DMS-ADAS协同：无响应驾驶员干预机制设计

Euro NCAP 2026核心要求

无响应驾驶员检测与干预是Euro NCAP 2026协议的重大升级。当系统检测到驾驶员无响应时，必须采取分级干预措施，最终可实现安全停车。

干预等级定义

等级	条件	干预措施	时间窗口
L0	驾驶员正常	无干预	-
L1	轻度疲劳/分心	视觉警告	即时
L2	中度疲劳/分心	视觉+听觉警告	≤5秒
L3	严重疲劳/分心	触觉警告+ADAS增强	≤10秒
L4	无响应	安全停车	≤30秒

系统架构

1. DMS-ADAS协同框架

from enum import Enum
from dataclasses import dataclass
from typing import Dict, Optional
import numpy as np

class DriverState(Enum):
    """驾驶员状态"""
    NORMAL = "normal"
    DISTRACTED_LIGHT = "distracted_light"
    DISTRACTED_SEVERE = "distracted_severe"
    FATIGUED_LIGHT = "fatigued_light"
    FATIGUED_SEVERE = "fatigued_severe"
    UNRESPONSIVE = "unresponsive"
    EMERGENCY = "emergency"


class InterventionLevel(Enum):
    """干预等级"""
    NONE = 0
    VISUAL_WARNING = 1
    AUDIBLE_WARNING = 2
    HAPTIC_WARNING = 3
    ADAS_ENHANCED = 4
    CONTROLLED_STOP = 5


@dataclass
class DMSOutput:
    """DMS输出"""
    driver_state: DriverState
    confidence: float
    fatigue_score: float
    distraction_score: float
    responsiveness_score: float
    eye_gaze: tuple
    head_pose: dict
    timestamp: float


@dataclass
class ADASCommand:
    """ADAS命令"""
    increase_sensitivity: bool
    enable_emergency_lane_keeping: bool
    enable_auto_braking: bool
    reduce_speed: bool
    controlled_stop: bool
    hazard_lights: bool
    ecall_trigger: bool


class DMSADASIntegration:
    """
    DMS-ADAS集成系统
    
    Euro NCAP 2026要求：
    - DMS检测驾驶员状态
    - ADAS根据状态调整策略
    - 无响应时实现安全停车
    """
    
    # 状态转换阈值
    THRESHOLDS = {
        'fatigue_light': 0.4,
        'fatigue_severe': 0.7,
        'distraction_light': 0.3,
        'distraction_severe': 0.6,
        'unresponsive': 0.9
    }
    
    # 干预时间窗口 (秒)
    TIME_WINDOWS = {
        InterventionLevel.VISUAL_WARNING: 2,
        InterventionLevel.AUDIBLE_WARNING: 5,
        InterventionLevel.HAPTIC_WARNING: 10,
        InterventionLevel.ADAS_ENHANCED: 15,
        InterventionLevel.CONTROLLED_STOP: 30
    }
    
    def __init__(self):
        self.dms = DriverMonitoringSystem()
        self.adas = ADASController()
        self.hmi = HMIController()
        
        # 状态追踪
        self.current_state = DriverState.NORMAL
        self.current_level = InterventionLevel.NONE
        self.state_duration = 0.0
        self.last_intervention_time = 0.0
    
    def process(self, vehicle_data: dict, dt: float) -> dict:
        """
        处理循环
        
        Args:
            vehicle_data: 车辆数据（速度、车道位置等）
            dt: 时间步长 (秒)
            
        Returns:
            {state, level, adas_command, hmi_command}
        """
        # 1. DMS检测
        dms_output = self.dms.detect()
        
        # 2. 状态判断
        new_state = self._determine_state(dms_output)
        
        # 3. 更新状态追踪
        if new_state == self.current_state:
            self.state_duration += dt
        else:
            self.current_state = new_state
            self.state_duration = dt
        
        # 4. 确定干预等级
        new_level = self._determine_intervention_level()
        
        # 5. 生成干预命令
        adas_command = self._generate_adas_command(new_level)
        hmi_command = self._generate_hmi_command(new_level)
        
