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

TL;DR

This paper presents a real-time object location prediction system using LSTM neural networks and polynomial regression, achieving high accuracy and low latency in various driving conditions.

Contribution

The paper introduces a novel combination of LSTM neural networks and polynomial regression for real-time object location prediction, outperforming traditional Kalman filter methods.

Findings

Average error of 0.11 meters with LSTM system

76% error reduction compared to Kalman filter

Inference time of 2 milliseconds

Abstract

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.