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| """
CPD 自动化测试管道
基于: Zyberaj et al., arXiv:2608.19752
功能:
1. 部署感知场景生成
2. CPD 算法测试
3. 自动评估与报告
依赖:
pip install numpy dataclasses json
"""
import numpy as np
from dataclasses import dataclass, field
from typing import List, Dict, Optional
from enum import Enum
import json
import time
class VehicleType(Enum):
SEDAN = "sedan"
SUV = "suv"
VAN = "van"
class SensorType(Enum):
UWB = "uwb"
RADAR_60GHZ = "60ghz"
CAMERA = "camera"
MICROPHONE = "microphone"
@dataclass
class DeploymentConfig:
"""部署配置"""
vehicle_type: VehicleType = VehicleType.SUV
sensors: List[SensorType] = field(default_factory=lambda: [
SensorType.UWB, SensorType.RADAR_60GHZ, SensorType.CAMERA
])
num_seats: int = 5
has_trunk: bool = True
climate_zone: str = "temperate"
temperature_range: tuple = (-20, 50)
humidity_range: tuple = (20, 80)
@dataclass
class CPDTestScenario:
"""CPD 测试场景"""
name: str
occupant_position: str
occupant_type: str
obstruction: str
environment: str
expected_detection: bool
response_time_limit: float = 6.0
class CPDTestPipeline:
"""CPD 自动化测试管道"""
def __init__(self, config: DeploymentConfig):
self.config = config
self.scenarios: List[CPDTestScenario] = []
self.results: List[dict] = []
def generate_scenarios(self) -> List[CPDTestScenario]:
"""根据部署配置动态生成测试场景"""
scenarios = []
positions = ['front_passenger', 'rear_left', 'rear_right']
if self.config.has_trunk:
positions.append('trunk')
occupants = ['adult', 'child', 'infant']
obstructions = ['none', 'blanket', 'seat', 'clothing']
environments = ['normal', 'dark', 'hot', 'cold']
for pos in positions:
for occ in occupants:
for obs in obstructions:
for env in environments:
detectable = self._is_detectable(pos, occ, obs)
scenario = CPDTestScenario(
name=f"{occ}_{pos}_{obs}_{env}",
occupant_position=pos,
occupant_type=occ,
obstruction=obs,
environment=env,
expected_detection=detectable
)
scenarios.append(scenario)
for pos in positions:
for env in environments:
scenarios.append(CPDTestScenario(
name=f"empty_{pos}_{env}",
occupant_position=pos,
occupant_type='none',
obstruction='none',
environment=env,
expected_detection=False
))
self.scenarios = scenarios
return scenarios
def _is_detectable(self, position: str, occupant: str, obstruction: str) -> bool:
"""判断场景是否应被检测到"""
if occupant == 'none':
return False
if SensorType.UWB in self.config.sensors:
return True
if SensorType.RADAR_60GHZ in self.config.sensors:
if obstruction in ['blanket', 'seat'] and occupant == 'infant':
return False
return True
if SensorType.CAMERA in self.config.sensors:
if obstruction != 'none':
return False
if position == 'trunk':
return False
return True
return True
def run_test(self, scenario: CPDTestScenario) -> dict:
"""运行单个测试场景"""
detection_result = self._simulate_cpd(scenario)
correct = detection_result['detected'] == scenario.expected_detection
tp = detection_result['detected'] and scenario.expected_detection
fp = detection_result['detected'] and not scenario.expected_detection
fn = not detection_result['detected'] and scenario.expected_detection
tn = not detection_result['detected'] and not scenario.expected_detection
return {
'scenario': scenario.name,
'expected': scenario.expected_detection,
'detected': detection_result['detected'],
'response_time': detection_result['response_time'],
'confidence': detection_result['confidence'],
'correct': correct,
'outcome': 'TP' if tp else ('FP' if fp else ('FN' if fn else 'TN'))
}
def _simulate_cpd(self, scenario: CPDTestScenario) -> dict:
"""模拟 CPD 算法"""
if scenario.occupant_type == 'none':
false_positive_rate = 0.02
detected = np.random.random() < false_positive_rate
return {
'detected': detected,
'response_time': np.random.exponential(3.0) if detected else 0,
'confidence': np.random.uniform(0.6, 0.8) if detected else 0.1
}
base_prob = 0.95
