7 papers
Boosting LiDAR-Based Localization with Semantic Insight: Camera Projection versus Direct LiDAR Segmentation
Sven Ochs, Philip Schörner, Marc René Zofka +1
Semantic segmentation of LiDAR data presents considerable challenges, particularly when dealing with diverse sensor types and configurations. However, incorporating semantic inform…
Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
Stefan Orf, Sven Ochs, Marc René Zofka +1
Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the uti…
Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
Stefan Orf, Sven Ochs, Valentin Marotta +3
In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of ind…
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving
Rupert Polley, Nikolai Polley, Dominik Heid +3
Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights t…
A Chefs KISS -- Utilizing semantic information in both ICP and SLAM framework
Sven Ochs, Marc Heinrich, Philip Schörner +2
For utilizing autonomous vehicle in urban areas a reliable localization is needed. Especially when HD maps are used, a precise and repeatable method has to be chosen. Therefore acc…
Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems
Stefan Orf, Sven Ochs, Jens Doll +4
Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous…