class DriverFunctionalReadiness:
    """
    Minieye DFR: 驾驶员功能就绪度模型
    
    从InCabin Europe 2026发布信息推断的架构
    """
    
    def __init__(self):
        # 座舱内感知（已有DMS能力扩展）
        self.in_cabin_sensing = {
            "fatigue_detection": "PERCLOS+打哈欠+点头",
            "distraction_detection": "视线偏离+手机使用",
            "alcohol_impairment": "行为分析+眼部特征",  # 新增
            "emotion_recognition": "面部表情+语音情绪"  # 新增
        }
        
        # ADAS道路场景感知
        self.adas_context = {
            "road_complexity": "道路复杂度评估",
            "traffic_density": "交通密度",
            "weather_condition": "天气状况",
            "driving_scenario": "高速/市区/匝道"
        }
    
    def assess_readiness(self, driver_state, road_context):
        """
        综合评估驾驶员功能就绪度
        
        关键创新：融合座舱内状态+外部ADAS场景
        
        Args:
            driver_state: 疲劳/分心/损伤/情绪
            road_context: 道路复杂度/交通/天气
        
        Returns:
            readiness_score: 0-100
            intervention_level: 0-3
        """
        # 座舱内状态评分（权重60%）
        cabin_score = self.score_cabin_state(driver_state)
        
        # 道路场景复杂度评分（权重40%）
        road_score = self.score_road_context(road_context)
        
        # 综合就绪度
        readiness = cabin_score * 0.6 + road_score * 0.4
        
        if readiness > 80:
            return readiness, 0  # 维持
        elif readiness > 60:
            return readiness, 1  # 提醒
        elif readiness > 40:
            return readiness, 2  # ADAS介入
        else:
            return readiness, 3  # 接管

class BamBamCabinButler:
    """
    Minieye BamBam: AI座舱管家
    
    基于VLM的座舱交互系统
    """
    
    def __init__(self):
        self.vlm_model = VisionLanguageModel()  # 视觉语言模型
        self.emotion_engine = EmotionRecognition()  # 情感计算
        self.context_awareness = ContextEngine()  # 情境理解
        
    def interact(self, occupant_input, cabin_state, driving_context):
        """
        多模态交互
        
        融合：语音+视觉+情境
        """
        # 情感状态识别
        emotion = self.emotion_engine.recognize(cabin_state["faces"])
        
        # 情境理解
        context = self.context_awareness.analyze(driving_context)
        
        # VLM生成响应
        response = self.vlm_model.generate(
            input=occupant_input,
            emotion=context_emotion,
            context=context
        )
        
        # 情感自适应
        response.tone = self.adapt_tone(emotion)
        
        return response