{"slug": "ai-tesla-fsdwaymo", "title": "AI Tesla FSDWaymo", "summary": "Evolution of autonomous driving from traditional modular systems to end-to-end solutions, highlighting Tesla FSD V12's proof of concept for pure vision end-to-end driving and Waymo's continued focus on Robotaxi services. It compares four technical architectures: modular, end-to-end (Tesla FSD V12), end-to-end with rule-based safeguards (Huawei, XPeng), and multimodal large model approaches (DriveGPT, UniAD). The article also includes Python code implementations for occupancy networks, bird's eye view feature extraction, planning networks, and a stacked hourglass network for autonomous driving systems.", "body_md": "# AI 自动驾驶与端到端深度学习完全指南：Tesla FSD、Waymo、端到端方案实战\n\n## 前言\n\n自动驾驶正在从\"模块化\"走向\"端到端\"。Tesla FSD V12 证明了纯视觉端到端方案的可行性，Waymo 则在 Robotaxi 领域持续深耕。2026年，端到端自动驾驶已经成为行业共识。\n\n## 自动驾驶技术概述\n\n```\n自动驾驶技术架构对比：\n\n1. 模块化方案 (传统)\n\n- 感知 → 规划 → 控制\n\n- 优点：可解释性强、易调试\n\n- 限制：误差累积、规则复杂\n\n- 代表：大多数传统车企\n\n2. 端到端方案 (Tesla FSD V12)\n\n- 传感器输入 → 驾驶输出\n\n- 优点：无误差累积、类人驾驶\n\n- 限制：可解释性差、需大量数据\n\n- 代表：Tesla、Wayve\n\n- 端到端 + 规则兜底\n\n- 优点：平衡安全与性能\n\n- 代表：华为、小鹏\n\n4. 多模态大模型方案\n\n- VLM + 驾驶策略\n\n- 优点：泛化能力强\n\n- 代表：DriveGPT、UniAD\n```\n\n## Tesla FSD 架构\n\n### FSD 核心实现\n\n``` python\nimport torch\n\nimport torch.nn as nn\n\nfrom typing import Dict, List, Tuple, Optional\n\nimport numpy as np\n\nclass OccupancyNetwork:\n\n\"\"\"占用网络 (Occupancy Network)\"\"\"\n\ndef __init__(self, voxel_size: float = 0.1):\n\nself.voxel_size = voxel_size\n\nself.encoder = None\n\nself.decoder = None\n\ndef build_encoder(self, input_channels: int = 12):\n\n\"\"\"构建编码器\"\"\"\n\nself.encoder = nn.Sequential(\n\nnn.Conv3d(input_channels, 64, kernel_size=3, padding=1),\n\nnn.BatchNorm3d(64),\n\nnn.ReLU(inplace=True),\n\nnn.MaxPool3d(2),\n\nnn.Conv3d(64, 128, kernel_size=3, padding=1),\n\nnn.BatchNorm3d(128),\n\nnn.ReLU(inplace=True),\n\nnn.MaxPool3d(2),\n\nnn.Conv3d(128, 256, kernel_size=3, padding=1),\n\nnn.BatchNorm3d(256),\n\nnn.ReLU(inplace=True),\n\ndef voxelize(\n\npoints: np.ndarray,\n\ngrid_range: Tuple[float, float, float] = (-50, 50, -50, 50, -5, 5)\n\n) -> torch.Tensor:\n\npoints: (N, 3) 点云\n\ngrid_range: (x_min, x_max, y_min, y_max, z_min, z_max)\n\nx_min, x_max, y_min, y_max, z_min, z_max = grid_range\n\nvoxel_x = ((points[:, 0] - x_min) / self.voxel_size).astype(int)\n\nvoxel_y = ((points[:, 1] - y_min) / self.voxel_size).astype(int)\n\nvoxel_z = ((points[:, 2] - z_min) / self.voxel_size).astype(int)\n\n(voxel_x >= 0) & (voxel_x < int((x_max - x_min) / self.voxel_size)) &\n\n(voxel_y >= 0) & (voxel_y < int((y_max - y_min) / self.voxel_size)) &\n\n(voxel_z >= 0) & (voxel_z < int((z_max - z_min) / self.voxel_size))\n\nreturn points[valid], voxel_x[valid], voxel_y[valid], voxel_z[valid]\n\ndef forward(self, points: np.ndarray) -> Dict:\n\nvalid_points, vx, vy, vz = self.voxelize(points)\n\ngrid_size = (\n\nint(100 / self.voxel_size),\n\nint(100 / self.voxel_size),\n\nint(10 / self.voxel_size)\n\nvoxel_grid = torch.zeros(grid_size, dtype=torch.float32)\n\n# 填充体素 (简化为密度)\n\nfor i in range(len(valid_points)):\n\nvoxel_grid[vx[i], vy[i], vz[i]] += 1\n\nvoxel_tensor = voxel_grid.unsqueeze(0).unsqueeze(0)  # (1, 1, D, H, W)\n\nfeatures = self.encoder(voxel_tensor)\n\n\"occupancy\": features,\n\n\"points\": valid_points\n\nclass BirdEyeView:\n\n\"\"\"鸟瞰图 (BEV) 特征提取\"\"\"\n\ndef __init__(self, feature_dim: int = 256):\n\nself.feature_dim = feature_dim\n\ndef build_bev(\n\nmulti_scale_features: List[torch.Tensor],\n\ntarget_size: Tuple[int, int] = (200, 