DMS-ADAS协同：无响应驾驶员紧急停车系统设计

Euro NCAP 2026新增要求： 无响应驾驶员干预（Emergency Stop Function）
核心场景： 驾驶员丧失响应能力时，系统自动接管并安全停车
技术要点： DMS检测 + ADAS协同 + 分级干预策略

---

Euro NCAP 2026干预要求

驾驶员状态分类

Euro NCAP驾驶员状态分类:
│
├── 瞬态状态 (Transient States)
│   ├── 分心（短时）
│   ├── 手机使用
│   ├── 视线偏离
│   └── 触发：警告
│
├── 非瞬态状态 (Non-Transient States)
│   ├── 疲劳（KSS ≥ 7）
│   ├── 损伤（酒精/药物）
│   ├── 触发：警告 + ADAS调整
│
└── 无响应状态 (Unresponsive)
    ├── 持续无反应
    ├── 意识丧失
    └── 触发：紧急停车 (EF)

干预策略矩阵

驾驶员状态	检测阈值	干预措施	时间要求
短时分心	视线偏离 ≥ 3s	一级警告	≤ 3s
长时分心	视线偏离 ≥ 10s	二级警告	≤ 10s
疲劳	PERCLOS ≥ 30%	一级/二级警告	≤ 60s
损伤	行为异常	警告 + ADAS调整	≤ 10min
无响应	无反应 ≥ 15s	紧急停车	≤ 30s

---

系统架构

DMS-ADAS协同架构

┌─────────────────────────────────────────────────────────────────┐
│                    DMS-ADAS协同系统架构                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                    DMS感知层                             │   │
│  │  ┌───────────┐  ┌───────────┐  ┌───────────┐           │   │
│  │  │ 眼动追踪  │  │ 头部姿态  │  │ 面部表情  │           │   │
│  │  └───────────┘  └───────────┘  └───────────┘           │   │
│  │         ↓             ↓             ↓                   │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │           驾驶员状态融合估计                      │   │   │
│  │  │  - 注意力状态                                    │   │   │
│  │  │  - 疲劳程度                                      │   │   │
│  │  │  - 损伤概率                                      │   │   │
│  │  │  - 响应能力                                      │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          ↓ DMS State                            │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                   决策层                                 │   │
│  │  ┌─────────────────────────────────────────────────┐   │   │
│  │  │              干预决策引擎                        │   │   │
│  │  │  ┌─────────┐  ┌─────────┐  ┌─────────┐         │   │   │
│  │  │  │ 警告级  │  │ ADAS调整│  │紧急停车 │         │   │   │
│  │  │  └─────────┘  └─────────┘  └─────────┘         │   │   │
│  │  └─────────────────────────────────────────────────┘   │   │
│  └─────────────────────────────────────────────────────────┘   │
│                          ↓ Intervention                         │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                   ADAS执行层                            │   │
│  │  ┌───────────┐  ┌───────────┐  ┌───────────┐           │   │
│  │  │ FCW/AEB   │  │ LKA/LDW   │  │ 紧急停车  │           │   │
│  │  └───────────┘  └───────────┘  └───────────┘           │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

---

核心代码实现

1. 驾驶员状态估计器

"""
驾驶员状态估计器
融合多模态信息判断驾驶员状态
"""

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


class DriverState(Enum):
    """驾驶员状态枚举"""
    NORMAL = "normal"                  # 正常
    DISTRACTED_BRIEF = "distracted_brief"  # 短时分心
    DISTRACTED_LONG = "distracted_long"    # 长时分心
    DROWSY_MILD = "drowsy_mild"            # 轻度疲劳
    DROWSY_SEVERE = "drowsy_severe"        # 重度疲劳
    IMPAIRED = "impaired"                  # 损伤
    UNRESPONSIVE = "unresponsive"          # 无响应


@dataclass
class DMSInput:
    """DMS输入数据"""
    timestamp: float
    
    # 眼动数据
    gaze_direction: np.ndarray          # (3,) 视线方向
    eye_openness: float                 # 0-1
    blink_rate: float                   # blinks/min
    perclos: float                      # % over 60s
    
