Jürgen Wiest

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The project “Autonomous Driving” at Ulm University aims at advancing highly-automated driving with close-to-market sensors while ensuring easy exchangeability of the particular components. In this contribution, the experimental vehicle that was realized during the project is presented along with its software modules. To achieve the mentioned(More)
This contribution presents a precise localization method for advanced driver assistance systems. A Maximally Stable Extremal Region (MSER) detector is used to extract bright areas, i.e. lane markings, from grayscale camera images. Furthermore, this algorithm is also used to extract features from a laser scanner grid map. These regions are automatically(More)
This paper presents a novel approach towards highly precise self-localization of a vehicle on a digital map. The proposed approach utilizes a map containing region descriptors extracted from ordinary occupancy grid maps. The Maximally Stable Extremal Regions (MSER) algorithm provides robust feature extraction from grid maps in a completely unsupervised(More)
A challenge for future advanced driver assistant systems is to establish a reliable environment perception. Recently, advanced multi-object tracking algorithms were presented. These algorithms consider spatial uncertainties and clutter detections from several different sensors and a fusion process combines all the information in a probabilistic framework.(More)
Advanced Driver Assistance Systems (ADAS) have witnessed a steady increase in complexity during the last few years. Many of these systems could benefit from a reliable long-term prediction of the vehicle's trajectory, for instance the prediction of a turning maneuver at an intersection. The application of probabilistic trajectory prediction provides(More)
A reliable confidence measure for the localization of road vehicles is a crucial requirement to enable highly automated driving with an inherent self-awareness of its robustness and capabilities. Therefore, the present contribution introduces a novel approach to estimate not only the spatial uncertainty of a feature-based localization algorithm but also an(More)