Waymo自動運転データを用いたCNNとLSTMによる運転モデリング
Waymo Driverless Car Data Analysis and Driving Modeling using CNN and LSTM
Waymoの公開データセットを使い、CNNで人間の操作を模倣し、LSTMで時系列問題として扱うことで自動運転車の加速度を予測する手法を提案した。
著者: Aashish Kumar Misraa, Naman Jain, Saurav Singh Dhakad
分類: cs.RO
原文アブストラクト
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.