Autonomy, AI Perception and Safety : A Safety Evaluation Framework for AI Perception Models Used In Agricultural Autonomous Vehicles

Abstract: Autonomous vehicle technology has seen rapid development thanks to advances in artificial intelligence. Among the various sectors, agriculture is one sector that is testing the potential of autonomous vehicle robots to meet the growing demands of society. Cutting or "Mowing" grass is one potential application that can be automated with AI-driven vehicles on large farms to increase efficiency. However, the increasing reliance on artificial intelligence models for decision-making, such as for navigation, raises the question of how safe these models are and how we can assess the safety of such algorithms. As the safety of AI is still an open challenge, very little research has addressed this problem, and even less in the field of agriculture. The aim of this work is to develop a framework for evaluating the safety of AI perception models used in autonomous vehicle robots in agriculture. The proposed methodology evaluates safety in three main stages: sub-system level, system-level, and post-deployment, along with a preliminary stage for defining boundaries. The feasibility of the framework was also tested on an AI perception system present in a prototype autonomous mowing vehicle to identify areas of safety concern.