无响应驾驶员干预系统：DMS与ADAS协同的Euro NCAP 2026新要求

Euro NCAP状态： 2026新增，强制要求紧急停车功能
核心能力： 检测无响应驾驶员 + 紧急停车 + 最小风险策略
技术难点： 区分疲劳/酒驾/医疗紧急情况、与ADAS协同控制

Euro NCAP无响应驾驶员干预要求

干预机制分级

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┌─────────────────────────────────────────────────────────────────┐
│                    无响应驾驶员干预流程                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  Level 1: 警告阶段                                              │
│  ├─ 视觉警告：仪表盘闪烁红灯                                    │
│  ├─ 声音警告：高频警报音                                        │
│  └─ 触觉警告：方向盘/座椅震动                                   │
│                                                                 │
│  Level 2: 辅助增强                                              │
│  ├─ 增强FCW/AEB灵敏度                                           │
│  ├─ 调整LKA/LDW阈值                                             │
│  └─ 降低车速                                                    │
│                                                                 │
│  Level 3: 紧急停车                                              │
│  ├─ 减速靠边                                                    │
│  ├─ 开启双闪警示灯                                              │
│  ├─ 自动拨打紧急救援                                            │
│  └─ 解锁车门便于救援                                            │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Euro NCAP评分细则（25分制）

检测项目 分值 检测要求
分心检测 8分 手机使用、视线偏离
疲劳检测 8分 KSS≥7，PERCLOS≥30%
损伤检测 5分 酒驾/药驾行为分析
无响应干预 4分 紧急停车功能

系统架构设计

DMS-ADAS协同架构

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┌─────────────────────────────────────────────────────────────────┐
│                    DMS-ADAS协同控制架构                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                    DMS感知层                              │   │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐    │   │
│  │  │眼动追踪 │  │面部表情 │  │头部姿态 │  │行为模式 │    │   │
│  │  └─────────┘  └─────────┘  └─────────┘  └─────────┘    │   │
│  └─────────────────────────────────────────────────────────┘   │
│                           │                                     │
│                           ▼                                     │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                    驾驶员状态估计                         │   │
│  │  ┌──────────────────────────────────────────────────┐   │   │
│  │  │ 状态分类：正常/分心/疲劳/损伤/无响应              │   │   │
│  │  │ 置信度估计：多模态融合                            │   │   │
│  │  │ 风险等级：低/中/高/紧急                           │   │   │
│  │  └──────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                           │                                     │
│                           ▼                                     │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                    干预决策层                             │   │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐    │   │
│  │  │警告策略 │  │ADAS调整 │  │减速控制 │  │紧急停车 │    │   │
│  │  └─────────┘  └─────────┘  └─────────┘  └─────────┘    │   │
│  └─────────────────────────────────────────────────────────┘   │
│                           │                                     │
│                           ▼                                     │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                    ADAS执行层                             │   │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐    │   │
│  │  │ACC控制  │  │LKA控制  │  │AEB控制  │  │EPS控制  │    │   │
│  │  └─────────┘  └─────────┘  └─────────┘  └─────────┘    │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

核心代码实现

1. 驾驶员状态估计

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"""
驾驶员状态估计模块
融合多模态信号判断驾驶员状态
"""

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


class DriverState(Enum):
    """驾驶员状态枚举"""
    NORMAL = 0           # 正常
    DISTRACTED = 1       # 分心
    DROWSY = 2           # 疲劳
    IMPAIRED = 3         # 损伤（酒驾/药驾）
    UNRESPONSIVE = 4     # 无响应
    EMERGENCY = 5        # 医疗紧急


