KR102184598B1 - Driving Prediction and Safety Driving System Based on Judgment of Driver Emergency Situation of Autonomous Driving Vehicle - Google Patents

Driving Prediction and Safety Driving System Based on Judgment of Driver Emergency Situation of Autonomous Driving Vehicle Download PDF

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KR102184598B1
KR102184598B1 KR1020180151149A KR20180151149A KR102184598B1 KR 102184598 B1 KR102184598 B1 KR 102184598B1 KR 1020180151149 A KR1020180151149 A KR 1020180151149A KR 20180151149 A KR20180151149 A KR 20180151149A KR 102184598 B1 KR102184598 B1 KR 102184598B1
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driving
vehicle
emergency
driver
information
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KR20200072581A (en
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박대혁
한경식
황현태
소상우
정용하
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(주)제인모터스
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Abstract

자율주행차량의 주행예측시스템을 개시한다. 실시예에 따른 주행예측시스템은 자율주행차량의 주행예측시스템에 있어서, 주행하는 차량에 인접한 차량을 감지하고, 인접 차량들과의 거리에 따라 속도 및 속도 변화량을 조정하는 가속제어모듈; 주행중인 차량이 위치한 지역의 도로상황과 위치 정보에 따라 충돌 예측정보 및 사고위험정보를 생성하는 안전주행모듈; 주행차량의 현재 위치와 목적지 입력정보, 지역별 교통 정보를 기반으로 주행경로를 생성하는 경로생성모듈; 및 전방차량, 후방차량 및 인접한 차선을 주행하는 주변객체를 인식하고, 주변객체에서 전송하는 운전신호와 주행경로정보를 수신하고 주변객체의 속도를 감지하여 주변객체들의 주행을 예측하고, 예측된 주행정보에 따라 차량의 자율주행을 조정하는 주행예측모듈; 을 포함하고, 주행예측모듈은 차량 내부에 설치된 생체신호 감지 센서로부터 운전자의 호흡주기를 포함하는 운전자생체 신호를 파악하여 응급상황 발생여부를 판단하고, 응급상황이 발생된 것으로 파악한 경우, 안전주행모드로 자동전환 한다.Initiate a driving prediction system for autonomous vehicles. A driving prediction system according to an embodiment is a driving prediction system for an autonomous vehicle, comprising: an acceleration control module for detecting a vehicle adjacent to a driving vehicle and adjusting a speed and a change in speed according to a distance between the adjacent vehicles; A safe driving module that generates collision prediction information and accident risk information according to road conditions and location information of an area in which the driving vehicle is located; A route generation module for generating a driving route based on the current location and destination input information of the driving vehicle, and regional traffic information; And the vehicle in front, the vehicle in the rear, and surrounding objects traveling in adjacent lanes are recognized, the driving signal and driving route information transmitted from the surrounding objects are received, and the speed of the surrounding objects is detected to predict the driving of the surrounding objects, and the predicted driving A driving prediction module for adjusting autonomous driving of the vehicle according to the information; Including, the driving prediction module determines whether an emergency situation has occurred by identifying the driver's biological signal including the driver's breathing cycle from a biological signal detection sensor installed inside the vehicle, and when it is determined that an emergency situation has occurred, the safe driving mode Automatically switch to.

Description

자율주행차량의 운전자 응급상황발생판단에 기반한 주행예측 및 안전주행시스템 {Driving Prediction and Safety Driving System Based on Judgment of Driver Emergency Situation of Autonomous Driving Vehicle}Driving Prediction and Safety Driving System Based on Judgment of Driver Emergency Situation of Autonomous Driving Vehicle}

운전자 응급상황발생판단에 기반한 주행예측 및 안전주행시스템에 관한 것으로 구체적으로, 자율주행차량의 센서를 통해 응급상황발생여부를 파악하고, 응급상황인 경우 안전주행 모드로 자율 주행하는 주행예측 및 안전주행 시스템에 관한 것이다.It relates to a driving prediction and safe driving system based on the driver's emergency situation determination. Specifically, it identifies whether an emergency situation has occurred through the sensor of an autonomous vehicle, and in case of an emergency, driving prediction and safe driving in which autonomous driving in safe driving mode is performed. It's about the system.

본 명세서에서 달리 표시되지 않는 한, 이 섹션에 설명되는 내용들은 이 출원의 청구항들에 대한 종래 기술이 아니며, 이 섹션에 포함된다고 하여 종래 기술이라고 인정되는 것은 아니다.Unless otherwise indicated herein, the content described in this section is not prior art to the claims of this application, and inclusion in this section is not admitted to be prior art.

자율주행차는 운전자가 직접 조작하지 않아도 주행환경을 인식해 위험을 판단하고 주행경로를 계획하여 운전자 주행조작을 최소화하며, 스스로 안전하게 운행하는 차량이다. 인공지능기술의 발달로 자율주행 차량과 무인자동차에 관한 연구 개발이 활발하게 이루어 지고 있는데, 무인자동차는 말 그대로 사람이 타고 있지 않은 차인 반면 자율주행자동차는 사람이 타는 것을 목적으로 한다.Self-driving cars are vehicles that recognize the driving environment without direct manipulation, determine risks, plan a driving route, minimize driver driving operations, and operate safely by themselves. With the development of artificial intelligence technology, research and development on self-driving vehicles and unmanned vehicles are being actively carried out. Unmanned vehicles are literally cars that are not riding by humans, whereas self-driving cars are aimed at riding by humans.

일반적으로 무인자동차는 군사용으로 많이 개발되고 있으며 사람이 탑승하지 않으므로 편의장치 등이 없고 승차감이 중요하지 않지만, 자율주행자동차는 탑승자를 위한 다양한 편의시설 및 편안한 주행 성능을 갖추고 있어야 한다.In general, driverless vehicles are being developed for military use, and there are no convenience devices and comfort is not important because no people are on board. However, autonomous vehicles must have various convenience facilities and comfortable driving performance for passengers.

현재 자율주행 차량은 실제 도로를 110km/h의 고속주행을 시험하는 실증단계까지 기술개발이 진행되어 있다. 빅데이터, 머신러닝, 딥러닝 등 다양한 인공지능 기술이 개발되면서 자율주행차가 미래 핵심성장동력으로 부상하며 자동차 업계는 물론, IT기업, 스타트업까지 업종과 규모에 관계없이 앞다투어 연구에 진입하고 있다. 구글은 총 58대의 시험차를 통해 2016년 6월까지 총 277만8,000㎞의 실제 도로상 순수 자율주행 기록을 세우며 자율주행기술 리더십 보유하고 있고, 더 빠른 확산이 기대되는 상용차 부문에서는 2015년 다임러의 자율주행 트럭이 독일과 미국 실제 고속도로를 주행하며 2년 내 상용화를 목표로 하고 있다. 일본에서는 2020년 올림픽을 대비해 로봇택시를 실 주행 테스트하고 있으며, 중국도 실도로에서 30km를 주행하는 시연을 하면서 국가 차원에서 육성하고 있다.Currently, technology development for autonomous vehicles is in progress until the test of high-speed driving at 110 km/h on the actual road. With the development of various artificial intelligence technologies such as big data, machine learning, and deep learning, autonomous vehicles are emerging as a key growth engine for the future, and not only the automobile industry, but also IT companies and start-ups are rushing into research regardless of the type of industry and size. With a total of 58 test cars, Google has set a record of real autonomous driving on roads of 2778,000 km by June 2016, and has leadership in autonomous driving technology. In the commercial vehicle sector, which is expected to spread faster, Daimler's Self-driving trucks drive on real highways in Germany and the United States, and are aiming for commercialization within two years. In Japan, robot taxis are being tested for actual driving in preparation for the 2020 Olympics, and China is also cultivating them at the national level by demonstrating driving 30 km on real roads.

