NVIDIA Releases Alpamayo 2 Super: A 34B Open Vision-Language-Action Model for Robotaxis and Autonomous Driving Under OpenMDW-1.1
https://www.marktechpost.com/2026/08/05/nvidia-alpamayo-2-super-open-vla-model-autonomous-driving/📌 【NVIDIA 重磅發佈】34B VLA 模型 Alpamayo 2 Super：專為自動駕駛「長尾事件」設計
TL;DR：NVIDIA 推出 34B VLA 模型，結合 VLM 與 Diffusion Decoder，能輸出路徑與因果解釋，且開放商用。
面對自動駕駛中最棘手的「長尾事件」（Long-tail events）——即那些罕見且涉及多個代理人（multi-agent）的複雜情境，傳統的「偵測與預測」技術架構往往難以應對。NVIDIA 此次推出的 Alpamayo 2 Super，正是為了填補這項技術空白而生。
🧩 32B VLM 結合 2.3B Diffusion Decoder 的強大架構
Alpamayo 2 Super 是一個擁有 34B 參數的視覺-語言-動作（Vision-Language-Action, VLA）模型，其核心設計包含：
- 主幹網路：採用 32B 的視覺語言模型（VLM）骨幹，基於 NVIDIA Cosmos 3 Super Reasoner 並經過強化學習（reinforcement learning）後訓練。
- 動作解碼器：使用 2.3B 的擴散模型（diffusion-based）作為 Action Decoder。
- 輸入資訊：模型接收多鏡頭 RGB 影片、文字指令以及包含時間戳的自我運動（egomotion）歷史紀錄。
- 輸出內容：僅需單次處理全方位環視相機影片，即可同時輸出：
- 規劃路徑（Planned trajectory）
- 該路徑的因果解釋（Causal explanation）
- 元動作（Meta-action，如讓路或變換車道）
📊 效能表現：在 LingoQA 評測中排名第一
在多項關鍵指標上，Alpamayo 2 Super 展現了極強的競爭力：
- LingoQA 評測：Lingo-Judge 分數為 79.2，在近 40 個模型中排名第一。
- 對比測試：
- 領先 Qwen2.5-VL 72B 17.0 分。
- 領先 Gemini 2.5 Pro 15.1 分。
- 領先 GPT-4o 23.3 分。
- 規劃能力：
- 在 AlpaSim 閉迴路評估中，取得 1.50 ± 0.13 的分數。
- 在 PhysicalAI-AV 開放迴路評估中，6.4 秒的 minADE₆ 為 0.911m。
💡 透過因果鏈（CoC）提升安全性與標記效率
該模型最引人注目的技術突破在於其「因果鏈」（Chain-of-Causation, CoC）技術。訓練資料中包含約 370 萬條結構化的因果解釋軌跡，這讓開發者能將「觀察到的情境」與「選擇的動作」直接關聯起來。
這不僅能與 NVIDIA Halos 安全驗證工作流整合，也符合 ISO/PAS 8800 的 AI 安全標準。此外，在實際應用中，NVIDIA 利用此模型作為自有車隊數據的自動標記工具（autolabeler），成功將標記週期從「數月」縮短至「數天」。
⚠️ 開放商用授權：OpenMDW-1.1
與以往僅供研究使用的模型不同，Alpamayo 2 Super 採用 OpenMDW-1.1 許可證（Linux Foundation 針對開源模型發佈的寬鬆授權），並提供 Apache 2.0 授權的原始碼。這意味著開發者從第一天起就可以對其進行微調（fine-tuning）、開發衍生模型並進行商業分發。
🎯 實務啟示
對於自動駕駛與機器人工程師而言，Alpamayo 2 Super 的出現標誌著從單純的「感知-決策」轉向「感知-推理-動作」的一體化趨勢。其具備的因果解釋能力，解決了自動駕駛模型長期存在的「黑箱」問題，為安全性驗證與大規模數據自動標記提供了新的解決方案。
🔗 來源
- 標題：NVIDIA Releases Alpamayo 2 Super: A 34B Open Vision-Language-Action Model for Robotaxis and Autonomous Driving Under OpenMDW-1.1
- 作者／機構：Asif Razzaq @ MarkTechPost
- 連結：https://www.marktechpost.com/2026/08/05/nvidia-alpamayo-2-super-open-vla-model-autonomous-driving/
#NVIDIA #Alpamayo2Super #VLA #AutonomousDriving #Robotaxis #MachineLearning #ComputerVision #OpenSource #AI #DeepLearning
原始資料 MarkTechPost · 收集於 2026-08-06
摘要原文
NVIDIA has released Alpamayo 2 Super , a 34B-parameter vision-language-action (VLA) mode l for autonomous driving, under an open commercial license. The stated design target is the long-tail events: rare, multi-agent situations that conventional detection-and-prediction stacks handle poorly. The model pairs a 32B VLM backbone, built on NVIDIA Cosmos 3 Super Reasoner and post-trained with reinforcement learning, with a 2.3B diffusion-based action decoder. From one pass over full-surround camera video it emits a planned trajectory, a causal explanation of that trajectory, and a meta-action. Yes, and for commercial use from day one. The weights are released under OpenMDW-1.1, the Linux Foundation’s permissive license for open model distributions; source code is Apache 2.0. The license covers fine-tuning, derivative models and commercial redistribution. NVIDIA is applying OpenMDW across the entire Alpamayo family, so earlier releases introduced for R&D are now deployable commercially without additional permission. Inputs are multi-camera RGB video, text, and egomotion history with timestamps. The validated public notebook profiles use six cameras and four historical frames per camera. Egomotion is 3D translation plus a 3×3 rotation matrix, multi-timestep. The trajectory API returns 64 waypoints spanning 0.1 to 6.4 seconds at 0.1-second intervals. Each waypoint carries ego-frame XYZ and a 3×3 rotation matrix. Training data is roughly 115,000 hours of multi-camera driving video with egomotion and trajectory annotations. It includes about 3,700,000 Chain-of-Causation (CoC) traces — structured, causally linked explanations of driving decisions. Image training data exceeds one billion images. On LingoQA , Alpamayo 2 Super records a Lingo-Judge score of 79.2 and ranks first among nearly 40 models evaluated. In NVIDIA’s testing it beat Qwen2.5-VL 72B by 17.0 points, Gemini 2.5 Pro by 15.1, and GPT-4o by 23.2. Two more numbers matter for planning work. Closed-loop evaluation with AlpaSim on 910 scenarios from the PhysicalAI-AV-NuRec dataset gives an AlpaSim score of 1.50 ± 0.13. Open-loop evaluation on 937 challenging samples from the PhysicalAI-AV dataset gives minADE₆ at 6.4s of 0.911m. For each driving situation, the model produces a trajectory, a CoC trace explaining the decision, a meta-action such as yield or lane change, reasoning auto-labels, and visual question answering with 2D grounding. That combination is what makes the release interesting operationally. Developers can tie what the model observed to the action it chose. CoC traces integrate with NVIDIA Halos safety-validation workflows and support AI safety aligned with ISO/PAS 8800. Used as an autolabeler on proprietary fleet data, NVIDIA says the model compresses annotation cycles from months to days. Check out the NVIDIA blog and Hugging Face model card . Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter . Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post NVIDIA Releases Alpamayo 2 Super: A 34B Open Vision-Language-Action Model for Robotaxis and Autonomous Driving Under OpenMDW-1.1 appeared first on MarkTechPost .
由 tencent/hy3:free 自動生成