6 papers · 1 filter
Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence
Robert Aufschläger, Youssef Shoeb, Azarm Nowzad +3
The collection and release of street-level recordings as Open Data play a vital role in advancing autonomous driving systems and AI research. However, these datasets pose significa…
Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art
Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk
In this paper, we review the state of the art in Out-of-Distribution (OoD) segmentation, with a focus on road obstacle detection in automated driving as a real-world application. W…
Adaptive Neural Networks for Intelligent Data-Driven Development
Youssef Shoeb, Azarm Nowzad, Hanno Gottschalk
Advances in machine learning methods for computer vision tasks have led to their consideration for safety-critical applications like autonomous driving. However, effectively integr…
Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios
Youssef Shoeb, Nazir Nayal, Azarm Nowzad +2
Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign…
A Likelihood Ratio-Based Approach to Segmenting Unknown Objects
Nazir Nayal, Youssef Shoeb, Fatma Güney
Addressing the Out-of-Distribution (OoD) segmentation task is a prerequisite for perception systems operating in an open-world environment. Large foundational models are frequently…
How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception
Mert Keser, Youssef Shoeb, Alois Knoll
Deep Neural Networks (DNNs) have become central for the perception functions of autonomous vehicles, substantially enhancing their ability to understand and interpret the environme…