서울시립대 박신형교수, 경기대 김정화교수, 명지대 박호철교수, 아주대 소재현교수, 계명대 권오훈교수, 카이스트 장기태 교수가 TUPA를 방문하여 교통전반에 대한 세미나를 하였습니다. 

코로나가 끝나면 더 많은 YSC가 모여서 연구교류를 했으면 좋겠습니다.

Congratulation my 7th PhD student, Bo Wang for the successful PhD final defense. 

The PhD title is “Short-term traffic state estimation and prediction based on spatiotemporal neural networks”

It has been a fantastic journey with you Bo. Good luck on your bright future step!!! I look forward to it!!!

축하합니다!!!

TUPA의 막둥이들 정재은, 유예지, 강지우, 박현철 학생이 2021 해양 수산 빅데이터 분석 경진대회에서 각각 대상, 최우수상, 우수상, 우수상을 수상하였습니다. 

대상: 해양수산부 장관상 – 정재은 (300만원)

최우수상: 한국해양교통안전공단 이사장상 – 유예지 (200만원)

우수상: 기술경영전문대학원장상 – 강지우(50만원)

우수상: 기술경영전문대학원장상 – 박현철 (50만원)

http://www.cstimes.com/news/articleView.html?idxno=474648

이 대회는 2021 해양수산 빅데이터 분석 경진대회는 해양수산부가 주최하고 울산항만공사와 UNIST 기술경영전문대학원이 공동 주관하여 참가자들은 2021년 8월 18일(수)부터 20일(금)까지 사전 교육을 받고, 20일(금)부터 23일(월)까지 비대면 방식으로 대회에 참여하였습니다.

이번 대회는 ‘해상물류 빅데이터 활용 활성화와 대학생의 창의적인 아이디어를 통한 신규 비즈니스 모델의 발굴 및 신규 부가가치 창출’을 주제로 펼쳐졌고, 참가 학생들은 공공기관과 민간기업의 데이터를 기반으로, 프로젝트를 수행하였습니다. 

수상자들은 추후 스마트 항만물류 지원센터의 창업지원 사업 지원 시 우대 특전도 받을 수 있게 되었습니다.

김인희 교수가 2021년도 우수신진연구 최초 혁신 실험실에 최종선정되었습니다. 연구재단으로 약 1억원이 지원될 예정이고 이로써 초기 연구실험실을 조기에 구축할수 있게 되었습니다.

TUPA연구실에 배치될 장비는 딥러닝용 서버 워크스테이션입니다. 

아래는 재원입니다.

김인희 교수는 사회시스템공학과 추연욱교수(PI), 김두기교수와 컴퓨터공학과 김정은교수와 함께 과학기술정보통신부가 주관하고 한국연구재단이 지원하는 2021년도 기초연구실 지원사업에 선정되었습니다.

이번 연구단은 2021년 6월부터 2024년 2월까지 33개월동안 13억 7천5천만원의 연구비를 지원받아 아래의 과제에 대해 연구를 진행하게 되었습니다.

과제명: 대심도 대공간 도시지하구조물의 데이터 융합 기반 지진재난 대응기술

Congratulation Wenhua!!

Wenhua has just passed the pre-submission today. She has made a fantastic progress since she joined Monash back in 2017. 

I hope your next journey waits for another superb moments with your partner!!!. Well done Wenhua. 

축하합니다.

우리 연구실 Zahra Nourmohammadi, 유예지 학생이 2021 춘계 ITS학술대회에서 “해양사고 예측을 위한 머신러닝 기법 적용방안 연구” 라는 주제로 우수논문상을 수상하였습니다.

모두 수고했습니다.!!

2021 춘계 ITS 학회가 2021년 4월 22~23일 강릉에서 개최되었습니다. 

