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| """
Minieye DFR 分级干预模型
DFR将DMS从"被动提醒"升级为"主动安全网关"
根据驾驶员功能准备度分数+ADAS场景，触发分级干预
"""
from dataclasses import dataclass
from enum import Enum
class InterventionLevel(Enum):
"""干预等级"""
NONE = 0
ALERT = 1
ASSIST = 2
TAKEOVER = 3
@dataclass
class DFRInput:
"""DFR输入数据"""
perclos: float
gaze_offroad: float
alcohol_impairment: float
head_pose_stable: bool
driver_identity: str
emotion_state: str
vehicle_speed: float
following_distance: float
ttc: float
lane_deviation: float
pedestrian_ahead: bool
class DFRModel:
"""驾驶员功能准备度模型"""
def __init__(self):
self.weights = {
"fatigue": 0.25,
"distraction": 0.20,
"alcohol": 0.30,
"head_stability": 0.10,
"emotion": 0.15
}
def assess(self, inp: DFRInput) -> dict:
"""评估功能准备度"""
fatigue_score = (1 - inp.perclos) * 100
distraction_score = (1 - inp.gaze_offroad) * 100
alcohol_score = (1 - inp.alcohol_impairment) * 100
head_score = 100 if inp.head_pose_stable else 50
emotion_score = self._emotion_score(inp.emotion_state)
dfr_score = (
fatigue_score * self.weights["fatigue"] +
distraction_score * self.weights["distraction"] +
alcohol_score * self.weights["alcohol"] +
head_score * self.weights["head_stability"] +
emotion_score * self.weights["emotion"]
)
if inp.ttc < 3.0:
dfr_score *= 0.7
elif inp.ttc < 5.0:
dfr_score *= 0.85
level = self._decide_level(dfr_score, inp)
return {
"dfr_score": round(dfr_score, 1),
"fatigue_score": round(fatigue_score, 1),
"distraction_score": round(distraction_score, 1),
"alcohol_score": round(alcohol_score, 1),
"intervention_level": level,
"action": self._get_action(level, inp)
}
def _emotion_score(self, emotion: str) -> float:
scores = {
"neutral": 90, "happy": 95, "calm": 92,
"angry": 40, "sad": 60, "stressed": 35,
"drowsy": 20, "unknown": 70
}
return scores.get(emotion, 70)
def _decide_level(self, score: float, inp: DFRInput) -> InterventionLevel:
"""决策干预等级"""
if score < 30 or inp.alcohol_impairment > 0.7:
return InterventionLevel.TAKEOVER
elif score < 50 or (inp.ttc < 3.0 and score < 70):
return InterventionLevel.ASSIST
elif score < 70:
return InterventionLevel.ALERT
else:
return InterventionLevel.NONE
def _get_action(self, level: InterventionLevel, inp: DFRInput) -> str:
actions = {
InterventionLevel.NONE: "正常驾驶",
InterventionLevel.ALERT: f"座舱警告: 疲劳/分心提醒",
InterventionLevel.ASSIST: f"ADAS增强: 增大跟车距离至{max(inp.following_distance*1.5, 50):.0f}m",
InterventionLevel.TAKEOVER: "ADAS接管: 减速靠边停车"
}
return actions[level]
if __name__ == "__main__":
model = DFRModel()
normal = DFRInput(
perclos=0.05, gaze_offroad=0.1, alcohol_impairment=0.0,
head_pose_stable=True, driver_identity="Alice",
emotion_state="neutral", vehicle_speed=80,
following_distance=40, ttc=10.0,
lane_deviation=0.1, pedestrian_ahead=False
)
fatigued = DFRInput(
perclos=0.35, gaze_offroad=0.2, alcohol_impairment=0.0,
head_pose_stable=True, driver_identity="Alice",
emotion_state="drowsy", vehicle_speed=80,
following_distance=20, ttc=2.5,
lane_deviation=0.3, pedestrian_ahead=True
)
alcohol = DFRInput(
perclos=0.1, gaze_offroad=0.15, alcohol_impairment=0.8,
head_pose_stable=False, driver_identity="Bob",
emotion_state="neutral", vehicle_speed=60,
following_distance=30, ttc=8.0,
lane_deviation=0.2, pedestrian_ahead=False
)
for name, inp in [("正常", normal), ("疲劳+紧急", fatigued), ("酒驾", alcohol)]:
result = model.assess(inp)
print(f"\n=== {name} ===")
print(f"DFR分数: {result['dfr_score']}")
print(f"疲劳: {result['fatigue_score']} | 分心: {result['distraction_score']} | 酒驾: {result['alcohol_score']}")
print(f"干预等级: {result['intervention_level'].name}")
print(f"动作: {result['action']}")
|
