From 39581fe1e6fd7153215913e9b1eacb329830434f Mon Sep 17 00:00:00 2001 From: wumei5 <2544077336@qq.com> Date: Fri, 3 Apr 2026 21:18:53 +0800 Subject: [PATCH 1/2] =?UTF-8?q?=E9=80=89=E9=A2=98=E5=B9=B6=E9=87=8D?= =?UTF-8?q?=E5=91=BD=E5=90=8D=E6=96=87=E4=BB=B6=E5=A4=B9=EF=BC=9AAutonomou?= =?UTF-8?q?s=5Fvehicle=5Fnavigation=5Fusing=5Fdeep=5Flearning=5Fmaster=20?= =?UTF-8?q?=E2=86=92=20automatic=5Fdrive=5Fdeep=5Flearning?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md | 0 .../carla_ros_ws/src/CMakeLists.txt | Bin .../src/carla_autonomous/CMakeLists.txt | 0 .../src/carla_autonomous/agents/__init__.py | 0 .../carla_autonomous/agents/navigation/__init__.py | 0 .../agents/navigation/basic_agent.py | 0 .../agents/navigation/behavior_agent.py | 0 .../agents/navigation/behavior_types.py | 0 .../agents/navigation/controller.py | 0 .../agents/navigation/global_route_planner.py | 0 .../agents/navigation/local_planner.py | 0 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a/src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/package.xml b/src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/package.xml similarity index 100% rename from src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/package.xml rename to src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/package.xml diff --git a/src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_node.py b/src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_node.py similarity index 100% rename from src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_node.py rename to src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_node.py diff --git a/src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_services.py b/src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_services.py similarity index 100% rename from src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_services.py rename to src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/scripts/carla_autonomous_services.py diff --git a/src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/scripts/carla_control_client.py b/src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/scripts/carla_control_client.py similarity index 100% rename from src/Autonomous_vehicle_navigation_using_deep_learning_master/carla_ros_ws/src/carla_autonomous/scripts/carla_control_client.py rename to 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a/src/Autonomous_vehicle_navigation_using_deep_learning_master/test/test_braking.py b/src/automatic_drive_deep_learning/test/test_braking.py similarity index 100% rename from src/Autonomous_vehicle_navigation_using_deep_learning_master/test/test_braking.py rename to