# Characterizing Driving Styles with Deep Learning

arXiv: https://arxiv.org/abs/1607.03611 · HTML: https://mlsanity.com/paper/1607.03611/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2016.

## Built on by (most cited first, 13 of 13)

- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Learning Preferences for Interactive Autonomy](https://mlsanity.com/paper/2210.10899/) (2022)
- [Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification](https://mlsanity.com/paper/2102.05843/) (2021)
- [A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems](https://mlsanity.com/paper/2401.06831/) (2024)
- [A Comparison of Decision Forest Inference Platforms from A Database Perspective](https://mlsanity.com/paper/2302.04430/) (2023)
- [GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation](https://mlsanity.com/paper/2207.00838/) (2022)
- [Deep Learning Approach for Aggressive Driving Behaviour Detection](https://mlsanity.com/paper/2111.04794/) (2021)
- [DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation](https://mlsanity.com/paper/2205.08356/) (2022)
- [Situation Awareness for Driver-Centric Driving Style Adaptation](https://mlsanity.com/paper/2403.19595/) (2024)
- [Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis](https://mlsanity.com/paper/2109.08996/) (2021)
- [Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification](https://mlsanity.com/paper/2408.09230/) (2024)
- [GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication](https://mlsanity.com/paper/2505.17530/) (2025)
- [A Prototypical Network with an Attention-based Encoder for Drivers Identification Application](https://mlsanity.com/paper/2510.17250/) (2025)

## Abstract (arXiv)

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.