Sensor Fusion for Automotive Applications (2011)
Abstract / truncated to 115 words
Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused ... toggle 8 keywordssensor fusion – phd filter – random set – road model – road edge – bicycle model – single track model – extended target tracking
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