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Found 6 papers, 2 papers with code
survAIval: Survival Analysis with the Eyes of AI
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-critical corner cases in a short period of time, as already presented in~\cite{kowol22simulator}.
Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey
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 in all kinds of situations.
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e. g., moving from point A to B.
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
We present the SOS data set containing 20 video sequences of street scenes and more than 1000 labeled frames with up to two OOD objects.
A-Eye: Driving with the Eyes of AI for Corner Case Generation
For the test rig, a real-time semantic segmentation network is trained and integrated into the driving simulation software CARLA in such a way that a human can drive on the network's prediction.
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors
In this work, we present an uncertainty-based method for sensor fusion with camera and radar data.
