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| import torch
import torch.nn as nn
import numpy as np
class TakeoverReadinessPredictor(nn.Module):
"""接管准备度预测模型
LSTM-BiLSTM-Attention 架构
"""
def __init__(self,
input_dim: int = 14,
hidden_dim: int = 128,
num_layers: int = 2,
dropout: float = 0.2):
super().__init__()
self.input_embedding = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.ReLU(),
nn.Dropout(dropout)
)
self.bilstm = nn.LSTM(
input_size=hidden_dim,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
dropout=dropout if num_layers > 1 else 0
)
self.attention = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.Tanh(),
nn.Linear(hidden_dim, 1),
nn.Softmax(dim=1)
)
self.predictor = nn.Sequential(
nn.Linear(hidden_dim * 2, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, 3)
)
def forward(self, x: torch.Tensor) -> dict:
"""
Args:
x: (B, T, D) 时序特征序列
B = batch size
T = 时间步（历史窗口）
D = 特征维度
Returns:
{
'readiness_score': (B, 1), # 0-1 准备度评分
'predicted_takeover_time': (B, 1), # 预测接管时间（秒）
'predicted_quality': (B, 1), # 预测接管质量
'attention_weights': (B, T, 1) # 注意力权重
}
"""
B, T, D = x.shape
embedded = self.input_embedding(x)
lstm_out, _ = self.bilstm(embedded)
attn_weights = self.attention(lstm_out)
weighted = torch.sum(attn_weights * lstm_out, dim=1)
predictions = self.predictor(weighted)
return {
'readiness_score': torch.sigmoid(predictions[:, 0:1]),
'predicted_takeover_time': torch.relu(predictions[:, 1:2]) + 1.0,
'predicted_quality': torch.sigmoid(predictions[:, 2:3]),
'attention_weights': attn_weights
}
class TakeoverDecisionSystem:
"""接管决策系统"""
def __init__(self,
model_path: str,
readiness_threshold: float = 0.7,
min_takeover_time: float = 5.0):
self.model = TakeoverReadinessPredictor()
self.model.load_state_dict(torch.load(model_path))
self.model.eval()
self.readiness_threshold = readiness_threshold
self.min_takeover_time = min_takeover_time
self.history = []
self.history_window = 30
def update(self, features: TakeoverReadinessFeatures) -> dict:
"""更新状态并预测
Returns:
{
'is_ready': bool,
'readiness_score': float,
'predicted_takeover_time': float,
'recommended_action': str
}
"""
self.history.append(features.to_feature_vector())
if len(self.history) > self.history_window:
self.history.pop(0)
if len(self.history) < self.history_window:
return {
'is_ready': False,
'readiness_score': 0.0,
'predicted_takeover_time': float('inf'),
'recommended_action': 'MONITOR'
}
x = torch.tensor(np.array(self.history), dtype=torch.float32).unsqueeze(0)
with torch.no_grad():
predictions = self.model(x)
readiness = predictions['readiness_score'].item()
takeover_time = predictions['predicted_takeover_time'].item()
quality = predictions['predicted_quality'].item()
if readiness >= self.readiness_threshold and takeover_time <= self.min_takeover_time:
is_ready = True
action = 'IMMEDIATE_HANDOVER'
elif readiness >= 0.5:
is_ready = False
action = 'EXTENDED_WARNING'
else:
is_ready = False
action = 'EMERGENCY_STOP'
return {
'is_ready': is_ready,
'readiness_score': readiness,
'predicted_takeover_time': takeover_time,
'predicted_quality': quality,
'recommended_action': action
}
|