Search papers, labs, and topics across Lattice.
This paper analyzes the shift in autonomous driving from modular, rule-based systems to end-to-end (E2E) learning systems, specifically large driving models (LDMs). It examines the architectural design, deployment strategies, safety considerations, and industry implications of systems like Tesla's FSD, Rivian's Unified Intelligence platform, and NVIDIA Cosmos. The analysis suggests that E2E learning is becoming a dominant commercial strategy due to its ability to handle the long tail distribution of real-world driving scenarios.
End-to-end autonomous driving systems, like Tesla's FSD, are proving commercially viable by effectively handling the long tail of real-world driving scenarios, signaling a major shift from rule-based approaches.
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.
