用概率推理提升机器人在伤员分类中的判断准确率
A Bayesian Reasoning Framework for Robotic Systems in Autonomous Casualty Triage
- 基于专家规则构建贝叶斯网络，融合多视觉算法输出
- 伤情评估准确率从15%提至42%，整体分类准确率达53%
- 适合应急救援机器人开发与智能决策系统研究者
在大规模伤亡事件（MCI）中，自主机器人面临基于不完整和噪声感知数据做出关键决策的挑战。本文提出一种用于伤员评估的自主机器人系统，融合多个基于视觉的算法输出，估计严重出血、可见创伤或身体警觉性等指标，生成连贯的分类结果。系统核心为基于专家定义规则构建的贝叶斯网络，可在感知信息缺失或冲突时进行概率推理。在达伯拉伤员分类挑战赛（DTC）的真实场景中，面对11名和9名伤员，系统相较于仅依赖视觉的基线方法，生理评估准确率分别从15%提升至42%，19%提升至46%；整体分类准确率从14%提升至53%，诊断覆盖范围也从31%扩大至95%。结果表明，将专家引导的概率推理与先进视觉感知结合，可显著增强自主系统在真实关键场景下的可靠性与决策能力。
原文摘要 · Abstract (English)
Autonomous robots deployed in mass casualty incidents (MCI) face the challenge of making critical decisions based on incomplete and noisy perceptual data. We present an autonomous robotic system for casualty assessment that fuses outputs from multiple vision-based algorithms, estimating signs of severe hemorrhage, visible trauma, or physical alertness, into a coherent triage assessment. At the core of our system is a Bayesian network, constructed from expert-defined rules, which enables probabilistic reasoning about a casualty's condition even with missing or conflicting sensory inputs. The system, evaluated during the DARPA Triage Challenge (DTC) in realistic MCI scenarios involving 11 and 9 casualties, demonstrated a nearly three-fold improvement in physiological assessment accuracy (from 15\% to 42\% and 19\% to 46\%) compared to a vision-only baseline. More importantly, overall triage accuracy increased from 14\% to 53\%, while the diagnostic coverage of the system expanded from 31\% to 95\% of cases. These results demonstrate that integrating expert-guided probabilistic reasoning with advanced vision-based sensing can significantly enhance the reliability and decision-making capabilities of autonomous systems in critical real-world applications.
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