src/automatic_drive_deep_learning/test/test_braking.py diff --git a/src/Autonomous_vehicle_navigation_using_deep_learning_master/test/test_driving.py b/src/automatic_drive_deep_learning/test/test_driving.py similarity index 100% rename from src/Autonomous_vehicle_navigation_using_deep_learning_master/test/test_driving.py rename to src/automatic_drive_deep_learning/test/test_driving.py From 65b8acbea7e44389f0ae8dde98ad9df8456a35d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E6=A2=A6=E7=8F=8D?= <2544077336@qq.com> Date: Fri, 12 Jun 2026 15:37:28 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E5=9F=BA=E4=BA=8E=E6=B7=B1=E5=BA=A6?= =?UTF-8?q?=E5=AD=A6=E4=B9=A0=E4=B8=8E=20DQN=20=E5=BC=BA=E5=8C=96=E5=AD=A6?= =?UTF-8?q?=E4=B9=A0=E7=9A=84=20CARLA=20=E8=87=AA=E5=8A=A8=E9=A9=BE?= =?UTF-8?q?=E9=A9=B6=E5=AF=BC=E8=88=AA=E7=B3=BB=E7=BB=9F=E6=8A=A5=E5=91=8A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/automatic_drive_deep_learning/README.md | 513 +++++++++++++++++++ docs/index.md | 2 + mkdocs.yml | 1 + 3 files changed, 516 insertions(+) create mode 100644 docs/automatic_drive_deep_learning/README.md diff --git a/docs/automatic_drive_deep_learning/README.md b/docs/automatic_drive_deep_learning/README.md new file mode 100644 index 0000000000..843e26c700 --- /dev/null +++ b/docs/automatic_drive_deep_learning/README.md @@ -0,0 +1,513 @@ +# 自动驾驶导航系统（DQN） + +基于深度学习与 DQN 强化学习的 CARLA 自动驾驶导航系统。 + +--- + + + + + + + + +
PythonCARLATensorFlowUbuntu
+ +--- + +## 📑 目录 + +- [项目简介](#项目简介) +- [核心功能](#核心功能) +- [系统架构](#系统架构) +- [模块说明](#模块说明) +- [状态空间与动作空间](#状态空间与动作空间) +- [奖励函数](#奖励函数) +- [DQN 模型架构](#dqn-模型架构) +- [配置系统](#配置系统) +- [数据收集与可视化](#数据收集与可视化) +- [环境要求](#环境要求) +- [部署流程](#部署流程) +- [运行方法](#运行方法) +- [注意事项](#注意事项) +- [参考](#参考) + +--- + +## 项目简介 + +`automatic_drive_deep_learning` 是一个在 [CARLA](https://carla.org/) 仿真环境中运行的自动驾驶系统，采用**双模型 DQN（Deep Q-Network）**策略，将刹车决策与驾驶转向决策解耦，分别由独立的神经网络负责。 + +系统利用深度摄像头（Depth Camera）与语义分割摄像头（Semantic Segmentation Camera）作为感知来源，实时获取前方障碍物距离与道路环境信息，结合全局路径规划器生成预定轨迹，驱动 Tesla Model 3 虚拟车辆从指定起点自主导航至终点。 + +项目同时提供 **ROS 版本**（位于 `carla_ros_ws/`），可通过 `roslaunch` 在 ROS Noetic 中部署与运行。 + +--- + +## 核心功能 + +1. **双模型协同决策**：刹车模型（Braking DQN）负责紧急制动判断，驾驶模型（Driving DQN）负责方向与速度控制，两者串联执行。 +2. **多传感器融合**：同时挂载深度摄像头和语义分割摄像头，障碍物距离由深度图计算，车辆/行人目标由语义分割图像精确识别。 +3. **全局路径规划**：使用 CARLA 内置 `GlobalRoutePlanner`，以 0.5m 采样精度在地图上生成从起点到终点的离散路径点序列。 +4. **实时路线可视化**：在 CARLA 世界中渲染规划路线（红色）、历史轨迹（蓝色）及车辆方向箭头（绿色）。 +5. **自适应视角跟随**：独立线程以 60Hz 平滑跟随车辆，支持自适应平滑系数，避免抖动。 +6. **可选交通流仿真**：支持在场景中批量生成 NPC 车辆与行人，模拟真实交通环境。 +7. **数据收集与图表生成**：逐步记录障碍物距离、横向偏差、角度差、速度、帧率和奖励值，Episode 结束后自动生成 CSV 和性能图表。 + +--- + +## 系统架构 + +``` +automatic_drive_deep_learning/ +├── main/ # 推理主程序（加载预训练模型运行） +│ ├── main.py # 主入口：初始化环境、加载模型、运行 Episode +│ ├── car_env.py # CARLA 车辆环境：传感器挂载、状态计算、奖励与终止判断 +│ ├── config.py # 全局配置：轨迹、模型路径、动作、可视化、传感器等 +│ ├── config_manager.py # 高级配置管理（动态参数校验与加载） +│ ├── data_collector.py # 数据收集与 Matplotlib 图表生成 +│ ├── route_visualizer.py # CARLA 世界中路线与标记的实时绘制 +│ ├── vehicle_tracker.py # 车辆位姿跟踪与视角平滑跟随线程 +│ ├── traffic_manager.py # NPC 车辆与行人的批量生成与管理 +│ ├── trajectory_manager.py # 轨迹路径点管理工具 +│ ├── model_manager.py # 模型加载与推理封装 +│ └── get_location.py # 获取 CARLA 中车辆当前坐标（用于配置轨迹） +├── test/ # DQN 训练脚本 +│ ├── braking_dqn.py # 刹车模型训练（2 状态 → 2 动作） +│ ├── driving_dqn.py # 驾驶模型训练（2 状态 → 5 动作） +│ ├── test_braking.py # 刹车模型推理测试 +│ ├── test_driving.py # 驾驶模型推理测试 +│ ├── pedestrians_1.py # 行人场景测试 1 +│ └── pedestrians_2.py # 行人场景测试 2 +├── carla_ros_ws/ # ROS 工作空间（可选） +│ └── src/ +│ ├── carla_autonomous/ # ROS 包：launch 文件、节点脚本、预训练模型 +│ └── CMakeLists.txt +└── requirements.txt # Python 依赖列表 +``` + +**三层分离设计**： + +| 层级 | 文件 | 职责 | +|:-----|:-----|:-----| +| **决策层** | `main.py`, `model_manager.py` | 加载双模型，串联推理，分发动作 | +| **环境层** | `car_env.py` | 传感器数据处理，状态提取，奖励计算，Episode 管理 | +| **仿真层** | CARLA Server | 物理仿真，Actor 管理，地图渲染 | + +--- + +## 模块说明 + +### `car_env.py` — 车辆环境 + +核心环境类 `CarEnv`，实现： + +- **`__init__(start, end)`**：连接 CARLA（localhost:2000），初始化车辆蓝图与目标终点。 +- **`reset()`**：生成车辆（Tesla Model 3），挂载深度摄像头、语义分割摄像头、碰撞传感器、车道入侵传感器，调用 `GlobalRoutePlanner` 生成路径，返回初始状态。 +- **`step(action, current_state)`**：执行 6 选 1 动作控制，处理图像，计算横向偏差与角度差，判断终止条件，返回 `(new_state, reward, done, waypoint)`。 +- **`process_images()`**：从深度图计算到最近车辆/行人的距离，从语义分割图提取障碍物像素索引。 + +### `main.py` — 主程序入口 + +- `setup_environment()`：初始化 CARLA 环境、交通管理器、路线可视化器与车辆跟踪器。 +- `load_models()`：加载刹车模型 `Braking___282.model` 和驾驶模型 `Driving__6030.model`。 +- `predict_action()`：先由刹车模型决定是否制动（action=0）；若允许行驶，再由驾驶模型选择方向动作（action=1~5）。 +- `run_episode()`：主循环，每步调用视角更新、状态获取、动作推理、环境执行与数据记录。 + +### `config.py` — 配置文件 + +集中管理所有参数，分为以下模块： + +| 配置段 | 内容 | +|:-------|:-----| +| `TRAJECTORIES` | 预定义轨迹（起点/终点坐标） | +| `MODEL_PATHS` | 刹车模型与驾驶模型文件路径 | +| `ACTION_NAMES` | 动作名称映射（中文） | +| 交通配置 | NPC 车辆数量、行人数量、安全模式开关 | +| 可视化配置 | 路线颜色、箭头长度、历史路径最大点数 | +| 视角配置 | 跟随高度、俯视角、平滑系数、自适应开关 | +| 传感器配置 | 摄像头分辨率（640×480）、FOV（40°） | +| 数据收集配置 | 保存目录、图表 DPI、采样率 | + +--- + +## 状态空间与动作空间 + +### 状态空间（4 维向量） + +主程序推理阶段使用完整的 4 维状态： + +| 索引 | 含义 | 归一化方式 | +|:----:|:-----|:----------| +| `s[0]` | 到前方障碍物的距离 | `(distance - 300) / 300` | +| `s[1]` | 车辆当前速度 | `(kmh - 30) / 30 - s[0]` | +| `s[2]` | 角度差 φ（车辆朝向 vs 路径朝向） | 原始度数 | +| `s[3]` | 横向偏差（到路径中心线的有符号距离） | `signed_dis × 15` | + +> **刹车模型**仅使用 `s[0:2]`（障碍物距离、速度）。 +> **驾驶模型**仅使用 `s[2:4]`（角度差、横向偏差）。 + +### 动作空间（6 离散动作） + +| 动作 ID | 名称 | 控制参数 | +|:-------:|:-----|:---------| +| **0** | 刹车 | `throttle=0, brake=1.0` | +| **1** | 直行 | `throttle=0.4, steer=0.0` | +| **2** | 左转 | `throttle=0.1, steer=-0.3` | +| **3** | 右转 | `throttle=0.1, steer=+0.3` | +| **4** | 微左 | `throttle=0.3, steer=-0.1` | +| **5** | 微右 | `throttle=0.3, steer=+0.1` | + +--- + +## 奖励函数 + +### 刹车模型奖励 + +| 条件 | 动作 | 奖励 | +|:-----|:-----|:-----| +| 障碍物距离 < 制动安全距离 | 刹车（0） | `+3` | +| 障碍物距离 < 制动安全距离 | 油门（1） | `-3` | +| 障碍物距离充足 | 油门（1） | `+2` | +| 已停车 + 障碍物距离 < 150m + kmh=0 | — | `+200`（成功） | +| 碰撞 | — | `-200`（失败） | + +### 驾驶模型奖励 + +| 条件 | 奖励 | +|:-----|:-----| +| `|φ| < 5°`，执行直行 | `+2` | +| `5° ≤ |φ| < 10°`，执行对应微调 | `+1~+2` | +| `|φ| ≥ 10°`，执行对应大幅转向 | `+1~+2` | +| 横向偏差 `|d| < 0.1` 且执行直行 | `+4` | +| 速度 > 20 km/h | `+1` | +| 速度 < 5 km/h | `-1` | +| 横向偏差 `|d| > 2m` | `-10` | +| 碰撞 / 角度偏差 > 100° / 偏离道路 > 3m | `-200`（终止） | +| 到达终点 (dist < 5m) | `+100`（成功） | + +--- + +## DQN 模型架构 + +### 刹车模型（`braking_dqn.py`） + +```text +输入: [障碍物距离, 速度] (shape: 2,) + │ + └─ Dense(2, activation='linear') + │ +输出: Q 值 (shape: 2,) # [刹车, 油门] +``` + +| 参数 | 值 | +|:-----|:---| +| 优化器 | Adam (lr=0.0001) | +| 损失函数 | MSE | +| 经验回放容量 | 5000 | +| Minibatch 大小 | 16 | +| 折扣因子 γ | 0.99 | +| ε 衰减 | 0.95（每 Episode） | +| 训练 Episodes | 40 | + +### 驾驶模型（`driving_dqn.py`） + +```text +输入: [角度差 φ, 横向偏差 d] (shape: 2,) + │ + ├─ Dense(8, activation='relu') + │ + └─ Dense(5, activation='linear') + │ +输出: Q 值 (shape: 5,) # [直行, 左转, 右转, 微左, 微右] +``` + +| 参数 | 值 | +|:-----|:---| +| 优化器 | Adam (lr=0.0001) | +| 损失函数 | MSE | +| 经验回放容量 | 5000 | +| Minibatch 大小 | 16 | +| 折扣因子 γ | 0.99 | +| ε 衰减 | 0.95（每 Episode） | +| 训练 Episodes | 40 | +| 目标网络更新频率 | 每 2 步 | + +--- + +## 配置系统 + +### 轨迹配置 + +```python +TRAJECTORIES = { + "test_trajectory": { + "start": [98.26, 99.16, 0.49, 0], # [x, y, z, yaw] + "end": [74.65, -56.01, 0.41], # [x, y, z] + "description": "测试轨迹 - 直线道路" + }, + "custom_trajectory": { + "start": [108.92, 101.36, 0.61, 0], + "end": [62.21, -68.13, 0.74], + "description": "自定义轨迹 - 城镇道路" + } +} +CURRENT_TRAJECTORY = "test_trajectory" # 切换此值选择轨迹 +``` + +### 训练配置 + +```python +TOTAL_EPISODES = 3 # 运行的总 Episode 数 +MAX_STEPS_PER_EPISODE = 2000 # 每个 Episode 最大步数 +EPISODE_INTERVAL = 2.0 # Episode 间隔（秒） +``` + +### 交通配置 + +```python +ENABLE_TRAFFIC = False # 是否生成 NPC 交通 +TRAFFIC_VEHICLES = 30 # NPC 车辆数量 +TRAFFIC_WALKERS = 50 # NPC 行人数量 +TRAFFIC_SAFE_MODE = True # 仅生成安全车型 +``` + +### 视角配置 + +```python +FOLLOW_DISTANCE = 8.0 # 跟随距离（米） +FOLLOW_HEIGHT = 3.0 # 跟随高度（米） +FOLLOW_PITCH = -20.0 # 俯视角（度） +VIEW_UPDATE_FPS = 60 # 视角更新频率（Hz） +VIEW_UPDATE_THREADED = True # 启用独立线程 +SMOOTH_FOLLOW_FACTOR = 0.01 # 平滑系数（越小越平滑） +``` + +--- + +## 数据收集与可视化 + +系统在每个 Episode 结束后自动生成以下输出： + +**CSV 文件**（`data_logs/episode_data_.csv`）： + +| 字段 | 含义 | +|:-----|:-----| +| 时间戳 | 相对运行时间（秒） | +| 障碍物距离 | 前方最近车辆/行人距离（米） | +| 动作编号/名称 | 执行的动作 ID 与中文名 | +| 横向偏差 | 到路径中心线的有符号偏移（米） | +| 角度差 | 车辆朝向与路径方向的夹角（度） | +| 速度_kmh | 车辆速度（km/h） | +| 帧率 | 推理帧率（FPS） | +| 奖励值 | 当步奖励 | + +**可视化图表**（`data_logs/plots/`）： + +- 障碍物距离与动作分布图 +- 横向偏差与角度差时序图 +- 速度与帧率时序图 +- 六合一综合性能图表 + +--- + +## 环境要求 + +| 组件 | 版本 | +|:-----|:-----| +| 操作系统 | Ubuntu 20.04 | +| Python | 3.7 | +| CARLA 模拟器 | 0.9.13 | +| ROS（可选） | Noetic | +| TensorFlow | 2.5.0 | +| CUDA（GPU 加速） | 推荐 11.x | + +--- + +## 部署流程 + +### 第一步：获取 CARLA 模拟器 + +从 CARLA 官方 Release 页面下载 0.9.13 版本并解压： + +```bash +# 下载 CARLA 0.9.13（Linux） +wget https://carla-releases.s3.eu-west-3.amazonaws.com/Linux/CARLA_0.9.13.tar.gz +tar -xzf CARLA_0.9.13.tar.gz -C ~/carla-0.9.13 +``` + +### 第二步：克隆项目 + +```bash +git clone https://github.com/zhang0323/nn.git +cd nn +``` + +### 第三步：创建 Python 环境并安装依赖 + +```bash +# 使用 conda 创建隔离环境（推荐） +conda create -n carla-env python=3.7 +conda activate carla-env + +# 安装依赖（使用国内镜像加速） +pip install -r src/automatic_drive_deep_learning/requirements.txt \ + -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + +> **注意**：`requirements.txt` 中的 `carla==0.9.13` 需要与 CARLA 安装版本一致。 +> 如果 `pip` 找不到该版本，可手动从 CARLA 安装目录安装： +> ```bash +> pip install ~/carla-0.9.13/PythonAPI/carla/dist/carla-0.9.13-cp37-cp37m-linux_x86_64.whl +> ``` + +### 第四步（可选）：构建 ROS 工作空间 + +> **仅在需要 ROS 集成时执行此步骤。** + +```bash +cd src/automatic_drive_deep_learning/carla_ros_ws + +# 安装 ROS Noetic（若未安装） +# http://wiki.ros.org/noetic/Installation/Ubuntu + +# 构建 catkin 工作空间 +catkin_make +source devel/setup.bash + +# 安装 carla_autonomous 包内的额外依赖 +cd src/carla_autonomous/utils +./install.sh +``` + +### 第五步：配置模型路径与轨迹 + +编辑 `src/automatic_drive_deep_learning/main/config.py`： + +```python +# 1. 确认模型路径正确（预训练模型位于 carla_ros_ws/src/carla_autonomous/models/） +MODEL_PATHS = { + 'braking': '/path/to/nn/src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/models/Braking___282.model', + 'driving': '/path/to/nn/src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/models/Driving__6030.model', +} + +# 2. 选择使用的轨迹 +CURRENT_TRAJECTORY = "test_trajectory" # 或 "custom_trajectory" +``` + +如需获取新的轨迹坐标，先在 CARLA 中运行： + +```bash +cd src/automatic_drive_deep_learning/main +python get_location.py # 打印当前车辆的 (x, y, z) 坐标 +``` + +--- + +## 运行方法 + +### 基础版本（无 ROS） + +**终端 1 — 启动 CARLA 模拟器：** + +```bash +cd ~/carla-0.9.13 +./CarlaUE4.sh +# 或使用无渲染模式（提升训练性能） +./CarlaUE4.sh -RenderOffScreen +``` + +**终端 2 — 运行主推理程序：** + +```bash +conda activate carla-env +cd src/automatic_drive_deep_learning/main +python main.py +``` + +**快速测试（单独测试刹车/驾驶模型）：** + +```bash +# 测试刹车模型推理 +python ../test/test_braking.py + +# 测试驾驶模型推理 +python ../test/test_driving.py +``` + +--- + +### 训练新模型 + +```bash +conda activate carla-env +cd src/automatic_drive_deep_learning/test + +# 训练刹车模型（约 40 个 Episode） +python braking_dqn.py +# 模型将保存至 test/models/Braking__max_....model + +# 训练驾驶模型（约 40 个 Episode） +python driving_dqn.py +# 模型将保存至 test/models/Driving__max_....model +``` + +训练完成后，将生成的 `.model` 文件复制到 `config.py` 的 `MODEL_PATHS` 路径中。 + +--- + +### ROS 版本 + +**终端 1 — 启动 CARLA：** + +```bash +./CarlaUE4.sh +``` + +**终端 2 — 一键启动 ROS 节点（推荐）：** + +```bash +cd src/automatic_drive_deep_learning/carla_ros_ws/src/carla_autonomous/utils +./run_carla.sh +``` + +**或手动分步启动：** + +```bash +# 终端 2：启动 ROS 节点 +cd src/automatic_drive_deep_learning/carla_ros_ws +source devel/setup.bash +roslaunch carla_autonomous carla_autonomous.launch + +# 终端 3：启动控制客户端 +cd src/automatic_drive_deep_learning/carla_ros_ws +source devel/setup.bash +python src/carla_autonomous/scripts/carla_control_client.py +``` + +--- + +## 注意事项 + +1. **CARLA 必须先于主程序启动**，否则客户端连接（`localhost:2000`）会超时失败。 +2. **模型文件路径**：`config.py` 中 `MODEL_PATHS` 使用绝对路径或相对于运行目录的路径，请根据实际情况修改。 +3. **ROS 使用前必须 source**：每次新开终端都需执行 `source devel/setup.bash`，或将其加入 `~/.bashrc`。 +4. **GPU 加速**：TensorFlow 默认使用 GPU（若可用），脚本会自动开启内存增长模式；如无 GPU，将降速运行于 CPU。 +5. **数据保存**：运行时若 `data_logs/` 目录不存在，程序会自动创建；训练脚本的 `logs/` 目录用于 TensorBoard，可通过 `tensorboard --logdir logs/` 查看训练曲线。 +6. **图表中文乱码**：Linux 下需安装中文字体（如文泉驿），程序会自动检测： + ```bash + sudo apt-get install fonts-wqy-microhei + ``` + +--- + +## 参考 + +- 源码目录：[src/automatic_drive_deep_learning](https://github.com/zhang0323/nn/tree/main/src/automatic_drive_deep_learning) +- 参考项目：[varunpratap222/Autonomous-Vehicle-Navigation-Using-Deep-Learning](https://github.com/varunpratap222/Autonomous-Vehicle-Navigation-Using-Deep-Learning) +- CARLA 官方文档：[https://carla.readthedocs.io/en/0.9.13/](https://carla.readthedocs.io/en/0.9.13/) +- CARLA Python API 参考：[https://carla.readthedocs.io/en/0.9.13/python_api/](https://carla.readthedocs.io/en/0.9.13/python_api/) + +## License + +MIT diff --git a/docs/index.md b/docs/index.md index 2d21bb1c9a..b16d67bb8f 100644 --- a/docs/index.md +++ b/docs/index.md @@ -75,6 +75,8 @@ title: 主页 [__基于自监督学习与PPO强化学习的自动驾驶仿真项目__](./autonomous_driving/README.md) - 基于CARLA的SSL+RL自动驾驶仿真系统 +[__自动驾驶导航系统__](./automatic_drive_deep_learning/README.md) - 基于深度学习与DQN强化学习的CARLA自动驾驶导航系统 + ## 规划 diff --git a/mkdocs.yml b/mkdocs.yml index 2b35b2641a..677dc18ece 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -52,6 +52,7 @@ nav: - 自动驾驶车辆语义分割: 'auto_drive_seg/README.md' - 双足人形机器人SAC步态仿真: 'mujoco_running\running.md' - 人形机器人项目: mujoco_hci_sim/README.md +- 自动驾驶导航系统：'automatic_drive_deep_learning/README.md' # - mdx_math 用于行内公式显示 markdown_extensions: