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| import torch
import torch.nn as nn
import numpy as np
class DMSARHUDIntegration:
"""
DMS + AR-HUD 协同系统架构
工作流：
1. DMS 检测驾驶员状态（注视点、PERCLOS、认知负荷）
2. 风险评估模块识别道路危险
3. AR-HUD 根据驾驶员状态和危险等级选择引导策略
4. AR-HUD 在挡风玻璃上投影引导信息
硬件配置：
| 组件 | 型号 | 参数 | 安装位置 |
|------|------|------|----------|
| DMS 摄像头 | IR RGB | 2MP, 30fps | A柱/仪表盘 |
| AR-HUD | DMD/LCoS | 1.5m 投影距离, 10°+ FoV | 仪表盘内 |
| 处理器 | QCS8255 | 26 TOPS | 中控 |
| 风险感知 | 前视摄像头+雷达 | ADAS级 | 前格栅 |
"""
def __init__(self):
self.dms = DMSModule()
self.risk = RiskAssessment()
self.hud = ARHUDController()
self.strategy = InterventionStrategy()
def process(self, dms_data, road_data):
driver_state = self.dms.evaluate(dms_data)
risks = self.risk.identify(road_data)
action = self.strategy.select(driver_state, risks)
hud_content = self.hud.render(action, risks)
return {
"driver_state": driver_state,
"risks": risks,
"intervention": action,
"hud_content": hud_content,
}
class DMSModule:
"""DMS 状态评估模块"""
def evaluate(self, data):
return {
"gaze_point": (0.3, 0.5),
"gaze_off_road_sec": 1.5,
"perclos_30s": 12.5,
"cognitive_load": 0.65,
"fatigue_level": "mild",
}
class RiskAssessment:
"""道路风险评估模块"""
def identify(self, road_data):
return [
{"type": "pedestrian", "position": (0.6, 0.7), "risk": 0.8, "ttc": 3.5},
{"type": "vehicle_brake", "position": (0.4, 0.5), "risk": 0.6, "ttc": 5.0},
]
class InterventionStrategy:
"""干预策略选择模块"""
def select(self, driver_state, risks):
gaze_off = driver_state["gaze_off_road_sec"]
cognitive = driver_state["cognitive_load"]
max_risk = max(risks, key=lambda r: r["risk"])
if gaze_off > 3.0 and max_risk["risk"] > 0.7:
return {
"type": "risk_priority_lsp",
"target": max_risk,
"intensity": "high",
"audio": True,
}
elif gaze_off > 1.5:
return {
"type": "gradual_redirect",
"target": max_risk,
"intensity": "medium",
"audio": False,
}
elif cognitive > 0.7:
return {
"type": "simplify_display",
"target": None,
"intensity": "low",
"audio": False,
}
else:
return {"type": "normal", "intensity": "none"}
class ARHUDController:
"""AR-HUD 渲染控制器"""
def render(self, action, risks):
if action["type"] == "risk_priority_lsp":
return {
"content": "arrow_to_risk",
"position": action["target"]["position"],
"color": "red",
"size": "large",
"duration_ms": 2000,
}
elif action["type"] == "gradual_redirect":
return {
"content": "soft_arrow",
"position": action["target"]["position"],
"color": "amber",
"size": "medium",
"duration_ms": 1500,
}
elif action["type"] == "simplify_display":
return {
"content": "essential_only",
"position": (0.5, 0.3),
"color": "green",
"size": "small",
"duration_ms": 5000,
}
else:
return {"content": "normal_hud", "color": "white"}
if __name__ == "__main__":
system = DMSARHUDIntegration()
result = system.process(
dms_data={"gaze_off": 4.0, "perclos": 15, "cognitive": 0.8},
road_data={"objects": ["pedestrian", "brake_lights"]}
)
print("=== DMS + AR-HUD 协同 ===")
print(f"干预策略: {result['intervention']}")
print(f"HUD内容: {result['hud_content']}")
|
