无响应驾驶员干预系统：DMS与ADAS协同方案

法规背景

Euro NCAP 2026要求

Unresponsive Driver Intervention (UDI)：当驾驶员失去响应能力时，系统必须能够安全停车。

场景	DMS检测	ADAS响应	时间要求
驾驶员昏迷	无眼动/无姿态变化	减速+停车	≤10秒
驾驶员睡眠	持续闭眼	警告→减速	≤15秒
驾驶员突发疾病	异常姿态	紧急停车	≤5秒

系统架构

DMS-ADAS协同流程

┌─────────────┐      ┌─────────────┐      ┌─────────────┐
│   DMS传感器  │──────→│  状态评估   │──────→│  ADAS决策   │
│  IR摄像头   │      │  意识检测   │      │  干预执行   │
└─────────────┘      └─────────────┘      └─────────────┘
                            ↑
                            │
                     ┌──────┴──────┐
                     │  车辆信号   │
                     │ 方向盘/踏板 │
                     └─────────────┘

核心算法

1. 无响应检测

import numpy as np
from typing import Dict, List, Tuple
from enum import Enum
from dataclasses import dataclass
import time

class DriverState(Enum):
    """驾驶员状态枚举"""
    NORMAL = "normal"
    DISTRACTED = "distracted"
    DROWSY = "drowsy"
    UNRESPONSIVE = "unresponsive"
    EMERGENCY = "emergency"


@dataclass
class DMSInput:
    """DMS输入数据"""
    timestamp: float
    eye_openness: float          # 0-1
    gaze_direction: Tuple[float, float]
    head_pose: Tuple[float, float, float]
    blink_rate: float            # bpm
    face_visible: bool
    hands_on_wheel: bool
    steering_torque: float       # Nm
    pedal_position: float        # 0-1


class UnresponsiveDriverDetector:
    """
    无响应驾驶员检测器
    
    多模态融合检测：
    1. 眼动检测
    2. 头部姿态
    3. 方向盘交互
    4. 踏板操作
    """
    
    def __init__(self):
        # 检测窗口
        self.window_size = 300  # 10秒@30fps
        self.history: List[DMSInput] = []
        
        # 阈值
        self.thresholds = {
            'eye_closure_duration': 3.0,   # 秒
            'no_gaze_change_duration': 5.0, # 秒
            'no_steering_duration': 8.0,    # 秒
            'no_pedal_duration': 10.0,      # 秒
            'head_drop_angle': 30,          # 度
        }
        
        # 状态追踪
        self.state = DriverState.NORMAL
        self.state_duration = 0.0
        
    def update(self, data: DMSInput) -> Tuple[DriverState, float]:
        """
        更新状态
        
        Args:
            data: DMS输入数据
        
        Returns:
            state: 驾驶员状态
            duration: 当前状态持续时间
        """
        # 缓存历史
        self.history.append(data)
        if len(self.history) > self.window_size:
            self.history.pop(0)
        
        # 检测各项指标
        eye_closure_time = self._detect_eye_closure()
        gaze_static_time = self._detect_static_gaze()
        no_interaction_time = self._detect_no_interaction()
        head_drop = self._detect_head_drop()
        
        # 状态判断（优先级：紧急 > 无响应 > 瞌睡 > 分心 > 正常）
        new_state = DriverState.NORMAL
        
        # 紧急情况：头部下垂 + 闭眼
        if head_drop and eye_closure_time > 2.0:
            new_state = DriverState.EMERGENCY
        
        # 无响应：长时间闭眼 或 多模态无响应
        elif (eye_closure_time > self.thresholds['eye_closure_duration'] or
              (no_interaction_time > 5.0 and gaze_static_time > 3.0)):
            new_state = DriverState.UNRESPONSIVE
        
        # 瞌睡：反复闭眼
        elif eye_closure_time > 1.5:
            new_state = DriverState.DROWSY
        
        # 分心：视线偏离
        elif gaze_static_time > 2.0 and not self._is_gaze_on_road(data.gaze_direction):
            new_state = DriverState.DISTRACTED
        
        # 更新状态持续时间
        if new_state == self.state:
            self.state_duration += 1/30  # 假设30fps
        else:
            self.state = new_state
            self.state_duration = 1/30
        
        return self.state, self.state_duration
    
    def _detect_eye_closure(self) -> float:
        """检测持续闭眼时间"""
        if len(self.history) < 10:
            return 0.0
        
        # 从最新帧往前数
        count = 0
        for data in reversed(self.history):
            if data.eye_openness < 0.2:  # 闭眼阈值
                count += 1
            else:
                break
        
        return count / 30  # 转换为秒
    
    def _detect_static_gaze(self) -> float:
        """检测视线静止时间"""
        if len(self.history) < 30:
            return 0.0
        
        recent = self.history[-30:]  # 最近1秒
        gaze_array = np.array([d.gaze_direction for d in recent])
        