@dataclass
class DriverStateEstimate:
    """驾驶员状态估计结果"""
    state: DriverState
    confidence: float
    risk_level: str  # "low", "medium", "high", "critical"
    duration_sec: float
    indicators: Dict[str, float]


class MultiModalStateEstimator(nn.Module):
    """
    多模态驾驶员状态估计器
    
    融合：
    - 眼动特征（PERCLOS、眨眼频率、注视方向）
    - 面部特征（表情、嘴部状态）
    - 头部姿态（俯仰、偏航、滚转）
    - 行为特征（方向盘操作、踏板使用）
    """
    
    def __init__(
        self,
        eye_feature_dim: int = 32,
        face_feature_dim: int = 64,
        head_feature_dim: int = 16,
        behavior_feature_dim: int = 32,
        hidden_dim: int = 128,
        num_states: int = 6
    ):
        super().__init__()
        
        # 眼动特征编码器
        self.eye_encoder = nn.Sequential(
            nn.Linear(eye_feature_dim, hidden_dim // 2),
            nn.ReLU(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim // 2, hidden_dim // 2)
        )
        
        # 面部特征编码器
        self.face_encoder = nn.Sequential(
            nn.Linear(face_feature_dim, hidden_dim // 2),
            nn.ReLU(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim // 2, hidden_dim // 2)
        )
        
        # 头部姿态编码器
        self.head_encoder = nn.Sequential(
            nn.Linear(head_feature_dim, hidden_dim // 4),
            nn.ReLU(inplace=True),
            nn.Linear(hidden_dim // 4, hidden_dim // 4)
        )
        
        # 行为特征编码器
        self.behavior_encoder = nn.Sequential(
            nn.Linear(behavior_feature_dim, hidden_dim // 2),
            nn.ReLU(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim // 2, hidden_dim // 2)
        )
        
        # 时序融合层（LSTM）
        self.temporal_fusion = nn.LSTM(
            input_size=hidden_dim + hidden_dim // 2 + hidden_dim // 4,
            hidden_size=hidden_dim,
            num_layers=2,
            batch_first=True,
            dropout=0.3,
            bidirectional=False
        )
        
        # 状态分类头
        self.state_classifier = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(inplace=True),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim // 2, num_states)
        )
        
        # 风险等级回归头
        self.risk_regressor = nn.Sequential(
            nn.Linear(hidden_dim, 32),
            nn.ReLU(inplace=True),
            nn.Linear(32, 1),
            nn.Sigmoid()
        )
        
        # 状态持续时间预测
        self.duration_predictor = nn.Sequential(
            nn.Linear(hidden_dim, 32),
            nn.ReLU(inplace=True),
            nn.Linear(32, 1),
            nn.Softplus()
        )
        
    def forward(
        self,
        eye_features: torch.Tensor,
        face_features: torch.Tensor,
        head_features: torch.Tensor,
        behavior_features: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Args:
            eye_features: (batch, seq_len, eye_dim)
            face_features: (batch, seq_len, face_dim)
            head_features: (batch, seq_len, head_dim)
            behavior_features: (batch, seq_len, behavior_dim)
            
        Returns:
            state_logits: (batch, seq_len, num_states)
            risk_scores: (batch, seq_len, 1)
            durations: (batch, seq_len, 1)
        """
        batch_size, seq_len, _ = eye_features.shape
        
        # 编码各模态特征
        eye_encoded = self.eye_encoder(eye_features)  # (batch, seq_len, hidden//2)
        face_encoded = self.face_encoder(face_features)
        head_encoded = self.head_encoder(head_features)
        behavior_encoded = self.behavior_encoder(behavior_features)
        
        # 融合
        fused = torch.cat([
            eye_encoded,
            face_encoded,
            head_encoded,
            behavior_encoded
        ], dim=-1)  # (batch, seq_len, hidden * 2)
        
        # 时序融合
        temporal_out, _ = self.temporal_fusion(fused)
        
        # 分类
        state_logits = self.state_classifier(temporal_out)
        
        # 风险
        risk_scores = self.risk_regressor(temporal_out)
        
        # 持续时间
        durations = self.duration_predictor(temporal_out)
        
        return state_logits, risk_scores, durations
    
    def estimate_state(
        self,
        features: Dict[str, torch.Tensor]
    ) -> DriverStateEstimate:
        """
        估计驾驶员状态
        
