"""
摄像头-雷达融合 vs 传统多传感器方案BOM对比

传统方案（4个独立传感器）:
- DMS摄像头: $15-25
- OMS摄像头: $15-25
- 重量垫: $8-12
- 电容传感器: $5-8
- CPD雷达(独立): $10-15
总计: $53-85

融合方案（2个传感器复用）:
- 共享摄像头(DMS+OMS): $15-25
- 60GHz雷达(CPD+存在+呼吸): $10-15
- 软件(Smart Eye AI): $5-10(授权)
总计: $30-50

节省: ~40%
"""

# BOM计算
traditional_bom = {
    'DMS摄像头': (20, 'USD'),
    'OMS摄像头': (20, 'USD'),
    '重量垫': (10, 'USD'),
    '电容传感器': (6, 'USD'),
    'CPD雷达': (12, 'USD'),
}

fusion_bom = {
    '共享摄像头': (20, 'USD'),
    '60GHz雷达': (12, 'USD'),
    'AI软件授权': (8, 'USD'),
}

trad_total = sum(v[0] for v in traditional_bom.values())
fusion_total = sum(v[0] for v in fusion_bom.values())
saving = (1 - fusion_total / trad_total) * 100

print("=== 座舱传感器BOM对比 ===")
print(f"传统多传感器方案: ${trad_total}")
print(f"摄像头-雷达融合方案: ${fusion_total}")
print(f"BOM节省: {saving:.0f}%")
print(f"减少传感器数量: {len(traditional_bom)} → {len(fusion_bom)}")

# 融合数据结构示例
class FusedCabinState:
    """摄像头-雷达融合输出"""
    # 驾驶员
    driver_fatigue: float      # 0-1 (摄像头PERCLOS + 雷达呼吸)
    driver_distraction: float   # 0-1 (摄像头视线 + 雷达距离)
    driver_gaze_zone: str       # road/instrument/phone (摄像头主导)
    driver_breathing_rate: float  # breaths/min (雷达)
    
    # 乘员
    passenger_present: bool     # 雷达+摄像头联合
    passenger_class: str        # adult/child/infant (摄像头+雷达体型)
    passenger_posture: str      # normal/oop/forward (摄像头+雷达深度)
    passenger_breathing: float  # breaths/min (雷达)
    
    # CPD
    rear_occupant_present: bool  # 雷达微动
    rear_occupant_class: str    # child/infant (雷达+摄像头)
    
    # 约束系统输入
    distance_to_wheel: float    # 雷达深度
    seatbelt_status: str         # 摄像头检测