KR20220068922A - Generating method and system for autonomous driving route - Google Patents

Abstract

Provided is a method for generating an autonomous driving route which can generate a driving route of an autonomous driving vehicle by predicting and analyzing an environment of the driving route or preference. The method predicts a driving environment with respect to the initial driving path to a set destination based on destination information to analyze driving preference of a user, generates the corrected driving route by correcting the initial driving route from an analysis result, and provides the corrected driving path to the autonomous driving vehicle.

Description

Autonomous driving route generation method and driving route generation system {Generating method and system for autonomous driving route}

The present invention relates to an autonomous driving route generating method and a driving route generating system capable of generating a driving route of an autonomous driving vehicle by predicting and analyzing an environment of a driving route preferred by a driver.

Recently, interest in self-driving vehicles is increasing. In particular, various additional functions related to autonomous driving are continuously being developed in order to solve the traffic congestion caused by the increase in automobile demand and to safely avoid obstacles such as people and other vehicles.

For example, there are numerous algorithms associated with lane keeping systems. In addition, as Internet connectivity expands, the amount of data generated from various devices or vehicles is rapidly increasing, and various services using the same are emerging. Accordingly, various studies are being conducted on methods for providing a user-friendly autonomous driving experience using various types of data in an autonomous vehicle.

On the other hand, in the currently commercialized navigation system for autonomous vehicles, a route is first searched under conditions such as the route that takes the shortest time to the destination, the route with the shortest distance, and the route with the minimum road cost, and the route is selected according to the user input. I am changing the path by adding it.

This method does not sufficiently provide the user with an intuitive understanding of the route, and there is a problem in that the preferred route according to the user's taste cannot be efficiently reflected.

Republic of Korea Patent Publication No. 2016-0013713 (2016.02.05.)

An object of the present invention is to provide an autonomous driving path generating method and driving path generating system capable of generating a driving path of an autonomous driving vehicle by predicting and analyzing the environment of a driving path preferred by a driver.

A method for generating an autonomous driving route according to an embodiment of the present invention includes: generating an initial driving route to a set destination based on destination information; predicting a driving environment for the first driving route based on a plurality of environmental information, and analyzing a user's driving preference based on the predicted driving environment; and generating a corrected driving path by correcting the initial driving path from the analysis result.

An autonomous driving route generating system according to an embodiment of the present invention includes a route generating server that is connected to an autonomous driving vehicle and one or more environment servers through a communication network, and the route generating server is configured to respond to destination information transmitted from the autonomous driving vehicle. Creates an initial driving route to a destination based on the environment, predicts a driving environment for the initial driving path based on a plurality of environmental information transmitted from the environment server, and determines a user's driving preference based on the predicted driving environment Analyze and correct the initial driving path from the analysis result to generate a corrected driving path and transmit it to the autonomous vehicle.

The autonomous driving route generation method of the present invention predicts the driving environment according to the traffic situation, weather situation, local situation, etc. of the road to be driven, and the user through the predicted driving environment and previous driving route information or learning about the previous preference environment. By predicting and analyzing the driving preference of

Accordingly, the present invention can control the autonomous driving of the vehicle by generating an autonomous driving route according to the user's driving preference, thereby increasing the efficiency and convenience of setting the driving route of the vehicle.

1 is a diagram illustrating an autonomous driving route generating system according to an embodiment of the present invention.
FIG. 2 is a diagram showing the configuration of the autonomous driving route generating system of FIG. 1 .
3 and 4 are diagrams illustrating a method for generating an autonomous driving route according to an embodiment of the present invention.
5 is a diagram showing an embodiment of the method for generating an autonomous driving route according to the present invention.

Terms used in this specification and claims have been selected in consideration of functions in various embodiments of the present invention. However, these terms may vary depending on the intention of a person skilled in the art, legal or technical interpretation, and emergence of new technology. Also, some terms may be arbitrarily selected by the applicant. These terms may be interpreted in the meanings defined herein, and in the absence of specific definitions, they may be interpreted based on the general content of the present specification and common technical common sense in the art.

