PURPOSES : This study is to develop an comprehensive validation methodology for autonomous mobility-on-demand system with level 4 automated driving system. METHODS : The proposed method includes the quantitative techniques for validating both automated driving system and center system using each optimal indicators. In addition, a novel method for validating the whole system applying multi-criteria decision methodology is suggested. RESULTS : The relative weights for the vehicle system was higher than the center systems. Moreover, the relative weights of failure rate for validating the vehicle system was the highest, in addition to, a relative weight for accuracy of dynamic routing algorithm within center system was the highest. CONCLUSIONS : The proposed methodology will be applicable to validate the autonomous mobility on demand system quantitatively considering the relative weights for each systems.

It is very important to measure and analyze various driving performance in the vehicle development stage. Particularly in racing vehicles, analysis of driving characteristics on various courses is very important, and data measurement and analysis technology using actual measurement equipment are widely used in racing strategies. In this paper, we present an analytical approach using vehicle acceleration, which is relatively easy to measure among various factors. Measured acceleration data is used to analyze optimal driving performance.

PURPOSES : The purpose of this study was to quantitatively evaluate the variability of LiDAR performance indicators, such as intensity and Number of Point Cloud(NPC), according to various environmental factors and material characteristics. METHODS : To consider the material characteristics of road safety facilities, various materials (Reference Material(RM), reflective sheet, matte sheet, granite, plastic, and rubber) were used in a darkroom, and the performance indicators of LiDAR were repeatedly measured in terms of changes in the measurement distance, rainfall, and angle of observation. RESULTS : In the case of standard reflective materials, the intensity measurement value decreased as the measurement distance and rainfall increased. The NPC showed a tendency to decrease as the measurement distance increased, regardless of rainfall intensity. For materials with high-intensity values, it was found that rainfall intensity and color had negligible effect on the change in intensity compared with the measurement distance. However, for materials with low-intensity values, it was found that the measurement distance, rainfall intensity, and color all had a significant effect on the change in intensity. CONCLUSIONS : For materials with high-intensity values, it was found that rainfall and color had negligible effect on change in intensity compared with the measurement distance. However, for materials with low-intensity values, the measurement distance, rainfall, and color all had a significant effect on the change in intensity value.

PURPOSES : The purpose of this study is to disseminate the process of deriving the truck performance curve of Korea’s representative trucks in detail such that the relevant experts can use it in the future when re-selecting the representative trucks and deriving the truck performance curve accordingly. METHODS : To collect relevant data required to derive the truck performance curve, this study used four trucks that implemented the weight/power of the nation’s representative trucks, and conducted a practical driving test on four different upgrade sections. RESULTS : The truck performance curve was derived for each driving position to represent the deceleration and acceleration capabilities of the truck through the actual driving test on the upgrade sections. CONCLUSIONS : The truck performance curve derived from this study indicated that the truck’s deceleration and acceleration capabilities increased as the total weight/power of Korea’s representative trucks improved, and the design of the climbing lane demonstrated that the design method used in this study saved approximately 60% of the construction cost compared to the existing method.

본 연구에서는 선수 돌출의 반전형 선수 형상을 가진 고속 쌍동선의 선체부착 부가물에 의한 주행성능 영향에 대하여 수치해 석과 회류수조 모형시험을 통하여 비교분석하였다. 반전형 선수 형상은 재래식 선수 형상보다 선수 발산파 파정의 생성위치를 선미방향 으로 이동시켜 개선된 조파형상을 보이며, 저항 및 안정된 항주자세에 효과적임을 보였다(Kim et al., 2019). 본 연구에서의 반전형 선수 내 측에 부착된 핀과 선미단 인터셉터(Interceptor)에 의한 파형과 항주자세 변화 등 주행성능에 대한 모형시험 결과에서는, 1) 반전형 선수의 Trim 특성 2) Fin에 의한 내측 파의 중첩 개선 3) Fin과 Interceptor에 의한 자세제어는 두 선체 연결갑판(Wetdeck) 충격을 줄이는데 효과적인 것으로 판단된다.

