驾驶员状态估计综述：从疲劳到认知分心

综述范围： DMS核心检测算法与Euro NCAP 2026合规
参考文献： IEEE Sensors Journal 2024, Euro NCAP Protocol

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驾驶员状态分类

状态空间

graph TD
    A[驾驶员状态] --> B[正常]
    A --> C[异常]
    C --> D[疲劳]
    C --> E[分心]
    C --> F[损伤]
    D --> D1[轻度]
    D --> D2[重度]
    E --> E1[视觉分心]
    E --> E2[认知分心]
    E --> E3[操作分心]
    F --> F1[酒精损伤]
    F --> F2[其他损伤]

检测方法矩阵

状态	主要方法	准确率	成熟度
疲劳	PERCLOS + 时序建模	95%+	✅ 成熟
视觉分心	视线估计 + 偏离检测	92%+	✅ 成熟
认知分心	眼动熵 + 转向熵	85%	⚠️ 待突破
酒精损伤	多模态融合	90%+	⚠️ 新兴
安全带误用	关键点检测 + 形状建模	97%+	✅ 成熟

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疲劳检测

PERCLOS方法

class PERCLOSDetector:
    """
    PERCLOS疲劳检测
    
    PERCLOS = 闭眼时间占比（百分比）
    
    阈值：
    - < 30%: 正常
    - 30-50%: 轻度疲劳
    - > 50%: 重度疲劳
    """
    
    def __init__(self, window_sec: int = 60, fps: int = 30):
        self.window_frames = window_sec * fps
        self.eye_openness_buffer = []
        
    def add_frame(self, eye_openness: float):
        """
        Args:
            eye_openness: 眼睑开度（0-1）
        """
        self.eye_openness_buffer.append(eye_openness)
        if len(self.eye_openness_buffer) > self.window_frames:
            self.eye_openness_buffer.pop(0)
    
    def compute_perclos(self) -> float:
        """
        计算PERCLOS值
        
        Returns:
            perclos: 闭眼时间占比（%）
        """
        if len(self.eye_openness_buffer) < self.window_frames * 0.5:
            return 0.0
        
        threshold = 0.2  # 闭眼阈值
        closed_count = sum(1 for x in self.eye_openness_buffer if x < threshold)
        
        perclos = closed_count / len(self.eye_openness_buffer) * 100
        return perclos
    
    def detect_fatigue(self) -> dict:
        """检测疲劳状态"""
        perclos = self.compute_perclos()
        
        if perclos < 30:
            return {'level': '正常', 'perclos': perclos}
        elif perclos < 50:
            return {'level': '轻度疲劳', 'perclos': perclos, 'alert': True}
        else:
            return {'level': '重度疲劳', 'perclos': perclos, 'alert': True, 'level_id': 2}

---

视觉分心检测

视线偏离检测

class VisualDistractionDetector:
    """
    视觉分心检测
    
    基于：
    1. 视线落点（ROI）
    2. 偏离道路时间
    3. 偏离频率
    """
    
    # ROI定义（前挡风玻璃区域）
    ROAD_ROI = {
        'x_min': 0.3,
        'x_max': 0.7,
        'y_min': 0.2,
        'y_max': 0.6
    }
    
    def __init__(self, deviation_threshold_sec: float = 3.0):
        self.deviation_threshold = deviation_threshold_sec
        self.gaze_history = []
        
    def add_frame(self, gaze_x: float, gaze_y: float, timestamp: float):
        """
        添加视线数据
        
        Args:
            gaze_x, gaze_y: 归一化视线位置（0-1）
            timestamp: 时间戳
        """
        is_on_road = self._check_roi(gaze_x, gaze_y)
        
        self.gaze_history.append({
            'x': gaze_x,
            'y': gaze_y,
            'timestamp': timestamp,
            'on_road': is_on_road
        })
        
        # 保持最近10秒
        cutoff = timestamp - 10.0
        self.gaze_history = [g for g in self.gaze_history if g['timestamp'] > cutoff]
    
    def _check_roi(self, x: float, y: float) -> bool:
        """检查是否在道路ROI内"""
        return (
            self.ROAD_ROI['x_min'] <= x <= self.ROAD_ROI['x_max'] and
            self.ROAD_ROI['y_min'] <= y <= self.ROAD_ROI['y_max']
        )
    
    def detect_distraction(self, current_time: float) -> dict:
        """
        检测视觉分心
        
