End-to-end (E2E) autonomous driving represents a paradigm shift by learning a direct mapping from raw sensor data to driving actions, replacing traditional perception–planning–control pipelines. This project focuses on evaluating and advancing Transformer-based E2E architectures for sensor-to-action learning, emphasizing performance, robustness, and interpretability.