Characterizing Driving Styles with Deep Learning

TL;DR

This paper introduces a deep learning approach to analyze and characterize human driving styles from GPS data, providing high-level, interpretable features that improve driver identification accuracy.

Contribution

It is the first to apply deep learning to driving behavior analysis based on GPS data, reducing reliance on handcrafted features and enhancing feature interpretability.

Findings

Deep learning effectively captures complex driving patterns.

The approach improves driver identification accuracy.

Features are highly interpretable and require less manual effort.

Abstract

Characterizing driving styles of human drivers using vehicle sensor data, e.g., GPS, is an interesting research problem and an important real-world requirement from automotive industries. A good representation of driving features can be highly valuable for autonomous driving, auto insurance, and many other application scenarios. However, traditional methods mainly rely on handcrafted features, which limit machine learning algorithms to achieve a better performance. In this paper, we propose a novel deep learning solution to this problem, which could be the first attempt of extending deep learning to driving behavior analysis based on GPS data. The proposed approach can effectively extract high level and interpretable features describing complex driving patterns. It also requires significantly less human experience and work. The power of the learned driving style representations are validated through the driver identification problem using a large real dataset.