KR102646435B1 - Apparatus and method for providing autonomous driving smoothness information - Google Patents

Abstract

The present invention relates to an autonomous driving smoothness information providing device, comprising: a first autonomous driving information collection unit that wirelessly collects autonomous driving information measured from an autonomous vehicle; a second autonomous driving information collection unit that wirelessly collects autonomous driving information measured by an external system; a control unit that calculates autonomous driving smoothness (%) according to a predetermined method based on autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit; and an autonomous driving smoothness map providing unit that generates a map (MAP) reflecting the calculated autonomous driving smoothness (%) and provides it to the autonomous vehicle or a vehicle terminal mounted on the autonomous vehicle.

Description

Apparatus and method for providing autonomous driving smoothness information {APPARATUS AND METHOD FOR PROVIDING AUTONOMOUS DRIVING SMOOTHNESS INFORMATION}

The present invention relates to an apparatus and method for providing autonomous driving smoothness information, and more specifically, to provide autonomous driving smoothness information indicating the degree to which autonomous driving is possible in the form of a map to a vehicle terminal. It relates to a device and method for providing seamless information.

In general, self-driving cars (or self-driving cars) use various sensors (e.g. lidar, radar, cameras, etc.) on the vehicle, such as precise maps and satellite navigation systems (GPS), without the driver having to operate the steering wheel, accelerator pedal, or brakes. This refers to a car that understands the surrounding situation and navigates to its destination on its own.

The stages of autonomous driving technology are defined by the Society of Automotive Engineers (SAE) as international standards and are classified into a total of 6 stages from stage 1 to stage 5.

Among these, Adaptive Cruise Control (ACC) and Lane Keeping Assist System (LKAS) are representative technologies in Stage 1, and the number of vehicles equipped with Advanced Driver Assistance Systems (ADAS) has recently been increasing.

At this time, the autonomous driving may or may not be performed smoothly depending on road conditions or weather (snow, rain, etc.).

However, there is a problem in that the self-driving car (or the vehicle terminal installed in the self-driving car) or the driver (user) cannot know in advance the degree to which the self-driving car can smoothly perform autonomous driving on each road (i.e., self-driving smoothness). .

Accordingly, the driver (user) cannot select in advance only routes that are suitable for autonomous driving, and in some cases, even though there are road sections that can be driven autonomously, road sections that cannot be driven autonomously are selected as the route. By making this choice, you may encounter the problem of having to drive only manually for the entire route.

Therefore, by providing autonomous driving smoothness information, which indicates the degree to which autonomous driving is possible, to the vehicle terminal in the form of a map, the autonomous vehicle can set a route and control the vehicle based on the autonomous driving smoothness information provided in the form of a map. There is a need for technology to enable this.

The background technology of the present invention is disclosed in Republic of Korea Patent Publication No. 10-2021-0100776 (published on August 18, 2021, vehicle performing autonomous driving using a camera and method of operating the same).

According to one aspect of the present invention, the present invention was created to solve the above problems, and provides autonomous driving smoothness information indicating the degree to which autonomous driving is possible in the form of a map to the vehicle terminal. The purpose is to provide a device and method for providing information on the smoothness of autonomous driving.

An autonomous driving smoothness information providing device according to one aspect of the present invention includes a first autonomous driving information collection unit that wirelessly collects autonomous driving information measured in an autonomous vehicle; a second autonomous driving information collection unit that wirelessly collects autonomous driving information measured by an external system; a control unit that calculates autonomous driving smoothness (%) according to a predetermined method based on autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit; and an autonomous driving smoothness map providing unit that generates a map (MAP) reflecting the calculated autonomous driving smoothness (%) and provides it to the autonomous vehicle or a vehicle terminal mounted on the autonomous vehicle. .

In the present invention, the autonomous driving information collected by the first autonomous driving information collection unit includes lane recognition rate information, GPS information (location information), and GPS reception sensitivity information.

In the present invention, the GPS reception sensitivity information is characterized as information including the number of satellites that can receive GPS signals.

In the present invention, the autonomous driving information collected by the second autonomous driving information collection unit includes weather information according to time and road condition information that can determine lane clarity.

In the present invention, the external system is characterized in that it includes a weather information system of the Korea Meteorological Administration and a traffic situation monitoring system of a traffic information center.

