Jakob Mayr — accepted AI-conference papers

2020

Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving

Alexander Frickenstein, Manoj-Rohit Vemparala, Jakob Mayr, Naveen-Shankar Nagaraja, Christian Unger, Federico Tombari +1

ICRA 2020poster

Driveable area detection is a key component for various applications in the field of autonomous driving (AD), such as ground-plane detection, obstacle detection and maneuver planning. Additionally, bulky and over-parameterized networks can be easily forgone and replaced with smaller networks for fas…

Cited by 28 Source ↗Scholar ↗

2018

Self-Supervised Learning of the Drivable Area for Autonomous Vehicles

Jakob Mayr, Christian Unger, Federico Tombari

IROS 2018poster

We propose a new approach for generating training data for the task of drivable area segmentation with deep neural networks (DNN). The impressive progress of deep learning in recent years demonstrated a superior performance of DNNs over traditional machine learning and deterministic algorithms for v…

Cited by 25 Source ↗Scholar ↗