Automotive Safety and Machine Learning: Initial Results from a Study on How to Adapt the ISO 26262 Safety Standard. - Jens Henriksson; Markus Borg; Cristofer Englund (SEFAIAS@ICSE, 2018)

Towards Structured Evaluation of Deep Neural Network Supervisors. - Jens Henriksson; Christian Berger 001; Markus Borg; Lars Tornberg; Cristofer Englund; Sankar Raman Sathyamoorthy; Stig Ursing (AITest, 2019)

Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks. - Jens Henriksson; Christian Berger 001; Markus Borg; Lars Tornberg; Sankar Raman Sathyamoorthy; Cristofer Englund (SEAA, 2019)

Controlled time series generation for automotive software-in-the-loop testing using GANs. - Dhasarathy Parthasarathy; Karl Bäckström; Jens Henriksson; Sólrún Einarsdóttir (AITest, 2020)

Understanding the Impact of Edge Cases from Occluded Pedestrians for ML Systems. - Jens Henriksson; Christian Berger 001; Stig Ursing (SEAA, 2021)

Performance analysis of out-of-distribution detection on trained neural networks. - Jens Henriksson; Christian Berger 001; Markus Borg; Lars Tornberg; Sankar Raman Sathyamoorthy; Cristofer Englund (Inf. Softw. Technol., 2021)

SMIRK: A machine learning-based pedestrian automatic emergency braking system with a complete safety case. - Kasper Socha; Markus Borg; Jens Henriksson (Softw. Impacts, 2022)

Evaluation of Out-of-Distribution Detection Performance on Autonomous Driving Datasets. - Jens Henriksson; Christian Berger 001; Stig Ursing; Markus Borg (AITest, 2023)

Out-of-Distribution Detection as Support for Autonomous Driving Safety Lifecycle. - Jens Henriksson; Stig Ursing; Murat Erdogan; Fredrik Warg; Anders Thorsén; Johan Jaxing; Ola Örsmark; Mathias Örtenberg Toftås (REFSQ, 2023)

Ergo, SMIRK is safe: a safety case for a machine learning component in a pedestrian automatic emergency brake system. - Markus Borg; Jens Henriksson; Kasper Socha; Olof Lennartsson; Elias Sonnsjö Lönegren; Thanh Bui; Piotr Tomaszewski; Sankar Raman Sathyamoorthy; Sebastian Brink; Mahshid Helali Moghadam (Softw. Qual. J., 2023)