Papers

End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems

This preprint introduces a Four-Dimensional Architecture Selection Framework that compares modular, end-to-end imitation learning, end-to-end reinforcement learning, and hybrid autonomous driving system designs. Tested against ten published systems from 1989 to 2023, the framework correctly identified the deployed architecture in seven of ten cases (four exact matches and three partial matches). It offers a structured decision-support tool for teams choosing an autonomous driving architecture, though it does not itself build or test a new driving system.