Safety integration components for robotic applications are a mandatory feature for any autonomous mobile application, including human avoidance behaviors. This paper proposes a novel parametrizable scene risk evaluator for openfield applications that use humans motion predictions and predefined hazard zones to estimate a braking factor. Parameters optimization uses simulated data. The evaluation is carried out by simulated and real-time scenarios, showing the impact of human predictions in favor of risk reductions on agricultural applications.

Tractors and heavy machinery have been used for decades to improve the quality and overall agriculture production. Moreover, agriculture is becoming a trend domain for robotics, and as a consequence, the efforts towards automatizing agricultural task increases year by year. However, for autonomous applications, accident prevention is of prior importance for warrantying human safety during operation in any scenario. This paper rephrases human safety as a classification problem using a custom distance criterion where each detected human gets a risk level classification. We propose the use of a neural network trained to detect and classify humans in the scene according to these criteria. The proposed approach learns from real-world data corresponding to an open-field scenario and is assessed with a custom risk assessment method.