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Towards Autonomous On-Road Driving via Multi-resolutional and Hierarchical Moving Object Prediction

机译：通过多分辨率和分层运动对象预测实现自动公路驾驶

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摘要

In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multi-resolutional approach 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) and provides information to planners at the level of granularity that is appropriate for their planning horizon. The lower levels of the framework utilize estimation theoretic short-term predictions based upon an extended Kalman filter that provide predictions and associated uncertainty measures. The upper levels utilize a probabilistic prediction approach based upon situation recognition with an underlying cost model that provide predictions that inc6rporate environmental information and constraints. These predictions are made at lower frequencies and at a level of resolution more in line with the needs of higher-level planners. PRIDE is run in the systems' world model independently of the planner and the control system. The results of the prediction are made available to a planner to allow it to make accurate plans in dynamic environments. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort.
机译：在本文中，我们介绍了PRIDE框架（动态环境中的预测），这是一种用于移动对象预测的分层多分辨率方法，该方法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），并以适合其计划范围的粒度级别向计划者提供信息。框架的较低级别利用基于扩展卡尔曼滤波器的估计理论短期预测，该滤波器提供了预测和相关的不确定性度量。较高级别利用基于情况识别的概率预测方法和基础成本模型，该模型提供了包含环境信息和约束的预测。这些预测是在较低的频率和更高的分辨率级别上进行的，与高级计划者的需求相符。 PRIDE在系统的世界模型中运行，而与计划者和控制系统无关。预测的结果可供计划人员使用，以使其能够在动态环境中制定准确的计划。我们已将此方法应用于作为DARPA移动自主机器人系统（MARS）努力的一部分而开发的道路驾驶控制层次结构。

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