    # 头部数据
    head_pose: np.ndarray               # (3,) pitch, yaw, roll
    head_velocity: np.ndarray           # (3,)
    
    # 面部数据
    mouth_openness: float               # 0-1
    facial_expression: str              # neutral, yawning, etc.
    
    # 响应数据
    steering_input: Optional[float]     # 方向盘输入
    pedal_input: Optional[float]        # 油门/刹车输入


@dataclass
class DriverStateEstimate:
    """驾驶员状态估计结果"""
    state: DriverState
    confidence: float
    intervention_level: int             # 0: 无, 1: 警告, 2: ADAS调整, 3: 紧急停车
    state_duration: float               # 状态持续时间（秒）
    details: Dict


class DriverStateEstimator:
    """
    驾驶员状态估计器
    
    融合眼动、头部、面部等信息，判断驾驶员当前状态
    """
    
    # Euro NCAP阈值
    PERCLOS_THRESHOLD_MILD = 15.0       # 轻度疲劳阈值
    PERCLOS_THRESHOLD_SEVERE = 30.0     # 重度疲劳阈值
    
    GAZE_OFF_ROAD_THRESHOLD = 3.0       # 视线偏离阈值（秒）
    GAZE_OFF_ROAD_LONG = 10.0           # 长时分心阈值
    
    UNRESPONSIVE_TIME_THRESHOLD = 15.0  # 无响应时间阈值
    
    def __init__(
        self,
        history_window: int = 1800     # 60秒 @ 30fps
    ):
        """
        Args:
            history_window: 历史窗口大小
        """
        self.history_window = history_window
        self.history: List[DMSInput] = []
        
        # 状态持续时间追踪
        self.state_start_time: Dict[DriverState, float] = {}
        self.current_state = DriverState.NORMAL
        
    def update(
        self,
        dms_input: DMSInput
    ) -> DriverStateEstimate:
        """
        更新状态估计
        
        Args:
            dms_input: DMS输入数据
            
        Returns:
            estimate: 状态估计结果
        """
        # 添加到历史
        self.history.append(dms_input)
        if len(self.history) > self.history_window:
            self.history.pop(0)
            
        # 多维度评估
        attention_score = self._assess_attention(dms_input)
        fatigue_score = self._assess_fatigue(dms_input)
        impairment_score = self._assess_impairment(dms_input)
        responsiveness = self._assess_responsiveness(dms_input)
        
        # 状态判断
        state, confidence = self._determine_state(
            attention_score,
            fatigue_score,
            impairment_score,
            responsiveness
        )
        
        # 计算干预级别
        intervention_level = self._get_intervention_level(state, dms_input.timestamp)
        
        # 构建结果
        estimate = DriverStateEstimate(
            state=state,
            confidence=confidence,
            intervention_level=intervention_level,
            state_duration=self._get_state_duration(state, dms_input.timestamp),
            details={
                "attention_score": attention_score,
                "fatigue_score": fatigue_score,
                "impairment_score": impairment_score,
                "responsiveness": responsiveness
            }
        )
        
        # 更新状态追踪
        if state != self.current_state:
            self.state_start_time[state] = dms_input.timestamp
            self.current_state = state
            
        return estimate
    
    def _assess_attention(
        self,
        dms_input: DMSInput
    ) -> float:
        """
        评估注意力水平
        
        Returns:
            attention_score: 0-1, 1为完全专注
        """
        # 视线偏离道路程度
        gaze_forward = np.array([0.0, 0.0, 1.0])
        gaze_deviation = 1.0 - np.dot(dms_input.gaze_direction, gaze_forward)
        
        # 头部朝向
        head_forward = abs(dms_input.head_pose[1]) < 15  # yaw
        
        # 眼睛睁开
        eyes_functional = dms_input.eye_openness > 0.3
        
        # 综合评分
        score = 1.0
        score -= gaze_deviation * 0.5
        if not head_forward:
            score -= 0.2
        if not eyes_functional:
            score -= 0.3
            
        return max(0.0, min(1.0, score))
    
    def _assess_fatigue(
        self,
        dms_input: DMSInput
    ) -> float:
        """
        评估疲劳程度
        
        Returns:
            fatigue_score: 0-1, 1为极度疲劳
        """
        score = 0.0
        
        # PERCLOS指标
        if dms_input.perclos >= self.PERCLOS_THRESHOLD_SEVERE:
            score += 0.5
        elif dms_input.perclos >= self.PERCLOS_THRESHOLD_MILD:
            score += 0.3
            
