Machine Learning Verification and Safety for Unmanned Aircraft - A Literature Study

[1]  Fabio Massimo Zanzotto.  Human-in-the-loop Artificial Intelligence , 2017, ArXiv.

[2]  Christoph Torens.  Safety Versus Security in Aviation, Comparing DO-178C with Security Standards , 2020 .

[3]  Amit Dhurandhar,et al.  Leveraging Latent Features for Local Explanations , 2019, KDD.

[4]  Christoph Torens,et al.  ASTM F3269 - An Industry Standard on Run Time Assurance for Aircraft Systems , 2020, AIAA Scitech 2021 Forum.

[5]  Suman Jana,et al.  DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).

[6]  Mykel J. Kochenderfer,et al.  Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning , 2020, J. Artif. Intell. Res..

[7]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[8]  Alexander Binder,et al.  Unmasking Clever Hans predictors and assessing what machines really learn , 2019, Nature Communications.

[9]  Rick Salay,et al.  Improving ML Safety with Partial Specifications , 2019, SAFECOMP Workshops.

[10]  Ernest Wozniak,et al.  A Safety Case Pattern for Systems with Machine Learning Components , 2020, SAFECOMP Workshops.

[11]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[12]  Mykel J. Kochenderfer,et al.  The Marabou Framework for Verification and Analysis of Deep Neural Networks , 2019, CAV.

[13]  Umut Durak,et al.  Adapting Scenario Definition Language for Formalizing UAS Concept of Operations , 2018 .

[14]  Jun Yang,et al.  Data Management in Machine Learning: Challenges, Techniques, and Systems , 2017, SIGMOD Conference.

[15]  Umut Durak,et al.  Formally Bounding UAS Behavior to Concept of Operation with Operation-Specific Scenario Description Language , 2019, AIAA Scitech 2019 Forum.

[16]  Petar Tsankov,et al.  Robustness Testing of AI Systems: A Case Study for Traffic Sign Recognition , 2021, AIAI.

[17]  Johann C. Dauer,et al.  HorizonUAM: Safety and Security Considerations for Urban Air Mobility , 2021, AIAA AVIATION 2021 FORUM.

[18]  Radu Grosu,et al.  Neural circuit policies enabling auditable autonomy , 2020, Nature Machine Intelligence.

[19]  Wojciech Samek,et al.  Methods for interpreting and understanding deep neural networks , 2017, Digit. Signal Process..

[20]  Sanjit A. Seshia,et al.  Compositional Falsification of Cyber-Physical Systems with Machine Learning Components , 2017, Journal of Automated Reasoning.

[21]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[22]  David W. Aha,et al.  DARPA's Explainable Artificial Intelligence (XAI) Program , 2019, AI Mag..

[23]  Nancy G Leveson,et al.  Software safety: why, what, and how , 1986, CSUR.

[24]  Francisco Herrera,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2020, Inf. Fusion.

[25]  Ali Sunyaev,et al.  Trustworthy artificial intelligence , 2020, Electronic Markets.

[26]  Keith Manville,et al.  The vulnerability of UAVs: an adversarial machine learning perspective , 2021, Defense + Commercial Sensing.

[27]  Junfeng Yang,et al.  DeepXplore: Automated Whitebox Testing of Deep Learning Systems , 2017, SOSP.

[28]  Eric D. Ragan,et al.  A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems , 2018, ACM Trans. Interact. Intell. Syst..

[29]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[30]  Paolo Tonella,et al.  Testing machine learning based systems: a systematic mapping , 2020, Empirical Software Engineering.

[31]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[32]  Jeannette M. Wing.  Trustworthy AI , 2020, Commun. ACM.

[33]  Johann C. Dauer,et al.  Considerations of Artificial Intelligence Safety Engineering for Unmanned Aircraft , 2018, SAFECOMP Workshops.

[34]  M. L. Cummings,et al.  Adaptation of Human Licensing Examinations to the Certification of Autonomous Systems , 2018, Safe, Autonomous and Intelligent Vehicles.

[35]  Lei Ma,et al.  DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems , 2018, 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE).

[36]  Franco Turini,et al.  A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..

[37]  Radu Grosu,et al.  Neural Simplex Architecture , 2019, NFM.

[38]  Zachary Chase Lipton.  The mythos of model interpretability , 2016, ACM Queue.

[39]  M. Niklaß,et al.  Urban Air Mobility Research at the DLR German Aerospace Center – Getting the HorizonUAM Project Started , 2021, AIAA AVIATION 2021 FORUM.