PURPOSES : Because a driving simulator typically focuses on analyzing a driver’s driving behavior, it is difficult to analyze the effect on the overall traffic flow. In contrast, traffic simulation can analyze traffic flow, that is, the interaction between vehicles; however, it has limitations in describing a driver’s driving behavior. Therefore, a method for integrating the simulator and traffic simulation was proposed. Information that could be controlled through driving experiments was used, and only the lane-change distance was considered so that a more natural driving behavior could be described in the traffic flow. METHODS : The simulated connection method proposed in this study was implemented under the assumption of specific traffic conditions. The driver’s lane-changing behavior (lane-changing distance, deceleration, and steering wheel) due to the occurrence of road debris was collected through a driving study. The lane-change distance was input as a parameter for the traffic simulation. Driving behavior and safety were compared between the basic traffic simulation setting, in which the driver's driving behavior information was not reflected, and the situation in which the driving simulator and traffic simulation were integrated. RESULTS : The number of conflicts between the traffic simulation default settings (Case 1) and the situation in which the driving simulator and traffic simulation were integrated (Case 2) was determined and compared for each analysis. The analysis revealed that the number of conflicts varied based on the level of service and road alignment of the analysis section. In addition, a statistical analysis was performed to verify the differences between the scenarios. There was a significant difference in the number of conflicts based on the level of service and road alignment. When analyzing a traffic simulation, it is necessary to replicate the driving behavior of the actual driver. CONCLUSIONS : We proposed an integration plan between the driving simulator and traffic simulation. This information can be used as fundamental data for the advancement of simulation integration methods.

자동차의 보급이 증가하고 삶의 질이 높아짐에 따라 자동차를 이용하는 운전자들의 안전에 대한 관심 도 높아지고 있다. 운전자들의 안전 확보 정도는 도로의 기하구조나 시설물의 상태 및 존재유무에 따라 달라지게 된다. 따라서 도로 설계기준이나 시설물의 설치기준 등은 운전자들의 주행행태를 고려하여 정립 되어야 한다. 각종 기준들이 운전자에게 적당한지를 판단하기 위한 방법 중 한 가지는 피험자를 활용하여 해당 기준이 적용된 환경을 구현하여 주행실험을 수행하는 것이다. 실험을 통해 분석된 운전자들의 평균 적인 주행행태는 피험자들이 많을수록 그 신뢰도가 높아지게 된다. 운전자의 주행행태를 정확하게 파악하 기 위해서는 운전자들의 주행정보가 정량적으로 도출되고, 허용범위 내에서 충분히 많은 피험자를 활용하 여야 한다. 이에 본 연구는 실험환경에서 운전자들의 주행행태를 파악하고, 인력과 시간소요를 최소화 할 수 있는 차량 정보 수집장비를 개발하였다. 본 프로그램은 LabVIEW를 이용하여 Windows7 환경에서 제작되었으며 차량 내에서 제공하는 CAN통 신을 이용하여 차량으로부터 수집되는 주행속도, 주행시간 등의 데이터를 실시간으로 디스플레이 할 수 있고 데이터의 저장이 가능도록 설계되었다. 그림 1은 본 프로그램의 메인화면으로써, 왼쪽에 위치한 그래프는 실시간으로 차량의 속도와 주행시간 을 표출한다. 본 프로그램은 최초 설정 시 미리 피험자의 실험순서와 정보를 입력하면 장비운영자가 없어 도 지속적인 실험이 가능하도록 제작하여 장비를 조작하는데 소요되는 시간과 피험자외의 인력을 최소화 할 수 있는데 개발의 의의가 있다. 본 프로그램 개발의 주 활용용도는 특정 환경에서 운전자들의 인지반응시간을 측정하는 것이다. 특정 이벤트 발생 시 운전자는 미리 설정한 행동을 하고 본 프로그램은 운전자의 주행행태를 분석한다. 예를들 어 도로주행 중 전방에 장애물이 존재할 때 운전자가 방향지시등을 점등하고 차선을 바꾸게 되면 본 프로 그램은 운전자가 장애물을 인지한 시간 및 거리를 분석하게 된다. 뿐만 아니라 본 프로그램은 모든 피험 자의 실험이 끝나고 나면 피험자들의 평균적인 주행행태를 계산한다. 본 프로그램은 향후 특정 시설물 혹 은 환경을 평가함에 있어 운전자들의 주행행태의 정량적인 수치를 얻어 평가의 타당성을 높여주고 주행실 험에 있어 인력과 시간의 소요를 줄이는 이점이 있을 것으로 판단된다.

PURPOSES : The use of virtual driving tests to determine actual road driving behavior is increasing. However, the results indicate a gap between real and virtual driving under same road conditions road based on ergonomic factors, such as anxiety and speed. In the future, the use of virtual driving tests is expected to increase. For this reason, the purpose of this study is to analyze the gap between real and virtual driving on same road conditions and to use a calibration formula to allow for higher reliability of virtual driving tests. METHODS : An intelligent driving recorder was used to capture real driving. A driving simulator was used to record virtual driving. Additionally, a virtual driving map was made with the UC-Win/Road software. We gathered data including geometric structure information, driving information, driver information, and road operation information for real driving and virtual driving on the same road conditions. In this study we investigated a range of gaps, driving speeds, and lateral positions, and introduced a calibration formula to the virtual record to achieve the same record as the real driving situation by applying the effects of the main causes of discrepancy between the two (driving speed and lateral position) using a linear regression model. RESULTS: In the virtual driving test, driving speed and lateral position were determined to be higher and bigger than in the real Driving test, respectively. Additionally, the virtual driving test reduces the concentration, anxiety, and reality when compared to the real driving test. The formula includes four variables to produce the calibration: tangent driving speed, curve driving speed, tangent lateral position, and curve lateral position. However, the tangent lateral position was excluded because it was not statistically significant . CONCLUSIONS: The results of analyzing the formula from MPB (mean prediction bias), MAD (mean absolute deviation) is after applying the formula to the virtual driving test, similar to the real driving test so that the formula works. Because this study was conducted on a national, two-way road, the road speed limit was 80 km/h, and the lane width was 3.0-3.5 m. It works in the same condition road restrictively.