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2 May 2007 Symbolic perception-based autonomous driving in dynamic environments using 4D/RCS
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Abstract
4D/RCS is a hierarchical architecture designed for the control of intelligent systems. One of the main areas that 4D/RCS has been applied to recently is the control of autonomous vehicles. To accomplish this, a hierarchical decomposition of on-road driving activities has been performed which has resulted in implementation of 4D/RCS tailored towards this application. This implementation has seven layers and ranges from a journey manager which determines the order of the places you wish to drive to, through a destination manager which provides turn-by-turn directions on how to get to a destination, through a route segment, drive behavior, elemental maneuver, goal path trajectory, and then finally to servo controllers. In this paper, we show, within the 4D/RCS architecture, how knowledge-driven top-down symbolic representations combined with low-level bottom-up tasks can synergistically provide valuable information for on-road driving better than what is possible with either of them alone. We demonstrate these ideas using field data obtained from an Unmanned Ground Vehicle (UGV) traversing urban on-road environments.
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M, Foedisch, R. Madhavan, and C. Schlenoff "Symbolic perception-based autonomous driving in dynamic environments using 4D/RCS", Proc. SPIE 6561, Unmanned Systems Technology IX, 65611T (2 May 2007); https://doi.org/10.1117/12.720225
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