Salus:一种新型数据驱动监视器,可实现自动驾驶系统的实时安全

Bohan Zhang, Yafan Huang, Guanpeng Li
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引用次数: 2

摘要

本文提出了Salus,一种数据驱动的实时安全监视器,可以检测和减轻自动驾驶汽车(AV)的安全违规行为。主要是交通情况导致AV安全违规行为分为可以识别模式和学习安全违规的AV。我们的方法是使用机器学习(ML)技术模型的交通行为,导致安全违规AV,描述他们的早期症状对于训练一个先发制人的模型,因此部署和实时检测在实际事故发生之前安全违规AV。为了训练我们毫升模型,我们利用一系列模糊测试技术来定制特定于自动驾驶汽车的安全违规症状,并通过数据论证技术生成训练数据。我们的评估表明,我们提出的技术有效地减少了97.2%以上的工业级自动驾驶系统的安全违规,如百度阿波罗,假阳性率不超过0.018。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Salus: A Novel Data-Driven Monitor that Enables Real-Time Safety in Autonomous Driving Systems
This paper proposes Salus, a data-driven real-time safety monitor, that detects and mitigates safety violations of an autonomous vehicle (AV). The key insight is that traffic situations that lead to AV safety violations fall into patterns and can be identified by learning from the safety violations of the AV. Our approach is to use machine learning (ML) techniques to model the traffic behaviors that result in safety violations in the AV, characterize their early symptoms for training a preemptive model, hence deploy and detect real-time safety violations before the actual crashes happen to the AV. In order to train our ML model, we leverage a pipeline of fuzzing techniques to tailor AV-specific safety violation symptoms and generate the training data via data argumentation techniques. Our evaluation demonstrates our proposed technique is effective in reducing over 97.2% of safety violations in industry-level autonomous driving systems, such as Baidu Apollo, with no more than 0.018 false positive values.
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