  This paper details the design and implementation of a system for predicting
and interpolating object location coordinates. Our solution is based on
processing inertial measurements and global positioning system data through a
Long Short-Term Memory (LSTM) neural network and polynomial regression. LSTM is
a type of recurrent neural network (RNN) particularly suited for processing
data sequences and avoiding the long-term dependency problem. We employed data
from real-world vehicles and the global positioning system (GPS) sensors. A
critical pre-processing step was developed to address varying sensor
frequencies and inconsistent GPS time steps and dropouts. The LSTM-based
system's performance was compared with the Kalman Filter. The system was tuned
to work in real-time with low latency and high precision. We tested our system
on roads under various driving conditions, including acceleration, turns,
deceleration, and straight paths. We tested our proposed solution's accuracy
and inference time and showed that it could perform in real-time. Our
LSTM-based system yielded an average error of 0.11 meters with an inference
time of 2 ms. This represents a 76\% reduction in error compared to the
traditional Kalman filter method, which has an average error of 0.46 meters
with a similar inference time to the LSTM-based system.
