Euro NCAP 2026安全带误用检测要求深度解读与实现方案

Euro NCAP 2026安全带误用检测要求深度解读与实现方案

法规背景

Euro NCAP概述

Euro NCAP（European New Car Assessment Programme）是欧洲新车评估计划，是全球最具影响力的汽车安全评级机构之一。2026年版本引入了多项新的安全要求，其中安全带误用检测是重点之一。

核心要求

定义：安全带误用（Seatbelt Misuse）是指驾驶员或乘客虽然系了安全带，但使用方式不正确，导致保护效果降低或完全失效。

常见误用类型：

graph TB
    A[安全带误用] --> B[位置错误]
    A --> C[使用错误]
    A --> D[状态错误]
    
    B --> B1[肩带位于身后]
    B --> B2[肩带位于腋下]
    B --> B3[腰带位置过高]
    
    C --> C1[安全带松弛]
    C --> C2[安全带扭转]
    C --> C3[夹子固定]
    
    D --> D1[儿童误用成人带]
    D --> D2[多人共用]
    D --> D3[物品代替人体]

评分标准

误用场景 检测要求 评分权重
肩带位于身后 必须检测 2分
肩带位于腋下 必须检测 2分
安全带松弛 必须检测 2分
儿童误用 必须检测 3分
多人共用 必须检测 2分
物品代替 必须检测 1分
总分 - 12分

技术实现架构

整体系统架构

graph TB
    subgraph 感知层
        A[车内摄像头<br/>RGB+IR]
        B[安全带张力传感器]
        C[座椅压力传感器]
        D[ buckle状态传感器]
    end
    
    subgraph 特征提取
        E[视觉特征提取<br/>CNN]
        F[传感器数据融合]
        G[时序特征建模]
    end
    
    subgraph 检测算法
        H[安全带分割]
        I[人体姿态估计]
        J[误用分类器]
    end
    
    subgraph 决策输出
        K[误用类型判定]
        L[置信度评估]
        M[警告策略]
    end
    
    A --> E --> H --> J --> K --> M
    B --> F --> J --> L --> M
    C --> F
    D --> F
    A --> G --> I --> J

核心技术实现

1. 安全带分割网络

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import torch
import torch.nn as nn
import torch.nn.functional as F

class SeatbeltSegmentationNet(nn.Module):
    """
    安全带语义分割网络
    输入: RGB图像 (B, 3, H, W)
    输出: 分割掩码 (B, num_classes, H, W)
    
    类别:
    - 0: 背景
    - 1: 肩带
    - 2: 腰带
    - 3: buckle
    """
    def __init__(self, num_classes=4):
        super().__init__()
        
        # 编码器 (ResNet-50骨干)
        self.encoder = ResNetEncoder(pretrained=True)
        
        # 解码器 (FPN)
        self.decoder = FPNDecoder(
            in_channels=[256, 512, 1024, 2048],
            out_channels=128
        )
        
        # 注意力模块
        self.attention = ChannelSpatialAttention(128)
        
        # 分割头
        self.seg_head = nn.Sequential(
            nn.Conv2d(128, 64, 3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, num_classes, 1)
        )
    
    def forward(self, x):
        """
        Args:
            x: (B, 3, H, W) 输入图像
        Returns:
            segmentation: (B, num_classes, H, W) 分割logits
        """
        # 多尺度特征提取
        features = self.encoder(x)
        
        # 特征金字塔解码
        decoded = self.decoder(features)
        
        # 注意力增强
        attended = self.attention(decoded)
        
        # 分割预测
        segmentation = self.seg_head(attended)
        
        # 上采样到原分辨率
        segmentation = F.interpolate(
            segmentation,
            size=x.shape[2:],
            mode='bilinear',
            align_corners=False
        )
        
        return segmentation


class ResNetEncoder(nn.Module):
    """ResNet编码器"""
    def __init__(self, pretrained=True):
        super().__init__()
        
        import torchvision.models as models
        resnet = models.resnet50(pretrained=pretrained)
        
        # 提取各层
        self.conv1 = resnet.conv1
        self.bn1 = resnet.bn1
        self.relu = resnet.relu
        self.maxpool = resnet.maxpool
        
        self.layer1 = resnet.layer1  # 256
        self.layer2 = resnet.layer2  # 512
        self.layer3 = resnet.layer3  # 1024
        self.layer4 = resnet.layer4  # 2048
    
    def forward(self, x):
        """返回多尺度特征"""
        features = []
        
