In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.

This paper describes a method of multiple cranes assignment to operators who call cranes in a factory. Due to a crane moving on a rail, there is a restriction on the movement. Therefore, the crane cannot be delivered without proper assignment. To solve the cranes assignment problem, we propose two methods. The first is the optimal solution search method which finds the minimum travel distance combination from the number of cases that are derived by calculating the distance between all the cranes and all the operators. The second is the dynamic mapping method which finds the number of operators between each crane. Simulation results show that the dynamic mapping method sometimes performs the same assignment results as the optimal solution search method, but on average, the result of dynamic mapping method is farther than the optimal solution as the number of cranes increases. When partial regions are created by using the dynamic mapping method and an optimal solution search method is applied to each region, optimal solutions can be acquired while reducing the number of calculations.