200)\n\n) -> torch.Tensor:\n\nmulti_scale_features: 多尺度特征列表\n\ntarget_size: (H, W)\n\nfused = torch.zeros(\n\n1, self.feature_dim, target_size[0], target_size[1]\n\nfor feat in multi_scale_features:\n\nfeat_up = torch.nn.functional.interpolate(\n\nsize=target_size,\n\nmode='bilinear',\n\nalign_corners=False\n\nfused += feat_up\n\nreturn fused\n\ndef bev_to_image_coords(\n\nbev_coords: np.ndarray,\n\norigin: Tuple[float, float] = (0, 0),\n\nresolution: float = 0.1\n\n) -> np.ndarray:\n\n\"\"\"BEV 坐标转图像坐标\"\"\"\n\nx, y = bev_coords[:, 0], bev_coords[:, 1]\n\nimg_x = ((x - origin[0]) / resolution).astype(int)\n\nimg_y = ((y - origin[1]) / resolution).astype(int)\n\nreturn np.stack([img_x, img_y], axis=-1)\n\nclass PlanningNetwork(nn.Module):\n\ndef __init__(self, input_dim: int = 512, hidden_dim: int = 256):\n\nsuper().__init__()\n\nself.planner = nn.Sequential(\n\nnn.Linear(input_dim, hidden_dim),\n\nnn.ReLU(inplace=True),\n\nnn.Dropout(0.1),\n\nnn.Linear(hidden_dim, hidden_dim),\n\nnn.ReLU(inplace=True),\n\nnn.Dropout(0.1),\n\nnn.Linear(hidden_dim, 2),  # (steering, throttle)\n\ndef forward(\n\nbev_features: torch.Tensor,\n\nroute_features: torch.Tensor,\n\ntraffic_features: torch.Tensor\n\n) -> Dict[str, torch.Tensor]:\n\nbev_features: BEV 特征\n\nroute_features: 路由特征\n\ntraffic_features: 交通状态特征\n\n{\"trajectory\": ..., \"control\": ...}\n\nbev_flat = bev_features.flatten(1)\n\ncombined = torch.cat([\n\nroute_features,\n\ntraffic_features\n\ntrajectory = self.planner(combined)\n\ncontrol = torch.tanh(trajectory)\n\n\"trajectory\": trajectory,\n\n\"control\": control\n\nclass FSDStackedHourglass(nn.Module):\n\n\"\"\"FSD 沙漏网络\"\"\"\n\ndef __init__(self, num_joints: int = 2):\n\nsuper().__init__()\n\nself.num_joints = num_joints\n\nself.encoder = nn.Sequential(\n\nnn.Conv2d(12, 64, kernel_size=7, stride=2, padding=3),\n\nnn.BatchNorm2d(64),\n\nnn.ReLU(inplace=True),\n\nnn.MaxPool2d(2),\n\nself._make_layer(64, 128),\n\nself._make_layer(128, 128),\n\nself._make_layer(128, 256),\n\nself.hourglass = self._make_hourglass(256, num_joints)\n\ndef _make_layer(self, in_ch: int, out_ch: int) -> nn.Module:\n\nreturn nn.Sequential(\n\nnn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(out_ch),\n\nnn.ReLU(inplace=True),\n\nnn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(out_ch),\n\nnn.ReLU(inplace=True),\n\ndef _make_hourglass(self, channels: int, num_joints: int) -> nn.Module:\n\n\"\"\"构建沙漏模块\"\"\"\n\nreturn nn.Sequential(\n\nself._make_layer(channels, channels),\n\nself._make_layer(channels, channels),\n\nnn.MaxPool2d(2),\n\nself._make_layer(channels, channels * 2),\n\nself._make_layer(channels * 2, channels * 2),\n\nnn.MaxPool2d(2),\n\nself._make_layer(channels * 2, channels * 2),\n\nnn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n\nself._make_layer(channels * 2, channels * 2),\n\nnn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),\n\nnn.Conv2d(channels * 2, num_joints, kernel_size=1),\n\ndef forward(self, x: torch.Tensor) -> List[torch.Tensor]:\n\nencoded = self.encoder(x)\n\noutputs = self.hourglass(encoded)\n\nreturn outputs\n```\n\n## 端到端自动驾驶\n\n### UniAD 架构\n\n``` python\nclass UniAD(nn.Module):\n\n\"\"\"UniAD 端到端自动驾驶\"\"\"\n\ndef __init__(self, config: Dict):\n\nsuper().