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| """
Cineon ELE + Smart Eye MicroBird 航空眼动系统
迁移到汽车DMS的架构设计
核心: 隐蔽安装摄像头 → 视线追踪 → AOI映射 → 行为分析
依赖: numpy
"""
import numpy as np
from typing import Dict, List
class MicroBirdSystem:
"""
Smart Eye MicroBird 环境眼动追踪
特点:
- 驾驶室隐蔽安装多个摄像头
- 支持处方眼镜
- 真实环境(非实验室)
- 仪表级AOI映射
"""
def __init__(self, cockpit_type='aviation'):
self.cockpit_type = cockpit_type
self.cameras = {
'left': {'position': '仪表板左侧上方', 'fov': [60, 40]},
'center': {'position': '仪表板中央上方', 'fov': [60, 40]},
'right': {'position': '仪表板右侧上方', 'fov': [60, 40]},
}
if cockpit_type == 'aviation':
self.aoi_zones = {
'PFD': {'desc': '主飞行显示', 'priority': 'high'},
'MFD': {'desc': '多功能显示', 'priority': 'medium'},
' standby': {'desc': '备用仪表', 'priority': 'low'},
'outside': {'desc': '挡风玻璃外', 'priority': 'high'},
'center_console': {'desc': '中央控制台', 'priority': 'medium'},
'overhead': {'desc': '头顶面板', 'priority': 'low'},
}
elif cockpit_type == 'automotive':
self.aoi_zones = {
'road_ahead': {'desc': '前方道路', 'priority': 'high'},
'hud': {'desc': 'HUD显示区', 'priority': 'medium'},
'dashboard': {'desc': '仪表盘', 'priority': 'medium'},
'center_screen': {'desc': '中控屏', 'priority': 'medium'},
'left_mirror': {'desc': '左后视镜', 'priority': 'low'},
'right_mirror': {'desc': '右后视镜', 'priority': 'low'},
'steering_wheel': {'desc': '方向盘区域', 'priority': 'low'},
'passenger': {'desc': '乘客侧', 'priority': 'low'},
}
def map_gaze_to_aoi(self, gaze_x, gaze_y):
"""将视线坐标映射到AOI"""
if self.cockpit_type == 'automotive':
if 0.3 < gaze_x < 0.7 and 0.0 < gaze_y < 0.4:
return 'road_ahead'
elif 0.3 < gaze_x < 0.7 and 0.4 < gaze_y < 0.6:
return 'dashboard'
elif 0.7 < gaze_x < 1.0 and 0.3 < gaze_y < 0.7:
return 'center_screen'
elif 0.0 < gaze_x < 0.2 and 0.3 < gaze_y < 0.6:
return 'left_mirror'
elif 0.8 < gaze_x < 1.0 and 0.3 < gaze_y < 0.6:
return 'right_mirror'
else:
return 'road_ahead'
return None
class EmpathicLearningEngine:
"""
Cineon 共情学习引擎 (ELE)
分析飞行员/驾驶员的仪表扫视模式:
- 扫视频率
- 注视时长
- 扫视顺序
- 信息获取效率
评估维度:
1. 仪表扫视完整性 (是否检查所有关键仪表)
2. 扫视效率 (最短路径)
3. 情境意识 (关键信息获取)
4. 决策延迟 (信息→行动时间)
"""
def __init__(self, aoi_zones):
self.aoi_zones = aoi_zones
self.scan_history = []
def analyze_scan_pattern(self, gaze_sequence, timestamps):
"""
分析仪表扫视模式
Args:
gaze_sequence: list of AOI names
timestamps: list of timestamps (秒)
Returns:
analysis: dict with scan metrics
"""
transitions = sum(1 for i in range(1, len(gaze_sequence))
if gaze_sequence[i] != gaze_sequence[i-1])
duration = timestamps[-1] - timestamps[0]
scan_rate = transitions / duration if duration > 0 else 0
dwell_times = {}
for i in range(len(gaze_sequence)):
if i > 0:
dt = timestamps[i] - timestamps[i-1]
aoi = gaze_sequence[i-1]
dwell_times[aoi] = dwell_times.get(aoi, 0) + dt
high_priority_aois = [k for k, v in self.aoi_zones.items()
if v['priority'] == 'high']
covered_high = [a for a in set(gaze_sequence)
if a in high_priority_aois]
coverage = len(covered_high) / len(high_priority_aois) if high_priority_aois else 0
