Evaluation of Warning Methods for Remotely Supervised Autonomous Agricultural Machines.

Abstract

Highlights: Humans who supervise autonomous agricultural machines require some type of warning to perceive abnormal conditions in the machine or its environment. Visual and tactile warnings were the most suitable warning methods for in-field and close-to-field remote supervision. This study will help improve the performance of remote supervisors and minimize unexpected incidents or liabilities during operation of autonomous machines.

Abstract: As agricultural machinery moves toward full autonomy, human supervisors will need to monitor the autonomous machines during operation and minimize system failures or malfunctions. However, to intervene in an emergency, the supervisor must first recognize the emergency in a timely manner. Existing warning devices rely on the human visual, auditory, and tactile senses. However, these warning methods vary in their ability to attract attention. Hence, it is important to determine which warning method is best suited to draw the attention of a remote supervisor of an autonomous machine in an emergency. To achieve this objective, participants were recruited and asked to interact with a simulation of an autonomous sprayer. Seven warning methods (presented alone or in combinations of visual, auditory, and tactile sensory cues) and four remote supervision scenarios (in-field, close-to-field, farm office, outside the farmland) were considered in this study. The findings revealed that a combination of tactile and visual methods was most suitable for in-field and close-to-field remote supervision, in comparison to the other warning methods. However, there was insufficient evidence to recommend the best warning methods for supervisors at the farm office or outside the farmland. This study will help improve the performance of remote supervisors and minimize unexpected incidents during field operations with autonomous agricultural machines.