__init__()\n\nself.config = config\n\nself.backbone = self._build_backbone()\n\nself.neck = self._build_neck()\n\nself.bev_encoder = BEVEncoder(\n\nin_channels=512,\n\nout_channels=config.get(\"bev_channels\", 256)\n\nself.map_head = MapHead(config)\n\nself.detection_head = DetectionHead(config)\n\nself.motion_head = MotionHead(config)\n\nself.planning_head = PlanningHead(config)\n\ndef _build_backbone(self) -> nn.Module:\n\n\"\"\"构建骨干网络\"\"\"\n\nreturn nn.Sequential(\n\nnn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),\n\nnn.BatchNorm2d(64),\n\nnn.ReLU(inplace=True),\n\nnn.MaxPool2d(3, stride=2, padding=1),\n\n# ResNet blocks\n\nself._make_resblock(64, 64, num_blocks=3),\n\nself._make_resblock(64, 128, num_blocks=4, stride=2),\n\nself._make_resblock(128, 256, num_blocks=6, stride=2),\n\nself._make_resblock(256, 512, num_blocks=3, stride=2),\n\ndef _make_resblock(\n\nin_ch: int,\n\nout_ch: int,\n\nnum_blocks: int,\n\nstride: int = 1\n\n) -> nn.Module:\n\n\"\"\"ResNet block\"\"\"\n\nlayers = []\n\nlayers.append(nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1))\n\nlayers.append(nn.BatchNorm2d(out_ch))\n\nlayers.append(nn.ReLU(inplace=True))\n\nfor _ in range(num_blocks - 1):\n\nlayers.append(nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))\n\nlayers.append(nn.BatchNorm2d(out_ch))\n\nlayers.append(nn.ReLU(inplace=True))\n\nreturn nn.Sequential(*layers)\n\ndef _build_neck(self) -> nn.Module:\n\n\"\"\"构建 Neck (FPN)\"\"\"\n\nreturn nn.Sequential(\n\nnn.Conv2d(512, 256, kernel_size=1),\n\nnn.BatchNorm2d(256),\n\nnn.ReLU(inplace=True),\n\nnn.Conv2d(256, 256, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(256),\n\nnn.ReLU(inplace=True),\n\ndef forward(\n\nimages: torch.Tensor,\n\nintrinsics: torch.Tensor,\n\nextrinsics: torch.Tensor\n\n) -> Dict[str, torch.Tensor]:\n\nimages: (B, N, 3, H, W) 多视角图像\n\nintrinsics: (B, N, 3, 3) 内参\n\nextrinsics: (B, N, 4, 4) 外参\n\n\"detection\": {...},\n\n\"tracking\": {...},\n\n\"map\": {...},\n\n\"motion\": {...},\n\n\"planning\": {...}\n\nB, N, C, H, W = images.shape\n\nmulti_view_features = []\n\nfor i in range(N):\n\nimg = images[:, i]  # (B, 3, H, W)\n\nfeat = self.backbone(img)  # (B, 512, H', W')\n\nfeat = self.neck(feat)\n\nmulti_view_features.append(feat)\n\n# 视角融合 + BEV\n\nbev_features = self.bev_encoder(\n\nmulti_view_features,\n\nintrinsics,\n\ndetection = self.detection_head(bev_features)\n\ntracking = self.detection_head(bev_features)  # 简化\n\nmap_pred = self.map_head(bev_features)\n\nmotion = self.motion_head(\n\nbev_features,\n\ndetection[\"boxes\"],\n\nmap_pred[\"lanes\"]\n\nplanning = self.planning_head(\n\nbev_features,\n\nmotion[\"trajectories\"]\n\n\"detection\": detection,\n\n\"tracking\": tracking,\n\n\"map\": map_pred,\n\n\"motion\": motion,\n\n\"planning\": planning\n\nclass BEVEncoder(nn.Module):\n\n\"\"\"BEV 编码器\"\"\"\n\ndef __init__(self, in_channels: int, out_channels: int):\n\nsuper().__init__()\n\nself.encoder = nn.Sequential(\n\nnn.Conv2d(in_channels, 128, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(128),\n\nnn.ReLU(inplace=True),\n\nnn.Conv2d(128, out_channels, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(out_channels),\n\nnn.ReLU(inplace=True),\n\ndef forward(\n\nmulti_view_features: List[torch.Tensor],\n\nintrinsics: torch.Tensor,\n\nextrinsics: torch.Tensor\n\n) -> torch.Tensor:\n\nmulti_view_features: 多视角特征列表\n\nintrinsics: 内参\n\nextrinsics: 外参\n\n# 实际使用 transformer 进行视角变换\n\nfused = torch.stack(multi_view_features, dim=1).mean(dim=1)  # (B, C, H, W)\n\nbev = self.encoder(fused)\n\nclass MapHead(nn.Module):\n\n\"\"\"地图感知头\"\"\"\n\ndef __init__(self, config: Dict):\n\nsuper().__init__()\n\nself.lane_head = LaneDetectionHead(config)\n\nself.segmentation_head = MapSegmentationHead(config)\n\ndef forward(self, bev_features: torch.Tensor) -> Dict[str, torch.Tensor]:\n\nlanes = self.lane_head(bev_features)\n\nsegmentation = self.segmentation_head(bev_features)\n\n\"lanes\": lanes,\n\n\"segmentation\": segmentation\n\nclass LaneDetectionHead(nn.Module):\n\n\"\"\"车道线检测头\"\"\"\n\ndef __init__(self, config: Dict):\n\nsuper().