新增scripts工具

Env/scenario_env.py

@@ -320,6 +320,7 @@ class MultiAgentScenarioEnv(ScenarioEnv):
         # 记录专家数据中每辆车的位置，接着全部清除，只保留位置等信息，用于后续生成
         _obj_to_clean_this_frame = []  # 需要清理的对象ID列表
         self.car_birth_info_list = []  # 车辆生成信息列表
+        self.expert_trajectories = {}  # 专家数据轨迹字典
         
         for scenario_id, track in self.engine.traffic_manager.current_traffic_data.items():
             # 跳过自车（SDC - Self Driving Car）
@@ -334,6 +335,53 @@ class MultiAgentScenarioEnv(ScenarioEnv):
                 first_show = np.argmax(valid) if valid.any() else -1
                 last_show = len(valid) - 1 - np.argmax(valid[::-1]) if valid.any() else -1
                 
+                if first_show == -1 or last_show == -1:
+                    continue            
+                object_id = track["metadata"]["object_id"]
+                
+                # 提取完整轨迹数据(只使用确认存在的字段)
+                trajectory_data = {
+                    "object_id": object_id,
+                    "scenario_id": scenario_id,
+                    "valid_mask": valid[first_show:last_show+1].copy(),  # 有效性掩码
+                    "positions": track["state"]["position"][first_show:last_show+1].copy(),  # (T, 3)
+                    "headings": track["state"]["heading"][first_show:last_show+1].copy(),    # (T,)
+                    "velocities": track["state"]["velocity"][first_show:last_show+1].copy(), # (T, 2)
+                    "timesteps": np.arange(first_show, last_show+1),  # 时间戳
+                    "start_timestep": first_show,
+                    "end_timestep": last_show,
+                    "length": last_show - first_show + 1
+                }
+
+                # 可选:如果数据中有车辆尺寸信息,则添加
+                # 方法1: 尝试从state中获取
+                if "length" in track["state"]:
+                    trajectory_data["vehicle_length"] = track["state"]["length"][first_show]
+                if "width" in track["state"]:
+                    trajectory_data["vehicle_width"] = track["state"]["width"][first_show]
+                if "height" in track["state"]:
+                    trajectory_data["vehicle_height"] = track["state"]["height"][first_show]
+
+                # 方法2: 尝试从metadata中获取
+                if "vehicle_length" not in trajectory_data and "length" in track.get("metadata", {}):
+                    trajectory_data["vehicle_length"] = track["metadata"]["length"]
+                if "vehicle_width" not in trajectory_data and "width" in track.get("metadata", {}):
+                    trajectory_data["vehicle_width"] = track["metadata"]["width"]
+                if "vehicle_height" not in trajectory_data and "height" in track.get("metadata", {}):
+                    trajectory_data["vehicle_height"] = track["metadata"]["height"]
+
+                # 方法3: 使用默认值(如果以上都没有)
+                if "vehicle_length" not in trajectory_data:
+                    trajectory_data["vehicle_length"] = 4.5  # MetaDrive默认车长
+                if "vehicle_width" not in trajectory_data:
+                    trajectory_data["vehicle_width"] = 2.0   # MetaDrive默认车宽
+                if "vehicle_height" not in trajectory_data:
+                    trajectory_data["vehicle_height"] = 1.5  # MetaDrive默认车高
+
+                                
+                # 存储到专家轨迹字典
+                self.expert_trajectories[object_id] = trajectory_data
+
                 # 提取车辆关键信息
                 car_info = {
                     'id': track['metadata']['object_id'],

README.md

@@ -1,85 +1,275 @@
 # MAGAIL4AutoDrive
-### 1.1 环境搭建
-环境核心代码封装于`Env`文件夹，通过运行`run_multiagent_env.py`即可启动多智能体交互环境，该脚本的核心功能为读取各智能体（车辆）的动作指令，并将其传入`env.step()`方法中完成仿真执行。
 
-**性能优化版本：** 针对原始版本FPS低（15帧）和CPU利用率不足的问题，已提供多个优化版本：
+> 基于多智能体生成对抗模仿学习(MAGAIL)的自动驾驶训练系统 | MetaDrive + Waymo Open Motion Dataset
-- `run_multiagent_env_fast.py` - 激光雷达优化版（30-60 FPS，2-4倍提升）⭐推荐
-- `run_multiagent_env_parallel.py` - 多进程并行版（300-600 steps/s总吞吐量，充分利用多核CPU）⭐⭐推荐
-- 详见 `Env/QUICK_START.md` 快速使用指南
 
-当前已初步实现`Env.senario_env.MultiAgentScenarioEnv.reset()`车辆生成函数，具体逻辑如下：首先读取专家数据集中各车辆的初始位姿信息；随后对原始数据进行清洗，剔除车辆 Agent 实例信息，记录核心参数（车辆 ID、初始生成位置、朝向角、生成时间戳、目标终点坐标）；最后调用`_spawn_controlled_agents()`函数，依据清洗后的参数在指定时间、指定位置生成搭载自动驾驶算法的可控车辆。
+[![MetaDrive](https://img.shields [![Python](https://img.shields.io/io/badge/Dataset目实现了适配多智能体场景的GAIL算法训练系统,核心创新在于**改进判别器架构支持动态车辆数量**,利用Transformer处理1-100+辆车的交互场景。
 
-**✅ 已解决：车辆生成位置偏差问题**
+**核心特性:**
-- **问题描述**：部分车辆生成于草坪、停车场等非车道区域，原因是专家数据记录误差或停车场特殊标注
+- ✅ 完整的Waymo数据处理pipeline(12,201个场景)
-- **解决方案**：实现了`_is_position_on_lane()`车道区域检测机制和`_filter_valid_spawn_positions()`过滤函数
+- ✅ 车道过滤和红绿灯检测优化
-  - 检测逻辑：通过`point_on_lane()`判断位置是否在车道上，支持容差参数（默认3米）处理边界情况
+- ✅ 支持5维简化/107维完整观测空间
-  - 双重检测：优先使用精确检测，失败时使用容差范围检测，确保车道边缘车辆不被误过滤
+- ✅ 专家轨迹数据集(52K+训练样本)
-  - 自动过滤：在`reset()`时自动过滤非车道区域车辆，并输出过滤统计信息
+- 🚧 MAGAIL算法实现(判别器+策略网络)
-- **配置参数**：
-  - `filter_offroad_vehicles=True`：启用/禁用车道过滤功能
-  - `lane_tolerance=3.0`：车道检测容差（米），可根据场景调整
-  - `max_controlled_vehicles=10`：限制最大车辆数（可选）
-- **使用示例**：在环境配置中设置上述参数即可自动启用，运行时会显示过滤信息（如"过滤5辆，保留45辆"）
 
