58 citations · 108 across the 7 of their papers we have counts for
7 papers
Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction
Sajad Marvi, Christoph Rist, Julian Schmidt +2
Accurate trajectory prediction is crucial for autonomous driving, yet uncertainty in agent behavior and perception noise makes it inherently challenging. While multi-modal trajecto…
Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations
Michael Kurenkov, Sajad Marvi, Julian Schmidt +6
The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is cruc…
A Realism Metric for Generated LiDAR Point Clouds
Larissa T. Triess, Christoph B. Rist, David Peter +1
A considerable amount of research is concerned with the generation of realistic sensor data. LiDAR point clouds are generated by complex simulations or learned generative models. T…
A Survey on Deep Domain Adaptation for LiDAR Perception
Larissa T. Triess, Mariella Dreissig, Christoph B. Rist +1
Scalable systems for automated driving have to reliably cope with an open-world setting. This means, the perception systems are exposed to drastic domain shifts, like changes in we…
Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data
Christoph B. Rist, David Emmerichs, Markus Enzweiler +1
Semantic scene completion is the task of jointly estimating 3D geometry and semantics of objects and surfaces within a given extent. This is a particularly challenging task on real…
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study
Larissa T. Triess, David Peter, Christoph B. Rist +1
Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep n…
CNN-based synthesis of realistic high-resolution LiDAR data
Larissa T. Triess, David Peter, Christoph B. Rist +2
This paper presents a novel CNN-based approach for synthesizing high-resolution LiDAR point cloud data. Our approach generates semantically and perceptually realistic results with…