Current approaches to identifying driving heterogeneity face challenges in comprehending fundamental patterns from the perspective of underlying driving behavior mechanisms. The concept of Action phases was proposed in our previous work, capturing the diversity of driving characteristics with physical meanings. This study presents a novel framework to further interpret driving patterns by classifying Action phases in an unsupervised manner. In this framework, a Resampling and Downsampling Method (RDM) is first applied to standardize the length of Action phases. Then the clustering calibration procedure including ''Feature Selection'', ''Clustering Analysis'', ''Difference/Similarity Evaluation'', and ''Action phases Re-extraction'' is iteratively applied until all differences among clusters and similarities within clusters reach the pre-determined criteria. Application of the framework using real-world datasets revealed six driving patterns in the I80 dataset, labeled as ''Catch up'', ''Keep away'', and ''Maintain distance'', with both ''Stable'' and ''Unstable'' states. Notably, Unstable patterns are more numerous than Stable ones. ''Maintain distance'' is the most common among Stable patterns. These observations align with the dynamic nature of driving. Two patterns ''Stable keep away'' and ''Unstable catch up'' are missing in the US101 dataset, which is in line with our expectations as this dataset was previously shown to have less heterogeneity. This demonstrates the potential of driving patterns in describing driving heterogeneity. The proposed framework promises advantages in addressing label scarcity in supervised learning and enhancing tasks such as driving behavior modeling and driving trajectory prediction.

翻译：当前识别驾驶异质性的方法在从底层驾驶行为机制角度理解基本模式方面面临挑战。动作阶段概念在我们先前工作中提出，以物理意义捕捉驾驶特征的多样性。本研究提出一种新颖框架，通过无监督方式对动作阶段进行分类来进一步解析驾驶模式。该框架首先应用重采样与降采样方法标准化动作阶段长度，随后迭代执行包含"特征选择"、"聚类分析"、"差异/相似性评估"和"动作阶段重提取"的聚类校准流程，直至类间差异与类内相似性均达到预设标准。应用该框架对真实数据集的分析显示：I80数据集中存在六种驾驶模式，标记为"追赶型"、"避让型"和"保持距离型"，且均包含"稳定"与"不稳定"两种状态。值得注意的是，不稳定模式数量多于稳定模式，而"保持距离型"在稳定模式中最为常见。这些发现与驾驶行为的动态特性相符。US101数据集中缺失"稳定避让型"和"不稳定追赶型"两种模式，这与我们预期一致，因为该数据集先前已被证明具有较低的异质性。这证明了驾驶模式在描述驾驶异质性方面的潜力。所提框架有望解决监督学习中的标签稀缺问题，并提升驾驶行为建模与驾驶轨迹预测等任务的性能。