이렇게 세계 각국에서 자율주행차량연구를 적극적으로 진행하고 있지만, 아직도 자율주행차량과 자율주행기능에 대한 안전성과 사고위험가능성에 대한 우려가 있다. In this way, many countries around the world are actively conducting research on autonomous vehicles, but there are still concerns about the safety of autonomous vehicles and autonomous driving functions and the possibility of accidents.

1. 한국 특허출원 제10-2017-0021431 (2017.02.17)1. Korean Patent Application No. 10-2017-0021431 (2017.02.17) 2. 한국 특허출원 제1020150063361 (2015.05.06)2. Korean Patent Application No. 1020150063361 (2015.05.06)

자율주행차량에 구비된 차량 내부 및 외부센서를 통해 운전자의 호흡, 심박 등의 생체정보를 획득하고 이를 분석하여 운전 중 응급상황발생을 파악한다. 운전 중 응급상황이 발생한 것으로 판단된 경우, 비상도로로 주차하도록 하는 자율주행을 수행하는 안전주행시스템을 제공한다.Biometric information such as the driver's breathing and heartbeat is acquired through the vehicle's internal and external sensors provided in the autonomous vehicle, and the occurrence of an emergency situation during driving is identified by analyzing it. When it is determined that an emergency situation has occurred while driving, a safe driving system that performs autonomous driving to park on an emergency road is provided.

주행중인 차량에 인접한 다른 차량들의 위치와 상대속도를 파악하여 인접차량들의 주행을 예측하고, 차량의 현재 위치와 설정된 목적지의 위치 및 주행중인 지역의 위험도를 파악하여 주행차량의 주행경로를 미리 산출하는 자율주행 차량의 주행예측 시스템을 제공한다. It predicts the driving of adjacent vehicles by grasping the location and relative speed of other vehicles adjacent to the driving vehicle, and calculates the driving route of the driving vehicle in advance by grasping the current position of the vehicle and the location of the set destination and the risk of the driving area. It provides a driving prediction system for autonomous vehicles.

특히 본 개시에서는 전방차량의 급정거나 인접차량의 끼어들기 등 위험상황이 발생할 확률을 예측 연산하여 차량의 안전한 주행경로를 예측할 수 있도록 한다. In particular, in the present disclosure, it is possible to predict a safe driving route of a vehicle by predicting and calculating a probability of occurrence of a dangerous situation such as an emergency stop of a vehicle in front or an interruption of an adjacent vehicle.

실시예에 따른 자율주행차량의 주행예측시스템은 주행하는 차량에 인접한 차량을 감지하고, 인접 차량들과의 거리에 따라 속도 및 속도 변화량을 조정하는 가속제어모듈; 주행중인 차량이 위치한 지역의 도로상황과 위치 정보에 따라 충돌 예측정보 및 사고위험정보를 생성하는 안전주행모듈; 주행차량의 현재 위치와 목적지 입력정보, 지역별 교통 정보를 기반으로 주행경로를 생성하는 경로생성모듈; 및 전방차량, 후방차량 및 인접한 차선을 주행하는 주변객체를 인식하고, 상기 주변객체에서 전송하는 운전신호와 주행경로정보를 수신하고 주변객체의 속도를 감지하여 주변객체들의 주행을 예측하고, 상기 예측된 주행정보에 따라 차량의 자율주행을 조정하는 주행예측모듈; 을 포함한다.The driving prediction system for an autonomous vehicle according to the embodiment includes an acceleration control module that detects a vehicle adjacent to a driving vehicle and adjusts a speed and a speed change amount according to a distance between the adjacent vehicles; A safe driving module that generates collision prediction information and accident risk information according to road conditions and location information of an area in which the driving vehicle is located; A route generation module for generating a driving route based on the current location and destination input information of the driving vehicle, and regional traffic information; And a vehicle in front, a vehicle in the rear, and surrounding objects traveling in an adjacent lane are recognized, and driving signals and driving route information transmitted from the surrounding objects are received, and the speed of the surrounding objects is detected to predict the driving of surrounding objects, and the prediction A driving prediction module that adjusts the autonomous driving of the vehicle according to the generated driving information; Includes.

바람직한 실시예에서 주행예측모듈은 차량 내부에 설치된 생체신호 감지 센서로부터 운전자의 호흡주기를 포함하는 운전자생체 신호를 기준신호와 비교하여 응급상황 발생여부를 판단하고, 응급상황이 발생된 것으로 판단한 경우, 안전주행모드로 자동전환 한다.In a preferred embodiment, the driving prediction module compares the driver's biological signal including the driver's breathing cycle with a reference signal from a biological signal detection sensor installed inside the vehicle to determine whether an emergency situation has occurred, and when it is determined that an emergency situation has occurred, It automatically switches to the safe driving mode.

심박수, 호흡주기, 뇌파, 동공감지를 포함하는 운전자의 생체신호 분석을 통해 운전 중 안전사고위험이 높은 긴급상황이나 응급상황이 발생한 것을 파악하여, 운전자가 정상적인 운전조작이 불가능한 응급상황이 발생한 경우, 안전하게 차량을 비상도로에 정차 시킬 수 있도록 하여 갑작스러운 응급상황에서도 안전운전을 가능하게 한다. By analyzing the driver's bio-signals including heart rate, respiratory cycle, brain wave, and pupil detection, it is possible to identify an emergency or emergency situation with a high risk of safety accidents while driving, and in the event of an emergency situation in which the driver cannot operate normally, The vehicle can be safely stopped on the emergency road, enabling safe driving even in sudden emergency situations.

인접차량의 위험주행 가능성을 예측하고, 전방차량의 주행변화를 예측하여 자율주행의 안전성을 향상 시킬 수 있다.It is possible to improve the safety of autonomous driving by predicting the possibility of dangerous driving of adjacent vehicles and by predicting driving changes of vehicles in front.

도로 위험도와 실시간 교통량을 파악하여 주행시간이 가장 적은 최적경로를 산출하고 최적경로에서 보다 안전한 자율주행이 가능하도록 한다. By grasping road risk and real-time traffic volume, the optimal route with the least driving time is calculated, and safer autonomous driving is possible on the optimum route.

본 발명의 효과는 상기한 효과로 한정되는 것은 아니며, 본 발명의 상세한 설명 또는 특허청구범위에 기재된 발명의 구성으로부터 추론 가능한 모든 효과를 포함하는 것으로 이해되어야 한다. The effects of the present invention are not limited to the above effects, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

도 1은 실시예에 따른 자율주행차량의 주행예측시스템의 구현 예를 설명하기 위한 도면
도 2는 실시예에 따른 자율주행예측시스템의 기술구성요소를 나타낸 도면
도 3은 실시예에 따른 자율주행 차량의 주행예측 시스템을 나타낸 블록도
도 4는 실시예에 따른 자율주행 차량의 주행예측 시스템의 구체적인 데이터 처리블록을 나타낸 도면1 is a view for explaining an implementation example of a driving prediction system for an autonomous vehicle according to an embodiment
2 is a diagram showing the technical components of an autonomous driving prediction system according to an embodiment
3 is a block diagram showing a driving prediction system for an autonomous vehicle according to an embodiment;
4 is a diagram showing a detailed data processing block of a driving prediction system for an autonomous vehicle according to an embodiment;

본 발명의 이점 및 특징, 그리고 그것들을 달성하는 방법은 첨부되는 도면과 함께 상세하게 후술되어 있는 실시 예들을 참조하면 명확해질 것이다. 그러나 본 발명은 이하에서 개시되는 실시 예들에 한정되는 것이 아니라 서로 다른 다양한 형태로 구현될 수 있으며, 단지 본 실시 예들은 본 발명의 개시가 완전하도록 하고, 본 발명이 속하는 기술분야에서 통상의 지식을 가진 자에게 발명의 범주를 완전하게 알려주기 위해 제공되는 것이며, 본 발명은 청구항의 범주에 의해 정의될 뿐이다. 명세서 전체에 걸쳐 동일 도면부호는 동일 구성 요소를 지칭한다. Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, only the present embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the art It is provided to completely inform the scope of the invention to those who have it, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same elements throughout the specification.

