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:各种流动应用,如先进的驾驶员协助系统,越来越多地利用人工智能功能; 通常使用深神经网络,因为这些网络在现实驱动环境中出现的具有挑战性的看法、预测或规划任务方面提供最佳业绩; 然而,欧洲经委会R155或ISO 262626等现行条例并不考虑与人工智能有关的方面,而只适用于传统的算法系统; 缺乏专门使用人工智能的标准或规范,妨碍实际应用,并可能损害用户的信任程度; 因此,有必要扩大现有的安保和安全标准化,以考虑针对人工智能的挑战和要求; 采取适当条例的步骤,我们提出50项技术要求或最佳做法,以扩大现有条例的范围,满足DNN系统的具体需求; 我们利用拟议要求中的三项,对基于DNN的交通标志识别系统进行示范性审计,表明拟议要求的适用性、有用性和意义。</s>