        Args:
            features: 特征字典
            
        Returns:
            estimate: 状态估计结果
        """
        self.eval()
        
        with torch.no_grad():
            state_logits, risk_scores, durations = self.forward(
                features['eye'],
                features['face'],
                features['head'],
                features['behavior']
            )
            
        # 获取最后时刻的预测
        state_probs = F.softmax(state_logits[:, -1, :], dim=-1)
        pred_state_idx = torch.argmax(state_probs, dim=-1).item()
        confidence = state_probs[0, pred_state_idx].item()
        
        risk_score = risk_scores[-1, -1, 0].item()
        duration = durations[-1, -1, 0].item()
        
        # 风险等级映射
        if risk_score < 0.25:
            risk_level = "low"
        elif risk_score < 0.5:
            risk_level = "medium"
        elif risk_score < 0.75:
            risk_level = "high"
        else:
            risk_level = "critical"
            
        return DriverStateEstimate(
            state=DriverState(pred_state_idx),
            confidence=confidence,
            risk_level=risk_level,
            duration_sec=duration,
            indicators={
                'eye_openness': features['eye'][-1, -1, 0].item(),
                'gaze_deviation': features['eye'][-1, -1, 1].item(),
                'head_pose': features['head'][-1, -1, 0].item()
            }
        )


# 测试
if __name__ == "__main__":
    # 创建模型
    model = MultiModalStateEstimator()
    
    # 模拟输入
    batch_size = 2
    seq_len = 30  # 30帧历史
    
    features = {
        'eye': torch.randn(batch_size, seq_len, 32),
        'face': torch.randn(batch_size, seq_len, 64),
        'head': torch.randn(batch_size, seq_len, 16),
        'behavior': torch.randn(batch_size, seq_len, 32)
    }
    
    # 前向传播
    state_logits, risk_scores, durations = model(
        features['eye'],
        features['face'],
        features['head'],
        features['behavior']
    )
    
    print("=== 驾驶员状态估计测试 ===")
    print(f"状态logits: {state_logits.shape}")
    print(f"风险分数: {risk_scores.shape}")
    print(f"持续时间: {durations.shape}")
    
    # 状态估计
    estimate = model.estimate_state(features)
    
    print(f"\n估计状态: {estimate.state.name}")
    print(f"置信度: {estimate.confidence:.2f}")
    print(f"风险等级: {estimate.risk_level}")
    print(f"持续时间: {estimate.duration_sec:.1f}s")

2. 干预决策模块

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"""
干预决策模块
根据驾驶员状态选择合适的干预策略
"""

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


class InterventionLevel(Enum):
    """干预级别"""
    NONE = 0            # 无干预
    WARNING_VISUAL = 1  # 视觉警告
    WARNING_AUDIO = 2   # 声音警告
    WARNING_HAPTIC = 3  # 触觉警告
    ASSIST_ENHANCED = 4 # 增强辅助
    SPEED_REDUCTION = 5 # 减速
    EMERGENCY_STOP = 6  # 紧急停车


@dataclass
class InterventionAction:
    """干预动作"""
    level: InterventionLevel
    action_type: str
    parameters: Dict
    duration_sec: float
    escalation_time_sec: float


class InterventionDecisionEngine:
    """
    干预决策引擎
    
    根据驾驶员状态和风险评估决定干预策略
    """
    
    # Euro NCAP规定的警告升级时间
    WARNING_ESCALATION = {
        'distraction': {
            'first_warning': 2.0,   # 秒
            'escalation': 4.0,
            'intervention': 6.0
        },
        'drowsiness': {
            'first_warning': 5.0,
            'escalation': 10.0,
            'intervention': 15.0
        },
        'impairment': {
            'first_warning': 10.0,  # 10分钟内检测
            'escalation': 15.0,
            'intervention': 20.0
        },
        'unresponsive': {
            'first_warning': 3.0,
            'escalation': 6.0,
            'emergency_stop': 10.0
        }
    }
    
    def __init__(
        self,
        config: Optional[Dict] = None
    ):
        if config is None:
            config = {}
            
        self.config = config
        self.state_history: List[DriverStateEstimate] = []
        self.intervention_history: List[InterventionAction] = []
        
    def decide(
        self,
        state_estimate: DriverStateEstimate,
        vehicle_state: Dict,
        road_context: Dict
    ) -> InterventionAction:
        """
        决定干预策略
        
