Abstract
This paper describes the development of an autonomous ground vehicle that participated in the 2007 DARPA urban challenge. Specifically, this paper introduces LADAR based static and moving obstacle detection algorithms for several traffic scenarios in the urban environment. The obstacle detection algorithm is critical for avoiding collisions with other vehicles and for safe driving at high speed. On the real road, the driver is faced with numerous traffic situations ranging from intersection traversal and lane changing to passing a stopped or slow moving car or performing a U-turn. For an unmanned vehicle to be successful, it must collect, interpret and act on sensor data about the surrounding world. The 2007 DARPA urban challenge demonstrated various approaches to navigate through these traffic scenarios. This paper outlines the approach used by the University of Floridapsilas Team Gator Nation to address the question of both static and dynamic obstracle detection.
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