to a conventional conventional vehicle that does not

Steps 1 to 4 are premised on the intervention of a human driver, but the fully autonomous vehicle in Level 5 drives autonomously without human intervention by recognizing the surrounding environment with images, radar, lidar, GPS, etc. and designating a destination. aim to do

The autonomous vehicle system is largely composed of Recognition, Judgment, and Operation layers as shown below.

1) Recognition layer: A layer that extracts and classifies the attribute information required for driving by using sensors (Camera, Radar, Lidar, etc.) mounted on the vehicle that play the same role as human eyes and ears.

2) Decision layer: A layer that determines the movement of a vehicle, such as creating a safe zone and judging dangerous situations to safely drive to a destination

3) Operation layer: A layer that controls the direct movement of the vehicle, such as controlling the speed or direction, like a human blood vessel, muscle, and nervous system (various actuators and controllers that control them)

Since autonomous vehicles drive themselves without human intervention or operate with minimal human intervention, define what functions should be executed in advance when various restrictions such as road types and weather occur while driving and refer to them to drive

These predefined items are called Operational Design Domain (ODD).

ODD setting is very important from the point of view of commercialization of autonomous vehicles, such as determining the limits of control performance and vehicle safety based on use cases and/or driving scenarios. , so far, the operation design area (ODD) has been set based on the vehicle's environmental sensors (image, radar, lidar, etc.) and dynamic models (speed, acceleration, angular velocity, etc.) and the autonomous vehicle is controlled. It was difficult to respond appropriately.

Korean Patent Publication No. 10-2020-0101517 (published on August 28, 2020)

Accordingly, the present invention has been devised in the background described above, and the operation design area using the operation information and road infrastructure information of specific surrounding vehicles operating within a predetermined area range based on the location of the autonomous vehicle, and the pre-learning information of the learning DB A method of controlling autonomous driving vehicle driving using infrastructure information that can increase the driving safety of autonomous driving vehicles by generating correction information and transmitting driving design area correction information to the autonomous driving vehicle, operating information of surrounding vehicles, and prior learning information and to provide a system.

The object of the present invention is not limited thereto, and other objects not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve this object, an embodiment of the present invention provides an autonomous vehicle driving control method using infrastructure information, driving information of surrounding vehicles, and prior learning information, (a) the communication unit 103 of the server 101 , receiving the first driving information DI1 from the autonomous vehicle 105; (b) receiving, by the communication unit 103 of the server 101 , respective second driving information DI2 from the surrounding autonomous vehicles 107 ; (c) receiving, by the communication unit (103) of the server (101), infrastructure information (RII) of a road section on which the autonomous vehicle (105) operates from an external server (109); (d) the selection unit 111 of the server 101 compares each of the second driving information DI2 with the first driving information DI1 to determine the location reference of the autonomous vehicle 105 selecting specific surrounding autonomous vehicles 107' that are running within an area range of ; (e) inquiring, by the determination unit 113 of the server 101, the pre-learning information DI3 on the road section in which the autonomous vehicle 105 operates in the learning DB 117; (f) the determination unit 113 of the server 101, by machine learning or deep learning, the first driving information DI1 and the second driving information of each of the specific surrounding autonomous vehicles 107' (DI2) and determining the correlation between the infrastructure information (RII) and the pre-learning information (DI3); (g) the setting unit 115 of the server 101, the correlated first driving information DI1, the second driving information DI2 of each of the specific surrounding autonomous vehicles 107', the infrastructure generating operation design area correction information (ODDCI) for the autonomous vehicle (105) using the information (RII) and the pre-learning information (DI3); and (h) transmitting, by the communication unit 103 of the server 101, the operation design area correction information (ODDCI) to the autonomous vehicle 105; A method for controlling the driving of an autonomous driving vehicle using driving information and prior learning information of driving vehicles is provided.

