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Florian Damerow, Stefan Klingelschmitt, Julian Eggert , "Spatio-temporal Trajectory Similarity and its Application to Predicting Lack of Interaction in Traffic Situations", Intelligent Transportation Systems Conference (ITSC) 2016, pp. 2512-2519, 2016.
AbstractThis work addresses two topics. On one hand we study the problem of general measures for spatio temporal trajectory similarity, based on the evaluation of longitudinal and lateral spatiotemporal distance. On the other hand we employ trajectory similarity measures to general situation classification problems for traffic scenarios, which include the well known problem of lane assignment and driving maneuver detection. We predict situation specific ...
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Stefan Klingelschmitt, Volker Willert, Julian Eggert , "Probabilistic, Discriminative Maneuver Estimation in Generic Traffic Scenes using Pairwise Probability Coupling", Intelligent Transportation Systems Conference (ITSC) 2016, pp. 1269-1276, 2016.
AbstractFuture advanced driver assistance system as well as autonomous vehicles are expected to further increase their areas of applicability. Reliable maneuver estimations are a prerequisite for many of the provided functionality. Accordingly, maneuver estimation systems need to cover a wide range of scenarios. The majority of recently presented approaches are targeted at fixed scenarios. However, having specialized maneuver estimation systems covering ...
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Julian Eggert , "Solomon Curve 2020: Relating Microscopic Risk Models to Accident Statistics", Intelligent Transportation Systems Conference (ITSC) 2016, pp. 2293-2300, 2016.
AbstractTraffic accident statistics as functions of the involved accident participant parameters are important sources of information to investigate accident causes. However, since traffic accidents are sparse events and the factors that lead to an accident can be very diverse, it is difficult to establish a direct link which relates microscopic risk models to the empirical findings. One seminal and widely debated work on accident statistics on mu...
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Gaoya Cao, Benedict Flade, Markus Helmling, Florian Damerow, Julian Eggert , "Camera to Map Alignment for Accurate Low-Cost Lane-Level Scene Interpretation ", 19th International IEEE Conference on Intelligent Transportation Systems, pp. 498-504, 2016.
AbstractTo be able to predict the evolution of the driving context, estimate the expected risks and plan future behavior alternatives, it is crucial to know where traffic participants can go and where they will most likely go. Consequently, for future Advanced Driver Assistance Systems (ADAS), precise, lane-accurate localization of the ego- as well as the other vehicles is a key technology. The proposed standard solutions to lane-accurate localization ar...
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Zhenyu Yang, Bernhard Sendhoff, Ke Tang, Xin Yao , "Target shape design optimization by evolving B-splines with cooperative coevolution", Applied Soft Computing, vol. 48, no. C, pp. 672-682, 2016.
AbstractGraphical abstractSince real-world design optimization is often computationally expensive, target shape design optimization problems (TSDOPs) was used as miniature model to check algorithmic performance for general shape design. With B-spline and adaptive encoding as representation, the TSDOPs can be modeled as complex numerical optimization problems which can be decomposed into two subproblems. i.e. components for control points and knot vector....
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Tobias Rodemann and Ken Nishikawa , "Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid Car Control?", Lecture Notes in Computer Science (EvoApplications Part I), 2016.
AbstractFinding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowledge Evolutionary Algorithms (EAs) have so far not been used for this setting, due to the success of DP. In this work we compare DP and EA for a well-s...
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Daniel Sieger, Sergius Gaulik, Jascha Achenbach, Stefan Menzel, Mario Botsch , "Constrained Space Deformation Techniques for Design Optimization", Computer-Aided Design, vol. 72, pp. 40-51, 2016.
AbstractWe present a novel shape deformation method for its use in design optimization tasks. Our space deformation technique based on moving least squares approximation improves upon existing approaches in crucial aspects: It offers the same level of modeling flexibility as surface-based deformations, but it is independent of the underlying geometry representation and therefore highly robust against defects in the input data. It overcomes the scalabilit...
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Dennis Orth, Milton Orlando Sarria Paja, Kersten Schaller, Andreas Pech, Dorothea Kolossa, Martin Ernst Heckmann , "A Maximum Likelihood Method for Driver-Specific Critical-Gap Estimation ", 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 553-558, 2016.
AbstractIn this work, we introduce a maximum likelihood (ML) method to estimate the smallest accepted gap of a specific driver, the so called critical gap. Previous methods, like Troutbeck’s or Raff’s method, are well known and widely used but define custom objective functions, which are not properly defined likelihoods. This makes them unusable for Bayesian inference, which would enable one to incorporate a prior into the estimation method, in order to ...
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Viktor Losing, Barbara Hammer, Heiko Wersing , "Choosing the Best Algorithm for an Incremental Learning Task", European Symposium on Artificial Neural Networks, 2016.
AbstractIncremental and on-line learning gained recently more atten- tion especially in the context of big data and learning from data streams, conflicting with the traditional assumption of complete data availability. Even though plenty of different methods are available, it often remains unclear which of them is suitable for a specific task and how they perform in comparison to each other. We analyze the key properties of seven in- cremental meth...
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Ran Cheng, Yaochu Jin, Markus Olhofer, Bernhard Sendhoff , "A Reference Vector Guided Evolutionary Algorithm for Many-objective Optimization", IEEE Transactions on Evolutionary Computation, vol. 20, no. 5, pp. 773-791, 2016.
AbstractIn evolutionary multi-objective optimization, maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms. In addition, it becomes increasingly important to incorporate user preferences because it will be less likely to achieve a representative subset of the Pareto optimal solutions using a limited population size as the number of objectives increases. This paper proposes ...
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