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| """
多传感器融合 DMS 架构
融合层级：
1. 数据层融合（原始数据）
2. 特征层融合（中间特征）
3. 决策层融合（最终输出）
"""
import numpy as np
from typing import Dict, List
class MultiSensorFusionDMS:
"""多传感器融合 DMS"""
def __init__(self):
self.camera_processor = CameraProcessor()
self.radar_processor = RadarProcessor()
self.steering_processor = SteeringProcessor()
self.seat_processor = SeatProcessor()
self.feature_fusion = FeatureFusion()
self.decision_fusion = DecisionFusion()
def process(self, sensor_data: Dict) -> Dict:
"""
处理多传感器数据
Args:
sensor_data: {
"camera": frame,
"radar": radar_frame,
"steering": torque_data,
"seat": pressure_map
}
Returns:
融合后的检测结果
"""
camera_features = self.camera_processor.extract_features(
sensor_data.get("camera")
)
radar_features = self.radar_processor.extract_features(
sensor_data.get("radar")
)
steering_features = self.steering_processor.extract_features(
sensor_data.get("steering")
)
seat_features = self.seat_processor.extract_features(
sensor_data.get("seat")
)
fused_features = self.feature_fusion.fuse({
"camera": camera_features,
"radar": radar_features,
"steering": steering_features,
"seat": seat_features
})
camera_decision = self.camera_processor.decide(camera_features)
radar_decision = self.radar_processor.decide(radar_features)
steering_decision = self.steering_processor.decide(steering_features)
final_decision = self.decision_fusion.fuse({
"camera": camera_decision,
"radar": radar_decision,
"steering": steering_decision
})
return {
"fatigue_level": final_decision.get("fatigue", "none"),
"distraction_level": final_decision.get("distraction", "none"),
"occupancy": fused_features.get("occupancy", True),
"vital_signs": fused_features.get("vital_signs"),
"confidence": final_decision.get("confidence", 0.0)
}
class CameraProcessor:
"""摄像头处理器"""
def extract_features(self, frame) -> dict:
if frame is None:
return {}
return {
"face_landmarks": [],
"gaze_vector": [],
"eye_closure": 0.3,
"head_pose": []
}
def decide(self, features) -> dict:
return {
"fatigue": "moderate" if features.get("eye_closure", 0) > 0.5 else "none",
"confidence": 0.85
}
class RadarProcessor:
"""雷达处理器"""
def extract_features(self, frame) -> dict:
if frame is None:
return {}
return {
"occupancy": True,
"breathing_rate": 16,
"micro_movement": True
}
def decide(self, features) -> dict:
return {
"fatigue": "none",
"confidence": 0.7
}
class SteeringProcessor:
"""方向盘处理器"""
def extract_features(self, data) -> dict:
if data is None:
return {}
return {
"torque_variance": 0.5,
"hands_on_wheel": True,
"correction_frequency": 2.0
}
def decide(self, features) -> dict:
if features.get("torque_variance", 0) > 1.0:
return {"fatigue": "moderate", "confidence": 0.6}
return {"fatigue": "none", "confidence": 0.6}
class SeatProcessor:
"""座椅处理器"""
def extract_features(self, data) -> dict:
if data is None:
return {}
return {
"posture": "normal",
"pressure_distribution": []
}
class FeatureFusion:
"""特征融合"""
def fuse(self, features_dict: Dict) -> dict:
"""融合多传感器特征"""
fused = {}
for sensor, features in features_dict.items():
if features:
fused.update(features)
return fused
class DecisionFusion:
"""决策融合"""
def __init__(self):
self.weights = {
"camera": 0.5,
"radar": 0.2,
"steering": 0.3
}
def fuse(self, decisions: Dict) -> dict:
"""融合决策"""
fatigue_votes = {"none": 0, "mild": 0, "moderate": 0, "severe": 0}
for sensor, decision in decisions.items():
level = decision.get("fatigue", "none")
weight = self.weights.get(sensor, 0.3)
confidence = decision.get("confidence", 0.5)
fatigue_votes[level] += weight * confidence
final_fatigue = max(fatigue_votes, key=fatigue_votes.get)
final_confidence = fatigue_votes[final_fatigue]
return {
"fatigue": final_fatigue,
"confidence": final_confidence
}
if __name__ == "__main__":
dms = MultiSensorFusionDMS()
sensor_data = {
"camera": np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8),
"radar": np.random.randn(64, 256),
"steering": {"torque": 1.5},
"seat": {"pressure": np.random.randn(16, 16)}
}
result = dms.process(sensor_data)
print(f"疲劳等级: {result['fatigue_level']}")
print(f"置信度: {result['confidence']:.2%}")
|