        # 6. 执行干预
        self.adas.execute(adas_command)
        self.hmi.execute(hmi_command)
        
        # 7. 更新等级
        if new_level != self.current_level:
            self.current_level = new_level
            self.last_intervention_time = 0.0
        else:
            self.last_intervention_time += dt
        
        return {
            'state': self.current_state,
            'level': self.current_level,
            'state_duration': self.state_duration,
            'adas_command': adas_command,
            'hmi_command': hmi_command
        }
    
    def _determine_state(self, dms_output: DMSOutput) -> DriverState:
        """确定驾驶员状态"""
        fatigue = dms_output.fatigue_score
        distraction = dms_output.distraction_score
        responsiveness = dms_output.responsiveness_score
        
        # 优先判断无响应
        if responsiveness > self.THRESHOLDS['unresponsive']:
            return DriverState.UNRESPONSIVE
        
        # 疲劳判断
        if fatigue > self.THRESHOLDS['fatigue_severe']:
            return DriverState.FATIGUED_SEVERE
        elif fatigue > self.THRESHOLDS['fatigue_light']:
            return DriverState.FATIGUED_LIGHT
        
        # 分心判断
        if distraction > self.THRESHOLDS['distraction_severe']:
            return DriverState.DISTRACTED_SEVERE
        elif distraction > self.THRESHOLDS['distraction_light']:
            return DriverState.DISTRACTED_LIGHT
        
        return DriverState.NORMAL
    
    def _determine_intervention_level(self) -> InterventionLevel:
        """确定干预等级"""
        # 状态到干预等级的映射
        STATE_LEVEL_MAP = {
            DriverState.NORMAL: InterventionLevel.NONE,
            DriverState.DISTRACTED_LIGHT: InterventionLevel.VISUAL_WARNING,
            DriverState.DISTRACTED_SEVERE: InterventionLevel.AUDIBLE_WARNING,
            DriverState.FATIGUED_LIGHT: InterventionLevel.VISUAL_WARNING,
            DriverState.FATIGUED_SEVERE: InterventionLevel.HAPTIC_WARNING,
            DriverState.UNRESPONSIVE: InterventionLevel.CONTROLLED_STOP
        }
        
        base_level = STATE_LEVEL_MAP.get(self.current_state, InterventionLevel.NONE)
        
        # 根据持续时间升级
        time_window = self.TIME_WINDOWS.get(base_level, float('inf'))
        
        if self.state_duration > time_window:
            # 升级到下一级
            if base_level.value < InterventionLevel.CONTROLLED_STOP.value:
                return InterventionLevel(base_level.value + 1)
        
        return base_level
    
    def _generate_adas_command(self, level: InterventionLevel) -> ADASCommand:
        """生成ADAS命令"""
        if level == InterventionLevel.NONE:
            return ADASCommand(
                increase_sensitivity=False,
                enable_emergency_lane_keeping=False,
                enable_auto_braking=False,
                reduce_speed=False,
                controlled_stop=False,
                hazard_lights=False,
                ecall_trigger=False
            )
        
        elif level == InterventionLevel.ADAS_ENHANCED:
            return ADASCommand(
                increase_sensitivity=True,
                enable_emergency_lane_keeping=True,
                enable_auto_braking=True,
                reduce_speed=True,
                controlled_stop=False,
                hazard_lights=False,
                ecall_trigger=False
            )
        
        elif level == InterventionLevel.CONTROLLED_STOP:
            return ADASCommand(
                increase_sensitivity=True,
                enable_emergency_lane_keeping=True,
                enable_auto_braking=True,
                reduce_speed=True,
                controlled_stop=True,
                hazard_lights=True,
                ecall_trigger=True
            )
        
        else:
            return ADASCommand(
                increase_sensitivity=False,
                enable_emergency_lane_keeping=False,
                enable_auto_braking=False,
                reduce_speed=False,
                controlled_stop=False,
                hazard_lights=False,
                ecall_trigger=False
            )
    
    def _generate_hmi_command(self, level: InterventionLevel) -> dict:
        """生成HMI命令"""
        HMI_COMMANDS = {
            InterventionLevel.NONE: {
                'display_message': None,
                'audio_alert': None,
                'seat_vibration': False
            },
            InterventionLevel.VISUAL_WARNING: {
                'display_message': "请注意驾驶",
                'audio_alert': None,
                'seat_vibration': False
            },
            InterventionLevel.AUDIBLE_WARNING: {
                'display_message': "警告：检测到疲劳/分心",
                'audio_alert': "fatigue_warning.wav",
                'seat_vibration': False
            },
            InterventionLevel.HAPTIC_WARNING: {
                'display_message': "严重警告：请立即休息",
                'audio_alert': "severe_warning.wav",
                'seat_vibration': True
            },
            InterventionLevel.ADAS_ENHANCED: {
                'display_message': "系统已增强安全辅助",
                'audio_alert': "adas_enhanced.wav",
                'seat_vibration': True
            },
            InterventionLevel.CONTROLLED_STOP: {
                'display_message': "紧急停车：驾驶员无响应",
                'audio_alert': "emergency_stop.wav",
                'seat_vibration': True
            }
        }
        
        return HMI_COMMANDS.get(level, HMI_COMMANDS[InterventionLevel.NONE])

