Abstract
This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.
Original language | English |
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Title of host publication | UbiComp & ISWC'15 |
Subtitle of host publication | proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers |
Place of Publication | New York |
Publisher | Association for Computing Machinery (ACM) |
Pages | 1169-1177 |
Number of pages | 9 |
ISBN (Print) | 978-1-4503-3575-1 |
DOIs | |
Publication status | Published - Sep 2015 |
Event | ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers - Osaka, Japan Duration: 7 Sep 2015 → 11 Sep 2015 |
Conference
Conference | ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers |
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Abbreviated title | UbiComp & ISWC |
Country/Territory | Japan |
City | Osaka |
Period | 7/09/15 → 11/09/15 |
Keywords
- EWI-26651
- Smartphone Sensing
- Driver Behaviour
- METIS-315138
- IR-98920
- Anomaly Detection
- Road Monitoring