├── 原始数据处理 ├── (1)添加特征.ipynb ├── (2)数据筛选.ipynb ├── (3)添加标签.ipynb ├── (4)特征提取标准化.ipynb ├── (5)右边道数据清洗.ipynb └── 标准化.ipynb ├── README.md ├── model ├── 1LSTM_HaveDrop.h5 ├── 1LSTM_HaveDrop_part.h5 ├── 1LSTM_HaveDrop_part_500.h5 ├── 1LSTM_NoDrop.h5 ├── 2LSTM_HaveDrop.h5 ├── 2LSTM_HaveDrop_500-515.h5 ├── 2LSTM_HaveDrop_merge.h5 ├── 2LSTM_HaveDrop_part.h5 ├── 2LSTM_new.h5 ├── train.h5 ├── train2.h5 └── 算法模型 │ └── bp.h5 ├── train ├── LSTM.py ├── LSTM_doubel.py ├── PRA.py ├── RANSAC随机采样一致性算法.py ├── SVM.py ├── new_answer.py ├── test.py └── train.py ├── 可视化 ├── 二维高斯分布.ipynb └── 可视化.ipynb ├── 数据新 ├── .ipynb_checkpoints │ ├── 80FPS-checkpoint.ipynb │ ├── Untitled-checkpoint.ipynb │ └── 轨迹新-checkpoint.ipynb ├── 80FPS.ipynb ├── Untitled.ipynb └── 轨迹新.ipynb ├── 算法代码 ├── .ipynb_checkpoints │ ├── BP变道可行性分析-checkpoint.ipynb │ ├── Logist-checkpoint.ipynb │ ├── SVM-checkpoint.ipynb │ ├── 神经网络-checkpoint.ipynb │ ├── 贝塞尔曲线绘制-checkpoint.ipynb │ ├── 逻辑回归-checkpoint.ipynb │ └── 随机森林-checkpoint.ipynb ├── BP变道可行性分析.ipynb ├── Logist.ipynb ├── SVM.ipynb ├── out.png ├── 神经网络.ipynb ├── 贝塞尔曲线绘制.ipynb ├── 逻辑回归.ipynb └── 随机森林.ipynb ├── 邻车道信息提取 ├── 变道邻车信息提取（2）.ipynb ├── 变道邻车信息提取（3）.ipynb ├── 最终版 │ ├── .ipynb_checkpoints │ │ ├── NB合并-checkpoint.ipynb │ │ ├── 变道-checkpoint.ipynb │ │ ├── 直行&变道-checkpoint.ipynb │ │ └── 直行-checkpoint.ipynb │ ├── NB合并.ipynb │ ├── 变道.ipynb │ ├── 直行&变道.ipynb │ └── 直行.ipynb ├── .ipynb_checkpoints │ ├── 变道邻车信息提取（2）-checkpoint.ipynb │ ├── 变道邻车信息提取（3）-checkpoint.ipynb │ ├── NB合并-checkpoint.ipynb │ ├── 单帧提取(变道）-checkpoint.ipynb │ ├── 单帧提取(直行）-checkpoint.ipynb │ ├── 单帧提取（变道2）-checkpoint.ipynb │ ├── 变道邻车信息提取（1）-checkpoint.ipynb │ └── 直行&变道-checkpoint.ipynb ├── NB合并.ipynb ├── 单帧提取(变道）.ipynb ├── 单帧提取（变道2）.ipynb ├── 变道邻车信息提取.ipynb └── 变道邻车信息提取（1）.ipynb └── 预测结果可视化.ipynb / 原始数据处理/(1)添加特征.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dotah88/Vehicle-Trajectory-Prediction-on-NGSIM/HEAD/ 原始数据处理/(1)添加特征.ipynb 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