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| """
舱内呼吸酒精传感器可行性分析
原理：舱内空气中酒精浓度 → 推算驾驶员 BAC
挑战：环境干扰源识别与消除
"""
import numpy as np
from dataclasses import dataclass
from typing import Optional
@dataclass
class BreathSample:
"""呼吸样本"""
alcohol_ppm: float
co2_ppm: float
timestamp: float
temperature: float
humidity: float
class CabinAlcoholSensor:
"""
舱内呼吸酒精检测系统
NHTSA 当前评估的三大方案之一
"""
LEGAL_BAC = 0.08
BAC_TO_PPM_RATIO = 0.21
INTERFERENCES = {
"passenger_drinking": {"alcohol_increase_ppm": 50, "duration_min": 30},
"hand_sanitizer": {"alcohol_increase_ppm": 200, "duration_min": 5},
"perfume": {"alcohol_increase_ppm": 15, "duration_min": 20},
"food_alcohol": {"alcohol_increase_ppm": 10, "duration_min": 15},
"cleaning_products": {"alcohol_increase_ppm": 100, "duration_min": 10},
}
def __init__(self):
self.sensor_position = "steering_column"
self.detection_range = 0.5
def estimate_bac(self, sample: BreathSample,
interference_flags: dict) -> Optional[float]:
"""
估算 BAC
Args:
sample: 呼吸样本
interference_flags: 干扰标志
{"passenger_drinking": True, "hand_sanitizer": False, ...}
Returns:
估算 BAC 或 None（置信度不足）
"""
alcohol = sample.alcohol_ppm
for source, active in interference_flags.items():
if active and source in self.INTERFERENCES:
interference = self.INTERFERENCES[source]
alcohol -= interference["alcohol_increase_ppm"] * 0.5
if sample.co2_ppm < 800:
return None
estimated_bac = alcohol / 1e6 / self.BAC_TO_PPM_RATIO
confidence = self._assess_confidence(sample, interference_flags)
if confidence < 0.95:
return None
return estimated_bac
def _assess_confidence(self, sample: BreathSample,
interferences: dict) -> float:
"""评估检测置信度"""
confidence = 1.0
active_interferences = sum(1 for v in interferences.values() if v)
confidence -= active_interferences * 0.15
if sample.temperature > 35 or sample.temperature < 5:
confidence -= 0.1
if sample.humidity > 80:
confidence -= 0.05
return max(0, confidence)
def analyze_false_positive_rate(self) -> dict:
"""
误报率分析
NHTSA: 美国2270亿次出行/年
即使99.9%准确率，仍有百万次错误
"""
annual_trips = 227e9
impaired_trips = 4.2e9
for accuracy in [0.99, 0.999, 0.9999, 0.99999]:
fp_rate = 1 - accuracy
fp_count = int(annual_trips * fp_rate * 0.982)
fn_count = int(impaired_trips * fp_rate)
print(f"精度 {accuracy*100}%: 误报={fp_count:,}/年, 漏报={fn_count:,}/年")
return {
"required_accuracy": 0.99999,
"current_best": 0.95,
"gap": "需要提升4个数量级"
}
sensor = CabinAlcoholSensor()
print("=== NHTSA 误报率分析 ===")
sensor.analyze_false_positive_rate()
print("\n=== 干扰源测试 ===")
sample1 = BreathSample(alcohol_ppm=5, co2_ppm=1200, timestamp=0,
temperature=25, humidity=50)
bac1 = sensor.estimate_bac(sample1, {"passenger_drinking": False,
"hand_sanitizer": False})
print(f"正常驾驶: BAC={bac1}")
sample2 = BreathSample(alcohol_ppm=80, co2_ppm=1500, timestamp=0,
temperature=25, humidity=50)
bac2 = sensor.estimate_bac(sample2, {"passenger_drinking": True,
"hand_sanitizer": False})
print(f"乘客喝酒: BAC={bac2} (干扰已扣除)")
sample3 = BreathSample(alcohol_ppm=250, co2_ppm=900, timestamp=0,
temperature=25, humidity=50)
bac3 = sensor.estimate_bac(sample3, {"passenger_drinking": False,
"hand_sanitizer": True})
print(f"刚用洗手液: BAC={bac3} (干扰已扣除)")
|