Also, the same reference numerals or reference numerals in each drawing appended hereto indicate parts or components that perform substantially the same functions. For convenience of description and understanding, the same reference numbers or reference numerals are used in different embodiments to be described. That is, even though all the components having the same reference number are shown in the plurality of drawings, the plurality of drawings do not mean one embodiment.

Also, in this specification and claims, terms including ordinal numbers such as 'first' and 'second' may be used to distinguish between elements. This ordinal number is used to distinguish the same or similar components from each other, and the meaning of the term should not be limitedly interpreted due to the use of the ordinal number. As an example, for components combined with such an ordinal number, the order of use or arrangement of the elements should not be construed as being limited by the number. If necessary, each ordinal number may be used interchangeably.

In this specification, the singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as 'comprise' or 'comprise' are intended to designate the existence of a characteristic, number, step, operation, component, part, or combination thereof described in the specification, but one or more other It should be understood that this does not preclude the possibility of addition or existence of features or numbers, steps, operations, components, parts, or combinations thereof.

Also, in an embodiment of the present invention, when it is said that a part is connected to another part, this includes not only direct connection but also indirect connection through another medium. In addition, the meaning that a certain part includes a certain component means that other components may be further included, rather than excluding other components, unless specifically stated to the contrary.

1 is a diagram showing an autonomous driving route generating system according to an embodiment of the present invention, and FIG. 2 is a diagram showing the configuration of the autonomous driving route generating system of FIG. 1 .

Referring to the drawings, the autonomous driving route generating system according to the present embodiment may include an autonomous driving vehicle 100 connected through a communication network 400 , a route generating server 200 , and one or more environment servers 300 . have.

The autonomous vehicle 100 and the route generating server 200 communicate through a wireless communication method through the communication network 400 , and the route generation server 200 and the environment server 300 communicate with each other through wired/wireless communication through the communication network 400 . way to communicate.

The autonomous driving vehicle 100 may perform autonomous driving from a departure point to a preset destination based on the driving route transmitted from the route generating server 200 . Here, the departure point of the autonomous vehicle 100 may be a current location of the autonomous vehicle 100 . The autonomous vehicle 100 may include an input unit 110 , a sensor unit 120 , a driving control unit 130 , and a display unit 140 .

The input unit 110 may receive various inputs from a user, for example, a driver of the autonomous vehicle 100 . For example, destination information such as an address for a driving destination of the autonomous vehicle 100 may be input to the input unit 110 . In addition, the user's condition information for generating the driving route to the destination may be input to the input unit 110 .

Here, the condition information is priority information for generating a driving route, and may be, for example, information on route generation conditions such as the shortest distance from the starting point to the destination, the minimum time, a toll road line, a toll road line, and the like. Such destination information and condition information may be transmitted to the route generating server 200 through the communication network 400 .

In addition, a user's selection signal may be input to the input unit 110 . The selection signal may be a signal for selecting and displaying one of a plurality of driving routes displayed on the display unit 140 to be described later. In other words, the route generating server 200 may generate a plurality of driving routes and transmit them to the autonomous vehicle 100 . Accordingly, the autonomous vehicle 100 may display a plurality of driving paths to be recognized by the user, and one of the plurality of driving paths may be selected and displayed according to a selection signal input through the input unit 110 .

A plurality of sensor units 120 may be installed inside/outside the autonomous vehicle 100 to detect a vehicle state and surrounding environment while the autonomous vehicle 100 is driving. The sensor unit 120 includes a plurality of sensors capable of detecting a vehicle state, such as a vehicle speed sensor, an acceleration sensor, a yaw rate sensor, a steering angle sensor, and a steering torque sensor, or an image sensor, a radar/lidar sensor, a GPS sensor, and the like. It may include a number of sensors capable of sensing the environment.