In the present invention, the control unit classifies the autonomous driving information collected through the first autonomous driving information collection unit by type and corrects it according to preset standards for each type of autonomous driving information, and to this end, the autonomous vehicle or autonomous driving information is It is characterized by additionally collecting information about the type of vehicle terminal installed in the car.

In the present invention, the control unit processes autonomous driving information collected from the external system, detects real-time road condition information including lane conditions reflecting actual weather conditions for areas where real-time monitoring is possible, and real-time monitoring is performed. For areas where this is not possible, road condition information, including lane conditions, is predicted by reflecting weather information and accumulated statistical weather information.

In the present invention, the control unit classifies and corrects the autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit by type, and accumulates the classified and corrected autonomous driving information. It is characterized by statistics.

In the present invention, assuming that the control unit recognizes the lane using only the camera, the autonomous driving smoothness (%) is calculated based on the lane recognition rate by the camera, but snow is detected on the road through the traffic surveillance camera. When accumulation is detected, the autonomous driving smoothness (%) in the area is calculated as 0%.

In the present invention, assuming that lanes are recognized using only GPS, the control unit calculates autonomous driving smoothness (%) based on GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received. It is characterized by

In the present invention, assuming that the control unit recognizes the lane by combining GPS and the camera, the GPS signal is received at the value obtained by multiplying the autonomous driving smoothness (%) calculated based on the lane recognition rate by the camera by 0.5. It is characterized by calculating the autonomous driving smoothness (%) by adding up the autonomous driving smoothness (%) calculated based on the GPS reception sensitivity corresponding to the number of GPS satellites multiplied by 0.5.

A method of providing autonomous driving smoothness information according to another aspect of the present invention includes the steps of a first autonomous driving information collection unit wirelessly collecting autonomous driving information measured in an autonomous vehicle; A second autonomous driving information collection unit wirelessly collecting autonomous driving information measured by an external system; A control unit calculating autonomous driving smoothness (%) according to a predetermined method based on autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit; And a step of the autonomous driving smoothness map providing unit generating a map (MAP) reflecting the calculated autonomous driving smoothness (%) and providing it to the autonomous vehicle or a vehicle terminal mounted on the autonomous vehicle. do.

In the present invention, after the step of wirelessly collecting autonomous driving information measured through the first autonomous driving information collection unit, the control unit classifies the autonomous driving information collected through the first autonomous driving information collection unit by type. and a step of correcting according to preset standards for each type of autonomous driving information, and for this purpose, information on the type of autonomous vehicle or vehicle terminal mounted on the autonomous vehicle is additionally collected.

In the present invention, after the step of wirelessly collecting autonomous driving information measured through the second autonomous driving information collection unit, the control unit processes the autonomous driving information collected from the external system to determine an area where real-time monitoring is possible. For areas where real-time monitoring is not possible, it detects real-time road condition information including lane conditions reflecting actual weather conditions, and predicts road condition information including lane conditions by reflecting weather information and accumulated statistical weather information for areas where real-time monitoring is not possible. It is characterized by

In the present invention, after the step of wirelessly collecting autonomous driving information measured through the first autonomous driving information collection unit and the second autonomous driving information collection unit, the control unit, the first autonomous driving information collection unit and the It is characterized by classifying and correcting autonomous driving information collected through the second autonomous driving information collection unit by type, and accumulating statistics on the classified and corrected autonomous driving information.

In the present invention, in the step of calculating the autonomous driving smoothness (%), assuming that the lane is recognized using only the camera, the control unit determines the autonomous driving smoothness (%) based on the lane recognition rate by the camera. However, if snow is detected on the road through a traffic surveillance camera, the autonomous driving smoothness (%) in the area is calculated as 0%.

In the present invention, in the step of calculating the autonomous driving smoothness (%), assuming that lanes are recognized using only GPS, the control unit determines the GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received. It is characterized by calculating the autonomous driving smoothness (%) based on .

In the present invention, in the step of calculating the autonomous driving smoothness (%), if it is assumed that the lane is recognized by fusing GPS and a camera, the control unit calculates the autonomous driving smoothness based on the lane recognition rate by the camera. Autonomous driving smoothness (%) is calculated by multiplying the degree (%) by 0.5 and multiplying the autonomous driving smoothness (%) calculated based on the GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received by 0.5. %).

