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Edge-VisionGuard：轻量信号处理+AI框架驾驶员状态与低能见度危险检测

论文信息

• 标题： Edge-VisionGuard: A Lightweight Signal-Processing and AI Framework for Driver State and Low-Visibility Hazard Detection
• 期刊： Applied Sciences (MDPI), Vol 16, Issue 2, 2026
• 链接： https://www.mdpi.com/2076-3417/16/2/1037

核心创新

Edge-VisionGuard 提出了一个双功能轻量框架：同时处理驾驶员状态监测和低能见度道路危险检测。不同于传统DMS只关注座舱内，本文将驾驶员状态与道路环境关联，实现”驾驶员-环境”协同感知。

三大特点

1. 双任务一体：驾驶员状态 + 道路能见度
2. 信号处理+AI混合：传统DSP + 深度学习
3. 边缘部署优化：低功耗实时推理

方法详解

1. 系统架构

graph TB
    A[摄像头输入] --> B[信号处理层]
    B --> C[驾驶员状态]
    B --> D[道路能见度]
    
    C --> E[面部检测]
    C --> F[PERCLOS计算]
    C --> G[行为分类]
    
    D --> H[对比度分析]
    D --> I[雾/雨检测]
    D --> J[能见度等级]
    
    E --> K[融合层]
    F --> K
    G --> K
    H --> K
    I --> K
    J --> K
    
    K --> L[风险评分]
    L --> M[分级警报]

2. 核心代码

"""
Edge-VisionGuard 轻量框架
驾驶员状态 + 低能见度危险检测

混合架构: 信号处理(DSP) + AI分类
部署: 边缘设备 (RPi/Jetson)
"""

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Dict
from dataclasses import dataclass


@dataclass
class DriverState:
    """驾驶员状态"""
    drowsiness_score: float  # 0-1
    distraction_type: str
    eye_openness: float
    head_pose: Tuple[float, float, float]
    behavior: str


@dataclass 
class RoadVisibility:
    """道路能见度"""
    visibility_level: str  # clear/fog/rain/snow/dark
    contrast_ratio: float
    brightness: float
    risk_score: float  # 0-1


@dataclass
class FusedRisk:
    """融合风险评分"""
    driver_risk: float
    road_risk: float
    combined_risk: float
    alert_level: int
    recommendations: list


class SignalProcessingLayer:
    """信号处理层 (传统DSP)"""
    
    def __init__(self):
        self.perplos_window = 60  # 60秒窗口
        self.eye_threshold = 0.3
    
    def compute_perclos(self, eye_openness_series: np.ndarray) -> float:
        """
        PERCLOS: 眼睑闭合度百分比
        
        Args:
            eye_openness_series: (N,) 眼部开度序列, 0-1
        
        Returns:
            perclos: 0-100%
        """
        closed_frames = np.sum(eye_openness_series < self.eye_threshold)
        perclos = closed_frames / len(eye_openness_series) * 100
        
        # 分级
        if perclos < 15:
            return perclos  # 正常
        elif perclos < 30:
            return perclos  # 轻度疲劳
        elif perclos < 50:
            return perclos  # 中度疲劳
        else:
            return perclos  # 重度疲劳
    
    def compute_contrast(self, image: np.ndarray) -> float:
        """计算图像对比度 (能见度指标)"""
        gray = np.mean(image, axis=2) if len(image.shape) == 3 else image
        contrast = np.std(gray.astype(np.float32)) / 255.0
        return contrast
    
    def compute_brightness(self, image: np.ndarray) -> float:
        """计算图像亮度"""
        return np.mean(image)
    
    def detect_fog(self, image: np.ndarray) -> float:
        """雾检测 (基于频率域)"""
        gray = np.mean(image, axis=2) if len(image.shape) == 3 else image
        # FFT
        fft = np.fft.fft2(gray)
        fft_shift = np.fft.fftshift(fft)
        magnitude = np.abs(fft_shift)
        
        # 高频能量比 (低→可能有雾)
        h, w = magnitude.shape
        center_h, center_w = h // 2, w // 2
        low_freq = magnitude[
            center_h-10:center_h+10, center_w-10:center_w+10
        ].sum()
        total = magnitude.sum()
        
        high_freq_ratio = 1 - (low_freq / total)
        
        # 雾天高频能量低
        fog_score = 1 - high_freq_ratio
        return fog_score


class DriverStateDetector:
    """驾驶员状态检测 (AI)"""
    
    def __init__(self):
        # 轻量行为分类器
        self.behavior_classifier = self._build_classifier()
        self.eye_history = []
    
    def _build_classifier(self):
        """轻量分类模型"""
        return nn.Sequential(
            nn.Conv2d(3, 32, 3, stride=2, padding=1),
            nn.BatchNorm2d(32), nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, 3, stride=2, padding=1),
            nn.BatchNorm2d(64), nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, 3, stride=2, padding=1),
            nn.BatchNorm2d(128), nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d(1),
            nn.Flatten(),
            nn.Linear(128, 64),
            nn.ReLU(inplace=True),
            nn.Linear(64, 5),  # normal/phone/eating/drowsy/talking
        )
    
    def detect(self, frame: np.ndarray) -> DriverState:
        """检测驾驶员状态"""
        # 简化: 模拟检测
        eye_openness = np.random.uniform(0.3, 1.0)
        self.eye_history.append(eye_openness)
        if len(self.eye_history) > 300:  # 10s@30fps
            self.eye_history.pop(0)
        
        return DriverState(
            drowsiness_score=1 - eye_openness,
            distraction_type='none',
            eye_openness=eye_openness,
            head_pose=(0, 0, 0),
            behavior='normal_driving'
        )


class RoadVisibilityDetector:
    """道路能见度检测"""
    
    def __init__(self):
        self.dsp = SignalProcessingLayer()
    
    def detect(self, frame: np.ndarray) -> RoadVisibility:
        """检测道路能见度"""
        contrast = self.dsp.compute_contrast(frame)
        brightness = self.dsp.compute_brightness(frame)
        fog_score = self.dsp.detect_fog(frame)
        
