用CNN和LSTM分析Waymo数据，预测自动驾驶车辆加速度
Waymo Driverless Car Data Analysis and Driving Modeling using CNN and LSTM
- 用CNN提取视觉特征，LSTM建模时间序列行为
- 在Waymo公开数据集上实现加速度预测，提升决策准确性
- 适合自动驾驶感知与控制方向研究者参考
自动驾驶汽车是汽车行业最大的创新之一，但要达到人类水平或接近人类水平的准确度仍是研究者面临的主要挑战。与依赖直觉的人类不同，自动驾驶车辆依靠机器学习模型处理训练数据来做出决策。随着深度学习的发展，自动驾驶研究迅速推进。本项目基于Waymo开放数据集，提出多种预测自动驾驶车辆加速度的方法。主要方法是使用卷积神经网络（CNN）模拟人类驾驶行为，并采用长短期记忆网络（LSTM）将加速度预测视为时间序列问题进行建模。
原文摘要 · Abstract (English)
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.
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