        Returns:
            result: 检测结果
        """
        # 连续偏离时间
        consecutive_offroad = 0
        
        for g in reversed(self.gaze_history):
            if not g['on_road']:
                consecutive_offroad += 1
            else:
                break
        
        # 偏离时长（秒）
        deviation_duration = consecutive_offroad / 30.0  # 假设30fps
        
        # 判断
        is_distracted = deviation_duration > self.deviation_threshold
        
        return {
            'is_distracted': is_distracted,
            'deviation_duration': deviation_duration,
            'alert_level': 1 if is_distracted else 0
        }

---

多状态融合

统一监控框架

class DriverStateMonitor:
    """
    驾驶员状态统一监控
    
    融合：
    1. 疲劳检测
    2. 视觉分心检测
    3. 认知分心检测
    4. 损伤检测
    """
    
    def __init__(self):
        self.fatigue_detector = PERCLOSDetector()
        self.visual_detector = VisualDistractionDetector()
        self.cognitive_detector = CognitiveDistractionDetector()
        
    def process_frame(self, frame_data: dict) -> dict:
        """
        处理单帧
        
        Args:
            frame_data: {
                'eye_openness': 眼睑开度,
                'gaze_x': 视线X,
                'gaze_y': 视线Y,
                'steering_angle': 转向角度,
                'timestamp': 时间戳
            }
            
        Returns:
            state: 综合状态评估
        """
        # 疲劳检测
        self.fatigue_detector.add_frame(frame_data['eye_openness'])
        fatigue_result = self.fatigue_detector.detect_fatigue()
        
        # 视觉分心检测
        self.visual_detector.add_frame(
            frame_data['gaze_x'],
            frame_data['gaze_y'],
            frame_data['timestamp']
        )
        visual_result = self.visual_detector.detect_distraction(frame_data['timestamp'])
        
        # 认知分心检测
        cognitive_result = self.cognitive_detector.process_frame(
            {
                'gaze_x': frame_data['gaze_x'],
                'gaze_y': frame_data['gaze_y'],
                'pupil_diameter': frame_data.get('pupil_diameter', 0),
                'blink': frame_data.get('blink', False)
            },
            frame_data['steering_angle']
        )
        
        # 综合判断
        alerts = []
        
        if fatigue_result.get('alert', False):
            alerts.append(('疲劳', fatigue_result['level']))
        
        if visual_result['is_distracted']:
            alerts.append(('视觉分心', f"偏离{visual_result['deviation_duration']:.1f}秒"))
        
        if cognitive_result.get('cognitive_distraction', False):
            alerts.append(('认知分心', f"置信度{cognitive_result['confidence']:.2f}"))
        
        return {
            'state': '异常' if alerts else '正常',
            'alerts': alerts,
            'fatigue': fatigue_result,
            'visual_distraction': visual_result,
            'cognitive_distraction': cognitive_result
        }

---

Euro NCAP 2026 合规检查清单

检测项	场景编号	要求	实现状态
疲劳检测	F-01~F-05	PERCLOS检测	✅ 已实现
视觉分心	D-01~D-08	视线偏离检测	✅ 已实现
手机使用	D-02/D-03	手持手机检测	✅ 已实现
认知分心	待定义	眼动熵/转向熵	⚠️ 优化中
安全带误用	SBR场景	关键点检测	✅ 已实现
酒驾检测	新增	多模态融合	⚠️ 开发中
CPD儿童检测	CPD-01~06	60GHz雷达	✅ 已实现

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参考资料

1. Euro NCAP Driver Monitoring Protocol 2026
2. IEEE Sensors Journal 2024, “Driver Distraction From the EEG Perspective”
3. NHTSA Driver Distraction Guidelines

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关键词： 驾驶员状态, DMS, 疲劳检测, 分心检测, Euro NCAP

发布时间： 2026-07-21

作者： OpenClaw AI Research