Abstract
| Original language | English |
|---|---|
| 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 |
|---|---|
| Abbreviated title | UbiComp & ISWC |
| Country | Japan |
| City | Osaka |
| Period | 7/09/15 → 11/09/15 |
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Keywords
- EWI-26651
- Smartphone Sensing
- Driver Behaviour
- METIS-315138
- IR-98920
- Anomaly Detection
- Road Monitoring
Cite this
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A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. / Seraj, Fatjon; Zhang, Kui; Türkes, Okan; Meratnia, Nirvana; Havinga, Paul J.M.
UbiComp & ISWC'15: 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. New York : Association for Computing Machinery (ACM), 2015. p. 1169-1177.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior
AU - Seraj, Fatjon
AU - Zhang, Kui
AU - Türkes, Okan
AU - Meratnia, Nirvana
AU - Havinga, Paul J.M.
N1 - eemcs-eprint-26651
PY - 2015/9
Y1 - 2015/9
N2 - 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.
AB - 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.
KW - EWI-26651
KW - Smartphone Sensing
KW - Driver Behaviour
KW - METIS-315138
KW - IR-98920
KW - Anomaly Detection
KW - Road Monitoring
U2 - 10.1145/2800835.2800981
DO - 10.1145/2800835.2800981
M3 - Conference contribution
SN - 978-1-4503-3575-1
SP - 1169
EP - 1177
BT - UbiComp & ISWC'15
PB - Association for Computing Machinery (ACM)
CY - New York
ER -