In addition, an embodiment of the present invention provides a communication unit 103, a selection unit 111, and a determination unit 113 in an autonomous vehicle driving control system using infrastructure information, driving information of surrounding vehicles, and prior learning information. and a server 101 including a setting unit 115 and a learning DB 117; and an external server 109 for storing infrastructure information (RII) of a road section in which the autonomous vehicle 105 operates; including, (a) the communication unit 103 of the server 101, Receives the first driving information DI1 from the vehicle 105 , and (b) the communication unit 103 of the server 101 receives each of the second driving information DI2 from the surrounding autonomous vehicles 107 . ), (c) the communication unit 103 of the server 101 receives the infrastructure information RII of the road section in which the autonomous vehicle 105 operates from the external server 109, , (d) the selection unit 111 of the server 101 compares each of the second driving information DI2 with the first driving information DI1 to determine the location of the autonomous vehicle 105 . Selects specific surrounding autonomous vehicles 107 ′ operating within a reference predetermined area range, and (e) the determination unit 113 of the server 101 performs the autonomous driving in the learning DB 117 . The vehicle 105 inquires the pre-learning information DI3 for the road section on which it travels, and (f) the determination unit 113 of the server 101 performs the first driving information by machine learning or deep learning. (G ) the setting unit 115 of the server 101, the first operation information (DI1) correlated, the second operation information (DI2) of each of the specific surrounding autonomous vehicles 107', the infrastructure information ( RII) and the prior learning information DI3 to generate operation design area correction information (ODDCI) for the autonomous vehicle 105, (h) the communication unit 103 of the server 101, Provided is an autonomous vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information, characterized in that the driving design area correction information (ODDCI) is transmitted to the autonomous driving vehicle 105 .

According to an embodiment of the present invention, operation design area correction information is obtained using operation information and road infrastructure information of specific surrounding vehicles operating within a predetermined area range based on the location of the autonomous vehicle, and pre-learning information of the learning DB. By creating and transmitting the operation design area correction information to the autonomous vehicle, there is an effect of increasing the driving safety of the autonomous vehicle.

1 is a block diagram illustrating an autonomous vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and prior learning information according to an embodiment of the present invention.
2 is a view showing an example of determining the correlation between the first operation information, the second operation information, the infrastructure information, and the prior learning information in the determination unit.
3 and 4 are flowcharts illustrating an autonomous vehicle driving control method using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information according to an embodiment of the present invention.

Hereinafter, some embodiments of the present invention will be described in detail with reference to exemplary drawings. In adding reference numerals to the components of each drawing, it should be noted that the same components are given the same reference numerals as much as possible even though they are indicated on different drawings. In addition, in describing the present invention, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present invention, the detailed description thereof will be omitted.

In addition, in describing the components of the present invention, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only for distinguishing the elements from other elements, and the essence, order, or order of the elements are not limited by the terms. When it is described that a component is “connected”, “coupled” or “connected” to another component, the component may be directly connected or connected to the other component, but another component is between each component. It should be understood that elements may be “connected,” “coupled,” or “connected.”

1 is a block diagram illustrating an autonomous vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and prior learning information according to an embodiment of the present invention. 2 is a view showing an example of determining the correlation between the first operation information, the second operation information, the infrastructure information, and the prior learning information in the determination unit. 3 and 4 are flowcharts illustrating an autonomous vehicle driving control method using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information according to an embodiment of the present invention.

As shown in these drawings, the method for controlling autonomous driving vehicle driving using infrastructure information and driving information of surrounding driving vehicles according to an embodiment of the present invention includes (a) the communication unit 103 of the server 101, receiving the first driving information DI1 from the autonomous vehicle 105; (b) receiving, by the communication unit 103 of the server 101 , respective second driving information DI2 from surrounding autonomous vehicles 107 ; (c) receiving, by the communication unit 103 of the server 101, infrastructure information (RII) of the road section on which the autonomous vehicle 105 operates from the external server 109; (d) the selection unit 111 of the server 101 compares each of the second driving information DI2 and the first driving information DI1 to be within a predetermined area range based on the location of the autonomous vehicle 105 selecting specific surrounding autonomous vehicles (107') in operation; (e) inquiring, by the determination unit 113 of the server 101, the pre-learning information DI3 on the road section in which the autonomous vehicle 105 operates in the learning DB 117; (f) the determination unit 113 of the server 101, the first driving information DI1 and the second driving information DI2 of each of the specific surrounding autonomous vehicles 107' by machine learning or deep learning , determining the correlation between the infrastructure information (RII) and the prior learning information (DI3); (g) the setting unit 115 of the server 101, the first operation information (DI1) correlated, the second operation information (DI2) of each of the specific surrounding autonomous vehicles 107', and the infrastructure information (RII) and generating operation design area correction information (ODDCI) for the autonomous vehicle 105 using the pre-learning information (DI3). and (h) transmitting, by the communication unit 103 of the server 101, the operation design area correction information (ODDCI) to the autonomous vehicle 105 .