• Zahra Nourmohammadi, 신대섭, 유예지, 김인희; 해양 사고 예측을 위한 머신러닝 기법 적용방안 연구
• Fatemeh Nourmohammadi, 노유나, 정재은, 김인희;국내 항적 데이터 시각화 및 밀도 연구
• 박솔은, 강지우, 김인희; 세종시 Super BRT 우선신호 효과 분석
• 권동현, 박현철, 이창희, 김인희; CNN-LSTM 모델 기반 RSE검지기 교통량 데이터 보정연구

프로그램 다운로드https://www.dropbox.com/s/3f18kda53uw1vfz/2021%20%EC%B6%98%EA%B3%84%ED%95%99%EC%88%A0%EB%8C%80%ED%9A%8C%20%EC%84%B8%EC%85%98%EB%B3%84%20%ED%94%84%EB%A1%9C%EA%B7%B8%EB%9E%A8.pdf?dl=0

Title: Personalized autonomous driving maneuver based on reinforcement learning

Abstract:

It is evident that road users will be changed from human-driven cars to automated-driven cars in the future by progressive research trends. However, the adoption of autonomous vehicles in our daily life is controversial in many different aspects since people have not ready to accept autonomous vehicles yet. Significant concerns are safety and reliability. According to Reuters[1], 67 percent of respondents considered that the safety standard of autonomous vehicle driving should be higher than conventional vehicle driving. Plus, on 2020 March, almost half of the Americans responded that they would never take autonomously driven cars, and 60% of respondents said they would have more trust in the auto-driven car if they understand how it works[2]. People tend to think that ten more years is required to believe automated driving system[2].
In order to answer the questions above, car manufacture such as Tesla, Hyundai, and so on have been putting so much effort to bring the autonomous vehicle era to us sooner [3, 4]. However, autonomous technology is still insufficient for being used in reality yet. The manufactures offer only driving assistance systems so far, such as the Advanced Driver Assistance System(ADAS), and recommend people to use the systems only on the highway. The primary reason for driving automatically in the urban area is complicated because of the lack of infrastructure for communication and plenty of components to consider for safe driving compared to highway[5-8].
When it comes to urban areas, the typical types of intersections can be categorized into a signalized and unsignalized intersections. In the case of the signalized intersection, traffic signal plays a role in maximizing the traffic flow efficiency under the premise of ensuring crossing priority. However, unsignalized intersection is a totally different story. Although fewer cars might cross the intersection than signalized one, the priority of crossing the intersection has to rely on each driver’s decision making since most countries outside of North America, and South Africa does not adjust stop sign rule[9]. Therefore, traffic at unsignalized intersections not only considers crossing the intersection safely but also needs to be react similarly to how humans drive. Many researchers have begun to focus on the study on unsignalized intersection control to give people trustworthy autonomous driving experience. Therefore, people would trust and ride autonomous vehicles in the future. In this study, the techniques that consider the human experience and make a car drive autonomously will be investigated.
When it comes to consider the human experience, human-in-the-loop(HITL) and Virtual Reality(VR) technology are implemented for this study. The HITL approach gets the spotlight due to the inability of a computer system to accurately accomplish tasks, which require human participation [10-12]. VR technology is prevalently used to offer a realistic experience to participants [13]. By integrating two techniques, it has the potential to carry out practical driving behavior datasets involving human experience.
In the case of getting an autonomously driven maneuver supervised(ref) and reinforcement learning(ref) have already been conducted. Among them, reinforcement learning(RL) has been paying attention due to its feature that the agent is able to do self-study cooperating with the environment. The remarkable benefit of reinforcement learning is that it could give a better solution that the human being even never thought about or show technique that very limited people could perform. In addition, in a computer science field, they have started considering putting human experience or human knowledge in the middle of the reinforcement learning procedure to find the optimized result quickly[14]. Likewise, the agent considering individual’s preference by human-in-the-loop technique has been used in a different area. For example, the fashion industry developed a personalized outfit recommendation system depending on each customer’s taste, StyleSnap service from Amazon. Therefore, it would be worthy of researching optimized autonomous driving maneuvor based on individual’s driving habit. The research gaps that can be filled are as follows: 

• Optimizing autonomous driving maneuver at Unsignalized intersection research is insufficient compared to research at the signalized intersection.
• Most of them do not consider human behavior and only focus on the efficiency of movement, but this study will consider people3’s characteristics.
• Training based on the personalized driving data is expected to shorten the time to reach the optimized value for reinforcement learning.
• It is possible to compare how realistic the trained model is by comparing the data of human driving in a virtual environment.