# Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver Identification

arXiv: https://arxiv.org/abs/2102.05843 · HTML: https://mlsanity.com/paper/2102.05843/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [Variational Recurrent Auto-Encoders](https://mlsanity.com/paper/1412.6581/) (2014)
- [Convolutional Recurrent Neural Networks for Music Classification](https://mlsanity.com/paper/1609.04243/) (2016)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture](https://mlsanity.com/paper/1601.00740/) (2016)
- [Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification](https://mlsanity.com/paper/1504.01561/) (2015)
- [Characterizing Driving Styles with Deep Learning](https://mlsanity.com/paper/1607.03611/) (2016)
- [Autoencoder Regularized Network For Driving Style Representation Learning](https://mlsanity.com/paper/1701.01272/) (2017)
- [Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network](https://mlsanity.com/paper/1706.02257/) (2017)
- [Driver Identification Using Automobile Sensor Data from a Single Turn](https://mlsanity.com/paper/1708.04636/) (2017)
- [Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data](https://mlsanity.com/paper/1806.04795/) (2018)
- [A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction](https://mlsanity.com/paper/1905.05614/) (2019)
- [Characterizing Driving Context from Driver Behavior](https://mlsanity.com/paper/1710.05733/) (2017)

## Built on by (most cited first, 5 of 5)

- [LIFT: Interpretable truck driving risk prediction with literature-informed fine-tuned LLMs](https://mlsanity.com/paper/2510.22333/) (2025)
- [Situation Awareness for Driver-Centric Driving Style Adaptation](https://mlsanity.com/paper/2403.19595/) (2024)
- [Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training](https://mlsanity.com/paper/2205.07870/) (2022)
- [Judge Me in Context: A Telematics-Based Driving Risk Prediction Framework in Presence of Weak Risk Labels](https://mlsanity.com/paper/2305.03740/) (2023)
- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)

## Abstract (arXiv)

Identifying driving styles is the task of analyzing the behavior of drivers in order to capture variations that will serve to discriminate different drivers from each other. This task has become a prerequisite for a variety of applications, including usage-based insurance, driver coaching, driver action prediction, and even in designing autonomous vehicles; because driving style encodes essential information needed by these applications. In this paper, we present a deep-neural-network architecture, we term D-CRNN, for building high-fidelity representations for driving style, that combine the power of convolutional neural networks (CNN) and recurrent neural networks (RNN). Using CNN, we capture semantic patterns of driver behavior from trajectories (such as a turn or a braking event). We then find temporal dependencies between these semantic patterns using RNN to encode driving style. We demonstrate the effectiveness of these techniques for driver identification by learning driving style through extensive experiments conducted on several large, real-world datasets, and comparing the results with the state-of-the-art deep-learning and non-deep-learning solutions. These experiments also demonstrate a useful example of bias removal, by presenting how we preprocess the input data by sampling dissimilar trajectories for each driver to prevent spatial memorization. Finally, this paper presents an analysis of the contribution of different attributes for driver identification; we find that engine RPM, Speed, and Acceleration are the best combination of features.