Physically Informed Driving Style Recognition for Intelligent Vehicle Assistance
提出一个融合多个物理跟驰模型与数据驱动方法的框架，从车辆轨迹数据中提取驾驶偏好，识别出三种驾驶风格，为个性化驾驶辅助提供可解释且可迁移的方案。
Recognizing driving styles is essential for enhancing advanced driver-assistance systems (ADASs), as it enables the provision of personalized driving experiences while improving road safety. Although trajectory-based driving style recognition has been widely studied, existing approaches often rely on behavioral statistical features, single-model parameters, or latent representations, which may compromise interpretability and cross-scenario generalizability. To bridge this gap, this article proposes a physically informed framework for driving style recognition that integrates multiple physical car-following models with data-driven clustering and classification techniques. The framework extracts driving preferences from vehicle trajectory data by calibrating parameters across multiple physical car-following models and identifying key style-discriminative features. This approach enhances the interpretability and robustness of trajectory-based representation while reducing reliance on sparse in-vehicle sensor data. Experimental results reveal three behaviorally distinct driving styles—cautious efficient, aggressive efficient, and conservative safe—each reflecting distinct tradeoffs among efficiency, safety, and stability. The extracted rules exhibit high classification accuracy and generalizability across datasets, forming a solid basis for style-aware ADAS strategies. The proposed framework offers an interpretable and transferable trajectory-based solution for scalable driving style analysis, offering a solid foundation for intelligent vehicle assistance.