__init__()\n\nself.head = nn.Sequential(\n\nnn.Conv2d(256, 128, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(128),\n\nnn.ReLU(inplace=True),\n\nnn.Conv2d(128, 64, kernel_size=3, padding=1),\n\nnn.BatchNorm2d(64),\n\nnn.ReLU(inplace=True),\n\nself.confidence = nn.Conv2d(64, 1, kernel_size=1)\n\nself.offset = nn.Conv2d(64, 2, kernel_size=1)  # 偏移\n\ndef forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]:\n\nfeat = self.head(x)\n\nconfidence = torch.sigmoid(self.confidence(feat))\n\noffset = self.offset(feat)\n\n\"confidence\": confidence,\n\n\"offset\": offset\n\nclass MotionHead(nn.Module):\n\n\"\"\"运动预测头\"\"\"\n\ndef __init__(self, config: Dict):\n\nsuper().__init__()\n\nself.num_agents = config.get(\"num_agents\", 64)\n\nself.future_frames = config.get(\"future_frames\", 40)\n\nself.motion_encoder = nn.GRU(\n\ninput_size=256,\n\nhidden_size=256,\n\nnum_layers=2,\n\nbatch_first=True\n\nself.trajectory_head = nn.Linear(256, self.future_frames * 2)\n\ndef forward(\n\nbev_features: torch.Tensor,\n\ndetection_boxes: torch.Tensor,\n\nmap_lanes: torch.Tensor\n\n) -> Dict[str, torch.Tensor]:\n\ndetection_boxes: (B, N, 5) [x, y, w, h, angle]\n\nmap_lanes: 地图信息\n\nB = bev_features.shape[0]\n\n# 简化：用检测框特征\n\nagent_features = detection_boxes.flatten(1)  # (B, N*5)\n\nencoded, _ = self.motion_encoder(agent_features)\n\ntrajectories = self.trajectory_head(encoded)\n\ntrajectories = trajectories.view(B, -1, self.future_frames, 2)\n\n\"trajectories\": trajectories,\n\n\"probabilities\": torch.softmax(torch.randn(B, self.num_agents), dim=-1)\n\nclass PlanningHead(nn.Module):\n\ndef __init__(self, config: Dict):\n\nsuper().__init__()\n\nself.planner = nn.Sequential(\n\nnn.Linear(512, 256),\n\nnn.ReLU(inplace=True),\n\nnn.Linear(256, 128),\n\nnn.ReLU(inplace=True),\n\nnn.Linear(128, 2),  # (x, y) 轨迹点\n\ndef forward(\n\nbev_features: torch.Tensor,\n\nmotion_trajectories: torch.Tensor\n\n) -> Dict[str, torch.Tensor]:\n\nmotion_trajectories: (B, N, T, 2) 预测轨迹\n\nB = bev_features.shape[0]\n\nbev_flat = bev_features.flatten(1)\n\nmotion_flat = motion_trajectories.flatten(1)\n\ncombined = torch.cat([bev_flat, motion_flat], dim=-1)\n\nplanning = self.planner(combined)\n\n\"trajectory\": planning.view(B, -1, 2),  # (B, T, 2)\n\n\"waypoints\": planning.view(B, -1, 2)\n```\n\n## 数据处理\n\n### 自动驾驶数据管道\n\n``` python\nimport numpy as np\n\nfrom typing import Dict, List, Tuple, Optional\n\nimport pickle\n\nclass DatasetLoader:\n\n\"\"\"数据加载器\"\"\"\n\ndef __init__(\n\ndata_root: str,\n\nsplit: str = \"train\"\n\nself.data_root = data_root\n\nself.split = split\n\nself.scene_ids = self._load_scene_list()\n\ndef _load_scene_list(self) -> List[str]:\n\n\"\"\"加载场景列表\"\"\"\n\n# 简化：从文件系统读取\n\ndef load_frame(self, scene_id: str, frame_id: int) -> Dict:\n\n\"\"\"加载单帧数据\"\"\"\n\ncamera_data = self._load_camera(scene_id, frame_id)\n\nlidar_data = self._load_lidar(scene_id, frame_id)\n\ncanbus_data = self._load_canbus(scene_id, frame_id)\n\nannotation = self._load_annotation(scene_id, frame_id)\n\n\"cameras\": camera_data,\n\n\"lidar\": lidar_data,\n\n\"canbus\": canbus_data,\n\n\"annotation\": annotation\n\ndef _load_camera(self, scene_id: str, frame_id: int) -> Dict[str, np.ndarray]:\n\n\"\"\"加载相机数据\"\"\"\n\ndef _load_lidar(self, scene_id: str, frame_id: int) -> np.ndarray:\n\n\"\"\"加载激光雷达数据\"\"\"\n\nreturn np.zeros((10000, 