# Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model

arXiv: https://arxiv.org/abs/2310.15057 · HTML: https://mlsanity.com/paper/2310.15057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models](https://mlsanity.com/paper/2508.13881/) (2025)
- [XAI-Driven Machine Learning System for Driving Style Recognition and Personalized Recommendations](https://mlsanity.com/paper/2509.00802/) (2025)

## Abstract (arXiv)

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, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers.