[Paper Review] Autoencoder Regularized Network For Driving Style Representation Learning

Weishan Dong, Ting Yuan+3 more|arXiv (Cornell University)|Jan 5, 2017

Autonomous Vehicle Technology and Safety21 references4 citations

TL;DR

This paper proposes ARNet, a deep neural network that combines supervised classification with autoencoder-based unsupervised feature regularization to learn generalized driving style representations from GPS trip data. By training an RNN to encode trip sequences and using an autoencoder to reconstruct the RNN's hidden state, ARNet learns a robust, low-dimensional bottleneck feature space that generalizes well to unseen drivers, achieving state-of-the-art performance on driver number estimation (0.68 average error) and driver identification (58.2% top-1 accuracy).

ABSTRACT

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.

Motivation & Objective

• To learn generalized driving style representations from GPS trip data that generalize to unseen drivers, overcoming limitations of traditional supervised learning.
• To address the challenge of limited labeled data and high diversity in unseen drivers' behaviors in real-world driving data.
• To improve representation learning by integrating unsupervised autoencoder regularization into a supervised deep learning framework.
• To develop a scalable trip encoding framework, trip2vec, for converting variable-length GPS trips into fixed-length style embeddings.
• To evaluate the method on two real-world benchmarks: driver number estimation and driver identification, with a focus on zero-shot generalization.

Proposed method

• ARNet uses a shared RNN encoder to process sequential GPS trip data, extracting temporal features from speed and turning patterns.
• The RNN's hidden state is fed into an autoencoder with an $l_1$-regularized bottleneck layer (fc1), which reconstructs the hidden state rather than the raw input.
• The network is jointly trained with two loss functions: a classification loss on driver identities and a reconstruction loss on the RNN hidden state.
• The autoencoder acts as a regularizer, enforcing that learned features are both discriminative for classification and reconstructible from the bottleneck layer.
• The bottleneck layer (fc1) serves as the final driving style representation, capturing generalized style patterns across diverse drivers.
• The trip2vec framework uses ARNet as a base encoder to convert entire trips into fixed-length vectors for downstream tasks.

Experimental results

Research questions

• RQ1 Can a deep learning model that combines supervised and unsupervised learning generalize better to unseen drivers than purely supervised models?
• RQ2 How effective is autoencoder regularization of hidden-layer representations in improving driving style representation learning from GPS data?
• RQ3 Can the proposed ARNet architecture outperform existing deep networks in both driver number estimation and driver identification tasks?
• RQ4 Does the integration of supervisory signals into unsupervised feature learning improve the quality and discriminative power of learned representations?
• RQ5 To what extent does the trip2vec framework enable effective, fixed-length encoding of variable-length GPS trips for style representation?

Key findings

• ARNet achieves an average absolute error of 0.68 in driver number estimation across 1 to 10 drivers, significantly lower than other methods and less than one driver.
• On the 50-class driver identification task, ARNet achieves 58.2% top-1 accuracy, outperforming the next best method (CONet) by 3 percentage points.
• The model improves top-5 accuracy to 78.3%, demonstrating strong generalization on unseen trips of known drivers.
• The autoencoder's reconstruction of the RNN hidden state, rather than input data, enables better feature generalization and robustness.
• ARNet’s performance gains are consistent across multiple evaluation settings, confirming its effectiveness in both zero-shot and few-shot scenarios.
• The method outperforms not only deep learning baselines (e.g., IRNN, StackedIRNN) but also traditional GBDT models, highlighting its superiority in representation learning.