SMIRK : A machine learning-based pedestrian automatic emergency braking system with a complete safety case
(2022) In Software Impacts 13.- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/c7b33f7b-b69f-483d-880c-0f4a8ea7cb4d
- author
- Socha, Kasper ; Borg, Markus LU and Henriksson, Jens
- organization
- publishing date
- 2022-08-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Advanced driver-assistance system, Automotive demonstrator, Computer vision, Machine learning, Pedestrian automatic emergency braking, Safety case
- in
- Software Impacts
- volume
- 13
- article number
- 100352
- publisher
- Elsevier
- external identifiers
-
- scopus:85134628533
- ISSN
- 2665-9638
- DOI
- 10.1016/j.simpa.2022.100352
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2022 The Authors
- id
- c7b33f7b-b69f-483d-880c-0f4a8ea7cb4d
- date added to LUP
- 2022-09-02 09:10:09
- date last changed
- 2023-11-20 09:30:31
@article{c7b33f7b-b69f-483d-880c-0f4a8ea7cb4d, abstract = {{<p>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.</p>}}, author = {{Socha, Kasper and Borg, Markus and Henriksson, Jens}}, issn = {{2665-9638}}, keywords = {{Advanced driver-assistance system; Automotive demonstrator; Computer vision; Machine learning; Pedestrian automatic emergency braking; Safety case}}, language = {{eng}}, month = {{08}}, publisher = {{Elsevier}}, series = {{Software Impacts}}, title = {{SMIRK : A machine learning-based pedestrian automatic emergency braking system with a complete safety case}}, url = {{http://dx.doi.org/10.1016/j.simpa.2022.100352}}, doi = {{10.1016/j.simpa.2022.100352}}, volume = {{13}}, year = {{2022}}, }