The result detected by the sensor unit 120 may include vehicle information such as the location, speed, and direction of the vehicle, and object information such as location and properties for objects such as people, things, or road facilities recognized while driving the vehicle. can

The driving controller 130 may control the driving of the autonomous vehicle 100 based on one driving path selected by a selection signal among a plurality of driving paths transmitted from the path generating server 200 . In this case, the driving control unit 130 may perform driving control of the autonomous vehicle 100 by using the detection result provided by the sensor unit 120 together.

The driving controller 130 controls the autonomous driving vehicle 100 to maintain or change the driving route, maintain or change the driving speed, maintain or change the driving direction, etc. You can make it drive along. In addition, the driving control unit 130 may perform on/off control for various lighting devices of the vehicle, instrument panel display control, and operation control for multimedia devices such as sound or video together with the above-described control. The driving controller 130 may perform driving control by controlling a plurality of driving modules of the autonomous vehicle 100 , for example, a steering module, a braking module, a driving module, or an HMI module.

The display unit 140 may display the driving path transmitted from the path generating server 200 on the screen so that the user can recognize it. In this case, when a plurality of driving routes are transmitted from the route generating server 200 , the display unit 140 may display them together by changing the shape of each driving route, for example, a shape or color of a line. Also, when one of the plurality of travel routes is selected by the selection signal input to the above-described input unit 110 , the display unit 140 may display only the selected travel route on the screen.

The travel route displayed on the display unit 140 may be a vehicle movement route to a destination set by the departure point and destination information corresponding to the current location of the vehicle. Accordingly, the driving route may be formed of a starting point, a destination, and a connecting line connecting them. In addition, a plurality of event information generated by the route generating server 200 may be displayed together on the driving route. The event information may be information generated related to vehicle driving.

Meanwhile, the display unit 140 may be configured as a display including a touch panel. In this case, the aforementioned input unit 110 may be configured together with the display unit 140 , and may receive destination information, condition information, or a selection signal according to a user's touch operation on the display unit 140 .

The route generating server 200 may generate one or more driving routes based on the destination information transmitted from the autonomous vehicle 100 . The route generating server 200 may include a driving route generating unit 210 , a driving route analyzing unit 220 , a driving route correcting unit 230 , and a route information database 240 (hereinafter referred to as a database).

The driving route generator 210 may generate an initial driving route, hereinafter, a first route, based on the destination information. The first route may be generated based on condition information transmitted together with destination information from the autonomous vehicle 100 .

For example, when condition information of the shortest distance is transmitted from the autonomous vehicle 100 , the driving route generator 210 may generate a first route formed as the shortest distance from the origin to the destination based on the condition information. Here, the departure point may be a current location detected by the sensor unit 120 of the autonomous vehicle 100 , and the destination may be a location set according to destination information.

Also, the first route may be generated in the form of a starting point and a destination and a connecting line connecting them.

The driving path analysis unit 220 may predict the driving environment for the first path generated by the driving path generation unit 210 and analyze the user's driving preference based on the predicted driving environment.

The driving route analysis unit 220 may predict the driving environment based on a plurality of environmental information transmitted from the external environment server 300 .

More specifically, the driving path analysis unit 220 may divide the first path into a plurality of driving sections. In addition, the driving environment may be predicted based on the environmental information provided from the environment server 300 for each divided driving section. Next, the driving path analysis unit 220 may generate one or more event information according to the predicted driving environment. The event information may be included in the first path.

Here, the environmental information provided by the environmental server 300 may include traffic situation information, weather situation information, and local situation information for each driving section of the first route. Accordingly, the driving route analysis unit 220 predicts the road environment for each driving section of the first route based on the traffic situation information, predicts the weather environment for each driving section based on the weather situation information, and the local situation Based on the information, it is possible to predict the local environment for each driving section. In addition, at least one of the predicted road environment, weather environment, and local environment may be output as the driving environment of the first route.

In addition, event information may be generated from at least one of a predicted road environment, a weather environment, and a local environment. The event information may be an event for a road environment, an event for a weather environment, or an event for a local environment.

