@inproceedings{ccc00eed22524efca37295d891724cc3,
title = "Uncertainty in machine learning: A safety perspective on autonomous driving",
abstract = "With recent efforts to make vehicles intelligent, solutions based on machine learning have been accepted to the ecosystem. These systems in the automotive domain are growing fast, speeding up the promising future of highly and fully automated driving, and respectively, raising new challenges regarding safety assurance approaches. Uncertainty in data and the machine learning methods is a key point to investigate one of the main origins of safety-related concerns. In this work, we inspect this issue in the domain of autonomous driving with an emphasis on four safety-related cases, then introduce our proposals to address the challenges and mitigate them. The core of our approach is on introducing monitoring limiters during development time of such intelligent systems.",
keywords = "Artificial intelligence, Safety, Uncertainty",
author = "Sina Shafaei and Stefan Kugele and Osman, {Mohd Hafeez} and Alois Knoll",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; Workshops: ASSURE, DECSoS, SASSUR, STRIVE, and WAISE 2018 co-located with 37th International Conference on Computer Safety, Reliability and Security, SAFECOMP 2018 ; Conference date: 18-09-2018 Through 21-09-2018",
year = "2018",
doi = "10.1007/978-3-319-99229-7_39",
language = "English",
isbn = "9783319992280",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "458--464",
editor = "Friedemann Bitsch and Amund Skavhaug and Barbara Gallina and Erwin Schoitsch",
booktitle = "Computer Safety, Reliability, and Security - SAFECOMP 2018 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Proceedings",
}