diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000000..359bb5307e
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000000..105ce2da2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000000..25d4c9c531
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000000..0e01bed9d6
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/nn.iml b/.idea/nn.iml
new file mode 100644
index 0000000000..a80c2ef563
--- /dev/null
+++ b/.idea/nn.iml
@@ -0,0 +1,14 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000000..35eb1ddfbb
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/src/driverless_car_yb/README.md b/src/driverless_car_yb/README.md
index 8c27ba2124..31c342a6a0 100644
--- a/src/driverless_car_yb/README.md
+++ b/src/driverless_car_yb/README.md
@@ -1 +1,23 @@
-无人车
\ No newline at end of file
+# 无人车项目
+
+## 项目简介
+无人车（自动驾驶汽车）是一种集成了人工智能、传感器技术、计算机视觉和机器学习等前沿技术的智能交通工具。该项目旨在开发能够在复杂道路环境中自主导航、避障、识别交通标志并安全行驶的无人驾驶系统。
+
+## 核心技术
+**计算机视觉**：通过摄像头识别道路标志、车辆、行人等
+**传感器融合**：整合激光雷达、毫米波雷达等传感器数据
+**路径规划**：基于实时环境信息规划最优行驶路径
+**决策系统**：处理复杂交通场景并做出驾驶决策
+**机器学习**：通过深度学习模型提升驾驶安全性
+
+## 主要功能
+自动跟车与变道
+智能避障与紧急制动
+自动泊车
+语音控制与导航
+实时路况分析
+
+## 技术优势
+本项目采用模块化架构设计，支持多种传感器配置和算法策略，确保系统的可扩展性和稳定性。通过大规模数据训练和仿真测试，持续优化算法性能。
+
+---
\ No newline at end of file
diff --git a/src/driverless_car_yb/main.py b/src/driverless_car_yb/main.py
new file mode 100644
index 0000000000..efc22d79eb
--- /dev/null
+++ b/src/driverless_car_yb/main.py
@@ -0,0 +1,275 @@
+import os
+import cv2
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Lambda
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
+
+
+# --------------------------
+# 1. 数据生成与预处理
+# --------------------------
+def create_simulated_data(data_dir, num_samples=1000):
+ """
+ 创建模拟的驾驶数据（图像和转向角）
+ """
+ os.makedirs(data_dir, exist_ok=True)
+ os.makedirs(os.path.join(data_dir, 'images'), exist_ok=True)
+
+ # 生成转向角（-45度到45度之间）
+ steering_angles = np.random.uniform(-45, 45, num_samples)
+
+ # 创建CSV文件
+ data = {'image_path': [], 'steering_angle': []}
+
+ for i in range(num_samples):
+ # 生成模拟图像（道路场景）
+ img = np.zeros((240, 320, 3), dtype=np.uint8)
+
+ # 绘制道路
+ cv2.rectangle(img, (100, 0), (220, 240), (100, 100, 100), -1)
+
+ # 绘制车道线
+ cv2.line(img, (130, 0), (160, 240), (255, 255, 255), 2)
+ cv2.line(img, (190, 0), (160, 240), (255, 255, 255), 2)
+
+ # 根据转向角调整车道线
+ angle_rad = np.radians(steering_angles[i])
+ offset = int(50 * np.tan(angle_rad))
+
+ cv2.line(img, (130 + offset, 0), (160 + offset, 240), (0, 255, 0), 2)
+ cv2.line(img, (190 + offset, 0), (160 + offset, 240), (0, 255, 0), 2)
+
+ # 保存图像
+ img_path = os.path.join(data_dir, 'images', f'{i:04d}.jpg')
+ cv2.imwrite(img_path, img)
+
+ data['image_path'].append(img_path)
+ data['steering_angle'].append(steering_angles[i])
+
+ # 保存CSV文件
+ df = pd.DataFrame(data)
+ df.to_csv(os.path.join(data_dir, 'driving_log.csv'), index=False)
+ print(f"生成 {num_samples} 个模拟样本")
+ return df
+
+
+# 创建模拟数据
+data_dir = 'simulated_data'
+df = create_simulated_data(data_dir, num_samples=2000)
+
+
+# 数据增强函数
+def augment_image(img, angle):
+ """
+ 对图像进行增强处理
+ """
+ # 随机水平翻转
+ if np.random.rand() > 0.5:
+ img = cv2.flip(img, 1)
+ angle = -angle
+
+ # 随机调整亮度
+ hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
+ brightness = np.random.uniform(0.7, 1.3)
+ hsv[:, :, 2] = np.clip(hsv[:, :, 2] * brightness, 0, 255)
+ img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
+
+ # 随机裁剪
+ img = img[50:190, :, :] # 裁剪天空和地面部分
+
+ # 随机平移
+ tx = np.random.randint(-20, 20)
+ ty = np.random.randint(-10, 10)
+ M = np.float32([[1, 0, tx], [0, 1, ty]])
+ img = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))
+
+ return img, angle
+
+
+# 数据生成器
+def data_generator(df, batch_size=32, augment=True):
+ """
+ 生成批量训练数据
+ """
+ while True:
+ batch_images = []
