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)
In the context of driver assistance, an accurate and reliable prediction of the vehicle's trajectory is beneficial. This can be useful either to increase the flexibility of comfort systems or, in the more interesting case, to detect potentially dangerous situations as early as possible. In this contribution, a novel approach for trajectory prediction is(More)
Robust lane detection is the precondition for advanced driver assistance systems like lane departure warning and overtaking assistants. While detecting the vehicle's lane is sufficient for lane departure warning, overtaking assistants or autonomous driving functions also need to detect adjacent lanes. In this contribution, a novel approach to multiple lane(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)
In many multi-object tracking applications, object individual single-target trackers are used to simultaneously track multiple objects. This requires a data association algorithm to decide which measurement belongs to which single-target tracker. In situations characterized by high clutter rates or high object densities, data association is often ambiguous.(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)
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)
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)
This paper introduces a precise self-localization method for road vehicles. The presented approach is based on a single grayscale camera in addition with a conventional estimation of the ego motion and a map of the environment. This map is built in advance and independently from the localization process utilizing the same techniques. The proposed algorithm(More)