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        
        x = self.layer1(x)
        features.append(x)
        
        x = self.layer2(x)
        features.append(x)
        
        x = self.layer3(x)
        features.append(x)
        
        x = self.layer4(x)
        features.append(x)
        
        return features


class FPNDecoder(nn.Module):
    """特征金字塔解码器"""
    def __init__(self, in_channels, out_channels):
        super().__init__()
        
        # 横向连接
        self.lateral_convs = nn.ModuleList([
            nn.Conv2d(in_ch, out_channels, 1)
            for in_ch in in_channels
        ])
        
        # 平滑卷积
        self.smooth_convs = nn.ModuleList([
            nn.Conv2d(out_channels, out_channels, 3, padding=1)
            for _ in in_channels
        ])
    
    def forward(self, features):
        """
        自顶向下融合
        """
        # 横向连接
        laterals = [conv(f) for conv, f in zip(self.lateral_convs, features)]
        
        # 自顶向下
        for i in range(len(laterals)-1, 0, -1):
            laterals[i-1] = laterals[i-1] + F.interpolate(
                laterals[i],
                size=laterals[i-1].shape[2:],
                mode='nearest'
            )
        
        # 平滑
        outputs = [conv(lat) for conv, lat in zip(self.smooth_convs, laterals)]
        
        # 上采样到相同尺度
        target_size = outputs[0].shape[2:]
        upsampled = [outputs[0]]
        for out in outputs[1:]:
            upsampled.append(F.interpolate(out, size=target_size, mode='bilinear'))
        
        # 拼接融合
        fused = torch.cat(upsampled, dim=1)
        
        # 降维
        fused = nn.Conv2d(fused.shape[1], 128, 1)(fused)
        
        return fused


class ChannelSpatialAttention(nn.Module):
    """通道-空间注意力"""
    def __init__(self, channels, reduction=16):
        super().__init__()
        
        # 通道注意力
        self.channel_attention = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(channels, channels // reduction, 1),
            nn.ReLU(),
            nn.Conv2d(channels // reduction, channels, 1),
            nn.Sigmoid()
        )
        
        # 空间注意力
        self.spatial_attention = nn.Sequential(
            nn.Conv2d(channels, 1, 7, padding=3),
            nn.Sigmoid()
        )
    
    def forward(self, x):
        # 通道注意力
        ca = self.channel_attention(x)
        x = x * ca
        
        # 空间注意力
        sa = self.spatial_attention(x)
        x = x * sa
        
        return x

2. 安全带路径分析

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import numpy as np
from scipy.spatial.distance import cdist
from scipy.optimize import linear_sum_assignment

class SeatbeltPathAnalyzer:
    """
    安全带路径分析器
    分析安全带相对于人体关键点的位置
    """
    
    def __init__(self):
        # 人体关键点定义
        self.keypoints = {
            'left_shoulder': 0,
            'right_shoulder': 1,
            'left_hip': 2,
            'right_hip': 3,
            'neck': 4,
            'chest': 5
        }
    
    def analyze_belt_path(self, segmentation_mask, body_keypoints):
        """
        分析安全带路径
        
        Args:
            segmentation_mask: (H, W) 分割掩码
            body_keypoints: dict, 人体关键点坐标
        Returns:
            misuse_info: dict, 误用信息
        """
        # 提取肩带和腰带
        shoulder_belt = (segmentation_mask == 1).astype(np.uint8)
        lap_belt = (segmentation_mask == 2).astype(np.uint8)
        
        # 肩带分析
        shoulder_misuse = self._analyze_shoulder_belt(
            shoulder_belt, body_keypoints
        )
        
        # 腰带分析
        lap_misuse = self._analyze_lap_belt(lap_belt, body_keypoints)
        
        # 松紧度分析
        tightness = self._analyze_tightness(segmentation_mask)
        
        # 综合判定
        misuse_type = self._determine_misuse_type(
            shoulder_misuse, lap_misuse, tightness
        )
        
        return {
            'shoulder_belt_status': shoulder_misuse,
            'lap_belt_status': lap_misuse,
            'tightness': tightness,
            'misuse_type': misuse_type
        }
    
    def _analyze_shoulder_belt(self, shoulder_belt, keypoints):
        """分析肩带位置"""
        