According to one aspect of the present invention, the present invention provides autonomous driving smoothness information indicating the degree to which autonomous driving is possible to a vehicle terminal in the form of a map, so that the autonomous vehicle can use the autonomous driving smoothness information provided in the form of a map. Based on this, it is possible to set routes and control vehicles.

1 is an exemplary diagram showing the schematic configuration of an autonomous driving smoothness information providing device according to an embodiment of the present invention.
Figure 2 is a flowchart illustrating a method for providing autonomous driving smoothness information according to an embodiment of the present invention.
Figure 3 is an example diagram showing a map (MAP) reflecting the autonomous driving smoothness (%) in Figure 1.

Hereinafter, an embodiment of an apparatus and method for providing autonomous driving smoothness information according to the present invention will be described with reference to the attached drawings.

In this process, the thickness of lines or sizes of components shown in the drawing may be exaggerated for clarity and convenience of explanation. In addition, the terms described below are terms defined in consideration of functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, definitions of these terms should be made based on the content throughout this specification.

Figure 1 is an exemplary diagram showing the schematic configuration of an autonomous driving smoothness information providing device according to an embodiment of the present invention.

As shown in FIG. 1, the autonomous driving smoothness information providing device 100 according to this embodiment includes a first autonomous driving information collection unit 110, a second autonomous driving information collection unit 120, and a control unit 130. ), and an autonomous driving smoothness map providing unit 140.

The first autonomous driving information collection unit 110 provides autonomous driving information (i.e., information detected for autonomous driving, or Information that affects performance) is collected wirelessly.

For example, autonomous driving information (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) collected by the first autonomous driving information collection unit 110 includes lane recognition rate information, GPS information (position information), and GPS reception sensitivity information.

For reference, the GPS can know the location with at least three GPS satellites, but the distance between the GPS satellites and the GPS receiver is calculated based on the arrival time of the radio waves sent from the satellites. The clock mounted on the satellite and the clock mounted on the receiver In reality, many errors are included because errors occur because they do not match.

Accordingly, recent GPS receivers can receive signals from 20 satellites and accurately calculate location.

Therefore, in this embodiment, the GPS reception sensitivity information is a concept that includes the number of satellites that can receive signals.

Meanwhile, the vehicle terminal installed in the autonomous vehicle refers to a terminal that detects or outputs information necessary for autonomous driving (eg, GPS information, lane recognition information, navigation information, etc.).

Additionally, the lane recognition rate information refers to lane recognition rate information recognized by LKAS (Lane Keeping Assist System).

However, the vehicle terminal that detects or outputs information necessary for autonomous driving (e.g. GPS information, lane recognition information, navigation information, etc.) uses different types of sensors or sensing methods for each product manufacturer, so the same type of autonomous driving is not used. Even if it is information (e.g. GPS information, lane recognition information, navigation information, etc.), the form or accuracy of the information output may be different.

For example, one autonomous vehicle (or vehicle terminal mounted on an autonomous vehicle) (VC) can recognize lanes using only a camera sensor, and another autonomous vehicle (or vehicle terminal mounted on an autonomous vehicle) can recognize lanes using only a camera sensor. )(VC) calculates the current location using high-precision GPS and can recognize the lane using the already determined lane position value, and can be connected to another autonomous vehicle (or vehicle terminal mounted on the autonomous vehicle) ( VC) is capable of recognizing lanes by combining high-precision GPS and camera sensors.

Accordingly, the control unit 130 collects autonomous driving information (i.e., autonomous driving information) collected from a plurality of autonomous vehicles (or vehicle terminals mounted on autonomous vehicles) (VC) through the first autonomous driving information collection unit 110. Information detected for driving (or information that affects the performance of autonomous driving) is classified by type and corrected according to preset standards for each type of autonomous driving information. In other words, the form or accuracy of the information output according to the type of the vehicle terminal is corrected and converted into the form or accuracy of standardized information.

To this end, the control unit 130 may additionally collect information about the type of the autonomous vehicle (or vehicle terminal mounted on the autonomous vehicle) (VC).

For example, when an autonomous vehicle (or a vehicle terminal mounted on an autonomous vehicle) (VC) transmits the autonomous driving information wirelessly, the autonomous vehicle (or autonomous vehicle) is transmitted including its own ID information. Additional information can be collected about the type of vehicle terminal (VC) installed in the driving vehicle.

