  In this paper, a driver's intention prediction near a road intersection is
proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM)
with an attention mechanism model based on a hybrid-state system (HSS)
framework. As intersection is considered to be as one of the major source of
road accidents, predicting a driver's intention at an intersection is very
crucial. Our method uses a sequence to sequence modeling with an attention
mechanism to effectively exploit temporal information out of the time-series
vehicular data including velocity and yaw-rate. The model then predicts ahead
of time whether the target vehicle/driver will go straight, stop, or take right
or left turn. The performance of the proposed approach is evaluated on a
naturalistic driving dataset and results show that our method achieves high
accuracy as well as outperforms other methods. The proposed solution is
promising to be applied in advanced driver assistance systems (ADAS) and as
part of active safety system of autonomous vehicles.
