Paul Savluc
Created December 11, 2023

"SafeRoute AI" - Intelligent Pathfinding for Emergencies

"SafeRoute AI" enhances emergency response efforts with AI-powered pathfinding. In crisis situations like natural or urban disasters.

10
"SafeRoute AI" - Intelligent Pathfinding for Emergencies

Things used in this project

Hardware components

AMD Instinct
×1
AVR IoT Mini Cellular Board
Microchip AVR IoT Mini Cellular Board
×1
nRF7002 Development Kit
Nordic Semiconductor nRF7002 Development Kit
×1
Jetson Orin Nano Developer Kit
NVIDIA Jetson Orin Nano Developer Kit
×1

Software apps and online services

linux
Windows 10
Microsoft Windows 10

Story

Read more

Schematics

Software Build Basic Schematics

Code

visual representation of path creating

Python
import networkx as nx

def create_graph():
    # Create a directed graph
    G = nx.DiGraph()

    # Add nodes and edges with weights (representing distances or time)
    # In a real application, this data would come from real-world sources
    G.add_edge('A', 'B', weight=1.5)
    G.add_edge('B', 'C', weight=2.0)
    G.add_edge('A', 'D', weight=1.0)
    G.add_edge('D', 'E', weight=3.0)
    G.add_edge('E', 'C', weight=1.0)
    G.add_edge('B', 'E', weight=2.5)

    return G

def find_optimal_route(G, start, end):
    # Find the shortest path using Dijkstra's Algorithm
    path = nx.dijkstra_path(G, start, end, weight='weight')
    return path

# Create the graph
G = create_graph()

# Define start and end points
start_point = 'A'
end_point = 'C'

# Find the optimal route
optimal_route = find_optimal_route(G, start_point, end_point)

print("Optimal route from {} to {}:".format(start_point, end_point), optimal_route)

pseudo code

Python
some pseudo code for generating and optimizing routs
# Function to collect data from various sources
def collect_data():
    # Collect real-time data from traffic, weather, emergency services, etc.
    # Also collect historical data for training the model
    pass

# Function to preprocess and format the data for the model
def preprocess_data(raw_data):
    # Clean, normalize, and transform data into a suitable format
    pass
  
# Define a machine learning model
class RouteOptimizationModel:
    def __init__(self):
        # Initialize the model parameters

    def train(self, training_data):
        # Train the model using historical data
        pass

    def predict(self, current_conditions):
        # Predict the best route based on current conditions
        pass

    def update_model(self, new_data):
        # Update the model with new data for continuous learning
        pass
      
# Function to generate the route using the model
def generate_route(model, current_conditions):
    optimal_route = model.predict(current_conditions)
    return optimal_route

# Function to collect feedback and update the model
def update_model_with_feedback(model, feedback_data):
    model.update_model(feedback_data)
    
def main():
    # Collect and preprocess historical data
    historical_data = collect_data()
    training_data = preprocess_data(historical_data)

    # Initialize and train the model
    model = RouteOptimizationModel()
    model.train(training_data)

    while True:
        # In a real-world application, this would be a continuous process

        # Collect and preprocess current data
        current_data = collect_data()
        current_conditions = preprocess_data(current_data)

        # Generate and provide the route
        route = generate_route(model, current_conditions)
        display_route(route)

        # Collect feedback after route completion
        feedback_data = collect_feedback(route)

        # Update the model with new data
        update_model_with_feedback(model, feedback_data)

Credits

Paul Savluc

Paul Savluc

1 project • 0 followers

Comments