How Does Perception Affect Safety: New Metrics and Strategy

Abstract

Perception plays a pivotal role in enhancing the functionality of autonomous agents. However, the intricate relationship between robotic perception metrics and actuation metrics remains unclear, leading to ambiguity in the development and fine-tuning of perception algorithms. In this paper, we introduce a methodology for quantifying this relationship, taking into account factors such as detection rate, detection quality, and latency. Furthermore, we introduce two novel perception metrics for Human-Robot Collaboration safety predicated upon basic perception metrics: Critical Collision Probability (CCP) and Average Collision Probability (ACP). To validate the utility of these metrics in facilitating algorithm development and tuning, we develop an attentive processing strategy that focuses exclusively on key input features. This approach significantly reduces computational time while preserving a similar level of accuracy. Experimental findings demonstrate that integrating this strategy into an object detector results in a notable maximum reduction of 30.09% in inference time and 26.53% in total time per frame. Additionally, the strategy lowers the CCP and ACP in a baseline model by 11.25% and 13.50%, respectively.