An Ensemble Learning-Based Vehicle Steering Detector Using Smartphones

Due to easy access to smartphones, recent years have witnessed an increasing interest in using the mobile phone as a sensing and computation platform for vehicle steering detection. However, relatively lower accuracy of smartphone sensors than on-board diagnostic (OBD)-based systems often leads to l...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2020-05, Vol.21 (5), p.1964-1975
Main Authors: Ouyang, Zhenchao, Niu, Jianwei, Liu, Yu, Liu, Xue
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Detectors
Diagnostic systems
Driving behavior
Energy consumption
Ensemble learning
Heuristic algorithms
Heuristic methods
Machine learning
Model accuracy
Onboard equipment
pattern recognition
Power consumption
Roads
Sensor systems
Smart phones
Smartphones
Steering
vehicle steering
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Summary:Due to easy access to smartphones, recent years have witnessed an increasing interest in using the mobile phone as a sensing and computation platform for vehicle steering detection. However, relatively lower accuracy of smartphone sensors than on-board diagnostic (OBD)-based systems often leads to lower accuracy. We propose an ensemble learning-based model combined with the heuristic algorithm for smartphone-based vehicle steering detection in this paper. Ensemble learning has been widely recognized for its powerful generalization capability, high accuracy, and rapid convergence. However, applying the ensemble learning approach to steering detection of the smartphone-based vehicle entails many challenges due to the limitation of smartphone storage, the constraint on power consumption, and the requirement of being real-time. To address these challenges, we propose a series of techniques to reduce the complexity of the model and energy consumption, while at the same time maintaining high detection accuracy. The performance of the proposed system has been demonstrated using a real dataset and can achieve an accuracy of 97.37%. We also conduct two case studies on real road environment in Beijing with different smartphones.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2019.2909107