        # 眨眼频率异常
        if dms_input.blink_rate > 30 or dms_input.blink_rate < 5:
            score += 0.2
            
        # 眼睛半闭
        if dms_input.eye_openness < 0.5:
            score += 0.2
            
        # 打哈欠
        if dms_input.facial_expression == "yawning":
            score += 0.1
            
        return min(1.0, score)
    
    def _assess_impairment(
        self,
        dms_input: DMSInput
    ) -> float:
        """
        评估损伤程度（酒精/药物）
        
        Returns:
            impairment_score: 0-1, 1为严重损伤
        """
        score = 0.0
        
        # 眼睛不稳定
        if len(self.history) > 30:
            recent_eye_openness = [h.eye_openness for h in self.history[-30:]]
            eye_variability = np.std(recent_eye_openness)
            if eye_variability > 0.2:
                score += 0.2
                
        # 头部不稳定
        head_velocity_mag = np.linalg.norm(dms_input.head_velocity)
        if head_velocity_mag > 10:  # deg/s
            score += 0.2
            
        # 反应迟钝（需要历史数据）
        # ... 实际中需要更复杂的模型
        
        return min(1.0, score)
    
    def _assess_responsiveness(
        self,
        dms_input: DMSInput
    ) -> float:
        """
        评估响应能力
        
        Returns:
            responsiveness: 0-1, 1为完全响应
        """
        # 检查是否有主动输入
        has_steering = dms_input.steering_input is not None and abs(dms_input.steering_input) > 0.1
        has_pedal = dms_input.pedal_input is not None and abs(dms_input.pedal_input) > 0.1
        
        # 眼睛是否有反应
        eyes_responsive = dms_input.eye_openness > 0.1
        
        # 头部是否有反应
        head_responsive = np.linalg.norm(dms_input.head_velocity) > 0.5
        
        # 综合判断
        if has_steering or has_pedal:
            return 1.0
        elif eyes_responsive or head_responsive:
            return 0.5
        else:
            return 0.0
    
    def _determine_state(
        self,
        attention: float,
        fatigue: float,
        impairment: float,
        responsiveness: float
    ) -> tuple:
        """确定最终状态"""
        
        # 优先级：无响应 > 损伤 > 疲劳 > 分心
        
        # 无响应
        if responsiveness == 0 and len(self.history) > 0:
            # 检查持续无响应时间
            unresponsive_duration = self._check_unresponsive_duration()
            if unresponsive_duration > self.UNRESPONSIVE_TIME_THRESHOLD:
                return DriverState.UNRESPONSIVE, 0.9
                
        # 损伤
        if impairment > 0.5:
            return DriverState.IMPAIRED, impairment
            
        # 疲劳
        if fatigue > 0.7:
            return DriverState.DROWSY_SEVERE, fatigue
        elif fatigue > 0.4:
            return DriverState.DROWSY_MILD, fatigue
            
        # 分心
        if attention < 0.5:
            # 检查持续时间
            distraction_duration = self._check_distraction_duration()
            if distraction_duration > self.GAZE_OFF_ROAD_LONG:
                return DriverState.DISTRACTED_LONG, 1 - attention
            elif distraction_duration > self.GAZE_OFF_ROAD_THRESHOLD:
                return DriverState.DISTRACTED_BRIEF, 1 - attention
                
        return DriverState.NORMAL, attention
    
    def _check_unresponsive_duration(self) -> float:
        """检查无响应持续时间"""
        # 实际中需要更精确的时间计算
        count = 0
        for h in reversed(self.history):
            if h.steering_input is None or abs(h.steering_input) < 0.1:
                count += 1
            else:
                break
        return count / 30.0  # 假设30fps
    
    def _check_distraction_duration(self) -> float:
        """检查分心持续时间"""
        count = 0
        for h in reversed(self.history):
            gaze_deviation = 1.0 - np.dot(h.gaze_direction, np.array([0, 0, 1]))
            if gaze_deviation > 0.3:
                count += 1
            else:
                break
        return count / 30.0
    
    def _get_intervention_level(
        self,
        state: DriverState,
        timestamp: float
    ) -> int:
        """获取干预级别"""
        intervention_map = {
            DriverState.NORMAL: 0,
            DriverState.DISTRACTED_BRIEF: 1,
            DriverState.DISTRACTED_LONG: 2,
            DriverState.DROWSY_MILD: 1,
            DriverState.DROWSY_SEVERE: 2,
            DriverState.IMPAIRED: 2,
            DriverState.UNRESPONSIVE: 3
        }
        return intervention_map.get(state, 0)
    
    def _get_state_duration(
        self,
        state: DriverState,
        timestamp: float
    ) -> float:
        """获取状态持续时间"""
        if state in self.state_start_time:
            return timestamp - self.state_start_time[state]
        return 0.0

