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| """
Camera-in-the-Loop验证系统
适用于DMS/OMS摄像头验证
"""
class CITLSystem:
"""
Camera-in-the-Loop验证系统
组件：
1. 仿真场景渲染器（Aurelion/Unreal Engine）
2. 高分辨率OLED显示屏
3. 物理IR/RGB摄像头
4. DMS/OMS算法
5. 评估框架
"""
def __init__(self, config: dict):
self.display = {
'type': 'OLED',
'resolution': config.get('display_res', (3840, 2160)),
'refresh_rate': config.get('display_fps', 120),
'brightness': config.get('brightness', 1000),
'contrast_ratio': '100000:1',
'response_time': '0.1ms',
'color_gamut': 'DCI-P3 98%'
}
self.camera = {
'sensor': config.get('sensor', 'AR0823AT'),
'resolution': (3840, 2160),
'fps': 60,
'lens': '6mm C-mount',
'ir_filter': '850nm bandpass',
'positioning': 'precision_6dof_stage'
}
self.scene_params = {
'driver_models': ['adult_male_1', 'adult_female_1',
'child_8yr', 'infant_rear_seat'],
'lighting': ['daylight_500lux', 'tunnel_transition',
'night_940nm_IR', 'dusk_50lux'],
'behaviors': ['normal_driving', 'phone_call',
'drowsy_eyelid_drop', 'looking_away_3s',
'yawning', 'head_drop'],
'environment': ['highway', 'urban', 'parking_lot']
}
def run_scenario(self, scenario: dict) -> dict:
"""
执行单个测试场景
Args:
scenario: {
'driver': 'adult_male_1',
'behavior': 'drowsy_eyelid_drop',
'lighting': 'night_940nm_IR',
'environment': 'highway',
'duration_sec': 60,
'expected_detection': 'fatigue_F02',
'expected_max_latency_ms': 3000
}
"""
rendered_frames = self._render_scene(scenario)
self._display_play(rendered_frames)
captured_frames = self._camera_capture(len(rendered_frames))
detections = self._run_dms(captured_frames)
result = self._evaluate(detections, scenario)
return result
def _render_scene(self, scenario):
"""渲染虚拟场景"""
n_frames = scenario['duration_sec'] * self.camera['fps']
return [{'frame_id': i, 'data': f'rendered_{i}'} for i in range(n_frames)]
def _display_play(self, frames):
"""显示屏播放"""
pass
def _camera_capture(self, n_frames):
"""物理摄像头采集"""
return [{'frame_id': i, 'data': f'captured_{i}'} for i in range(n_frames)]
def _run_dms(self, frames):
"""DMS算法处理"""
return [{'frame_id': i, 'detection': 'fatigue', 'confidence': 0.92}
for i in range(len(frames))]
def _evaluate(self, detections, scenario):
"""评估检测结果"""
expected = scenario['expected_detection']
detected = any(d['detection'] == expected for d in detections)
latency = 250
return {
'scenario': f"{scenario['driver']}_{scenario['behavior']}_{scenario['lighting']}",
'expected': expected,
'detected': detected,
'latency_ms': latency,
'max_allowed_ms': scenario['expected_max_latency_ms'],
'pass': detected and latency <= scenario['expected_max_latency_ms']
}
def batch_run(self, scenarios: list) -> dict:
"""
批量执行场景
"""
results = []
for s in scenarios:
results.append(self.run_scenario(s))
pass_rate = sum(1 for r in results if r['pass']) / len(results)
return {
'total': len(results),
'passed': sum(1 for r in results if r['pass']),
'failed': sum(1 for r in results if not r['pass']),
'pass_rate': pass_rate,
'results': results
}
if __name__ == "__main__":
citl = CITLSystem({
'display_res': (3840, 2160),
'display_fps': 120,
'brightness': 1000,
'sensor': 'AR0823AT'
})
euro_ncap_scenarios = [
{'driver': 'adult_male_1', 'behavior': 'drowsy_eyelid_drop',
'lighting': 'night_940nm_IR', 'environment': 'highway',
'duration_sec': 30, 'expected_detection': 'fatigue_F02',
'expected_max_latency_ms': 3000},
{'driver': 'adult_female_1', 'behavior': 'yawning',
'lighting': 'daylight_500lux', 'environment': 'urban',
'duration_sec': 30, 'expected_detection': 'fatigue_F04',
'expected_max_latency_ms': 3000},
{'driver': 'adult_male_1', 'behavior': 'phone_call',
'lighting': 'daylight_500lux', 'environment': 'highway',
'duration_sec': 30, 'expected_detection': 'distraction_D02',
'expected_max_latency_ms': 3000},
{'driver': 'adult_female_1', 'behavior': 'looking_away_3s',
'lighting': 'tunnel_transition', 'environment': 'highway',
'duration_sec': 30, 'expected_detection': 'distraction_D05',
'expected_max_latency_ms': 3000},
{'driver': 'adult_male_1', 'behavior': 'drowsy_eyelid_drop',
'lighting': 'night_940nm_IR', 'environment': 'urban',
'duration_sec': 30, 'expected_detection': 'fatigue_F02',
'expected_max_latency_ms': 3000},
{'driver': 'adult_female_1', 'behavior': 'head_drop',
'lighting': 'tunnel_transition', 'environment': 'highway',
'duration_sec': 30, 'expected_detection': 'fatigue_F03',
'expected_max_latency_ms': 3000},
]
results = citl.batch_run(euro_ncap_scenarios)
print("=" * 60)
print("CITL Euro NCAP DSM场景批量验证结果")
print("=" * 60)
print(f"总场景数: {results['total']}")
print(f"通过: {results['passed']}")
print(f"失败: {results['failed']}")
print(f"通过率: {results['pass_rate']:.1%}")
print("\n详细结果:")
for r in results['results']:
status = "✅ PASS" if r['pass'] else "❌ FAIL"
print(f" {status} | {r['scenario']}")
print(f" 检测: {r['detected']}, 延迟: {r['latency_ms']}ms")
|
