activity
20172022
most citedINTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

354 citations · 382 across the 20 of their papers we have counts for

collaborators
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12 papers · 1 filter

cs.RO2022

Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

Kevin Rösch, Florian Heidecker, Julian Truetsch +5

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns.…

cs.RO2022

Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

Sascha Wirges, Kevin Rösch, Frank Bieder +1

We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle. The ground surface is modeled as a UBS which is robust towards v…

cs.RO2021

Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

Danial Kamran, Tizian Engelgeh, Marvin Busch +2

Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for r…

cs.RO20209 cited

Decision-Time Postponing Motion Planning for Combinatorial Uncertain Maneuvering

Ömer Şahin Taş, Felix Hauser, Christoph Stiller

Motion planning involves decision making among combinatorial maneuver variants in urban driving. A planner must consider uncertainties and associated risks of the maneuver variants…

cs.RO2020

SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation

Juncong Fei, Wenbo Chen, Philipp Heidenreich +2

3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. L…

cs.RO2020

Tackling Existence Probabilities of Objects with Motion Planning for Automated Urban Driving

Omer Sahin Tas, Christoph Stiller

Motion planners take uncertain information about the environment as an input. The environment information is often quite noisy and has a tendency to contain false positive object d…