A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 201

Wirtschaftliche Generierung von Belieferungssimulationen unter Verwendung rechnerunterstützter Plausibilisierungsmethoden für die Bewertung der Eingangsdaten

The concept of the Digital-Factory provides a “single source of truth” Planning-System. This establishes a possibility to generate intra-logistic-simulations with validated input data through a standardized method. Therefore we have to take a closer look on input-data and its quality in order to understand its way back through all the IT-systems to the point of birth. This dissertation provides an approach how to validate this input-data with different, independent methods, not only double-checking whether the information is complete but also giving an answer to the question: is this data correct? Those methods will be combined in a quality module in the Digital-Factory-Planning-System, showing inconsistencies which should be inspected. The objective is collecting all necessary input-data in the required quality in order to semi-automatic-generate a reliable simulation model in intra-logistics, avoiding costly iterations or even wrong conclusions.Das Konzept der Digitalen Fabrik bietet die Möglichkeit, ein Planungssystem mit einer einheitlichen Datengrundlage aufzubauen. Dies schafft die Voraussetzungen, um Belieferungssimulationen mit plausibilisierten Eingangsdaten weitestgehend automatisiert generieren zu können. Die Eingangsdaten sind der Schlüssel zum Erfolg. Nur wer in gewachsenen IT-Landschaften mit unzähligen Schnittstellen Transparenz schaffen kann, weiß, woher welche Daten kommen und wie diese erhoben werden, kann damit auch die Qualität der Eingangsdaten – und dadurch auch die Qualität der Simulationsergebnisse beurteilen. Diese Dissertation möchte einen Beitrag leisten, wie diese Eingangsdaten mit unterschiedlichen, unabhängigen Methoden untersucht werden können. Es sollen nicht nur Fragen zur Vollständigkeit der Eingangsdaten beantwortet werden, sondern vielmehr Antworten gegeben werden, ob die Eingangsdaten korrekt sind. Es wird aufgezeigt, wie diese Methoden im Umfeld der Digitalen Fabrik zentral in einem Simulationsgerüst gebündelt werden können, um eine einheitliche Plattform zur Plausibilisierung zu schaffen. Darauf aufbauend können diese plausibilisierten Daten verwendet werden, um eine Belieferungssimulation aufzubauen. Dies spart nicht nur Zeit, sondern kann auch falsche Schlussfolgerungen auf Basis fehlerhafter Eingangsdaten verhindern

Continuous-Time Estimation of Attitude Using B-Splines on Lie Groups

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140656/1/1.g001149.pd

Advancing Protocol Diversity in Network Security Monitoring

With information technology entering new fields and levels of deployment, e.g., in areas of energy, mobility, and production, network security monitoring needs to be able to cope with those environments and their evolution. However, state-of-the-art Network Security Monitors (NSMs) typically lack the necessary flexibility to handle the diversity of the packet-oriented layers below the abstraction of TCP/IP connections. In this work, we advance the software architecture of a network security monitor to facilitate the flexible integration of lower-layer protocol dissectors while maintaining required performance levels. We proceed in three steps: First, we identify the challenges for modular packet-level analysis, present a refined NSM architecture to address them and specify requirements for its implementation. Second, we evaluate the performance of data structures to be used for protocol dispatching, implement the proposed design into the popular open-source NSM Zeek and assess its impact on the monitor performance. Our experiments show that hash-based data structures for dispatching introduce a significant overhead while array-based approaches qualify for practical application. Finally, we demonstrate the benefits of the proposed architecture and implementation by migrating Zeek\u27s previously hard-coded stack of link and internet layer protocols to the new interface. Furthermore, we implement dissectors for non-IP based industrial communication protocols and leverage them to realize attack detection strategies from recent applied research. We integrate the proposed architecture into the Zeek open-source project and publish the implementation to support the scientific community as well as practitioners, promoting the transfer of research into practice

Ausprägungen und Nutzungsgrad der Logistiksimulation im Umfeld der Automobilindustrie

In Wissenschaft und Praxis wird die Materialflusssimulation für eine Vielzahl von Anwendungsszenarien in Produktion und Logistik verwendet. Fokus dieses Beitrags sind die unter dem Oberbegriff „Logistiksimulation“ im automobilen Umfeld zusammengefassten Ausprägungen dieser Simulationsmethode. Diese werden von den einzelnen Automobilherstellern in unterschiedlichen Detaillierungsgraden und unterschiedlichen Bandbreiten eingesetzt. Bisher konnten sich allerdings keine gemeinsamen Bezeichnungen für diese Ausprägungen der Logistiksimulation durchsetzen. Um einen einheitlichen Standard im Umfeld der automobilen Logistik zu definieren, wurde eine Umfrage bei den deutschen Automobilherstellern durchgeführt. Das Ergebnis zeigt, dass aktuell vier Ausprägungen der Simulation im Bereich der Automotive-Logistik eingesetzt werden, die auf Grund ihrer Fragestellungen sehr unterschiedlich zu behandeln sind. Dies sind die Werkssimulation, die Belieferungssimulation, die Supply-Chain-Simulation und die Verkehrsflusssimulation. Durch die übergreifende Definition der verwendeten Simulationen, Festlegung der Betrachtungsumfänge und Systemgrenzen sowie die Rolle im Produktentstehungsprozess wird eine Vergleichbarkeit geschaffen. Auf der Basis dieser Standardisierung können Zusammenarbeitsmodelle vereinbart und gemeinsame Forschungsvorhaben angestoßen werden

A Primer on the Differential Calculus of 3D Orientations

The proper handling of 3D orientations is a central element in many optimization problems in engineering. Unfortunately many researchers and engineers struggle with the formulation of such problems and often fall back to suboptimal solutions. The existence of many different conventions further complicates this issue, especially when interfacing multiple differing implementations. This document discusses an alternative approach which makes use of a more abstract notion of 3D orientations. The relative orientation between two coordinate systems is primarily identified by the coordinate mapping it induces. This is combined with the standard exponential map in order to introduce representation-independent and minimal differentials, which are very convenient in optimization based methods