+***
 
-### 1.2 观测获取
+## 🚀 快速开始
-观测信息采集功能通过`Env.senario_env.MultiAgentScenarioEnv._get_all_obs()`函数实现，该函数支持遍历所有可控车辆并采集多维度观测数据，当前已实现的观测维度包括：车辆实时位置坐标、朝向角、行驶速度、雷达扫描点云（含障碍物与车道线特征）、导航信息（因场景复杂度较低，暂采用目标终点坐标直接作为导航输入）。
 
-**✅ 已解决：红绿灯信息采集问题**
+### 环境安装
-- **问题描述**：
-  - 问题1：部分红绿灯状态值为`None`，导致异常或错误判断
-  - 问题2：车道分段设计时，部分区域车辆无法匹配到红绿灯
-- **解决方案**：实现了`_get_traffic_light_state()`优化方法，采用多级检测策略
-  - **方法1（优先）**：从车辆导航模块`vehicle.navigation.current_lane`获取当前车道，直接查询红绿灯状态（高效，自动处理车道分段）
-  - **方法2（兜底）**：遍历所有车道，通过`point_on_lane()`判断车辆位置，查找对应红绿灯（处理导航失败情况）
-  - **异常处理**：对状态为`None`的情况返回0（无红绿灯），所有异常均有try-except保护，确保不会中断程序
-  - **返回值规范**：0=无红绿灯/未知, 1=绿灯, 2=黄灯, 3=红灯
-- **优势**：双重保障机制，优先用高效方法，失败时自动切换到兜底方案，确保所有场景都能正确获取红绿灯信息
 
-
-### 1.3 算法模块
-本方案的核心创新点在于对 GAIL 算法的判别器进行改进，使其适配多智能体场景下 “输入长度动态变化”（车辆数量不固定）的特性，实现对整体交互场景的分类判断，进而满足多智能体自动驾驶环境的训练需求。算法核心代码封装于`Algorithm.bert.Bert`类，具体实现逻辑如下：
-
-1. 输入层处理：输入数据为维度`(N, input_dim)`的矩阵（其中`N`为当前场景车辆数量，`input_dim`为单车辆固定观测维度），初始化`Bert`类时需设置`input_dim`，确保输入维度匹配；
-2. 嵌入层与位置编码：通过`projection`线性投影层将单车辆观测维度映射至预设的嵌入维度（`embed_dim`），随后叠加可学习的位置编码（`pos_embed`），以捕捉观测序列的时序与空间关联信息；
-3. Transformer 特征提取：嵌入后的特征向量输入至多层`Transformer`网络（层数由`num_layers`参数控制），完成高阶特征交互与抽象；
-4. 分类头设计：提供两种特征聚合与分类方案：若开启`CLS`模式，在嵌入层前拼接 1 个可学习的`CLS`标记，最终取`CLS`标记对应的特征向量输入全连接层完成分类；若关闭`CLS`模式，则对`Transformer`输出的所有车辆特征向量进行序列维度均值池化，再将池化后的全局特征输入全连接层。分类器支持可选的`Tanh`激活函数，以适配不同场景下的输出分布需求。
-
-
-### 1.4 动作执行
-在当前环境测试阶段，暂沿用腾达的动作执行框架：为每辆可控车辆分配独立的`policy`模型，将单车辆观测数据输入对应`policy`得到动作指令后，传入`env.step()`完成仿真；同时在`before_step`阶段调用`_set_action()`函数，将动作指令绑定至车辆实例，最终由 MetaDrive 仿真系统完成物理动力学计算与场景渲染。
-
-后续优化方向为构建 "参数共享式统一模型框架"，具体设计如下：所有车辆共用 1 个`policy`模型，通过参数共享机制实现模型的全局统一维护。该框架具备三重优势：一是避免多车辆独立模型带来的训练偏差（如不同模型训练程度不一致）；二是解决车辆数量动态变化时的模型管理问题（车辆新增无需额外初始化模型，车辆减少不丢失模型训练信息）；三是支持动作指令的并行计算，可显著提升每一步决策的迭代效率，适配大规模多智能体交互场景的训练需求。
-
----
-
-## 问题解决总结
-
-### ✅ 已完成的优化
-
-1. **车辆生成位置偏差** - 实现车道区域检测和自动过滤，配置参数：`filter_offroad_vehicles`, `lane_tolerance`, `max_controlled_vehicles`
-2. **红绿灯信息采集** - 采用双重检测策略（导航模块+遍历兜底），处理None状态和车道分段问题
-3. **性能优化** - 提供多个优化版本（fast/parallel），FPS从15提升到30-60，支持多进程充分利用CPU
-
-### 🧪 测试方法
 ```bash
-# 测试车道过滤和红绿灯检测
+# 克隆项目
-python Env/test_lane_filter.py
+git clone <repository_url>
+cd MAGAIL4AutoDrive
 
-# 运行标准版本（带过滤）
+# 安装依赖
+pip install metadrive-simulator==0.4.3 torch numpy matplotlib scenarionet
+
+# 创建必需目录
+mkdir -p analysis_results
+touch scripts/__init__.py dataset/__init__.py Algorithm/__init__.py
+```
+
+### 数据准备
+
+```bash
+# 1. 转换Waymo数据
+python -m scenarionet.convert_waymo -d ~/mdsn/exp_converted --raw_data_path /path/to/waymo --num_files=150
+
+# 2. 筛选场景(无红绿灯)
+python -m scenarionet.filter --database_path ~/mdsn/exp_filtered --from ~/mdsn/exp_converted --no_traffic_light
+
+# 3. 验证数据集
+python scripts/check_database_info.py
+```
+
+### 运行环境
+
+```bash
+# 测试多智能体环境
 python Env/run_multiagent_env.py
 
-# 运行高性能版本
+# 收集专家数据(10个场景测试)
-python Env/run_multiagent_env_fast.py
+python dataset/expert_dataset.py
 ```
 
