关键词: 驾驶风格；CAN 总线；加速度；车队管理

Abstract: With the rapid development of the automotive industry, the quantification and classification of driver behavior characteristics have become key factors in improving testing efficiency and driving safety. This paper proposes a driver style identification model that relies solely on raw vehicle dynamics data from the onboard controller area network (CAN) bus, without the need for additional sensors. By collecting key parameters such as vehicle speed, throttle opening, longitudinal and lateral acceleration, steering angle, and combining descriptive statistical analysis with interval distribution methods, driver behavior is clustered under three typical driving conditions: urban, highway, and mountain roads. The study results indicate that aggressive drivers have significantly higher acceleration standard deviation, proportion of extreme maneuvers, and steering frequency than stable and cautious drivers. By constructing a two-dimensional threshold system based on standard deviation and interval proportions, automated classification of driver styles can be achieved, with high accuracy. The model has been validated with actual fleet data, providing practical technical support for driver selection, safety warnings, and driving behavior interventions. This paper also points out the current model's limitations in complex road conditions and multi-vehicle adaptability, and suggests future improvements by introducing weight adaptive mechanisms and automatic condition recognition modules.

Key words: driver style; CAN bus; acceleration; fleet management