Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.Comment: 6 pages, 5 figures, accepted at 2019 IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, New Zealand, October 201

Machine learning challenges in theoretical HEP

In these proceedings we perform a brief review of machine learning (ML) applications in theoretical High Energy Physics (HEP-TH). We start the discussion by defining and then classifying machine learning tasks in theoretical HEP. We then discuss some of the most popular and recent published approaches with focus on a relevant case study topic: the determination of parton distribution functions (PDFs) and related tools. Finally, we provide an outlook about future applications and developments due to the synergy between ML and HEP-TH.Comment: 7 pages, 3 figures, in proceedings of the 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017