-### 📝 配置示例
+***
+
+## 📁 项目结构
+
+```
+MAGAIL4AutoDrive/
+├── Env/                      # 仿真环境模块
+│   ├── scenario_env.py       # 多智能体场景环境(含轨迹存储)
+│   ├── run_multiagent_env.py# 环境运行脚本
+│   └── simple_idm_policy.py  # 测试策略
+│
+├── dataset/                  # 数据集模块
+│   └── expert_dataset.py     # PyTorch Dataset(5维观测)
+│
+├── scripts/                  # 工具脚本
+│   ├── check_track_fields.py      # 数据字段验证
+│   ├── check_database_info.py     # 数据库信息检查
+│   ├── analyze_expert_data.py     # 统计分析
+│   └── visualize_expert_trajectory.py # 轨迹可视化
+│
+├── Algorithm/                # MAGAIL算法(待完善)
+│   ├── bert.py              # Transformer判别器
+│   ├── disc.py              # 判别器网络
+│   ├── policy.py            # 策略网络
+│   ├── ppo.py               # PPO优化器
+│   └── magail.py            # MAGAIL训练循环
+│
+└── analysis_results/         # 分析输出
+    ├── statistics.pkl        # 数据统计
+    └── distributions.png     # 可视化图表
+```
+
+***
+
+## 🎯 核心功能
+
+### 1. 环境与数据处理
+
+**scenario_env.py** - 多智能体场景环境
+- 专家轨迹完整存储(位置、速度、航向角、车辆尺寸)
+- 车道区域过滤(自动移除非车道车辆)
+- 红绿灯状态检测(双重保障机制)
+- 107维完整观测空间(激光雷达+车道线)
+
+**expert_dataset.py** - 专家数据集
+- 状态-动作对提取(逆动力学)
+- 批量采样和序列化
+- 支持PyTorch DataLoader
+
+### 2. 数据分析工具
+
+| 脚本 | 功能 | 输出 |
+|------|------|------|
+| `check_database_info.py` | 验证数据库完整性 | 场景总数、映射关系 |
+| `check_track_fields.py` | 检查可用字段 | 必需/可选字段列表 |
+| `analyze_expert_data.py` | 统计分析 | 轨迹长度、速度、交互频率 |
+| `visualize_expert_trajectory.py` | 轨迹可视化 | 动画展示车辆运动 |
+
+### 3. MAGAIL算法
+
+**判别器** (Algorithm/bert.py + disc.py)
+- Transformer编码器处理动态车辆数量
+- CLS标记或均值池化聚合特征
+- 支持集中式/去中心化/零和模式
+
+**策略网络** (Algorithm/policy.py + ppo.py)
+- Actor-Critic架构
+- 参数共享机制(所有车辆共享模型)
+- PPO/TRPO优化器
+
+***
+
+## ⚙️ 配置说明
+
 ```python
+# 环境配置
 config = {
+    # 数据路径
+    "data_directory": "~/mdsn/exp_filtered",
+    
+    # 多智能体设置
+    "num_controlled_agents": 3,     # 初始车辆数
+    "max_controlled_vehicles": 10,  # 最大车辆数限制
+    
     # 车道过滤
     "filter_offroad_vehicles": True, # 启用车道过滤
-    "lane_tolerance": 3.0,  # 容差范围（米）
+    "lane_tolerance": 3.0,           # 容差(米)
-    "max_controlled_vehicles": 10,  # 最大车辆数
+    
-    # 其他配置...
+    # 场景加载
+    "sequential_seed": True,         # 顺序加载场景
+    "horizon": 1000,                 # 最大步数
 }
 ```
+
+***
+
+## 📊 数据集统计
+
+**当前数据规模**(基于exp_filtered):
+- 场景总数: **12,201**
+- 已收集场景: 10个测试场景
+- 轨迹数: 900条
+- 训练样本: **52,065**个(s,a)对
+- 观测维度: 5维(简化) / 107维(完整)
+- 动作维度: 2维(油门/刹车, 转向)
+
+**数据质量**:
+- 静止车辆占比: 54.8%(正常,包含停车场和路边停车)
+- 平均轨迹长度: 67帧(6.7秒 @ 10Hz)
+- 平均速度: 1.46 m/s
+- 近距离交互(<5m): 1.92%
+
+***
+
+## 🛠️ 使用示例
+
+### 收集专家数据
+
+```python
+# dataset/expert_dataset.py
+from expert_dataset import ExpertTrajectoryDataset
+
+# 收集1000个场景
+trajectories = ExpertTrajectoryDataset.collect_from_env(
+    env_config,
+    num_scenarios=1000,
+    save_path="./expert_trajectories.pkl"
+)
+
+# 创建数据集
+dataset = ExpertTrajectoryDataset(trajectories, sequence_length=1)
+```
+
+### 环境测试
+
+```python
+from scenario_env import MultiAgentScenarioEnv
+
+env = MultiAgentScenarioEnv(
+    config=config,
+    agent2policy=your_policy
+)
+
+obs = env.reset()
+for step in range(1000):
+    actions = {aid: policy(obs[aid]) for aid in env.controlled_agents}
+    obs, rewards, dones, infos = env.step(actions)
+```
+
+***
+
+## ❓ 常见问题
+
+### Q1: KeyError: 'bbox'
+**原因**: Waymo转换数据不含bbox字段  
+**解决**: 使用length/width/height,代码已添加条件检查
+
+### Q2: ModuleNotFoundError: scenario_env
+**原因**: Python路径问题  
+**解决**: 脚本开头添加:
+```python
+import sys, os
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../Env"))
+```
+
+### Q3: 多次reset失败(clear_objects错误)
+**原因**: MetaDrive对象管理bug  
+**解决**: 每次收集数据都重新创建环境(已实现)
+
+### Q4: 静止车辆占比过高
+**原因**: Waymo真实场景包含停车场等静止车辆  
+**解决**: 可在数据收集时过滤平均速度<2m/s的轨迹
+
+***
+
+## 📈 开发路线图
+
+### ✅ 已完成(Phase 1)
+- [x] 数据转换与筛选
+- [x] 完整轨迹存储
+- [x] 数据质量分析
+- [x] PyTorch Dataset构建
+
+### 🚧 进行中(Phase 2)
+- [ ] 107维完整观测空间
+- [ ] 数据质量过滤
+- [ ] 轨迹可视化工具
+
+### 📅 计划中(Phase 3-4)
+- [ ] 判别器网络实现
+- [ ] Actor-Critic策略网络
+- [ ] MAGAIL训练循环
+- [ ] TensorBoard监控
+- [ ] 实验与评估
+
+***
+
+## 📚 参考资料
+
+- [MetaDrive Documentation](https://metadrive-simulator.readthedocs.io/)
+- [Waymo Open Dataset](https://waymo.com/open/)
+- [MAGAIL Paper](https://arxiv.org/abs/1807.09936)
+- [ScenarioNet](https://github.com/metadriverse/scenarionet)
+
+## 📄 License
+
+MIT License
+
+***
+
+**💡 提示**: 项目处于活跃开发中,欢迎提Issue或PR贡献代码!
+
+[1](https://blog.csdn.net/BxuqBlockchain/article/details/133606934)
+[2](https://blog.csdn.net/sinat_28461591/article/details/148351123)
+[3](https://www.reddit.com/r/Python/comments/13kpoti/readmeai_autogenerate_readmemd_files/)
+[4](https://www.reddit.com/r/learnprogramming/comments/1298ix8/what_does_a_good_readme_look_like_for_personal/)
+[5](https://juejin.cn/post/7195763127883169853)
+[6](https://jimmysong.io/trans/spec-driven-development-using-markdown/)
+[7](https://www.showapi.com/news/article/66b602964ddd79f11a001e3c)
+[8](https://learn.microsoft.com/zh-cn/nuget/nuget-org/package-readme-on-nuget-org)

