Identification of Dynamic Driving Styles based on Behavioral Primitives: A Research from Data to Insights

Driving style reflects a driver's vehicle manipulation and driving habits, it is essential for understanding and analyzing a driver’s dynamic behavior and decision-making process. To better understand the intrinsic characteristics of how drivers make decisions and control their vehicles based on external conditions, this paper proposes an unsupervised framework for dynamic driving style recognition based on driving behavior primitives. The framework consists of three key stages: primitive extraction, model development, and dynamic driving style recognition. Unsupervised techniques are employed to extract meaningful driving behavior primitives from long-term driving data. The primitive serves as the fundamental unit for dynamic driving style analysis, and a driving style assessment model is built using a linear weighting approach. This model quantifies the risks of primitives and the transition risks between adjacent primitives. The thresholds for classifying cautious, average, and aggressive driving styles are determined using an improved particle swarm optimization algorithm. The proposed dynamic driving style recognition framework comprehensively considers the characteristics of the current primitive and its surrounding context. By preserving the temporal features of driving behavior, the framework provides fine-grained recognition of dynamic driving styles. Additionally, a deeper understanding of a driver’s dynamic driving style and their long-term driving behavior can be gained based on this framework, which will benefit the development of AVs and ADASs.