In addition, the driving route analysis unit 220 performs a plurality of learning by using the existing data stored in the database 240 , for example, the previous driving route information as learning data, and the first route, that is, the initial driving generated based on the destination information. It is possible to predict and analyze the user's driving preference for the route. In addition, the driving route analysis unit 220 extracts information corresponding to the user's driving preference from the traffic situation information, weather situation information, and local situation information for the driving section provided by the environment server 300 as learning data to learn a plurality of learning data. or after completion of driving of the autonomous vehicle 100, a plurality of learning can be performed by linking information on the selected driving path, that is, the path traveled according to the user's selection, with the information provided by the environment server 300. . Accordingly, the driving path analysis unit 220 may analyze the user's driving preference for the driving environment of the pre-predicted first path according to the learning result.

The driving path analysis unit 220 includes machine learning such as an artificial neural network (ANN), a deep neural network (DNN), a convolutional neural network (CNN), and a regression analysis. Algorithms may be loaded.

The driving route analysis unit 220 includes a plurality of data stored in the database 240, that is, one having the same driving section as the driving section of the first route or the same event information as the event information of the first route among a plurality of previous driving route information. The above previous driving route information can be extracted.

In addition, the driving path analysis unit 220 may predict the user's driving preference based on the learning result from the extracted previous driving path information, and analyze the driving preference for the first path through this.

Here, the previous driving route information stored in the database 240 includes information on the driving route of the autonomous vehicle 100 by the same user, that is, the initial driving route information generated from the previous destination information, and predicted from the first driving route. It may include the driving environment and event information generated therefrom, corrected driving path information through correction of the initial driving path, and selected driving path information selected by the user.

In addition, the driving route analysis unit 220 predicts whether the vehicle avoids or participates in the event based on the learning result from the extracted previous driving route information, and analyzes the user's driving preference for the event of the first route through this can do.

The driving path correction unit 230 may generate a corrected driving path, hereinafter, a second path, obtained by correcting the previously generated first path based on the driving preference analyzed by the driving path analysis unit 220 .

The driving route correction unit 230 may correct the driving pattern for each of the plurality of driving sections of the first route based on the driving preference. Next, the driving path corrector 230 may generate a corrected driving path for the first path, that is, a second path by combining the corrected driving patterns of each driving section.

Accordingly, the route generating server 200 may transmit the first route and the second route, that is, the initial route and the corrected route to the autonomous vehicle 100 . In addition, the route generating server 200 may store the driving route information in the database 240 by combining the selected driving route selected in the autonomous vehicle 100 together with the initial driving route, the corrected driving route, the driving environment and event information. .

The external environment server 300 may include a traffic information server 310 , a weather information server 320 , and a local information server 330 . The environmental server 300 may transmit a plurality of environmental information, ie, traffic situation information, weather situation information, and local situation information, as environmental information to the route generating server 200 through the communication network 400 .

Here, the traffic situation information transmitted from the traffic information server 310 may include traffic accident status information, vehicle congestion status information, road construction status information, and the like corresponding to a plurality of driving sections of the first route. The weather condition information transmitted from the weather information server 320 may include information such as temperature, rainfall, snowfall, wind direction, and wind speed corresponding to a plurality of driving sections of the first route. The local context information transmitted from the local information server 330 may include local event information, restaurant information, and resting facility information corresponding to a plurality of driving sections of the first route. Such local situation information may be information that is comprehensively analyzed so as to recommend rest information according to the driving time of the vehicle or local restaurant information related to the driving time.

3 and 4 are diagrams illustrating an autonomous driving route generating method according to an embodiment of the present invention, and FIG. 5 is a diagram illustrating an autonomous driving route generating method according to an embodiment of the present invention.

First, the driving path generating unit 210 of the path generating server 200 may generate an initial driving path, for example, a first path, based on the destination information transmitted from the autonomous vehicle 100 ( S10 ).