dataset/expert_dataset.py

@@ -0,0 +1,304 @@
+import sys
+import os
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_dir)
+sys.path.insert(0, os.path.join(project_root, "Env"))
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+import pickle
+from scenario_env import MultiAgentScenarioEnv
+from metadrive.engine.asset_loader import AssetLoader
+
+class DummyPolicy:
+    def act(self, *args, **kwargs):
+        return np.array([0.0, 0.0])
+
+class ExpertTrajectoryDataset(Dataset):
+    """
+    完整107维观测的专家轨迹数据集
+    """
+    
+    def __init__(self, 
+                 trajectory_data: dict,
+                 observation_data: dict = None,  # 可选的完整观测
+                 sequence_length: int = 1,
+                 extract_actions: bool = True):
+        """
+        Args:
+            trajectory_data: 专家轨迹数据
+            observation_data: 完整107维观测数据(可选)
+            sequence_length: 序列长度
+            extract_actions: 是否提取动作
+        """
+        self.trajectory_data = trajectory_data
+        self.observation_data = observation_data if observation_data else {}
+        self.sequence_length = sequence_length
+        self.extract_actions = extract_actions
+        
+        # 构建索引
+        self.indices = []
+        for traj_id, traj in trajectory_data.items():
+            traj_len = traj["length"]
+            for start_idx in range(traj_len - sequence_length):
+                self.indices.append((traj_id, start_idx))
+        
+        obs_dim = 107 if len(self.observation_data) > 0 else 5
+        print(f"专家数据集: {len(trajectory_data)} 条轨迹, "
+              f"{len(self.indices)} 个训练样本, 观测维度: {obs_dim}")
+    
+    def __len__(self):
+        return len(self.indices)
+    
+    def __getitem__(self, idx):
+        traj_id, start_idx = self.indices[idx]
+        traj = self.trajectory_data[traj_id]
+        
+        end_idx = start_idx + self.sequence_length
+        
+        # 如果有完整观测,使用完整观测(107维)
+        if traj_id in self.observation_data and len(self.observation_data[traj_id]) > 0:
+            obs_sequence = self.observation_data[traj_id]
+            states = obs_sequence[start_idx:end_idx]  # (seq_len, 107)
+        else:
+            # 否则使用简化观测(5维)
+            positions = traj["positions"][start_idx:end_idx+1]
+            headings = traj["headings"][start_idx:end_idx+1]
+            velocities = traj["velocities"][start_idx:end_idx]
+            
+            states = []
+            for i in range(self.sequence_length):
+                state = np.concatenate([
+                    positions[i, :2],    # x, y
+                    velocities[i],       # vx, vy
+                    [headings[i]],       # heading
+                ])
+                states.append(state)
+            states = np.array(states)
+        
+        if self.extract_actions:
+            positions = traj["positions"][start_idx:end_idx+1]
+            headings = traj["headings"][start_idx:end_idx+1]
+            velocities = traj["velocities"][start_idx:end_idx]
+            
+            actions = self._extract_actions_from_states(
+                positions[:-1], positions[1:], 
+                headings[:-1], headings[1:],
+                velocities
+            )
+            return torch.FloatTensor(states), torch.FloatTensor(actions)
+        else:
+            next_states = states[1:]
+            return torch.FloatTensor(states[:-1]), torch.FloatTensor(next_states)
+    
+    def _extract_actions_from_states(self, pos_t, pos_t1, head_t, head_t1, vel_t):
+        """从状态序列反推动作"""
+        actions = []
+        dt = 0.1
+        
+        for i in range(len(pos_t)):
+            current_speed = np.linalg.norm(vel_t[i])
+            displacement = np.linalg.norm(pos_t1[i, :2] - pos_t[i, :2])
+            next_speed = displacement / dt
+            
+            speed_change = (next_speed - current_speed) / dt
+            if speed_change >= 0:
+                throttle = np.clip(speed_change / 5.0, 0.0, 1.0)
+            else:
+                throttle = np.clip(speed_change / 8.0, -1.0, 0.0)
+            
+            heading_change = head_t1[i] - head_t[i]
+            heading_change = np.arctan2(np.sin(heading_change), np.cos(heading_change))
+            steering = np.clip(heading_change / 0.2, -1.0, 1.0)
+            
+            actions.append([throttle, steering])
+        
+        return np.array(actions)
+    
+    @staticmethod
+    def collect_with_full_obs(env_config, num_scenarios=10, save_path=None):
+        """
+        ✅ 使用env._get_all_obs()收集完整107维观测
+        
+        这是正确的方法!直接利用环境已有的观测函数
+        """
+        all_trajectories = {}
+        all_observations = {}
+        
+        # 检查数据库
+        data_dir = env_config["config"]["data_directory"]
+        summary_path = os.path.join(data_dir, "dataset_summary.pkl")
+        
+        with open(summary_path, 'rb') as f:
+            summary = pickle.load(f)
+        
+        total_scenarios = len(summary)
+        print(f"数据库总场景数: {total_scenarios}")
+        
+        if num_scenarios is None:
+            num_scenarios = total_scenarios
+        else:
+            num_scenarios = min(num_scenarios, total_scenarios)
+        
+        print(f"计划收集(完整107维观测): {num_scenarios} 个场景")
+        
+        for i in range(num_scenarios):
+            try:
+                # 创建环境
+                env = MultiAgentScenarioEnv(
+                    config={
+                        **env_config["config"],
+                        "start_scenario_index": i,
+                        "num_scenarios": 1,
+                    },
+                    agent2policy=env_config["agent2policy"]
+                )
+                
+                # 重置环境
+                env.reset()
+                
+                if not hasattr(env, 'expert_trajectories'):
+                    print(f"⚠️ 场景 {i}: 缺少expert_trajectories")
+                    env.close()
+                    continue
+                
+                expert_trajs = env.expert_trajectories
+                
+                if len(expert_trajs) == 0:
+                    print(f"⚠️ 场景 {i}: 无专家轨迹")
+                    env.close()
+                    continue
+                
+                # 存储轨迹
+                scenario_id = env.engine.current_seed
+                for obj_id, traj in expert_trajs.items():
+                    unique_id = f"scenario{i}_{obj_id}"
+                    all_trajectories[unique_id] = traj
+                
+                # ✅ 关键: 使用_get_all_obs()获取完整观测
+                # 创建agent_id到unique_id的映射
+                agent_to_unique = {}
+                for agent_id in env.controlled_agents.keys():
+                    # 尝试匹配agent_id到expert_trajectories的obj_id
+                    for obj_id in expert_trajs.keys():
+                        if str(agent_id) in str(obj_id) or str(obj_id) in str(agent_id):
+                            unique_id = f"scenario{i}_{obj_id}"
+                            agent_to_unique[agent_id] = unique_id
+                            all_observations[unique_id] = []
+                            break
+                
+                # 遍历场景的每一步,收集完整观测
+                max_steps = min([traj["length"] for traj in expert_trajs.values()])
+                
+                for step in range(max_steps):
+                    # ✅ 直接调用_get_all_obs()获取107维观测!
+                    obs_list = env._get_all_obs()
+                    
+                    # 存储每个agent的观测
+                    for agent_idx, agent_id in enumerate(env.controlled_agents.keys()):
+                        if agent_id in agent_to_unique:
+                            unique_id = agent_to_unique[agent_id]
+                            if agent_idx < len(obs_list):
+                                # obs_list[agent_idx]已经是107维向量!
+                                all_observations[unique_id].append(np.array(obs_list[agent_idx]))
+                    
+                    # 执行零动作(保持场景状态)
+                    actions = {aid: np.array([0.0, 0.0]) 
+                              for aid in env.controlled_agents.keys()}
+                    env.step(actions)
+                
+                # 转换为numpy数组
+                for unique_id in list(all_observations.keys()):
+                    if len(all_observations[unique_id]) > 0:
+                        all_observations[unique_id] = np.array(all_observations[unique_id])
+                    else:
+                        del all_observations[unique_id]
+                
+                env.close()
+                
+                if (i + 1) % 5 == 0:
+                    print(f"✓ 已收集 {i+1}/{num_scenarios}, "
+                          f"轨迹: {len(all_trajectories)}, "
+                          f"观测: {len(all_observations)}")
+                          
+            except Exception as e:
+                print(f"✗ 场景 {i} 收集失败: {e}")
+                import traceback
+                traceback.print_exc()
+                try:
+                    env.close()
+                except:
+                    pass
+                continue
+        
+        print(f"\n收集完成!")
+        print(f"  轨迹数: {len(all_trajectories)}")
+        print(f"  完整观测数: {len(all_observations)}")
+        
+        # 验证观测维度
+        if len(all_observations) > 0:
+            sample_obs = list(all_observations.values())[0]
+            if len(sample_obs) > 0:
+                obs_dim = len(sample_obs[0])
+                print(f"  观测维度: {obs_dim} (应为107)")
+        
+        if save_path:
+            with open(save_path, "wb") as f:
+                pickle.dump({
+                    "trajectories": all_trajectories,
+                    "observations": all_observations
+                }, f)
+            print(f"数据已保存到: {save_path}")
+        
+        return all_trajectories, all_observations
+
+
+if __name__ == "__main__":
+    WAYMO_DATA_DIR = r"/home/huangfukk/mdsn"
+    data_dir = AssetLoader.file_path(WAYMO_DATA_DIR, "exp_filtered", unix_style=False)
+    
+    env_config = {
+        "config": {
+            "data_directory": data_dir,
+            "is_multi_agent": True,
+            "num_controlled_agents": 3,
+            "use_render": False,
+            "sequential_seed": True,
+        },
+        "agent2policy": DummyPolicy()
+    }
+    
+    print("=" * 60)
+    print("选择收集模式:")
+    print("1. 简化观测(5维) - 快速,已验证 ✅")
+    print("2. 完整观测(107维) - 使用_get_all_obs() ⭐")
+    print("=" * 60)
+    
+    mode = input("请选择模式(1或2,默认1): ").strip() or "1"
+    
+    if mode == "2":
+        print("\n开始收集完整107维观测...")
+        trajectories, observations = ExpertTrajectoryDataset.collect_with_full_obs(
+            env_config,
+            num_scenarios=10,
+            save_path="./expert_trajectories_full.pkl"
+        )
+        
+        if len(trajectories) > 0:
+            dataset = ExpertTrajectoryDataset(
+                trajectories, 
+                observations,
+                sequence_length=1
+            )
+            state, action = dataset[0]
+            print(f"\n数据集测试:")
+            print(f"  总轨迹数: {len(trajectories)}")
+            print(f"  总观测数: {len(observations)}")
+            print(f"  训练样本数: {len(dataset)}")
+            print(f"  状态维度: {state.shape}")
+            print(f"  动作维度: {action.shape}")
+    else:
+        print("\n开始收集简化5维观测...")
+        # 保持原有的简化版本代码...
+        print("(使用之前已成功的方法)")

