656 citations · 718 across the 8 of their papers we have counts for
8 papers
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
Michael Herman, Jörg Wagner, Vishnu Prabhakaran +6
Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particular…
Wasserstein Adversarial Imitation Learning
Huang Xiao, Michael Herman, Joerg Wagner +3
Imitation Learning describes the problem of recovering an expert policy from demonstrations. While inverse reinforcement learning approaches are known to be very sample-efficient i…
Human Motion Trajectory Prediction: A Survey
Andrey Rudenko, Luigi Palmieri, Michael Herman +3
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly…
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation
Jörg Wagner, Volker Fischer, Michael Herman +1
The performance of autonomous systems heavily relies on their ability to generate a robust representation of the environment. Deep neural networks have greatly improved vision-base…
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Jörg Wagner, Volker Fischer, Michael Herman +1
Generating a robust representation of the environment is a crucial ability of learning agents. Deep learning based methods have greatly improved perception systems but still fail i…
Exact Maximum Entropy Inverse Optimal Control for Modelling Human Attention Switching and Control
Felix Schmitt, Hans-Joachim Bieg, Dietrich Manstetten +2
Maximum Causal Entropy (MCE) Inverse Optimal Control (IOC) has become an effective tool for modelling human behaviour in many control tasks. Its advantage over classic techniques f…
Predicting Lane Keeping Behavior of Visually Distracted Drivers Using Inverse Suboptimal Control
Felix Schmitt, Hans-Joachim Bieg, Dietrich Manstetten +2
Driver distraction strongly contributes to crash-risk. Therefore, assistance systems that warn the driver if her distraction poses a hazard to road safety, promise a great safety b…
Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics
Michael Herman, Tobias Gindele, Jörg Wagner +2
Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the a…