1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
| class VirtualAssessmentPipeline:
"""
Euro NCAP 2029虚拟评估数据管道
基于NVIDIA Omniverse + OpenUSD构建
"""
def __init__(self):
self.platform = "NVIDIA Omniverse + OpenUSD"
self.scenarios = {
"CPD": self._gen_cpd_scenarios(),
"OOP": self._gen_oop_scenarios(),
"seatbelt": self._gen_seatbelt_scenarios(),
"fatigue": self._gen_fatigue_scenarios(),
"distraction": self._gen_distraction_scenarios()
}
def _gen_cpd_scenarios(self):
"""CPD仿真场景生成"""
return {
"child_seats": "100+种（各品牌/型号/朝向）",
"ages": "0-6岁（不同体型）",
"positions": "正常坐/侧倾/前倾/躺",
"occlusions": "毯子/衣物/前座遮挡/太阳帽",
"lighting": "白天/夜间/隧道/逆光",
"sensors": ["ir_camera", "rgb_camera", "radar_60ghz"],
"annotations": ["bbox", "landmark", "breathing", "classification"]
}
def _gen_oop_scenarios(self):
"""OOP仿真场景"""
return {
"postures": ["normal", "leaning_forward", "side_lean",
"feet_on_dash", "lying_down", "kneeling"],
"occupants": "50+种Metahuman（不同体型/年龄/性别）",
"seat_positions": "多角度调节",
"annotations": ["3d_pose", "pressure_map", "risk_level"]
}
def generate_dataset(self, scenario_type: str, n_samples: int = 10000):
"""生成指定类型的仿真数据集"""
config = self.scenarios.get(scenario_type, {})
return {
"samples": n_samples,
"images": f"output/{scenario_type}/images/",
"annotations": f"output/{scenario_type}/annotations/",
"metadata": config,
"cost_estimate": n_samples * 0.005,
"time_estimate": n_samples / 1000
}
pipeline = VirtualAssessmentPipeline()
for scenario, config in pipeline.scenarios.items():
result = pipeline.generate_dataset(scenario, n_samples=5000)
print(f"{scenario.upper()}: {result['samples']}帧, "
f"成本${result['cost_estimate']:.0f}, "
f"时间{result['time_estimate']:.0f}分钟")
total_cost = sum(pipeline.generate_dataset(s, 5000)['cost_estimate'] for s in pipeline.scenarios)
print(f"\n总计: 25000帧, ${total_cost:.0f}, 数小时完成")
print(f"vs 真实数据: ${25000*3:,} = ${75000:,}")
print(f"节省: {75000/total_cost:.0f}x")
|