The second autonomous driving information collection unit 120 wirelessly collects autonomous driving information (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) measured by the external system (ES). do.

For example, autonomous driving information (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) collected by the second autonomous driving information collection unit 120 may include weather information (e.g., snow, rain, etc.). , cloudy, daytime, night, etc.), and road condition information (e.g., no lanes (lanes not visible due to snow/rain), clear lanes, unclear lanes, etc.).

Here, the external system (ES) includes the Korea Meteorological Administration's weather information system and the traffic information center's traffic situation monitoring system (eg, including CCTV).

For reference, the weather information (e.g. weather forecast) provided by the Korea Meteorological Administration's weather information system allows you to know approximate weather information for each region in advance, and the traffic situation monitoring system (e.g. including CCTV) of the traffic information center provides detailed information. It allows you to check actual weather information for the area in real time, and also allows you to know real-time road conditions (including lane conditions) that reflect actual weather conditions.

Accordingly, the control unit 130 processes autonomous driving information (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) collected from the external system (ES), enabling real-time monitoring. For areas, real-time road condition (including lane status) information reflecting actual weather conditions is detected (obtained), and for areas where real-time monitoring is not possible, weather information (e.g. weather forecast) and accumulated statistical weather information are reflected to determine the road condition. Predict (guess) state (including suboptimal state) information.

At this time, the autonomous driving information collected from the external system (ES) (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) is actually information that affects the lane condition. This is important information for determining whether or not the weather can be recognized.

The control unit 130 classifies and corrects the information collected through the first autonomous driving information collection unit 110 and the autonomous driving information collected through the second autonomous driving information collection unit 120 by type, and Cumulative statistics are provided on classified and corrected autonomous driving information. At this time, apart from the accumulated statistics of the autonomous driving information, the autonomous driving smoothness (%) is calculated according to a predetermined method based on the classified and corrected autonomous driving information.

For example, assuming that lanes are recognized using only cameras, the autonomous driving smoothness (%) directly reflects the camera's lane recognition rate: 100% for ultra-high quality, 80% for high quality, 60% for normal, It can be calculated as 40% for low quality and 0% for very low quality. However, if snow is piled up on the road through traffic surveillance cameras (e.g. CCTV), the autonomous driving smoothness (%) in the area may be calculated as 0%.

In addition, assuming that lanes are recognized using only GPS, the autonomous driving smoothness (%) is based on GPS reception sensitivity (i.e., the number of GPS satellites received), for example, if the number of satellites is 6 or more, it is 100%. , it can be calculated as 80% when the number of satellites is 5, 70% when the number of satellites is 4, 60% when the number of satellites is 3, and 0% when the number of satellites is 2 or less.

In addition, assuming that the lane is recognized by fusing the GPS and the camera, the autonomous driving smoothness (%) is "autonomous driving smoothness calculated by GPS reception sensitivity (i.e., the number of GPS satellites received) * 0.5 + Autonomous driving smoothness calculated by the camera's lane recognition rate can be calculated as *0.5". However, if snow is piled up on the road through traffic surveillance cameras (e.g. CCTV), the autonomous driving smoothness (%) in the area may be calculated as 0%.

If the control unit 130 determines that the lane cannot be recognized based on autonomous driving information predicted (guessed) by reflecting the actually detected (acquired) autonomous driving information and the accumulated statistical weather information, the autonomous driving smoothness level is adjusted. (%) can be calculated as 0%.

For example, if the weather is such that the lanes cannot be recognized (e.g., the lanes are covered with snow), or even if the weather information (e.g., weather forecast) says it is clear, the snow is actually frozen and the lanes are not visible. In this case, the autonomous driving smoothness (%) can be calculated as 0%.

In addition, when the autonomous driving smoothness (%) is calculated according to a predetermined method as described above, the control unit 130 generates a map (MAP) reflecting the calculated autonomous driving smoothness (%) to drive the autonomous vehicle (or It is provided to the vehicle terminal (VC) mounted on the autonomous vehicle (see Figure 3).

Figure 2 is a flowchart illustrating a method of providing autonomous driving smoothness information according to an embodiment of the present invention.