4))\n\ndef _load_canbus(self, scene_id: str, frame_id: int) -> Dict:\n\n\"\"\"加载车辆 CAN 总线数据\"\"\"\n\n\"speed\": 0.0,\n\n\"steering\": 0.0,\n\n\"acceleration\": 0.0,\n\n\"heading\": 0.0\n\ndef _load_annotation(self, scene_id: str, frame_id: int) -> Dict:\n\n\"boxes_3d\": [],  # 3D 检测框\n\n\"track_ids\": [],\n\n\"traffic_lights\": [],\n\n\"lanes\": []\n\nclass DataAugmentation:\n\n@staticmethod\n\ndef random_flip(\n\nimage: np.ndarray,\n\nboxes: np.ndarray,\n\nprob: float = 0.5\n\n) -> Tuple[np.ndarray, np.ndarray]:\n\n\"\"\"随机水平翻转\"\"\"\n\nif np.random.random() < prob:\n\nimage = np.flip(image, axis=1).copy()\n\nif len(boxes) > 0:\n\nboxes[:, [0, 2]] = image.shape[1] - boxes[:, [2, 0]]\n\nreturn image, boxes\n\n@staticmethod\n\ndef random_scaling(\n\nimage: np.ndarray,\n\nscale_range: Tuple[float, float] = (0.9, 1.1)\n\n) -> np.ndarray:\n\nscale = np.random.uniform(*scale_range)\n\nfrom scipy.ndimage import zoom\n\nh, w = image.shape[:2]\n\nscaled_h = int(h * scale)\n\nscaled_w = int(w * scale)\n\nscaled = zoom(image, (scaled_h/h, scaled_w/w, 1))\n\n# 裁剪或填充到原尺寸\n\nresult = np.zeros_like(image)\n\nif scale >= 1:\n\nstart_h = (scaled_h - h) // 2\n\nstart_w = (scaled_w - w) // 2\n\nresult = scaled[start_h:start_h+h, start_w:start_w+w]\n\nstart_h = (h - scaled_h) // 2\n\nstart_w = (w - scaled_w) // 2\n\nresult[start_h:start_h+scaled_h, start_w:start_w+scaled_w] = scaled\n\nreturn result\n\n@staticmethod\n\ndef random_rotation(\n\nimage: np.ndarray,\n\nangle_range: Tuple[float, float] = (-15, 15)\n\n) -> np.ndarray:\n\nfrom scipy.ndimage import rotate\n\nangle = np.random.uniform(*angle_range)\n\nrotated = rotate(image, angle, reshape=False)\n\nreturn rotated\n\nclass BEVGenerator:\n\n\"\"\"BEV 视角生成器\"\"\"\n\ndef __init__(\n\nx_range: Tuple[float, float] = (-50, 50),\n\ny_range: Tuple[float, float] = (-50, 50),\n\nz_range: Tuple[float, float] = (-5, 5),\n\nresolution: float = 0.1\n\nself.x_range = x_range\n\nself.y_range = y_range\n\nself.z_range = z_range\n\nself.resolution = resolution\n\nself.grid_size = (\n\nint((x_range[1] - x_range[0]) / resolution),\n\nint((y_range[1] - y_range[0]) / resolution),\n\nint((z_range[1] - z_range[0]) / resolution)\n\ndef points_to_bev(\n\npoints: np.ndarray,\n\nintensity: np.ndarray = None\n\n) -> np.ndarray:\n\npoints: (N, 3) [x, y, z]\n\nintensity: (N,) 反射强度\n\n(C, H, W) BEV 特征图\n\n# 初始化网格 (使用反射强度作为通道)\n\nheight_channels = self.grid_size[2]\n\nchannels = height_channels + (1 if intensity is not None else 0)\n\nbev = np.zeros((channels, self.grid_size[0], self.grid_size[1]))\n\nx_idx = ((points[:, 0] - self.x_range[0]) / self.resolution).astype(int)\n\ny_idx = ((points[:, 1] - self.y_range[0]) / self.resolution).astype(int)\n\nz_idx = ((points[:, 2] - self.z_range[0]) / self.resolution).astype(int)\n\n(x_idx >= 0) & (x_idx < self.grid_size[0]) &\n\n(y_idx >= 0) & (y_idx < self.grid_size[1]) &\n\n(z_idx >= 0) & (z_idx < self.grid_size[2])\n\nx_idx = x_idx[valid]\n\ny_idx = y_idx[valid]\n\nz_idx = z_idx[valid]\n\nfor i in range(len(x_idx)):\n\nif z_idx[i] >= 0 and z_idx[i] < self.grid_size[2]:\n\nbev[z_idx[i], x_idx[i], y_idx[i]] = 1.0\n\nif intensity is not None:\n\nintensity_valid = intensity[valid]\n\nbev[-1, x_idx, y_idx] = intensity_valid\n```\n\n## 仿真与测试\n\n### 自动驾驶仿真\n\n``` python\nimport numpy as np\n\nfrom typing import Dict, List, Tuple\n\nfrom dataclasses import dataclass\n\nclass VehicleState:\n\nheading: float\n\nspeed: float\n\nsteering: float\n\nclass TrafficActor:\n\n\"\"\"交通参与者\"\"\"\n\nvehicle_id: str\n\nheading: float\n\nspeed: float\n\nvehicle_type: str  # car, pedestrian, cyclist\n\nclass DrivingSimulator:\n\n\"\"\"驾驶仿真器\"\"\"\n\ndef __init__(\n\ndt: float = 0.1,\n\nmap_size: Tuple[float, float] = (200, 200)\n\nself.dt = dt\n\nself.map_size = map_size\n\nself.ego_vehicle = None\n\nself.traffic_actors: List[TrafficActor] = []\n\nself.traffic_lights = []\n\ndef set_ego(self, state: VehicleState):\n\n\"\"\"设置自车状态\"\"\"\n\nself.ego_vehicle = state\n\ndef add_traffic(self, actor: TrafficActor):\n\n\"\"\"添加交通参与者\"\"\"\n\nself.traffic_actors.append(actor)\n\ndef step(self, control: Dict[str, float]) -> Dict:\n\ncontrol: {\"steering\": ..., \"throttle\": ..., \"brake\": ...}\n\n{\"observation\": ..., \"reward\": ..., \"done\": ...}\n\nself._update_ego(control)\n\nself._update_traffic()\n\nobservation = self._get_observation()\n\nreward = self._compute_reward()\n\ndone = self._check_done()\n\n\"observation\": observation,\n\n\"reward\": reward,\n\n\"done\": done\n\ndef _update_ego(self, control: Dict[str, float]):\n\nif self.ego_vehicle is None:\n\nwheelbase = 2.7  # 轴距\n\nsteering = control.get(\"steering\", 0)\n\nthrottle = control.get(\"throttle\", 0)\n\nbrake = control.get(\"brake\", 0)\n\nspeed = self.ego_vehicle.speed\n\nacceleration = throttle * 2.0 - brake * 5.0\n\nnew_speed = speed + acceleration * self.dt\n\nnew_speed = max(0, new_speed)  # 不倒车\n\nheading = self.ego_vehicle.heading\n\nnew_heading = heading + new_speed / wheelbase * np.tan(steering) * self.dt\n\nnew_x = self.ego_vehicle.x + new_speed * np.cos(new_heading) * self.dt\n\nnew_y = self.ego_vehicle.y + new_speed * np.sin(new_heading) * self.dt\n\nself.ego_vehicle = VehicleState(\n\nheading=new_heading,\n\nspeed=new_speed,\n\nsteering=steering\n\ndef _update_traffic(self):\n\nfor actor in self.traffic_actors:\n\nactor.x += actor.speed * np.cos(actor.heading) * self.dt\n\nactor.y += actor.speed * np.sin(actor.heading) * self.dt\n\ndef _get_observation(self) -> Dict:\n\n\"ego_state\": {\n\n\"x\": self.ego_vehicle.x,\n\n\"y\": self.ego_vehicle.y,\n\n\"heading\": self.ego_vehicle.heading,\n\n\"speed\": self.ego_vehicle.speed\n\n\"traffic\": [\n\n\"heading\": a.heading,\n\n\"speed\": a.speed,\n\n\"type\": a.vehicle_type\n\nfor a in self.traffic_actors\n\n\"lidar\": self._simulate_lidar(),\n\n\"camera\": self._simulate_camera()\n\ndef _simulate_lidar(self) -> np.ndarray:\n\n\"\"\"模拟激光雷达\"\"\"\n\nreturn np.zeros((10000, 3))\n\ndef _simulate_camera(self) -> np.ndarray:\n\nreturn np.zeros((375, 1242, 3))\n\ndef _compute_reward(self) -> float:\n\nreward = 0.0\n\nif self.ego_vehicle.speed > 10:\n\nreward += 0.1\n\nfor actor in self.traffic_actors:\n\ndist = np.sqrt(\n\n(actor.x - self.ego_vehicle.x) ** 2 +\n\n(actor.y - self.ego_vehicle.y) ** 2\n\nif dist < 2.0:\n\nreward -= 100.0\n\nreturn reward\n\ndef _check_done(self) -> bool:\n\n\"\"\"检查是否结束\"\"\"\n\nfor actor in self.traffic_actors:\n\ndist = np.sqrt(\n\n(actor.x - self.ego_vehicle.x) ** 2 +\n\n(actor.y - self.ego_vehicle.y) ** 2\n\nif dist < 1.0:\n\nreturn True\n\nself.ego_vehicle.x < 0 or self.ego_vehicle.x > self.map_size[0] or\n\nself.ego_vehicle.y < 0 or self.ego_vehicle.y > self.map_size[1]\n\nreturn True\n\nreturn False\n\nclass ScenarioRunner:\n\n\"\"\"场景运行器\"\"\"\n\ndef __init__(self, simulator: DrivingSimulator):\n\nself.simulator = simulator\n\ndef run_scenario(\n\nscenario: Dict,\n\nmax_steps: int = 1000\n\nscenario: 场景定义\n\npolicy_fn: 策略函数 (observation -> control)\n\nmax_steps: 最大步数\n\nself.simulator.set_ego(VehicleState(\n\nx=scenario[\"ego_start\"][0],\n\ny=scenario[\"ego_start\"][1],\n\nheading=scenario[\"ego_start\"][2],\n\nfor