Here, the autonomous vehicle 100 may transmit condition information together with destination information. Accordingly, the driving route generating unit 210 sets the current location of the autonomous vehicle 100 as the starting point, sets the destination according to the destination information, and generates a first route from the origin to the destination based on the condition information. can Here, the condition information may be information on the conditions of route creation, for example, conditions such as the shortest distance, minimum time, toll road line, toll road line, and the like.

Next, the driving path analysis unit 220 of the path generating server 200 divides the first path into a plurality of driving sections, analyzes each divided driving section, and a corrected driving path for the first path, for example, the second A path may be created (S20).

As shown in FIG. 5 , the driving route generator 210 may generate an initial driving route, that is, a first route, from the current location of the vehicle to a destination set according to the set departure point and destination information.

Next, the driving path analysis unit 220 may extract one or more nodes from the first path, for example, the first node (A), the second node (B), and the third node (C). The first node (A) corresponds to the set origin, the third node (C) corresponds to the set destination, and the second node (B) is a specific point between the first node (A) and the third node (C). can correspond to Here, the specific point may be an intersection or a point of interest existing in the first route.

Next, the driving path analysis unit 220 divides the first path into a first driving section (R1) and a second driving section (R2) according to the extracted first nodes (A) to (C). can The first driving section (R1) is a section connected from the first node (A) to the second node (B), and the second driving section (R2) is a section connected from the second node (B) to the third node (C) can be

The driving route analysis unit 220 predicts the driving environment for each of the plurality of driving sections R1 and R2 based on one or more environmental information provided from the environment server 300, and generates event information according to the predicted driving environment. It can be done (S21).

The environmental server 300 includes a traffic information server 310 that provides traffic situation information as environmental information, a weather information server 320 that provides weather situation information as environmental information, and a local information server that provides local situation information as environmental information. 330 may be included. Accordingly, the driving route analysis unit 220 may predict the driving environment for the plurality of driving sections R1 and R2 of the first route based on at least one of traffic situation information, weather situation information, and local situation information.

For example, the driving route analysis unit 220 may predict the road environment for the first driving section R1 and the second driving section R2 based on the traffic situation information provided from the traffic information server 310 . The driving route analysis unit 220 may generate one or more event information about the traffic situation of the first route based on the predicted road environment. Here, the traffic situation information may include information such as the traffic accident status of the first route, the vehicle congestion status, the road construction status, and the like.

Also, the driving route analysis unit 220 may predict the weather environment for the first driving section R1 and the second driving section R2 based on the weather situation information provided from the weather information server 320 . The driving route analysis unit 220 may generate one or more event information about the weather conditions of the first route based on the predicted weather environment. Here, the weather situation information may include information such as temperature, humidity, rainfall, snowfall, wind direction, and wind speed of the first route.

Also, the driving route analysis unit 220 may predict the local environment for the first driving section R1 and the second driving section R2 based on the local context information provided from the local information server 330 . The driving route analysis unit 220 may generate one or more event information about the local situation of the first route based on the predicted local environment. Here, the local situation information may include information such as local event information, restaurant information, and resting facilities in the corresponding region. Such local situation information may be information analyzed comprehensively so as to recommend rest information according to the driving time of the vehicle or local restaurant information related to the driving time.

Also, the driving route analysis unit 220 may include one or more generated event information E1 , E2 , and E3 on the first route.

For example, the driving route analysis unit 220 may generate first event information E1 for traffic conditions, second event information E2 for weather conditions, and third event information E3 for local conditions. . In addition, the first event information E1 may be included in the first driving section R1 , and the second event information E2 and the third event information E3 may be included in the second driving section R2 , respectively. . At the same time, when the event information is touched or clicked through the display unit 140 of the autonomous vehicle 100 , the driving path analysis unit 220 displays a detailed description of the event in the form of a separate pop-up window. Event information can be included in the path.