Referring to FIG. 3, the control unit 130 controls autonomous driving information (i.e., information detected for autonomous driving) measured from an autonomous vehicle (or a vehicle terminal mounted on an autonomous vehicle) (VC) and an external system (ES). , or information affecting the performance of autonomous driving) is collected wirelessly (S101).

For example, autonomous driving information (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) collected by the first autonomous driving information collection unit 110 includes lane recognition rate information, GPS information (position information), and GPS reception sensitivity information. Additionally, the autonomous driving information (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) collected by the second autonomous driving information collection unit 120 is weather information (e.g., snow, rain, etc.). , cloudy, daytime, night, etc.), and road condition information (e.g., no lanes (lanes not visible due to snow/rain), clear lanes, unclear lanes, etc.).

Here, the external system (ES) includes the Korea Meteorological Administration's weather information system and the traffic information center's traffic situation monitoring system (eg, including CCTV).

In addition, the control unit 130 collects autonomous driving information from a plurality of autonomous vehicles (or vehicle terminals mounted on autonomous vehicles) (VC) through the first autonomous driving information collection unit 110 and the second autonomous driving information. Autonomous driving information collected through the driving information collection unit 120 (i.e., information detected for autonomous driving, or information affecting the performance of autonomous driving) is classified by type, and criteria for each type of autonomous driving information are preset. Correct according to (S102).

In other words, the form or accuracy of the information output according to the type of the vehicle terminal is corrected and converted into the form or accuracy of standardized information.

To this end, the control unit 130 may additionally collect information about the type of the autonomous vehicle (or vehicle terminal mounted on the autonomous vehicle) (VC).

Additionally, the control unit 130 accumulates the classified and corrected autonomous driving information and then calculates statistics (S103).

At this time, the accumulated statistical autonomous driving information reflects the weather information (e.g. weather forecast) and the accumulated statistical weather information for areas where real-time monitoring is not possible (e.g. areas where CCTV monitoring is not possible) and road conditions (lane conditions). can be used to predict (guess) including

Additionally, the control unit 130 calculates the autonomous driving smoothness (%) according to a predetermined method based on the classified and corrected autonomous driving information (S104).

For example, assuming that lanes are recognized using only cameras, the autonomous driving smoothness (%) directly reflects the camera's lane recognition rate: 100% for ultra-high quality, 80% for high quality, 60% for normal, It can be calculated as 40% for low quality and 0% for very low quality. However, if snow is piled up on the road through traffic surveillance cameras (e.g. CCTV), the autonomous driving smoothness (%) in the area may be calculated as 0%.

In addition, assuming that lanes are recognized using only GPS, the autonomous driving smoothness (%) is based on GPS reception sensitivity (i.e., the number of GPS satellites received), for example, if the number of satellites is 6 or more, it is 100%. , it can be calculated as 80% when the number of satellites is 5, 70% when the number of satellites is 4, 60% when the number of satellites is 3, and 0% when the number of satellites is 2 or less.

In addition, assuming that the lane is recognized by fusing the GPS and the camera, the autonomous driving smoothness (%) is "autonomous driving smoothness calculated by GPS reception sensitivity (i.e., the number of GPS satellites received) * 0.5 + Autonomous driving smoothness calculated by the camera's lane recognition rate can be calculated as *0.5". However, if snow is piled up on the road through traffic surveillance cameras (e.g. CCTV), the autonomous driving smoothness (%) in the area may be calculated as 0%.

In addition, if the control unit 130 determines that the lane cannot be recognized based on the autonomous driving information predicted (guessed) by reflecting the actually detected (acquired) autonomous driving information and the accumulated statistical weather information, the autonomous driving is smooth. The degree (%) can be calculated as 0%.

In addition, when the autonomous driving smoothness (%) is calculated according to a predetermined method as described above, the control unit 130 generates a map (MAP) reflecting the calculated autonomous driving smoothness (%) to drive the autonomous vehicle (or It is provided to the vehicle terminal (VC) mounted on the autonomous vehicle (S105) (see FIG. 3).

Figure 3 is an example diagram showing a map (MAP) reflecting the autonomous driving smoothness (%) in Figure 1. Figure 3 shows the autonomous driving smoothness (%) changing depending on the weather and time in the same area, By creating a map (MAP) reflecting the changed self-driving smoothness (%) and providing it to the self-driving car (or vehicle terminal mounted on the self-driving car) (VC), the self-driving car provides smooth self-driving in the form of the map. It allows route setting and vehicle control based on road information.

