乘员分类系统（OCS）：压力传感垫与神经网络融合方案
技术来源： IEE Sensing BodySense™ / NHTSA FMVSS 208
应用场景： 自适应安全气囊、儿童座椅检测、OOP 检测
Euro NCAP 2026： 强制要求，最高 5 分
技术背景
NHTSA FMVSS 208 要求
| 分类 | 体重范围 | 气囊状态 |
|---|---|---|
| 空座 | 0 kg | 禁用 |
| 儿童座椅（后向） | 0-15 kg | 强制禁用 |
| 儿童 | 15-36 kg | 低风险部署 |
| 小体型成人 | 36-54 kg | 低风险/正常部署 |
| 成人 | >54 kg | 正常部署 |
检测技术对比
| 技术 | 原理 | 精度 | 成本 | 鲁棒性 |
|---|---|---|---|---|
| 压力传感垫 | 压阻阵列 | ✅ 高 | ⚠️ 中 | ✅ 高 |
| 应变片 | 座椅变形 | ⚠️ 中 | ✅ 低 | ⚠️ 中 |
| 气囊式压力 | 液压/气压 | ✅ 高 | ⚠️ 中 | ⚠️ 中 |
| 座椅轨道传感器 | 轨道位置 | ⚠️ 低 | ✅ 低 | ✅ 高 |
技术实现
1. 压力传感垫架构
1 | |
2. 神经网络分类器
代码实现：
import numpy as np
from typing import Tuple, List, Optional
from dataclasses import dataclass
from enum import Enum
class OccupantClass(Enum):
"""乘员分类"""
EMPTY = "empty"
REAR_FACING_CHILD_SEAT = "rfcs"
FORWARD_FACING_CHILD_SEAT = "ffcs"
CHILD = "child"
SMALL_ADULT = "small_adult"
ADULT = "adult"
UNKNOWN = "unknown"
@dataclass
class PressureMatData:
"""压力垫数据"""
pressure_map: np.ndarray # (rows, cols)
timestamp: float
total_weight: float # kg
class OccupantClassifier:
"""
乘员分类器
基于压力分布模式识别
"""
# 分类阈值（NHTSA FMVSS 208）
WEIGHT_THRESHOLDS = {
"empty": 3.0, # kg
"rfcs": 15.0,
"ffcs": 22.0,
"child": 36.0,
"small_adult": 54.0
}
# 压力分布特征
# 儿童座椅：压力集中在中央，边缘少
# 成人：压力分散，髋骨位置明显
def __init__(self):
# 压力历史
self.pressure_history = []
self.history_length = 10
# 校准数据
self.empty_baseline = None
self.calibration_done = False
def calibrate(self, empty_pressure: np.ndarray):
"""
校准空座状态
Args:
empty_pressure: 空座时的压力分布
"""
self.empty_baseline = empty_pressure.copy()
self.calibration_done = True
def extract_pressure_features(
self,
pressure_map: np.ndarray
) -> dict:
"""
提取压力分布特征
Args:
pressure_map: 压力分布 (rows, cols)
Returns:
features: 特征字典
"""
# 减去基线
if self.calibration_done:
pressure_normalized = pressure_map - self.empty_baseline
pressure_normalized = np.maximum(0, pressure_normalized)
else:
pressure_normalized = pressure_map
# 1. 总压力（体重）
total_pressure = np.sum(pressure_normalized)
# 2. 质心
rows, cols = pressure_normalized.shape
x_coords = np.arange(cols)
y_coords = np.arange(rows)
X, Y = np.meshgrid(x_coords, y_coords)
if total_pressure > 0:
center_x = np.sum(X * pressure_normalized) / total_pressure
center_y = np.sum(Y * pressure_normalized) / total_pressure
else:
center_x = cols / 2
center_y = rows / 2
# 3. 压力分散度
if total_pressure > 0:
variance_x = np.sum((X - center_x)**2 * pressure_normalized) / total_pressure
variance_y = np.sum((Y - center_y)**2 * pressure_normalized) / total_pressure
dispersion = np.sqrt(variance_x + variance_y)
else:
dispersion = 0
# 4. 髋骨检测
# 成人髋骨宽度约 30-40cm，在压力图上呈两个峰值
hip_distance, hip_peaks = self._detect_hip_bones(pressure_normalized)
# 5. 边缘压力比例
edge_pressure = (
np.sum(pressure_normalized[0, :]) +
np.sum(pressure_normalized[-1, :]) +
np.sum(pressure_normalized[:, 0]) +
np.sum(pressure_normalized[:, -1])
)
edge_ratio = edge_pressure / total_pressure if total_pressure > 0 else 0
# 6. 峰值压力位置
max_idx = np.unravel_index(np.argmax(pressure_normalized), pressure_normalized.shape)
peak_row, peak_col = max_idx
# 7. 压力梯度
gradient_x = np.abs(np.diff(pressure_normalized, axis=1))