---

2. 干预决策引擎

"""
干预决策引擎
根据DMS状态和ADAS状态决定干预策略
"""

from typing import Dict, List, Optional
from dataclasses import dataclass
from enum import Enum


class InterventionType(Enum):
    """干预类型"""
    NONE = "none"
    VISUAL_WARNING = "visual_warning"
    AUDIO_WARNING = "audio_warning"
    HAPTIC_WARNING = "haptic_warning"
    ADAS_SENSITIVITY_INCREASE = "adas_sensitivity_increase"
    LKA_ADJUSTMENT = "lka_adjustment"
    EMERGENCY_STOP = "emergency_stop"


@dataclass
class InterventionCommand:
    """干预指令"""
    intervention_type: InterventionType
    priority: int                    # 1-5, 5最高
    target_system: str               # FCW, AEB, LKA, etc.
    parameters: Dict
    duration: Optional[float]        # 持续时间（秒）


class InterventionDecisionEngine:
    """
    干预决策引擎
    
    根据DMS状态和道路情况决定干预策略
    """
    
    # Euro NCAP要求的干预策略
    INTERVENTION_STRATEGIES = {
        DriverState.DISTRACTED_BRIEF: [
            InterventionCommand(
                intervention_type=InterventionType.VISUAL_WARNING,
                priority=2,
                target_system="instrument_cluster",
                parameters={"icon": "eyes_on_road"},
                duration=2.0
            )
        ],
        DriverState.DISTRACTED_LONG: [
            InterventionCommand(
                intervention_type=InterventionType.AUDIO_WARNING,
                priority=3,
                target_system="audio_system",
                parameters={"sound": "attention_tone", "volume": 0.7},
                duration=1.0
            ),
            InterventionCommand(
                intervention_type=InterventionType.HAPTIC_WARNING,
                priority=3,
                target_system="steering_wheel",
                parameters={"vibration_pattern": "pulse"},
                duration=1.0
            )
        ],
        DriverState.DROWSY_MILD: [
            InterventionCommand(
                intervention_type=InterventionType.VISUAL_WARNING,
                priority=3,
                target_system="instrument_cluster",
                parameters={"icon": "take_break", "message": "考虑休息"},
                duration=5.0
            )
        ],
        DriverState.DROWSY_SEVERE: [
            InterventionCommand(
                intervention_type=InterventionType.AUDIO_WARNING,
                priority=4,
                target_system="audio_system",
                parameters={"sound": "fatigue_alert", "volume": 0.9},
                duration=2.0
            ),
            InterventionCommand(
                intervention_type=InterventionType.LKA_ADJUSTMENT,
                priority=4,
                target_system="lka",
                parameters={"sensitivity": "high", "intervention_threshold": 0.5},
                duration=60.0
            )
        ],
        DriverState.IMPAIRED: [
            InterventionCommand(
                intervention_type=InterventionType.AUDIO_WARNING,
                priority=4,
                target_system="audio_system",
                parameters={"sound": "impairment_alert", "volume": 1.0},
                duration=3.0
            ),
            InterventionCommand(
                intervention_type=InterventionType.ADAS_SENSITIVITY_INCREASE,
                priority=4,
                target_system="fcw_aeb",
                parameters={"fcw_threshold": 0.7, "aeb_threshold": 0.8},
                duration=300.0
            )
        ],
        DriverState.UNRESPONSIVE: [
            InterventionCommand(
                intervention_type=InterventionType.EMERGENCY_STOP,
                priority=5,
                target_system="adas",
                parameters={
                    "deceleration": 3.0,  # m/s²
                    "activate_hazard_lights": True,
                    "sound_horn": True,
                    "lane_position": "rightmost"
                },
                duration=None  # 直到完全停车
            )
        ]
    }
    
    def __init__(self):
        self.active_interventions: List[InterventionCommand] = []
        self.last_intervention_time: Dict[InterventionType, float] = {}
        
    def decide(
        self,
        driver_state: DriverStateEstimate,
        adas_state: Dict,
        vehicle_state: Dict,
        timestamp: float
    ) -> List[InterventionCommand]:
        """
        决策干预策略
        
