2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets
Marc Uecker, J. Marius Zöllner
Recently, LiDAR segmentation methods for autonomous vehicles, powered by deep neural networks, have experienced steep growth in performance on classic benchmarks, such as nuScenes…
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…
Can you see me now? Blind spot estimation for autonomous vehicles using scenario-based simulation with random reference sensors
Marc Uecker, J. Marius Zöllner
In this paper, we introduce a method for estimating blind spots for sensor setups of autonomous or automated vehicles and/or robotics applications. In comparison to previous method…
Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception
Marc Uecker, Tobias Fleck, Marcel Pflugfelder +1
LiDAR sensors are an integral part of modern autonomous vehicles as they provide an accurate, high-resolution 3D representation of the vehicle's surroundings. However, it is comput…
AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation
David Kügler, Marc Uecker, Arjan Kuijper +1
Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical inst…