Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 21, Issue 8, Pages 3281-3293Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2019.2925510
Keywords
Adaptation models; Vehicles; Data models; Manifolds; Principal component analysis; Dimensionality reduction; Wheels; Driver behavior modeling; driver model adaptation; manifold alignment; local Procrustes analysis
Categories
Funding
- National Natural Science Foundation of China [61703041]
- Beijing Institute of Technology Research Fund Program for Young Scholars
- Key Laboratory of Biomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education [2017CX02005]
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Driver model adaptation (DMA) provides a way to model the target driver when sufficient data are not available. Traditional DMA methods running at the model level are restricted by the specific model structures and cannot make full use of the historical data. In this paper, a novel DMA framework based on transfer learning (TL) is proposed to deal with the adaptation of driver models in lane-changing scenarios at the data level. Under the proposed DMA framework, a new TL approach named DTW-LPA that combines dynamic time warping (DTW) and local Procrustes analysis (LPA) is developed. Using the DTW, the relationship between the datasets for different drivers can be found automatically. Based on this relationship, the LPA can transfer the data in the historical dataset to the dataset of a newly-involved driver (target driver). In this way, sufficient data can be obtained for the target driver. After the data transferring process, a proper modeling method, such as the Gaussian mixture regression (GMR), can be applied to train the model for the target driver. Data collected from a driving simulator and realistic driving scenes are used to validate the proposed method in various experiments. Compared with the GMR-only and GMR-MAP methods, the DTW-LPA shows better performance on the model accuracy with much lower predicting errors in most cases.
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