2. 无响应检测

class UnresponsiveDriverDetector:
    """
    无响应驾驶员检测器
    
    检测方法：
    1. 眼动无响应（长时间闭眼/固定视线）
    2. 头部姿态异常（下垂/后仰）
    3. 无方向盘输入
    4. 无踏板输入
    5. 对警告无反应
    """
    
    def __init__(self):
        self.eye_tracker = EyeTracker()
        self.steering_monitor = SteeringMonitor()
        self.pedal_monitor = PedalMonitor()
        
        # 阈值
        self.EYE_CLOSURE_THRESHOLD_SEC = 5.0
        self.NO_INPUT_THRESHOLD_SEC = 10.0
        self.NO_RESPONSE_THRESHOLD_SEC = 15.0
    
    def detect(self, dms_output: DMSOutput, vehicle_data: dict) -> dict:
        """
        检测无响应
        
        Returns:
            {
                'is_unresponsive': bool,
                'indicators': list,
                'confidence': float,
                'recommended_action': str
            }
        """
        indicators = []
        
        # 1. 眼动检测
        eye_indicator = self._check_eye_response(dms_output)
        if eye_indicator:
            indicators.append(eye_indicator)
        
        # 2. 头部姿态检测
        head_indicator = self._check_head_pose(dms_output)
        if head_indicator:
            indicators.append(head_indicator)
        
        # 3. 方向盘输入检测
        steering_indicator = self._check_steering_input(vehicle_data)
        if steering_indicator:
            indicators.append(steering_indicator)
        
        # 4. 踏板输入检测
        pedal_indicator = self._check_pedal_input(vehicle_data)
        if pedal_indicator:
            indicators.append(pedal_indicator)
        
        # 综合判断
        is_unresponsive = len(indicators) >= 2
        confidence = min(len(indicators) / 3.0, 1.0)
        
        return {
            'is_unresponsive': is_unresponsive,
            'indicators': indicators,
            'confidence': confidence,
            'recommended_action': 'controlled_stop' if is_unresponsive else 'monitor'
        }
    
    def _check_eye_response(self, dms_output: DMSOutput) -> Optional[str]:
        """检查眼动响应"""
        # 长时间闭眼
        if dms_output.fatigue_score > 0.8:
            return 'prolonged_eye_closure'
        
        # 视线固定不动
        gaze_history = self.eye_tracker.get_gaze_history()
        if len(gaze_history) > 100:  # ~3秒历史
            gaze_variance = np.var(gaze_history, axis=0)
            if np.mean(gaze_variance) < 0.01:  # 视线几乎不动
                return 'fixed_gaze'
        
        return None
    
    def _check_head_pose(self, dms_output: DMSOutput) -> Optional[str]:
        """检查头部姿态"""
        pitch = dms_output.head_pose.get('pitch', 0)
        
        # 头部下垂
        if pitch > 20:  # 下垂超过20度
            return 'head_drooping'
        
        # 头部后仰（可能失去意识）
        if pitch < -15:
            return 'head_tilted_back'
        
        return None
    
    def _check_steering_input(self, vehicle_data: dict) -> Optional[str]:
        """检查方向盘输入"""
        last_input_time = vehicle_data.get('last_steering_input_time', 0)
        current_time = vehicle_data.get('timestamp', 0)
        
        time_since_input = current_time - last_input_time
        
        if time_since_input > self.NO_INPUT_THRESHOLD_SEC:
            return 'no_steering_input'
        
        return None
    
    def _check_pedal_input(self, vehicle_data: dict) -> Optional[str]:
        """检查踏板输入"""
        last_input_time = vehicle_data.get('last_pedal_input_time', 0)
        current_time = vehicle_data.get('timestamp', 0)
        
        time_since_input = current_time - last_input_time
        
        if time_since_input > self.NO_INPUT_THRESHOLD_SEC:
            return 'no_pedal_input'
        
