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. Successfully completed and defended with an exceptional grade of 1.0/4.0 (S-tier), demonstrating outstanding research quality and implementation.
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
- Research paper with comprehensive experimental results
Technical Challenges
- Handling unpredictable human driver behavior
- Ensuring safety in critical traffic scenarios
- Balancing efficiency and safety in coordination strategies
- Real-time processing requirements