        # 计算标准差
        std = np.std(gaze_array, axis=0)
        
        if np.max(std) < 0.05:  # 静止阈值
            return len(self.history) / 30  # 返回总静止时间
        
        return 0.0
    
    def _detect_no_interaction(self) -> float:
        """检测无交互时间"""
        if len(self.history) < 30:
            return 0.0
        
        # 检查方向盘和踏板
        count = 0
        for data in reversed(self.history):
            if not data.hands_on_wheel and data.pedal_position < 0.05:
                count += 1
            else:
                break
        
        return count / 30
    
    def _detect_head_drop(self) -> bool:
        """检测头部下垂"""
        if len(self.history) < 1:
            return False
        
        latest = self.history[-1]
        pitch = latest.head_pose[1]  # 俯仰角
        
        return abs(pitch) > self.thresholds['head_drop_angle']
    
    def _is_gaze_on_road(self, gaze: Tuple[float, float]) -> bool:
        """判断视线是否在道路上"""
        # 简化：前方区域
        x, y = gaze
        return -0.3 < x < 0.3 and -0.2 < y < 0.2


# ============ ADAS协同控制器 ============

class ADASInterventionController:
    """
    ADAS干预控制器
    
    根据DMS状态决定干预策略
    """
    
    def __init__(self):
        self.detector = UnresponsiveDriverDetector()
        
        # 干预策略
        self.strategies = {
            DriverState.NORMAL: self._no_intervention,
            DriverState.DISTRACTED: self._warn_driver,
            DriverState.DROWSY: self._alert_and_prepare,
            DriverState.UNRESPONSIVE: self._emergency_slowdown,
            DriverState.EMERGENCY: self._emergency_stop
        }
        
        # ADAS能力
        self.adas_capabilities = {
            'lka': True,      # 车道保持
            'acc': True,      # 自适应巡航
            'aeb': True,      # 紧急制动
            'hazards': True   # 危险警报灯
        }
        
    def process(self, dms_data: DMSInput, 
                vehicle_speed: float,
                lane_info: Dict) -> Dict:
        """
        处理DMS数据并决定干预
        
        Args:
            dms_data: DMS数据
            vehicle_speed: 车速 (m/s)
            lane_info: 车道信息
        
        Returns:
            intervention: 干预指令
        """
        # 更新状态
        state, duration = self.detector.update(dms_data)
        
        # 选择策略
        strategy = self.strategies[state]
        
        # 执行策略
        intervention = strategy(duration, vehicle_speed, lane_info)
        
        intervention['driver_state'] = state.value
        intervention['state_duration'] = duration
        
        return intervention
    
    def _no_intervention(self, duration: float, 
                         speed: float, lane: Dict) -> Dict:
        """无干预"""
        return {
            'action': 'none',
            'warning': None,
            'adas_control': None
        }
    
    def _warn_driver(self, duration: float, 
                     speed: float, lane: Dict) -> Dict:
        """警告驾驶员"""
        return {
            'action': 'warn',
            'warning': {
                'type': 'audio',
                'message': '请注意前方道路',
                'intensity': 'medium'
            },
            'adas_control': None
        }
    
    def _alert_and_prepare(self, duration: float, 
                           speed: float, lane: Dict) -> Dict:
        """警报并准备干预"""
        return {
            'action': 'alert_and_prepare',
            'warning': {
                'type': 'audio_visual',
                'message': '检测到疲劳，请休息',
                'intensity': 'high'
            },
            'adas_control': {
                'lka': 'active',
                'acc': 'increase_distance'
            }
        }
    
    def _emergency_slowdown(self, duration: float, 
                            speed: float, lane: Dict) -> Dict:
        """紧急减速"""
        return {
            'action': 'emergency_slowdown',
            'warning': {
                'type': 'audio_visual_haptic',
                'message': '驾驶员无响应，正在减速',
                'intensity': 'critical'
            },
            'adas_control': {
                'lka': 'active_strong',
                'acc': 'decelerate',
                'target_speed': max(0, speed - 10),
                'hazards': True
            }
        }
    
    def _emergency_stop(self, duration: float, 
                        speed: float, lane: Dict) -> Dict:
        """紧急停车"""
        return {
            'action': 'emergency_stop',
            'warning': {
                'type': 'all_channels',
                'message': '紧急停车！请联系救援',
                'intensity': 'critical'
            },
            'adas_control': {
                'lka': 'active_strong',
                'aeb': True,
                'target_speed': 0,
                'hazards': True,
                'unlock_doors': True,
                'call_emergency': True
            }
        }


# ============ 完整系统集成 ============

class DMStoADASBridge:
    """
    DMS到ADAS的完整集成桥
    
    处理：
    1. DMS数据采集
    2. 状态评估
    3. ADAS指令生成
    4. 安全检查
    """
    
    def __init__(self):
        self.controller = ADASInterventionController()
        self.last_intervention = None
        
    def process_frame(self, 
                      eye_openness: float,
                      gaze: Tuple[float, float],
                      head_pose: Tuple[float, float, float],
                      vehicle_signals: Dict) -> Dict:
        """
        处理单帧数据
        
