LSTM 来预测车辆轨迹的 Python 代码

以下是使用 LSTM 预测车辆轨迹的 Python 代码示例：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 读取数据
dataset = pd.read_csv('car_trajectory.csv')
train_set = dataset.iloc[:, 1:2].values

# 特征缩放
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range = (0, 1))
train_set_scaled = scaler.fit_transform(train_set)

# 创建输入和输出序列
X_train = []
y_train = []
for i in range(60, len(train_set_scaled)):
    X_train.append(train_set_scaled[i-60:i, 0])
    y_train.append(train_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)

# 转换为 LSTM 可接受的格式
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

# 创建 LSTM 模型
model = Sequential()
model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
model.add(LSTM(units = 50))
model.add(Dense(units = 1))
model.compile(optimizer = 'adam', loss = 'mean_squared_error')

# 训练模型
model.fit(X_train, y_train, epochs = 50, batch_size = 32)

# 预测结果
inputs = train_set_scaled[len(train_set_scaled) - len(X_train):]
inputs = np.reshape(inputs, (inputs.shape[0], inputs.shape[1], 1))
predicted = model.predict(inputs)
predicted = scaler.inverse_transform(predicted)

# 可视化结果
plt.plot(train_set, color = 'red', label = 'Real car trajectory')
plt.plot(predicted, color = 'blue', label = 'Predicted car trajectory')
plt.title('Car Trajectory Prediction')
plt.xlabel('Time')
plt.ylabel('Position')
plt.legend()
plt.show()

需要注意的是，这里的 `car_trajectory.csv` 是已经处理好的车辆轨迹数据集，需要自行准备或者从公开数据集中获取。同时，代码中的参数和超参数需要根据具体情况进行调整和优化。