Hereinafter, each step will be described in detail.

First, step (a) is a step in which the communication unit 103 of the server 101 receives the first driving information DI1 from the autonomous vehicle 105 .

Here, the first driving information DI1 includes steering information, location information, speed information, acceleration/deceleration information, control information of actuators such as gears, lighting on/off information, vehicle internal instrument panel display information, and vehicle of the autonomous vehicle 105 . At least one of information necessary for or generated by the operation of the autonomous vehicle 105, such as internal sound control information, is included.

In addition, the first driving information DI1 includes at least one or more of pedestrian movement information sensed by the autonomous vehicle 105, obstacle state information, and signal information of traffic lights (color information of traffic lights such as red, green, and yellow) do.

In addition, the first driving information DI1 includes specific surrounding driving vehicles 108 ′ sensed by the autonomous driving vehicle 105 with a camera, LiDAR, Radar, etc., a general vehicle that is not an autonomous vehicle. )) for sensing information (SI).

Subsequently, step (b) is a step in which the communication unit 103 of the server 101 receives each of the second driving information DI2 from the surrounding autonomous vehicles 107 .

The second driving information DI2 transmitted by the surrounding autonomous vehicles 107 is also the steering information, location information, speed information, addition and subtraction of each of the surrounding autonomous driving vehicles 107 , similar to the above-described first driving information DI1 . It includes speed information, control information of actuators such as gears, lighting on/off information, instrument panel display information inside the vehicle, and sound control information inside the vehicle.

Subsequently, step (c) is a step in which the communication unit 103 of the server 101 receives, from the external server 109, the infrastructure information RII of the road section on which the autonomous vehicle 105 operates.

Here, the infrastructure information (RII) of the road section includes at least one of road type information, road surface information, and signal state information of a traffic light.

More specifically, the road type information may include information on lanes, intersections, whether there is an autonomous vehicle-only road, straightness, inclination, and whether or not there is a pedestrian protection zone.

In addition, the road surface information may include information on the material of the road and the state of the road (presence of rainfall, snowfall, black ice, etc.).

In addition, the signal state information may include information on whether the signal lamp operates normally, the current signal type, and the remaining time until the signal is changed.

On the other hand, the communication unit 103 of the server 101 , the signal information of the traffic light included in the first driving information DI1 received from the autonomous vehicle 105 , and the infrastructure information RII received from the external server 109 . ), it is also possible to compare the signal state information of the traffic lights included in ) to transmit a notification signal for whether the traffic lights are normally operated to the external server 109 .

Next, in step (d), the selection unit 111 of the server 101 compares each of the second driving information DI2 and the first driving information DI1, and the location reference of the autonomous vehicle 105 is This is a step of selecting specific surrounding autonomous vehicles 107 ′ operating within a predetermined area range.

Here, the selection unit 111 includes location information of each of the surrounding autonomous vehicles 107 included in each of the second driving information DI2 and the autonomous driving vehicle 105 included in the first driving information DI1. ), and selects nearby autonomous vehicles 107 operating within a predetermined area range based on the location of the autonomous vehicle 105 as specific surrounding autonomous vehicles 107'.

For example, the selection unit 111 sets a 500 m radius range based on the location of the autonomous driving vehicle 105 as a predetermined area range, and selects surrounding autonomous driving vehicles 107 operating within this range for specific surrounding autonomous driving. Vehicles 107' can be selected.

Of course, the reference radius of the location of the autonomous driving vehicle 105 set by the selection unit 111 may be variously changed.

In addition, the selection unit 111 selects, among the surrounding driving vehicles 108 , the surrounding driving vehicles 108 that are within a range in which the autonomous driving vehicle 105 can collect the sensing information SI. 108' can be selected.

