Driver aggressivity analysis within the prediction in dynamic environments (PRIDE) framework

Abstract

PRIDE is a hierarchical multiresolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (Real-time Control System) reference model architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. This framework supports the prediction of the future location of moving objects at various levels of resolution, thus providing prediction information at the frequency and level of abstraction necessary for planners at different levels within the hierarchy. To date, two prediction approaches have been applied to this framework. In this paper, we provide an overview of the PRIDE (Prediction in Dynamic Environments) framework and describe the approach that has been used to model different aggressivities of drivers. We then explore different aggressivity models to determine their impact on the location predictions that are provided through the PRIDE framework. We also describe recent efforts to implement PRIDE in USARSim, which provides high-fidelity simulation of robots and environments based on the Unreal Tournament game engine.