Nature 2025：Transformer架构实时驾驶员疲劳检测新方法

论文来源： Nature Scientific Reports
期刊： Scientific Reports, May 20, 2025
核心创新： Transformer + IR摄像头 → Raspberry Pi 实时部署

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论文信息

项目	内容
标题	Real-time driver drowsiness detection using transformer architectures
期刊	Nature Scientific Reports
日期	May 20, 2025
链接	https://www.nature.com/articles/s41598-025-02111-x

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核心创新：边缘实时部署

硬件配置：

Raspberry Pi 3 Model B + IR摄像头
- 成本：< $50
- 功耗：< 5W
- 帧率：≥ 30fps
- 检测延迟：< 100ms

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Transformer 疲劳检测架构

import torch
import torch.nn as nn

class DrowsinessTransformer(nn.Module):
    """
    疲劳检测 Transformer
    
    Nature 2025 论文核心：
    用 Transformer 处理眼动序列
    
    输入：眼睑开度序列 (EAR)
    输出：疲劳状态分类
    """
    
    def __init__(self,
                 seq_len: int = 30,  # 30帧序列
                 d_model: int = 64,
                 nhead: int = 4,
                 num_layers: int = 2):
        super().__init__()
        
        # 输入嵌入（EAR值 → 向量）
        self.input_embedding = nn.Linear(1, d_model)
        
        # 位置编码
        self.pos_encoding = nn.Parameter(
            torch.randn(seq_len, d_model)
        )
        
        # Transformer 编码器
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=128,
            dropout=0.1,
            batch_first=True
        )
        self.transformer_encoder = nn.TransformerEncoder(
            encoder_layer,
            num_layers=num_layers
        )
        
        # 疲劳分类头
        self.classifier = nn.Sequential(
            nn.Linear(d_model, 32),
            nn.ReLU(inplace=True),
            nn.Linear(32, 3)  # 0:清醒, 1:轻度疲劳, 2:严重疲劳
        )
        
        self.seq_len = seq_len
    
    def forward(self, ear_sequence: torch.Tensor) -> torch.Tensor:
        """
        前向传播
        
        Args:
            ear_sequence: EAR序列, shape=(B, seq_len, 1)
        
        Returns:
            fatigue_class: 疲劳分类, shape=(B, 3)
        """
        # 嵌入
        x = self.input_embedding(ear_sequence)  # (B, seq_len, d_model)
        
        # 位置编码
        x = x + self.pos_encoding
        
        # Transformer编码
        encoded = self.transformer_encoder(x)  # (B, seq_len, d_model)
        
        # 全局平均池化
        pooled = encoded.mean(dim=1)  # (B, d_model)
        
        # 分类
        fatigue_logits = self.classifier(pooled)
        
        return fatigue_logits


# Raspberry Pi部署优化
class LightweightDrowsinessTransformer(nn.Module):
    """
    轻量化版本（Raspberry Pi部署）
    
    优化：
    - 减少层数
    - 减少维度
    - 量化友好
    """
    
    def __init__(self, seq_len=30):
        super().__init__()
        
        # 简化Transformer
        self.embedding = nn.Linear(1, 32)
        self.attention = nn.MultiheadAttention(32, 2, batch_first=True)
        self.fc = nn.Linear(32, 3)
    
    def forward(self, x):
        x = self.embedding(x)
        attn_out, _ = self.attention(x, x, x)
        pooled = attn_out.mean(dim=1)
        return self.fc(pooled)

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Euro NCAP 2026 F-01~F05疲劳场景对应

场景	Transformer检测指标	预期检测时间
F-01: PERCLOS ≥ 30%	EAR序列均值 < 0.7	≤ 5秒
F-02: 微睡眠 1.5秒	EAR < 0.2持续15帧	≤ 3秒
F-03: 眨眼频率异常	Transformer注意力权重异常	≤ 5秒
F-04: 闭眼 ≥ 2秒	EAR持续低值20帧	≤ 3秒
F-05: 综合疲劳指标	分类结果 ≥ 1	≤ 5秒

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IMS开发启示

• 🔴高：Transformer疲劳检测模型训练
• 🟡中：Raspberry Pi边缘部署优化
• 🟢低：量化加速（INT8）

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参考资料

1. Nature Scientific Reports 2025
2. Euro NCAP 2026 DSM Fatigue Scenarios