Unsupervised Representation Learning of Complex Time Series for Maneuverability State Identification in Smart Mobility

TL;DR

This work aims to address challenges associated with modeling MTS data collected from a vehicle using sensors by investigating the effectiveness of two distinct unsupervised representation learning approaches in identifying maneuvering states in smart mobility.

Abstract

Multivariate Time Series (MTS) data capture temporal behaviors to provide invaluable insights into various physical dynamic phenomena. In smart mobility, MTS plays a crucial role in providing temporal dynamics of behaviors such as maneuver patterns, enabling early detection of anomalous behaviors while facilitating pro-activity in Prognostics and Health Management (PHM). In this work, we aim to address challenges associated with modeling MTS data collected from a vehicle using sensors. Our goal is to investigate the effectiveness of two distinct unsupervised representation learning approaches in identifying maneuvering states in smart mobility. Specifically, we focus on some bivariate accelerations extracted from 2.5 years of driving, where the dataset is non-stationary, long, noisy, and completely unlabeled, making manual labeling impractical. The approaches of interest are Temporal Neighborhood Coding for Maneuvering (TNC4Maneuvering) and Decoupled Local and Global Representation learner for Maneuvering (DLG4Maneuvering). The main advantage of these frameworks is that they capture transferable insights in a form of representations from the data that can be effectively applied in multiple subsequent tasks, such as time-series classification, clustering, and multi-linear regression, which are the quantitative measures and qualitative measures, including visualization of representations themselves and resulting reconstructed MTS, respectively. We compare their effectiveness, where possible, in order to gain insights into which approach is more effective in identifying maneuvering states in smart mobility.

Figures (4)

• Figure 1: Representation learning and generative modeling frameworks for maneuverability extraction in smart transportation.
• Figure 2: Accelerations and corresponding vector representations $(M=16)$ encoded using static window $W_t = 19$.
• Figure 3: Original $(a_{lat}, a_{lon})$ and reconstructions $(\hat{a}_{lon}, \hat{a}_{lat})$ with error bars $(\pm \sigma_{lon}, \pm \sigma_{lat})$ of bivariate signals using DLG4Maneuvering with a static window size of $W_t = 19$.
• Figure 4: Global objectives and workflow.