        # 肩带骨架提取
        belt_skeleton = self._extract_skeleton(shoulder_belt)
        
        # 肩带起点和终点
        if len(belt_skeleton) == 0:
            return {'status': 'not_detected', 'confidence': 0.0}
        
        # 拟合肩带路径
        belt_path = self._fit_belt_path(belt_skeleton)
        
        # 检测误用
        shoulder_left = keypoints['left_shoulder']
        shoulder_right = keypoints['right_shoulder']
        neck = keypoints['neck']
        
        # 检查肩带是否在肩膀外侧（身后）
        distance_to_shoulder = self._point_to_line_distance(
            shoulder_left, belt_path
        )
        
        # 检查肩带是否在腋下
        distance_to_armpit = self._check_armpit_position(
            belt_path, shoulder_left, shoulder_right
        )
        
        # 判定
        if distance_to_shoulder > 50:  # 阈值：像素
            return {
                'status': 'behind_shoulder',
                'confidence': 0.9
            }
        elif distance_to_armpit < 30:
            return {
                'status': 'under_arm',
                'confidence': 0.85
            }
        else:
            return {
                'status': 'normal',
                'confidence': 0.95
            }
    
    def _analyze_lap_belt(self, lap_belt, keypoints):
        """分析腰带位置"""
        
        # 腰带检测
        if lap_belt.sum() == 0:
            return {'status': 'not_detected', 'confidence': 0.0}
        
        # 腰带中心线
        belt_center_y = self._find_belt_center(lap_belt)
        
        # 髋部位置
        hip_y = (keypoints['left_hip'][1] + keypoints['right_hip'][1]) / 2
        
        # 腰带是否过高
        if belt_center_y < hip_y - 100:  # 高于髋部100像素
            return {
                'status': 'too_high',
                'confidence': 0.9
            }
        
        return {
            'status': 'normal',
            'confidence': 0.9
        }
    
    def _analyze_tightness(self, segmentation_mask):
        """分析安全带松紧度"""
        
        shoulder_belt = (segmentation_mask == 1)
        lap_belt = (segmentation_mask == 2)
        
        # 计算安全带宽度
        if shoulder_belt.sum() > 0:
            shoulder_width = self._compute_belt_width(shoulder_belt)
        else:
            shoulder_width = 0
        
        # 正常宽度范围
        normal_width_min = 30
        normal_width_max = 60
        
        # 判定松紧度
        if shoulder_width < normal_width_min:
            return {
                'status': 'too_tight',
                'confidence': 0.7
            }
        elif shoulder_width > normal_width_max:
            return {
                'status': 'too_loose',
                'confidence': 0.8
            }
        else:
            return {
                'status': 'normal',
                'confidence': 0.9
            }
    
    def _extract_skeleton(self, binary_mask):
        """提取骨架"""
        from skimage.morphology import skeletonize
        
        skeleton = skeletonize(binary_mask)
        points = np.argwhere(skeleton)
        
        return points
    
    def _fit_belt_path(self, skeleton_points):
        """拟合安全带路径"""
        # 使用RANSAC拟合直线
        from sklearn.linear_model import RANSACRegressor
        
        if len(skeleton_points) < 10:
            return None
        
        X = skeleton_points[:, 1].reshape(-1, 1)  # x坐标
        y = skeleton_points[:, 0]  # y坐标
        
        ransac = RANSACRegressor()
        ransac.fit(X, y)
        
        # 返回拟合直线参数
        return {
            'slope': ransac.estimator_.coef_[0],
            'intercept': ransac.estimator_.intercept_
        }
    
    def _point_to_line_distance(self, point, line_params):
        """计算点到直线距离"""
        if line_params is None:
            return float('inf')
        
        x, y = point
        slope = line_params['slope']
        intercept = line_params['intercept']
        
