Holger Mielenz

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Future requirements for drastic reduction of CO<sub>2</sub> production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times.(More)
The objective of this paper is the modelling of an unbounded environment of a human-driven car that may contain multilevel structures such as bridges or parking decks. Such a model might be used by a driver assistant system (DAS) where one drives through an urban environment, requests for an assistance and the DAS should immediately be able to give the user(More)
The goal of this paper is the localization of a car within an unstructured, outdoor area, based on low level algorithms. This is contrary to the current main focus of many researchers, which mainly choose either highly accurate environment observations, e.g. provided by laser sensors, or information rich vision based localization that requires computational(More)
In this contribution we introduce a framework for precise vehicle localization in dense urban environments which are characterized by high rates of dynamic and semi-static objects. The proposed localization method is specifically designed to handle inconsistencies between map material and sensor measurements. This is achieved by means of a robust map(More)
For future automated driving functions it is necessary to be able to reason about the typical behavior, intentions and future movements of vulnerable road users in urban traffic scenarios. It is crucial to have this information as early as possible, given the typical reaction time of human drivers. Since this is a highly complex problem, it needs to be(More)
Landmark-based localization in dynamic environments poses high demands on the perception system of a mobile robot. The pose estimate generally has to fulfill specific accuracy requirements which might be necessitated by dependent systems, such as behavior planning. Thus, in this contribution we focus on the model-based derivation of perception requirements,(More)
In this contribution, we address the model-based derivation of perception requirements based on upper bounds on vehicle localization uncertainty for urban driver assistance (UDA) and urban automated driving (UAD). We show that a probabilistic model for the estimation of map-relative localization accuracy can be obtained and utilized for proper(More)
Future automated driving systems will require a comprehensive scene understanding. Considering these systems in an urban environment it becomes immediately clear that reasoning about the future behavior and trajectories of pedestrians represents one major challenge. In this paper we focus on predicting the pedestrians' time-to-cross when approaching a(More)
Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times. Automated cars(More)
In the context of future urban automated driving many important problems remain unsolved. A critical one is the analysis and prediction of pedestrian movements around urban roads. Especially the analysis of non-critical situations has not received much attention in the past. This paper focuses on analyzing and predicting movements of pedestrians approaching(More)