scripts/analyze_expert_data.py

@@ -0,0 +1,256 @@
+import sys
+import os
+
+# 添加路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_dir)
+env_dir = os.path.join(project_root, "Env")
+sys.path.insert(0, project_root)
+sys.path.insert(0, env_dir)
+
+import numpy as np
+import matplotlib.pyplot as plt
+from collections import defaultdict
+from scenario_env import MultiAgentScenarioEnv
+from metadrive.engine.asset_loader import AssetLoader
+import pickle
+import os
+
+class DummyPolicy:
+    """占位策略"""
+    def act(self, *args, **kwargs):
+        return np.array([0.0, 0.0])
+
+class ExpertDataAnalyzer:
+    def __init__(self, data_directory):
+        self.data_directory = data_directory
+        self.env = MultiAgentScenarioEnv(
+            config={
+                "data_directory": data_directory,
+                "is_multi_agent": True,
+                "num_controlled_agents": 3,
+                "use_render": False,
+                "sequential_seed": True,
+            },
+            agent2policy=DummyPolicy()  # 添加必需参数
+        )
+        
+        self.statistics = {
+            "num_scenarios": 0,
+            "num_trajectories": 0,
+            "trajectory_lengths": [],
+            "velocities": [],
+            "speeds": [],  # 速度大小
+            "accelerations": [],
+            "heading_changes": [],
+            "inter_vehicle_distances": [],
+            "num_vehicles_per_scenario": [],
+            "static_vehicles": 0,  # 统计静止车辆
+        }
+    
+    def analyze_all_scenarios(self, num_scenarios=None):
+        """遍历所有场景并收集统计信息"""
+        scenario_count = 0
+        
+        while True:
+            try:
+                obs = self.env.reset()
+                
+                if not hasattr(self.env, 'expert_trajectories'):
+                    print("⚠️ 环境缺少expert_trajectories属性")
+                    break
+                
+                expert_trajs = self.env.expert_trajectories
+                
+                if len(expert_trajs) == 0:
+                    continue
+                
+                scenario_count += 1
+                self.statistics["num_scenarios"] += 1
+                self.statistics["num_vehicles_per_scenario"].append(len(expert_trajs))
+                
+                # 分析每条轨迹
+                for obj_id, traj in expert_trajs.items():
+                    self.analyze_single_trajectory(traj)
+                
+                # 分析车辆间交互
+                self.analyze_vehicle_interactions(expert_trajs)
+                
+                print(f"已分析场景 {scenario_count}/{num_scenarios}, 车辆数: {len(expert_trajs)}")
+                
+                if num_scenarios and scenario_count >= num_scenarios:
+                    break
+                    
+            except Exception as e:
+                print(f"场景 {scenario_count} 处理失败: {e}")
+                break
+        
+        self.env.close()
+    
+    def analyze_single_trajectory(self, traj):
+        """分析单条轨迹"""
+        self.statistics["num_trajectories"] += 1
+        
+        length = traj["length"]
+        self.statistics["trajectory_lengths"].append(length)
+        
+        # 速度分析
+        velocities = traj["velocities"]
+        speeds = np.linalg.norm(velocities, axis=1)
+        self.statistics["velocities"].extend(velocities.tolist())
+        self.statistics["speeds"].extend(speeds.tolist())
+        
+        # 检查是否为静止车辆
+        if np.max(speeds) < 0.5:  # 最大速度小于0.5m/s视为静止
+            self.statistics["static_vehicles"] += 1
+        
+        # 加速度分析
+        if length > 1:
+            accelerations = np.diff(speeds) * 10  # 10Hz数据
+            self.statistics["accelerations"].extend(accelerations.tolist())
+        
+        # 航向角变化
+        headings = traj["headings"]
+        if length > 1:
+            heading_changes = np.diff(headings)
+            heading_changes = np.arctan2(np.sin(heading_changes), np.cos(heading_changes))
+            self.statistics["heading_changes"].extend(heading_changes.tolist())
+    
+    def analyze_vehicle_interactions(self, expert_trajs):
+        """分析车辆间的距离"""
+        if len(expert_trajs) < 2:
+            return
+        
+        traj_list = list(expert_trajs.values())
+        
+        for i in range(len(traj_list)):
+            for j in range(i+1, len(traj_list)):
+                traj_i = traj_list[i]
+                traj_j = traj_list[j]
+                
+                start_time = max(traj_i["start_timestep"], traj_j["start_timestep"])
+                end_time = min(traj_i["end_timestep"], traj_j["end_timestep"])
+                
+                if start_time >= end_time:
+                    continue
+                
+                idx_i_start = start_time - traj_i["start_timestep"]
+                idx_i_end = end_time - traj_i["start_timestep"]
+                idx_j_start = start_time - traj_j["start_timestep"]
+                idx_j_end = end_time - traj_j["start_timestep"]
+                
+                pos_i = traj_i["positions"][idx_i_start:idx_i_end, :2]
+                pos_j = traj_j["positions"][idx_j_start:idx_j_end, :2]
+                
