端到端自主时代：从基于规则的驾驶转向大型驾驶模型
The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
AI总结：
本文探讨自动驾驶从模块化规则系统向端到端学习系统过渡，分析大型驾驶模型在复杂环境中的表现及行业影响。
摘要：
自动驾驶正从模块化规则管道转向端到端（E2E）学习系统。本文通过追溯从经典感知-规划-控制架构到能够直接将原始传感器输入映射到驾驶动作的大型驾驶模型（LDMs）的演变，分析了最近的发展，包括特斯拉的Full Self Driving（FSD）V12 V14、Rivian的统一智能平台、NVIDIA Cosmos以及新兴的商业机器人出租车部署，重点探讨了架构设计、部署策略、安全考虑和行业影响。关键的新兴产品类别是监督的端到端驾驶，通常称为FSD（监督）或L2 Plus Plus，多家制造商计划从2026年起部署。这些系统能够在复杂环境中完成大多数动态驾驶任务（DDT），同时需要人类监督，将驾驶员的角色转变为安全监督。早期的操作证据表明，端到端学习能够处理现实世界驾驶场景的长尾分布，并成为主导的商业策略。本文还讨论了类似的架构进步可能扩展到其他具身AI系统，包括人形机器人。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
