提出SET框架，系统分析环境因素对自动驾驶感知的负面影响。
The SET Perceptual Factors Framework: Towards Assured Perception for Autonomous Systems
- 构建自、环境、目标三类因素的树状模型，定位感知失效根源。
- 通过因子树量化天气、遮挡等对检测与位姿估计的影响程度。
- 为安全验证与公众信任提供透明可解释的风险分析工具。
未来自主系统虽具重大社会价值，但部署中仍存安全与可信度疑虑。感知可靠性是安全决策的基础，而感知失败常源于天气、遮挡或传感器限制等常见环境因素，易引发事故并削弱公众信任。为此，本文提出SET（Self, Environment, and Target）感知因素框架，通过SET状态树分类因素来源，通过SET因子树建模其对目标检测、位姿估计等任务的影响路径。进而结合两类树构建感知因子模型，量化特定任务下的不确定性。该框架旨在推动严谨的安全保障，提升公众对自主系统的理解与信任，提供一种透明、标准化的感知风险识别与传播方法。
原文摘要 · Abstract (English)
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.
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