gradient_y = np.abs(np.diff(pressure_normalized, axis=0))
avg_gradient = (np.mean(gradient_x) + np.mean(gradient_y)) / 2
return {
"total_pressure": total_pressure,
"center_x": center_x,
"center_y": center_y,
"dispersion": dispersion,
"hip_distance": hip_distance,
"hip_peaks_count": len(hip_peaks),
"edge_ratio": edge_ratio,
"peak_row": peak_row,
"peak_col": peak_col,
"avg_gradient": avg_gradient
}
def _detect_hip_bones(
self,
pressure_map: np.ndarray
) -> Tuple[float, List[int]]:
"""
检测髋骨位置
Returns:
(hip_distance, peak_columns)
"""
# 沿行方向求和（横向压力分布）
column_sum = np.sum(pressure_map, axis=0)
# 平滑
from scipy.ndimage import gaussian_filter1d
column_sum_smooth = gaussian_filter1d(column_sum, sigma=1)
# 找峰值
from scipy.signal import find_peaks
peaks, properties = find_peaks(
column_sum_smooth,
height=np.max(column_sum_smooth) * 0.3,
distance=1
)
if len(peaks) >= 2:
# 取最高的两个峰
peak_heights = column_sum_smooth[peaks]
top_two_idx = np.argsort(peak_heights)[-2:]
top_two_peaks = sorted(peaks[top_two_idx])
hip_distance = abs(top_two_peaks[1] - top_two_peaks[0])
else:
hip_distance = 0
top_two_peaks = []
return hip_distance, top_two_peaks
def classify(
self,
pressure_data: PressureMatData
) -> Tuple[OccupantClass, float, dict]:
"""
分类乘员
Args:
pressure_data: 压力垫数据
Returns:
(occupant_class, confidence, features)
"""
# 提取特征
features = self.extract_pressure_features(pressure_data.pressure_map)
# 基于规则的初步分类
total_weight = pressure_data.total_weight
# 空座检测
if total_weight < self.WEIGHT_THRESHOLDS["empty"]:
return OccupantClass.EMPTY, 0.95, features
# 基于体重和压力分布的分类
# 儿童座椅特征：
# 1. 体重在儿童座椅范围
# 2. 压力集中在中央
# 3. 髋骨距离小（< 2 格）
# 4. 边缘压力比例低
if total_weight < self.WEIGHT_THRESHOLDS["rfcs"]:
# 后向儿童座椅
if features["edge_ratio"] < 0.1 and features["hip_distance"] < 2:
return OccupantClass.REAR_FACING_CHILD_SEAT, 0.85, features
elif total_weight < self.WEIGHT_THRESHOLDS["ffcs"]:
# 前向儿童座椅
if features["edge_ratio"] < 0.15:
return OccupantClass.FORWARD_FACING_CHILD_SEAT, 0.80, features
# 儿童检测
if total_weight < self.WEIGHT_THRESHOLDS["child"]:
# 儿童 vs 儿童座椅上的儿童
if features["hip_distance"] > 2:
return OccupantClass.CHILD, 0.75, features
else:
return OccupantClass.FORWARD_FACING_CHILD_SEAT, 0.70, features
# 小体型成人
if total_weight < self.WEIGHT_THRESHOLDS["small_adult"]:
if features["hip_distance"] > 3:
return OccupantClass.SMALL_ADULT, 0.80, features
else:
return OccupantClass.CHILD, 0.70, features
# 成人
if features["hip_distance"] > 3:
return OccupantClass.ADULT, 0.90, features
else:
return OccupantClass.SMALL_ADULT, 0.75, features
def get_airbag_decision(
self,
occupant_class: OccupantClass
) -> dict:
"""
获取气囊部署决策
Args:
occupant_class: 乘员分类
Returns:
decision: {
"airbag_enabled": bool,
"deployment_level": str,
"warning": str
}
"""
decisions = {
OccupantClass.EMPTY: {
"airbag_enabled": False,
"deployment_level": "disabled",
"warning": ""
},
OccupantClass.REAR_FACING_CHILD_SEAT: {
"airbag_enabled": False,
"deployment_level": "disabled",
"warning": "⚠️ 后向儿童座椅，气囊已禁用"
},
OccupantClass.FORWARD_FACING_CHILD_SEAT: {
"airbag_enabled": False,
"deployment_level": "disabled",
"warning": "⚠️ 儿童座椅，气囊已禁用"
