• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.409-411
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• 2018

자율주행차에서 경로계획기법은 지도, 목적지 경로와 다른 정적/동적 장애물에 대한 예측 정보를 바탕으로, 안전하고, 합법적이며 효율적으로 차량을 조종하는 목표를 가진다. 고속도로 환경에서, 차량이 차선을 유지하고, 다른 차량들과 충돌을 회피하며, 더 느리게 움직이는 트래픽을 지나쳐 효율적이면서 안전한 경로를 생성하는 기법이 요구된다. 본 연구에서는, 시스템의 행위를 모델링하는 기법 중의 하나인 유한상태기계를 적용하였다. 시뮬레이터를 통해, 급가속/감속과, 충돌 없이, 차선을 유지/변경을 힐 수 있음을 보였다. 자율주행차의 고속도로 주행의 경우, 유한상태기계를 적용하여, 효율적이고 안전한 경로계획을 수행할 수 있다.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.16-22
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• 2011

This paper presents the potential of in-vehicle data recorder system for monitoring aggressive driving patterns and providing feedback to drivers on their on road behaviour. This system can detect 10 risky types of drivers' driving patterns such as aggressive lane change, sudden brakes and turns with acceleration etc. Vehicle dynamics simulation and vehicle road test have been performed in order to develop driving pattern recognition algorithms. Recorder systems are installed to 50 buses in a single company. Drivers' driving behaviour are monitored for 1 month. The drivers' risky driving data collected by the system are analyzed. Aggressive lane change in 50km/h below is a cause in overwhelming majority of risky driving pattern.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.