Next, step (e) is a step in which the determination unit 113 of the server 101 inquires the pre-learning information DI3 for the road section in which the autonomous vehicle 107 operates in the learning DB 117 . .

In one embodiment of the present invention, the learning DB 117 is described as an example in which the server 101 is included as one configuration, but otherwise, the learning DB 117 may be a separate and independently constructed database.

Of course, the learning DB 117 may be provided as a cloud-type database.

Subsequently, in step (f), the determination unit 113 of the server 101 performs the first driving information DI1 and the second of each of the specific surrounding autonomous vehicles 107 ′ by machine learning or deep learning. It is a step of determining the correlation between the operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3).

For example, in the case of the autonomous driving vehicle 105, specific surrounding autonomous driving vehicles 107', and specific surrounding driving vehicles 108' operating in situation 1 (holiday, morning, snowfall, child protection area), the second Speed information included in the first driving information (DI1) and the second driving information (DI2), road type information included in infrastructure information (RII), road surface information, and prior learning information (DI3) for the relevant road section in situation 1 The learning information included in the .

Subsequently, in step (g), the setting unit 115 of the server 101 performs the interrelated first driving information DI1, the second driving information DI2 of each of the specific surrounding autonomous vehicles 107', This is a step of generating operation design area correction information (ODDCI) for the autonomous vehicle 105 by using the infrastructure information (RII) and the prior learning information (DI3).

An example of the details of the operation design area correction information (ODDCI) is shown in Table 1 below.

On the other hand, the operation design area correction information (ODDCI) is calculated using the interrelated first operation information (DI1), the second operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3), autonomous driving It may include speed control information (VCI) of the vehicle 105 .

A specific example of the speed control information (VCI) is as follows.

When the speed limit based on the ODD (Operation Design Area) of the road on which the autonomous vehicle 105 is running is 30Km/h, the first driving information (DI1), the second driving information (DI2), the infrastructure information (RII), and the prior The speed limit of the autonomous vehicle 105 is increased or decreased using the learning information DI3.

- In the afternoon on holidays, the speed limit has been raised

- Lowering the speed limit in the afternoon on holidays and in winter

- Lowering the speed limit during weekday commuting hours

- Lowering the speed limit when road congestion increases

- Lowering the speed limit when there is construction and accident information on the driving route of the autonomous vehicle 105

In addition, the operation design area correction information (ODDCI) is calculated using the interrelated first operation information (DI1), the second operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3), autonomous driving Steering control information (SCI) of the vehicle 105 may be included.

A specific example of the steering control information (SCI) is as follows.

Assuming that the road on which the autonomous driving vehicle 105 is currently driving is a straight road and it is necessary to turn right after driving 300 m, the degree of congestion of the road, the presence of construction, the first operation information (DI1) of the autonomous driving vehicle 105, the specific The lane change timing is adjusted by using the second driving information DI2 of the surrounding autonomous vehicles 107 ′.

- Set the default lane change location based on pre-learning information (DI3) (for example, change lanes before 50m during commuting time on weekdays, and change lanes before 30m during morning hours on holidays)

- Change lanes in advance at night or in wet conditions

- Change lanes ahead of time when congestion of vehicles driving on the right increases

- Adjusting the lane change position by using the driving direction and acceleration/deceleration information of the vehicle driving on the right when changing lanes

- Adjust the lane change location when construction is in progress at the lane change location

In addition, the operation design area correction information (ODDCI) is calculated using the interrelated first operation information (DI1), the second operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3), autonomous driving It may include path control information (RCI) of the vehicle 105 .

A specific example of the path control information (RCI) is as follows.

Change the driving route to the destination from time to time based on pre-learning information (DI3) and infrastructure information (RII)

- Set the basic driving route based on pre-learning information (DI3) (eg, weekday daytime: A route, weekday commute time: B route, holiday: C route)

- Determination of whether to change the driving route according to real-time infrastructure information (RII)

Subsequently, step (h) is a step in which the communication unit 103 of the server 101 transmits the operation design area correction information ODDCI to the autonomous vehicle 105 .

