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Advancement in Predictive Vehicle Collision Avoidance Technologies using ML Algorithms & Traffic Simulation

Overview

This repository contains the code, simulation models, and related resources for our project, Advancement in Predictive Vehicle Collision Avoidance Technologies & Traffic Simulation. The project explores the integration of machine learning algorithms and traffic simulation techniques to predict and prevent vehicle collisions, enhancing road safety and efficiency.

Project Goals

1. Improve Road Safety: Develop predictive models using ML to anticipate collisions.
2. Enhance Traffic Flow: Use traffic simulation tools to optimize vehicle movement and reduce congestion.
3. Enable Real-Time Communication: Implement V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communication for proactive collision avoidance.

Key Features

• Collision Prediction Algorithm: Uses GPS, speed, and acceleration data to calculate collision probability and issue warnings.
• Traffic Simulations: Simulations using SUMO (Simulation of Urban Mobility) to analyze traffic scenarios and validate collision avoidance measures.
• V2V Communication: Real-time message exchange for warning nearby vehicles about potential hazards.
• Data-Driven Insights: Models trained on real-world and synthetic datasets for better accuracy.

Technologies Used

• Programming Languages: Python
• Libraries & Tools: SUMO, TraCI, TensorFlow
• Communication Protocols: IEEE 802.11p, WAVE
• Simulation Tools: NetEdit for road network modeling

Structure of the Repository

• models/: Machine learning models for collision prediction.
• simulations/: SUMO configuration files and simulation results.
• scripts/: Python scripts for algorithm implementation and analysis.
• docs/: Detailed project documentation and reports.

How to Use

1. Clone this repository:

   https://github.com/Tushar2952/Vehicle-Collision-Avoidance-Technologies-Traffic-Simulation.git

2. Install dependencies:

   pip install -r requirements.txt

3. Run simulations:
   Navigate to the simulations folder and follow the instructions in README.md to run traffic simulations using SUMO.
4. Use the predictive model:
   Load the machine learning models from the models directory for collision prediction tasks.

Future Scope

• Enhance the algorithm to consider diverse weather conditions and human factors.
• Expand V2I communication for better integration with smart city infrastructure.
• Optimize simulations for scalability in real-world urban environments.

Acknowledgments

This project was conducted as part of our final year Bachelor of Technology degree in Electronics and Communication with Specialization in Biomedical Engineering at Vellore Institute of Technology, under the guidance of Dr. Ravi Kumar C.V.

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