        Args:
            state_estimate: 驾驶员状态估计
            vehicle_state: 车辆状态
            road_context: 道路上下文
            
        Returns:
            action: 干预动作
        """
        # 记录状态历史
        self.state_history.append(state_estimate)
        
        # 根据状态选择干预策略
        state = state_estimate.state
        risk = state_estimate.risk_level
        duration = state_estimate.duration_sec
        
        if state == DriverState.NORMAL:
            return self._no_intervention()
            
        elif state == DriverState.DISTRACTED:
            return self._distraction_intervention(
                risk, duration, vehicle_state
            )
            
        elif state == DriverState.DROWSY:
            return self._drowsiness_intervention(
                risk, duration, vehicle_state
            )
            
        elif state == DriverState.IMPAIRED:
            return self._impairment_intervention(
                risk, duration, vehicle_state
            )
            
        elif state == DriverState.UNRESPONSIVE:
            return self._unresponsive_intervention(
                risk, duration, vehicle_state, road_context
            )
            
        elif state == DriverState.EMERGENCY:
            return self._emergency_intervention(
                vehicle_state, road_context
            )
            
        return self._no_intervention()
    
    def _no_intervention(self) -> InterventionAction:
        """无干预"""
        return InterventionAction(
            level=InterventionLevel.NONE,
            action_type='none',
            parameters={},
            duration_sec=0,
            escalation_time_sec=0
        )
    
    def _distraction_intervention(
        self,
        risk: str,
        duration: float,
        vehicle_state: Dict
    ) -> InterventionAction:
        """分心干预"""
        escalation_times = self.WARNING_ESCALATION['distraction']
        
        if duration < escalation_times['first_warning']:
            # 一级警告：视觉
            return InterventionAction(
                level=InterventionLevel.WARNING_VISUAL,
                action_type='visual_alert',
                parameters={
                    'display_type': 'icon_flash',
                    'color': 'amber',
                    'message': '请保持注意力集中'
                },
                duration_sec=3.0,
                escalation_time_sec=escalation_times['first_warning']
            )
            
        elif duration < escalation_times['escalation']:
            # 二级警告：声音+触觉
            return InterventionAction(
                level=InterventionLevel.WARNING_AUDIO,
                action_type='audio_haptic_alert',
                parameters={
                    'sound_type': 'chime',
                    'volume': 0.7,
                    'vibration_pattern': 'pulse',
                    'message': '请注视道路'
                },
                duration_sec=5.0,
                escalation_time_sec=escalation_times['escalation']
            )
            
        elif duration < escalation_times['intervention']:
            # 三级：增强LKA
            return InterventionAction(
                level=InterventionLevel.ASSIST_ENHANCED,
                action_type='enhanced_lka',
                parameters={
                    'lka_sensitivity': 'high',
                    'ldw_threshold': 0.5,  # 降低阈值
                    'auto_steering': True
                },
                duration_sec=10.0,
                escalation_time_sec=escalation_times['intervention']
            )
            
        else:
            # 四级：减速
            return InterventionAction(
                level=InterventionLevel.SPEED_REDUCTION,
                action_type='gradual_slowdown',
                parameters={
                    'target_speed': vehicle_state.get('speed', 60) * 0.8,
                    'deceleration': 0.5  # m/s²
                },
                duration_sec=30.0,
                escalation_time_sec=0
            )
    
    def _drowsiness_intervention(
        self,
        risk: str,
        duration: float,
        vehicle_state: Dict
    ) -> InterventionAction:
        """疲劳干预"""
        escalation_times = self.WARNING_ESCALATION['drowsiness']
        
