Affiliation:
1. Baidu Research
2. Civil Aviation Management Institute of China
3. Beijing University of Posts and Telecommunications
4. University of Electronic Science and Technology of China
5. IBM Research - China
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 drivers' 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.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
23 articles.
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