In this case, the communication unit 103 of the server 101 may transmit the operation design area correction information ODDCI to the autonomous vehicle 105 through one-to-one communication or broadcasting.

On the other hand, according to an embodiment of the present invention, in the autonomous driving vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and prior learning information, the communication unit 103, the selection unit 111, and the determination unit ( 113) and a server 101 including a setting unit 115 and a learning DB 117; and an external server 109 for storing infrastructure information (RII) of a road section in which the autonomous vehicle 105 operates; including, (a) the communication unit 103 of the server 101, ) receives the first driving information DI1 from, (b) the communication unit 103 of the server 101 receives each of the second driving information DI2 from the surrounding autonomous vehicles 107, ( c) the communication unit 103 of the server 101 receives the infrastructure information (RII) of the road section on which the autonomous vehicle 105 operates from the external server 109, (d) the selection unit of the server 101 111 compares each of the second driving information DI2 and the first driving information DI1 to determine specific surrounding autonomous driving vehicles ( 107 '), (e) the determination unit 113 of the server 101 inquires the pre-learning information DI3 for the road section in which the autonomous vehicle 105 operates in the learning DB 117, , (f) the determination unit 113 of the server 101, the first driving information DI1 and the second driving information DI2 of each of the specific surrounding autonomous driving vehicles 107' by machine learning or deep learning And, determine the correlation between the infrastructure information (RII) and the prior learning information (DI3), (g) the setting unit 115 of the server 101, the correlated first operation information (DI1), the specific surrounding autonomy The driving design area correction information (ODDCI) for the autonomous driving vehicle 105 is generated using the second driving information DI2, the infrastructure information RII, and the pre-learning information DI3 of each of the driving vehicles 107'. and (h) the communication unit 103 of the server 101 transmits the operation design area correction information ODDCI to the autonomous vehicle 105 .

In addition, the first driving information DI1 includes at least one of steering information, location information, speed information of the autonomous vehicle 105 , pedestrian movement information sensed by the autonomous vehicle 105 , obstacle state information, and signal information such as traffic lights. It is characterized in that it contains one or more.

More specifically, the first driving information DI1 includes steering information, location information, speed information, acceleration/deceleration information, control information of actuators such as gears, lighting on/off information, and instrument panel display inside the autonomous vehicle 105 of the autonomous vehicle 105 . It includes at least one or more of information necessary for or generated by the operation of the autonomous vehicle 105 , such as information and sound control information inside the vehicle.

In addition, the first driving information DI1 includes at least one or more of pedestrian movement information sensed by the autonomous vehicle 105, obstacle state information, and signal information of traffic lights (color information of traffic lights such as red, green, and yellow) do.

In addition, the first driving information DI1 includes surrounding driving vehicles 108 sensed by the autonomous driving vehicle 105 with a camera, LiDAR, Radar, etc. (a general vehicle other than an autonomous vehicle)) may include sensing information (SI) for

In addition, the infrastructure information (RII) is characterized in that it includes at least one of road type information, road surface information, and signal state information such as traffic lights.

In addition, the operation design area correction information (ODDCI) is calculated using the interrelated first operation information (DI1), the second operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3), autonomous driving It is characterized in that it includes the speed control information (VCI) of the vehicle (105).

In addition, the operation design area correction information (ODDCI) is calculated using the interrelated first operation information (DI1), the second operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3), autonomous driving It is characterized in that it includes the speed control information (VCI) of the vehicle (105).

In addition, the operation design area correction information (ODDCI) is calculated using the interrelated first operation information (DI1), the second operation information (DI2), the infrastructure information (RII), and the prior learning information (DI3), autonomous driving It is characterized in that it includes the steering control information (SCI) of the vehicle (105).

Features and functions of each configuration of the autonomous driving vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information according to an embodiment of the present invention are described above in the infrastructure according to an embodiment of the present invention. Since it is the same as that described in the method for controlling the driving of an autonomous driving vehicle using information and driving information and prior learning information of surrounding driving vehicles, a detailed description thereof will be omitted.