        if risk == 'medium':
            # 一级警告
            return InterventionAction(
                level=InterventionLevel.WARNING_AUDIO,
                action_type='drowsiness_alert',
                parameters={
                    'sound_type': 'alert_tone',
                    'volume': 0.8,
                    'message': '您可能感到疲劳，请考虑休息',
                    'rest_stop_suggestion': True
                },
                duration_sec=5.0,
                escalation_time_sec=escalation_times['first_warning']
            )
            
        elif risk == 'high':
            # 二级：增强警告 + 建议休息
            return InterventionAction(
                level=InterventionLevel.WARNING_HAPTIC,
                action_type='strong_drowsiness_alert',
                parameters={
                    'sound_type': 'loud_alert',
                    'volume': 1.0,
                    'vibration_intensity': 'high',
                    'message': '检测到严重疲劳，请立即休息',
                    'auto_reduce_speed': True
                },
                duration_sec=10.0,
                escalation_time_sec=escalation_times['escalation']
            )
            
        else:  # critical
            # 三级：减速 + 寻找停车点
            return InterventionAction(
                level=InterventionLevel.SPEED_REDUCTION,
                action_type='emergency_slowdown',
                parameters={
                    'target_speed': 30,  # km/h
                    'deceleration': 1.0,
                    'find_rest_area': True,
                    'auto_park_suggestion': True
                },
                duration_sec=60.0,
                escalation_time_sec=0
            )
    
    def _impairment_intervention(
        self,
        risk: str,
        duration: float,
        vehicle_state: Dict
    ) -> InterventionAction:
        """损伤（酒驾/药驾）干预"""
        escalation_times = self.WARNING_ESCALATION['impairment']
        
        # 增强 FCW/AEB 灵敏度
        return InterventionAction(
            level=InterventionLevel.ASSIST_ENHANCED,
            action_type='impairment_assist',
            parameters={
                'fcw_sensitivity': 'maximum',
                'aeb_threshold': 0.3,  # 提前触发
                'safe_following_distance': 3.0,  # 增大跟车距离
                'speed_limit_reduction': 0.8,  # 降低限速20%
                'message': '检测到驾驶能力受损，已增强安全辅助'
            },
            duration_sec=float('inf'),  # 持续整个行程
            escalation_time_sec=escalation_times['first_warning']
        )
    
    def _unresponsive_intervention(
        self,
        risk: str,
        duration: float,
        vehicle_state: Dict,
        road_context: Dict
    ) -> InterventionAction:
        """无响应驾驶员干预"""
        escalation_times = self.WARNING_ESCALATION['unresponsive']
        
        if duration < escalation_times['first_warning']:
            # 尝试唤醒
            return InterventionAction(
                level=InterventionLevel.WARNING_HAPTIC,
                action_type='wake_attempt',
                parameters={
                    'sound_type': 'loud_siren',
                    'volume': 1.0,
                    'vibration_intensity': 'maximum',
                    'light_flash': True
                },
                duration_sec=3.0,
                escalation_time_sec=escalation_times['first_warning']
            )
            
        elif duration < escalation_times['escalation']:
            # 减速准备停车
            return InterventionAction(
                level=InterventionLevel.SPEED_REDUCTION,
                action_type='prepare_stop',
                parameters={
                    'target_speed': 20,
                    'deceleration': 2.0,
                    'hazard_lights': True
                },
                duration_sec=6.0,
                escalation_time_sec=escalation_times['escalation']
            )
            
        else:
            # 紧急停车
            return InterventionAction(
                level=InterventionLevel.EMERGENCY_STOP,
                action_type='emergency_stop',
                parameters={
                    'stop_type': 'controlled',  # 受控停车
                    'pull_over': road_context.get('shoulder_available', True),
                    'deceleration': 3.0,
                    'hazard_lights': True,
                    'unlock_doors': True,
                    'emergency_call': True,
                    'eCall_message': '驾驶员无响应，请求救援'
                },
                duration_sec=float('inf'),
                escalation_time_sec=0
            )
    
    def _emergency_intervention(
        self,
        vehicle_state: Dict,
        road_context: Dict
    ) -> InterventionAction:
        """医疗紧急情况干预"""
        return InterventionAction(
            level=InterventionLevel.EMERGENCY_STOP,
            action_type='medical_emergency_stop',
            parameters={
                'stop_type': 'immediate',
                'pull_over': True,
                'deceleration': 4.0,  # 较大减速度
                'hazard_lights': True,
                'unlock_doors': True,
                'emergency_call': True,
                'eCall_message': '医疗紧急情况，请求急救',
                'transmit_location': True
            },
            duration_sec=float('inf'),
            escalation_time_sec=0
        )