        # 点到直线距离
        distance = abs(slope * x - y + intercept) / np.sqrt(slope**2 + 1)
        
        return distance
    
    def _check_armpit_position(self, belt_path, left_shoulder, right_shoulder):
        """检查是否在腋下"""
        # 简化实现
        return 50  # 返回距离
    
    def _find_belt_center(self, lap_belt):
        """找到腰带中心Y坐标"""
        y_coords = np.where(lap_belt)[0]
        return y_coords.mean() if len(y_coords) > 0 else 0
    
    def _compute_belt_width(self, belt_mask):
        """计算安全带宽度"""
        # 沿着安全带方向计算宽度
        widths = []
        for col in range(belt_mask.shape[1]):
            col_pixels = np.where(belt_mask[:, col])[0]
            if len(col_pixels) > 1:
                width = col_pixels.max() - col_pixels.min()
                widths.append(width)
        
        return np.mean(widths) if widths else 0
    
    def _determine_misuse_type(self, shoulder_status, lap_status, tightness):
        """综合判定误用类型"""
        
        misuses = []
        
        if shoulder_status['status'] == 'behind_shoulder':
            misuses.append(('behind_shoulder', shoulder_status['confidence']))
        
        if shoulder_status['status'] == 'under_arm':
            misuses.append(('under_arm', shoulder_status['confidence']))
        
        if lap_status['status'] == 'too_high':
            misuses.append(('lap_belt_too_high', lap_status['confidence']))
        
        if tightness['status'] == 'too_loose':
            misuses.append(('belt_too_loose', tightness['confidence']))
        
        if tightness['status'] == 'too_tight':
            misuses.append(('belt_too_tight', tightness['confidence']))
        
        if not misuses:
            return [('normal', 0.95)]
        
        return misuses

3. 多传感器融合

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class SeatbeltMisuseDetector(nn.Module):
    """
    多传感器融合的安全带误用检测器
    """
    def __init__(self):
        super().__init__()
        
        # 视觉模态
        self.vision_encoder = VisionEncoder()
        
        # 传感器模态
        self.sensor_encoder = SensorEncoder()
        
        # 跨模态融合
        self.fusion = CrossModalFusion(
            vision_dim=256,
            sensor_dim=64,
            fusion_dim=128
        )
        
        # 误用分类器
        self.misuse_classifier = nn.Sequential(
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(64, 7)  # 6种误用 + 1种正常
        )
    
    def forward(self, image, sensor_data):
        """
        Args:
            image: (B, 3, H, W) RGB图像
            sensor_data: dict包含:
                - belt_tension: 安全带张力
                - seat_pressure: 座椅压力分布
                - buckle_status: buckle状态
        Returns:
            misuse_logits: (B, 7) 误用类别logits
        """
        # 视觉特征
        vision_features = self.vision_encoder(image)
        
        # 传感器特征
        sensor_features = self.sensor_encoder(sensor_data)
        
        # 融合
        fused_features = self.fusion(vision_features, sensor_features)
        
        # 分类
        misuse_logits = self.misuse_classifier(fused_features)
        
        return misuse_logits


class VisionEncoder(nn.Module):
    """视觉特征编码器"""
    def __init__(self):
        super().__init__()
        
        # 共享骨干
        self.backbone = ResNetEncoder(pretrained=True)
        
        # 安全带分割
        self.segmentation_head = SeatbeltSegmentationNet()
        
        # 人体姿态估计
        self.pose_head = PoseEstimationHead()
        
        # 特征融合
        self.fusion = nn.Conv2d(256 + 17, 256, 1)  # 分割 + 姿态
    
    def forward(self, image):
        # 分割
        seg_mask = self.segmentation_head(image)
        
        # 姿态
        keypoints = self.pose_head(image)
        
        # 融合
        combined = torch.cat([seg_mask, keypoints], dim=1)
        features = self.fusion(combined)
        
        # 全局池化
        features = F.adaptive_avg_pool2d(features, 1).squeeze()
        
        return features


class PoseEstimationHead(nn.Module):
    """人体姿态估计头"""
    def __init__(self, num_keypoints=17):
        super().__init__()
        
        # 简化的姿态估计网络
        self.conv = nn.Sequential(
            nn.Conv2d(2048, 256, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, num_keypoints, 1)
        )
    
    def forward(self, image):
        # 简化：返回热图
        return torch.randn(image.size(0), 17, image.size(2)//32, image.size(3)//32)


class SensorEncoder(nn.Module):
    """传感器数据编码器"""
    def __init__(self):
        super().__init__()
        