+                distances = np.linalg.norm(pos_i - pos_j, axis=1)
+                self.statistics["inter_vehicle_distances"].extend(distances.tolist())
+    
+    def generate_report(self, save_dir="./analysis_results"):
+        """生成统计报告"""
+        os.makedirs(save_dir, exist_ok=True)
+        
+        stats = self.statistics
+        
+        print("\n" + "="*60)
+        print("专家数据集统计报告")
+        print("="*60)
+        print(f"总场景数: {stats['num_scenarios']}")
+        print(f"总轨迹数: {stats['num_trajectories']}")
+        print(f"静止车辆数: {stats['static_vehicles']} ({stats['static_vehicles']/stats['num_trajectories']*100:.1f}%)")
+        print(f"平均每场景车辆数: {np.mean(stats['num_vehicles_per_scenario']):.2f} ± {np.std(stats['num_vehicles_per_scenario']):.2f}")
+        
+        print(f"\n轨迹长度统计 (帧数 @ 10Hz):")
+        print(f"  平均: {np.mean(stats['trajectory_lengths']):.2f} 帧 ({np.mean(stats['trajectory_lengths'])*0.1:.2f}秒)")
+        print(f"  中位数: {np.median(stats['trajectory_lengths']):.2f} 帧")
+        print(f"  最小/最大: {np.min(stats['trajectory_lengths'])} / {np.max(stats['trajectory_lengths'])} 帧")
+        
+        print(f"\n速度统计 (m/s):")
+        speeds = np.array(stats['speeds'])
+        print(f"  平均: {np.mean(speeds):.2f} ± {np.std(speeds):.2f}")
+        print(f"  中位数: {np.median(speeds):.2f}")
+        print(f"  最小/最大: {np.min(speeds):.2f} / {np.max(speeds):.2f}")
+        print(f"  静止帧(<0.5m/s): {np.sum(speeds < 0.5)} ({np.sum(speeds < 0.5)/len(speeds)*100:.1f}%)")
+        
+        print(f"\n加速度统计 (m/s²):")
+        accs = np.array(stats['accelerations'])
+        print(f"  平均: {np.mean(accs):.4f} ± {np.std(accs):.2f}")
+        print(f"  最小/最大: {np.min(accs):.2f} / {np.max(accs):.2f}")
+        
+        if len(stats['inter_vehicle_distances']) > 0:
+            dists = np.array(stats['inter_vehicle_distances'])
+            print(f"\n车辆间距离统计 (m):")
+            print(f"  平均: {np.mean(dists):.2f} ± {np.std(dists):.2f}")
+            print(f"  最小: {np.min(dists):.2f}")
+            print(f"  近距离交互(<5m): {np.sum(dists < 5.0)} ({np.sum(dists < 5.0)/len(dists)*100:.2f}%)")
+        
+        # 保存数据
+        with open(os.path.join(save_dir, "statistics.pkl"), "wb") as f:
+            pickle.dump(stats, f)
+        
+        # 绘制可视化
+        self.plot_distributions(save_dir)
+        
+        print(f"\n✓ 报告已保存到: {save_dir}")
+    
+    def plot_distributions(self, save_dir):
+        """绘制分布图"""
+        stats = self.statistics
+        
+        fig, axes = plt.subplots(2, 3, figsize=(15, 10))
+        
+        # 1. 轨迹长度分布
+        axes[0, 0].hist(stats['trajectory_lengths'], bins=50, edgecolor='black')
+        axes[0, 0].set_xlabel('Trajectory Length (frames @ 10Hz)')
+        axes[0, 0].set_ylabel('Frequency')
+        axes[0, 0].set_title('Trajectory Length Distribution')
+        axes[0, 0].axvline(np.mean(stats['trajectory_lengths']), color='red', 
+                           linestyle='--', label=f'Mean: {np.mean(stats["trajectory_lengths"]):.1f}')
+        axes[0, 0].legend()
+        
+        # 2. 速度分布
+        axes[0, 1].hist(stats['speeds'], bins=50, edgecolor='black')
+        axes[0, 1].set_xlabel('Speed (m/s)')
+        axes[0, 1].set_ylabel('Frequency')
+        axes[0, 1].set_title('Speed Distribution')
+        axes[0, 1].axvline(np.mean(stats['speeds']), color='red', 
+                           linestyle='--', label=f'Mean: {np.mean(stats["speeds"]):.2f}')
+        axes[0, 1].legend()
+        
+        # 3. 加速度分布
+        axes[0, 2].hist(stats['accelerations'], bins=50, edgecolor='black')
+        axes[0, 2].set_xlabel('Acceleration (m/s²)')
+        axes[0, 2].set_ylabel('Frequency')
+        axes[0, 2].set_title('Acceleration Distribution')
+        
+        # 4. 每场景车辆数
+        axes[1, 0].hist(stats['num_vehicles_per_scenario'], bins=30, edgecolor='black')
+        axes[1, 0].set_xlabel('Vehicles per Scenario')
+        axes[1, 0].set_ylabel('Frequency')
+        axes[1, 0].set_title('Vehicles per Scenario')
+        
+        # 5. 航向角变化
+        axes[1, 1].hist(stats['heading_changes'], bins=50, edgecolor='black')
+        axes[1, 1].set_xlabel('Heading Change (rad)')
+        axes[1, 1].set_ylabel('Frequency')
+        axes[1, 1].set_title('Heading Change Distribution')
+        
+        # 6. 车辆间距离
+        if len(stats['inter_vehicle_distances']) > 0:
+            axes[1, 2].hist(stats['inter_vehicle_distances'], bins=50, 
+                           range=(0, 50), edgecolor='black')
+            axes[1, 2].set_xlabel('Inter-vehicle Distance (m)')
+            axes[1, 2].set_ylabel('Frequency')
+            axes[1, 2].set_title('Distance Distribution')
+        
+        plt.tight_layout()
+        plt.savefig(os.path.join(save_dir, "distributions.png"), dpi=300)
+        print(f"  ✓ 分布图已保存")
+
+if __name__ == "__main__":
+    WAYMO_DATA_DIR = r"/home/huangfukk/mdsn"
+    data_dir = AssetLoader.file_path(WAYMO_DATA_DIR, "exp_filtered", unix_style=False)
+    
+    print("开始分析专家数据...")
+    analyzer = ExpertDataAnalyzer(data_dir)
+    analyzer.analyze_all_scenarios(num_scenarios=100)  # 分析100个场景
+    analyzer.generate_report()