# 测试
if __name__ == "__main__":
    # 创建决策引擎
    engine = InterventionDecisionEngine()
    
    # 模拟驾驶员状态
    state_estimate = DriverStateEstimate(
        state=DriverState.UNRESPONSIVE,
        confidence=0.95,
        risk_level='critical',
        duration_sec=12.0,
        indicators={}
    )
    
    # 车辆状态
    vehicle_state = {
        'speed': 80,  # km/h
        'lane_position': 0.1
    }
    
    # 道路上下文
    road_context = {
        'shoulder_available': True,
        'traffic_density': 'low'
    }
    
    # 决策
    action = engine.decide(state_estimate, vehicle_state, road_context)
    
    print("=== 干预决策测试 ===")
    print(f"干预级别: {action.level.name}")
    print(f"动作类型: {action.action_type}")
    print(f"参数: {action.parameters}")
    print(f"持续时间: {action.duration_sec}s")

3. ADAS控制接口

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"""
ADAS控制接口
实现DMS与ADAS系统的协同控制
"""

from typing import Dict, Optional
from dataclasses import dataclass
import time


@dataclass
class ADASCommand:
    """ADAS控制命令"""
    command_type: str
    value: float
    priority: int  # 1-10, 10最高
    source: str  # "dms", "adas", "driver"


class ADASController:
    """
    ADAS控制器
    
    接收DMS干预命令并控制车辆系统
    """
    
    def __init__(self):
        self.active_commands: Dict[str, ADASCommand] = {}
        self.fcw_sensitivity = 1.0
        self.aeb_threshold = 1.0
        self.lka_sensitivity = 1.0
        
    def execute_intervention(
        self,
        action: InterventionAction
    ) -> Dict:
        """
        执行干预动作
        
        Args:
            action: 干预动作
            
        Returns:
            result: 执行结果
        """
        results = {}
        
        action_type = action.action_type
        params = action.parameters
        
        if action_type == 'visual_alert':
            results['visual'] = self._trigger_visual_alert(params)
            
        elif action_type == 'audio_haptic_alert':
            results['audio'] = self._trigger_audio_alert(params)
            results['haptic'] = self._trigger_haptic_alert(params)
            
        elif action_type == 'enhanced_lka':
            results['lka'] = self._enhance_lka(params)
            
        elif action_type == 'gradual_slowdown':
            results['speed'] = self._gradual_slowdown(params)
            
        elif action_type == 'enhanced_fcw_aeb':
            results['fcw_aeb'] = self._enhance_fcw_aeb(params)
            
        elif action_type == 'emergency_stop':
            results['emergency'] = self._emergency_stop(params)
            
        return results
    
    def _trigger_visual_alert(self, params: Dict) -> Dict:
        """触发视觉警告"""
        return {
            'status': 'success',
            'display_type': params.get('display_type', 'icon'),
            'color': params.get('color', 'amber'),
            'message': params.get('message', '')
        }
    
    def _trigger_audio_alert(self, params: Dict) -> Dict:
        """触发声音警告"""
        return {
            'status': 'success',
            'sound_type': params.get('sound_type', 'chime'),
            'volume': params.get('volume', 0.7)
        }
    
    def _trigger_haptic_alert(self, params: Dict) -> Dict:
        """触发触觉警告"""
        return {
            'status': 'success',
            'vibration_pattern': params.get('vibration_pattern', 'pulse'),
            'intensity': params.get('vibration_intensity', 'medium')
        }
    
    def _enhance_lka(self, params: Dict) -> Dict:
        """增强LKA功能"""
        sensitivity = params.get('lka_sensitivity', 'high')
        
