Quality assurance methodologies for automated driving; [Qualitätssicherungsmaßnahmen für das automatisierte Fahren]

被引：0

作者：

Wotawa F. ^{[1
]}

Peischl B. ^{[1
]}

Klück F. ^{[1
]}

Nica M. ^{[2
]}

机构：

[1] Institute for Software Technology, Christian Doppler Laboratory for Quality Assurance Methodologies for Autonomous Cyber-Physical Systems (QAMCAS), Graz University of Technology, Inffeldgasse 16b/2, Graz

[2] AVL LIST GmbH, Hans-List-Platz 1, Graz

来源：

e & i Elektrotechnik und Informationstechnik | 2018年 / 135卷 / 4-5期

关键词：

ADAS; functional safety; SOTIF; testing; verification;

D O I：

10.1007/s00502-018-0630-7

中图分类号：

学科分类号：

摘要：

For safety critical systems like cars, trains, or airplanes quality assurance methods and techniques are crucial for preventing situations that may harm people. The case of automated driving represents the next level of safety critical systems where additional challenges arise. This includes the question of how to assure that artificial intelligence and machine learning based systems fulfill safety criticality requirements under all potential conditions and situations that may emerge during operation. In this paper, we first review simulation-based verification and validation methods for such systems and afterwards discuss necessary requirements and future research challenges that have to be solved in order to bring automated driving into practice without compromising safety requirements. © 2018, Springer-Verlag GmbH Austria, ein Teil von Springer Nature.

引用

页码：322 / 327

页数：5