There is an increase in interest to model driving maneuver patterns via the automatic unsupervised clustering of naturalistic sequential kinematic driving data. The patterns learned are often used in transportation research areas such as eco-driving, road safety, and intelligent vehicles. One such model capable of modeling these patterns is the Hierarchical Dirichlet Process Hidden Semi-Markov Model (HDP-HSMM), as it is often used to estimate data segmentation, state duration, and transition probabilities. While this model is a powerful tool for automatically clustering observed sequential data, the existing HDP-HSMM estimation suffers from an inherent tendency to overestimate the number of states. This can result in poor estimation, which can potentially impact impact transportation research through incorrect inference of driving patterns. In this paper, a new robust HDP-HSMM (rHDP-HSMM) method is proposed to reduce the number of redundant states and improve the consistency of the model's estimation. Both a simulation study and a case study using naturalistic driving data are presented to demonstrate the effectiveness of the proposed rHDP-HSMM in identifying and inference of driving maneuver patterns.

翻译：驾驶操作模式的建模需求日益增长，其实现途径是对自然驾驶时序运动数据进行无监督自动聚类。所习得模式常应用于生态驾驶、道路安全及智能汽车等交通研究领域。层级狄利克雷过程隐半马尔可夫模型（HDP-HSMM）是此类模式建模的有效工具之一，因其常用于估计数据分段、状态持续时间及状态转移概率。尽管该模型可自动聚类观测到的时序数据，但现有HDP-HSMM估计存在固有缺陷：易过度估计状态数量。这可能导致估计精度不足，进而因驾驶模式推断错误对交通研究产生潜在影响。本文提出一种新型鲁棒HDP-HSMM（rHDP-HSMM）方法，旨在减少冗余状态并提升模型估计的一致性。通过仿真实验与基于自然驾驶数据的案例研究，验证了所提rHDP-HSMM方法在驾驶操作模式识别与推断中的有效性。