Nature 2025：多模态神经网络优化驾驶员疲劳检测方法

论文来源： Nature Scientific Reports
期刊： Scientific Reports, April 10, 2025
核心创新： EAR + PERCLOS + MAR + LSTM → 多模态融合

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论文信息

项目	内容
标题	Optimized driver fatigue detection method using multimodal neural networks
期刊	Nature Scientific Reports
日期	April 10, 2025

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多模态特征融合

import torch
import torch.nn as nn

class MultimodalFatigueNet(nn.Module):
    """
    多模态疲劳检测网络
    
    Nature 2025 论文核心：
    眼部 + 口部 + 头部特征融合
    
    输入：
    - EAR (Eye Aspect Ratio)
    - PERCLOS (Percentage of Eyelid Closure)
    - MAR (Mouth Aspect Ratio)
    - Head Movement
    """
    
    def __init__(self):
        super().__init__()
        
        # 眼部特征编码器
        self.eye_encoder = nn.Sequential(
            nn.Linear(3, 32),  # EAR, PERCLOS, blink_freq
            nn.ReLU(inplace=True)
        )
        
        # 口部特征编码器
        self.mouth_encoder = nn.Sequential(
            nn.Linear(2, 16),  # MAR, yawn_freq
            nn.ReLU(inplace=True)
        )
        
        # 头部特征编码器
        self.head_encoder = nn.Sequential(
            nn.Linear(3, 16),  # pitch, yaw, roll变化率
            nn.ReLU(inplace=True)
        )
        
        # LSTM时序建模
        self.lstm = nn.LSTM(64, 32, batch_first=True)
        
        # 疲劳分类头
        self.classifier = nn.Linear(32, 4)  # 清醒/轻度/中度/严重
    
    def forward(self, features_seq):
        """
        features_seq: (B, seq_len, 8) 包含所有特征
        """
        # 分离特征
        eye_feat = features_seq[:, :, :3]
        mouth_feat = features_seq[:, :, 3:5]
        head_feat = features_seq[:, :, 5:8]
        
        # 编码
        eye_encoded = self.eye_encoder(eye_feat)
        mouth_encoded = self.mouth_encoder(mouth_feat)
        head_encoded = self.head_encoder(head_feat)
        
        # 融合
        fused = torch.cat([eye_encoded, mouth_encoded, head_encoded], dim=2)
        
        # LSTM时序建模
        lstm_out, _ = self.lstm(fused)
        
        # 分类
        fatigue_class = self.classifier(lstm_out[:, -1, :])
        
        return fatigue_class

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Euro NCAP 2026 疲劳检测指标映射

论文指标	Euro NCAP对应场景	检测阈值
PERCLOS	F-01	≥ 30%
EAR平均值	F-02 微睡眠	< 0.2持续≥15帧
MAR（打哈欠）	补充指标	> 0.6
头部运动变化率	姿态异常	> 15°/s

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IMS开发启示

• 🔴高：实现 EAR/PERCLOS/MAR 计算
• 🟡中：多模态特征融合训练
• 🟢低：时序窗口优化

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参考资料

1. Nature Scientific Reports 2025