世界模型驱动自动驾驶：从 Waymo Genie 3 到 NVIDIA Cosmos 的技术全景

# 世界模型自动驾驶仿真概念代码
# 展示世界模型如何生成驾驶场景并用于决策

import torch
import torch.nn as nn
from typing import Dict, Tuple, Optional

class WorldModelSimulator:
    """
    基于世界模型的自动驾驶仿真器
    概念代码：展示核心架构
    """
    
    def __init__(self, config: dict):
        # 感知编码器：多模态传感器融合
        self.perception_encoder = PerceptionEncoder(
            camera_channels=3,
            lidar_channels=1,
            hidden_dim=512
        )
        
        # 动态预测网络：预测下一时刻状态
        self.dynamics_predictor = DynamicsPredictor(
            state_dim=512,
            action_dim=4,  # [steering, throttle, brake, yield]
            horizon=config.get("prediction_horizon", 10)
        )
        
        # 场景生成器：基于条件生成完整场景
        self.scene_generator = SceneGenerator(
            latent_dim=512,
            output_modalities=["camera", "lidar"],
            resolution=(720, 1280)
        )
        
        # VLM 语义推理模块
        self.vlm_reasoner = VLMReasoner(
            model_name="gemini-driving-v2"
        )
    
    def simulate_scenario(
        self,
        initial_state: torch.Tensor,
        text_prompt: Optional[str] = None,
        driving_input: Optional[torch.Tensor] = None
    ) -> Dict[str, torch.Tensor]:
        """
        生成一个完整的驾驶场景仿真
        
        Args:
            initial_state: 初始传感器观测 (B, C, H, W)
            text_prompt: 场景描述（如"十字路口，左转卡车"）
            driving_input: 驾驶控制输入
        
        Returns:
            多模态仿真结果
        """
        # Step 1: 编码初始状态
        state_embedding = self.perception_encoder(initial_state)
        
        # Step 2: 如果有文本提示，融合语义信息
        if text_prompt:
            semantic_context = self.vlm_reasoner.encode(text_prompt)
            state_embedding = state_embedding + semantic_context
        
        # Step 3: 自回归生成未来帧
        generated_frames = []
        current_state = state_embedding
        
        for t in range(self.dynamics_predictor.horizon):
            # 预测动作
            if driving_input is not None:
                action = driving_input[:, t]
            else:
                action = self.dynamics_predictor.predict_action(current_state)
            
            # 预测下一状态
            next_state = self.dynamics_predictor.step(current_state, action)
            
            # 生成多传感器输出
            frame_output = self.scene_generator.render(next_state)
            generated_frames.append(frame_output)
            
            current_state = next_state
        
        return {
            "camera_frames": torch.stack([f["camera"] for f in generated_frames]),
            "lidar_frames": torch.stack([f["lidar"] for f in generated_frames]),
            "predicted_actions": torch.stack([f["action"] for f in generated_frames])
        }
    
    def generate_rare_scenario(
        self,
        scenario_type: str,
        difficulty: str = "hard"
    ) -> Dict[str, torch.Tensor]:
        """
        生成罕见的长尾场景
        例如：极端天气、异常障碍物、罕见交通参与者
        """
        rare_prompts = {
            "tornado": "高速公路行驶中，前方出现龙卷风，能见度极低",
            "animal_crossing": "城市道路，一头大象突然穿越马路",
            "construction": "施工区域，交通锥和临时标志，车道变窄",
            "emergency_vehicle": "紧急车辆从后方接近，需要安全让行"
        }
        
        prompt = rare_prompts.get(scenario_type, scenario_type)
        
        # 生成初始场景
        initial_state = self.scene_generator.generate_initial(prompt)
        
        # 运行仿真
        return self.simulate_scenario(
            initial_state=initial_state,
            text_prompt=prompt
        )

# 使用示例
simulator = WorldModelSimulator({"prediction_horizon": 30})

# 生成一个罕见的龙卷风场景
scenario = simulator.generate_rare_scenario("tornado", difficulty="hard")
print(f"Generated {scenario['camera_frames'].shape[0]} frames of simulation data")