        Args:
            driver_state: 驾驶员状态估计
            adas_state: ADAS状态
            vehicle_state: 车辆状态
            timestamp: 时间戳
            
        Returns:
            interventions: 干预指令列表
        """
        # 获取基础策略
        base_interventions = self.INTERVENTION_STRATEGIES.get(
            driver_state.state,
            []
        )
        
        # 根据实际情况调整
        adjusted_interventions = []
        
        for intervention in base_interventions:
            # 考虑当前车速
            speed = vehicle_state.get("speed", 0)
            
            if intervention.intervention_type == InterventionType.EMERGENCY_STOP:
                # 紧急停车需要特殊处理
                if speed > 0:
                    # 选择安全停车位置
                    safe_stop_params = self._plan_safe_stop(
                        vehicle_state,
                        adas_state
                    )
                    intervention = InterventionCommand(
                        intervention_type=intervention.intervention_type,
                        priority=intervention.priority,
                        target_system=intervention.target_system,
                        parameters=safe_stop_params,
                        duration=intervention.duration
                    )
                    adjusted_interventions.append(intervention)
            else:
                adjusted_interventions.append(intervention)
                
        return adjusted_interventions
    
    def _plan_safe_stop(
        self,
        vehicle_state: Dict,
        adas_state: Dict
    ) -> Dict:
        """
        规划安全停车
        
        Returns:
            stop_params: 停车参数
        """
        # 获取道路信息
        lane_info = adas_state.get("lane", {})
        obstacles = adas_state.get("obstacles", [])
        
        # 默认参数
        params = {
            "deceleration": 3.0,
            "target_speed": 0,
            "activate_hazard_lights": True,
            "sound_horn": True,
            "call_emergency_services": False
        }
        
        # 选择停车位置
        if lane_info.get("right_lane_available"):
            params["lane_change"] = "right"
            params["target_lane"] = "rightmost"
        else:
            params["lane_change"] = "none"
            params["target_lane"] = "current"
            
        # 检查是否有障碍物
        if obstacles:
            nearest_obstacle = min(obstacles, key=lambda x: x.get("distance", float('inf')))
            if nearest_obstacle.get("distance", float('inf')) < 50:
                # 前方有障碍物，减速更快
                params["deceleration"] = 5.0
                
        return params


# 测试
if __name__ == "__main__":
    # 创建状态估计器
    estimator = DriverStateEstimator()
    
    # 创建决策引擎
    decision_engine = InterventionDecisionEngine()
    
    print("=== DMS-ADAS协同干预测试 ===\n")
    
    # 模拟场景
    scenarios = [
        {
            "name": "短时分心",
            "input": DMSInput(
                timestamp=1.0,
                gaze_direction=np.array([0.5, 0.0, 0.866]),
                eye_openness=0.85,
                blink_rate=15.0,
                perclos=5.0,
                head_pose=np.array([0, -30, 0]),
                head_velocity=np.array([0, 0, 0]),
                mouth_openness=0.1,
                facial_expression="neutral",
                steering_input=None,
                pedal_input=0.5
            )
        },
        {
            "name": "重度疲劳",
            "input": DMSInput(
                timestamp=60.0,
                gaze_direction=np.array([0.0, -0.2, 0.98]),
                eye_openness=0.4,
                blink_rate=35.0,
                perclos=45.0,
                head_pose=np.array([10, 0, 2]),
                head_velocity=np.array([0, 0, 0]),
                mouth_openness=0.3,
                facial_expression="yawning",
                steering_input=None,
                pedal_input=0.3
            )
        },
        {
            "name": "无响应",
            "input": DMSInput(
                timestamp=200.0,
                gaze_direction=np.array([0.0, 0.0, 1.0]),
                eye_openness=0.0,
                blink_rate=0.0,
                perclos=100.0,
                head_pose=np.array([15, 0, 0]),
                head_velocity=np.array([0, 0, 0]),
                mouth_openness=0.1,
                facial_expression="neutral",
                steering_input=None,
                pedal_input=None
            )
        }
    ]
    
    for scenario in scenarios:
        print(f"场景: {scenario['name']}")
        
