• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2018년도 춘계학술대회
• /
• pp.229-231
• /
• 2018

자율주행 차량 기술이 점차 발전해 오면서 점차 산업 현장 및 사고 현장과 같이 인명하고가 많이 일어나는 분야에 자율주행 시스템을 도입한 로봇으로 대처를 많이 해 오고 있다. 이러한 이유로 자율주행시스템을 탑재한 무인이송장치는 사람의 접근이 어려운 유해환경 등에 많이 이용된다. 또한 자율주행 시스템의 도입은 산업현장과 같이 정신없이 움직이는 환경 속에서 일어나는 충돌 사고 및 인명피해를 줄이고, 효율성 있는 업무처리를 도와준다. 또한 자율주행 차량끼리 매인서버로 차량별 주변 환경을 전송하여 매인서버에서 이를 통재하면 더욱 넓은 지역에서 보다 안전하고 신속한 업무처리가 가능하다. 본 논문에서는 자율주행이 가능한 무인지게차 시스템에 대한 V2X 통신을 활용함으로써, 보다 넓은 지역의 지게차들을 통재하여 산업 업무량을 높이며, 인명피해와 재산피해를 줄일 수 있다.

• 한국전자통신학회논문지
• /
• 제8권2호
• /
• pp.329-337
• /
• 2013

본 논문에서는 자율 주행 자동차의 연구 방향을 결정하기 위한 기관별 개인별 논문 분석을 통한 계량정보 분석을 검토한다. 기관별 개인별 논문 수 분석, 수준 분석, 국제협력연구 네트워크 분석, 핵심 기관과 개인의 Q-L분포를 이용하여 자율 주행 자동차의 연구 동향을 확인한다.

• 한국전자통신학회논문지
• /
• 제8권2호
• /
• pp.321-328
• /
• 2013

본 논문에서는 자율 주행 자동차의 연구 방향을 결정하기 위한 국가별 논문 분석을 통한 계량정보 분석을 수행한다. 이를 위해 국가별 수준 분석, 국제협력연구 네트워크 분석을 통하여 자율 주행 자동차의 연구 동향을 확인한다.

• 한국ITS학회 논문지
• /
• 제21권1호
• /
• pp.258-272
• /
• 2022

안전한 자율주행을 위하여 차량 센서에만 의존하는 Stand-alone 방식의 한계를 극복하기 위한 방법으로, 노변의 인프라와 자율주행차간 정보를 교환하는 '자율협력주행' 방식의 기술 개발이 이루어지고 있다. 이 과정에서 협력의 대상이 되는 동적정보는 통신 데이터 손실 측면의 평가방법이 일반적이지만, 정보로서 역할 중심의 평가방법이 필요하다. 본 연구에서는 자율협력주행에서 동적정보 서비스 역할의 적정성을 평가하기 위하여 역할 기반 평가방법을 제안하였다. 평가 척도로 검출률, 검출 소요시간, LDM 처리시간을 제안하였고, 평가방법론을 검증하기 위하여 실제 도로에서 보행자 정보를 대상으로 실증 실험을 시행하였다. 실험 결과로는 검출률 99%, 소요시간 200ms/건, 처리시간 19ms/건을 얻었다. 향후 제안된 동적정보 서비스 평가 방법이 관련 정보제공 서비스의 평가에 활용되기를 기대한다.

• 인터넷정보학회논문지
• /
• 제24권1호
• /
• pp.39-47
• /
• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
• /
• pp.274-281
• /
• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• International journal of advanced smart convergence
• /
• 제10권4호
• /
• pp.263-272
• /
• 2021

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

• 자동차안전학회지
• /
• 제9권2호
• /
• pp.6-12
• /
• 2017

An autonomous driving system based on a precise digital map is developed. The system is implemented to the Hyundai's Tucsan fuel cell car, which has a camera, smart cruise control (SCC) and Blind spot detection (BSD) radars, 4-Layer LiDARs, and a standard GPS module. The precise digital map has various information such as lanes, speed bumps, crosswalks and land marks, etc. They can be distinguished as lane-level. The system fuses sensed data around the vehicle for localization and estimates the vehicle's location in the precise map. Objects around the vehicle are detected by the sensor fusion system. Collision threat assessment is performed by detecting dangerous vehicles on the precise map. When an obstacle is on the driving path, the system estimates time to collision and slow down the speed. The vehicle has driven autonomously in the Hyundai-Kia Namyang Research Center.

• 자동차안전학회지
• /
• 제11권3호
• /
• pp.30-36
• /
• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• ETRI Journal
• /
• 제41권4호
• /
• pp.506-514
• /
• 2019

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three-lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.