The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
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 ...
| Authors | Eduardo Nebot, Julie Stephany Berrio Perez |
|---|---|
| Primary category | cs.RO |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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