The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
It addresses the shift towards more capable autonomous driving systems for commercial deployment, though it is incremental as it builds on existing trends.
This paper examines the transition from modular rule-based pipelines to end-to-end learning systems in autonomous driving, analyzing recent developments like Tesla's FSD and Rivian's Unified Intelligence, and notes that early evidence suggests these systems handle complex real-world scenarios effectively.
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.