        if sensitivity == 'high':
            self.lka_sensitivity = 1.5
        elif sensitivity == 'maximum':
            self.lka_sensitivity = 2.0
        else:
            self.lka_sensitivity = 1.0
            
        return {
            'status': 'success',
            'lka_sensitivity': self.lka_sensitivity,
            'auto_steering': params.get('auto_steering', False)
        }
    
    def _enhance_fcw_aeb(self, params: Dict) -> Dict:
        """增强FCW/AEB"""
        sensitivity = params.get('fcw_sensitivity', 'normal')
        
        if sensitivity == 'maximum':
            self.fcw_sensitivity = 2.0
            self.aeb_threshold = 0.5  # 提前触发
        elif sensitivity == 'high':
            self.fcw_sensitivity = 1.5
            self.aeb_threshold = 0.7
        else:
            self.fcw_sensitivity = 1.0
            self.aeb_threshold = 1.0
            
        return {
            'status': 'success',
            'fcw_sensitivity': self.fcw_sensitivity,
            'aeb_threshold': self.aeb_threshold
        }
    
    def _gradual_slowdown(self, params: Dict) -> Dict:
        """逐渐减速"""
        target_speed = params.get('target_speed', 30)
        deceleration = params.get('deceleration', 1.0)
        
        return {
            'status': 'success',
            'target_speed_kmh': target_speed,
            'deceleration_ms2': deceleration
        }
    
    def _emergency_stop(self, params: Dict) -> Dict:
        """紧急停车"""
        stop_type = params.get('stop_type', 'controlled')
        pull_over = params.get('pull_over', True)
        hazard_lights = params.get('hazard_lights', True)
        unlock_doors = params.get('unlock_doors', True)
        emergency_call = params.get('emergency_call', False)
        
        result = {
            'status': 'success',
            'stop_type': stop_type,
            'pull_over': pull_over,
            'hazard_lights_activated': hazard_lights,
            'doors_unlocked': unlock_doors
        }
        
        if emergency_call:
            result['eCall'] = {
                'initiated': True,
                'message': params.get('eCall_message', 'Emergency'),
                'location_shared': params.get('transmit_location', True)
            }
            
        return result


# 测试
if __name__ == "__main__":
    # 创建控制器
    controller = ADASController()
    
    # 创建紧急停车动作
    emergency_action = InterventionAction(
        level=InterventionLevel.EMERGENCY_STOP,
        action_type='emergency_stop',
        parameters={
            'stop_type': 'controlled',
            'pull_over': True,
            'hazard_lights': True,
            'unlock_doors': True,
            'emergency_call': True,
            'eCall_message': '驾驶员无响应'
        },
        duration_sec=float('inf'),
        escalation_time_sec=0
    )
    
    # 执行
    result = controller.execute_intervention(emergency_action)
    
    print("=== ADAS控制测试 ===")
    print(f"执行结果: {result}")

Euro NCAP测试场景

场景ID 描述 检测时限 干预动作
UR-01 突然失去意识 ≤5s 紧急停车
UR-02 睡着（无响应） ≤10s 唤醒→紧急停车
UR-03 医疗紧急情况 ≤3s 立即紧急停车
UR-04 极度酒驾 ≤10min 增强FCW/AEB

总结

维度 内容
检测类型 无响应、医疗紧急、极度疲劳/损伤
干预级别 警告→辅助增强→减速→紧急停车
检测时限 3-10秒（根据场景）
关键技术 DMS-ADAS协同、最小风险策略
Euro NCAP分值 4分（25分制）

发布时间： 2026-04-22
标签： #无响应驾驶员 #紧急停车 #DMS-ADAS协同 #EuroNCAP2026

技术研究
#DMS #IMS
无响应驾驶员干预系统：DMS与ADAS协同的Euro NCAP 2026新要求
https://dapalm.com/2026/04/22/2026-04-22-unresponsive-driver-intervention/
作者
Mars
发布于
2026年4月22日
许可协议