        Args:
            eye_openness: 眼睛开度
            gaze: 视线方向
            head_pose: 头部姿态
            vehicle_signals: 车辆信号
        
        Returns:
            result: 处理结果
        """
        # 构造DMS输入
        dms_data = DMSInput(
            timestamp=time.time(),
            eye_openness=eye_openness,
            gaze_direction=gaze,
            head_pose=head_pose,
            blink_rate=vehicle_signals.get('blink_rate', 15),
            face_visible=True,
            hands_on_wheel=vehicle_signals.get('hands_on_wheel', True),
            steering_torque=vehicle_signals.get('steering_torque', 0),
            pedal_position=vehicle_signals.get('pedal_position', 0.5)
        )
        
        # 处理
        intervention = self.controller.process(
            dms_data,
            vehicle_signals.get('speed', 20),
            vehicle_signals.get('lane_info', {})
        )
        
        # 安全检查：防止指令跳变
        intervention = self._safety_filter(intervention)
        
        self.last_intervention = intervention
        
        return intervention
    
    def _safety_filter(self, intervention: Dict) -> Dict:
        """
        安全过滤器
        
        防止危险指令
        """
        action = intervention['action']
        
        # 紧急停车只能从减速状态进入
        if action == 'emergency_stop':
            if self.last_intervention and \
               self.last_intervention['action'] not in ['emergency_slowdown', 'emergency_stop']:
                # 降级为减速
                intervention['action'] = 'emergency_slowdown'
        
        return intervention


# ============ Euro NCAP测试场景 ============

def euro_ncap_udi_scenarios():
    """
    Euro NCAP 2026 UDI测试场景
    """
    scenarios = [
        {
            "id": "UDI-01",
            "name": "驾驶员突发昏迷",
            "test_sequence": [
                "正常驾驶30秒",
                "驾驶员闭眼、头部下垂",
                "手离开方向盘",
                "脚离开踏板"
            ],
            "expected_response": [
                "≤3秒：声音警告",
                "≤5秒：开始减速",
                "≤10秒：安全停车",
                "自动拨打紧急电话"
            ],
            "pass_criteria": "停车位置在车道内"
        },
        {
            "id": "UDI-02",
            "name": "驾驶员疲劳入睡",
            "test_sequence": [
                "正常驾驶30秒",
                "PERCLOS逐渐增加",
                "闭眼超过5秒"
            ],
            "expected_response": [
                "PERCLOS>30%：警告",
                "闭眼3秒：强烈警告",
                "闭眼5秒：减速"
            ],
            "pass_criteria": "检测延迟≤3秒"
        },
        {
            "id": "UDI-03",
            "name": "驾驶员突发疾病",
            "test_sequence": [
                "正常驾驶",
                "异常头部运动",
                "身体僵硬或抽搐"
            ],
            "expected_response": [
                "≤2秒：检测异常",
                "≤5秒：紧急停车"
            ],
            "pass_criteria": "识别异常姿态"
        }
    ]
    
    return scenarios


# ============ 实际测试 ============

if __name__ == "__main__":
    # 初始化
    bridge = DMStoADASBridge()
    
    print("=" * 60)
    print("无响应驾驶员干预系统测试")
    print("=" * 60)
    
    # 模拟正常驾驶
    print("\n场景1：正常驾驶")
    print("-" * 40)
    
    for i in range(10):
        result = bridge.process_frame(
            eye_openness=0.8,
            gaze=(0.1, 0.05),
            head_pose=(0, 5, 0),
            vehicle_signals={
                'speed': 25,
                'hands_on_wheel': True,
                'pedal_position': 0.5
            }
        )
    
    print(f"状态: {result['driver_state']}")
    print(f"动作: {result['action']}")
    
    # 模拟无响应
    print("\n场景2：驾驶员昏迷（模拟）")
    print("-" * 40)
    
    for i in range(150):  # 5秒@30fps
        result = bridge.process_frame(
            eye_openness=0.1,  # 闭眼
            gaze=(0, 0),       # 静止
            head_pose=(0, 35, 0),  # 头部下垂
            vehicle_signals={
                'speed': 25,
                'hands_on_wheel': False,
                'pedal_position': 0
            }
        )
        
        if i % 30 == 29:  # 每1秒打印
            print(f"[{i//30 + 1}秒] 状态: {result['driver_state']}, 动作: {result['action']}")
    
    # Euro NCAP测试场景
    print("\n" + "=" * 60)
    print("Euro NCAP 2026 UDI测试场景")
    print("=" * 60)
    
    scenarios = euro_ncap_udi_scenarios()
    for s in scenarios:
        print(f"\n{s['id']}: {s['name']}")
        print("测试序列:")
        for step in s['test_sequence']:
            print(f"  - {step}")
        print("预期响应:")
        for resp in s['expected_response']:
            print(f"  ✓ {resp}")
        print(f"通过标准: {s['pass_criteria']}")