The SET Perceptual Factors Framework: Towards Assured Perception for Autonomous Systems

TL;DR

The paper introduces the SET Perceptual Factors Framework, a systematic approach to analyze, model, and communicate perceptual uncertainties in autonomous systems to enhance safety and public trust.

Contribution

It presents a novel framework with structured trees and models for identifying and quantifying perceptual risks in autonomous perception systems.

Findings

Framework effectively categorizes environmental factors affecting perception.

Models quantify uncertainty in perceptual tasks.

Framework promotes safety assurance and transparency.

Abstract

Future autonomous systems promise significant societal benefits, yet their deployment raises concerns about safety and trustworthiness. A key concern is assuring the reliability of robot perception, as perception seeds safe decision-making. Failures in perception are often due to complex yet common environmental factors and can lead to accidents that erode public trust. To address this concern, we introduce the SET (Self, Environment, and Target) Perceptual Factors Framework. We designed the framework to systematically analyze how factors such as weather, occlusion, or sensor limitations negatively impact perception. To achieve this, the framework employs SET State Trees to categorize where such factors originate and SET Factor Trees to model how these sources and factors impact perceptual tasks like object detection or pose estimation. Next, we develop Perceptual Factor Models using both trees to quantify the uncertainty for a given task. Our framework aims to promote rigorous safety assurances and cultivate greater public understanding and trust in autonomous systems by offering a transparent and standardized method for identifying, modeling, and communicating perceptual risks.