Efficient Navigation in Crowded Environments for Autonomous Electric Wheelchairs Using Human Flow Activity Trend and Most Frequent Direction

Human flow data is being acquired and utilized in various situations. In this paper, we propose a navigation system for autonomous electric wheelchairs that makes use of usage trends in the driving environment obtained from human flow data. This system is expected to generate globally optimal paths and plan highly effective driving strategies. As a result, the navigation system may reduce the physical and mental burden on the user and harmonize with the surrounding pedestrians. In particular, this paper verifies through simulation experiments the usefulness of the system that takes into account environmental usage information, such as stay history, transit history, and most frequent travel directions.