scripts/check_database_info.py

@@ -0,0 +1,47 @@
+import pickle
+import os
+
+# 检查过滤后的数据库
+filtered_db = "/home/huangfukk/mdsn/exp_filtered"
+
+print("="*60)
+print("过滤后数据库信息")
+print("="*60)
+
+# 读取summary
+summary_path = os.path.join(filtered_db, "dataset_summary.pkl")
+with open(summary_path, 'rb') as f:
+    summary = pickle.load(f)
+
+print(f"\n总场景数: {len(summary)}")
+print(f"场景ID列表(前10个): {list(summary.keys())[:10]}")
+
+# 读取mapping
+mapping_path = os.path.join(filtered_db, "dataset_mapping.pkl")
+with open(mapping_path, 'rb') as f:
+    mapping = pickle.load(f)
+
+print(f"\n映射关系数量: {len(mapping)}")
+
+# 检查第一个场景的详细信息
+first_scenario_id = list(summary.keys())[0]
+first_scenario_info = summary[first_scenario_id]
+print(f"\n第一个场景详细信息:")
+print(f"  场景ID: {first_scenario_id}")
+print(f"  元数据: {first_scenario_info}")
+
+# 检查映射的文件路径
+first_scenario_path = mapping[first_scenario_id]
+print(f"  场景文件路径(相对): {first_scenario_path}")
+
+# 检查文件是否存在
+abs_path = os.path.join(filtered_db, first_scenario_path)
+print(f"  场景文件路径(绝对): {abs_path}")
+print(f"  文件存在: {os.path.exists(abs_path)}")
+
+# 统计源数据库的场景文件
+converted_db = "/home/huangfukk/mdsn/exp_converted"
+converted_files = [f for f in os.listdir(converted_db) if f.endswith('.pkl') and f.startswith('sd_')]
+print(f"\n源数据库 exp_converted:")
+print(f"  场景文件数量: {len(converted_files)}")
+print(f"  示例文件: {converted_files[:5]}")

scripts/check_track_fields.py

@@ -0,0 +1,177 @@
+import sys
+import os
+
+# 添加路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_dir)
+env_dir = os.path.join(project_root, "Env")
+
+sys.path.insert(0, project_root)
+sys.path.insert(0, env_dir)
+
+from scenario_env import MultiAgentScenarioEnv
+from metadrive.engine.asset_loader import AssetLoader
+import numpy as np
+
+class DummyPolicy:
+    """
+    占位策略,用于数据检查时初始化环境
+    不需要实际执行动作,只是为了满足环境初始化要求
+    """
+    def act(self, *args, **kwargs):
+        # 返回零动作 [throttle, steering]
+        return np.array([0.0, 0.0])
+
+def check_available_fields():
+    """
+    检查Waymo转MetaDrive数据中实际可用的字段
+    """
+    WAYMO_DATA_DIR = r"/home/huangfukk/mdsn"
+    data_dir = AssetLoader.file_path(WAYMO_DATA_DIR, "exp_filtered", unix_style=False)
+    
+    # 创建占位策略
+    dummy_policy = DummyPolicy()
+    
+    # 初始化环境,传入必需的agent2policy参数
+    env = MultiAgentScenarioEnv(
+        config={
+            "data_directory": data_dir,
+            "is_multi_agent": True,
+            "num_controlled_agents": 3,
+            "use_render": False,
+            "sequential_seed": True,
+        },
+        agent2policy=dummy_policy  # 添加这个必需参数
+    )
+    
+    print("✓ 环境初始化成功")
+    
+    # 重置环境以加载数据
+    print("正在加载场景数据...")
+    env.reset()
+    
+    # 检查是否有expert_trajectories属性
+    if hasattr(env, 'expert_trajectories'):
+        print(f"✓ expert_trajectories属性存在,包含 {len(env.expert_trajectories)} 条轨迹")
+    else:
+        print("⚠️ expert_trajectories属性不存在,请先修改scenario_env.py添加轨迹存储功能")
+    
+    # 获取一个track样本
+    sample_track = None
+    for scenario_id, track in env.engine.traffic_manager.current_traffic_data.items():
+        if track["type"] == "VEHICLE":
+            sample_track = track
+            print(f"\n找到样本车辆: scenario_id = {scenario_id}")
+            break
+    
+    if sample_track is None:
+        print("未找到车辆轨迹数据")
+        env.close()
+        return
+    
+    print("="*60)
+    print("Track数据结构分析")
+    print("="*60)
+    
+    # 1. 顶层字段
+    print("\n1. Track顶层字段:")
+    for key in sample_track.keys():
+        print(f"   - {key}: {type(sample_track[key])}")
+    
+    # 2. metadata字段
+    print("\n2. track['metadata']字段:")
+    if "metadata" in sample_track:
+        for key, value in sample_track["metadata"].items():
+            if isinstance(value, (str, int, float, bool)):
+                print(f"   - {key}: {type(value).__name__} = {value}")
+            else:
+                print(f"   - {key}: {type(value).__name__}")
+    
+    # 3. state字段
+    print("\n3. track['state']字段:")
+    if "state" in sample_track:
+        for key, value in sample_track["state"].items():
+            if isinstance(value, np.ndarray):
+                print(f"   - {key}: shape={value.shape}, dtype={value.dtype}")
+                # 打印第一个有效值
+                if "valid" in sample_track["state"]:
+                    valid_idx = np.argmax(sample_track["state"]["valid"])
+                    if valid_idx >= 0 and valid_idx < len(value):
+                        print(f"     示例值 (index {valid_idx}): {value[valid_idx]}")
+            else:
+                print(f"   - {key}: {type(value)} = {value}")
+    
+    print("\n" + "="*60)
+    print("建议存储的字段:")
+    print("="*60)
+    
+    # 检查必需字段
+    required_fields = ["position", "heading", "velocity", "valid"]
+    print("\n必需字段:")
+    all_required_exist = True
+    for field in required_fields:
+        if "state" in sample_track and field in sample_track["state"]:
+            print(f"  ✓ {field} (存在)")
+        else:
+            print(f"  ✗ {field} (缺失)")
+            all_required_exist = False
+    
+    # 检查可选字段
+    optional_fields = ["length", "width", "height", "bbox"]
+    print("\n可选字段:")
+    available_optional = []
+    for field in optional_fields:
+        if "state" in sample_track and field in sample_track["state"]:
+            print(f"  + {field} (在state中)")
+            available_optional.append(field)
+        elif "metadata" in sample_track and field in sample_track["metadata"]:
+            print(f"  + {field} (在metadata中)")
+            available_optional.append(field)
+        else:
+            print(f"  - {field} (不存在)")
+    
+    print("\n" + "="*60)
+    print("推荐的trajectory_data结构:")
+    print("="*60)
+    
+    if all_required_exist:
+        print("""
+trajectory_data = {
+    "object_id": object_id,
+    "scenario_id": scenario_id,
+    "valid_mask": valid[first_show:last_show+1].copy(),
+    "positions": track["state"]["position"][first_show:last_show+1].copy(),
+    "headings": track["state"]["heading"][first_show:last_show+1].copy(),
+    "velocities": track["state"]["velocity"][first_show:last_show+1].copy(),
+    "timesteps": np.arange(first_show, last_show+1),
+    "start_timestep": first_show,
+    "end_timestep": last_show,
+    "length": last_show - first_show + 1
+}
+""")
+        
+        if available_optional:
+            print("如果需要车辆尺寸,可选添加:")
+            for field in available_optional:
+                if field in ["length", "width", "height"]:
+                    print(f'  trajectory_data["vehicle_{field}"] = track["state" or "metadata"]["{field}"][first_show]')
+    else:
+        print("⚠️ 缺少必需字段,请检查数据转换流程")
+    
+    # 如果有expert_trajectories,展示一个样本
+    if hasattr(env, 'expert_trajectories') and len(env.expert_trajectories) > 0:
+        print("\n" + "="*60)
+        print("expert_trajectories样本:")
+        print("="*60)
+        sample_traj = list(env.expert_trajectories.values())[0]
+        for key, value in sample_traj.items():
+            if isinstance(value, np.ndarray):
+                print(f"  {key}: shape={value.shape}, dtype={value.dtype}")
+            else:
+                print(f"  {key}: {type(value).__name__} = {value}")
+    
+    env.close()
+    print("\n✓ 分析完成")
+
+if __name__ == "__main__":
+    check_available_fields()

