Test scenario selection for system-level verification and validation of geolocation-dependent automotive control systems. - Johannes Bach; Jacob Langner; Stefan Otten; Eric Sax; Marc Holzäpfel (ICE/ITMC, 2017)

Estimating the Uniqueness of Test Scenarios derived from Recorded Real-World-Driving-Data using Autoencoders. - Jacob Langner; Johannes Bach; Lennart Ries; Stefan Otten; Marc Holzäpfel; Eric Sax (Intelligent Vehicles Symposium, 2018)

A Driving Scenario Representation for Scalable Real-Data Analytics with Neural Networks. - Lennart Ries; Jacob Langner; Stefan Otten; Johannes Bach; Eric Sax (IV, 2019)

Logical Scenario Derivation by Clustering Dynamic-Length-Segments Extracted from Real-World-Driving-Data. - Jacob Langner; Hannes Grolig; Stefan Otten; Marc Holzäpfel; Eric Sax (VEHITS, 2019)

Leveraging Regular Expressions for Flexible Scenario Detection in Recorded Driving Data. - Philip Elspas; Jacob Langner; Michael Aydinbas; Johannes Bach; Eric Sax (ISSE, 2020)

A Taxonomy and Survey on Validation Approaches for Automated Driving Systems. - Christian King; Lennart Ries; Jacob Langner; Eric Sax (ISSE, 2020)

A Process Reference Model for the Virtual Application of Predictive Control Features. - Jacob Langner; Kai-Lukas Bauer; Marc Holzäpfel; Eric Sax (IV, 2020)

Qualitative Feature Assessment for Longitudinal and Lateral Control-features. - Jacob Langner; Christian Seiffer; Stefan Otten; Kai-Lukas Bauer; Marc Holzäpfel; Eric Sax (VEHITS, 2020)

Capturing the Variety of Urban Logical Scenarios from Bird-view Trajectories. - Christian King; Thilo Braun; Constantin Braess; Jacob Langner; Eric Sax (VEHITS, 2021)

An Efficient Strategy for Testing ADAS on HiL Test Systems with Parallel Condition-based Assessments. - Christian Steinhauser; Maciej Boncler; Jacob Langner; Steffen Strebel; Eric Sax (VEHITS, 2022)

Reducing Computer Vision Dataset Size via Selective Sampling. - Hanno Stage; Lukas Ewecker; Jacob Langner; Tin Stribor Sohn; Thomas Villmann; Eric Sax (ITSC, 2023)

Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets. - Philipp Rigoll; Jacob Langner; Eric Sax (CoRR, 2023)

Contextualizing the Sim-to-Real Gap for Hardware-in-the-Loop Testing of Highly Automated Driving Functions. - Christian Steinhauser; Joshua Ransiek; Jacob Langner; Eric Sax (ICSRS, 2024)

GOOSE: Goal-Conditioned Reinforcement Learning for Safety-Critical Scenario Generation. - Joshua Ransiek; Johannes Plaum; Jacob Langner; Eric Sax (ITSC, 2024)

Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept. - Lukas Ewecker; Niklas Wagner; Tim Brühl; Robin Schwager; Tin Stribor Sohn; Alexander Engelsberger; Jensun Ravichandran; Hanno Stage; Jacob Langner; Sascha Saralajew (IV, 2024)

Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets. - Philipp Rigoll; Jacob Langner; Lennart Ries; Eric Sax (IV, 2024)

Data Quality Matters: Quantifying Image Quality Impact on Machine Learning Performance. - Christian Steinhauser; Philipp Reis; Hubert Padusinski; Jacob Langner; Eric Sax (CoRR, 2025)

Point Cloud Recombination: Systematic Real Data Augmentation Using Robotic Targets for LiDAR Perception Validation. - Hubert Padusinski; Christian Steinhauser; Christian Scherl; Julian Gaal; Jacob Langner (CoRR, 2025)

A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording. - Philipp Reis; Joshua Ransiek; David Petri; Jacob Langner; Eric Sax (CoRR, 2025)