The objective of this study is to analyze the difference between the theoretically calculated torque values of lead screws used in vehicle seat rails and the required torque values due to various disturbances that occur in actual systems. Lead screws were classified into square and trapezoidal threads and modeled by two lead type. Dynamic analysis models were constructed by applying contact conditions and rotational joints between the lead screw and nut. The validity of the dynamic model was verified by comparing the torque values obtained from rigid body dynamic analysis with the theoretically calculated torque values. Then, the lead screw was modeled as a flexible body to investigate the torque variation required for the lead screw when dynamic loads are considered. This study will help predict the actual torque values of lead screws for seat rails.

This research has been conducted to design upright parts of hand-made vehicles with the purpose of reducing material and machining cost while ensuring structural safety. Aluminum knuckles were modelled with three parts in order to enhance design flexibility as well as to reduce CNC machining cost. A vehicle model was constructed in CAD program and simulated in ADAMS View in order to estimate joint forces developing during 20 degree step steering condition at 60km/h. The joint forces obtained in the vehicle dynamics simulation were used for the structural analysis in ANSYS and dimensions of knuckle parts were adjusted until the lowest safety factor reached 2.0. The weight of knuckle decreased by 50% compared to the previous version that was designed without the structural analysis. The overall manufacturing cost decreased by 33% due to the reduction in the material as well as the CNC machining effort.

An automotive company have developed corporate requirements for vehicle characteristics for dynamic response which must be met before a product is delivered to the customer. To provide early predictions of vehicle handling performance, prior to the construction and testing of prototypes, it is necessary to predict dynamic behavior due to road inputs. This paper describes an application of the “virtual proving ground” approach for vehicle handling characteristics for a vehicle on proving ground road surfaces. I developed generation program of the virtual road profile for vehicle dynamics simulation.

As the development of autonomous vehicles becomes realistic, many automobile manufacturers and components producers aim to develop ‘completely autonomous driving’. ADAS (Advanced Driver Assistance Systems) which has been applied in automobile recently, supports the driver in controlling lane maintenance, speed and direction in a single lane based on limited road environment. Although technologies of obstacles avoidance on the obstacle environment have been developed, they concentrates on simple obstacle avoidances, not considering the control of the actual vehicle in the real situation which makes drivers feel unsafe from the sudden change of the wheel and the speed of the vehicle. In order to develop the ‘completely autonomous driving’ automobile which perceives the surrounding environment by itself and operates, ability of the vehicle should be enhanced in a way human driver does. In this sense, this paper intends to establish a strategy with which autonomous vehicles behave human-friendly based on vehicle dynamics through the reinforcement learning that is based on Q-learning, a type of machine learning. The obstacle avoidance reinforcement learning proceeded in 5 simulations. The reward rule has been set in the experiment so that the car can learn by itself with recurring events, allowing the experiment to have the similar environment to the one when humans drive. Driving Simulator has been used to verify results of the reinforcement learning. The ultimate goal of this study is to enable autonomous vehicles avoid obstacles in a human-friendly way when obstacles appear in their sight, using controlling methods that have previously been learned in various conditions through the reinforcement learning.