DeepVIP: Deep Learning-Based Vehicle Indoor Positioning Using Smartphones

The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor position...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2022-12, Vol.71 (12), p.13299-13309
Main Authors: Zhou, Baoding, Gu, Zhining, Gu, Fuqiang, Wu, Peng, Yang, Chengjing, Liu, Xu, Li, Linchao, Li, Yan, Li, Qingquan
Format: Article
Language:English
Subjects:
Accelerometers
Autonomous cars
Data-driven inertial navigation
Deep learning
deep neural networks
Electronic devices
Equipment costs
indoor localization
Inertial navigation
Inertial sensing devices
Location awareness
Navigation
Parking
Sensors
Smart cars
Smart phones
smartphone sensors
Smartphones
Wireless fidelity
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Summary:The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor positioning solutions either use infrastructures (e.g., WiFi access points) or inertial sensors, which suffer from low positioning accuracy, limited coverage, or high cost to deploy new equipment. To tackle these challenges, in this work we propose a novel Deep Learning-based Vehicle Indoor Positioning (DeepVIP) approach using smartphone built-in sensors, including accelerometer, gyroscope, magnetometer, and gravity sensor. Experiments are conducted in indoor parking areas. Experimental results show that the proposed method outperforms the state-of-the-art methods.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3199507