  As a combination of various kinds of technologies, autonomous vehicles could
complete a series of driving tasks by itself, such as perception,
decision-making, planning, and control. Since there is no human driver to
handle the emergency situation, future transportation information is
significant for automated vehicles. This paper proposes different methods to
forecast the time series for autonomous vehicles, which are the nearest
neighborhood (NN), fuzzy coding (FC), and long short term memory (LSTM). First,
the formulation and operational process for these three approaches are
introduced. Then, the vehicle velocity is regarded as a case study and the
real-world dataset is utilized to predict future information via these
techniques. Finally, the performance, merits, and drawbacks of the presented
methods are analyzed and discussed.