For example, (a) in Figure 3 is a map reflecting the autonomous driving smoothness (%) in sunny daytime weather, and the blue road section has good autonomous driving smoothness (%) (e.g., more than 90%). , yellow road sections indicate normal autonomous driving smoothness (%) (e.g., 70% or more), and red road sections indicate poor autonomous driving smoothness (%) (e.g., 50% or less). indicates.

Figure 3(b) is a map reflecting the smoothness (%) of autonomous driving in sunny weather at night, with the red road section increased, and Figure 3(c) is a map reflecting autonomous driving smoothness (%) in sunny weather at night. As a map (MAP) reflecting the smoothness (%), the red road section is increasing, and (d) in Figure 3 is a map (MAP) reflecting the smoothness (%) of autonomous driving in snowy weather at night. As all roads are either red road sections or changed to yellow road sections, it can be seen that there are no sections in which autonomous driving is possible.

Meanwhile, the self-driving car (or vehicle terminal mounted on the self-driving car) (VC) that receives the map (MAP) reflecting the autonomous driving smoothness (%) as described above is in the section where the autonomous driving smoothness (%) is 100%. If you enter the 50% section after passing , you will be warned that “the line recognition rate will drop sharply from 100 meters ahead” and “the LKAS function or autonomous driving function may not operate from 100 meters ahead” and that autonomous driving or lane The maintenance function can be turned off, which has the effect of enabling safe driving.

In addition, it has the effect of allowing drivers (users) to comfortably and safely drive autonomously to their destination by setting a route that includes only roads (sections) on which autonomous driving is possible.

As described above, the present invention has been described with reference to the embodiments shown in the drawings, but these are merely illustrative, and various modifications and equivalent embodiments can be made by those skilled in the art. You will understand the point. Therefore, the scope of technical protection of the present invention should be determined by the scope of the patent claims below. Implementations described herein may also be implemented as, for example, a method or process, device, software program, data stream, or signal. Although discussed only in the context of a single form of implementation (eg, only as a method), implementations of the features discussed may also be implemented in other forms (eg, devices or programs). The device may be implemented with appropriate hardware, software, firmware, etc. The method may be implemented in a device such as a processor, which generally refers to a processing device that includes a computer, microprocessor, integrated circuit, or programmable logic device. Processors also include communication devices such as computers, cell phones, portable/personal digital assistants (“PDAs”) and other devices that facilitate communication of information between end-users.

100: Autonomous driving smoothness information provision device
110: 1st autonomous driving information collection unit
120: Second autonomous driving information collection unit
130: control unit
140: Self-driving smoothness map provision unit

Claims (18)