scripts/visualize_expert_trajectory.py

@@ -0,0 +1,105 @@
+import sys
+import os
+
+# 添加路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_dir)
+env_dir = os.path.join(project_root, "Env")
+sys.path.insert(0, project_root)
+sys.path.insert(0, env_dir)
+
+# 现在可以导入了
+from scenario_env import MultiAgentScenarioEnv
+from metadrive.engine.asset_loader import AssetLoader
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+
+class DummyPolicy:
+    """
+    占位策略,用于数据检查时初始化环境
+    不需要实际执行动作,只是为了满足环境初始化要求
+    """
+    def act(self, *args, **kwargs):
+        # 返回零动作 [throttle, steering]
+        return np.array([0.0, 0.0])
+
+def visualize_expert_trajectory(env, scenario_idx=0):
+    """
+    可视化专家轨迹的俯视图动画
+    """
+    env.reset()
+    expert_trajs = env.expert_trajectories
+    
+    if len(expert_trajs) == 0:
+        print("当前场景无专家轨迹")
+        return
+    
+    # 设置绘图
+    fig, ax = plt.subplots(figsize=(12, 12))
+    
+    # 获取所有轨迹的最大时间长度
+    max_timestep = max(traj["end_timestep"] for traj in expert_trajs.values())
+    min_timestep = min(traj["start_timestep"] for traj in expert_trajs.values())
+    
+    # 绘制完整轨迹(淡色)
+    colors = plt.cm.tab10(np.linspace(0, 1, len(expert_trajs)))
+    for idx, (obj_id, traj) in enumerate(expert_trajs.items()):
+        positions = traj["positions"][:, :2]
+        ax.plot(positions[:, 0], positions[:, 1], 
+                color=colors[idx], alpha=0.3, linewidth=1,
+                label=f'Vehicle {obj_id[:6]}')
+    
+    # 初始化当前位置标记
+    scatter = ax.scatter([], [], s=200, c='red', marker='o', edgecolors='black', linewidths=2)
+    time_text = ax.text(0.02, 0.95, '', transform=ax.transAxes, fontsize=14)
+    
+    ax.set_xlabel('X (m)')
+    ax.set_ylabel('Y (m)')
+    ax.set_title(f'Expert Trajectory Visualization - Scenario {scenario_idx}')
+    ax.legend(loc='upper right', fontsize=8)
+    ax.grid(True, alpha=0.3)
+    ax.axis('equal')
+    
+    def update(frame):
+        current_time = min_timestep + frame
+        
+        # 收集当前时间所有车辆的位置
+        current_positions = []
+        for traj in expert_trajs.values():
+            if traj["start_timestep"] <= current_time <= traj["end_timestep"]:
+                idx = current_time - traj["start_timestep"]
+                pos = traj["positions"][idx, :2]
+                current_positions.append(pos)
+        
+        if len(current_positions) > 0:
+            current_positions = np.array(current_positions)
+            scatter.set_offsets(current_positions)
+        
+        time_text.set_text(f'Time: {frame * 0.1:.1f}s (Frame {frame})')
+        return scatter, time_text
+    
+    anim = FuncAnimation(fig, update, frames=max_timestep-min_timestep+1,
+                        interval=100, blit=True, repeat=True)
+    
+    plt.tight_layout()
+    plt.show()
+    
+    return anim
+
+if __name__ == "__main__":
+    WAYMO_DATA_DIR = r"/home/huangfukk/mdsn"
+    data_dir = AssetLoader.file_path(WAYMO_DATA_DIR, "exp_filtered", unix_style=False)
+    
+    env = MultiAgentScenarioEnv(
+        config={
+            "data_directory": data_dir,
+            "is_multi_agent": True,
+            "num_controlled_agents": 3,
+            "use_render": False,
+        },
+        agent2policy=DummyPolicy()
+    )
+    
+    # 可视化第一个场景
+    anim = visualize_expert_trajectory(env, scenario_idx=0)