        # 状态估计
        estimate = estimator.update(scenario['input'])
        print(f"  状态: {estimate.state.value}")
        print(f"  置信度: {estimate.confidence:.2f}")
        print(f"  干预级别: {estimate.intervention_level}")
        
        # 干预决策
        interventions = decision_engine.decide(
            estimate,
            {"lane": {"right_lane_available": True}},
            {"speed": 80},
            scenario['input'].timestamp
        )
        
        print(f"  干预措施:")
        for intervention in interventions:
            print(f"    - {intervention.intervention_type.value}")
        print()

---

3. 紧急停车执行器

"""
紧急停车执行器
执行无响应驾驶员场景下的安全停车
"""

from typing import Dict, Optional
from dataclasses import dataclass
from enum import Enum


class EmergencyStopPhase(Enum):
    """紧急停车阶段"""
    INITIATION = "initiation"         # 启动
    LANE_CHANGE = "lane_change"       # 变道
    DECELERATION = "deceleration"     # 减速
    STOPPING = "stopping"             # 停车
    STOPPED = "stopped"               # 已停止


@dataclass
class EmergencyStopState:
    """紧急停车状态"""
    phase: EmergencyStopPhase
    target_deceleration: float        # m/s²
    current_speed: float              # m/s
    target_lane: Optional[int]
    hazard_lights_on: bool
    horn_active: bool
    emergency_call_initiated: bool


class EmergencyStopExecutor:
    """
    紧急停车执行器
    
    执行无响应驾驶员场景下的安全停车
    """
    
    # Euro NCAP要求
    MAX_DECELERATION = 5.0            # 最大减速度 m/s²
    MIN_DECELERATION = 2.0            # 最小减速度 m/s²
    TARGET_STOP_TIME = 30.0           # 目标停车时间 秒
    
    def __init__(self):
        self.state: Optional[EmergencyStopState] = None
        self.start_time: Optional[float] = None
        
    def initiate(
        self,
        vehicle_state: Dict,
        road_state: Dict,
        timestamp: float
    ) -> EmergencyStopState:
        """
        启动紧急停车
        
        Args:
            vehicle_state: 车辆状态
            road_state: 道路状态
            timestamp: 时间戳
            
        Returns:
            initial_state: 初始状态
        """
        current_speed = vehicle_state.get("speed", 0) / 3.6  # km/h -> m/s
        
        # 计算目标减速度
        if current_speed > 0:
            target_decel = min(
                self.MAX_DECELERATION,
                max(self.MIN_DECELERATION, current_speed / self.TARGET_STOP_TIME)
            )
        else:
            target_decel = 0
            
        # 确定目标车道
        target_lane = self._select_target_lane(road_state)
        
        self.state = EmergencyStopState(
            phase=EmergencyStopPhase.INITIATION,
            target_deceleration=target_decel,
            current_speed=current_speed,
            target_lane=target_lane,
            hazard_lights_on=False,
            horn_active=False,
            emergency_call_initiated=False
        )
        
        self.start_time = timestamp
        
        return self.state
    
    def update(
        self,
        vehicle_state: Dict,
        adas_state: Dict,
        timestamp: float
    ) -> Dict:
        """
        更新紧急停车状态
        
        Args:
            vehicle_state: 车辆状态
            adas_state: ADAS状态
            timestamp: 时间戳
            
        Returns:
            control_commands: 控制指令
        """
        if self.state is None:
            return {}
            
        current_speed = vehicle_state.get("speed", 0) / 3.6
        
        # 更新状态
        self.state.current_speed = current_speed
        
        # 阶段转换
        commands = {}
        
        if self.state.phase == EmergencyStopPhase.INITIATION:
            # 启动阶段：开启危险报警灯、鸣笛
            commands["hazard_lights"] = True
            commands["horn"] = True
            self.state.hazard_lights_on = True
            self.state.horn_active = True
            
