An Inertial Sequence Learning Framework for Vehicle Speed Estimation via Smartphone IMU

2025/05/24 by Xuan Xiao, Xiao, Xuan, Xiaotong Ren +3
Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #IoT and GPS-based Vehicle Safety Systems #Robotics (cs.RO) #Vehicle Dynamics and Control Systems

paper · pdf · doi:10.48550/arxiv.2505.18490

openalex publication_date 2025/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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Related

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• Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
• RIDI: Robust IMU Double Integration
• Novel velocity model to improve indoor localization using inertial navigation with sensors on a smartphone
• Inertial Odometry on Handheld Smartphones