A fair simple car driving simulator was created based on the open source engine TORCS and used in carfollowing experiments aimed at studying the basic features of human behavior in car driving. Four subjects with different skill in driving real cars participated in these experiments. The subjects were instructed to drive a car without overtaking and losing sight of a lead car driven by computer at a fixed speed. Based on the collected data the distributions of the headway distance, the car velocity, acceleration, and jerk are constructed and compared with the available experimental data for the real traffic flow. A new model for the carfollowing is proposed to capture the found properties. As the main result, we draw a conclusion that human actions in car driving should be categorized as generalized intermittent control with noise-driven activation. Besides, we hypothesize that the car jerk together with the car acceleration are additional phase variables required for describing the dynamics of car motion governed by human drivers.

Recently, virtual reality (VR) has become popular for a variety of applications, such as manufacturing and entertainment. In this study, considering that a driver’s head moves according to the motion of turning the steering wheel, we explored the effectiveness of head movement as a means for steering a vehicle in a virtual reality driving simulation. First, we analyzed the motion axes that are effective for control and found that the x (horizontal) direction, yaw rotation, and roll rotation are potential candidates. Through the implementation of a simulator, which allows participants to steer the vehicle by means of head movement, it was found that the x-axis movement was the most reliable as it reduced VR sickness while guaranteeing better usability and realistic motion. Human–machine interaction can become conceived of as symmetrical in the sense that if a machine is truly easy for humans to handle, it means that they can get the best out of it.

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The first modelling to predict the moisture content of perlite in container cultivation was performed. In recent years, information technology-based agriculture (smart agriculture), such as automated cultivation of crops, has received considerable attention. One of the important issues in crop cultivation is how to control soil moisture content to achieve optimal conditions for plants. Models that accurately predict the water content in soil are needed. Perlite is often used in containers as a hydroponic soil. Although methods to maintain the moisture content in containers have been studied in the past, there has been no study on how to maintain the moisture content of perlite. In this study, we develop a measurement system, created a model for the water content and drainage of perlite in a container, and analysed the data. The results reveal for the first time a nonlinear trend in the moisture of perlite in containers. The nonlinear model obtained can be used to establish more efficient water supply methods for perlite used in container cultivation.

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