A knowledge-integrated learning framework for accurate quantification and semantic interpretation of driving aggressiveness

Aggressive driving is a major contributor to traffic fatalities, necessitating reliable assessment methods to guide driver interventions. Existing methods, however, lack granularity in assessing both the severity and specific maneuver categories of aggressive driving behaviors. This paper proposes a novel framework for multidimensional aggressiveness assessment using lateral-longitudinal acceleration and vehicle speed. The framework combines domain-specific prior knowledge with a non-parametric statistical method to quantify aggressiveness levels and automatically extract aggressive driving samples. We then classify them into distinct maneuver categories through fuzzy clustering and semantic analysis, assigning each sample a membership degree for every category. Finally, we integrate the samples’ levels with their membership distribution across the maneuvers to generate comprehensive profiles of individuals’ driving aggressiveness. Experimental validation with real-world driving data (N=90 drivers) and real-time in-vehicle testing confirms our framework's effectiveness and practicality. Additionally, a spatiotemporal analysis of driving maneuvers reveals insights into the evolution of aggressive driving and its relationship with environmental factors.