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http://dx.doi.org/10.12815/kits.2017.16.4.153

Automated Vehicle Research by Recognizing Maneuvering Modes using LSTM Model  

Kim, Eunhui (CGS Graduate School of Green Transportation / Korea Advanced Institute of Science Technology(KAIST))
Oh, Alice (School of Computing / Korea Advanced Institute of Science Technology(KAIST))
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.16, no.4, 2017 , pp. 153-163 More about this Journal
Abstract
This research is based on the previous research that personally preferred safe distance, rotating angle and speed are differentiated. Thus, we use machine learning model for recognizing maneuvering modes trained per personal or per similar driving pattern groups, and we evaluate automatic driving according to maneuvering modes. By utilizing driving knowledge, we subdivided 8 kinds of longitudinal modes and 4 kinds of lateral modes, and by combining the longitudinal and lateral modes, we build 21 kinds of maneuvering modes. we train the labeled data set per time stamp through RNN, LSTM and Bi-LSTM models by the trips of drivers, which are supervised deep learning models, and evaluate the maneuvering modes of automatic driving for the test data set. The evaluation dataset is aggregated of living trips of 3,000 populations by VTTI in USA for 3 years and we use 1500 trips of 22 people and training, validation and test dataset ratio is 80%, 10% and 10%, respectively. For recognizing longitudinal 8 kinds of maneuvering modes, RNN achieves better accuracy compared to LSTM, Bi-LSTM. However, Bi-LSTM improves the accuracy in recognizing 21 kinds of longitudinal and lateral maneuvering modes in comparison with RNN and LSTM as 1.54% and 0.47%, respectively.
Keywords
autonomous driving; maneuvering modes; deep learning; RNN; LSTM;
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