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CarSpeedNet: A Deep Neural Network-based Car Speed Estimation from Smartphone Accelerometer (2401.07468v2)

Published 15 Jan 2024 in cs.LG and cs.AI

Abstract: We introduce the CarSpeedNet, a deep learning model designed to estimate car speed using three-axis accelerometer data from smartphones. Using 13 hours of data collected from a smartphone in cars across various roads, CarSpeedNet accurately models the relationship between smartphone acceleration and car speed. Ground truth speed data was collected at 1 [Hz] from GPS receivers. The model provides high-frequency speed estimation by incorporating historical data and achieves a precision of less than 0.72 [m/s] during extended driving tests, relying solely on smartphone accelerometer data without any connection to the car.

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