Using Machine Learning to Optimize Energy Consumption of HVAC Systems in Vehicles. - Martin Böhme; Andreas Lauber; Marco Stang; Luyi Pan; Eric Sax (IHIET, 2019)

An Evolutionary Approach to Hyper-Parameter Optimization of Neural Networks. - Marco Stang; Christopher Meier; Vinzenz Rau; Eric Sax (IHIET, 2019)

FEEDI - A Smart Wearable Foot-Band for Navigation and Guidance Using Haptic Feedback. - Simon Stock; Alain Bertemes; Marco Stang; Martin Böhme; Daniel Grimm; Wilhelm Stork (IHIET (Lausanne), 2020)

TalkyCars: A Distributed Software Platform for Cooperative Perception. - Martin Böhme; Marco Stang; Ferdin Muetsch; Eric Sax (IV, 2020)

Evaluation of Deep Reinforcement Learning Algorithms for Autonomous Driving. - Marco Stang; Daniel Grimm; Moritz Gaiser; Eric Sax (IV, 2020)

Ein LAMA geht online: Wie Machine-Learning-Kompetenzen durch praxisnahe Problemstellungen gefördert werden können. - Gabriela Molinar 001; Marco Stang; David Lohner; Manuela Schmidt (DELFI, 2021)

Acceptance of Smart Automated Comfort Functionalities in Vehicles. - Maria Guinea; Marco Stang; Irma Nitsche; Eric Sax (IHIET (Strasbourg), 2021)

Introduction of an Algorithm Based on Convolutional Neural Networks for an Automated Online Correction of Braided Cardiovascular Implants. - Benedikt Haas; Marco Stang; Valentin Khan-Blouki; Eric Sax (IHIET (Strasbourg), 2021)

CAGEN - Context-Action Generation for Testing Self-learning Functions. - Marco Stang; Maria Guinea Marquez; Eric Sax (IHIET (Strasbourg), 2021)

Context-Aware Security for Vehicles and Fleets: A Survey. - Daniel Grimm; Marco Stang; Eric Sax (IEEE Access, 2021)

Development of a self-learning automotive comfort function: an adaptive gesture control with few-shot-learning. - Marco Stang; Simon Stock; Simon Müller; Eric Sax; Wilhelm Stork (ICCVE, 2022)

Improving the Validation of Automotive Self-Learning Systems through the Synergy of Scenario-Based Testing and Metamorphic Relations. - Marco Stang; Martin Sommer; David Kraus; Eric Sax (BDCAT, 2023)