본 발명의 실시 예들을 설명함에 있어서 공지 기능 또는 구성에 대한 구체적인 설명이 본 발명의 요지를 불필요하게 흐릴 수 있다고 판단되는 경우에는 그 상세한 설명을 생략할 것이다. 그리고 후술되는 용어들은 본 발명의 실시 예에서의 기능을 고려하여 정의된 용어들로서 이는 사용자, 운용자의 의도 또는 관례 등에 따라 달라질 수 있다. 그러므로 그 정의는 본 명세서 전반에 걸친 내용을 토대로 내려져야 할 것이다.In describing embodiments of the present invention, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted. In addition, terms to be described later are terms defined in consideration of functions in an embodiment of the present invention, which may vary according to the intention or custom of users or operators. Therefore, the definition should be made based on the contents throughout this specification.

본 개시는 산업통상자원부 산업기술혁신사업의 연구비 지원 (과제번호: 10082585)에 의해 수행된 연구과제로서, 주행중인 차량에 인접한 다른 차량들의 위치와 상대속도를 파악하여 인접차량들의 주행을 예측하고, 차량의 현재 위치와 설정된 목적지의 위치 및 주행중인 지역의 위험도를 파악하여 주행차량의 주행경로를 미리 산출하는 자율주행 차량의 주행예측 시스템을 제공을 목적으로 한다. 또한, 자율주행 차량에 설치된 센서로 운전자 생체신호를 분석하고 뇌졸증, 호흡곤란, 의식불명, 경련 등 정상적인 운전을 지속할 수 없는 응급상황 발생을 예측하여, 응급상황이 발생한 경우에는 비상도로로 안전하게 차량을 주차시키도록 하는 안전주행모드로 변환하도록 한다. 또한, 본 개시에서는 전방차량의 급정거나 인접차량의 끼어들기 등 위험상황이 발생할 확률을 예측 연산하여 차량의 안전한 주행경로를 예측할 수 있도록 한다. This disclosure is a research project carried out by the Ministry of Trade, Industry and Energy's industrial technology innovation project support (project number: 10082585), and predicts the driving of adjacent vehicles by grasping the location and relative speed of other vehicles adjacent to the driving vehicle, An object of the present invention is to provide a driving prediction system for an autonomous vehicle that calculates a driving route of a driving vehicle in advance by grasping the current position of the vehicle, the location of a set destination, and the risk of a driving area. In addition, sensors installed in autonomous vehicles analyze the driver's vital signs and predict the occurrence of emergencies that cannot continue normal driving such as stroke, breathing difficulties, unconsciousness, and convulsions, and in the event of an emergency, drive the vehicle safely to the emergency road. Switch to the safe driving mode to park. In addition, in the present disclosure, it is possible to predict a safe driving path of the vehicle by predicting and calculating a probability of occurrence of a dangerous situation such as an emergency stop of a vehicle in front or an interruption of an adjacent vehicle.

도 1은 실시예에 따른 자율주행차량의 주행예측시스템의 구현 예를 설명하기 위한 도면이다.1 is a diagram illustrating an implementation example of a driving prediction system for an autonomous vehicle according to an embodiment.

도 1에 도시된 바와 같이, 실시예에 따른 자율주행차량의 주행예측 시스템은 주행중인 차량의 전방차량과 인접한 차선을 주행하는 근접차량의 상대속도와 위치를 실시간으로 인식하고, 인식된 근접차량의 주행정보와 주해차량에 미리 설정된 경로를 기반으로 자율주행이 안전하게 지속되도록 주행경로 및 주행상태를 예측할 수 있다. 실시예에서는 인접차량의 위험주행 가능성을 예측하고, 전방차량의 주행변화를 예측하여 자율주행의 안전성을 향상 시킬 수 있다. 또한, 도로 위험도와 실시간 교통량을 파악하여 주행시간이 가장 적은 최적경로를 산출하고 최적경로에서 보다 안전한 자율주행이 가능하도록 한다.As shown in FIG. 1, the driving prediction system of an autonomous vehicle according to an embodiment recognizes in real time the relative speed and position of a vehicle in front of a vehicle in front of a vehicle and a nearby vehicle traveling in an adjacent lane, and It is possible to predict the driving route and driving condition so that autonomous driving can continue safely based on the driving information and the route preset in the parking vehicle. In the embodiment, it is possible to improve the safety of autonomous driving by predicting the possibility of dangerous driving of an adjacent vehicle and predicting a driving change of a vehicle in front. In addition, by grasping road risk and real-time traffic volume, the optimal route with the least driving time is calculated and safer autonomous driving is possible on the optimum route.

또한 실시예에서는 자율주행 차량에 설치된 공기밀도 감지센서, 생체신호 감지센서, 운전자 호흡 감지센서, 뇌파감지센서, 뇌졸증 발생 감지 센서 등 복수개의 센서로부터 운전자 생체정보를 전달받고 이를 분석하여 응급상황 발생 여부를 파악할 수 있다. 실시예에서 응급상황은 호흡곤란, 의식불명 등 정상적인 운전 조작을 지속적으로 수행할 수 없는 상태로 운전자 신체 상태가 변화하는 것이 될 수 있다. 실시예에서는 응급상황이 발생된 것으로 판단되는 경우, 주행중인 차량을 비상도로에 정차시키는 안전주행 모드로 자율주행 하도록 하여 응급상황 발생 시에도 안전사고를 방지하고 안전하게 대응할 수 있도록 한다.In addition, in an embodiment, driver biometric information is received from a plurality of sensors such as an air density sensor installed in an autonomous vehicle, a biometric signal sensor, a driver's breath sensor, an EEG sensor, and a stroke detection sensor, and analyzes it to determine whether an emergency situation occurs. Can grasp. In an embodiment, the emergency situation may be a state in which a driver's body state changes such that normal driving operations such as difficulty breathing and unconsciousness cannot be continuously performed. In the embodiment, when it is determined that an emergency situation has occurred, autonomous driving is performed in a safe driving mode in which a vehicle in motion is stopped on an emergency road, so that a safety accident can be prevented and a safe response is possible even in the event of an emergency situation.

도 2는 실시예에 따른 자율주행예측시스템의 기술구성요소를 나타낸 도면이다.2 is a diagram showing the technical components of an autonomous driving prediction system according to an embodiment.

도 2를 참조하면, 자율주행 예측 시스템을 구현하는 자율주행 차량은 운전자인터페이스, 운전기록장치, 별도기록장치, 전후방카메라, 전방영상기록장치, 후방영상기록장치, 후측방레이더, 전측방레이더, 전방레이더 등을 포함하여 구성될 수 있다. 2, the autonomous driving vehicle implementing the autonomous driving prediction system includes a driver interface, a driving recorder, a separate recorder, a front and rear camera, a front image recorder, a rear image recorder, a rear side radar, a front side radar, and a front side. It can be configured including a radar and the like.

