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Estimating the Uniqueness of Test Scenarios derived from Recorded Real-World-Driving-Data using Autoencoders.

, , , , , and . Intelligent Vehicles Symposium, page 1860-1866. IEEE, (2018)

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Estimating the Uniqueness of Test Scenarios derived from Recorded Real-World-Driving-Data using Autoencoders., , , , , and . Intelligent Vehicles Symposium, page 1860-1866. IEEE, (2018)Semantic Comparison of Driving Sequences by Adaptation of Word Embeddings., , , and . ITSC, page 1-7. IEEE, (2020)A Taxonomy and Survey on Validation Approaches for Automated Driving Systems., , , and . ISSE, page 1-8. IEEE, (2020)Scalable Data Set Distillation for the Development of Automated Driving Functions., , and . ITSC, page 3139-3145. IEEE, (2022)Trajectory-Based Clustering of Real-World Urban Driving Sequences with Multiple Traffic Objects., , , , , and . ITSC, page 1251-1258. IEEE, (2021)Focus on the Challenges: Analysis of a User-friendly Data Search Approach with CLIP in the Automotive Domain., , , , and . ITSC, page 168-174. IEEE, (2023)Collection of Requirements and Model-based Approach for Scenario Description., , , , , and . VEHITS, page 634-645. SCITEPRESS, (2021)A Driving Scenario Representation for Scalable Real-Data Analytics with Neural Networks., , , , and . IV, page 2215-2222. IEEE, (2019)CLIPping the Limits: Finding the Sweet Spot for Relevant Images in Automated Driving Systems Perception Testing., , , and . CoRR, (2024)Unveiling Objects with SOLA: An Annotation-Free Image Search on the Object Level for Automotive Data Sets., , , and . IV, page 1053-1059. IEEE, (2024)