Search Results for author: J. Marius Zöllner

Found 17 papers, 1 papers with code

Quantifying point cloud realism through adversarially learned latent representations

no code implementations24 Sep 2021 Larissa T. Triess, David Peter, Stefan A. Baur, J. Marius Zöllner

In a series of experiments, we confirm the soundness of our metric by applying it in controllable task setups and on unseen data.

Anomaly Detection Metric Learning +1

Self-Supervised Action-Space Prediction for Automated Driving

no code implementations21 Sep 2021 Faris Janjoš, Maxim Dolgov, J. Marius Zöllner

In this paper, we present a novel learned multi-modal trajectory prediction architecture for automated driving.

Trajectory Prediction

Description of Corner Cases in Automated Driving: Goals and Challenges

no code implementations20 Sep 2021 Daniel Bogdoll, Jasmin Breitenstein, Florian Heidecker, Maarten Bieshaar, Bernhard Sick, Tim Fingscheidt, J. Marius Zöllner

Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC).

Safe Continuous Control with Constrained Model-Based Policy Optimization

1 code implementation14 Apr 2021 Moritz A. Zanger, Karam Daaboul, J. Marius Zöllner

Further, we provide theoretical and empirical analyses regarding the implications of model-usage on constrained policy optimization problems and introduce a practical algorithm that accelerates policy search with model-generated data.

Continuous Control Safe Exploration

Temporal Feature Networks for CNN based Object Detection

no code implementations22 Mar 2021 Michael Weber, Tassilo Wald, J. Marius Zöllner

For reliable environment perception, the use of temporal information is essential in some situations.

Object Detection Temporal Information Extraction

Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning

no code implementations12 Feb 2021 Karl Kurzer, Philip Schörner, Alexander Albers, Hauke Thomsen, Karam Daaboul, J. Marius Zöllner

Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability.

Decision Making

Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study

no code implementations6 Apr 2020 Larissa T. Triess, David Peter, Christoph B. Rist, J. Marius Zöllner

Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment.

Autonomous Vehicles Semantic Segmentation

Parallelization of Monte Carlo Tree Search in Continuous Domains

no code implementations30 Mar 2020 Karl Kurzer, Christoph Hörtnagl, J. Marius Zöllner

Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari.

Trajectory Planning

Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search

no code implementations2 Feb 2020 Karl Kurzer, Marcus Fechner, J. Marius Zöllner

Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others.

Automated Focal Loss for Image based Object Detection

no code implementations19 Apr 2019 Michael Weber, Michael Fürst, J. Marius Zöllner

With automated focal loss we introduce a new loss function which substitutes this hyperparameter by a parameter that is automatically adapted during the training progress and controls the amount of focusing on hard training examples.

Object Detection

Learning to Predict Ego-Vehicle Poses for Sampling-Based Nonholonomic Motion Planning

no code implementations3 Dec 2018 Holger Banzhaf, Paul Sanzenbacher, Ulrich Baumann, J. Marius Zöllner

This paper introduces therefore a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently towards the optimal solution.

Motion Planning

Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

no code implementations10 Sep 2018 Karl Kurzer, Florian Engelhorn, J. Marius Zöllner

Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency.

Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

no code implementations10 Sep 2018 Peter Wolf, Karl Kurzer, Tobias Wingert, Florian Kuhnt, J. Marius Zöllner

This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training.

Autonomous Driving

Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search

no code implementations25 Jul 2018 Karl Kurzer, Chenyang Zhou, J. Marius Zöllner

This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments.

Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps

no code implementations10 Sep 2017 Florian Piewak, Timo Rehfeld, Michael Weber, J. Marius Zöllner

Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications.

Object Detection

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