Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior

TL;DR

This paper introduces a semi-supervised learning approach using RCGANs to augment IMU data for classifying driving behavior, significantly improving classification accuracy with limited labeled data.

Contribution

It presents a novel semi-supervised method employing RCGANs to generate labeled IMU data for better driving behavior classification.

Findings

Classification improved in 79% of cases with generated data.

Using RCGANs enhances driver behavior classification accuracy.

Method addresses limited labeled data issue in IMU-based classification.

Abstract

Over the past years, interest in classifying drivers' behavior from data has surged. Such interest is particularly relevant for car insurance companies who, due to privacy constraints, often only have access to data from Inertial Measurement Units (IMU) or similar. In this paper, we present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally based on such IMU data. Since the amount of labeled IMU data is limited and costly to generate, we utilize Recurrent Conditional Generative Adversarial Networks (RCGAN) to generate more labeled data. Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of the cases, compared to when the drivers are classified with no generated data.