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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.
AbstractIn 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), pp. 2205-2211, 2017.
AbstractToday'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.
AbstractA 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 enviro...
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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.
AbstractTopology 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.
AbstractWe 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.
AbstractAccurate 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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Dennis Orth, Dorothea Kolossa, Martin Ernst Heckmann , "Predicting Driver Left-Turn Behavior from Few Training Samples using a Maximum A Posteriori Method ", ITSC 2017, pp. 1121-1126, 2017.
AbstractIn this work, we introduce a novel maximum a posteriori (MAP) method, which can predict driver left-turn behavior from only a few training samples. For the prediction of the driver behavior in this scenario we utilize the so-called critical gap. It signifies how large a gap minimally has to be for the driver to accept it and take the turn. The latter is especially important for the personalization of an intersection assistant, which we are curre...
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Matti Krüger, Christiane Wiebel, Heiko Wersing , "From Tools Towards Cooperative Assistants", HAI 2017, 5th International Conference on Human-Agent Interaction, 2017.
AbstractEndowing assistant systems with more autonomy establishes the transition from a human-controlled tool towards a self-directed agent capable of own decisions and goals. In this concept paper we suggest to perform the design of such an assistant agent according to principles of cooperativity. We first review definitions of cooperation between animals, humans and machines and then discuss advantages of cooperation also for a human-machine interactio...
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Handing Wang, Markus Olhofer, Yaochu Jin , "A Mini-Review on Preference Modeling and Articulation in Multi-Objective Optimization: Current Status and Challenges", Complex & Intelligent Systems, 2017.
AbstractEvolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. However, decision makers are usually interested in only a small part of the Pareto front of the multi-objective optimization problem. Over the past decades, preference-based multi-objective optimization has attracted increasing attention from both academia and industry. Significant progress has been made in the evolutionary mu...
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Sebastian Schrom and Stephan Hasler , "Effects of Domain Awareness in Generalizing over Cameras in Road Detection", New Challenges in Neural Computation (NC2), 2017.
AbstractIn this paper we investigate the effects of domain awareness when using a pre-trained covolutional neural network (CNN). Usually, the only adaptation when using such a CNN for the same task is to normalize the input data by an individual RGB mean from own data. We show that it plays a major role whether the test domain was included during training and if training was aware of domains. For this we investigate generalization over few cameras in a r...
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