• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.107-114
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• 2022

In this paper, proposes a delivery robot that can be autonomous driving learning. The proposed robot is designed to be used in park-type apartments without ground parking facilities. Compared to the existing apartments with complex ground and underground routes, park-type apartments have a standardized movement path, allowing the robot to run stably, making it suitable for students' initial education environment. The delivery robot is configured to enable delivery of parcels through machine learning technology for route learning and autonomous driving using cameras and LiDAR sensors. In addition, the control MCU was designed by separating it into three parts to enable learning by level, and it was confirmed that it can be used as a delivery robot for learning through operation tests such as autonomous driving and obstacle recognition. In the future, we plan to develop it into an educational delivery robot for various delivery services by linking with the precision indoor location information recognition technology and the public technology platform of the apartment.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.65-70
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• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.138-144
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• 2020

Currently, an autonomous vehicle studies are working to develop a four-level autonomous vehicle that can cope with emergencies. In order to flexibly respond to an emergency, the autonomous vehicle must move in a direction to minimize the damage, which must be conducted by judging all the states of the road, such as the surrounding pedestrians, road conditions, and surrounding vehicle conditions. Therefore, in this paper, we suggest a passenger detection and sharing system to detect the passenger situation inside the autonomous vehicle and share it with V2V to the surrounding vehicles to assist in the operation of the autonomous vehicle. Passenger detection and sharing system improve the weighting method that recognizes passengers in the current vehicle to identify the passenger's position accurately inside the vehicle, and shares the passenger's position of each vehicle with other vehicles around it in case of emergency. So, it can help determine the driving of a vehicle. As a result of the experiment, the body pressure sensor applied to the passenger recognition sub-module showed about 8% higher accuracy than the conventional resonant sensor and about 17% higher than the piezoelectric sensor.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.253-273
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• 2022

Automated vehicles rely on information collected through sensors to drive. Therefore, the uncertainty of the information collected from a sensor is an important to address. To this end, research is conducted in the field of road and traffic to solve the uncertainty of these sensors through infrastructure or facilities. Therefore, this study developed a traffic cone that can maintaing the gaze guidance function in the construction site by securing sufficient LiDAR detection performance even in rainy conditions and verified its improvement effect through demonstration. Two types of cones were manufactured, a cross-type and a flat-type, to increase the reflective performance compared to an existing cone. The demonstration confirms that the flat-type traffic cone has better detection performance than an existing cone, even in 50 mm/h rainfall, which affects a driver's field of vision. In addition, it was confirmed that the detection level on a clear day was maintained at the 20 mm/h rain for both cones. In the future, improvement measures should be developed so that the traffic cones, that can improve the safety of automated driving, can be applied.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.15-23
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• 2020

Automated driving car is drawing attention as a seminal service representing 4th industrial revolution era based on 5G network, AI, IOT and sensor technology. automated driving car is expected to evolve into the final level which does not require driver's input. Drivers are able to consume new additional time in private space. Many industries started to compete to control these time and space. Media industry is expecting quite big change due to the introduction of automated driving cars. This research examines the impact of the media industry and social & institutional issues of automated driving cars based on depth interviews of experts. The introduction of automated driving cars is giving new opportunity for media industry as contents provider. Telcos and IT corporations are expected to compete each other to get the control of infotainment systems of automated driving cars. The reform of current regulations regarding car driving is pointed as important task to protect private information and the introduction of automated driving cars.

• Journal of Sensor Science and Technology
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• v.12 no.5
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• pp.211-217
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• 2003

This paper is proposed that roadway recognition performance improvement for autonomous vehicle using magnetic markers that are embedded along the road center and the sensors mounted on a vehicle, and which changing of magnetic field that is measured along with vehicle driving. For Retrenchment of equipment cost, interval of markers is more expensive than existing method. In order to this, This paper is proposed that interval of markers is founded using magnetic field analysis, and which arrangement method of six magnetic sensors and control method of neural network. This paper is carried out magnetic field analysis, the acquiring of the training patterns, the training of the neural network and composition of steering control, and is verified that roadway recognition performance can improve using computer simulation with proposed methods.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2010.10a
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• pp.1-3
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• 2010

수중 무인항체(Autonomous Underwater Vehicle, AUV)를 고정밀, 고위험 임무수행 분야에 이용하기 위해서는 연속적이고 정확한 항법정보를 제공하는 기술이 반드시 필요하다. 특히, 최근에는 항공분야에서 국내외적으로 연속적이고 정확한 항법정보를 제공하기 위하여 여러 가지 센서를 결합한 통합 항법시스템에 관한 연구가 활발하며, GPS나 음향장치를 관성센서와 통합하는 방법이 대표적이다. 하지만 수중 무인항체에 경우는 해수면 노출로 인한 탐사시간 장기화와 음향장치 설치 및 회수의 한계로 인하여 GPS나 음향장치 이외에 센서를 이용한 통합 항법시스템의 필요성이 커지고 있다. 본 논문에서는 자율성이 높으면서, 적은 비용으로 설치가 가능한 영상센서를 이용하여 항법성능을 효과적으로 증대시키는 Vision/INS 통합 항법을 제안한다. 제안한 통합 항법알고리즘은 외부표정요소 직접결정기법을 이용하여 영상 데이터로부터 항체의 위치와 자세를 추정하고, 추정된 결과를 INS의 추정치와 비교한다. 그리고 추정한 위치와 자세오차를 입력으로 칼만필터를 구동하도록 설계하였다. 모의실험을 통해 제안한 방법의 유효성을 확인하였다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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• pp.160-163
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• 2009

본 연구는 생리신호에 의한 집중된 시선과 집중하지 않는 시선을 분류하고자 한다. 이를 검증하기 위해 시각적으로 높은 집중과 낮은 집중을 요구하는 두가지 과제를 피실험자에게 제시하고 PPG(Photoplethysmogram), GSR(Galvanic Skin Response) 그리고 SKT(Skin Temperature)센서를 사용한 자율신경계 반응과 시선 움직임을 측정하였다. 과제는 $3{\times}3$으로 화면 구역을 나누고 각 구역에 문자를 제시하고 역방향 문자를 찾도록 하였다. 실험에는 20 명의 대학생이 참여하였으며, 1 번의 실험에 12 종류의 다른 문자배열을 제시 받았으며 1 번의 연습을 포함하여 총 5 회 실시후 데이터를 분석하였다. 높은 집중일 경우와 낮은 집중일 경우를 T-test 분석 결과, 자율신경계에서는 높은 집중일 경우 PPG 주파수가 증가하고 GSR과 SKT는 감소한 결과를 보였다. 따라서 시선의 집중도에 따라 다른 자율신경계 반응과 시선반응을 보이는 것을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.843-845
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• 2012

전기자동차 또는 로봇의 자율 주행에 필요한 여러 가지 기술 가운데 위치 인식과 진행 방향을 결정하는 외부 환경인지 능력은 매우 중요하다. 본 논문에서는 GPS 수신 장치와 각종센서를 내장한 안드로이드 폰을 활용하여 자율 운행 중인 골프카트의 위치 정보를 획득하고 도로이탈 여부를 판별할 수 있는 시뮬레이션 시스템을 개발한다.