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| """
L2自动驾驶瞳孔认知负荷监测系统
基于: Pupillary dynamics during hands-off L2 driving (2026)
核心: 持续监测基线瞳孔直径，而非等待任务事件
依赖:opencv, numpy
"""
import numpy as np
from typing import Tuple, List
from dataclasses import dataclass
@dataclass
class PupilMetrics:
"""瞳孔指标数据类"""
baseline_diameter: float
current_diameter: float
tepr: float
cognitive_load_index: float
arousal_level: float
load_trend: str
class PupillaryCognitiveMonitor:
"""
瞳孔认知负荷监测器
论文核心结论:
- 基线瞳孔直径均值是认知负荷的可靠指标
- 基线越高 → TEPR越小 (负荷饱和效应)
- 持续监测比事件触发更实用
部署: DMS摄像头 + IR补光 → 瞳孔检测 → 认知负荷评估
"""
def __init__(self,
baseline_window: int = 30,
sampling_rate: int = 60,
plr_correction: bool = True,
load_threshold: float = 0.7):
self.baseline_window = baseline_window
self.sampling_rate = sampling_rate
self.plr_correction = plr_correction
self.load_threshold = load_threshold
self.pupil_history: List[float] = []
self.luminance_history: List[float] = []
self.max_history = baseline_window * sampling_rate
self.resting_baseline = 4.0
def update(self, pupil_diameter: float, luminance: float) -> PupilMetrics:
"""
更新瞳孔数据并计算指标
Args:
pupil_diameter: 当前瞳孔直径 (mm)
luminance: 当前环境照度 (lux)
Returns:
PupilMetrics: 瞳孔指标
"""
if self.plr_correction:
corrected_diameter = self._correct_plr(pupil_diameter, luminance)
else:
corrected_diameter = pupil_diameter
self.pupil_history.append(corrected_diameter)
self.luminance_history.append(luminance)
if len(self.pupil_history) > self.max_history:
self.pupil_history.pop(0)
self.luminance_history.pop(0)
baseline = np.mean(self.pupil_history) if len(self.pupil_history) >= 10 else corrected_diameter
tepr = corrected_diameter - baseline
cognitive_index = self._compute_load_index(baseline)
arousal = self._compute_arousal(baseline)
trend = self._compute_trend()
return PupilMetrics(
baseline_diameter=baseline,
current_diameter=corrected_diameter,
tepr=tepr,
cognitive_load_index=cognitive_index,
arousal_level=arousal,
load_trend=trend
)
def _correct_plr(self, diameter: float, luminance: float) -> float:
"""
瞳孔光反射(PLR)校正
瞳孔大小同时受认知负荷和光照影响
需要去除光照引起的瞳孔变化
PLR模型: D = D_cognitive * f(luminance)
f(l) = 4.0 + 1.5 * exp(-l / 100) # 简化模型
"""
plr_predicted = 4.0 + 1.5 * np.exp(-luminance / 100)
plr_ratio = diameter / (plr_predicted + 1e-6)
corrected = self.resting_baseline * plr_ratio
return np.clip(corrected, 2.0, 8.0)
def _compute_load_index(self, baseline: float) -> float:
"""
计算认知负荷指数 (0-1)
基线瞳孔直径越大 → 负荷越高
归一化到静息基线
"""
load = (baseline - self.resting_baseline) / (self.resting_baseline * 0.5)
return np.clip(load, 0.0, 1.0)
def _compute_arousal(self, baseline: float) -> float:
"""
计算唤醒水平 (LC-NE系统代理)
蓝斑核-去甲肾上腺素系统活动
与瞳孔基线直接相关
"""
arousal = 1.0 / (1.0 + np.exp(-(baseline - self.resting_baseline) / 0.3))
return float(arousal)
def _compute_trend(self) -> str:
"""计算负荷趋势"""
if len(self.pupil_history) < 30:
return "stable"
recent = np.mean(self.pupil_history[-30:])
older = np.mean(self.pupil_history[-60:-30]) if len(self.pupil_history) >= 60 else recent
diff = recent - older
if diff > 0.1:
return "rising"
elif diff < -0.1:
return "falling"
return "stable"
def should_alert(self, metrics: PupilMetrics) -> Tuple[bool, str]:
"""是否需要发出认知负荷警告"""
if metrics.cognitive_load_index > self.load_threshold:
if metrics.load_trend == "rising":
return True, "认知负荷持续上升，建议接管"
elif metrics.cognitive_load_index > 0.85:
return True, "认知负荷过高，存在分心风险"
return False, ""
if __name__ == "__main__":
monitor = PupiliaryCognitiveMonitor(
baseline_window=30,
sampling_rate=60,
plr_correction=True
)
np.random.seed(42)
print("=== 场景1: 正常L2驾驶 ===")
for t in range(60):
pupil = 4.2 + np.random.randn() * 0.1
lux = 500 + np.random.randn() * 20
m = monitor.update(pupil, lux)
print(f"基线瞳孔: {m.baseline_diameter:.2f}mm")
print(f"认知负荷: {m.cognitive_load_index:.2f}")
print(f"唤醒水平: {m.arousal_level:.2f}")
alert, msg = monitor.should_alert(m)
print(f"警告: {alert} {msg}")
print("\n=== 场景2: 高认知负荷 ===")
for t in range(120):
pupil = 5.0 + np.random.randn() * 0.15
lux = 480 + np.random.randn() * 20
m = monitor.update(pupil, lux)
print(f"基线瞳孔: {m.baseline_diameter:.2f}mm")
print(f"认知负荷: {m.cognitive_load_index:.2f}")
print(f"唤醒水平: {m.arousal_level:.2f}")
print(f"趋势: {m.load_trend}")
alert, msg = monitor.should_alert(m)
print(f"警告: {alert} {msg}")
print("\n=== 场景3: 极端认知负荷 ===")
for t in range(120):
pupil = 5.8 + np.random.randn() * 0.2
lux = 460 + np.random.randn() * 20
m = monitor.update(pupil, lux)
print(f"基线瞳孔: {m.baseline_diameter:.2f}mm")
print(f"认知负荷: {m.cognitive_load_index:.2f}")
alert, msg = monitor.should_alert(m)
print(f"警告: {alert} {msg}")
|