과학 기술 분야에 변화가 일어나고 있다. 미래 도로 산업의 지속적인 발전을 위해서는 자율주행, 인공지능, 3D 프리트 등 최신의 기술을 도로 실정에 맞게 적용하는 방안이 마련되어야 한다. 특히 자율주행은 운전자 오류를 줄여 안전한 도로 환경을 조성하는데 크게 기여할 것으로 전망되고 있다. 본 발표는 도로 자율주행 성능지표 도입에 관한 것이다. 자율주행이 시대적인 소명이며 시급하게 도입될 기술 혁명이지만 국내 도로 여건에 대한 충분한 고민 없이 도입된다면 그 부작용이 클 것으로 전망된다. ‘사람이 운전하기 좋은 도로’가 ‘자동차가 운전하기도 좋은 도로’라는 전제하에 자율주행 효과가 최대한 발휘될 것으로 예상되는 도로를 자율주행 성능이 높은 도로로 정의한 것이다. 본 발표는 도로 자율주행 성능지표 도입 필요성, 지표 구성 내용, 정량화 방안 등에 대해 제시코자 한다. 도로 안전성, 이동성, 접근성과 같은 도로 성능과 유사한 지표를 추가함으로써 자율주행 효과가 극대화될 수 있는 데 기어코자 한다. 아울러 자율주행 성능지표라는 정량화된 목표를 통해 자율주행을 위한 도로 정비 로드맵 및 신규 투자에 대한 합리적인 의사결정에 보탬이 될 것으로 기대한다.

The major complaint of hybrid vehicle driver is that real fuel economy is lower than the certified fuel economy. Therefore, it is important to analyze the cause of low fuel economy and to improve the fuel consumption at real driving condition. In this study, the various speed profile is measured by driving urban road with considering different traffic jam. By using backward simulation, the fuel economy characteristics of the acquired driving modes are analyzed. From the simulation results, the operating points of engine and motor analyzed and the cause of decrease of real fuel economy is examined.

Kart-Racing which is called a microcosm of F1`s racing is the most basic step in the car racing. A Kart consisting of the very fundamental structure which does not include differential gear and suspension is sampler than a typical car. It is necessary to reduce the Lab time at the Kart-racing`s corner so the research investigate the Kart through check of racing condition and of driving factors about cornering. The study measured to make the kart closely factual condition which was showed the main parameters for more actual decision of kart`s dynamic characteristics and estimated the real-time data from the developed kart whose sensor was installed at all tires and which was pursued through the GPS.

In a four-wheel independent drive platform, four wheels and motors are connected directly, and the rotation of the motors generates the power of the platform. It uses a skid steering system that steers based on the difference in rotational power between wheel motors. The platform can control the speed of each wheel individually and has excellent mobility on dirt roads. However, the difficulty of the straight-running is caused due to torque distribution variation in each wheel’s motor, and the direction of rotation of the wheel, and moving direction of the platform, and the difference of the platform’s target direction. This paper describes an algorithm to detect the slip generated on each wheel when a four-wheel independent drive platform is traveling in a harsh environment. When the slip is detected, a compensation control algorithm is activated to compensate the torque of the motor mounted on the platform to improve the trajectory tracking performance of the platform. The four-wheel independent drive platform developed for this study verified the algorithm. The wheel slip detection and the compensation control algorithm of the platform are expected to improve the stability of trajectory tracking.

Legged locomotion has high mobility on irregular surfaces by touching the ground at discrete points. Inspired by the creature’s legged locomotion, legged robots have been developed to explore unstructured environments. In this paper, we propose a modular crawler that can easily adjust the number of legs for adapting the environment that the robot should move. One module has a pair of legs, so the number of legs can be adjusted by changing the number of modules. All legs are driven by a single driving motor for simple and compact design, so the driving axle of each module is connected by the universal joint. Universal joints between modules enable the body flexion for steering or overcoming higher obstacles. A prototype of crawler with three modules is built and the driving performance and the effect of module lifting on the ability to overcome obstacles are demonstrated by the experiments.

Inspired by small insects, which perform rapid and stable locomotion based on body softness and tripod gait, various milli-scale six-legged crawling robots were developed to move rapidly in harsh environment. In particular, cockroach’s leg compliance was resembled to enhance the locomotion performance of the crawling robots. In this paper, we investigated the effects of changing leg compliance for the locomotion performance of the small light weight legged crawling robot under various payload condition. First, we developed robust milli-scale six-leg crawling robot which actuated by one motor and fabricated in SCM method with light and soft material. Using this robot platform, we measured the running velocity of the robot depending on the leg stiffness and payload. In result, there was optimal range of the leg stiffness enhancing the locomotion ability at each payload condition in the experiment. It suggests that the performance of the crawling robot can be improved by adjusting stiffness of the legs in given payload condition.

One of the requirements for autonomous vehicles on off-road is to move stably in unstructured environments. Such capacity of autonomous vehicles is one of the most important abilities in consideration of mobility. So, many researchers use contact and/or non-contact methods to determine a terrain whether the vehicle can move on or not. In this paper we introduce an algorithm to classify terrains using visual information(one of the non-contacting methods). As a pre-processing, a contrast enhancement technique is introduced to improve classification of terrain. Also, for conducting classification algorithm, training images are grouped according to materials of the surface, and then Bayesian classification are applied to new images to determine membership to each group. In addition to the classification, we can build Traversability map specified by friction coefficients on which autonomous vehicles can decide to go or not. Experiments are made with Load-Cell to determine real friction coefficients of various terrains.