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Michael Herman

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3 papers

2 author rows

ICRA Conference 2018 Conference Paper

Predicting Ego-Vehicle Paths from Environmental Observations with a Deep Neural Network

Ulrich Baumann
Claudius Guiser
Michael Herman
J. Marius Zöllner

Advanced driver assistance systems allow for increasing user comfort and safety by sensing the environment and anticipating upcoming hazards. Often, this requires to accurately predict how situations will change. Recent approaches make simplifying assumptions on the predictive model of the Ego-Vehicle motion or assume prior knowledge, such as road topologies, to be available. However, in many urban areas this assumption is not satisfied. Furthermore, temporary changes (e. g. construction areas, vehicles parked on the street) are not considered by such models. Since many cars observe the environment with several different sensors, predictive models can benefit from them by considering environmental properties. In this work, we present an approach for an Ego-Vehicle path prediction from such sensor measurements of the static vehicle environment. Besides proposing a learned model for predicting the driver's multi-modal future path as a grid-based prediction, we derive an approach for extracting paths from it. In driver assistance systems both can be used to solve varying assistance tasks. The proposed approach is evaluated on real driving data and outperforms several baseline approaches.

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AAAI Conference 2017 Conference Paper

I See What You See: Inferring Sensor and Policy Models of Human Real-World Motor Behavior

Felix Schmitt
Hans-Joachim Bieg
Michael Herman
Constantin Rothkopf

Human motor behavior is naturally guided by sensing the environment. To predict such sensori-motor behavior, it is necessary to model what is sensed and how actions are chosen based on the obtained sensory measurements. Although several models of human sensing haven been proposed, rarely data of the assumed sensory measurements is available. This makes statistical estimation of sensor models problematic. To overcome this issue, we propose an abstract structural estimation approach building on the ideas of Herman et al. ’s Simultaneous Estimation of Rewards and Dynamics (SERD). Assuming optimal fusion of sensory information and rational choice of actions the proposed method allows to infer sensor models even in absence of data of the sensory measurements. To the best of our knowledge, this work presents the ﬁrst general approach for joint inference of sensor and policy models. Furthermore, we consider its concrete implementation in the important class of sensor scheduling linear quadratic Gaussian problems. Finally, the effectiveness of the approach is demonstrated for prediction of the behavior of automobile drivers. Speciﬁcally, we model the glance and steering behavior of driving in the presence of visually demanding secondary tasks. The results show, that prediction beneﬁts from the inference of sensor models. This is the case, especially, if also information is considered, that is contained in gaze switching behavior.

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ICRA Conference 2015 Conference Paper

Inverse reinforcement learning of behavioral models for online-adapting navigation strategies

Michael Herman
Volker Fischer 0003
Tobias Gindele
Wolfram Burgard

To increase the acceptance of autonomous systems in populated environments, it is indispensable to teach them social behavior. We would expect a social robot, which plans its motions among humans, to consider both the social acceptability of its behavior as well as task constraints, such as time limits. These requirements are often contradictory and therefore resulting in a trade-off. For example, a robot has to decide whether it is more important to quickly achieve its goal or to comply with social conventions, such as the proximity to humans, i. e. , the robot has to react adaptively to task-specific priorities. In this paper, we present a method for priority-adaptive navigation of mobile autonomous systems, which optimizes the social acceptability of the behavior while meeting task constraints. We learn acceptability-dependent behavioral models from human demonstrations by using maximum entropy (MaxEnt) inverse reinforcement learning (IRL). These models are generative and describe the learned stochastic behavior. We choose the optimum behavioral model by maximizing the social acceptability under constraints on expected time-limits and reliabilities. This approach is evaluated in the context of driving behaviors based on the highway scenario of Levine et al. [1].

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