梳理机器学习感知安全监控的关键技术与挑战
Safety Monitoring of Machine Learning Perception Functions: a Survey
- 按威胁识别到评估的流程，系统归纳安全监控设计要点
- 指出当前在故障检测与响应机制上的核心难点
- 适合关注自动驾驶、医疗机器人等安全系统的研究者
机器学习模型（如深度神经网络）广泛应用于自动驾驶、手术机器人等自主系统中，执行复杂感知任务。当这些模型用于安全关键场景时，其预测结果的可靠性带来新的可信性挑战。因此，必须引入容错机制，如安全监控，以确保系统在出现故障时仍能保持安全行为。本文对安全关键环境下基于机器学习的感知功能安全监控进行了全面文献综述。我们系统梳理了现有研究，提炼出设计安全监控需考虑的关键环节：威胁识别、需求获取、故障检测、响应策略及评估方法。同时，文章指出了当前面临的主要挑战，并为未来研究方向提供了建议。
原文摘要 · Abstract (English)
Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety-critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation. We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research.
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