点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 编辑 | 自动驾驶之心 首先看上图，展示了Tesla标准的端到端自动驾驶方案，其中Large Neural Network可以为LLM（Large Language Model），也可以为非LLM架构，总之是一个具有强大 表征能力的大规模神经网络。这种架构通过直接从传感器输入到控制输出的映射，避免了传统模块化系统的复杂性和错误累积问题。然而，这种端到端架构在实际部 署中面临着如下三大核心挑战： 论文标题 ：UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs 论文链接 ：https://arxiv.org/pdf/2511.01768 项目链接 ：https://github.com/happinesslz/UniLION UniLION主要特点：统一的3D骨干网络架构，基于线性组RNN实现线性计算复杂度，解决了传统Transformer在处理长序列数据时的计算效率瓶 ...

Core Viewpoint - The article discusses the advancements in end-to-end (E2E) and VLA (Vision-Language Architecture) algorithms in the autonomous driving industry, highlighting their potential to enhance driving capabilities through unified perception and control modeling, despite their higher technical complexity [1][5]. Summary by Sections End-to-End Algorithms - End-to-end approaches are categorized into single-stage and two-stage methods, with the latter focusing more on joint prediction, where perception serves as input for trajectory planning and prediction [3]. - Single-stage end-to-end models include various methods such as UniAD, DiffusionDrive, and Drive-OccWorld, each emphasizing different aspects and likely to be optimized by combining their strengths in production [3][37]. VLA Algorithms - VLA extends the capabilities of large models to enhance scene understanding in production models, with internal discussions on language models as interpreters and various algorithm summaries for modular and unified end-to-end VLA [5][45]. - The community has compiled over 40 technical routes, facilitating quick access to industry applications, benchmarks, and learning pathways [7]. Community and Resources - The community provides a platform for knowledge exchange among members from renowned universities and leading companies in the autonomous driving sector, offering resources such as open-source projects, datasets, and learning routes [19][35]. - A comprehensive technical stack and roadmap for beginners and advanced researchers are available, covering various aspects of autonomous driving technology [12][15]. Job Opportunities and Networking - The community has established job referral mechanisms with multiple autonomous driving companies, encouraging members to connect and share job opportunities [10][17]. - Regular discussions on industry trends, research directions, and practical applications are held, fostering a collaborative environment for learning and professional growth [20][83].