자율주행차량은 기존의 자동차 주요 수송 기능을 수행 할 수 있는 자동 운전 차량이다. 자동 운전 차량은 인간의 개입이 없이 주위의 환경을 감지하고, 자동항법 운행이 가능하다. 현재 로봇 자동차가 프로토 타입(prototype)으로 존재하고 주행 차량은 레이더, 라이다(LIDAR), GPS, 및 컴퓨터 비전(vision) 기술 등으로 주변 환경을 감지한다. 보다 발전된 제어 시스템은 해당 내비게이션 경로뿐만 아니라 장애물과 관련된 표지 등을 식별하는 정보를 해석한다. 실시예에 따른 주행예측 시스템에서 무인자동차는 등록되지 않은 환경이나 조건이 변한 상황에서도 경로를 유지할 수 있도록 센서 입력에 따라 지도를 자동 갱신할 수 있도록 한다. 또한, 실시예에 따른 자율주행 예측시스템에서는 전면의 장거리 · 단거리 레이더와 입체 카메라 그리고 적응형 순항 제어기술 등을 통해 구현하는데, '액티브 크루즈 컨트롤(Active Cruise Control, ACC)'과 '액티브 브레이크 어시스트(Active Brake Assist, ABA)'가 장거리 레이더와 단거리 레이더를 이용해 주행과 감속을 조정하며, 자동차 간 거리를 자동으로 조절할 수 있도록 한다.Autonomous vehicles are self-driving vehicles that can perform the main transport functions of existing automobiles. The self-driving vehicle senses the surrounding environment without human intervention and enables automatic navigation. Currently, a robotic vehicle exists as a prototype, and a driving vehicle detects the surrounding environment with radar, LIDAR, GPS, and computer vision technology. A more advanced control system interprets information that identifies not only the navigation path, but also signs related to obstacles. In the driving prediction system according to the embodiment, the driverless vehicle automatically updates a map according to a sensor input so that a route can be maintained even in an unregistered environment or a situation where conditions change. In addition, the autonomous driving prediction system according to the embodiment is implemented through a front long-range and short-range radar, a stereoscopic camera, and an adaptive cruise control technology, etc.,'Active Cruise Control (ACC)' and'Active Brake Assist ( Active Brake Assist (ABA)' uses long-range radar and short-range radar to adjust driving and deceleration, and automatically adjust the distance between cars.

실시예에서 자율주행 차량의 장거리 레이더는 18° 시야각으로 전방 250m까지 탐색하고, 단거리 레이더는 130° 시야각으로 전방 70m까지 탐색한다. 그리고 트럭 전면 유리에 부착된 입체 카메라는 수평 45° · 수직 27° 시야각으로 100m까지 탐색하며 차선 표시를 인식하고, '하이웨이 파일럿(Highway Pilot)' 시스템은 전면 레이더와 입체 카메라를 연결해 차선 유지, 충돌 회피, 속도 제어, 감속 등의 기능을 제공한다. 이러한 차량 자체적인 실시간 주변상황 감지 외에도 GPS(Global Positioning System) 등을 활용해 정밀지도를 통한 예측시스템도 작동하고, 이 모든 것들을 종합적으로 판단해서 자동차 구동장치를 사람이 아닌 컴퓨터가 실제로 제어하도록 만드는 것이다. 자율주행 자동차 지붕에 탑재된 센서 장비는 레이더(LiDAR)라고 부른다. 원격 레이저 시스템이 들어가 있는 기술로서 음파 장비와 3D 카메라, 레이더 장비도 포함되어 있다. 레이더는 사물과 사물의 거리를 측정하고, 위험을 감지할 수 있도록 돕는다. In the embodiment, a long-range radar of an autonomous vehicle searches up to 250 m in front with an 18° viewing angle, and a short-range radar searches up to 70 m in front with a 130° viewing angle. In addition, the stereoscopic camera attached to the front glass of the truck navigates up to 100m at a viewing angle of 45° horizontally and 27° vertically to recognize lane markings, and the'Highway Pilot' system connects the front radar and the stereoscopic camera to maintain lanes and collide. It provides functions such as avoidance, speed control, and deceleration. In addition to the real-time detection of the vehicle's own real-time surroundings, a prediction system through precision maps is also operated using GPS (Global Positioning System), and all these are judged comprehensively so that a computer, not a human, can actually control the vehicle's driving system. . The sensor equipment mounted on the roof of an autonomous vehicle is called a radar (LiDAR). A technology that incorporates a remote laser system includes sound wave equipment, 3D cameras, and radar equipment. Radar measures the distance between an object and an object and helps to detect danger.

자율주행 차량에 설치된 각 센서의 역할은 모두 다르고 감지할 수 있는 거리도 차이가 난다. 예를 들어 레이저 장비는 사물과 충돌해 반사되는 원리를 이용해 거리를 측정한다. 360도 모두 감지할 수 있도록 설계되고, 1초에 160만번이나 정보를 처리한다. 또한, 전방을 주시하기 위해 탑재된 3D 카메라는 차량이 도로 상황을 실시간으로 파악하기 위해 탑재된 기술로서, 3D 카메라는 카메라 하나로 사물을 촬영하는 것과 비교해 거리 측정의 정확도를 높인다. 사람의 눈이 2개의 눈으로 거리를 감지하는 것과 같은 원리다. 3D 카메라는 30m 거리까지 탐지하도록 설계된다. 자율주행차량에는 이밖에 GPS와 구글지도 등 다양한 장비와 기술이 탑재되어 있다. 각종 첨단 센서 장비를 목적과 기능에 맞게 활용해 자동차가 감지할 수 없는 사각을 줄이는 것이 구글 자율주행 자동차 기술의 핵심이다.The roles of each sensor installed in an autonomous vehicle are all different, and the distances that can be detected are also different. For example, laser equipment measures distance using the principle of collision and reflection of objects. It is designed to detect all 360 degrees, and processes information 1.6 million times per second. In addition, a 3D camera mounted to look ahead is a technology that a vehicle is equipped with to grasp road conditions in real time, and a 3D camera increases the accuracy of distance measurement compared to photographing an object with a single camera. It is the same principle as the human eye detects the distance with two eyes. The 3D camera is designed to detect up to 30m distance. In addition to this, autonomous vehicles are equipped with various equipment and technologies such as GPS and Google Maps. The core of Google's self-driving car technology is to reduce blind spots that cars cannot detect by using various advanced sensor equipment according to purpose and function.

도 3은 실시예에 따른 자율주행 차량의 주행예측 시스템을 나타낸 블록도이다. 3 is a block diagram showing a driving prediction system for an autonomous vehicle according to an embodiment.

도 3을 참조하면, 자율주행차량의 주행예측 시스템은 가속제어모듈(110), 안전주행모듈(150), 경로생성모듈(170) 및 주행예측모듈(190)을 포함하여 구성될 수 있다. 본 명세서에서 사용되는 '모듈' 이라는 용어는 용어가 사용된 문맥에 따라서, 소프트웨어, 하드웨어 또는 그 조합을 포함할 수 있는 것으로 해석되어야 한다. 예를 들어, 소프트웨어는 기계어, 펌웨어(firmware), 임베디드코드(embedded code), 및 애플리케이션 소프트웨어일 수 있다. 또 다른 예로, 하드웨어는 회로, 프로세서, 컴퓨터, 집적 회로, 집적 회로 코어, 센서, 멤스(MEMS; Micro-Electro-Mechanical System), 수동 디바이스, 또는 그 조합일 수 있다.Referring to FIG. 3, a driving prediction system for an autonomous vehicle may include an acceleration control module 110, a safe driving module 150, a path generation module 170, and a driving prediction module 190. The term'module' used in this specification should be interpreted as being capable of including software, hardware, or a combination thereof, depending on the context in which the term is used. For example, the software may be machine language, firmware, embedded code, and application software. As another example, the hardware may be a circuit, a processor, a computer, an integrated circuit, an integrated circuit core, a sensor, a MEMS (Micro-Electro-Mechanical System), a passive device, or a combination thereof.

가속제어모듈(110)은 주행하는 차량에 인접한 차량을 감지하고, 인접 차량들과의 거리에 따라 속도 및 속도 변화량을 조정한다. 예컨대, 가속제어모듈(110)은 주행중인 차량과 인접한 차량의 상대속도 및 인접한 차량과 주행중인 차량의 거리와 주행방향에 따라 속도 및 속도변화량을 조정하고, 전방차량의 좌회전신호, 우회전신호를 포함하는 주행신호에 따라 차량 속도를 조정한다.The acceleration control module 110 detects a vehicle adjacent to the driving vehicle, and adjusts the speed and the amount of change in speed according to the distance to the adjacent vehicles. For example, the acceleration control module 110 adjusts the speed and the amount of change in speed according to the relative speed of the vehicle being driven and the vehicle adjacent to the vehicle and the distance and the driving direction of the vehicle and the vehicle being driven, and includes a left turn signal and a right turn signal of the vehicle ahead. The vehicle speed is adjusted according to the driving signal.

