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The UNFC Podcast2025-12-1918 minThe UNFC Podcast
The UNFC PodcastEpisode 9: Discussion with the UNECE - UNFC'S Past, Present and Future🇺🇳 Join us in conversation with a video panel of members of the United Nations Economic Commission for Europe (UNECE), who are 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘃𝗼𝗹𝘃𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗨𝗻𝗶𝘁𝗲𝗱 𝗡𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (𝗨𝗡𝗙𝗖).  We’ll be discussing with the UNECE just 𝗵𝗼𝘄 𝗨𝗡𝗙𝗖 𝗵𝗮𝘀 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 since the 1990s, what 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗼 𝗶𝘁 𝗮𝗰𝗵𝗶𝗲𝘃𝗶𝗻𝗴 𝗶𝘁𝘀 𝗳𝘂𝗹𝗹 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹, and what opportunities exist for UNFC to become 𝗮 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝘁𝗼 𝗵𝗲𝗹𝗽 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗺𝗼𝗿𝗲 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗹𝗱.  𝗢𝘂𝗿 𝗴𝘂𝗲𝘀𝘁𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗲𝗽𝗶𝘀𝗼𝗱𝗲 𝗮𝗿𝗲: Erika Ingvald, Economic Affairs Officer, Sustainable Energy Division Hari Tulsidas, Economic Affairs Officer, Sustainable Energy Division Ghadi Sabra, UNECE Consultant Maria Victoria Oliver, UNECE Consultant  🎙️Hosted by Chris Stockey, Matthew Grimshaw, and Alexander Fuentes. Find out more about The UNFC Podcast here: https://satarla.com/unfc-podcast/  Podcast produced by Critical Productions.   #UNFCPodcast #UNFC #UNECE #SustainableEnergyDivision #UnitedNations #UN #CRMA #CriticalRawMaterials #RawMaterials #Mining #Sustainability 
2025-12-171h 00Laternenparker: E-Mobilität und Tesla2025-11-121h 03科技前緣
科技前緣2025/10/20 Tesla FSD V14 衝擊波:純視覺 AI 的極限挑戰與算力晶片三國戰 (vs. NVIDIA vs. Qualcomm)🎙️ 節目摘要 本集深入解析自動駕駛兩大技術路線:Tesla FSD V14/Supervised 的「端到端」純視覺 AI,與 Waymo/Cruise 的「多感測器融合」(LiDAR、雷達、攝影機)。我們將探討 FSD V14 如何透過 10 倍參數規模的神經網路模型和 Mad Max/Sloth 等新模式,顯著提升停車及應對緊急車輛能力。同時,節目將聚焦硬體競爭:NVIDIA (DRIVE Thor/Orin) 如何以 2000 TOPS 算力與「雲到車」方案領跑 L3+ 市場,以及 Qualcomm (Snapdragon Ride Flex) 如何利用「艙駕融合」策略加速 ADAS 普及。最後,我們將討論自動駕駛在網路安全、功能安全 (SOTIF) 及弱勢道路使用者 (VRUs) 感知公平性等方面的倫理與法規挑戰。 📌 重點整理 Key Points Tesla V14 AI 大躍進:採用 10 倍大 AI 模型與端到端神經網路,提升駕駛流暢度、停車能力,新增 Sloth 和 Mad Max 等駕駛模式。 感測器路線爭議:Tesla 堅持「純視覺」路線,但面臨冗餘性及惡劣天氣下可靠性風險;競爭者則採 LiDAR/雷達/攝影機多感測器融合,提升 L4 級無人駕駛安全性。 算力硬體競爭:NVIDIA 以 DRIVE Thor 的 2000 TOPS 算力瞄準 L4/Robotaxi 市場;Qualcomm Snapdragon Ride Flex 晶片實現座艙與駕駛融合,降低成本並達 ASIL-D 安全標準,加速 ADAS 普及。 安全與公平性挑戰:自動駕駛需遵循 ISO 26262/SOTIF 標準應對風險。此外,訓練數據集缺乏代表性導致系統對輪椅使用者或導盲犬等 VRUs 偵測準確度較低,亟需法規要求提升感知公平性。 📚 延伸閱讀/相關連結 一、 研究論文與學術報告 A Review of Cybersecurity Challenges and Solutions for Autonomous Vehicles (The Science and Information (SAI) Organization, 2025, Vol. 16, No. 2) Autonomous Vehicle Pedestrian Detection: Minimum Safety Standards Needed to Protect Disabled Road Users (International Journal of Scientific Research & Engineering Trends (IJSRET), Sep-Oct 2025, Volume 11, Issue 5) End-to-End Deep Learning for Self-Driving Cars (NVIDIA Technical Blog, Aug 17, 2016) Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor J3016_202104 (SAE International, 2021 年 4 月) The Cityscapes Dataset for Semantic Urban Scene Understanding (Computer Vision Foundation (Cordts, M., et al.), 2016) Bias Behind the Wheel: Fairness Analysis of Autonomous Driving Systems (arXiv preprint (Li, Z., et al.), 2023) The social dilemma of autonomous vehicles (Science (Bonnefon, Jean-François/Shariff, Azim/Rahwan, Iyad), 2016) Autonomous Driving: Technical, Legal and Social Aspects (Springer (M. Maurer, B. Lenz, H. Winner, and J. C. Gerdes), 2016) Perception, Planning, Control, and Coordination for Autonomous Vehicles (Machines (Pendleton et al.), 2017) 二、 官方文件與國際標準 (功能安全與法規) Ethics Commission Automated...
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