actor_def in scenario.get(\"traffic\", []):\n\nself.simulator.add_traffic(TrafficActor(\n\nvehicle_id=actor_def[\"id\"],\n\nx=actor_def[\"start\"][0],\n\ny=actor_def[\"start\"][1],\n\nheading=actor_def[\"start\"][2],\n\nspeed=actor_def.get(\"speed\", 0),\n\nvehicle_type=actor_def.get(\"type\", \"car\")\n\ntotal_reward = 0.0\n\nfor step in range(max_steps):\n\nobs = self.simulator._get_observation()\n\ncontrol = policy_fn(obs)\n\nresult = self.simulator.step(control)\n\ntotal_reward += result[\"reward\"]\n\nif result[\"done\"]:\n\n\"total_reward\": total_reward,\n\n\"steps\": steps,\n\n\"success\": steps < max_steps\n```\n\n## 规划与控制\n\n### 轨迹规划\n\n``` python\nimport numpy as np\n\nfrom typing import List, Tuple, Optional\n\nclass TrajectoryPlanner:\n\n\"\"\"轨迹规划器\"\"\"\n\ndef __init__(self):\n\nself.waypoints = []\n\ndef plan_to_goal(\n\nstart: Tuple[float, float, float],\n\ngoal: Tuple[float, float],\n\nobstacles: List[Tuple[float, float, float, float]] = None\n\n) -> np.ndarray:\n\nstart: (x, y, heading)\n\ngoal: (x, y)\n\nobstacles: [(x, y, w, h), ...]\n\n# 简化的 A* 规划\n\nif obstacles is None:\n\nobstacles = []\n\ntrajectories = self._generate_candidate_trajectories(start, goal)\n\nbest_trajectory = self._select_best_trajectory(\n\ntrajectories,\n\nreturn best_trajectory\n\ndef _generate_candidate_trajectories(\n\nstart: Tuple[float, float, float],\n\ngoal: Tuple[float, float]\n\n) -> List[np.ndarray]:\n\n\"\"\"生成候选轨迹\"\"\"\n\ntrajectories = []\n\nfor curvature in np.linspace(-0.5, 0.5, 5):\n\ntrajectory = self._generate_spline_trajectory(\n\nstart, goal, curvature\n\ntrajectories.append(trajectory)\n\nreturn trajectories\n\ndef _generate_spline_trajectory(\n\nstart: Tuple[float, float, float],\n\ngoal: Tuple[float, float],\n\ncurvature: float\n\n) -> np.ndarray:\n\n\"\"\"生成样条轨迹\"\"\"\n\nx0, y0, theta0 = start\n\nxg, yg = goal\n\nnum_points = 50\n\ntrajectory = []\n\nfor i in range(num_points):\n\nt = i / (num_points - 1)\n\nif abs(curvature) < 1e-6:\n\nx = x0 + (xg - x0) * t\n\ny = y0 + (yg - y0) * t\n\nr = 1.0 / curvature\n\ncx = x0 - r * np.sin(theta0)\n\ncy = y0 + r * np.cos(theta0)\n\nstart_angle = theta0 + np.pi / 2\n\nend_angle = np.arctan2(yg - cy, xg - cx)\n\ncurrent_angle = start_angle + (end_angle - start_angle) * t\n\nx = cx + r * np.sin(current_angle)\n\ny = cy - r * np.cos(current_angle)\n\ntrajectory.append([x, y])\n\nreturn np.array(trajectory)\n\ndef _select_best_trajectory(\n\ntrajectories: List[np.ndarray],\n\nobstacles: List[Tuple[float, float, float, float]]\n\n) -> np.ndarray:\n\n\"\"\"选择最优轨迹\"\"\"\n\nbest_cost = float('inf')\n\nbest_trajectory = trajectories[0] if trajectories else None\n\nfor traj in trajectories:\n\ncost = self._compute_trajectory_cost(traj, obstacles)\n\nif cost < best_cost:\n\nbest_cost = cost\n\nbest_trajectory = traj\n\nreturn best_trajectory\n\ndef _compute_trajectory_cost(\n\ntrajectory: np.ndarray,\n\nobstacles: List[Tuple[float, float, float, float]]\n\n) -> float:\n\n\"\"\"计算轨迹代价\"\"\"\n\npath_length = np.sum(np.linalg.norm(\n\nnp.diff(trajectory, axis=0),\n\ncost += path_length\n\nfor obs_x, obs_y, obs_w, obs_h in obstacles:\n\nfor point in trajectory:\n\ndist = np.sqrt(\n\n(point[0] - obs_x) ** 2 +\n\n(point[1] - obs_y) ** 2\n\nif dist < 3.0:\n\ncost += 1000.0 * (3.0 - dist)\n\nreturn cost\n\nclass PIDController:\n\n\"\"\"PID 控制器\"\"\"\n\ndef __init__(\n\nkp: float = 1.0,\n\nki: float = 0.0,\n\nkd: float = 0.0\n\nself.kp = kp\n\nself.ki = ki\n\nself.kd = kd\n\nself.prev_error = 0.0\n\nself.integral = 0.0\n\ndef compute(\n\nsetpoint: