Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 5, Pages 1964-1975Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2909107
Keywords
Driving behavior; ensemble learning; pattern recognition; vehicle steering; smartphones
Categories
Funding
- National Key R&D Program of China [2017YFB1301100]
- National Natural Science Foundation of China [61572060, 61772060, 61728201]
- State Key Laboratory of Software Development Environment [SKLSDE-2017ZX-18]
- China Education and Research Network (CERNET) Innovation Project [NGII20160316, NGII20170315]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available