#CARLA

This project uses CARLA simulation and Deep Reinforcement Learning with Proximal Policy Optimization to improve autonomous driving capabilities. It trains agents in hyper-realistic urban environments, leveraging a Variational Autoencoder for efficient learning. By focusing on continuous state and action spaces, it aims to provide reliable autonomous navigation on predetermined routes, offering a comprehensive end-to-end driving solution.

Explore the complexities of end-to-end autonomous driving models by uncovering hidden biases through a CARLA-based research initiative. The repository provides efficient, configurable code, exhaustive documentation, and pre-trained models, presenting a solid foundation for autonomous driving research. Key features include dataset generation, model evaluation, and advanced training methods designed for parallel processing to boost research efficiency. Ideal for developers progressing in complex autonomous driving benchmarks, this resource bypasses promotional language, focusing on practical benefits relevant to the field.

InterFuser enhances autonomous driving safety through interpretable sensor fusion, integrating multi-modal sensor data for comprehensive scene insights. This method provides semantics to ensure actions stay within safe limits, setting new standards on the CARLA AD Leaderboard. The repository offers complete setup guidance, including dataset configuration, model training, and evaluation instructions, with access to pre-trained weights for immediate application.