a first autonomous driving information collection unit that wirelessly collects autonomous driving information measured from the autonomous vehicle;
a second autonomous driving information collection unit that wirelessly collects autonomous driving information measured by an external system;
a control unit that calculates autonomous driving smoothness (%) according to a predetermined method based on autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit; and
Including a self-driving smoothness map providing unit that generates a map (MAP) reflecting the calculated self-driving smoothness (%) and provides it to the self-driving car or a vehicle terminal mounted on the self-driving car,
The control unit,
By processing autonomous driving information collected from the external system,
For areas where real-time monitoring is possible, real-time road condition information including lane conditions reflecting actual weather conditions is detected,
An autonomous driving smoothness information providing device that predicts road condition information, including lane conditions, by reflecting weather information and accumulated statistical weather information for areas where real-time monitoring is not possible.
According to clause 1,
The autonomous driving information collected by the first autonomous driving information collection unit is:
An autonomous driving smoothness information providing device comprising lane recognition rate information, GPS information (location information), and GPS reception sensitivity information.
The method of claim 2, wherein the GPS reception sensitivity information is:
An autonomous driving smoothness information providing device, characterized in that the information includes the number of satellites capable of receiving GPS signals.
According to clause 1,
The autonomous driving information collected by the second autonomous driving information collection unit is:
An autonomous driving smoothness information providing device that includes weather information over time and road condition information that provides information on lane clarity.
The method of claim 1, wherein the external system:
An autonomous driving smoothness information providing device comprising a weather information system of the Korea Meteorological Administration and a traffic situation monitoring system of a traffic information center.
The method of claim 1, wherein the control unit:
Classifies the autonomous driving information collected through the first autonomous driving information collection unit by type and corrects it according to preset standards for each type of autonomous driving information,
For this purpose, an autonomous driving smoothness information providing device characterized in that it additionally collects information about the type of autonomous vehicle or vehicle terminal mounted on the autonomous vehicle.
delete The method of claim 1, wherein the control unit:
Smooth autonomous driving, characterized by classifying and correcting the autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit by type, and accumulating statistics on the classified and corrected autonomous driving information. Also an information provision device.
The method of claim 1, wherein the control unit:
Calculated as autonomous driving smoothness (%) based on the lane recognition rate by the camera,
An autonomous driving smoothness information providing device that calculates the autonomous driving smoothness (%) in the area as 0% when snow is detected on the road through a traffic surveillance camera.
The method of claim 1, wherein the control unit:
An autonomous driving smoothness information providing device characterized by calculating autonomous driving smoothness (%) based on GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received.
The method of claim 1, wherein the control unit:
The autonomous driving smoothness (%) calculated based on the lane recognition rate by the camera is multiplied by 0.5 and the autonomous driving smoothness (%) calculated based on the GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received. An autonomous driving smoothness information providing device characterized in that the autonomous driving smoothness (%) is calculated by adding the value multiplied by 0.5.
A first autonomous driving information collection unit collecting autonomous driving information measured from the autonomous vehicle in a wireless manner;
A second autonomous driving information collection unit wirelessly collecting autonomous driving information measured by an external system;
A control unit calculating autonomous driving smoothness (%) according to a predetermined method based on autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit; and
A self-driving smoothness map providing unit generates a map (MAP) reflecting the calculated self-driving smoothness (%) and provides it to the self-driving car or a vehicle terminal mounted on the self-driving car.
After the step of wirelessly collecting autonomous driving information measured through the second autonomous driving information collection unit,
The control unit,
By processing autonomous driving information collected from the external system,
For areas where real-time monitoring is possible, real-time road condition information including lane conditions reflecting actual weather conditions is detected,
A method of providing autonomous driving smoothness information, which is characterized by predicting road condition information, including lane conditions, by reflecting weather information and accumulated statistical weather information for areas where real-time monitoring is not possible.
The method of claim 12, after the step of wirelessly collecting autonomous driving information measured through the first autonomous driving information collection unit,
The control unit,
Classifying the autonomous driving information collected through the first autonomous driving information collection unit by type and correcting it according to preset standards for each type of autonomous driving information,
To this end, a method of providing information on the smoothness of autonomous driving, characterized in that additional information is collected about the type of autonomous vehicle or vehicle terminal mounted on the autonomous vehicle.
delete The method of claim 12, after the step of wirelessly collecting autonomous driving information measured through the first autonomous driving information collection unit and the second autonomous driving information collection unit,
The control unit,
Smooth autonomous driving, characterized by classifying and correcting the autonomous driving information collected through the first autonomous driving information collection unit and the second autonomous driving information collection unit by type, and accumulating statistics on the classified and corrected autonomous driving information. How to provide information.
The method of claim 12, wherein in calculating the autonomous driving smoothness (%),
The control unit,
Calculated as autonomous driving smoothness (%) based on the lane recognition rate by the camera,
A method of providing autonomous driving smoothness information, characterized in that when snow accumulation on the road is detected through a traffic surveillance camera, the autonomous driving smoothness (%) in the area is calculated as 0%.
The method of claim 12, wherein in calculating the autonomous driving smoothness (%),
The control unit,
A method of providing autonomous driving smoothness information, characterized by calculating autonomous driving smoothness (%) based on GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received.
The method of claim 12, wherein in calculating the autonomous driving smoothness (%),
The control unit,
The autonomous driving smoothness (%) calculated based on the lane recognition rate by the camera is multiplied by 0.5 and the autonomous driving smoothness (%) calculated based on the GPS reception sensitivity corresponding to the number of GPS satellites from which GPS signals are received. A method of providing autonomous driving smoothness information, characterized in that the autonomous driving smoothness (%) is calculated by multiplying by 0.5 and adding the values.

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