Autoencoder Regularized Network For Driving Style Representation Learning
Authors: Weishan Dong, Ting Yuan, Kai Yang, Changsheng Li, Shilei Zhang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Collaboration | 1Baidu Research 2Civil Aviation Management Institute of China 3Beijing University of Posts and Telecommunications 4University of Electronic Science and Technology of China 5IBM Research China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code publicly available, nor does it provide a link to a code repository for the methodology described. |
| Open Datasets | No | We use a large real yet private dataset in experiments. The dataset is collected by an insurance company, containing over 500,000 GPS trips from over 2,500 drivers. |
| Dataset Splits | Yes | For each driver, we use 80% trips as training data, and the rest 20% as classification validation data. Training ARNet and CONet stops until the validation accuracy is maximized (at epochs 33 and 116, respectively). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. It only mentions "in training these networks" without specifying the hardware. |
| Software Dependencies | No | The paper mentions using "ADADELTA optimizer [Zeiler, 2012]" and the "scikit-learn implementation of AP [Pedregosa et al., 2011]" but does not provide specific version numbers for these software components or other ancillary software used in the experimental setup. |
| Experiment Setup | Yes | For all the nets, we set 256 hidden units in gru1 and gru2... We use dropout probability 0.5. We set 50 hidden units in fc1... We use λ=1e-5, ADADELTA optimizer [Zeiler, 2012] with learning rate 1.0, ρ=0.95 and ϵ=1e-8, and batch size 2560 in training these networks. |