系统评估自动驾驶感知中深度神经网络的潜在风险
Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception
- 融合ISO 26262与ISO/SAE 21434，构建联合风险评估流程
- 识别出DNN在泛化、鲁棒性等方面缺陷引发的具体风险
- 为自动驾驶安全设计提供可落地的风险分析框架
自动驾驶车辆的安全与可信度至关重要。深度神经网络（DNN）广泛应用于自动驾驶感知等环节，但其普遍存在泛化能力不足、效率低、可解释性差、合理性欠缺及鲁棒性弱等局限性，可能对系统安全构成重大威胁。然而，目前尚未系统研究这些DNN固有缺陷带来的危害、威胁与风险。本文提出一种联合工作流程，结合ISO 26262中的危害分析与风险评估（HARA）以及ISO/SAE 21434中的威胁分析与风险评估（TARA），系统识别并分析自动驾驶感知中由DNN内在局限性引发的风险。
原文摘要 · Abstract (English)
Safety and security are essential for the admission and acceptance of automated and autonomous vehicles. Deep neural networks (DNNs) are widely used for perception and further components of the autonomous driving (AD) stack. However, they possess several limitations, including lack of generalization, efficiency, explainability, plausibility, and robustness. These insufficiencies can pose significant risks to autonomous driving systems. However, hazards, threats, and risks associated with DNN limitations in this domain have not been systematically studied so far. In this work, we propose a joint workflow for risk assessment combining the hazard analysis and risk assessment (HARA) following ISO 26262 and threat analysis and risk assessment (TARA) following the ISO/SAE 21434 to identify and analyze risks arising from inherent DNN limitations in AD perception.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。