Driving maneuvers prediction based on cognition-driven and data-driven
  method
  Advanced Driver Assistance Systems (ADAS) improve driving safety
significantly. They alert drivers from unsafe traffic conditions when a
dangerous maneuver appears. Traditional methods to predict driving maneuvers
are mostly based on data-driven models alone. However, existing methods to
understand the driver's intention remain an ongoing challenge due to a lack of
intersection of human cognition and data analysis. To overcome this challenge,
we propose a novel method that combines both the cognition-driven model and the
data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN)
which fuses the data inside the vehicle and the data outside the vehicle in a
cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM)
branches regulated by human reaction time. Experiments on the Brain4Cars
benchmark dataset demonstrate that the proposed method outperforms previous
methods and achieves state-of-the-art performance.
