Extraction of driving behavior primitives considering driver expectation and vehicle dynamics.

ABSTRACT: Extracting meaningful driving behavior primitives is crucial for fine-grained behavior analysis. To develop primitives that more closely mirror the hierarchical nature of human driving, this paper introduces a novel framework and carries out on a 16 drivers' dataset. The proposed method operates in two synergistic stages. First, for behavior segmentation, a multi-type feature space is constructed to capture both the objective vehicle motion states and the subjective driver expectations on vehicle performance. This space is then input into a Bayesian Model-based Agglomerative Sequence Segmentation (BMASS) model to achieve precise segmentation. Segment durations and the positioning of segmentation points serve as key metrics to assess the quality of behavior segmentation. Next, for primitive

SUBMITTER: Ren Y 

PROVIDER: S-EPMC12755792 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature