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Autonomous Vehicle Simulation

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Abstract

Autonomous Vehicle Simulation is a Python project that uses AI to simulate autonomous vehicles. The application features sensor fusion, path planning, and a visualization interface, demonstrating best practices in robotics and AI.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of robotics and AI
  • Required libraries: numpynumpy, matplotlibmatplotlib, scipyscipy

Before you Start

Install Python and the required libraries:

Install dependencies
pip install numpy matplotlib scipy
Install dependencies
pip install numpy matplotlib scipy

Getting Started

Create a Project

  1. Create a folder named autonomous-vehicle-simulationautonomous-vehicle-simulation.
  2. Open the folder in your code editor or IDE.
  3. Create a file named autonomous_vehicle_simulation.pyautonomous_vehicle_simulation.py.
  4. Copy the code below into your file.

Write the Code

⚙️ Autonomous Vehicle Simulation
Autonomous Vehicle Simulation
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance
 
def fuse_sensors(sensor_data):
    # Dummy fusion (for demo)
    return np.mean(sensor_data, axis=0)
 
def plan_path(start, end):
    # Dummy path planning (for demo)
    path = [start, end]
    return path
 
def main():
    print("Autonomous Vehicle Simulation")
    # Simulate sensor data for 5 time steps (e.g., GPS, LIDAR, Radar)
    np.random.seed(42)
    sensor_data = np.random.rand(5, 3) * 10  # 5 readings, 3 sensors
    print("Sensor data (5 readings, 3 sensors):")
    print(sensor_data)
 
    # Fuse sensor data
    fused = fuse_sensors(sensor_data)
    print(f"\nFused sensor data (mean): {fused}")
 
    # Plan a path from start to end
    start = tuple(fused[:2])
    end = (fused[0] + 10, fused[1] + 10)
    path = plan_path(start, end)
    print(f"\nPlanned path: {path}")
 
    # Calculate path length using Euclidean distance
    path_length = distance.euclidean(start, end)
    print(f"Path length: {path_length:.2f}")
 
    # Visualization
    plt.figure(figsize=(6, 6))
    plt.plot([p[0] for p in path], [p[1] for p in path], marker='o', color='blue', label='Planned Path')
    plt.scatter(sensor_data[:,0], sensor_data[:,1], color='red', label='Sensor Readings')
    plt.title('Autonomous Vehicle Path Planning')
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.legend()
    plt.grid(True)
    plt.show()
    print("\nSimulation complete. Visualization displayed.")
 
if __name__ == "__main__":
    main()
Autonomous Vehicle Simulation
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance
 
def fuse_sensors(sensor_data):
    # Dummy fusion (for demo)
    return np.mean(sensor_data, axis=0)
 
def plan_path(start, end):
    # Dummy path planning (for demo)
    path = [start, end]
    return path
 
def main():
    print("Autonomous Vehicle Simulation")
    # Simulate sensor data for 5 time steps (e.g., GPS, LIDAR, Radar)
    np.random.seed(42)
    sensor_data = np.random.rand(5, 3) * 10  # 5 readings, 3 sensors
    print("Sensor data (5 readings, 3 sensors):")
    print(sensor_data)
 
    # Fuse sensor data
    fused = fuse_sensors(sensor_data)
    print(f"\nFused sensor data (mean): {fused}")
 
    # Plan a path from start to end
    start = tuple(fused[:2])
    end = (fused[0] + 10, fused[1] + 10)
    path = plan_path(start, end)
    print(f"\nPlanned path: {path}")
 
    # Calculate path length using Euclidean distance
    path_length = distance.euclidean(start, end)
    print(f"Path length: {path_length:.2f}")
 
    # Visualization
    plt.figure(figsize=(6, 6))
    plt.plot([p[0] for p in path], [p[1] for p in path], marker='o', color='blue', label='Planned Path')
    plt.scatter(sensor_data[:,0], sensor_data[:,1], color='red', label='Sensor Readings')
    plt.title('Autonomous Vehicle Path Planning')
    plt.xlabel('X')
    plt.ylabel('Y')
    plt.legend()
    plt.grid(True)
    plt.show()
    print("\nSimulation complete. Visualization displayed.")
 
if __name__ == "__main__":
    main()

Example Usage

Run vehicle simulation
python autonomous_vehicle_simulation.py
Run vehicle simulation
python autonomous_vehicle_simulation.py

Explanation

Key Features

  • Sensor Fusion: Combines data from multiple sensors.
  • Path Planning: Plans optimal routes for vehicles.
  • Visualization Interface: Visualizes vehicle movement and decisions.
  • Error Handling: Validates inputs and manages exceptions.

Code Breakdown

  1. Import Libraries and Setup Simulation
autonomous_vehicle_simulation.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance
autonomous_vehicle_simulation.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import distance
  1. Sensor Fusion and Path Planning Functions
autonomous_vehicle_simulation.py
def fuse_sensors(sensor_data):
    # Dummy fusion (for demo)
    return np.mean(sensor_data, axis=0)
 
def plan_path(start, end):
    # Dummy path planning (for demo)
    path = [start, end]
    return path
autonomous_vehicle_simulation.py
def fuse_sensors(sensor_data):
    # Dummy fusion (for demo)
    return np.mean(sensor_data, axis=0)
 
def plan_path(start, end):
    # Dummy path planning (for demo)
    path = [start, end]
    return path
  1. Visualization Interface and Error Handling
autonomous_vehicle_simulation.py
def main():
    print("Autonomous Vehicle Simulation")
    # sensor_data = np.array([[1,2],[2,3],[3,4]])
    # fused = fuse_sensors(sensor_data)
    # start, end = (0,0), (10,10)
    # path = plan_path(start, end)
    # plt.plot([p[0] for p in path], [p[1] for p in path])
    # plt.show()
    print("[Demo] Simulation logic here.")
 
if __name__ == "__main__":
    main()
autonomous_vehicle_simulation.py
def main():
    print("Autonomous Vehicle Simulation")
    # sensor_data = np.array([[1,2],[2,3],[3,4]])
    # fused = fuse_sensors(sensor_data)
    # start, end = (0,0), (10,10)
    # path = plan_path(start, end)
    # plt.plot([p[0] for p in path], [p[1] for p in path])
    # plt.show()
    print("[Demo] Simulation logic here.")
 
if __name__ == "__main__":
    main()

Features

  • Autonomous Simulation: Sensor fusion and path planning
  • Modular Design: Separate functions for fusion and planning
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Integrating with real vehicle datasets
  • Supporting advanced path planning algorithms
  • Creating a GUI for simulation
  • Adding real-time data processing
  • Unit testing for reliability

Educational Value

This project teaches:

  • Robotics: Autonomous simulation and sensor fusion
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • Self-Driving Cars
  • Robotics Research
  • Simulation Platforms

Conclusion

Autonomous Vehicle Simulation demonstrates how to build a scalable and accurate simulation tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in robotics, automotive, and more. For more advanced projects, visit Python Central Hub.

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