+ batch_angles = []
+
+ # 随机打乱数据
+ shuffled_df = df.sample(frac=1).reset_index(drop=True)
+
+ for i in range(batch_size):
+ # 获取图像路径和转向角
+ img_path = shuffled_df.iloc[i]['image_path']
+ angle = shuffled_df.iloc[i]['steering_angle']
+
+ # 读取图像
+ img = cv2.imread(img_path)
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+ # 数据增强
+ if augment:
+ img, angle = augment_image(img, angle)
+
+ # 图像预处理
+ img = cv2.resize(img, (200, 66)) # 适应NVIDIA模型输入
+ img = img / 255.0 # 归一化
+
+ batch_images.append(img)
+ batch_angles.append(angle)
+
+ yield np.array(batch_images), np.array(batch_angles)
+
+
+# 划分训练集和验证集
+train_df, val_df = train_test_split(df, test_size=0.2, random_state=42)
+
+# 创建数据生成器
+train_generator = data_generator(train_df, batch_size=32, augment=True)
+val_generator = data_generator(val_df, batch_size=32, augment=False)
+
+
+# --------------------------
+# 2. 构建深度学习模型
+# --------------------------
+def build_model():
+ """
+ 构建基于CNN的转向角预测模型（参考NVIDIA架构）
+ """
+ model = Sequential()
+
+ # 图像预处理层
+ model.add(Lambda(lambda x: x / 255.0 - 0.5, input_shape=(66, 200, 3)))
+
+ # 卷积层
+ model.add(Conv2D(24, (5, 5), strides=(2, 2), activation='relu'))
+ model.add(Conv2D(36, (5, 5), strides=(2, 2), activation='relu'))
+ model.add(Conv2D(48, (5, 5), strides=(2, 2), activation='relu'))
+ model.add(Conv2D(64, (3, 3), activation='relu'))
+ model.add(Conv2D(64, (3, 3), activation='relu'))
+
+ # 全连接层
+ model.add(Flatten())
+ model.add(Dense(100, activation='relu'))
+ model.add(Dropout(0.5))
+ model.add(Dense(50, activation='relu'))
+ model.add(Dropout(0.5))
+ model.add(Dense(10, activation='relu'))
+ model.add(Dense(1)) # 输出转向角
+
+ # 编译模型
+ model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
+
+ return model
+
+
+# 构建模型
+model = build_model()
+model.summary()
+
+# --------------------------
+# 3. 训练模型
+# --------------------------
+# 定义回调函数
+checkpoint = ModelCheckpoint(
+ 'best_model.h5',
+ monitor='val_loss',
+ save_best_only=True,
+ mode='min',
+ verbose=1
+)
+
+early_stop = EarlyStopping(
+ monitor='val_loss',
+ patience=5,
+ mode='min',
+ verbose=1,
+ restore_best_weights=True
+)
+
+# 训练模型
+history = model.fit(
+ train_generator,
+ steps_per_epoch=len(train_df) // 32,
+ epochs=30,
+ validation_data=val_generator,
+ validation_steps=len(val_df) // 32,
+ callbacks=[checkpoint, early_stop],
+ verbose=1
+)
+
+# 绘制训练曲线
+plt.figure(figsize=(10, 5))
+plt.plot(history.history['loss'], label='训练损失')
+plt.plot(history.history['val_loss'], label='验证损失')
+plt.xlabel('Epoch')
+plt.ylabel('MSE损失')
+plt.legend()
+plt.title('训练过程')
+plt.show()
+
+# --------------------------
+# 4. 模型评估与实时预测
+# --------------------------
+# 加载最佳模型
+best_model = load_model('best_model.h5')
+
+# 在验证集上评估
+val_loss = best_model.evaluate(val_generator, steps=len(val_df) // 32, verbose=1)
+print(f"验证集损失: {val_loss:.4f}")
+
+
+# 实时预测演示
+def real_time_prediction(model):
+ """
+ 使用摄像头进行实时转向角预测
+ """
+ cap = cv2.VideoCapture(0)
+ cap.set(3, 320) # 设置宽度
+ cap.set(4, 240) # 设置高度
+
+ while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ # 图像预处理
+ img = frame[50:190, :, :] # 裁剪
+ img = cv2.resize(img, (200, 66)) # 调整尺寸
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 转换颜色空间
+ img = img / 255.0 # 归一化
+ img = np.expand_dims(img, axis=0) # 增加批次维度
+
+ # 预测转向角
+ steering_angle = model.predict(img)[0][0]
+
+ # 在图像上显示转向角
+ cv2.putText(
+ frame,
+ f"Steering Angle: {steering_angle:.2f} deg",
+ (10, 30),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 1,
+ (0, 255, 0),
+ 2
+ )
+
+ # 显示图像
+ cv2.imshow('Autonomous Driving', frame)
+
+ # 按'q'退出
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ cap.release()
+ cv2.destroyAllWindows()
+
+
+# 运行实时预测
+print("开始实时预测... 按'q'退出")
+real_time_prediction(best_model)
\ No newline at end of file