            # 检查是否需要变道
            if self.state.target_lane is not None:
                self.state.phase = EmergencyStopPhase.LANE_CHANGE
            else:
                self.state.phase = EmergencyStopPhase.DECELERATION
                
        elif self.state.phase == EmergencyStopPhase.LANE_CHANGE:
            # 变道阶段
            commands["steering"] = self._calculate_lane_change_steering(
                vehicle_state,
                adas_state
            )
            
            # 检查是否完成变道
            if self._is_lane_change_complete(vehicle_state, adas_state):
                self.state.phase = EmergencyStopPhase.DECELERATION
                
        elif self.state.phase == EmergencyStopPhase.DECELERATION:
            # 减速阶段
            commands["brake"] = self._calculate_brake_pressure(
                self.state.target_deceleration
            )
            commands["horn"] = False
            self.state.horn_active = False
            
            # 检查是否接近停车
            if current_speed < 2.0:  # m/s
                self.state.phase = EmergencyStopPhase.STOPPING
                
        elif self.state.phase == EmergencyStopPhase.STOPPING:
            # 停车阶段
            commands["brake"] = 1.0  # 最大刹车
            commands["parking_brake"] = True
            
            # 检查是否完全停车
            if current_speed < 0.1:
                self.state.phase = EmergencyStopPhase.STOPPED
                commands["emergency_call"] = True
                self.state.emergency_call_initiated = True
                
        elif self.state.phase == EmergencyStopPhase.STOPPED:
            # 已停车
            commands["parking_brake"] = True
            commands["hazard_lights"] = True
            
        return commands
    
    def _select_target_lane(
        self,
        road_state: Dict
    ) -> Optional[int]:
        """选择目标车道"""
        # 简化：选择最右侧车道
        # 实际中需要考虑道路类型、紧急停车带等
        return road_state.get("rightmost_lane_index")
    
    def _calculate_lane_change_steering(
        self,
        vehicle_state: Dict,
        adas_state: Dict
    ) -> float:
        """计算变道方向盘转角"""
        # 简化实现
        # 实际中需要考虑车道宽度、车辆速度等
        return 0.1  # 轻微右转
    
    def _is_lane_change_complete(
        self,
        vehicle_state: Dict,
        adas_state: Dict
    ) -> bool:
        """判断变道是否完成"""
        # 简化实现
        current_lane = adas_state.get("current_lane_index")
        return current_lane == self.state.target_lane
    
    def _calculate_brake_pressure(
        self,
        target_deceleration: float
    ) -> float:
        """计算刹车压力"""
        # 简化：线性映射
        # 实际中需要考虑刹车系统特性
        return min(1.0, target_deceleration / self.MAX_DECELERATION)


# Euro NCAP测试场景
EURO_NCAP_EMERGENCY_STOP_SCENARIOS = [
    {
        "id": "ES-01",
        "name": "高速公路无响应",
        "setup": "速度 120 km/h，直线道路",
        "expected_behavior": "减速停车，靠右",
        "expected_duration": "≤30秒"
    },
    {
        "id": "ES-02",
        "name": "城市道路无响应",
        "setup": "速度 50 km/h，有交通",
        "expected_behavior": "安全停车，避免碰撞",
        "expected_duration": "≤15秒"
    },
    {
        "id": "ES-03",
        "name": "弯道无响应",
        "setup": "速度 80 km/h，弯道",
        "expected_behavior": "保持车道，减速停车",
        "expected_duration": "≤25秒"
    }
]


# 测试
if __name__ == "__main__":
    print("=== 紧急停车执行器测试 ===\n")
    
    executor = EmergencyStopExecutor()
    
    # 初始化
    vehicle_state = {"speed": 120}  # km/h
    road_state = {"rightmost_lane_index": 2}
    
    state = executor.initiate(vehicle_state, road_state, 0.0)
    
    print(f"初始状态: {state.phase.value}")
    print(f"目标减速度: {state.target_deceleration:.2f} m/s²")
    print(f"当前速度: {state.current_speed:.2f} m/s")
    print(f"目标车道: {state.target_lane}")

---

总结

维度	内容
新增要求	无响应驾驶员紧急停车
检测阈值	无反应 ≥ 15秒
停车时间	≤ 30秒
干预级别	0-3级分级干预
技术要点	DMS-ADAS协同、状态融合、分级决策

---

发布时间： 2026-04-22
标签： #DMS-ADAS协同 #无响应驾驶员 #紧急停车 #Euro NCAP #干预策略