Drive2Vec: Multiscale State-Space Embedding of Vehicular Sensor Data

TL;DR

Drive2Vec is a deep learning method using stacked GRUs to embed vehicle sensor data into a compact, actionable form, enabling accurate prediction, contextual inference, and risk detection from short-term sensor inputs.

Contribution

We introduce Drive2Vec, a novel deep learning approach that creates low-dimensional embeddings of vehicular sensor data for diverse automotive tasks.

Findings

Outperforms baseline methods by up to 90% in experiments

Accurately predicts short-term sensor values and long-term averages

Enables inference of driver identity and risky states

Abstract

With automobiles becoming increasingly reliant on sensors to perform various driving tasks, it is important to encode the relevant CAN bus sensor data in a way that captures the general state of the vehicle in a compact form. In this paper, we develop a deep learning-based method, called Drive2Vec, for embedding such sensor data in a low-dimensional yet actionable form. Our method is based on stacked gated recurrent units (GRUs). It accepts a short interval of automobile sensor data as input and computes a low-dimensional representation of that data, which can then be used to accurately solve a range of tasks. With this representation, we (1) predict the exact values of the sensors in the short term (up to three seconds in the future), (2) forecast the long-term average values of these same sensors, (3) infer additional contextual information that is not encoded in the data, including the identity of the driver behind the wheel, and (4) build a knowledge base that can be used to auto-label data and identify risky states. We evaluate our approach on a dataset collected by Audi, which equipped a fleet of test vehicles with data loggers to store all sensor readings on 2,098 hours of driving on real roads. We show in several experiments that our method outperforms other baselines by up to 90%, and we further demonstrate how these embeddings of sensor data can be used to solve a variety of real-world automotive applications.