The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models

TL;DR

This paper explores the transition from rule-based to end-to-end learning systems in autonomous driving, highlighting recent developments, safety considerations, and industry implications of large driving models capable of raw sensor-to-action mapping.

Contribution

It provides a comprehensive analysis of recent advancements in large driving models and discusses their potential to transform autonomous vehicle deployment and beyond.

Findings

E2E driving models handle complex real-world scenarios effectively

Supervised E2E systems like FSD (Supervised) are set for widespread deployment from 2026

E2E approaches are becoming the dominant commercial strategy in autonomous driving

Abstract

Autonomous driving is undergoing a shift from modular rule based pipelines toward end to end (E2E) learning systems. This paper examines this transition by tracing the evolution from classical sense perceive plan control architectures to large driving models (LDMs) capable of mapping raw sensor input directly to driving actions. We analyze recent developments including Tesla's Full Self Driving (FSD) V12 V14, Rivian's Unified Intelligence platform, NVIDIA Cosmos, and emerging commercial robotaxi deployments, focusing on architectural design, deployment strategies, safety considerations and industry implications. A key emerging product category is supervised E2E driving, often referred to as FSD (Supervised) or L2 plus plus, which several manufacturers plan to deploy from 2026 onwards. These systems can perform most of the Dynamic Driving Task (DDT) in complex environments while requiring human supervision, shifting the driver's role to safety oversight. Early operational evidence suggests E2E learning handles the long tail distribution of real world driving scenarios and is becoming a dominant commercial strategy. We also discuss how similar architectural advances may extend beyond autonomous vehicles (AV) to other embodied AI systems, including humanoid robotics.