Authors:
Devi Alagarswamy
1
;
Christian Berghoff
2
;
Vasilios Danos
3
;
Fabian Langer
3
;
Thora Markert
3
;
Georg Schneider
4
;
Arndt von Twickel
2
and
Fabian Woitschek
4
Affiliations:
1
ZF Friedrichshafen AG, System House Autonomous Mobility Systems, Friedrichshafen, Germany
;
2
German Federal Office for Information Security (BSI), Bonn, Germany
;
3
TÜV Informationstechnik GmbH, IT Security Hardware Evaluation, Essen, Germany
;
4
ZF Friedrichshafen AG, Artificial Intelligence Lab, Saarbrücken, Germany
Keyword(s):
Artificial Intelligence, Neural Networks, Security, Safety, Trustworthiness, Regulation, AD, ADAS.
Abstract:
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 p
roposed requirements by performing an exemplary audit of a DNN-based traffic sign recognition system using three of the proposed requirements.
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