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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches
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Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches

机译：贝叶斯非参数方法的原始驾驶模式驾驶风格分析

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摘要

Driving style analysis plays a pivotal role in intelligent vehicle design. This paper presents a novel framework for driving style analysis based on primitive driving patterns. To this end, a Bayesian nonparametric approach based on a hidden semi-Markov model (HSMM) is introduced to extract the primitive driving patterns from muti-dimensional time-series driving data without prior knowledge of these driving patterns. For the Bayesian nonparametric approach, a hierarchical Dirichlet process (HDP) is applied to learn the unknown smooth dynamical modes in the HSMM, called primitive driving patterns. Two other types of Bayesian nonparametric approaches (HDP-HMM and sticky HDP-HMM) are developed as comparatives in order to show the advantages of the HDP-HSMM. The naturalistic car-following data of 18 drivers are collected from the University of Michigan Safety Pilot Model Deployment database. For each driver, 75 primitive driving patterns are semantically predefined according to their physical and psychological perception thresholds. The individual driving styles are then semantically analyzed based on the distribution over primitive driving patterns, and the similarity of driving styles among drivers is then evaluated. Experimental results demonstrate that the utilization of driving primitive pattern provides a semantically interpretable way to analyze driver's behavior and driving style.
机译：驾驶风格分析在智能车辆设计中起着至关重要的作用。本文提出了一种基于原始驾驶模式的新型驾驶风格分析框架。为此，引入了基于隐藏半马尔可夫模型（HSMM）的贝叶斯非参数方法，以从多维时间序列驾驶数据中提取原始驾驶模式，而无需事先了解这些驾驶模式。对于贝叶斯非参数方法，应用分层Dirichlet过程（HDP）来学习HSMM中未知的平滑动力学模式，称为原始驾驶模式。为了显示HDP-HSMM的优势，还开发了另外两种贝叶斯非参数方法（HDP-HMM和粘性HDP-HMM）作为比较。从密歇根大学安全飞行员模型部署数据库中收集了18位驾驶员的自然驾驶数据。对于每个驾驶员，根据其身体和心理感知阈值在语义上预定义了75种原始驾驶模式。然后基于原始驾驶模式的分布对各个驾驶风格进行语义分析，然后评估驾驶员之间驾驶风格的相似性。实验结果表明，驾驶原始模式的利用提供了一种语义可解释的方式来分析驾驶员的行为和驾驶方式。

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