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| import numpy as np
from typing import List, Tuple
class InstrumentScanAnalysis:
"""
仪表扫描模式分析
航空领域成熟技术，迁移到汽车座舱
分析驾驶员对仪表/中控屏/道路的注意力分配
"""
ROI_AVIATION = {
'ADI': '姿态指示器',
'HSI': '水平态势指示器',
'altimeter': '高度表',
'airspeed': '空速表',
'engine': '发动机仪表',
'outside': '外部场景',
}
ROI_AUTOMOTIVE = {
'road_ahead': '前方道路',
'dashboard': '仪表盘',
'center_display': '中控屏',
'left_mirror': '左后视镜',
'right_mirror': '右后视镜',
'rear_mirror': '车内后视镜',
'steering': '方向盘区域',
'passenger': '副驾区域',
}
def analyze_scan_pattern(
self,
gaze_sequence: List[Tuple[float, str]],
total_duration: float = 60.0
) -> dict:
"""
分析注视序列的扫描模式
Args:
gaze_sequence: [(timestamp, roi_name), ...]
total_duration: 总时长（秒）
Returns:
analysis: 扫描模式分析结果
"""
roi_durations = {}
for i in range(len(gaze_sequence) - 1):
roi = gaze_sequence[i][1]
duration = gaze_sequence[i+1][0] - gaze_sequence[i][0]
roi_durations[roi] = roi_durations.get(roi, 0) + duration
attention_distribution = {
roi: dur / total_duration
for roi, dur in roi_durations.items()
}
transitions = sum(1 for i in range(1, len(gaze_sequence))
if gaze_sequence[i][1] != gaze_sequence[i-1][1])
scan_rate = transitions / (total_duration / 60)
fixations = []
for i in range(len(gaze_sequence) - 1):
duration = gaze_sequence[i+1][0] - gaze_sequence[i][0]
fixations.append(duration)
avg_fixation = np.mean(fixations) if fixations else 0
road_attention = attention_distribution.get('road_ahead', 0) + \
attention_distribution.get('outside', 0)
if scan_rate > 30 and avg_fixation < 0.5:
cognitive_state = 'high_workload'
elif scan_rate < 10 and avg_fixation > 2.0:
cognitive_state = 'fatigue_risk'
elif road_attention < 0.6:
cognitive_state = 'distraction_risk'
else:
cognitive_state = 'normal'
return {
'attention_distribution': attention_distribution,
'road_attention_ratio': road_attention,
'scan_rate_per_min': scan_rate,
'avg_fixation_sec': avg_fixation,
'cognitive_state': cognitive_state,
'total_transitions': transitions,
'safety_assessment': self._assess_safety(
road_attention, scan_rate, avg_fixation
)
}
def _assess_safety(
self, road_attn: float,
scan_rate: float, avg_fix: float
) -> str:
if road_attn < 0.5:
return "⚠️ 危险：道路注意力不足50%"
elif avg_fix > 2.0 and road_attn < 0.7:
return "⚠️ 疲劳风险：注视时间过长"
elif scan_rate > 40:
return "⚠️ 高负荷：扫视频率过高"
else:
return "✅ 正常"
if __name__ == "__main__":
analyzer = InstrumentScanAnalysis()
np.random.seed(42)
rois = ['road_ahead', 'dashboard', 'center_display',
'left_mirror', 'right_mirror', 'rear_mirror']
timestamps = np.cumsum(np.random.exponential(1.5, 40))
gaze_seq = [(t, np.random.choice(rois, p=[0.7, 0.1, 0.08, 0.04, 0.04, 0.04]))
for t in timestamps]
result = analyzer.analyze_scan_pattern(gaze_seq, total_duration=60)
print("仪表扫描模式分析")
print("=" * 50)
print(f"道路注意力: {result['road_attention_ratio']:.1%}")
print(f"扫视频率: {result['scan_rate_per_min']:.1f}次/分")
print(f"平均注视: {result['avg_fixation_sec']:.2f}秒")
print(f"认知状态: {result['cognitive_state']}")
print(f"安全评估: {result['safety_assessment']}")
print(f"\n各区域注意力分配:")
for roi, ratio in sorted(result['attention_distribution'].items(),
key=lambda x: -x[1]):
print(f" {roi:20s}: {ratio:.1%}")
|