안전주행모듈(150)은 주행중인 차량이 위치한 지역의 도로상황과 위치 정보에 따라 충돌 예측정보 및 사고위험정보를 생성하는 한다. The safe driving module 150 generates collision prediction information and accident risk information according to road conditions and location information of an area where the vehicle being driven is located.

경로생성모듈(170)은 주행차량의 현재 위치와 목적지 입력정보, 지역별 교통 정보를 기반으로 주행경로를 생성한다.The route generation module 170 generates a driving route based on the current location and destination input information of the driving vehicle, and regional traffic information.

주행예측모듈(190)은 전방차량, 후방차량 및 인접한 차선을 주행하는 주변객체를 인식하고, 주변객체에서 전송하는 운전신호와 주행경로정보를 수신하고 주변객체의 속도를 감지하여 주변객체들의 주행을 예측하고, 예측된 주행정보에 따라 차량의 자율주행을 조정한다. 또한 실시예에 따른 주행예측모듈(190)은 차량 내부에 설치된 생체신호 감지 센서로부터 운전자의 호흡주기를 포함하는 운전자생체 신호를 파악하여 응급상황 발생여부를 판단하고, 응급상황이 발생된 것으로 파악한 경우, 안전주행모드로 자동전환 한다. 실시예에서 안전주행모드는 자율주행차량 구비된 센서에 의해 운전자가 정상 운전행동을 지속할 수 없는 응급상황으로 판단된 경우, 비상등 점등 후 비상도로에 주행차량을 정차시키는 자율주행모드이다. 실시예에서 주행예측모듈(190)은 차량에 설치된 센서를 통해 운전자의 동공이 일정시간 이상 감지되지 않거나, 운전자의 호흡주기와 뇌파가 정상수치에서 일정비율 이상 차이 나거나, 핸들조작 및 터치신호가 일정시간 이상 입력되지 않는 등의 응급상황발생조건을 중복하여 만족시키는 경우 응급상황 발생으로 판단하고 자율주행차량을 안전주행모드로 진입시킬 수 있다. The driving prediction module 190 recognizes a vehicle in front, a vehicle in the rear, and surrounding objects traveling in an adjacent lane, receives driving signals and driving route information transmitted from the surrounding objects, and detects the speed of the surrounding objects to detect the speed of the surrounding objects. It predicts and adjusts the autonomous driving of the vehicle according to the predicted driving information. In addition, the driving prediction module 190 according to the embodiment determines whether an emergency situation has occurred by grasping the driver's biological signal including the driver's breathing cycle from a biosignal detection sensor installed inside the vehicle, and when it is determined that an emergency situation has occurred. , Automatically switch to safe driving mode. In the embodiment, the safe driving mode is an autonomous driving mode in which the driving vehicle is stopped on the emergency road after the emergency light is turned on when it is determined by a sensor equipped with the autonomous vehicle as an emergency situation in which the driver cannot continue normal driving behavior. In an embodiment, the driving prediction module 190 is that the driver's pupil is not detected for more than a certain period of time through a sensor installed in the vehicle, the driver's breathing cycle and EEG differ by more than a certain percentage from the normal value, or the steering wheel operation and the touch signal are constant. If an emergency condition such as not being input for more than a period of time is satisfied by overlapping, it is judged that an emergency condition has occurred and the autonomous vehicle can be entered into the safe driving mode.

또한, 주행예측모듈(190)은 차량 센서로부터 전달된 예측상황에 따라 일반도로, 고속도로, 골목길주행 등 주행중인 도로의 종류와 상태에 기반하여 긴급주차 및 비상주차를 위한 자율주행제어를 수행할 수 있다. 예컨대, 주행예측모듈(190)은 응급상황 발생 판단 후 차량이 고속도로를 주행중인 경우, 고속도로의 규정속도로 주행하면서 비상도로로 차량이 근접하도록 한다. 이후 비상도로 인접 차선에 진입하면 차량 속도를 감속하고 비상도로에 안전정차 하도록 자율주행 차량을 제어한다.In addition, the driving prediction module 190 can perform autonomous driving control for emergency parking and emergency parking based on the type and state of roads being driven, such as general roads, highways, and alleyways, according to the predicted situation transmitted from the vehicle sensor. have. For example, when the vehicle is traveling on a highway after determining the occurrence of an emergency situation, the driving prediction module 190 makes the vehicle approach the emergency road while driving at a prescribed speed of the highway. Then, when entering the lane adjacent to the emergency road, the vehicle speeds up and controls the autonomous vehicle to stop safely on the emergency road.

다른 예로 주행예측모듈(190)은 골목길 주행 중 응급상황이 발생한 것으로 판단된 경우, 차량 속도를 감속시키고, 비상등을 켠 후 정차 위치를 탐색한다. 이후 정차위치가 탐색되면 정차위치로 차량을 안전하게 정차시키도록 제어할 수 있다. As another example, when it is determined that an emergency situation has occurred while driving on an alley, the driving prediction module 190 slows the vehicle speed, turns on the emergency light, and then searches for a stop position. After that, when the stop position is searched, the vehicle can be controlled to safely stop at the stop position.

만일, 자율주행 차량이 인적이 드문 외곽지역의 갓길에서 응급상황 발생을 판단한 경우, 주행예측모듈(190)은 차량을 정차시킨 후 119 등 구조기관으로 차량 위치정보를 전달하거나, 운전자 스마트 단말과 연동하여 스마트 단말에 기 저장된 긴급연락처로 차량 위치정보와 응급상황 발생 정보를 전송하여, 응급상황에 신속하게 대처하도록 한다. 실시예에 따른 주행예측모듈(190)은 주행상황, 주행 위치 및 응급상황발생여부에 따른 주행모드를 예측하여 운용할 수 있다. If the autonomous vehicle determines that an emergency situation occurs on the shoulder of an outlying area, the driving prediction module 190 stops the vehicle and transmits the vehicle location information to a rescue agency such as 119 or interlocks with the driver's smart terminal. Thus, the vehicle location information and emergency situation occurrence information are transmitted to the emergency contact information previously stored in the smart terminal to quickly respond to the emergency situation. The driving prediction module 190 according to the embodiment may predict and operate a driving mode according to a driving situation, a driving position, and whether an emergency situation occurs.

실시예에 따른 생체신호 감지센서는 공기밀도감지센서, 생체신호감지센서, 운전자호흡감지센서, 운전자 뇌졸증 발생 감지센서를 포함하고, 복수개의 센서들로부터 수집된 운전자 생체신호를 통해 운전자 응급상황발생 여부를 파악 가능하다.The biometric signal detection sensor according to the embodiment includes an air density detection sensor, a biosignal detection sensor, a driver's breath detection sensor, and a driver's stroke detection sensor, and whether a driver emergency situation occurs through the driver's biosignals collected from a plurality of sensors. It is possible to grasp.

도 4는 실시예에 따른 자율주행 차량의 주행예측 시스템의 구체적인 데이터 처리블록을 나타낸 도면이다.4 is a diagram showing a detailed data processing block of a driving prediction system for an autonomous vehicle according to an embodiment.

도 4를 참조하면, 가속제어모듈(110)은 주변객체감지부(111) 및 상대속도연산부(113)을 포함하여 구성될 수 있고, 안전주행모듈(150)은 위험도 산출부(151) 및 주행 최대속도 설정부(153)을 포함하여 구성될 수 있고, 경로생성모듈(170)은 교통상황정보 수집부(171) 및 상대속도 연산부(173)을 포함하여 구성될 수 있고, 주행예측모듈(190)은 확률산출부(191) 및 주행 예측부(193)을 포함하여 구성될 수 있다.4, the acceleration control module 110 may be configured to include a peripheral object detection unit 111 and a relative speed calculation unit 113, the safety driving module 150 is a risk calculation unit 151 and driving It may be configured to include the maximum speed setting unit 153, the route generation module 170 may be configured to include a traffic condition information collection unit 171 and a relative speed calculation unit 173, and the driving prediction module 190 ) May be configured to include a probability calculation unit 191 and a driving prediction unit 193.