float,\n\nmeasurement: float,\n\ndt: float = 0.1\n\n) -> float:\n\nsetpoint: 设定值\n\nmeasurement: 测量值\n\nerror = setpoint - measurement\n\nself.integral += error * dt\n\nderivative = (error - self.prev_error) / dt\n\noutput = self.kp * error + self.ki * self.integral + self.kd * derivative\n\nself.prev_error = error\n\nreturn output\n\nclass LateralController:\n\n\"\"\"横向控制器 (Pure Pursuit)\"\"\"\n\ndef __init__(self, lookahead_distance: float = 5.0):\n\nself.lookahead_distance = lookahead_distance\n\nself.wheelbase = 2.7\n\ndef compute_steering(\n\nvehicle_state: Tuple[float, float, float],\n\ntrajectory: np.ndarray\n\n) -> float:\n\nvehicle_state: (x, y, heading)\n\ntrajectory: (N, 2) 轨迹点\n\nvx, vy, heading = vehicle_state\n\nlookahead_point = self._find_lookahead_point(\n\ndx = lookahead_point[0] - vx\n\ndy = lookahead_point[1] - vy\n\nangle_to_point = np.arctan2(dy, dx)\n\nalpha = angle_to_point - heading\n\nsteering = np.arctan(2 * self.wheelbase * np.sin(alpha) / self.lookahead_distance)\n\nreturn np.clip(steering, -0.5, 0.5)\n\ndef _find_lookahead_point(\n\nvehicle_pos: Tuple[float, float],\n\ntrajectory: np.ndarray\n\n) -> np.ndarray:\n\n\"\"\"找到前瞻点\"\"\"\n\nvx, vy = vehicle_pos\n\n# 找到轨迹上距离车一定距离的点\n\ndists = np.linalg.norm(trajectory - np.array([vx, vy]), axis=1)\n\nvalid_indices = np.where(dists >= self.lookahead_distance * 0.8)[0]\n\nif len(valid_indices) == 0:\n\nreturn trajectory[-1]\n\nreturn trajectory[valid_indices[0]]\n\nclass LongitudinalController:\n\n\"\"\"纵向控制器\"\"\"\n\ndef __init__(self):\n\nself.speed_pid = PIDController(kp=1.0, ki=0.1, kd=0.1)\n\nself.desired_speed = 0.0\n\ndef compute_throttle_brake(\n\ndesired_speed: float,\n\ncurrent_speed: float,\n\ndt: float = 0.1\n\n) -> Tuple[float, float]:\n\n(throttle, brake)\n\nself.desired_speed = desired_speed\n\ncontrol = self.speed_pid.compute(\n\ndesired_speed,\n\ncurrent_speed,\n\nif control > 0:\n\nreturn (min(control, 1.0), 0.0)\n\nreturn (0.0, min(-control, 1.0))\n```\n\n## 变现路径\n\n### 自动驾驶服务变现\n\n```\n1. 自动驾驶仿真平台\n\n- 产品：自动驾驶仿真 SaaS\n\n- 内容：场景仿真、数据回放\n\n- 收益：按仿真时长计费\n\n- 产品：3D 点云标注平台\n\n- 内容：目标检测、车道线标注\n\n- 收益：按标注量计费\n\n3. ADAS 算法模块\n\n- 产品：车道保持、自动泊车算法\n\n- 内容：视觉/LiDAR 算法\n\n- 收益：技术授权\n\n- 产品：自动驾驶课程\n\n- 内容：算法、仿真、实车\n\n- 收益：课程销售\n\n- 产品：商用车队管理\n\n- 内容：监控、调度、路径规划\n\n- 收益：SaaS 订阅\n\n6. Robotaxi 服务\n\n- 产品：无人出租车服务\n\n- 内容：城市运营\n\n- 收益：按里程计费\n```\n\n## 总结\n\n**占用网络**：3D 空间表示，Tesla 核心** BEV 视角**：多视角统一到鸟瞰图**端到端 UniAD**：感知→预测→规划一体化**规划控制**：Pure Pursuit、Stanley、PID**仿真测试**：场景仿真、安全测试**数据管道**：点云、图像、多传感器融合**地图感知**：车道线、边界、语义地图**运动预测**：多智能体轨迹预测**安全验证**：仿真回放、脱离报告**变现模式**：仿真平台、数据标注、技术授权\n\n*本文是自动驾驶系列之一。*\n\n*This article contains affiliate links. If you sign up through the links above, I may earn a commission at no additional cost to you.*\n\n## Ready to Build Your AI Business?\n\n** Get started with Systeme.io for free** — All-in-one platform for building your online business with AI tools.", "url": "https://wpnews.pro/news/ai-tesla-fsdwaymo", "canonical_source": "https://dev.to/zny10289/ai-tesla-fsdwaymo-hjm", "published_at": "2026-05-21 00:10:17+00:00", "updated_at": "2026-05-21 00:32:59.336322+00:00", "lang": "en", "topics": ["autonomous-vehicles", "artificial-intelligence", "machine-learning", "research", "products"], "entities": ["Tesla", "Waymo", "Wayve", "华为", "小鹏", "DriveGPT", "UniAD"], "alternates": {"html": "https://wpnews.pro/news/ai-tesla-fsdwaymo", "markdown": "https://wpnews.pro/news/ai-tesla-fsdwaymo.md", "text": "https://wpnews.pro/news/ai-tesla-fsdwaymo.txt", "jsonld": "https://wpnews.pro/news/ai-tesla-fsdwaymo.jsonld"}}