Name
Abstract | ||
|---|---|---|
In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn driversu0027 driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unified architecture. Experiments on a challenging driver number estimation problem and the driver identification problem show that ARNet can learn a good generalized driving style representation: It significantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0.68, less than one driver) and the highest identification accuracy (by at least 3% improvement) compared with traditional supervised learning methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.24963/ijcai.2017/222 | IJCAI |
DocType | Volume | Citations |
Conference | abs/1701.01272 | 5 |
PageRank | References | Authors |
0.44 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weishan Dong | 1 | 19 | 3.13 |
| Ting Yuan | 2 | 43 | 4.83 |
| kai yang | 3 | 116 | 30.39 |
| Changsheng Li | 4 | 71 | 10.11 |
| Shilei Zhang | 5 | 57 | 9.81 |