Safety Concerns and Mitigation Approaches Regarding the Use of Deep
Learning in Safety-Critical Perception Tasks

Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks

22 January 2020

Sebastian Sudholt

Shervin Raafatnia

Stephanie Abrecht

Papers citing "Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks"

17 / 17 papers shown

Title
A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems Milin Patel Rolf Jung M. Khatun 67 0 0 04 Mar 2025
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
Characterizing Perspective Error in Voxel-Based Lidar Scan Matching Jason Rife Matthew McDermott 22 3 0 24 Jan 2024
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement Holger Boche Adalbert Fono Gitta Kutyniok FaML 28 4 0 18 Jan 2024
Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing Ayoosh Bansal Yang Zhao James Zhu Sheng Cheng Yuliang Gu Hyung-Jin Yoon Hunmin Kim N. Hovakimyan Lui Sha 13 2 0 05 Dec 2023
Labeling Neural Representations with Inverse Recognition Kirill Bykov Laura Kopf Shinichi Nakajima Marius Kloft Marina M.-C. Höhne BDL 21 15 0 22 Nov 2023
Combating noisy labels in object detection datasets K. Chachula Jakub Lyskawa Bartlomiej Olber Piotr Fratczak A. Popowicz Krystian Radlak NoLa 21 4 0 25 Nov 2022
Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults Ayoosh Bansal Hunmin Kim Simon Yu Bo-wen Li N. Hovakimyan Marco Caccamo L. Sha AAML 26 4 0 04 Sep 2022
Verifiable Obstacle Detection Ayoosh Bansal Hunmin Kim Simon Yu Bo-Yi Li N. Hovakimyan Marco Caccamo L. Sha 23 6 0 30 Aug 2022
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 Sonnsjo Lonegren T. Bui Piotr Tomaszewski S. Sathyamoorthy Sebastian Brink M. H. Moghadam 22 23 0 16 Apr 2022
Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges Huaming Chen Muhammad Ali Babar AAML 29 21 0 12 Jan 2022
Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey M. Askarpour Alan Wassyng M. Lawford R. Paige Z. Diskin 15 0 0 29 Nov 2021
Exposing Previously Undetectable Faults in Deep Neural Networks Isaac Dunn Hadrien Pouget Daniel Kroening T. Melham AAML 23 28 0 01 Jun 2021
Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases Md. Abdullah Al Alamin Gias Uddin 26 11 0 03 May 2021
Requirement Engineering Challenges for AI-intense Systems Development Hans-Martin Heyn E. Knauss Amna Pir Muhammad O. Eriksson Jennifer Linder P. Subbiah S. K. Pradhan Sagar Tungal 22 33 0 18 Mar 2021
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving Di Feng Ali Harakeh Steven Waslander Klaus C. J. Dietmayer AAML UQCV EDL 24 222 0 20 Nov 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015