Next, the driving path analysis unit 220 selects a plurality of previous driving path information stored in the database 240 , a plurality of pieces of information provided from the environment server 300 , and the driving at a previous point in time of the autonomous vehicle 100 . At least one of the driving route information is used as learning data to perform learning a plurality of times, and according to the result, the user's driving preference for the first route can be predicted and analyzed.

The driving route analysis unit 220 may extract at least one previous driving route information having the same event information as the previously generated event information (E1, E2, E3) from among a plurality of previous driving route information stored in the database 240 . There is (S22). The driving route analysis unit 220 may analyze the driving preference by predicting the user's driving preference on the previous driving path information extracted based on the learning result (S23).

Each of the plurality of previous driving route information stored in the database 240 includes information on the autonomous driving route performed by the autonomous driving vehicle 100 at the previous point in time, that is, previous destination information, initial driving route information, event information, and corrected driving route. It may include information and selected driving route information according to the selection signal.

The driving path analysis unit 220 may learn whether the user avoids or participates in each event of a plurality of previous driving path information. Accordingly, the driving path analysis unit 220 extracts one or more previous driving path information having the same event information as the previously generated event information E1, E2, E3 from among a plurality of previous driving path information, and extracts the extracted previous driving path It is possible to predict whether the user avoids or participates in the information event according to the learning result, and analyzes the user's driving preference from this.

Next, the driving route correction unit 230 corrects the driving pattern for the first driving section R1 and the second driving section R2 of the first route based on the analyzed driving preference (S24), and the corrected section A corrected driving path, that is, a second path according to each driving pattern may be generated (S25).

As shown in FIG. 5 , the first event information E1 regarding the traffic situation may be generated and displayed in the first driving section R1 of the first route. Then, the driving preference for avoidance of the first event information E1 may be predicted and analyzed by the driving route analysis unit 220 .

Accordingly, the driving path correction unit 230 extracts a new fourth node D between the first node A and the second node B, and the existing driving pattern of the first driving section R1, that is, the first The driving pattern from the first node (A) to the second node (B) may be corrected to lead to the first node (A), the fourth node (D), and the second node (B). The driving path corrector 230 may generate a new driving section according to the corrected driving pattern, that is, the third driving section R3.

In addition, the second event information E2 of the weather condition and the third event information E3 of the local condition may be generated and displayed in the second driving section R2 of the first route. Then, the driving preference of the avoidance of the second event information E2 and the participation in the third event information E3 may be predicted and analyzed by the driving route analysis unit 220 .

Accordingly, the driving path correction unit 230 extracts a new fifth node (E) between the second node (B) and the third node (C), and the existing driving pattern of the second driving section (R2), that is, the first The driving pattern from the 2nd node (B) to the 3rd node (C) can be corrected to lead to the 2nd node (B), the 5th node (E), the 3rd event information (E3), and the 3rd node (C). can The driving path corrector 230 may generate a new driving section according to the corrected driving pattern, that is, the fourth driving section R4.

In addition, the driving route correcting unit 230 is a new driving section generated according to the corrected driving pattern, that is, a corrected driving route including the third driving section R3 and the fourth driving section R4, that is, the second path. can create

Next, the route generating server 200 includes a first route and a second route, that is, an initial route having a first running section R1 and a second running section R2, a third running section R3, and a fourth route. The corrected driving route having the driving section R4 may be transmitted to the autonomous vehicle 100 (S30).

The autonomous vehicle 100 may display the first route and the second route through the display unit 140 . Here, the first path and the second path may be displayed in different line shapes or in different colors.

As described above, one or more event information (E1, E2, E3) is displayed on the first path, and when a touch or click occurs thereon, a detailed description of the corresponding event information may appear in the form of a pop-up.

When a selection signal is input to the input unit 110 of the autonomous vehicle 100 , the display unit 140 may display one path selected in response to the selection signal among the first path and the second path. The autonomous vehicle 100 may transmit the input selection signal to the route generating server 200 .

