Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

Personalisierung internetbasierter Nachhaltigkeitskommunikation : theoretische Analyse und empirische Einsichten am Beispiel der Automobilindustrie

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Facilitating Conflict Resolution of Models for Automated Enterprise Architecture Documentation

Enterprise Architecture (EA) management relies on solid and up-to-date information about the current state of an EA. In current practices the manual collection of information is prevailing resulting in an error-prone, time-consuming, and expensive task. Recent research efforts seek to automate this task by integrating existing information sources in the organization to optimize the EA documentation process. While automation of EA documentation enables many advantages, the transformation of the collected information to an EA model remains an unresolved challenge since it cannot be automated completely. In particular, conflicts resulting from partial transformations require involvement of EA Stakeholders possibly not having a technical background. In this paper we propose an approach for the conflict resolution facilitating our long-term goal of automated EA documentation. We illustrate our approach using a productive Enterprise Service Bus from a leading organization of the fashion industry and evaluate our approach with expert interviews

Enterprise Architecture Documentation: Current Practices and Future Directions

Over the past decade Enterprise Architecture (EA) management matured to a discipline commonly perceived as a strategic advantage. Among others, EA management helps to identify and realize cost saving potentials in organizations. EA initiatives commonly start by documenting the status-quo of the EA. The respective management discipline analyzes this so-called current state and derives intermediate planned states heading towards a desired target state of the architecture. Several EA frameworks describe this process in theory. However, during practical application, organizations struggle with documenting the EA and lack concrete guidance during the process. To underline our observations and confirm our hypotheses, we conducted a survey among 140 EA practitioners to analyze issues organizations face while documenting the EA and keeping the documentation up to date. In this paper we present results on current practices, challenges, and automation techniques for EA documentation in a descriptive manner

DECISION SUPPORT FOR SELECTING AN APPLICATION LANDSCAPE INTEGRATION STRATEGY IN MERGERS AND ACQUISITIONS

Mergers and Acquisitions (M&A) represent a powerful strategic instrument increasingly applied in today\u27s business environment. Besides juridical, financial, and organizational challenges, it is crucial to rapidly integrate the existing application landscapes in order to capitalize the aspired synergies. Literature documents four commonly agreed strategies: \u27best-of-breed\u27, \u27absorption\u27, \u27co-existence\u27, and \u27new-build\u27. However, no consolidated set of criteria exists to ease the selection of an integration strategy most suitable for the merger or the acquisition. Based on the results of a literature study, this paper proposes four integration profiles enabling a structured decision support for selecting the appropriate application landscape strategy during M&A. Each profile comprises relevant driving factors and resulting consequences as selection criteria. The identified literature statements regarding the criteria are validated by means of 12 confirmatory interviews with M&A experts. Furthermore, collected findings from an additional exploratory interview part with the practitioners complement the devised strategy profiles

Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.Comment: To appear in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, France, June 2020 (Virtual Conference). Accepted version. Corrected figure fon

Automating Enterprise Architecture Documentation using an Enterprise Service Bus

Currently the documentation of Enterprise Architectures (EA) requires manual collection of data resulting in an error prone, expensive, and time consuming process. Recent approaches seek to automate and improve EA documentation by employing the productive system environment of organizations. In this paper, we investigate a specific Enterprise Service Bus (ESB) considered as the nervous system of an enterprise interconnecting business applications and processes as an information source. We evaluate the degree of coverage to which data of a productive system can be used for EA documentation. A vendor-specific ESB data model is reverse-engineered and transformation rules for three representative EA information models are derived. These transformation rules are employed to perform automated model transformations making the first step towards an automated EA documentation. We evaluate our approach using a productive ESB system from a leading enterprise of the fashion industry