mlsanityData to 7 Oct 2026

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

arXiv 2603.16050 ↗First posted 17 Mar 2026cs.RO cs.CV eess.IVcited by 3

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 17 of its 43 references that are in the map. First posted on arXiv 17 Mar 2026.

Builds on

17 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey10 Jul
  2. 2023End-to-end Autonomous Driving: Challenges and Frontiers29 Jun
  3. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving31 May

Other references · 14

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2017Deep reinforcement learning from human preferencescited by 3,846
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  4. 2016End to End Learning for Self-Driving Carscited by 1,000
  5. 2023LMDrive: Closed-Loop End-to-End Driving with Large Language Modelscited by 301
  6. 2020PnPNet: End-to-End Perception and Prediction with Tracking in the Loopcited by 143
  7. 2025Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tailcited by 136
  8. 2020Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systemscited by 100
  9. 2021Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Surveycited by 81
  10. 2022"Curse of rarity" for autonomous vehiclescited by 62
  11. 2024RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Modelscited by 20
  12. 2024High Definition Map Mapping and Update: A General Overview and Future Directionscited by 7
2 more
  1. 2023Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenariocited by 2
  2. 2021Real-World Evaluation of the Impact of Automated Driving System Technology on Driver Gaze Behavior, Reaction Time and Trustcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systemscited by 1
  2. 2026GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environmentcited by 0
  3. 2026Stochastic World Models for Verifying Vision-Based Neural Feedback Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
  2. 2026End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems
  3. 2026StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
  4. 2026Planning-Oriented End-to-End Autonomous Driving: Architectures, Evaluation, and Emerging Paradigms
  5. 2026Large Multimodal Models for Embodied Intelligent Driving: The Next Frontier in Self-Driving?
  6. 2025LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving
  7. 2025DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving
  8. 2026What Matters for Scalable and Robust Learning in End-to-End Driving Planners?