가속제어모듈(110)의 주변객체감지부(111)는 주행차량의 전방 및 후방차량을 감지하고, 인접한 차량에서 주행중인 근접차량을 감지한다. 상대속도 연산부(113)는 주행중인 차량과 인접한 차량의 상대속도 및 인접한 차량과 주행중인 차량의 거리와 주행방향에 따라 속도 및 속도변화량을 조정하고, 전방차량의 좌회전신호, 우회전신호를 포함하는 주행신호에 따라 차량 속도를 조정한다. 예컨대, 주행중인 차량에 인접한 차선에서 끼어들기 하려는 근접차량이 감지되는 경우, 근접차량과 주행차량의 상대속도를 연산하여 주행차량의 가속 및 감속을 조정하도록 한다. The peripheral object detection unit 111 of the acceleration control module 110 detects vehicles in front of and behind the driving vehicle, and detects vehicles in proximity to the vehicle running in the adjacent vehicle. The relative speed calculation unit 113 adjusts the speed and the amount of change in speed according to the relative speed of the vehicle being driven and the vehicle adjacent to the vehicle and the distance and driving direction of the vehicle and the vehicle being driven, and includes a left turn signal and a right turn signal of the vehicle ahead. Adjust the vehicle speed according to the signal. For example, when a proximity vehicle to be interrupted in a lane adjacent to a driving vehicle is detected, a relative speed between the proximity vehicle and the driving vehicle is calculated to adjust acceleration and deceleration of the driving vehicle.

안전주행모듈(150)의 위험도 산출부(151)는 주행중인 차량이 위치한 지역의 도로위험도 및 교통사고 누적 량을 포함하는 사고 위험도를 산출한다. 예컨대, 차량 시스템 외부의 교통관리 서버와 통신하여 위치 별 누적 사고량과 날씨정보 등 주행 중 차량 안전사고 발생에 영향을 줄 수 있는 데이터를 전달받아, 주행중인 지역의 사고 위험도를 실시간으로 산출 할 수 있다.The risk calculation unit 151 of the safe driving module 150 calculates an accident risk including a road risk and a cumulative amount of traffic accidents in an area where the vehicle being driven is located. For example, by communicating with the traffic management server outside the vehicle system, it is possible to receive data that may affect the occurrence of vehicle safety accidents while driving, such as accumulated accidents by location and weather information, and calculate the accident risk in the driving area in real time. have.

이후, 주행최대속도 설정부(153)는 산출된 사고 위험도에 따라 주행 최대속도를 설정할 수 있다. 예컨대, 비가 오는 날씨에 누적사고량이 많은 위험지역을 주행중인 경우, 법적으로 정해진 주행속도의 90% 수준을 주행 최대속도로 설정할 수 있고, 맑은 날 누적사고량이 적은 지역을 주행하는 경우, 법적 주행속도의 100% 를 주행 최대 속도로 설정할 수 있다. Thereafter, the maximum driving speed setting unit 153 may set the maximum driving speed according to the calculated accident risk. For example, when driving in a dangerous area with a large amount of cumulative accidents in rainy weather, 90% of the legally determined driving speed can be set as the maximum driving speed, and when driving in an area with less cumulative accidents on a sunny day, the legal driving speed 100% of can be set as the maximum driving speed.

경로생성모듈(170)의 교통상황정보수집부(171)는 차량의 현재위치와 목적지를 파악하고, 실시간 교통상황 정보를 수집한다. The traffic condition information collection unit 171 of the route generation module 170 identifies the current location and destination of the vehicle, and collects real-time traffic condition information.

최적경로생성부(173)는 차량의 현재위치와 입력된 목적지 정보 및 실시간 교통상황 정보를 수집하여, 최단거리경로, 최단시간 경로 등 목적지까지 도달할 수 있는 복수개의 주행경로를 생성한다. 이후, 생성된 주행경로 중 최단시간으로 도착 가능한 최적경로를 선택하여, 선택된 최적경로로 자율주행차량이 주행 할 수 있도록 한다. 실시예에 따라, 자율주행차량 사용자는 교통상황정보를 반영하지 않고 주행경로를 생성할 수도 있고, 최단거리경로를 최적경로로 설정하도록 시스템을 파라미터를 지정할 수 있다.The optimal route generation unit 173 collects the current location of the vehicle, input destination information, and real-time traffic condition information, and generates a plurality of driving routes that can reach a destination, such as a shortest distance route and a shortest time route. Thereafter, an optimum route that can be reached in the shortest time among the generated driving routes is selected, so that the autonomous vehicle can travel on the selected optimum route. Depending on the embodiment, a user of an autonomous vehicle may generate a driving route without reflecting traffic condition information, or designate a system parameter to set the shortest-distance route as an optimal route.

주행예측모듈(190)의 확률산출부(191)는 전후방차량 및 근접차선 차량의 실시간 위치, 주행신호, 인접차량의 상대속도와 상대위치를 파악하여 전방차량의 주행 변화를 예측한다. 예컨대, 전방차량의 상대속도를 통해 급정거 확률 및 근접차량들이 끼어들기를 진행할 확률 등 안전사고 발생 위험이 있는 주변 차량의 주행상태 변화를 모니터링하고, 끼어들기나 급정거 등이 발생할 확률을 산출한다.The probability calculation unit 191 of the driving prediction module 190 predicts a driving change of the vehicle in front by grasping the real-time position, driving signal, and relative speed and relative position of the vehicle in front of and behind the vehicle in the vicinity of the vehicle in the vicinity. For example, through the relative speed of the vehicle in front, changes in driving conditions of nearby vehicles at risk of safety accidents, such as the probability of an emergency stop and the probability of intervening vehicles in proximity, are monitored, and the probability of an interruption or sudden stop is calculated.

주행예측부(193)는 급정거나 끼어들기가 발생할 확률을 기반으로 주행차량의 감속제어 여부를 예측하고, 전방차량 및 근접차량들의 상대속도와 주행방향을 실시간으로 감지하면서 주변 차량들의 주행변화를 함께 예측할 수 있다.The driving prediction unit 193 predicts whether to control the deceleration of the driving vehicle based on the probability of a sudden stop or interruption, and detects the relative speed and driving direction of the vehicle in front and the adjacent vehicles in real time, and monitors driving changes of surrounding vehicles together. It is predictable.

또한, 주행예측부(193)은 주행중인 도로와 긴급상황 발생여부에 따라 주행예측을 수행하고 각 상황에 따라 자율주행 모드를 다르게 운용하도록 한다. 예컨대, 차량이 도심의 일반도로를 주행 중일 때는 일반도로주행모드가 운용되고, 신호등 유무 및 전방객체와 근접객체의 상대속도 파악을 중점적으로 수행하며 안전 주행하도록 한다. 주행예측부(193)는 차량이 고속도로 주행중인 경우에는 고속도로주행모드가 운용되고, 고속도로 주행모드는 주행중인 고속도로의 규정속도와 전방차량과의 거리 및 도로의 차량 분포도를 일차적으로 파악하여 자율 주행하도록 한다.Further, the driving prediction unit 193 performs driving prediction according to the driving road and whether an emergency situation occurs, and operates the autonomous driving mode differently according to each situation. For example, when a vehicle is driving on a general road in an urban area, the general road driving mode is operated, and it focuses on grasping the presence or absence of a traffic light and the relative speed of a front object and a nearby object to ensure safe driving. The driving prediction unit 193 operates the highway driving mode when the vehicle is driving on the highway, and in the highway driving mode, it primarily identifies the regulated speed of the highway being driven, the distance to the vehicle in front, and the vehicle distribution map on the road to drive autonomously. do.