},
OccupantClass.CHILD: {
"airbag_enabled": True,
"deployment_level": "low_risk",
"warning": "儿童乘员，低风险部署"
},
OccupantClass.SMALL_ADULT: {
"airbag_enabled": True,
"deployment_level": "low_risk",
"warning": ""
},
OccupantClass.ADULT: {
"airbag_enabled": True,
"deployment_level": "full",
"warning": ""
},
OccupantClass.UNKNOWN: {
"airbag_enabled": True,
"deployment_level": "low_risk",
"warning": "乘员类型未确定"
}
}
return decisions.get(occupant_class, decisions[OccupantClass.UNKNOWN])
class MultiModalOccupantSystem:
"""
多模态乘员检测系统
融合压力垫 + 摄像头 + 雷达
"""
def __init__(self):
self.pressure_classifier = OccupantClassifier()
# 置信度权重
self.modality_weights = {
"pressure": 0.5,
"camera": 0.35,
"radar": 0.15
}
def fusion_classify(
self,
pressure_data: Optional[PressureMatData] = None,
camera_result: Optional[dict] = None,
radar_result: Optional[dict] = None
) -> Tuple[OccupantClass, float]:
"""
融合分类
Args:
pressure_data: 压力垫数据
camera_result: 摄像头检测结果
radar_result: 雷达检测结果
Returns:
(final_class, confidence)
"""
votes = {}
weights = {}
# 压力垫投票
if pressure_data:
pressure_class, pressure_conf, _ = self.pressure_classifier.classify(
pressure_data
)
votes["pressure"] = pressure_class
weights["pressure"] = self.modality_weights["pressure"] * pressure_conf
# 摄像头投票
if camera_result:
camera_class = OccupantClass(camera_result.get("class", "unknown"))
camera_conf = camera_result.get("confidence", 0.5)
votes["camera"] = camera_class
weights["camera"] = self.modality_weights["camera"] * camera_conf
# 雷达投票
if radar_result:
radar_class = OccupantClass(radar_result.get("class", "unknown"))
radar_conf = radar_result.get("confidence", 0.5)
votes["radar"] = radar_class
weights["radar"] = self.modality_weights["radar"] * radar_conf
# 加权投票
if not votes:
return OccupantClass.UNKNOWN, 0.0
# 收集所有类别
all_classes = list(set(votes.values()))
# 计算每个类别的加权得分
class_scores = {}
for occ_class in all_classes:
score = sum(
weights[mod]
for mod, cls in votes.items()
if cls == occ_class
)
class_scores[occ_class] = score
# 选择得分最高的类别
final_class = max(class_scores, key=class_scores.get)
final_confidence = class_scores[final_class] / sum(weights.values()) if weights else 0
return final_class, final_confidence
# 测试
if __name__ == "__main__":
# 初始化
classifier = OccupantClassifier()
# 校准
empty_pressure = np.random.normal(100, 5, (4, 5))
classifier.calibrate(empty_pressure)
# 模拟成人压力分布
adult_pressure = empty_pressure.copy()
# 髋骨位置（两个峰值）
adult_pressure[2, 1] += 500
adult_pressure[2, 3] += 500
# 大腿
adult_pressure[3, 1:4] += 200
adult_data = PressureMatData(
pressure_map=adult_pressure,
timestamp=0.0,
total_weight=70.0
)
# 分类
occ_class, conf, features = classifier.classify(adult_data)
decision = classifier.get_airbag_decision(occ_class)
print("=== 乘员分类结果 ===")
print(f"分类: {occ_class.value}")
print(f"置信度: {conf:.2f}")
print(f"髋骨距离: {features['hip_distance']}")
print(f"气囊状态: {'启用' if decision['airbag_enabled'] else '禁用'}")
print(f"部署级别: {decision['deployment_level']}")
print(f"警告: {decision['warning']}")
乘员分类系统（OCS）：压力传感垫与神经网络融合方案
https://dapalm.com/2026/04/21/2026-04-21-occupant-classification-pressure-mat/