  The Waymo Open Dataset has been released recently, providing a platform to
crowdsource some fundamental challenges for automated vehicles (AVs), such as
3D detection and tracking. While~the dataset provides a large amount of
high-quality and multi-source driving information, people in academia are more
interested in the underlying driving policy programmed in Waymo self-driving
cars, which is inaccessible due to AV manufacturers' proprietary protection.
Accordingly, academic researchers have to make various assumptions to implement
AV components in their models or simulations, which may not represent the
realistic interactions in real-world traffic. Thus, this paper introduces an
approach to learn a long short-term memory (LSTM)-based model for imitating the
behavior of Waymo's self-driving model. The proposed model has been evaluated
based on Mean Absolute Error (MAE). The experimental results show that our
model outperforms several baseline models in driving action prediction. In
addition, a visualization tool is presented for verifying the performance of
the model.
