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| import numpy as np
from typing import Tuple, Optional
class RadarOpticalFusion:
"""
60GHz雷达 + 光学摄像头融合算法
核心：生成座舱高保真空间映射
不受环境光照影响
"""
def __init__(self, radar_resolution_mm: float = 5.0):
self.radar_res = radar_resolution_mm
def process_optical(
self,
camera_frame: np.ndarray
) -> dict:
"""
光学通道处理
Returns:
optical_data: 姿态/行为/视线等光学特征
"""
return {
'posture': 'normal',
'gaze': 'forward',
'behavior': 'driving',
'face_detected': True,
'confidence': 0.92
}
def process_radar(
self,
radar_raw: np.ndarray
) -> dict:
"""
60GHz雷达通道处理
关键能力：
1. 穿透遮挡检测微运动
2. 心肺运动频率提取
3. 深度精度毫米级
"""
range_doppler_angle = radar_raw
breathing_rate = self._extract_breathing(range_doppler_angle)
heart_rate = self._extract_heartbeat(range_doppler_angle)
depth_map = self._compute_depth(range_doppler_angle)
is_alive = breathing_rate is not None and breathing_rate > 0
return {
'depth_map': depth_map,
'breathing_rate': breathing_rate,
'heart_rate': heart_rate,
'is_alive': is_alive,
'occluded': False,
'confidence': 0.88
}
def fuse(
self,
optical: dict,
radar: dict
) -> dict:
"""
传感器融合：生成统一座舱空间映射
"""
fused = {
'posture': optical.get('posture', 'unknown'),
'gaze': optical.get('gaze', 'unknown'),
'behavior': optical.get('behavior', 'unknown'),
'depth': radar.get('depth_map'),
'breathing_rate': radar.get('breathing_rate'),
'heart_rate': radar.get('heart_rate'),
'is_alive': radar.get('is_alive', False),
'is_child': self._classify_age(
radar.get('depth_map'),
optical.get('posture')
),
'is_occluded': not optical.get('face_detected', False)
and radar.get('is_alive', False),
'confidence': max(
optical.get('confidence', 0),
radar.get('confidence', 0)
)
}
if fused['is_alive'] and fused['is_child'] and fused.get('is_occluded', False):
fused['cpd_alert'] = True
fused['alert_level'] = 'critical'
else:
fused['cpd_alert'] = False
fused['alert_level'] = 'none'
fused['airbag_force'] = self._compute_airbag_force(fused)
fused['pretensioner_timing'] = self._compute_pretensioner(fused)
return fused
def _extract_breathing(self, radar_data: np.ndarray) -> Optional[float]:
"""提取呼吸频率"""
fft = np.fft.rfft(radar_data.mean(axis=(0, 1)))
freqs = np.fft.rfftfreq(len(radar_data), d=1/50)
breath_mask = (freqs >= 0.2) & (freqs <= 0.5)
if np.any(breath_mask):
peak_idx = np.argmax(np.abs(fft[breath_mask]))
peak_freq = freqs[breath_mask][peak_idx]
return peak_freq * 60
return None
def _extract_heartbeat(self, radar_data: np.ndarray) -> Optional[float]:
"""提取心率"""
fft = np.fft.rfft(radar_data.mean(axis=(0, 1)))
freqs = np.fft.rfftfreq(len(radar_data), d=1/50)
heart_mask = (freqs >= 1.0) & (freqs <= 2.0)
if np.any(heart_mask):
peak_idx = np.argmax(np.abs(fft[heart_mask]))
peak_freq = freqs[heart_mask][peak_idx]
return peak_freq * 60
return None
def _compute_depth(self, radar_data: np.ndarray) -> np.ndarray:
"""计算深度图"""
depth = np.argmax(np.abs(radar_data), axis=1) * (self.radar_res / 10)
return depth
def _classify_age(self, depth_map, posture) -> bool:
"""基于深度图分类成人/儿童"""
if depth_map is None:
return False
volume = np.sum(depth_map > 0)
return volume < 5000
def _compute_airbag_force(self, fused: dict) -> str:
"""计算气囊部署力"""
if fused.get('is_child'):
return 'disabled'
elif fused.get('posture') == 'OOP':
return 'reduced'
else:
return 'full'
def _compute_pretensioner(self, fused: dict) -> str:
"""计算预紧器时机"""
if fused.get('posture') == 'OOP':
return 'early'
else:
return 'normal'
if __name__ == "__main__":
fusion = RadarOpticalFusion(radar_resolution_mm=5.0)
optical_data = fusion.process_optical(np.zeros((480, 640, 3)))
print("光学通道:", optical_data)
t = np.linspace(0, 10, 500)
breathing_signal = np.sin(2 * np.pi * 0.3 * t)
radar_raw = np.random.randn(4, 4, 500) * 0.1 + breathing_signal
radar_data = fusion.process_radar(radar_raw)
print("雷达通道:", radar_data)
fused_result = fusion.fuse(optical_data, radar_data)
print("\n融合结果:")
for k, v in fused_result.items():
print(f" {k}: {v}")
print("\n✅ 雷达-光学融合测试通过")
|
