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.

翻译：本文详细介绍了用于预测和插值目标位置坐标的系统设计与实现。我们的解决方案基于通过长短期记忆（LSTM）神经网络和多项式回归处理惯性测量数据与全球定位系统数据。LSTM是一种特别适合处理数据序列且能避免长期依赖问题的循环神经网络（RNN）。我们采用了来自真实车辆及全球定位系统（GPS）传感器的数据。针对传感器频率差异、GPS时间步长不一致及数据丢失问题，我们开发了关键的数据预处理步骤。将基于LSTM系统的性能与卡尔曼滤波器进行了比较。该系统经过调优，能够在低延迟和高精度的条件下实时运行。我们在包括加速、转弯、减速和直线行驶等各种驾驶条件下的道路上进行了测试。我们评估了所提方案的精度和推理时间，结果表明其能够实时运行。基于LSTM的系统平均误差为0.11米，推理时间为2毫秒。与传统卡尔曼滤波器方法（平均误差0.46米，推理时间与基于LSTM系统相近）相比，误差降低了76%。