Name
Abstract | ||
|---|---|---|
This paper proposes a continuous identification scheme monitoring a driver's behavior and a detection scheme for an impostor. The proposed scheme uses Long Short-Term Memory (LSTM) to create a classifier of the driver's behavior according to acceleration data and classifies a driver. Additionally, it also detects an impostor according to statistical information of output from the classifier. Experimental results show that an acceleration sensor on a real vehicle is enough to classify 15 drivers according to real-time driver's behavior. Additionally, the proposed scheme can detect an impostor through the verification of real experimental data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCVE45908.2019.8965241 | 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) |
Keywords | Field | DocType |
Driver identification,Impostor detection,Acceleration sensor,Driver's behavior | Identification scheme,Experimental data,Pattern recognition,Computer science,Artificial intelligence,Acceleration,Classifier (linguistics) | Conference |
ISSN | ISBN | Citations |
2378-1289 | 978-1-7281-0143-9 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Mori | 1 | 0 | 0.34 |
| Ryota Ono | 2 | 0 | 0.34 |
| Takuya Mitani | 3 | 0 | 0.68 |
| Katsuhiro Naito | 4 | 64 | 28.81 |
| takaya yamazato | 5 | 239 | 45.32 |