Shortly, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detec More
Shortly, preventing collisions with fixed or moving, alive, and inanimate obstacles will appear to be a severe challenge due to the increased use of Unmanned Ground Vehicles (UGVs). Light Detection and Ranging (LIDAR) sensors and cameras are usually used in UGV to detect obstacles. The tracing and classification of moving obstacles is a significant dimension in developed driver assistance systems. The present study indicated a multi-hypotheses monitoring and classifying approach, which allows solving ambiguities rising with the last methods of associating and classifying targets and tracks in a highly volatile vehicular situation. We proposed a recursive method based on Bayesian Algorithm for using classification information of obstacles in the tracking information of them and vice versa. The results are shown that the proposed method can improve classifying and tracking together.This method was tested through real data from various driving scenarios and focusing on two obstacles of interest vehicle and pedestrian.
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