As described above, according to an embodiment of the present invention, by using the driving information of specific surrounding driving vehicles operating within a predetermined area range based on the location of the autonomous vehicle, road infrastructure information, and pre-learning information of the learning DB, By generating the operation design area correction information and transmitting the operation design area correction information to the autonomous vehicle, it is possible to increase the driving safety of the autonomous vehicle.

In the above, even though all the components constituting the embodiment of the present invention are described as being combined or operating in combination, the present invention is not necessarily limited to this embodiment. That is, within the scope of the object of the present invention, all the components may operate by selectively combining one or more.

The above description is merely illustrative of the technical spirit of the present invention, and various modifications and variations will be possible without departing from the essential characteristics of the present invention by those skilled in the art to which the present invention pertains. Therefore, the embodiments disclosed in the present invention are not intended to limit the technical spirit of the present invention, but to explain, and the scope of the technical spirit of the present invention is not limited by these embodiments. The protection scope of the present invention should be construed by the following claims, and all technical ideas within the equivalent range should be construed as being included in the scope of the present invention.

101: server
103: communication department
105: autonomous vehicle
107: Nearby autonomous vehicles
107': Autonomous vehicles around a specific area
108: surrounding vehicles
108': specific surrounding vehicles
109: external server
111: selection part
113: judgment unit
115: setting unit
117: Learning DB
DI1: 1st operation information
DI2: 2nd operation information
DI3: Pre-learning information
ODDCI: Operation design area correction information
RII: Infrastructure information
SI: Sensing information

Claims (12)

