Various mobility applications like advanced driver assistance systems increasingly utilize artificial intelligence (AI) based functionalities. Typically, deep neural networks (DNNs) are used as these provide the best performance on the challenging perception, prediction or planning tasks that occur in real driving environments. However, current regulations like UNECE R 155 or ISO 26262 do not consider AI-related aspects and are only applied to traditional algorithm-based systems. The non-existence of AI-specific standards or norms prevents the practical application and can harm the trust level of users. Hence, it is important to extend existing standardization for security and safety to consider AI-specific challenges and requirements. To take a step towards a suitable regulation we propose 50 technical requirements or best practices that extend existing regulations and address the concrete needs for DNN-based systems. We show the applicability, usefulness and meaningfulness of the proposed requirements by performing an exemplary audit of a DNN-based traffic sign recognition system using three of the proposed requirements.

翻译：各类移动应用，如高级驾驶辅助系统，正越来越多地采用基于人工智能的功能。通常，深度神经网络因其在真实驾驶环境中具有挑战性的感知、预测或规划任务上表现最佳而被使用。然而，现行法规如UNECE R 155或ISO 26262并未考虑AI相关方面，仅适用于传统基于算法的系统。AI特定标准或规范的缺失阻碍了实际应用，并可能损害用户的信任度。因此，有必要扩展现有的安全与安保标准化，以涵盖AI特有的挑战和要求。为迈向合适的监管，我们提出了50项技术要求或最佳实践，这些内容扩展了现有法规，并针对基于深度神经网络的系统解决了具体需求。我们通过使用其中三项要求对基于深度神经网络的交通标志识别系统进行示范性审计，展示了所提要求的适用性、实用性和有效性。