# Autoencoder Regularized Network For Driving Style Representation Learning

arXiv: https://arxiv.org/abs/1701.01272 · HTML: https://mlsanity.com/paper/1701.01272/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2017.

## Built on by (most cited first, 9 of 9)

- [Learning to Navigate Intersections with Unsupervised Driver Trait Inference](https://mlsanity.com/paper/2109.06783/) (2021)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [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)
- [Driver2vec: Driver Identification from Automotive Data](https://mlsanity.com/paper/2102.05234/) (2021)
- [DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation](https://mlsanity.com/paper/2205.08356/) (2022)
- [Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification](https://mlsanity.com/paper/2408.09230/) (2024)
- [A Prototypical Network with an Attention-based Encoder for Drivers Identification Application](https://mlsanity.com/paper/2510.17250/) (2025)

## Abstract (arXiv)

In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers' driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture. Experiments on a challenging driver number estimation problem and the driver identification problem show that ARNet can learn a good generalized driving style representation: It significantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0.68, less than one driver) and the highest identification accuracy (by at least 3% improvement) compared with traditional supervised learning methods.