In a method for controlling the driving of an autonomous driving vehicle using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information,
(a) receiving, by the communication unit 103 of the server 101 , the first driving information DI1 from the autonomous vehicle 105 ;
(b) receiving, by the communication unit 103 of the server 101, respective second driving information DI2 from the surrounding autonomous vehicles 107;
(c) receiving, by the communication unit (103) of the server (101), infrastructure information (RII) of a road section on which the autonomous vehicle (105) operates from an external server (109);
(d) the selection unit 111 of the server 101 compares each of the second driving information DI2 with the first driving information DI1 to determine the location reference of the autonomous vehicle 105 selecting specific surrounding autonomous vehicles 107' that are running within an area range of ;
(e) inquiring, by the determination unit 113 of the server 101, the pre-learning information DI3 on the road section in which the autonomous vehicle 105 operates in the learning DB 117;
(f) the determination unit 113 of the server 101, by machine learning or deep learning, the first driving information DI1 and the second driving information of each of the specific surrounding autonomous vehicles 107' (DI2) and determining the correlation between the infrastructure information (RII) and the prior learning information (DI3);
(g) the setting unit 115 of the server 101, the first driving information DI1 correlated, the second driving information DI2 of each of the specific surrounding autonomous vehicles 107', the infrastructure generating operation design area correction information (ODDCI) for the autonomous vehicle (105) using the information (RII) and the pre-learning information (DI3); and
(h) transmitting, by the communication unit (103) of the server (101), the operation design area correction information (ODDCI) to the autonomous vehicle (105);
Self-driving vehicle driving control method using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information, comprising: The method of claim 1,
In step (a), the first driving information DI1 is
At least one of steering information, location information, speed information of the autonomous vehicle 105, pedestrian movement information sensed by the autonomous vehicle 105, obstacle state information, and signal information such as traffic lights A method of controlling the driving of autonomous vehicles using infrastructure information, driving information of surrounding vehicles, and prior learning information. The method of claim 1,
In the step (c), the infrastructure information (RII) of the road section is,
An autonomous vehicle driving control method using infrastructure information, driving information of surrounding vehicles, and prior learning information, comprising at least one of road type information, road surface information, and signal state information of traffic lights. The method of claim 1,
In step (g), the operation design area correction information (ODDCI) is,
The speed control of the autonomous vehicle 105, calculated using the correlated first driving information DI1, the second driving information DI2, the infrastructure information RII, and the pre-learning information DI3 An autonomous vehicle driving control method using infrastructure information, driving information of surrounding vehicles, and pre-learning information, characterized in that it includes information (VCI). The method of claim 1,
In step (g), the operation design area correction information (ODDCI) is,
Steering control of the autonomous driving vehicle 105, calculated using the correlated first driving information DI1, the second driving information DI2, the infrastructure information RII, and the prior learning information DI3 An autonomous vehicle driving control method using infrastructure information, driving information of surrounding vehicles, and prior learning information, characterized in that it includes information (SCI). The method of claim 1,
In step (g), the operation design area correction information (ODDCI) is,
Path control of the autonomous vehicle 105, calculated using the correlated first driving information DI1, the second driving information DI2, the infrastructure information RII, and the pre-learning information DI3 An autonomous vehicle driving control method using infrastructure information, driving information of surrounding driving vehicles, and pre-learning information, characterized in that it includes information (RCI). In an autonomous driving vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and prior learning information,
a server 101 including a communication unit 103 , a selection unit 111 , a determination unit 113 , a setting unit 115 , and a learning DB 117 ; and an external server 109 for storing infrastructure information (RII) of a road section in which the autonomous vehicle 105 operates;
(a) the communication unit 103 of the server 101 receives the first driving information DI1 from the autonomous vehicle 105;
(b) the communication unit 103 of the server 101 receives each of the second driving information DI2 from the surrounding autonomous vehicles 107,
(c) the communication unit 103 of the server 101 receives, from the external server 109, the infrastructure information (RII) of a road section in which the autonomous vehicle 105 operates,
(d) the selection unit 111 of the server 101 compares each of the second driving information DI2 with the first driving information DI1 to determine the location of the autonomous vehicle 105 Selecting specific surrounding autonomous vehicles 107' operating within a predetermined area range,
(e) the determination unit 113 of the server 101 searches the learning DB 117 for the pre-learning information DI3 on the road section in which the autonomous vehicle 105 operates,
(f) the determination unit 113 of the server 101, by machine learning or deep learning, the first driving information DI1 and the second driving information of each of the specific surrounding autonomous vehicles 107' (DI2) and determine the correlation between the infrastructure information (RII) and the pre-learning information (DI3),
(g) the setting unit 115 of the server 101, the correlated first driving information DI1, the second driving information DI2 of each of the specific surrounding autonomous vehicles 107', the infrastructure generating operation design area correction information (ODDCI) for the autonomous vehicle 105 using the information RII and the pre-learning information DI3;
(h) infrastructure information, characterized in that the communication unit 103 of the server 101 transmits the operation design area correction information (ODDCI) to the autonomous vehicle 105; and operation information of surrounding vehicles; Autonomous vehicle driving control system using pre-learning information. 8. The method of claim 7,
The first driving information DI1 is
At least one of steering information, location information, speed information of the autonomous vehicle 105, pedestrian movement information sensed by the autonomous vehicle 105, obstacle state information, and signal information such as traffic lights An autonomous vehicle driving control system using infrastructure information, driving information of surrounding vehicles, and pre-learning information. 8. The method of claim 7,
The infrastructure information (RII) is,
An autonomous vehicle driving control system using infrastructure information, driving information of surrounding vehicles, and prior learning information, characterized in that it includes at least one of road type information, road surface information, and signal state information of traffic lights. 8. The method of claim 7,
The operation design area correction information (ODDCI) is,
The speed control of the autonomous vehicle 105, calculated using the correlated first driving information DI1, the second driving information DI2, the infrastructure information RII, and the pre-learning information DI3 An autonomous driving vehicle driving control system using information (VCI) and driving information and prior learning information of surrounding driving vehicles and infrastructure information. 8. The method of claim 7,
The operation design area correction information (ODDCI) is,
The speed control of the autonomous vehicle 105, calculated using the correlated first driving information DI1, the second driving information DI2, the infrastructure information RII, and the pre-learning information DI3 An autonomous driving vehicle driving control system using information (VCI) and driving information and prior learning information of surrounding driving vehicles and infrastructure information. 8. The method of claim 7,
The operation design area correction information (ODDCI) is,
Steering control of the autonomous driving vehicle 105, calculated using the correlated first driving information DI1, the second driving information DI2, the infrastructure information RII, and the prior learning information DI3 An autonomous driving vehicle driving control system using infrastructure information, driving information of surrounding driving vehicles, and prior learning information, characterized in that it includes information (SCI).

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