obs_penalty = {
'none': 0.0,
'blanket': 0.05,
'seat': 0.10,
'clothing': 0.03
}
env_penalty = {
'normal': 0.0,
'dark': 0.05,
'hot': 0.02,
'cold': 0.03
}
occ_bonus = {
'adult': 0.03,
'child': 0.0,
'infant': -0.05
}
sensor_bonus = len(self.config.sensors) * 0.01
prob = (base_prob
- obs_penalty.get(scenario.obstruction, 0)
- env_penalty.get(scenario.environment, 0)
+ occ_bonus.get(scenario.occupant_type, 0)
+ sensor_bonus)
detected = np.random.random() < prob
response_time = np.random.uniform(1.0, 5.5) if detected else 0
return {
'detected': detected,
'response_time': response_time,
'confidence': np.random.uniform(0.85, 0.99) if detected else 0.1
}
def run_all_tests(self) -> Dict:
"""运行所有测试"""
self.results = []
for scenario in self.scenarios:
result = self.run_test(scenario)
self.results.append(result)
return self.generate_report()
def generate_report(self) -> Dict:
"""生成测试报告"""
total = len(self.results)
correct = sum(1 for r in self.results if r['correct'])
tp = sum(1 for r in self.results if r['outcome'] == 'TP')
fp = sum(1 for r in self.results if r['outcome'] == 'FP')
fn = sum(1 for r in self.results if r['outcome'] == 'FN')
tn = sum(1 for r in self.results if r['outcome'] == 'TN')
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
response_times = [r['response_time'] for r in self.results if r['detected']]
return {
'total_scenarios': total,
'correct_predictions': correct,
'accuracy': round(correct / total * 100, 1) if total else 0,
'true_positives': tp,
'false_positives': fp,
'false_negatives': fn,
'true_negatives': tn,
'precision': round(precision * 100, 1),
'recall': round(recall * 100, 1),
'f1_score': round(f1 * 100, 1),
'mean_response_time': round(np.mean(response_times), 2) if response_times else 0,
'max_response_time': round(max(response_times), 2) if response_times else 0,
'false_alarm_rate_per_24h': round(fp / total * 24, 2),
}
if __name__ == "__main__":
print("=" * 70)
print("CPD 自动化测试管道")
print("基于: Zyberaj et al., arXiv:2608.19752")
print("=" * 70)
config = DeploymentConfig(
vehicle_type=VehicleType.SUV,
sensors=[SensorType.UWB, SensorType.RADAR_60GHZ, SensorType.CAMERA],
num_seats=5,
has_trunk=True,
)
print(f"\n部署配置:")
print(f" 车型: {config.vehicle_type.value}")
print(f" 传感器: {[s.value for s in config.sensors]}")
print(f" 座位数: {config.num_seats}")
print(f" 有后备箱: {config.has_trunk}")
pipeline = CPDTestPipeline(config)
scenarios = pipeline.generate_scenarios()
print(f"\n生成测试场景: {len(scenarios)} 个")
by_type = {}
for s in scenarios:
by_type[s.occupant_type] = by_type.get(s.occupant_type, 0) + 1
print(f" 乘员类型分布: {by_type}")
by_env = {}
for s in scenarios:
by_env[s.environment] = by_env.get(s.environment, 0) + 1
print(f" 环境条件分布: {by_env}")
print(f"\n运行测试...")
report = pipeline.run_all_tests()
print(f"\n{'='*70}")
print("测试报告")
print(f"{'='*70}")
print(f"\n{'指标':<25s} {'数值':>10s}")
print("-" * 40)
for key, val in report.items():
if isinstance(val, float):
print(f" {key:<23s} {val:>10.2f}")
else:
print(f" {key:<23s} {val:>10}")
print(f"\n{'='*70}")
print("不同传感器配置对比")
print(f"{'='*70}")
configs = [
("UWB + 60GHz + Camera", [SensorType.UWB, SensorType.RADAR_60GHZ, SensorType.CAMERA]),
("UWB + 60GHz", [SensorType.UWB, SensorType.RADAR_60GHZ]),
("60GHz + Camera", [SensorType.RADAR_60GHZ, SensorType.CAMERA]),
("仅 UWB", [SensorType.UWB]),
("仅 60GHz", [SensorType.RADAR_60GHZ]),
("仅 Camera", [SensorType.CAMERA]),
]
print(f"\n{'传感器组合':<25s} {'准确率':>8s} {'精确率':>8s} {'召回率':>8s} {'F1':>6s} {'误报/24h':>10s}")
print("-" * 68)
for name, sensors in configs:
cfg = DeploymentConfig(sensors=sensors)
pipe = CPDTestPipeline(cfg)
pipe.generate_scenarios()
rep = pipe.run_all_tests()
print(f"{name:<25s} {rep['accuracy']:>7.1f}% {rep['precision']:>7.1f}% "
f"{rep['recall']:>7.1f}% {rep['f1_score']:>5.1f} {rep['false_alarm_rate_per_24h']:>9.2f}")
|
