A “semi-lazy” approach to probabilistic path prediction in dynamic environments

Jingbo Zhou, A. Tung, Wei Wu, W. Ng
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引用次数: 49

Abstract

Path prediction is useful in a wide range of applications. Most of the existing solutions, however, are based on eager learning methods where models and patterns are extracted from historical trajectories and then used for future prediction. Since such approaches are committed to a set of statistically significant models or patterns, problems can arise in dynamic environments where the underlying models change quickly or where the regions are not covered with statistically significant models or patterns. We propose a "semi-lazy" approach to path prediction that builds prediction models on the fly using dynamically selected reference trajectories. Such an approach has several advantages. First, the target trajectories to be predicted are known before the models are built, which allows us to construct models that are deemed relevant to the target trajectories. Second, unlike the lazy learning approaches, we use sophisticated learning algorithms to derive accurate prediction models with acceptable delay based on a small number of selected reference trajectories. Finally, our approach can be continuously self-correcting since we can dynamically re-construct new models if the predicted movements do not match the actual ones. Our prediction model can construct a probabilistic path whose probability of occurrence is larger than a threshold and which is furthest ahead in term of time. Users can control the confidence of the path prediction by setting a probability threshold. We conducted a comprehensive experimental study on real-world and synthetic datasets to show the effectiveness and efficiency of our approach.
动态环境中概率路径预测的“半惰性”方法
路径预测在很多应用中都很有用。然而,大多数现有的解决方案都是基于渴望学习方法,从历史轨迹中提取模型和模式,然后用于未来预测。由于这些方法致力于一组统计上重要的模型或模式,因此在底层模型快速变化的动态环境中,或者在没有统计上重要的模型或模式覆盖的区域中,可能会出现问题。我们提出了一种“半懒惰”的路径预测方法,使用动态选择的参考轨迹在飞行中构建预测模型。这种方法有几个优点。首先,要预测的目标轨迹在建立模型之前是已知的,这使我们能够构建被认为与目标轨迹相关的模型。其次,与惰性学习方法不同,我们使用复杂的学习算法基于少量选定的参考轨迹来获得具有可接受延迟的准确预测模型。最后,我们的方法可以持续自我修正,因为如果预测的运动与实际的运动不匹配,我们可以动态地重建新的模型。我们的预测模型可以构造一个概率路径,它的发生概率大于某个阈值,并且在时间上遥遥领先。用户可以通过设置概率阈值来控制路径预测的置信度。我们对真实世界和合成数据集进行了全面的实验研究,以显示我们方法的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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