만일 주행예측부(193)가 운전자 생체 신호 분석 결과 호흡곤란, 의식불명 등 정상 운전 조작을 수행할 수 없는 긴급 상황이 발생한 것으로 판단한 경우, 주행중인 차량의 비상등을 켜고, 속도를 줄인 후 정차위치를 파악하는 안전주행모드로 변환하여 주행하도록 한다. 실시예에서 안전주행모드는 고속도로, 일반도로, 골목길 등 차량이 주행중인 도로에 따라 안전주행모드로 변환하는 과정이 달라지고, 차량의 주행 위치에 따라서도 안전주행 모드로의 변환 과정이 달라질 수 있다. 예컨대, 안전주행모드는 도심지역 주행 중 응급상황 발생이 파악되고 안전주행 모드로 변환된 경우, 비상등을 켜고 감속 및 정차위치 파악 후, 응급상황발생 메시지를 차량 오디오로 출력하여 외부 사람들에게 알리도록 설정할 수 있다.If the driving prediction unit 193 determines that there is an emergency situation in which normal driving operations such as difficulty breathing or unconsciousness have occurred as a result of analyzing the driver's vital signs, turn on the emergency light of the vehicle being driven, reduce the speed, and adjust the stop position. Convert it to a safe driving mode that you know and drive. In the embodiment, the process of converting the safe driving mode to the safe driving mode varies depending on the road on which the vehicle is driving, such as a highway, a general road, and an alley, and the conversion process to the safe driving mode may also vary depending on the driving position of the vehicle. . For example, in the safe driving mode, when an emergency situation occurs while driving in an urban area and is converted to a safe driving mode, turn on the emergency light, determine the deceleration and stop position, and output an emergency message to outside people by outputting the vehicle audio. I can.

또한 주행예측부(193)는 운전자의 호흡주기가 기 저장된 기준 주기보다 일정 비율 미만으로 떨어지는 경우, 운전자의 동공이 일정시간 이상 인식되지 않는 경우, 핸들조작 및 터치가 일정시간 이상 감지되지 않는 경우 및 운전자의 뇌파가 정상 뇌파와 일정수준 이상 차이를 보이는 경우 등을 응급상황발생 판단 조건으로 설정할 수 있다.In addition, when the driver's breathing cycle falls below a certain ratio than the previously stored reference period, the driving prediction unit 193 is configured when the driver's pupil is not recognized for a predetermined period of time or longer, and steering and touch are not detected for a predetermined time or longer. When the driver's EEG shows a difference of more than a certain level from the normal EEG, it can be set as a condition for determining an emergency situation.

실시예에 따른 심박수, 호흡주기, 뇌파, 동공감지를 포함하는 운전자의 생체신호 분석을 통해 운전 중 안전사고위험이 높은 긴급상황이나 응급상황이 발생한 것을 파악하여, 운전자가 정상적인 운전조작이 불가능한 응급상황이 발생한 경우, 안전하게 차량을 비상도로에 정차 시킬 수 있도록 하여 갑작스러운 응급상황에서도 안전운전을 가능하게 한다.An emergency situation in which a driver's normal driving operation is impossible through an analysis of the driver's bio-signals including heart rate, respiratory cycle, brain wave, and pupil detection according to an embodiment to identify an emergency or emergency situation with a high risk of safety accident while driving. When this occurs, the vehicle can be safely stopped on the emergency road, enabling safe driving even in sudden emergencies.

실시예에 따른 자율주행차량의 운전자 응급상황발생판단에 기반한 주행예측 및 안전주행시스템은 자율주행차량의 주행예측 시스템은 인접차량의 위험주행 가능성을 예측하고, 전방차량의 주행변화를 예측하여 자율주행의 안전성을 향상 시킬 수 있다. 또한, 도로 위험도와 실시간 교통량을 파악하여 주행시간이 가장 적은 최적경로를 산출하고 최적경로에서 보다 안전한 자율주행이 가능하도록 한다.The driving prediction and safety driving system based on the driver's emergency situation determination of the autonomous vehicle according to the embodiment, the driving prediction system of the autonomous vehicle predicts the possibility of dangerous driving of the adjacent vehicle, and predicts the driving change of the vehicle in front of the vehicle. Can improve the safety of. In addition, by grasping road risk and real-time traffic volume, the optimal route with the least driving time is calculated and safer autonomous driving is possible on the optimum route.

개시된 내용은 예시에 불과하며, 특허청구범위에서 청구하는 청구의 요지를 벗어나지 않고 당해 기술분야에서 통상의 지식을 가진 자에 의하여 다양하게 변경 실시될 수 있으므로, 개시된 내용의 보호범위는 상술한 특정의 실시예에 한정되지 않는다.The disclosed contents are only examples, and various changes can be made by those of ordinary skill in the art without departing from the gist of the claims claimed in the claims, so the scope of protection of the disclosed contents is It is not limited to the examples.

Claims (8)

