29 citations · 121 across the 63 of their papers we have counts for
10 papers · 1 filter
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving
Daniel Bogdoll, Stefani Guneshka, J. Marius Zöllner
The core obstacle towards a large-scale deployment of autonomous vehicles currently lies in the long tail of rare events. These are extremely challenging since they do not occur of…
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…
Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detection
Svetlana Pavlitskaya, Jonas Hendl, Sebastian Kleim +3
Adversarial patch-based attacks aim to fool a neural network with an intentionally generated noise, which is concentrated in a particular region of an input image. In this work, we…
DLCSS: Dynamic Longest Common Subsequences
Daniel Bogdoll, Jonas Rauch, J. Marius Zöllner
Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility model…
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers
Daniel Bogdoll, Meng Zhang, Maximilian Nitsche +1
Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a l…
Adversarial Vulnerability of Temporal Feature Networks for Object Detection
Svetlana Pavlitskaya, Nikolai Polley, Michael Weber +1
Taking into account information across the temporal domain helps to improve environment perception in autonomous driving. However, it has not been studied so far whether temporally…
Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detection
Svetlana Pavlitskaya, Bianca-Marina Codău, J. Marius Zöllner
Standard approaches for adversarial patch generation lead to noisy conspicuous patterns, which are easily recognizable by humans. Recent research has proposed several approaches to…
Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability
Svetlana Pavlitska, Christian Hubschneider, Lukas Struppek +1
Sparsely-gated Mixture of Expert (MoE) layers have been recently successfully applied for scaling large transformers, especially for language modeling tasks. An intriguing side eff…
Point Cloud Generation with Continuous Conditioning
Larissa T. Triess, Andre Bühler, David Peter +2
Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper…
Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search
Karl Kurzer, Matthias Bitzer, J. Marius Zöllner
Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants. However, for coop…