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Stefan Klingelschmitt, Florian Damerow, Volker Willert, Julian Eggert , "Probabilistic Situation Assessment Framework for Multiple Interacting Traffic Participants in Generic Traffic Scenes", Intelligent Vehicles Symposium (IV), pp. 1141-1148, 2016.
AbstractSituation recognition is a prerequisite for many advanced drivers assistance systems as well as partially and fully automated vehicles. Various approaches have been targeted at estimating maneuvers of single scene entities. However, assessing multiple, possibly interacting, traffic participants simultaneously is crucial in complex traffic scenes and has hardly been investigated. Considering the variability and combinatorics of such scenarios, hav...
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Nico Andreas Steinhardt, Stefan Kaufmann, Sven Rebhan, Ulrich Lages, Christian Goerick, Yves Noutangnin , "Lidar based object tracking evaluation for automotive applications", 23rd World Congress on Intelligent Transport Systems 2016, 2016.
AbstractThe evaluation and verification of advanced driver assistance systems (ADAS) is a crucial element for guaranteeing safe operation of such systems on the road. To cover a wide variety of real-world situations, ground-truth data needs to be generated for large datasets, rendering complete manual annotation infeasible. However, when employing automatic ground-truth reference systems, the performance and accuracy of these systems becomes subject to v...
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Mariusz Bujny, Nikola Aulig, Markus Olhofer, Fabian Duddeck , "Evolutionary Crashworthiness Topology Optimization of Thin-Walled Structures", Proceedings of the 11th ASMO UK/ISSMO Conference, 2016.
AbstractAs in many other disciplines, also in crashworthiness, the extensive growth of computers' power led to the development of techniques for numerical simulations. In particular, this allows to use numerical optimization methods to develop better structures and shorten the vehicle design cycle, what is a must in case of the hard competition on the car market. A basis for most of the state-of-the art methods for crashworthiness topology optimization...
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Lydia Fischer, Barbara Hammer, Heiko Wersing , "Optimal Local Rejection for Classifiers", Neurocomputing, vol. 214, pp. 445-457, 2016.
AbstractWe analyse optimal reject strategies for classifiers with input space partitioning, e.g. prototype-based classifiers, support vector machines or decision trees based on real-valued, distance-based certainty measures such as the distance to the closest decision border. We compare reject schemes with global thresholds, and local thresholds for the partitions of the space induced by the classifiers. For the latter, we develop a polynomial-time algo...
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Jörg Deigmöller and Julian Eggert , "Stereo Visual Odometry without Temporal Filtering", German Conference on Pattern Recognition, 2016.
AbstractVisual Odometry is one of the key technology for navigating and percepting the environment of an autonomous vehicle. Within the last ten years, a common sense has been established how to implement a high precision and robust system. This paper goes one step back by avoiding temporal filtering and relying only on pure measurements that have been carefully selected. The focus here is on estimating the ego-motion rather a detailed reconstructio...
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Benjamin Metka, Ute Bauer-Wersing, Mathias Franzius , "Improving Robustness of Slow Feature Analysis Based Localization Using Loop Closure Events", International Conference on Artificial Neural Networks (ICANN), 2016.
AbstractHierarchical Slow Feature Analysis (SFA) extracts a spatial representation of the environment by directly processing images from a training run and has been shown to enable self-localization of a mobile robot by encoding its position as slowly varying features. However, in real world outdoor scenarios other variables, like global illumination or location of dynamic objects, might vary on an equal or slower time scale than the position of th...
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Martin Ernst Heckmann , "Feature-level Decision Fusion for Audio-Visual Word Prominence Detection", INTERSPEECH, 2016.
AbstractCommon fusion techniques in audio-visual speech processing operate on the modality level. I.e. they either combine the features extracted from the two modalities directly or derive a decision for each modality separately and then combine the modalities on the decision level. We investigate the audio-visual processing of linguistic prosody, more precisely the extraction of word prominence. In this context the different features for each modality c...
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Florian Damerow, Benedict Flade, Julian Eggert , "Extensions for the Foresighted Driver Model: Tactical Lane Change, Overtaking and Continuous Lateral Control", Intelligent Vehicles Symposium (IV), pp. 186-193, 2016.
AbstractThe Foresighted Driver Model (FDM) is a microscopic driver model which is based on the idea that a driver balances risk with utility. This paper deals with the modeling of advanced driving maneuvers for the FDM with a special focus on lateral positioning scenarios, such as lane changes in highway traffic. When driving at high speeds, tactical preparation for a safe lane change is of high importance. In this context, the paper presents maneuvers t...
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Henrik Rusche, Hrvoje Jasak, Sebastian Schmitt , "Stability improvements of pressure-based compressible solver and validation for industrial turbomachinery applications ", 4th Annual OpenFOAM User Conference 2016, 2016.
AbstractFOAM's pressure-based compressible solver family has been suffering from stability issues hampering its usability for industrial applications. The stability issues can be overcome by using a physically motivated linearisation of the density change in the pressure equation. In the presentation, the new pressure-equation will be derived and discussed. In addition, alternative formulations of the energy equation and a special boundary conditio...
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Kaname Narukawa, Yu Setoguchi, Yuki Tanigaki, Markus Olhofer, Bernhard Sendhoff, Hisao Ishibuchi , "Preference representation using Gaussian functions on a hyperplane in evolutionary many-objective optimization", Soft Computing, vol. 20, no. 7, pp. 2733-2757, 2016.
AbstractMany-objective optimization has attracted much attention in evolutionary multi-objective optimization (EMO). This is because EMO algorithms developed so far often degrade their search ability for optimize problems with four or more objectives, which are frequently referred to as many-objective problems. One of promising approaches to handle many objectives is to incorporate the preference of a decision maker (DM) into EMO algorithms. With the pre...
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