        # 分级
        if contrast > 0.3 and brightness > 80:
            level = 'clear'
            risk = 0.1
        elif fog_score > 0.7:
            level = 'fog'
            risk = 0.7
        elif brightness < 30:
            level = 'dark'
            risk = 0.5
        elif contrast < 0.15:
            level = 'rain'
            risk = 0.6
        else:
            level = 'cloudy'
            risk = 0.3
        
        return RoadVisibility(
            visibility_level=level,
            contrast_ratio=contrast,
            brightness=brightness,
            risk_score=risk
        )


class EdgeVisionGuard:
    """Edge-VisionGuard 完整系统"""
    
    def __init__(self):
        self.driver_detector = DriverStateDetector()
        self.road_detector = RoadVisibilityDetector()
        self.dsp = SignalProcessingLayer()
        
    def process(self, frame: np.ndarray) -> FusedRisk:
        """处理单帧"""
        # 驾驶员状态
        driver_state = self.driver_detector.detect(frame)
        
        # 道路能见度
        road_vis = self.road_detector.detect(frame)
        
        # PERCLOS
        if len(self.driver_detector.eye_history) > 10:
            perclos = self.dsp.compute_perclos(
                np.array(self.driver_detector.eye_history)
            )
        else:
            perclos = 0
        
        # 融合风险评分
        driver_risk = max(
            driver_state.drowsiness_score,
            perclos / 100
        )
        
        road_risk = road_vis.risk_score
        
        # 协同风险: 疲劳+低能见度 = 放大风险
        if driver_risk > 0.5 and road_risk > 0.5:
            combined = min(driver_risk + road_risk, 1.0) * 1.2
        else:
            combined = (driver_risk + road_risk) / 2
        combined = min(combined, 1.0)
        
        # 警报等级
        if combined > 0.8:
            level = 3
            recs = ['立即停车休息', '开启双闪', '减速行驶']
        elif combined > 0.6:
            level = 2
            recs = ['建议休息', '提高注意力', '减速']
        elif combined > 0.3:
            level = 1
            recs = ['注意路况', '保持警觉']
        else:
            level = 0
            recs = []
        
        return FusedRisk(
            driver_risk=driver_risk,
            road_risk=road_risk,
            combined_risk=combined,
            alert_level=level,
            recommendations=recs
        )


# ============ 测试 ============

if __name__ == "__main__":
    print("=" * 60)
    print("Edge-VisionGuard 测试")
    print("=" * 60)
    
    system = EdgeVisionGuard()
    
    scenarios = [
        ('正常白天驾驶', np.random.randint(100, 200, (480, 640, 3))),
        ('夜间疲劳+隧道', np.random.randint(20, 50, (480, 640, 3))),
        ('雾天驾驶', np.random.randint(150, 180, (480, 640, 3))),
        ('雨天驾驶', np.random.randint(60, 120, (480, 640, 3))),
    ]
    
    for name, frame in scenarios:
        risk = system.process(frame)
        print(f"\n{name}:")
        print(f"  驾驶员风险: {risk.driver_risk:.2f}")
        print(f"  道路风险: {risk.road_risk:.2f}")
        print(f"  综合风险: {risk.combined_risk:.2f}")
        print(f"  警报等级: L{risk.alert_level}")
        if risk.recommendations:
            print(f"  建议: {', '.join(risk.recommendations)}")

3. 协同风险模型

驾驶员状态	道路状态	放大系数	综合风险
清醒(0.2)	晴朗(0.1)	1.0	0.15
疲劳(0.6)	晴朗(0.1)	1.0	0.35
清醒(0.2)	雾天(0.7)	1.0	0.45
疲劳(0.6)	雾天(0.7)	1.2	0.78
重度疲劳(0.8)	暴雨(0.8)	1.3	1.0

IMS 开发启示

1. “驾驶员-环境”协同的价值

• 传统DMS只看驾驶员，忽略了环境因素
• 疲劳+低能见度的协同风险放大效应
• 可作为ADAS-DMS联动的决策依据

2. 信号处理+AI混合架构

组件	方法	优势
PERCLOS	DSP(阈值统计)	可解释/低计算
行为分类	AI(CNN)	高精度
雾检测	DSP(FFT)	无需训练
风险融合	规则+加权	透明可调

3. 部署优势

• 信号处理部分不需要GPU
• AI部分模型极轻量(128维特征)
• 适合低成本MCU+AI加速器方案

总结

Edge-VisionGuard的创新在于将驾驶员状态和道路环境统一在一个轻量框架中：

1. 双任务：DMS + 环境感知一体
2. 混合架构：DSP + AI 各取所长
3. 协同风险：疲劳+低能见度放大效应
4. 边缘友好：低功耗/低延迟

对 IMS 的价值：

• “驾驶员-环境”协同风险模型可直接采用
• 信号处理部分可作为ADAS前处理
• 雾检测算法可集成到环境感知模块