2 citations · 2 across the 3 of their papers we have counts for
3 papers
Diffusion-Based Generation and Imputation of Driving Scenarios from Limited Vehicle CAN Data
Julian Ripper, Ousama Esbel, Rafael Fietzek +2
Training deep learning methods on small time series datasets that also include corrupted samples is challenging. Diffusion models have shown to be effective to generate realistic a…
Unsupervised Driving Event Discovery Based on Vehicle CAN-data
Thomas Kreutz, Ousama Esbel, Max Mühlhäuser +1
The data collected from a vehicle's Controller Area Network (CAN) can quickly exceed human analysis or annotation capabilities when considering fleets of vehicles, which stresses t…
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time Series
Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea
In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are invo…