Project Overview
This research focuses on developing a sophisticated Coordinated Multi-Agent Reinforcement Learning (CMARL) framework that enables autonomous vehicles to effectively coordinate with human drivers in complex mixed-traffic scenarios. The project utilizes the CARLA simulator for realistic traffic environment simulation and PyTorch for implementing deep reinforcement learning algorithms.
Key Features
- Real-time coordination between autonomous and human-driven vehicles
- Advanced traffic scenario simulation using CARLA
- Deep Q-Network (DQN) implementation for decision making
- Safety-critical situation handling
- Performance metrics evaluation and analysis
Technical Challenges
- Handling unpredictable human driver behavior
- Ensuring safety in critical traffic scenarios
- Balancing efficiency and safety in coordination strategies
- Real-time processing requirements