  In this paper, we propose an efficient vehicle trajectory prediction
framework based on recurrent neural network. Basically, the characteristic of
the vehicle's trajectory is different from that of regular moving objects since
it is affected by various latent factors including road structure, traffic
rules, and driver's intention. Previous state of the art approaches use
sophisticated vehicle behavior model describing these factors and derive the
complex trajectory prediction algorithm, which requires a system designer to
conduct intensive model optimization for practical use. Our approach is
data-driven and simple to use in that it learns complex behavior of the
vehicles from the massive amount of trajectory data through deep neural network
model. The proposed trajectory prediction method employs the recurrent neural
network called long short-term memory (LSTM) to analyze the temporal behavior
and predict the future coordinate of the surrounding vehicles. The proposed
scheme feeds the sequence of vehicles' coordinates obtained from sensor
measurements to the LSTM and produces the probabilistic information on the
future location of the vehicles over occupancy grid map. The experiments
conducted using the data collected from highway driving show that the proposed
method can produce reasonably good estimate of future trajectory.
