• Journal of Korean Society of Transportation
• /
• v.36 no.1
• /
• pp.38-50
• /
• 2018

Emerging automated driving environments will lead to a mixed traffic flow depending on the interaction between automated vehicles (AVs) and manually driven vehicles (MVs) because the market penetration rate (MPR) of AVs will gradually increase over time. Understanding the characteristics of mixed traffic conditions, and developing a method to control both AV and MV maneuverings smoothly is a backbone of the traffic management in the era of automated driving. To facilitate smooth vehicle interactions, the maneuvering of AVs should be properly determined by various traffic and road conditions, which motivates this study. This study investigated whether the aggressiveness of AV maneuvering, defined as automated driving aggressiveness (ADA), affect the performance of mixed traffic flow. VISSIM microscopic simulation experiments were conducted to derive proper ADAs for satisfying both the traffic safety and the operational efficiency. Traffic conflict rates and average travel speeds were used as indicators for the performance of safety and operations. While conducting simulations, level of service(LOS) and market penetration rate(MPR) of AVs were also taken into considerations. Results implies that an effective guideline to manage the ADA under various traffic and road conditions needs to be developed from the perspective of traffic operations to optimize traffic performances.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.3 no.2
• /
• pp.1-15
• /
• 1995

The driving patterns in Seoul metropolitan area were surveyed in an experiment involving 1,212km of driving along seventeen representative routes. The speed and fuel consumption data were recorded and the influence of driving patterns on vehicle fuel economy was analyzed by statistical techniques. The results showed that characteristics of driving in Seoul metropolitan area are far different from that of CVS-75 mode and then on-road fuel economy in Seoul may be small as compared with that of CVS-75 mode. Finally, it was proposed that CVS-75 mode fuel economy should be modified by applying adjustment factor to represent actual on-road fuel economy.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2011.01a
• /
• pp.101-104
• /
• 2011

본 논문에서는 레이저 센서를 장착한 자율 이동 로봇을 이용하여 실내에서 장애물들을 검출한다. 데이터에서 나오는 패턴을 인식해 사람과 정적 장애물을 실시간으로 구분한 후 사람의 속도와 로봇의 속도를 각각 비교하여 따로 지정해준 안전거리를 유지하며 주행한다. 예상치 못한 상황이 발생될 것을 대비해 로봇의 전방에 범퍼 센서를 장착하여 안전성을 고려하였다. 로봇의 자기 위치 인식을 위해 StarGazer센서를 이용하였다. 패턴은 레이저 센서 데이터의 거리, 각 값을 이용하여 다리 패턴의 너비를 구하고 너비의 가운데 점을 중심점으로 지정해 추적하며 구동하는 알고리즘을 구현하였다.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.41 no.10
• /
• pp.991-996
• /
• 2017

Gait stability is partly characterized by an extended stance phase that comprises 60 of the gait ％ cycle. In this study, a gait pattern was employed for a crank drive system that allows for stable lower limb kinematics during stand-up cycling. A quick return mechanism was applied to the crank system to allow for a slow rotation of the crank during the stance phase and for a quick return during the swing phase. Design parameters for the quick return crank mechanism were defined, and kinematic simulations were performed to understand the behavior of the mechanism. To evaluate the design, an experimental instrument was fabricated, and the cycling motion was analyzed. The results indicated that this new drive system can stabilize the center of mass of the user. This study can contribute to the development of a stand-up bicycle that allows for more comfortable leg kinematics.

