Shareable Driving Style Learning and Analysis With a Hierarchical Latent Model

Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving styles through a hierarchical latent model. This model successfully extracts latent driving styles from extensive driving behavior data without the need for manual labeling, offering an interpretable statistical structure. Through real-world testing involving 100 drivers, our developed model is validated, demonstrating a subjective-objective consistency exceeding 90%, outperforming the benchmark method. Experimental results reveal that individuals share driving styles within and between them. We also found that individuals inclined towards aggressiveness only exhibit a higher proportion of such behavior rather than persisting consistently to be aggressive.