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Martin Herrmann

2 accepted papers

2022

Deep Kernel Learning for Uncertainty Estimation in Multiple Trajectory Prediction Networks

IROS 2022poster

Predicting future paths of vehicles or pedestrians is an essential task for automated vehicles to allow for planning the own trajectory. Using predicted paths, a planning algorithm can, e.g., react to anticipated manoeuvres of other traffic participants. For calculating risks of planned manoeuvres,…

Cited by 7SourceScholar
2020

Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks

IROS 2020poster

Automated vehicles need to not only perceive their environment, but also predict the possible future behavior of all detected traffic participants in order to safely navigate in complex scenarios and avoid critical situations, ranging from merging on highways to crossing urban intersections. Due to…

Cited by 46SourceScholar