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Obstacles classification plays an important role in driving assistance systems. Any classification system should accurately distinguish, in real-time, between a set of well-known object classes such as pedestrians, cars and poles and other obstacles. If the object class is determined then the driving assistance system may take the right decision, in case of an imminent impact, in correlation to the...
This paper proposes an accurate localization method, which consists of a non-probabilistic motion model and Generalized Iterative Closet Point (GICP). The most encountered problem of using motion models is to determine empirical parameters, which represent the systemic errors and the non-systemic errors. The perfect representation of those errors is an extremely difficult task, and it is practically...
This paper proposes a probabilistic model for automated reasoning for identifying the lane on which the vehicle is driving on. The solution is based on the visual information from an on-board stereo-vision camera and a priori information from an extended digital map. The visual perception system provides information about on-the-spot detected lateral landmarks, as well as about other important traffic...
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