• Journal of Korean Society of Transportation
• /
• v.27 no.2
• /
• pp.207-214
• /
• 2009

This study proposed an advanced warning information system based on real-time traffic conflict analysis. An algorithm to detect and analyze unsafe traffic events associated with car-following and lane-changes using individual vehicle trajectories was developed. A positive guidance procedure was adopted to provide warning information to alert drivers to hazardous traffic conditions derived from the outcomes of the algorithm. In addition, autoregressive integrated moving average (ARIMA) analyses were conducted to investigate the predictability of warning information for the enhancement of information reliability.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.4
• /
• pp.30-37
• /
• 2014

The Electronic Toll Collection System based on Free-Flow Multi-Lane is needed to solve some yearly problems such as traffic congestion, safety accident, maintenance by hold-up of one way hi-pass system. The hi-pass communication system is first-in, first-out method in one way environment so, it can't handle various running patterns such as vehicle platoon, switching lane and passing in multi-lane environment. In this thesis we compared and analyzed the communication system of foreign countries ETCS operating system and domestic hi-pass communication system, then studied communication system which can run in multiple-way accepting existing hi-pass OBU. And we formed the communication system of Free-Flow Multi-Lane environment as a plan using incoherent of IR antenna and coherent of RF antenna. The communication system in Free-Flow Multi-Lane environment can be used not alone in parking garages but in expressways of ETCS and toll roads from now on.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.05a
• /
• pp.211-214
• /
• 2000

This paper describes a lateral guidance system of an autonomous vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in simulations. As the neural network controller acquires magnetic field values(B$\sub$x/, B$\sub$y/, B$\sub$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the teaming pattern, learning itself, and the adequacy of the design controller. Also, the performance of the controller can be verified through simulation.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.19 no.5
• /
• pp.1-9
• /
• 2015

Recently Honam high-speed railroad line is constructed in southern part of Korea. This line is for next generation HST named HEMU-430X. But there is a limestone mine near this line and this mine will make a process to dig a passageway under the railway line. In this case, safety of railroad system and stability of mine are crucial problems on both sides. By measuring mine blasting vibration and calculating regression equation, effect of mine blasting to train running is investigated quantitatively. 0.5 kine (cm/sec) is applied as a management specification of vibration based on field measurement. In this study, changes of blasting patterns are suggested to control vibration of mine blasting. And the effect of train vibration to mine is also invesitigated by numerical analysis.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.07a
• /
• pp.260-263
• /
• 2010

본 논문에서는 주행 중인 차량에서 전방을 향해 장착된 카메라를 통해 입력된 영상에서 측면에 부분적으로 나타나는 차량을 검출하는 방법을 제안한다. 기존 연구에서는 모션 벡터를 이용하여 주변 배경과 관측되는 차량 사이의 모션 벡터 차이를 이용하여 측면 차량을 검출하고 있다. 그러나 모션 벡터를 이용할 경우 정지된 차량이나 전방에서 다가오는 차량의 경우 검출하기 어려운 점이 있다. 이러한 문제를 해결하기 위해 본 논문에서는 모션 벡터를 사용하지 않고 차량 측면 모습에서 특징 정보를 추출하여 SVM 분류기를 통해 측면 차량을 검출하는 방법을 제안한다. 차량 측면 모습의 특징을 뽑기 위해 영상의 밝기 변화에 강인한 국부 이진 패턴을 사용하였고 ROI영역 내에서 차량이 나타나는 위치에 상관없이 차량의 측면 모습을 찾아내기 위해 국부 이진 패턴의 히스토그램을 이용하였다. 실험결과에서는 제안하는 방법이 정지된 차량을 포함하여 88.5%의 정확도로 측면 차량을 검출하는 것을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.667-670
• /
• 2016

최근 철도는 다양한 분야에서 사용됨에 따라 그 중요성이 점차 증가하고 있다. 따라서 철도의 안전한 주행을 위하여 철도를 구성하는 요소 관리 역시 중요하다. 철도를 구성하는 요소 중 열차의 진행 방향을 결정하는 선로전환기의 비정상 상황 탐지는 열차의 탈선 등과 같은 대형 사고를 예방하기 위해 매우 중요한 문제이다. 본 논문에서는 Fast Shapelets 알고리즘을 이용하여 선로전환기의 전류 패턴을 분류할 수 있는 방법을 제안한다. 실험 결과, Fast Shapelets 알고리즘을 이용하여 선로전환기의 전류 패턴들을 자동으로 분류할 수 있음을 확인하였다.