        # 张力编码
        self.tension_encoder = nn.Sequential(
            nn.Linear(10, 32),
            nn.ReLU()
        )
        
        # 压力分布编码
        self.pressure_encoder = nn.Sequential(
            nn.Conv2d(1, 16, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((4, 4)),
            nn.Flatten(),
            nn.Linear(16 * 4 * 4, 32)
        )
        
        # Buckle状态编码
        self.buckle_encoder = nn.Linear(3, 16)  # 左/右/后排
    
    def forward(self, sensor_data):
        """
        编码传感器数据
        """
        # 张力特征
        tension = sensor_data['belt_tension']  # (B, 10)
        tension_feat = self.tension_encoder(tension)
        
        # 压力特征
        pressure = sensor_data['seat_pressure']  # (B, 1, H, W)
        pressure_feat = self.pressure_encoder(pressure)
        
        # Buckle特征
        buckle = sensor_data['buckle_status']  # (B, 3)
        buckle_feat = self.buckle_encoder(buckle)
        
        # 拼接
        combined = torch.cat([tension_feat, pressure_feat, buckle_feat], dim=1)
        
        return combined


class CrossModalFusion(nn.Module):
    """跨模态融合"""
    def __init__(self, vision_dim, sensor_dim, fusion_dim):
        super().__init__()
        
        # 注意力融合
        self.attention = nn.MultiheadAttention(
            embed_dim=fusion_dim,
            num_heads=4
        )
        
        # 特征投影
        self.vision_proj = nn.Linear(vision_dim, fusion_dim)
        self.sensor_proj = nn.Linear(sensor_dim, fusion_dim)
        
        # 融合层
        self.fusion_layer = nn.Sequential(
            nn.Linear(fusion_dim * 2, fusion_dim),
            nn.ReLU()
        )
    
    def forward(self, vision_feat, sensor_feat):
        # 投影
        vision_proj = self.vision_proj(vision_feat).unsqueeze(1)
        sensor_proj = self.sensor_proj(sensor_feat).unsqueeze(1)
        
        # 注意力
        stacked = torch.cat([vision_proj, sensor_proj], dim=1)
        attended, _ = self.attention(stacked, stacked, stacked)
        
        # 融合
        fused = self.fusion_layer(attended.view(attended.size(0), -1))
        
        return fused

4. 训练与评估

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import torch.optim as optim
from torch.utils.data import DataLoader

class SeatbeltMisuseTrainer:
    """训练器"""
    
    def __init__(self, model, device):
        self.model = model.to(device)
        self.device = device
        
        self.criterion = nn.CrossEntropyLoss()
        self.optimizer = optim.AdamW(model.parameters(), lr=1e-4)
    
    def train_epoch(self, dataloader):
        """训练一个epoch"""
        self.model.train()
        total_loss = 0
        
        for batch in dataloader:
            image = batch['image'].to(self.device)
            sensor_data = {
                k: v.to(self.device) for k, v in batch['sensor'].items()
            }
            label = batch['label'].to(self.device)
            
            # 前向
            logits = self.model(image, sensor_data)
            loss = self.criterion(logits, label)
            
            # 反向
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()
            
            total_loss += loss.item()
        
        return total_loss / len(dataloader)
    
    def evaluate(self, dataloader):
        """评估"""
        self.model.eval()
        
        correct = 0
        total = 0
        
        with torch.no_grad():
            for batch in dataloader:
                image = batch['image'].to(self.device)
                sensor_data = {
                    k: v.to(self.device) for k, v in batch['sensor'].items()
                }
                label = batch['label'].to(self.device)
                
                logits = self.model(image, sensor_data)
                pred = logits.argmax(dim=1)
                
                correct += (pred == label).sum().item()
                total += label.size(0)
        
        return correct / total


def evaluate_per_class(model, dataloader, device, num_classes=7):
    """各类别评估"""
    model.eval()
    
    confusion_matrix = np.zeros((num_classes, num_classes), dtype=int)
    
    class_names = [
        'normal',
        'behind_shoulder',
        'under_arm',
        'lap_too_high',
        'too_loose',
        'too_tight',
        'child_misuse'
    ]
    
    with torch.no_grad():
        for batch in dataloader:
            image = batch['image'].to(device)
            sensor_data = {k: v.to(device) for k, v in batch['sensor'].items()}
            label = batch['label'].to(device)
            
            logits = model(image, sensor_data)
            pred = logits.argmax(dim=1)
            
            for true, pred_label in zip(label.cpu().numpy(), pred.cpu().numpy()):
                confusion_matrix[true, pred_label] += 1
    
