9 citations · 24 across the 13 of their papers we have counts for
13 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…
DigiT4TAF -- Bridging Physical and Digital Worlds for Future Transportation Systems
Maximilian Zipfl, Pascal Zwick, Patrick Schulz +22
In the future, mobility will be strongly shaped by the increasing use of digitalization. Not only will individual road users be highly interconnected, but also the road and associa…
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…
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…
Empowering Autonomous Shuttles with Next-Generation Infrastructure
Sven Ochs, Melih Yazgan, Rupert Polley +9
As cities strive to address urban mobility challenges, combining autonomous transportation technologies with intelligent infrastructure presents an opportunity to transform how peo…
CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research
Marc Heinrich, Maximilian Zipfl, Marc Uecker +9
Real world testing is of vital importance to the success of automated driving. While many players in the business design purpose build testing vehicles, we designed and build a mod…
One Stack to Rule them All: To Drive Automated Vehicles, and Reach for the 4th level
Sven Ochs, Jens Doll, Daniel Grimm +15
Most automated driving functions are designed for a specific task or vehicle. Most often, the underlying architecture is fixed to specific algorithms to increase performance. There…
Leveraging Swarm Intelligence to Drive Autonomously: A Particle Swarm Optimization based Approach to Motion Planning
Sven Ochs, Jens Doll, Marc Heinrich +4
Motion planning is an essential part of autonomous mobile platforms. A good pipeline should be modular enough to handle different vehicles, environments, and perception modules. Th…
From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving
Ferdinand Mütsch, Helen Gremmelmaier, Nicolas Becker +3
Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simula…
KIGLIS: Smart Networks for Smart Cities
Daniel Bogdoll, Patrick Matalla, Christoph Füllner +11
Smart cities will be characterized by a variety of intelligent and networked services, each with specific requirements for the underlying network infrastructure. While smart city a…
Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality
Lars Töttel, Maximilian Zipfl, Daniel Bogdoll +2
One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex trafficscenarios with many, hard to predict traffic participants…