CITATION.cff

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cff-version: 1.2.0
title: >-
  SMIRK: A Machine Learning-Based Pedestrian
  Automatic Emergency Braking System With a Complete
  Safety Case, Software Impacts, Volume 13
message: >-
  If you use this software, please cite it using the
  metadata from this file. To cite the corresponding
  safety case, please cite: M. Borg et al., “Ergo,
  SMIRK is Safe: A Safety Case for a Machine Learning
  Component in a Pedestrian Automatic Emergency Brake
  System,” Software Quality Journal, 2023.
type: Original Software Publication
authors:
  - given-names: Kasper
    family-names: Socha
    email: kasper.socha@ri.se
    affiliation: RISE Research Institutes of Sweden
  - given-names: Markus
    family-names: Borg
    email: markus.borg@ri.se
    affiliation: CodeScene
    orcid: 'https://orcid.org/0000-0001-7879-4371'
  - given-names: Jens
    family-names: Henriksson
    email: jens.henriksson@semcon.com
    affiliation: Semcon
identifiers:
  - type: doi
    value: 10.1016/j.simpa.2022.100352
    description: Original Software Publication
repository-code: 'https://github.com/RI-SE/smirk/'
url: >-
  https://www.softwareimpacts.com/article/S2665-9638(22)00068-9/fulltext
abstract: >-
  SMIRK is a pedestrian automatic emergency braking
  system that facilitates research on safety-critical
  systems embedding machine learning components. As a
  fully transparent driver-assistance system, SMIRK
  can support future research on trustworthy AI
  systems, e.g., verification & validation,
  requirements engineering, and testing. SMIRK is
  implemented for the simulator ESI Pro-SiVIC with
  core components including a radar sensor, a mono
  camera, a YOLOv5 model, and an anomaly detector.
  ISO/PAS 21448 SOTIF guided the development, and we
  present a complete safety case for a restricted ODD
  using the AMLAS methodology. Finally, all training
  data used to train the perception system is
  publicly available.
keywords:
  - Automotive demonstrator
  - Advanced driver-assistance system
  - Pedestrian automatic emergency braking
  - Machine learning
  - Computer vision
  - Safety case
license: GPL-3.0
version: '0.99'
date-released: '2022-06-30'