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)
— 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)
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)
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)
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)
In this contribution, we propose multilayer adaptive Monte Carlo localization (ML-AMCL) in combination with 3D point registration algorithms as a GPS-independent framework for precise global vehicle pose estimation in challenging urban environments. Scans from a 3D LIDAR sensor are split into a set of horizontal layers which are then used for localization(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)
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)
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)
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)