Driving safety of a semi-trailer is greatly reduced when driving in a section with a narrow turning radius, so a dynamic study of driving and road conditions is required. In this study, the driving stability of the semi-trailer was investigated using the RecurDyn program in consideration of the velocity and weight of the semi-trailer in the entrance curve section of the highway, and the turning angle and radius of the curved road. In order to select the model and analysis conditions according to the road type, the sloping curved road was modeled by selecting the curvature, entry length, height difference, and entrance angle of the curved section. From the analysis results, the higher the semi-trailer's entrance velocity, the heavier the weight, the narrower the entrance angle of the curved road, and the smaller the curvature, the greater the semi-trailer's maximum running angular speed which had an effect on driving stability.

본 논문은 인체에 영향을 미치는 진동에 따라 도로노면의 주행성을 정량화하고 향후 공용중인 도로 및 신설도로의 주행 안전성 평가에 적용할 수 있는 방법론을 정립하는 것이 주요 목적이다. 이를 위해 평탄성 및 인체 피로도 관련 조사를 수행하여 이론적 근거를 수립하였고 다양한 고속도로 표본 구간을 선정하여 차량 내 3축 가속도를 측정하였다. 도로 상태별로 ISO-2631에 준한 주파수별 가속도 값을 분류화함으로써 노면상태에 따른 인체에 미치는 진동 가속도 수준을 정량화할 수 있었다. 연구 결과, 전반적으로 인체에 미치는 피로도는 IRI가 높은 콘크리트 포장이 크게 유발시키는 것으로 나타났으며, SMA 포장과 다이아몬드그라인딩이 적용된 콘크리트 포장은 상대적으로 피로도가 낮은 것으로 나타났다.

Methods for measuring or estimating of ground shape by a laser range finder and a vision sensor(exteroceptive sensors) have critical weakness in terms that these methods need prior database built to distinguish acquired data as unique surface condition for driving. Also, ground information by exteroceptive sensors does not reflect the deflection of ground surface caused by the movement of UGVs. Thereby, UGVs have some difficulties regarding to finding optimal driving conditions for maximum maneuverability. Therefore, this paper proposes a method of recognizing exact and precise ground shape using Inertial Measurement Unit(IMU) as a proprioceptive sensor. In this paper, firstly this method recognizes attitude of a robot in real-time using IMU and compensates attitude data of a robot with angle errors through analysis of vehicle dynamics. This method is verified by outdoor driving experiments of a real mobile robot.