Accordingly, the route generating server 200 determines the destination information transmitted from the autonomous vehicle 100 , the first route generated based on the destination information, the second route corrected for the first route, and the driving selected according to the selection signal. Information on the route may be generated as driving route information and stored in the route information database 240 .

Also, the driving control unit 130 of the autonomous driving vehicle 100 may perform driving control so that the autonomous driving vehicle 100 travels along a selected driving path based on the detection result of the sensor unit 120 (S40). ).

As described above, the driving route generation method of this embodiment predicts the driving environment according to the traffic situation, weather situation, local situation, etc. of the road to be driven, and the user's driving preference based on the predicted driving environment and previous driving route information. By performing the analysis, it is possible to generate a user-customized driving route for the autonomous vehicle 100 .

Accordingly, the present invention can control the autonomous driving of the autonomous driving vehicle 100 by generating an autonomous driving route according to the user's taste, thereby increasing the efficiency and convenience of setting the driving route of the vehicle.

As described above, an embodiment of the present invention has been described, but those of ordinary skill in the art can add, change, delete or add components within the scope that does not depart from the spirit of the present invention described in the claims. It will be possible to variously modify and change the present invention by, etc., which will also be included within the scope of the present invention.

In addition, the above-mentioned terms are terms defined in consideration of functions in the present invention, which may vary according to intentions or customs of users and operators. Therefore, definitions of these terms should be made based on the content throughout this specification.

100: autonomous vehicle 110: input unit
120: sensor unit 130: driving control unit
140: display unit 200: path generation server
210: driving path generation unit 220: driving path analysis unit
230: driving route correction unit 240: route information database
310: traffic information server 320: weather information server
330: local information server 400: communication network

Claims (8)

generating an initial driving route to a set destination based on destination information;
predicting a driving environment for the first driving route based on a plurality of environmental information, and analyzing a user's driving preference based on the predicted driving environment; and
and generating a corrected driving path by correcting the initial driving path from the analysis result. According to claim 1,
The step of analyzing the driving preference is,
dividing the initial driving route into a plurality of driving sections;
predicting the driving environment for each driving section based on the environment information;
generating one or more event information for the driving section based on the predicted driving environment;
extracting at least one piece of previous driving path information having the same event as the event information from among a plurality of previous driving path information; and
and analyzing the driving preference based on the extracted previous driving path information. 3. The method of claim 2,
The step of analyzing the driving preference is,
The method for generating an autonomous driving route, characterized in that the step of analyzing the previous driving route information by determining whether to avoid or participate in the event. 3. The method of claim 2,
Predicting the driving environment comprises:
predicting a road environment of the driving section based on traffic situation information among the environmental information;
predicting a weather environment of the driving section based on weather condition information among the environment information;
predicting a local environment of the driving section based on local context information among the environmental information; and
and outputting at least one of the predicted road environment, weather environment, and local environment as the driving environment. 3. The method of claim 2,
The method for generating an autonomous driving route, characterized in that it further comprises the step of including the event information in the initial driving route. According to claim 1,
The step of generating the corrected driving route includes:
correcting a driving pattern for each driving section of the first driving route based on the driving preference; and
and generating the corrected driving path by combining the corrected driving patterns for each driving section. According to claim 1,
Further comprising the step of transmitting the initial driving route and the corrected driving route to the autonomous vehicle,
The autonomous driving path generating method, characterized in that the autonomous driving vehicle performs driving control to the destination based on one driving path selected according to a selection signal. An autonomous driving route generating system for generating the autonomous driving route according to any one of claims 1 to 7,
The autonomous driving route generation system,
A route generating server connected to the autonomous vehicle and one or more environmental servers through a communication network,
The route generating server generates an initial driving route to a destination based on the destination information transmitted from the autonomous vehicle, and predicts a driving environment for the initial driving route based on a plurality of environmental information transmitted from the environment server and analyzing the user's driving preference based on the predicted driving environment, and correcting the initial driving path from the analysis result to generate a corrected driving path and transmitting it to the autonomous vehicle. .

Images