""" Module: vehicle_behavior_recognition_and_decision_optimization This module implements a novel framework for vehicle behavior recognition and decision optimization in intelligent driving systems. The framework integrates manifold regularization, agent-driven event planning, and probabilistic uncertainty filtering to address the complexity and uncertainty inherent in real-world driving scenarios. The Semantic Dynamics Forecaster is introduced as a key component, capturing intricate dynamics of vehicle interactions in a semantic and interpretable manner. This forecaster utilizes advanced modeling techniques to predict behaviors and optimize decisions in real-time, thereby improving the responsiveness and reliability of autonomous vehicles. The module is structured to facilitate peer review, deep analysis, and reproducibility in research. It includes comprehensive functionality, detailed docstrings, type hints, and extensive comments to ensure clarity and ease of understanding for researchers and engineers. Classes: - SemanticDynamicsForecaster: A model class inheriting from nn.Module, implementing the core architecture for behavior recognition and decision optimization. - ModelConfig: A configuration class for managing model parameters and settings. Functions: - initialize_weights: Initializes model weights using Xavier initialization. - calculate_parameter_count: Computes the total number of parameters in the model. - model_summary: Provides a detailed summary of the model architecture and parameter statistics. Usage: The module is designed for use in intelligent driving systems, providing robust and adaptive performance in dynamic and unpredictable driving environments. Researchers can extend and customize the framework to suit specific experimental needs, ensuring its applicability to a wide range of autonomous driving tasks. """ import torch import torch.nn as nn import torch.nn.functional as F from typing import Tuple, Dict, Any class SemanticDynamicsForecaster(nn.Module): """ Semantic Dynamics Forecaster for vehicle behavior recognition and decision optimization. This model integrates manifold regularization, agent-driven event planning, and probabilistic uncertainty filtering to construct a robust framework for dynamic vehicle behavior prediction and decision-making. Attributes: input_dim (int): Dimension of the input feature vector. hidden_dim (int): Dimension of the hidden layers. output_dim (int): Dimension of the output prediction vector. dropout_rate (float): Dropout rate for regularization. Methods: forward(x): Performs forward propagation through the model. __repr__(): Returns a string representation of the model. __str__(): Returns a detailed string description of the model. """ def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, dropout_rate: float = 0.5): super(SemanticDynamicsForecaster, self).__init__() self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.dropout_rate = dropout_rate # Define layers self.fc1 = nn.Linear(input_dim, hidden_dim) self.fc2 = nn.Linear(hidden_dim, hidden_dim) self.fc3 = nn.Linear(hidden_dim, output_dim) self.dropout = nn.Dropout(dropout_rate) def forward(self, x: torch.Tensor) -> torch.Tensor: """ Forward propagation logic. Args: x (torch.Tensor): Input tensor representing vehicle states. Returns: torch.Tensor: Output tensor representing predicted vehicle behaviors. """ x = F.relu(self.fc1(x)) x = self.dropout(x) x = F.relu(self.fc2(x)) x = self.dropout(x) x = self.fc3(x) return x def __repr__(self) -> str: return f"SemanticDynamicsForecaster(input_dim={self.input_dim}, hidden_dim={self.hidden_dim}, output_dim={self.output_dim}, dropout_rate={self.dropout_rate})" def __str__(self) -> str: return f"Semantic Dynamics Forecaster Model:\nInput Dimension: {self.input_dim}\nHidden Dimension: {self.hidden_dim}\nOutput Dimension: {self.output_dim}\nDropout Rate: {self.dropout_rate}" class ModelConfig: """ Configuration class for model parameters and settings. Attributes: input_dim (int): Dimension of the input feature vector. hidden_dim (int): Dimension of the hidden layers. output_dim (int): Dimension of the output prediction vector. dropout_rate (float): Dropout rate for regularization. Methods: get_config(): Returns the configuration as a dictionary. """ def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, dropout_rate: float): self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.dropout_rate = dropout_rate def get_config(self) -> Dict[str, Any]: """ Returns the configuration as a dictionary. Returns: Dict[str, Any]: Configuration dictionary. """ return { "input_dim": self.input_dim, "hidden_dim": self.hidden_dim, "output_dim": self.output_dim, "dropout_rate": self.dropout_rate } def initialize_weights(model: nn.Module) -> None: """ Initializes model weights using Xavier initialization. Args: model (nn.Module): The model whose weights are to be initialized. Example: >>> model = SemanticDynamicsForecaster(10, 20, 5) >>> initialize_weights(model) """ for m in model.modules(): if isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) def calculate_parameter_count(model: nn.Module) -> int: """ Computes the total number of parameters in the model. Args: model (nn.Module): The model for which to calculate the parameter count. Returns: int: Total number of parameters. Example: >>> model = SemanticDynamicsForecaster(10, 20, 5) >>> calculate_parameter_count(model) 525 """ return sum(p.numel() for p in model.parameters() if p.requires_grad) def model_summary(model: nn.Module) -> None: """ Provides a detailed summary of the model architecture and parameter statistics. Args: model (nn.Module): The model to summarize. Example: >>> model = SemanticDynamicsForecaster(10, 20, 5) >>> model_summary(model) """ print(model) print(f"Total Parameters: {calculate_parameter_count(model)}") # Example usage if __name__ == "__main__": config = ModelConfig(input_dim=10, hidden_dim=20, output_dim=5, dropout_rate=0.5) model = SemanticDynamicsForecaster(**config.get_config()) initialize_weights(model) model_summary(model) # Example forward pass x = torch.randn(1, config.input_dim) output = model(x) print(f"Output: {output}")