Enhancing Autonomous Vehicles Using Deep Reinforcement Learning

Our project focuses on enhancing collision avoidance between autonomous vehicles using deep reinforcement learning (DRL), and it also aims to improve the collision avoidance between autonomous vehicles using deep reinforcement learning. By leveraging advanced DRL algorithms and ensuring robust, realtime decision-making, the project seeks to create a safer and more efficient autonomous driving system. The approach integrates various state representations, actions, and reward functions to optimize driving behaviors. The system's state representation includes vehicle positions, velocities, distances to obstacles, and relative locations of other vehicles. Actions encompass accelerating, decelerating, and turning maneuvers. The reward function is designed to promote safe driving behaviors, such as maintaining speed and lane discipline, while penalizing collisions and erratic movements. The project also incorporates Multi-Agent Reinforcement Learning (MARL) to enable vehicle coordination, where each vehicle learns to maximize its own reward while considering the actions of others. Communication protocols between vehicles enhance decision- making and collision avoidance here vehicles share information about their states and intended actions, improving overall coordination and reducing the likelihood of collisions.Safety and robustness are ensured by integrating safe reinforcement learning techniques and making the policy resilient to environmental uncertainties. Real-world challenges addressed include transferring learned policies from simulation to real-world applications, ensuring scalability across different traffic conditions, and compliance with traffic regulations. The effectiveness of the proposed solution is demonstrated through case studies and validation in various driving scenarios. This project aims to advance the development of safer and more efficient autonomous driving systems