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

Object Location Prediction in Real-time using LSTM Neural Network and Polynomial Regression