An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU
Abstract
Accurately estimating vehicle velocity via smartphone is critical for mobile navigation and transportation. This paper introduces a cutting-edge framework for velocity estimation that incorporates temporal learning models, utilizing Inertial Measurement Unit (IMU) data and is supervised by Global Navigation Satellite System (GNSS) information. The framework employs a noise compensation network to fit the noise distribution between sensor measurements and actual motion, and a pose estimation network to align the coordinate systems of the phone and the vehicle. To enhance the model's generalizability, a data augmentation technique that mimics various phone placements within the car is proposed. Moreover, a new loss function is designed to mitigate timestamp mismatches between GNSS and IMU signals, effectively aligning the signals and improving the velocity estimation accuracy. Finally, we implement a highly efficient prototype and conduct extensive experiments on a real-world crowdsourcing dataset, resulting in superior accuracy and efficiency.
Cite
@article{arxiv.2505.18490,
title = {An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU},
author = {Xuan Xiao and Xiaotong Ren and Haitao Li},
journal= {arXiv preprint arXiv:2505.18490},
year = {2025}
}