23 citations · 42 across the 6 of their papers we have counts for
6 papers
survAIval: Survival Analysis with the Eyes of AI
Kamil Kowol, Stefan Bracke, Hanno Gottschalk
In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-cri…
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
Daniel Bogdoll, Svenja Uhlemeyer, Kamil Kowol +1
Deep neural networks (DNN) which are employed in perception systems for autonomous driving require a huge amount of data to train on, as they must reliably achieve high performance…
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Kevin Rösch, Florian Heidecker, Julian Truetsch +5
Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns.…
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
Kira Maag, Robin Chan, Svenja Uhlemeyer +2
In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD objects are understood as objects…
A-Eye: Driving with the Eyes of AI for Corner Case Generation
Kamil Kowol, Stefan Bracke, Hanno Gottschalk
The overall goal of this work is to enrich training data for automated driving with so called corner cases. In road traffic, corner cases are critical, rare and unusual situations…
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
Kamil Kowol, Matthias Rottmann, Stefan Bracke +1
In this work, we present an uncertainty-based method for sensor fusion with camera and radar data. The outputs of two neural networks, one processing camera and the other one radar…