        return None


class EyeTracker:
    """眼动追踪器"""
    
    def __init__(self):
        self.gaze_history = []
        self.max_history = 300  # 10秒 @ 30fps
    
    def update(self, gaze: tuple):
        """更新视线历史"""
        self.gaze_history.append(gaze)
        if len(self.gaze_history) > self.max_history:
            self.gaze_history.pop(0)
    
    def get_gaze_history(self) -> list:
        return self.gaze_history


class SteeringMonitor:
    """方向盘监控"""
    pass


class PedalMonitor:
    """踏板监控"""
    pass

3. 安全停车控制

class ControlledStopController:
    """
    安全停车控制器
    
    Euro NCAP要求：
    - 逐步减速
    - 保持车道
    - 开启危险报警灯
    - 触发eCall
    """
    
    def __init__(self):
        self.target_speed = 0
        self.deceleration_rate = 2.0  # m/s²
        self.lane_keeping_enabled = True
        self.hazard_lights_on = False
        self.ecall_triggered = False
    
    def execute_controlled_stop(self, vehicle_state: dict) -> dict:
        """
        执行安全停车
        
        Args:
            vehicle_state: {speed, lane_position, ...}
            
        Returns:
            {control_commands, status}
        """
        current_speed = vehicle_state.get('speed', 0)  # m/s
        
        # 计算减速目标
        if current_speed > 0:
            # 计算停车距离
            stopping_distance = current_speed ** 2 / (2 * self.deceleration_rate)
            
            # 生成减速曲线
            target_speed = max(0, current_speed - self.deceleration_rate * 0.1)
            self.target_speed = target_speed
        
        # 车道保持
        lane_keeping_command = self._generate_lane_keeping_command(vehicle_state)
        
        # 开启危险报警灯
        if not self.hazard_lights_on:
            self.hazard_lights_on = True
        
        # 触发eCall
        if not self.ecall_triggered and current_speed < 1.0:
            self.ecall_triggered = True
        
        return {
            'control_commands': {
                'target_speed': self.target_speed,
                'deceleration': self.deceleration_rate,
                'lane_keeping': lane_keeping_command,
                'hazard_lights': self.hazard_lights_on,
                'ecall': self.ecall_triggered
            },
            'status': {
                'phase': self._determine_phase(current_speed),
                'stopping_distance': stopping_distance if current_speed > 0 else 0
            }
        }
    
    def _generate_lane_keeping_command(self, vehicle_state: dict) -> dict:
        """生成车道保持命令"""
        lane_position = vehicle_state.get('lane_position', 0)  # -1左偏, 0居中, 1右偏
        
        # 简单的比例控制
        steering_correction = -lane_position * 0.5
        
        return {
            'enabled': self.lane_keeping_enabled,
            'steering_correction': steering_correction
        }
    
    def _determine_phase(self, current_speed: float) -> str:
        """确定停车阶段"""
        if current_speed > 10:
            return 'decelerating'
        elif current_speed > 1:
            return 'low_speed_approach'
        else:
            return 'stopped'


class ADASController:
    """ADAS控制器"""
    
    def __init__(self):
        self.controlled_stop = ControlledStopController()
    
    def execute(self, command: ADASCommand):
        """执行ADAS命令"""
        if command.controlled_stop:
            # 执行安全停车
            pass
        
        if command.increase_sensitivity:
            # 增加AEB/LSS灵敏度
            pass
        
        if command.enable_emergency_lane_keeping:
            # 启用紧急车道保持
            pass


class HMIController:
    """HMI控制器"""
    
    def execute(self, command: dict):
        """执行HMI命令"""
        if command.get('display_message'):
            # 显示警告消息
            pass
        
        if command.get('audio_alert'):
            # 播放音频警告
            pass
        
        if command.get('seat_vibration'):
            # 座椅振动
            pass


class DriverMonitoringSystem:
    """驾驶员监控系统"""
    
    def __init__(self):
        self.eye_tracker = EyeTracker()
        self.unresponsive_detector = UnresponsiveDriverDetector()
    
    def detect(self) -> DMSOutput:
        """检测驾驶员状态"""
        # 简化实现
        return DMSOutput(
            driver_state=DriverState.NORMAL,
            confidence=0.95,
            fatigue_score=0.1,
            distraction_score=0.05,
            responsiveness_score=0.02,
            eye_gaze=(0.0, 0.0),
            head_pose={'pitch': 0, 'yaw': 0, 'roll': 0},
            timestamp=0.0
        )

Euro NCAP测试场景

无响应驾驶员测试

class UnresponsiveDriverTest:
    """
    无响应驾驶员测试场景
    
    Euro NCAP 2026定义
    """
    
    TEST_SCENARIOS = {
        'UD-01': {
            'name': '医疗紧急情况',
            'simulation': {
                'eye_closure': True,
                'head_droop': True,
                'no_input': True,
                'duration_sec': 30
            },
            'expected': {
                'detection_time_sec': 5,
                'warning_sequence': ['visual', 'audible', 'haptic'],
                'stop_initiation_sec': 15,
                'lane_keeping': True
            }
        },
        'UD-02': {
            'name': '深度睡眠',
            'simulation': {
                'eye_closure': True,
                'head_droop': False,
                'no_input': True,
                'duration_sec': 60
            },
            'expected': {
                'detection_time_sec': 10,
                'warning_sequence': ['visual', 'audible'],
                'stop_initiation_sec': 30
            }
        },
        'UD-03': {
            'name': '严重醉酒',
            'simulation': {
                'eye_closure': False,
                'slow_response': True,
                'erratic_input': True,
                'duration_sec': 120
            },
            'expected': {
                'detection_time_sec': 15,
                'adas_sensitivity_increase': True
            }
        }
    }
    
    def execute_test(self, test_id: str) -> dict:
        """执行测试"""
        scenario = self.TEST_SCENARIOS[test_id]
        
        # 模拟测试执行
        result = {
            'test_id': test_id,
            'scenario': scenario['name'],
            'passed': True,
            'details': {}
        }
        
        return result

部署配置

系统架构

┌─────────────────────────────────────────────────────────┐
│                    DMS-ADAS 协同系统                      │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  ┌─────────┐    ┌──────────────┐    ┌─────────────┐   │
│  │  DMS    │───▶│ 状态判断     │───▶│ 干预决策    │   │
│  │ 检测模块 │    │ (状态机)     │    │ (策略引擎)  │   │
│  └─────────┘    └──────────────┘    └─────────────┘   │
│       │                                    │            │
│       ▼                                    ▼            │
│  ┌─────────┐                        ┌─────────────┐   │
│  │ 无响应  │                        │    ADAS     │   │
│  │ 检测器  │                        │   控制器    │   │
│  └─────────┘                        └─────────────┘   │
│       │                                    │            │
│       └──────────────┬─────────────────────┘            │
│                      ▼                                  │
│               ┌─────────────┐                          │
│               │  安全停车   │                          │
│               │  控制器     │                          │
│               └─────────────┘                          │
└─────────────────────────────────────────────────────────┘

性能要求

指标	要求	说明
无响应检测延迟	≤5秒	从状态开始到检测
警告响应时间	≤2秒	从检测到警告
安全停车启动	≤15秒	从无响应到减速
车道保持精度	≤0.2m	停车过程中

开发启示

1. 关键技术点

1. 多源融合判断：眼动+头部+输入+响应
2. 分级干预策略：渐进式警告
3. 安全停车规划：减速曲线+车道保持
4. 紧急呼叫集成：停车后自动eCall

2. Euro NCAP合规要点

• ✅ 无响应检测（≥2个指标）
• ✅ 分级警告（视觉→听觉→触觉）
• ✅ ADAS灵敏度增强
• ✅ 安全停车功能
• ✅ 危险报警灯自动开启
• ✅ eCall自动触发

3. 测试验证

# Euro NCAP测试清单
TEST_CHECKLIST = [
    "检测到闭眼≥5秒 → 触发警告",
    "无方向盘输入≥10秒 → 增强ADAS",
    "无响应≥15秒 → 启动安全停车",
    "停车过程中保持车道",
    "停车后自动开启危险报警灯",
    "停车后自动触发eCall"
]

---

参考资料：

1. Euro NCAP Safe Driving Protocol v1.1 (2025)
2. Smart Eye: “Driver Monitoring 2.0: How Euro NCAP is Raising the Bar in 2026”
3. ETSC: “Euro NCAP New 2026 Protocols Target Distraction, Impairment, and Speeding”
4. AB Dynamics: “Euro NCAP 2026 Protocols: What Does It Mean for ADAS Testing”