Andreas D. Lattner

Authentication of free hand drawings by pattern recognition methods

Proof of authenticity of free hand drawings of artists is one of the major problems in history of arts especially with respect to unsigned works of famous artists. Usually the authentication is performed manually by experts based on a physical analysis of the drawing materials used, and visual inspection. This often leads to ambiguous results as the visual inspection is influenced by subjective criteria, experience, and background information. Here we show how automatic pattern recognition methods can be applied to decide if a drawing belongs to the work of a certain artist. As example we compare drawings by Delacroix (1798-1863) with the work of contemporary artists whose drawings show similar characteristics. Based on higher order features we are able to achieve a correct classification for about 87% of the drawings.

Knowledge-based risk assessment for intelligent vehicles

In order to set up assistance systems in intelligent vehicles or to control an autonomous vehicle a number of cognitive functions has to be realized in an integrated architecture. In this paper we propose a knowledge-based risk assessment procedure in order to identify objects which might be dangerous for the own vehicle. Having an advanced vision system with gaze control in mind a knowledge-based risk assessment can determine where to concentrate the attention. The approach is evaluated by simulating different traffic scenes.

Dynamic-preserving qualitative motion description for intelligent vehicles

Planning, acting, and recognizing intentions of participants in traffic situations requires the processing of complex spatio-temporal situations. If spatio-temporal information was represented quantitatively it would result in a huge amount of data. We claim that an abstraction to a qualitative description leads to more stable representations as similar situations at the quantitative level are mapped to one qualitative representation. Our approach is evaluated by emulating traffic situations with settings in the Robocup small-sized league.

Don't Go With the Ant Flow: Ant-Inspired Traffic Routing in Urban Environments

Traffic routing is a well-established optimization problem in traffic management. Here, we address dynamic routing problems where the load of roads is taken into account dynamically, aiming at the optimization of required travel times. We investigate ant-based algorithms that can handle dynamic routing problems, but suffer from negative emergent effects like road congestions. These negative effects are inherent in the design of ant-based algorithms. In this article we propose an inverse ant-based routing algorithm to (a) maintain the positive features of ant-based algorithms for dynamic routing problems, while (b) avoiding the occurrence of negative emerging effects, like road congestion. We evaluated the performance of the proposed algorithm by comparing its results with two alternative routing algorithms, namely, A*, which is a static routing algorithm, and an iterative approach. In particular, the iterative approach is used for providing an upper bound, as it uses routing knowledge in a number of calibration runs, to determine the actual load, before the effective routing is done. For the evaluation we used the agent-based traffic simulation system MAINSIM. The evaluation was done with one synthetic and two real-world scenarios, to outline the practical relevance of our findings. Based on these evaluations, we can conclude that the inverse ant-based routing approach is particularly suited for a scenario with a high traffic density, as it can adapt the routing of each vehicle, while avoiding the negative emerging effects of conventional ant-based routing algorithms.

A knowledge-based approach to behavior decision in intelligent vehicles

Recent advances in the field of intelligent vehicles have shown that it is possible nowadays to provide the driver with useful assistance systems, or even letting a car drive autonomously over long distances on highways. Usually these approaches are on a rather quantitative level. A knowledge-based approach as presented here has the advantage of a better comprehensibility and allows for formulating and using common sense knowledge and traffic rules while reasoning. In our approach a knowledge base is the central component for higher-level functionality. A qualitative mapping module abstracts from the quantitative data and stores symbolic facts in the knowledge base. The knowledge-based approach allows for easily integrating and adjusting background knowledge. Higher-level modules can query the knowledge base in order to evaluate the situation and decide what actions to perform. For the evaluation of the approach a prototype was developed in order to simulate traffic scenarios. In experiments behavior decision was applied for controlling the vehicle and its gaze.

Learning dynamic adaptation strategies in agent-based traffic simulation experiments

The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.

Towards assisted input and output data analysis in manufacturing simulation: the EDASim approach

Discrete-event simulation has been established as an important methodology in various domains. In particular in the automotive industry, simulation is used to plan, control, and monitor processes including the flow of material and information. Procedure models help to perform simulation studies in a structured way and tools for data preparation or statistical analysis provide assistance in some phases of simulation studies. However, there is no comprehensive data assistance following all phases of such procedure models. In this article, a new approach combining assistance functionalities for input and output data analysis is presented. The developed tool - EDASim - focuses on supporting the user in selection, validation, and preparation of input data as well as to assist the analysis of output data. The proposed methods have been implemented and initial evaluations of the concepts have led to promising feedback from practitioners.

A knowledge-based approach to automated simulation model adaptation

Simulation has become a widely accepted technology for analyzing or planning systems in various domains. In production logistics, for instance, many companies use simulation to evaluate scenarios before actually the construction or modifications of the production hall or processes are performed in order to get insights about the performance of planned configurations. In this paper, we propose an approach to knowledge-based adaptation of simulation models. The vision of this work is to go one step beyond parameter optimization, namely to provide means for automated structural changes in simulation models, and thus for the generation of simulation model variants. For a first evaluation of our approach, we introduce a system consisting of a simulation control as well as a model adaptation module with a set of adaptation operations. Our implementation is coupled to the simulation system Plant Simulation in order to perform simulation runs. For illustration we apply our system to a test scenario and present first results.

Real-Time Spatio-Temporal Analysis of Dynamic Scenes in 3D Soccer Simulation

We propose a framework for spatio-temporal real-time analysis of dynamic scenes. It is designed to improve the grounding situation of autonomous agents in (simulated) physical domains. We introduce a knowledge processing pipeline ranging from relevance-driven compilation of a qualitative scene description to a knowledge-based detection of complex event and action sequences, conceived as a spatio-temporal pattern matching problem. A methodology for the formalization of motion patterns and their inner composition is introduced and applied to capture human expertise about domain-specific motion situations. We present extensive experimental results from the 3D soccer simulation that substantiate the online applicability of our approach under tournament conditions, based on 5 Hz a) precise and b) noisy/incomplete perception.

GIS-Based Traffic Simulation Using OSM

This chapter demonstrates how to build up a traffic simulation on the base of a Geographic Information System (GIS). Maps from the OpenStreetMap (OSM) initiative have shown to be appropriate for usage in this field. Essential steps from OSM over GIS to a graph data structure for use in traffic simulation are described. The work is done with the focus on urban scenarios. The crucial decision, which types of road users to integrate into a simulation and how to model them, is discussed. A case scenario shows the utility of data mining techniques in the field of traffic simulation. The scenario aims at predicting traffic jams in the city of Frankfurt am Main with help of a learned classifier. Our results show that taking into account simple and partial information about the traffic situation can lead to a huge gain of knowledge when using data mining techniques in the face of predicting of traffic situations.