Vehicle-Edge Server Simulation with Object Detection and Route Planning

Overview

This project demonstrates a 5G-inspired vehicle-edge server simulation that integrates edge computing to handle computationally intensive tasks. Using gRPC for communication, it showcases the following capabilities:

1. Object Detection: Utilizing YOLOv5 to detect objects in images captured by the vehicle.
2. Route Planning: Implementing the A* algorithm for efficient navigation.

Project Demonstration

Link: https://drive.google.com/file/d/12E35PFODAug7Wxw7jHCauiQ6RLrnpLoC/view?usp=sharing

Features

• Real-Time Communication: Leverages gRPC for low-latency, high-efficiency communication.
• Edge Computing Integration: Offloads heavy tasks to the edge server for optimal performance.
• Scalability: Designed to work in 5G environments with potential for multi-vehicle setups.

System Components

1. Vehicle Simulation:
   • Captures images and sends them to the edge server.
   • Requests route planning computations.
2. Edge Server:
   • Processes object detection using YOLOv5.
   • Computes navigation routes using the A* algorithm.
3. Communication Protocol:
   • Uses gRPC and Protocol Buffers for efficient data serialization.

Installation Steps

Prerequisites

• Python 3.8 or higher
• Required Python libraries:
  • torch
  • opencv-python
  • grpcio
  • grpcio-tools
  • numpy

Clone the Repository

git clone https://github.com/IIITV-5G-and-Edge-Computing-Activity/5G-Edge-Drive.git
cd 5G-Edge-Drive-master

Install Dependencies

pip install -r requirements.txt

Generate gRPC Files

Run the following command to generate the gRPC client and server code:

python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. vehicle_edge.proto

Start the Edge Server

Run the edge server to handle incoming requests:

python edge_server.py

Run the Vehicle Simulation

Simulate the vehicle sending data to the edge server:

python vehicle.py

File Structure

• vehicle.py: Simulates the vehicle capturing images and sending requests to the edge server.
• edge_server.py: Handles object detection and route planning requests.
• route_planning.py: Implements the A* algorithm for navigation.
• vehicle_edge.proto: Defines the gRPC communication schema.

Future Work

• Integration with CARLA Simulator:
  • Test the system in realistic driving environments.
• Docker Deployment:
  • Package components into Docker containers for scalability.

References

• YOLOv5 Documentation
• gRPC Documentation
• A* Algorithm Overview
• CARLA Simulator
• Docker Documentation

Contributors

• Arkesh Choudhury 202111012
• Nisarg Patel 202111058
• Rajan Yadav 202111068
• Sahil Sonkar 202111075
• Saurya Gupta 202111076