Driving Resistance is calculated for emission test defines total vehicle resistance forces. Resistance factors of running vehicle are sum of rolling resistance, transmission loss and aerodynamic drag force. To measure this resistance, Coastdown test is conventional method and it needs a long level driving road. In this study coastdown test is executed on short driving road. And also each resistance factors are calculated. This test is based on S(Distance)-Time Method. From the result, it is shown that this method is reliable and can be used for initial vehicle test.

도로 주행의 안전성 측면에서 타이어-노면간 미끄럼 마찰력은 주행 차량의 제동거리와 직접적인 요인으로 작용한다. 포장재료와 공법은 노출되는 포장 표면에 적절한 노면의 조직(Texture)을 형성하여 노면의 미끄럼 마찰력을 증가시킨다. 도로 표면에 노출되는 사용골재의 크기와 종류를 달리하거나 인위적인 홈을 주어 Macrotexture와 Microtexture를 형성 한다. 형성된 노면 조직은 시간이 경과 됨에 따라 환경하중과 교통하중이 반복 재하되면서 표면마모가 급격히 진행된다. 교통량의 흐름에 따라 마모로 인해 Microtexture 뿐만 아니라 Macrotexture의 노면조직은 매끄러운 표면으로 변해간다. 교통량의 흐름은 다양하다. 교통량 통계자료에 따르면 고속도로 이용차량의 약 70%는 승용차와 같은 2축 1단위로 구성 된 1종 차량이 차지하고 있다. 이는 국내 교통 특성은 포장 마모에 취약한 환경임을 말해주고 있다. 주행 차량들의 좌/ 우 바퀴의 간격과 주행위치의 다른 궤적에 따라 차량바퀴의 횡방향 변동을 원더링(Wandering)이라하는데, 도로포장 분 야에서 교통특성이 포장에 미치는 영향으로 원더링에 대한 연구 많이 진행 되어왔다. 본 연구에서는 실제 고속도로와 시 험도로에서 횡방향 위치별 미끄럼 마찰을 반복 조사하여 차량의 원더링에 따라 미끄럼 마찰저항이 다르게 분포함을 정 량적으로 입증하였다.

This empirical study explores determinants of consumer resistance towards self-driving cars by considering the level of car autonomy. Based on a literature review, this research distinguishes between the effects of functional and psychological barriers on behavioral intention. Several studies have clarified that technological innovation in particular, need to overcome several barriers as a first step, before (potential) users will even start to favor buying such an innovation. Data was collected by an online-survey in December 2017, resulting in an effective sample of 182 respondents. The sample has an average age of M = 24.46 years with 70% male participants and a total of 95% were in possession of a driver license. To ensure that the respondents are able to differentiate between the characteristics or levels of autonomous driving, two independent samples were surveyed on the basis of different scenarios (low and high autonomy). In addition, a structural equation model (SEM) was used to perform an analysis of measurement and structural models using SmartPLS 3.0 software. The findings show that functional and psychological aspects explain consumer resistance towards self-driving cars. Interestingly, the results of a moderation analysis illustrate that the effects of both psychological barriers (i.e., image and traditions/norms) on behavioral intentions vary between a high and a low level of car autonomy. In detail, for those who evaluated the high autonomy scenario (N=92), significant results can be presented for both psychological barriers. Surprisingly, no significant relationship between risk barrier as functional barrier and behavioral intention can be verified. Conclusively, marketers and OEM’s, respectively, should elaborate specific strategies for the different levels of autonomous driving that will be introduced to the market over the next decades. To support these findings, it would be helpful to test the model with a larger sample and new items to test for a potential usage barrier. Moreover, it would be prudent to test additional scenarios and levels of autonomous driving.