    # 打印结果
    print("\nPer-Class Performance:")
    print("-" * 60)
    
    for i, name in enumerate(class_names):
        tp = confusion_matrix[i, i]
        fp = confusion_matrix[:, i].sum() - tp
        fn = confusion_matrix[i, :].sum() - tp
        
        precision = tp / (tp + fp + 1e-6)
        recall = tp / (tp + fn + 1e-6)
        f1 = 2 * precision * recall / (precision + recall + 1e-6)
        
        print(f"{name:20s} | Precision: {precision:.3f} | Recall: {recall:.3f} | F1: {f1:.3f}")
    
    print("-" * 60)

性能要求与测试

Euro NCAP测试场景

测试场景 数量 要求检出率 允许误报率
肩带身后 50 ≥95% ≤3%
肩带腋下 50 ≥95% ≤3%
安全带松弛 50 ≥90% ≤5%
腰带过高 50 ≥90% ≤5%
儿童误用 30 ≥98% ≤1%
多人共用 30 ≥95% ≤2%

实现难点与解决方案

难点 描述 解决方案
遮挡 安全带被手臂/衣物遮挡 多摄像头 + 传感器融合
光照 夜间/逆光条件 红外照明 + HDR
颜色 黑色安全带难识别 边缘检测 + 纹理特征
个体差异 体型/服装多样性 数据增强 + 合成数据

IMS开发启示

1. 系统集成建议

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# IMS集成代码示例
class IMSSeatbeltMonitor:
    """IMS安全带监测模块"""
    
    def __init__(self):
        self.detector = SeatbeltMisuseDetector()
        self.path_analyzer = SeatbeltPathAnalyzer()
    
    def monitor(self, camera_frame, sensor_data):
        """
        实时监测
        
        Returns:
            alert_level: 0=正常, 1=警告, 2=严重
            misuse_type: 误用类型
            confidence: 置信度
        """
        # 检测
        misuse_logits = self.detector(camera_frame, sensor_data)
        misuse_class = misuse_logits.argmax(dim=1).item()
        confidence = torch.softmax(misuse_logits, dim=1).max().item()
        
        # 分类映射
        if misuse_class == 0:  # 正常
            return 0, 'normal', confidence
        elif misuse_class in [1, 2, 4]:  # 严重误用
            return 2, self._get_misuse_name(misuse_class), confidence
        else:  # 轻度误用
            return 1, self._get_misuse_name(misuse_class), confidence
    
    def _get_misuse_name(self, class_id):
        names = {
            0: 'normal',
            1: 'behind_shoulder',
            2: 'under_arm',
            3: 'lap_too_high',
            4: 'too_loose',
            5: 'too_tight',
            6: 'child_misuse'
        }
        return names.get(class_id, 'unknown')

2. 部署优化

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# 模型量化
def quantize_model(model):
    """量化模型以适应嵌入式平台"""
    model.eval()
    
    # 动态量化
    quantized_model = torch.quantization.quantize_dynamic(
        model,
        {nn.Linear, nn.Conv2d},
        dtype=torch.qint8
    )
    
    return quantized_model

# TensorRT优化
def export_to_onnx(model, dummy_input, output_path):
    """导出ONNX用于TensorRT"""
    torch.onnx.export(
        model,
        dummy_input,
        output_path,
        opset_version=11,
        input_names=['image', 'sensor_data'],
        output_names=['misuse_logits'],
        dynamic_axes={
            'image': {0: 'batch_size'},
            'misuse_logits': {0: 'batch_size'}
        }
    )

作者: IMS技术团队
版本: v1.0
最后更新: 2026-06-12

法规解读 > 安全系统
#OMS #CPD #法规解读 #Euro-NCAP #安全带误用
Euro NCAP 2026安全带误用检测要求深度解读与实现方案
https://dapalm.com/2026/06/12/2026-06-12-euro-ncap-seatbelt-misuse-detection/
作者
Mars
发布于
2026年6月12日
许可协议