avg_dwell = np.mean(list(dwell_times.values())) if dwell_times else 0
unique_aois = list(dict.fromkeys(gaze_sequence))
efficiency = len(unique_aois) / len(gaze_sequence) if gaze_sequence else 0
return {
'scan_rate_per_sec': scan_rate,
'dwell_times': dwell_times,
'high_priority_coverage': coverage,
'avg_dwell_sec': avg_dwell,
'scan_efficiency': efficiency,
'unique_aois': unique_aois,
'total_aois': len(set(gaze_sequence)),
}
def assess_competency(self, analysis):
"""
评估驾驶员/飞行员能力等级
基于扫视模式的完整性、效率、覆盖率
"""
score = 0
score += analysis['high_priority_coverage'] * 0.4
rate = analysis['scan_rate_per_sec']
if 2 < rate < 6:
score += 0.2
elif 1 < rate < 8:
score += 0.1
dwell = analysis['avg_dwell_sec']
if 0.3 < dwell < 1.0:
score += 0.2
elif 0.1 < dwell < 2.0:
score += 0.1
score += analysis['scan_efficiency'] * 0.2
if score > 0.8:
level = 'Expert'
elif score > 0.6:
level = 'Proficient'
elif score > 0.4:
level = 'Novice'
else:
level = 'At Risk'
return {'score': score, 'level': level}
if __name__ == "__main__":
microbird = MicroBirdSystem(cockpit_type='automotive')
ele = EmpathicLearningEngine(microbird.aoi_zones)
print("=" * 60)
print("Cineon ELE + Smart Eye MicroBird → 汽车DMS")
print("=" * 60)
print(f"\n汽车座舱AOI区域 ({len(microbird.aoi_zones)}个):")
for name, info in microbird.aoi_zones.items():
print(f" {name}: {info['desc']} [优先级: {info['priority']}]")
np.random.seed(42)
n = 60 * 30
timestamps = np.arange(n) / 30
normal_gaze = []
for t in timestamps:
if t % 3 < 0.5:
normal_gaze.append('dashboard')
elif t % 5 < 0.3:
normal_gaze.append('left_mirror')
else:
normal_gaze.append('road_ahead')
distracted_gaze = []
for t in timestamps:
if t < 10:
distracted_gaze.append('road_ahead')
elif t < 20:
distracted_gaze.append('center_screen')
elif t < 25:
distracted_gaze.append('road_ahead')
elif t < 35:
distracted_gaze.append('center_screen')
else:
distracted_gaze.append('road_ahead')
print(f"\n--- 正常驾驶员扫视分析 ---")
normal_analysis = ele.analyze_scan_pattern(normal_gaze, timestamps)
normal_assess = ele.assess_competency(normal_analysis)
print(f" 扫视频率: {normal_analysis['scan_rate_per_sec']:.2f}/s")
print(f" 高优先AOI覆盖率: {normal_analysis['high_priority_coverage']:.0%}")
print(f" 平均注视时长: {normal_analysis['avg_dwell_sec']:.2f}s")
print(f" 扫视效率: {normal_analysis['scan_efficiency']:.2f}")
print(f" 能力评分: {normal_assess['score']:.2f} ({normal_assess['level']})")
print(f"\n--- 分心驾驶员扫视分析 ---")
distracted_analysis = ele.analyze_scan_pattern(distracted_gaze, timestamps)
distracted_assess = ele.assess_competency(distracted_analysis)
print(f" 扫视频率: {distracted_analysis['scan_rate_per_sec']:.2f}/s")
print(f" 高优先AOI覆盖率: {distracted_analysis['high_priority_coverage']:.0%}")
print(f" 平均注视时长: {distracted_analysis['avg_dwell_sec']:.2f}s")
print(f" 扫视效率: {distracted_analysis['scan_efficiency']:.2f}")
print(f" 能力评分: {distracted_assess['score']:.2f} ({distracted_assess['level']})")
|
