Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262

5 August 2018

Papers citing "Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262"

21 / 21 papers shown

Title
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review Anton Kuznietsov Balint Gyevnar Cheng Wang Steven Peters Stefano V. Albrecht XAI 28 26 0 08 Feb 2024
STEAM & MoSAFE: SOTIF Error-and-Failure Model & Analysis for AI-Enabled Driving Automation Krzysztof Czarnecki Hiroshi Kuwajima 26 0 0 15 Dec 2023
Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges Hans-Martin Heyn K. M. Habibullah E. Knauss Jennifer Horkoff Markus Borg Alessia Knauss Polly Jing Li 25 4 0 10 Mar 2023
A Probabilistic Framework for Mutation Testing in Deep Neural Networks Florian Tambon Foutse Khomh G. Antoniol MedIm 16 13 0 11 Aug 2022
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components Boyue Caroline Hu Lina Marsso Krzysztof Czarnecki Rick Salay Huakun Shen Marsha Chechik 16 21 0 08 Feb 2022
Safe AI -- How is this Possible? Harald Ruess Simon Burton 16 0 0 25 Jan 2022
How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review Florian Tambon Gabriel Laberge Le An Amin Nikanjam Paulina Stevia Nouwou Mindom Y. Pequignot Foutse Khomh G. Antoniol E. Merlo François Laviolette 30 66 0 26 Jul 2021
Software Engineering for AI-Based Systems: A Survey Silverio Martínez-Fernández Justus Bogner Xavier Franch Marc Oriol Julien Siebert Adam Trendowicz Anna Maria Vollmer Stefan Wagner 19 211 0 05 May 2021
Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI? Markus Borg Ronald Jabangwe Simon Åberg Arvid Ekblom Ludwig Hedlund August Lidfeldt 14 18 0 02 Mar 2021
A Review of Testing Object-Based Environment Perception for Safe Automated Driving Michael Hoss Maike Scholtes L. Eckstein 30 48 0 16 Feb 2021
Safety Case Templates for Autonomous Systems Robin Bloomfield Gareth Fletcher Heidy Khlaaf Luke Hinde Philippa Ryan 22 15 0 29 Jan 2021
A Safety Framework for Critical Systems Utilising Deep Neural Networks Xingyu Zhao Alec Banks James Sharp Valentin Robu David Flynn Michael Fisher Xiaowei Huang AAML 50 48 0 07 Mar 2020
Towards Probability-based Safety Verification of Systems with Components from Machine Learning H. Kaindl Stefan Kramer 17 1 0 02 Mar 2020
Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 1 Robin Bloomfield Gareth Fletcher Heidy Khlaaf Philippa Ryan Shuji Kinoshita ... M. Takeyama Yamato Matsubara Peter Popov Peter Popov Kazuki Imai Yoshinori Tsutake 26 3 0 28 Feb 2020
PaRoT: A Practical Framework for Robust Deep Neural Network Training Edward W. Ayers Francisco Eiras Majd Hawasly I. Whiteside OOD 23 19 0 07 Jan 2020
Organization of machine learning based product development as per ISO 26262 and ISO/PAS 21448 Krystian Radlak Michal Szczepankiewicz Tim Jones Piotr Serwa 20 1 0 07 Oct 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 22 345 0 11 Jun 2019
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges Rob Ashmore R. Calinescu Colin Paterson AI4TS 24 116 0 10 May 2019
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability Xiaowei Huang Daniel Kroening Wenjie Ruan M. Kwiatkowska Youcheng Sun Emese Thamo Min Wu Xinping Yi AAML 16 50 0 18 Dec 2018
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015