# DrivingGen 评估指标概念实现
# 展示如何评估世界模型生成质量

import torch
import numpy as np
from typing import Dict, List

class DrivingGenEvaluator:
    """世界模型生成质量评估器"""
    
    def __init__(self):
        self.metrics = {}
    
    def evaluate_visual_fidelity(
        self, 
        generated: torch.Tensor, 
        real: torch.Tensor
    ) -> Dict[str, float]:
        """评估视觉保真度"""
        # FID (Fréchet Inception Distance)
        fid_score = self.compute_fid(generated, real)
        
        # LPIPS (Learned Perceptual Image Patch Similarity)
        lpips_score = self.compute_lpips(generated, real)
        
        return {
            "fid": fid_score,      # 越低越好
            "lpips": lpips_score,   # 越低越相似
            "psnr": self.compute_psnr(generated, real),  # 越高越好
            "ssim": self.compute_ssim(generated, real)   # 越高越相似
        }
    
    def evaluate_physical_consistency(
        self,
        frames: torch.Tensor,
        vehicle_states: torch.Tensor
    ) -> Dict[str, float]:
        """评估物理一致性"""
        scores = {}
        
        # 检查车辆是否遵循运动学约束
        # 位置变化 = 速度 × 时间
        positions = vehicle_states[:, :3]  # x, y, z
        velocities = vehicle_states[:, 3:6]  # vx, vy, vz
        dt = 0.1  # 帧间隔
        
        predicted_positions = positions[:-1] + velocities[:-1] * dt
        actual_positions = positions[1:]
        
        position_error = torch.norm(
            predicted_positions - actual_positions, dim=-1
        ).mean()
        scores["kinematics_error"] = position_error.item()
        
        # 检查加速度是否在物理合理范围内
        accelerations = (velocities[1:] - velocities[:-1]) / dt
        max_lateral_acc = 9.8  # 约 1g
        lateral_acc = accelerations[:, :2].norm(dim=-1)
        scores["max_lateral_acceleration"] = lateral_acc.max().item()
        scores["physical_plausibility"] = (
            lateral_acc.max() < max_lateral_acc
        ).float().item()
        
        return scores
    
    def evaluate_temporal_consistency(
        self,
        frames: torch.Tensor
    ) -> Dict[str, float]:
        """评估时间一致性"""
        # 计算相邻帧之间的光流一致性
        flow_consistency = self.compute_optical_flow_consistency(frames)
        
        # 计算帧间变化率（过高表示闪烁）
        frame_diffs = torch.diff(frames, dim=0).abs().mean()
        
        return {
            "flow_consistency": flow_consistency.item(),
            "temporal_smoothness": 1.0 - frame_diffs.item(),
            "flicker_rate": frame_diffs.item()
        }
    
    def evaluate_controllability(
        self,
        generated_trajectory: torch.Tensor,
        commanded_trajectory: torch.Tensor
    ) -> Dict[str, float]:
        """评估可控性——生成是否响应了控制输入"""
        # 轨迹跟踪误差
        tracking_error = torch.norm(
            generated_trajectory - commanded_trajectory, dim=-1
        ).mean()
        
        # 方向一致性
        gen_direction = torch.diff(generated_trajectory, dim=0)
        cmd_direction = torch.diff(commanded_trajectory, dim=0)
        direction_cosine = torch.nn.functional.cosine_similarity(
            gen_direction, cmd_direction, dim=-1
        ).mean()
        
        return {
            "tracking_error_meters": tracking_error.item(),
            "direction_consistency": direction_cosine.item()
        }
    
    def run_full_evaluation(
        self,
        generated_data: dict,
        real_data: dict
    ) -> Dict[str, Dict[str, float]]:
        """运行完整评估"""
        results = {}
        
        results["visual_fidelity"] = self.evaluate_visual_fidelity(
            generated_data["camera_frames"],
            real_data["camera_frames"]
        )
        
        results["physical_consistency"] = self.evaluate_physical_consistency(
            generated_data["camera_frames"],
            generated_data["vehicle_states"]
        )
        
        results["temporal_consistency"] = self.evaluate_temporal_consistency(
            generated_data["camera_frames"]
        )
        
        if "commanded_trajectory" in generated_data:
            results["controllability"] = self.evaluate_controllability(
                generated_data["generated_trajectory"],
                generated_data["commanded_trajectory"]
            )
        
        return results