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首页> 外文会议>Unmanned Systems Technology IX; Proceedings of SPIE-The International Society for Optical Engineering; vol.6561 >Driver Aggressivity Analysis within the Prediction In Dynamic Environments (PRIDE) Framework
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Driver Aggressivity Analysis within the Prediction In Dynamic Environments (PRIDE) Framework

机译：动态环境（PRIDE）框架中的预测中的驾驶员主动性分析

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摘要

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.
机译：PRIDE是用于移动对象预测的分层多分辨率框架，该框架将多个预测算法合并到一个统一的框架中。 PRIDE基于4D / RCS（实时控制系统）参考模型体系结构，并以适合其计划范围的粒度级别向计划者提供信息。该框架支持以各种分辨率级别预测移动对象的未来位置，从而以层次结构内不同级别的计划者所需的抽象频率和级别提供预测信息。迄今为止，两种预测方法已应用于此框架。在本文中，我们提供了PRIDE（动态环境中的预测）框​​架的概述，并描述了已被用来对驱动程序的不同攻击性进行建模的方法。然后，我们探索不同的攻击性模型，以确定它们对通过PRIDE框架提供的位置预测的影响。我们还描述了最近在USARSim中实施PRIDE的努力，该过程基于Unreal Tournament游戏引擎提供了机器人和环境的高保真模拟。

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