Self driving cars has been the biggest innovation in the automotive industry, but to achieve human level accuracy or near human level accuracy is the biggest challenge that research scientists are facing today. Unlike humans autonomous vehicles do not work on instincts rather they make a decision based on the training data that has been fed to them using machine learning models using which they can make decisions in different conditions they face in the real world. With the advancements in machine learning especially deep learning the self driving car research skyrocketed. In this project we have presented multiple ways to predict acceleration of the autonomous vehicle using Waymo's open dataset. Our main approach was to using CNN to mimic human action and LSTM to treat this as a time series problem.
@article{arxiv.2505.01446,
title = {Waymo Driverless Car Data Analysis and Driving Modeling using CNN and LSTM},
author = {Aashish Kumar Misraa and Naman Jain and Saurav Singh Dhakad},
journal= {arXiv preprint arXiv:2505.01446},
year = {2025}
}
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This work contributed to research acknowledged in https://doi.org/10.3390/app10062046