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Sheng Dong, Lars Gräning, Allen Sheldon, "Parametric Optimization for CAE Models of Carbon-Fiber Reinforced Plastic (CFRP) Composite Material ", NAFEMS World Congress 2017, 2017.

Abstract

Carbon-fiber-reinforced plastic (CFRP) composite material, due to its high strength but light weight, has been increasingly employed in aerospace, automotive, and civil engineering. However, the non-isotropic properties across the layers of composites, due to different fiber orientations, create challenges in modeling the CFRP parts both all by themselves and when integrated into entire mechanical systems. The basic properties in and out of fiber...



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Edoardo Casapietra, Thomas Weisswange, Franz Kummert, Christian Goerick, "Improving spatial trajectory planning by using an enhanced road representation", 4th International Symposium on Future Active Safety Technology: Toward zero traffic accidents (FAST-zero'17), 2017.

Abstract

The detection of road layout and semantics is an important issue in modern ADAS and autonomous driving systems. In particular, trajectory planning needs a spatial road representation to operate on. As typical trajectories are computed for time-spans in the order of a few seconds, the spatial range needed for the road representation to achieve a stable and smooth trajectory can go from tenths to hundreds of meters. This range is very hard to achie...



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Nils Einecke, Keiji Muro, Jörg Deigmöller, Mathias Franzius, "Working area mapping with an autonomous lawn mower", Conference on Field and Service Robotics, 2017.

Abstract

In this work we show a new technique for loop closing using the special setup of autonomous lawn mowers. By estimating the movement while travelling around the border wire we get a first shot estimation of the the boundary. This will not be very precise. Additionally, using a loop closing at the base station and by distributing the end point error depending on the estimated motion at each time step, the results are strongly improved. Furthermore...



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Yuka Ogino, Ryoya Iida, Tobias Rodemann, "Using Desirability Functions for Many-Objective Optimization of a Hybrid Car Controller", GECCO 2017 Conference Companion, 2017.

Abstract

In this work we investigate the recently proposed concept of desirability functions for a many-objective optimization of a prototypical application problem, a hybrid car controller where the seven objectives are from many different domains. We compare the results of the optimization with a standard optimization in terms of hypervolume and the ease with which a solution can be picked. We also analyze the impact of wrongly defined desirability func...



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Nikola Aulig, Emily Nutwell, Stefan Menzel, Duane Detwiler, "Preference-based Topology Optimization for Vehicle Concept Design with Concurrent Static and Crash Load Cases ", Journal of Structural and Multidisciplinary Optimization, 2017.

Abstract

In the simulation-based design process of automotive structures, an increasing amount of multi-disciplinary requirements have to be considered. Methods of topology optimization can be used to devise structural concepts early in the design process to obtain the best possible structural layout as starting point for further development steps. Especially relevant for the vehicle design process is the concurrent consideration of static load requiremen...



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Martina Hasenjäger and Heiko Wersing, "Personalization in Advanced Driver Assistance Systems and Autonomous Vehicles: A Review", 2017 IEEE20th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 2205-2211, 2017.

Abstract

Today's possibilities to collect, store and process huge amounts of data open the opportunity to tailor technical systems to the preferences of individual users. At the same time the field of advanced driver assistance systems (ADAS) has matured to the point where an optimized driving experience with the systems gains importance. In parallel, the development of algorithms for autonomous driving opens a fresh view on the implementation of individu...



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Benjamin Metka, Mathias Franzius, Ute Bauer-Wersing, "Efficient Navigation Using Slow Feature Gradients", International Conference on Intelligent Robots and Systems (IROS), 2017.

Abstract

A model of hierarchical Slow Feature Analysis (SFA) enables a mobile robot to learn a spatial representation of its environment directly from images captured during a random walk. After the unsupervised learning phase a subset of the resulting representations are orientation invariant and code for the position of the robot. Hence, they change monotonically over space even though the variation of the sensory signals received from the environmen...



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Mariusz Bujny, Nikola Aulig, Markus Olhofer, Fabian Duddeck, "Topology Optimization of Crash Structures with the Hybrid Evolutionary Level Set Method", 12th World Congress of Structural and Multidisciplinary Optimisation, 2017.

Abstract

Topology optimization plays an important role in many engineering fields, including crashworthiness. In most of the crash topology optimization methods, very strong simplifications of the underlying problem are made and often heuristic approaches are used. This makes the optimality of the obtained topologies arguable and limits the applicability of those methods just to selected use cases. Presented in previous works of the authors, Evolutionary ...



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Viktor Losing, Barbara Hammer, Heiko Wersing, "Personalized Maneuver Prediction at Intersections", International Conference on Intelligent Transportation Systems, pp. 1-6, 2017.

Abstract

We investigate a new approach towards maneuver prediction that is based on personalization and incremental learning. The prediction accuracy is continuously improved by incorporating only the individual driving history. The study is based on a collection of commuting drivers who recorded their daily routes with a standard smart phone and GPS receiver. Prediction target is the expected maneuver on the next intersection with three classes: stop,...



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Benedict Flade, Edoardo Casapietra, Christian Goerick, Julian Eggert, "Behavior-Based Relative Self-Localization in Intersection Scenarios", 20th International IEEE Conference on Intelligent Transportation Systems, 2017.

Abstract

Accurate map-relative localization is crucial when dealing with navigation or other driver assistance tasks. Global Navigation Satellite Systems (GNSS), such as GPS, GLONASS or Galileo, meet the requirements demanded from road-level navigation systems, but with lane-level assistance being the next step, more accurate localization technologies are needed. A lot of effort has been put in improving localization relative to lanes in scenarios with si...



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