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In this paper, we present a method to estimate abstract parameters of high definition (HD) maps from sensor data. Parameters we estimate include the distance from ego-vehicle to road boundary, orientation of the ego-vehicle with respect to lanes, number of lanes, and street type. Our method is realized as a Convolutional Neural Network (CNN) that takes pre-processed sensor information in the form...
Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared to tracking approaches, that use e.g. a particle filter...
The increasing ubiquity of mobile handheld devices paved the way for the dynamic ridesharing which could save travel cost and reduce the environmental pollution. The ridematching problem with time windows in dynamic ridesharing considers matching drivers and riders with similar routes (with drivers detour flexibility) and time schedules on short notice. This problem is hard to solve. In this work,...
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