  In order to drive safely and efficiently on public roads, autonomous vehicles
will have to understand the intentions of surrounding vehicles, and adapt their
own behavior accordingly. If experienced human drivers are generally good at
inferring other vehicles' motion up to a few seconds in the future, most
current Advanced Driving Assistance Systems (ADAS) are unable to perform such
medium-term forecasts, and are usually limited to high-likelihood situations
such as emergency braking. In this article, we present a first step towards
consistent trajectory prediction by introducing a long short-term memory (LSTM)
neural network, which is capable of accurately predicting future longitudinal
and lateral trajectories for vehicles on highway. Unlike previous work focusing
on a low number of trajectories collected from a few drivers, our network was
trained and validated on the NGSIM US-101 dataset, which contains a total of
800 hours of recorded trajectories in various traffic densities, representing
more than 6000 individual drivers.