자율주행차량의 주행예측시스템에 있어서,
주행하는 차량에 인접한 차량을 감지하고, 인접 차량들과의 거리에 따라 속도 및 속도 변화량을 조정하는 가속제어모듈;
주행중인 차량이 위치한 지역의 도로상황과 위치 정보에 따라 충돌 예측정보 및 사고위험정보를 생성하는 안전주행모듈;
주행차량의 현재 위치와 목적지 입력정보, 지역별 교통 정보를 기반으로 주행경로를 생성하는 경로생성모듈; 및
전방차량, 후방차량 및 인접한 차선을 주행하는 주변객체를 인식하고, 상기 주변객체에서 전송하는 운전신호와 주행경로정보를 수신하고 주변객체의 속도를 감지하여 주변객체들의 주행을 예측하고, 상기 예측된 주행정보에 따라 차량의 자율주행을 조정하는 주행예측모듈; 을 포함하고
상기 주행예측모듈은
차량 내부에 설치된 생체신호 감지 센서로부터 운전자의 호흡주기를 포함하는 운전자생체 신호를 파악하여 응급상황 발생여부를 판단하고, 응급상황이 발생된 것으로 파악한 경우, 안전주행모드로 자동전환하고
상기 안전주행모드는
차량 센서에 의해 응급상황으로 판단된 경우, 비상등 점등 후 비상도로에 주행차량을 정차시키는 자율주행을 수행하고,
상기 주행예측모듈은
차량 센서로부터 전달된 예측상황에 따라 일반도로, 고속도로, 골목길주행을 포함하는 주행중인 도로의 종류와 상태에 기반하여 긴급주차 및 비상주차를 위한 자율주행제어를 수행하고
응급상황 발생 판단 후 차량이 고속도로를 주행중인 경우, 고속도로의 규정속도로 주행하면서 비상도로로 차량이 근접하도록 하고, 비상도로 인접 차선에 진입하면 차량 속도를 감속하고 비상도로에 안전정차 하도록 자율주행 차량을 제어하고,
상기 주행예측모듈은
차량이 도심의 일반도로를 주행 중일 때는 일반도로주행모드가 운용되어, 신호등 유무 및 전방객체와 근접객체의 상대속도 파악을 수행하며 차량이 고속도로를 주행중인 경우에는 고속도로주행모드가 운용되어, 고속도로 주행모드는 주행중인 고속도로의 규정속도와 전방차량과의 거리 및 도로의 차량 분포도를 일차적으로 파악하여 자율 주행하고,
상기 주행예측모듈은
전후방차량 및 근접차선 차량의 실시간 위치, 주행신호, 인접차량의 상대속도와 상대위치를 파악하여 전방차량의 주행 변화를 예측하기 위해, 전방차량의 상대속도를 통해 급정거 확률 및 근접차량들이 끼어들기를 진행할 확률을 산출하고, 안전사고 발생 위험이 있는 주변 차량의 주행상태 변화를 모니터링하고,
자율주행 차량이 외곽지역의 갓길에서 응급상황 발생을 판단한 경우, 상기 주행예측모듈은 차량을 정차시킨 후 구조기관으로 차량 위치정보를 전달하고, 운전자 스마트 단말과 연동하여 스마트 단말에 기 저장된 긴급연락처로 차량 위치정보와 응급상황 발생 정보를 전송하고,
차량에 설치된 센서를 통해 응급상황발생조건을 중복하여 만족시키는 경우 응급상황 발생으로 판단하고 자율주행차량을 안전주행모드로 진입시키고,
응급상황이 발생한 것으로 판단된 경우, 차량 속도를 감속시키고, 비상등을 켠 후 정차 위치를 탐색하고, 정차위치가 탐색되면 정차위치로 차량을 안전하게 정차시키도록 제어하고,
상기 응급상황발생조건은 운전자의 동공이 일정시간 이상 감지되지 않거나, 운전자의 호흡주기와 뇌파가 정상수치에서 일정비율 이상 차이 나거나, 핸들조작 및 터치신호가 일정시간 이상 입력되지 않는 경우, 운전자의 호흡주기가 기 저장된 기준 주기보다 일정 비율 미만으로 떨어지는 경우, 운전자의 동공이 일정시간 이상 인식되지 않는 경우, 핸들조작 및 터치가 일정시간 이상 감지되지 않는 경우 및 운전자의 뇌파가 정상 뇌파와 일정수준 이상 차이를 보이는 경우를 포함하고,
상기 생체신호 감지센서는
공기밀도감지센서, 생체신호감지센서, 운전자호흡감지센서, 운전자 뇌졸증 발생 감지센서를 포함하고, 상기 센서들로부터 수집된 운전자 생체신호를 통해 운전자 응급상황발생 여부를 파악하고
상기 가속제어모듈; 은
주행중인 차량과 인접한 차량의 상대속도 및 인접한 차량과 주행중인 차량의 거리와 주행방향에 따라 속도 및 속도변화량을 조정하고, 전방차량의 좌회전신호, 우회전신호를 포함하는 주행신호에 따라 차량 속도를 조정하고
상기 안전주행모듈; 은
상기 주행중인 차량이 위치한 지역의 도로위험도, 날씨정보 및 교통사고 누적 량을 이용하여 사고 위험도를 산출하여, 상기 산출된 사고 위험도에 따라 주행 최대속도를 설정하고
상기 경로생성모듈; 은
차량의 현재위치와 목적지를 파악하고, 실시간 교통상황을 인식 한 후 목적지에 도달하는 복수개의 주행경로를 생성하고, 상기 생성된 주행경로 중 최단시간으로 도착 가능한 최적경로를 선택하여, 상기 선택된 최적경로를 기반으로 주행경로를 생성하고
상기 주행예측 시스템은
무인자동차는 등록되지 않은 환경이나 조건이 변한 상황에서도 경로를 유지하도록, 센서 입력에 따라 지도를 자동 갱신하고, 주행과 감속을 조정하며, 자동차 간 거리를 자동으로 조절하는 것을 특징으로 하는 자율주행 차량의 주행예측 시스템.
In the driving prediction system of an autonomous vehicle,
An acceleration control module that detects a vehicle adjacent to a driving vehicle and adjusts a speed and a change in speed according to a distance between the adjacent vehicles;
A safe driving module that generates collision prediction information and accident risk information according to road conditions and location information of an area in which the driving vehicle is located;
A route generation module for generating a driving route based on the current location and destination input information of the driving vehicle, and regional traffic information; And
Recognizes a vehicle in front, a vehicle in the rear, and surrounding objects traveling in an adjacent lane, receives driving signals and driving route information transmitted from the surrounding objects, detects the speed of the surrounding objects, and predicts the driving of surrounding objects, and the predicted A driving prediction module for adjusting autonomous driving of the vehicle according to driving information; Including
The driving prediction module
It determines whether an emergency situation has occurred by grasping the driver's biological signal including the driver's breathing cycle from the bio-signal detection sensor installed inside the vehicle, and automatically switches to the safe driving mode when it is determined that an emergency situation has occurred.
The safe driving mode is
When it is judged as an emergency situation by the vehicle sensor, autonomous driving is performed to stop the driving vehicle on the emergency road after the emergency light is turned on,
The driving prediction module
It performs autonomous driving control for emergency parking and emergency parking based on the type and condition of the road being driven, including general roads, highways, and alleyways, according to the predicted situation transmitted from the vehicle sensor.
If the vehicle is driving on the highway after determining the occurrence of an emergency, make sure that the vehicle approaches the emergency road while driving at the regulated speed of the highway, and when entering the lane adjacent to the emergency road, the vehicle slows down and stops the vehicle safely on the emergency road. Control,
The driving prediction module
When a vehicle is driving on a general road in the city, the general road driving mode is operated to determine the presence or absence of a traffic light and the relative speed of the front object and the adjacent object. When the vehicle is driving on a highway, the highway driving mode is operated and the highway is driven. The mode is to drive autonomously by first grasping the regulated speed of the highway being driven, the distance to the vehicle in front, and the vehicle distribution of the road.
The driving prediction module
In order to predict the driving change of the vehicle in front by grasping the real-time position, driving signal, and relative speed and relative position of front and rear vehicles and vehicles in the adjacent lane, the probability of sudden stop and intervening vehicles are determined by Calculate the probability of proceeding, monitor changes in driving conditions of nearby vehicles at risk of safety accidents,
When the autonomous vehicle determines that an emergency situation occurs on the shoulder of an outlying area, the driving prediction module stops the vehicle and transmits the vehicle location information to the rescue agency, and interlocks with the driver's smart terminal to an emergency contact previously stored in the smart terminal. Transmit vehicle location information and emergency situation information,
If an emergency condition occurs through a sensor installed in the vehicle, it is judged as an emergency condition and the autonomous vehicle enters the safe driving mode.
When it is determined that an emergency situation has occurred, the vehicle speed is reduced, the emergency light is turned on, and the stopping position is searched, and when the stopping position is detected, the vehicle is controlled to stop safely at the stopping position,
The emergency condition occurs when the driver's pupils are not detected for a certain period of time or longer, the driver's breathing cycle and brain waves differ by more than a certain percentage from the normal value, or the steering wheel manipulation and touch signals are not input for a certain time or longer, the driver's breathing. If the cycle falls below a certain percentage of the previously stored reference cycle, the driver's pupil is not recognized for a certain period of time or longer, the steering wheel operation and touch are not detected for a certain period of time, and the driver's EEG differs from the normal EEG by more than a certain level. Including the case where is shown,
The biological signal detection sensor
It includes an air density sensor, a bio-signal sensor, a driver's breathing sensor, and a driver's stroke detection sensor, and identifies whether a driver has an emergency situation through the driver's bio-signals collected from the sensors.
The acceleration control module; silver
Adjusts the speed and the amount of change in speed according to the relative speed of the vehicle being driven and the vehicle adjacent to the vehicle being driven and the distance and driving direction of the vehicle being driven and the vehicle in front, and adjusts the vehicle speed according to the driving signals including the left and right turn signals and
The safe driving module; silver
The accident risk is calculated using the road risk, weather information, and the accumulated amount of traffic accidents in the area where the driving vehicle is located, and the maximum driving speed is set according to the calculated accident risk.
The path generation module; silver
After recognizing the current location and destination of the vehicle, recognizing real-time traffic conditions, generating a plurality of driving routes to reach the destination, selecting the optimal route that can arrive in the shortest time among the generated driving routes, and selecting the selected optimal route. Create a driving route based on
The driving prediction system
A driverless vehicle is an autonomous vehicle characterized in that it automatically updates the map according to sensor input, adjusts driving and deceleration, and automatically adjusts the distance between vehicles so that the route is maintained even in an unregistered environment or condition changed. Of driving prediction system.
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