跟讀練習: Enhancing AV Simulation with Generative AI - DRIVE Labs Ep. 36 - 通過影片學習英語口語
選擇練習模式
每種模式都會開啟全屏練習室。
33 句
如果句子過短或過長，請點選 Edit 進行調整。
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Simulation is critical to ensuring the safety of autonomous driving systems before real -world deployment.
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However, creating realistic scenarios that accurately depict rare and potentially dangerous situations can be extremely labor -intensive,
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especially when creating 3D assets and adding natural behavior.
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In this episode of Dryer Labs, together we'll discuss three key advancements from NVIDIA that uses generative AI to create realistic environments,
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generate natural driving behaviors, and edit the resulting scenarios to enable rigorous AV evaluation and training.
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First, NVIDIA has developed Map LLM,
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a large language model that generates realistic driving environments in the form of high -definition maps based on user input. For example,
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it can use text from a crash report to generate multiple possible high -definition maps
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that match the original description the generation of diverse driving environments
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provides the first key components of general realistic av simulation the
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road layout the second key capability is placing agents within these environments
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and generating natural driving behaviors once the virtual world is constructed
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we can use nvidia lctgen a language condition traffic generation model to populate vehicles pedestrians
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and other agents exhibiting natural realistic behaviors lctgen is a transformer based language model
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that interprets text descriptions of traffic scenarios it predicts where both
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agents are likely to be as well as their motion through the environment all according to the given text prompt.
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Again, using the example of a crash report,
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LCTGen can place vehicles at their specified locations and generate the trajectories described in the report, as well as possible variations.
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This is designed to help AV developers turn previously dormant data sources, such as text -based crash reports, into SIM -ready evaluation scenarios.
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This not only increases data diversity,
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but also enhances the training and testing of AVStacks in simulation to maintain safety across a wider variety of scenarios.
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If we still need to fine -tune the scenario, we can use editing tools built on NVIDIA Omniverse,
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which incorporates large language models to easily modify elements of the virtual world using simple language props.
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In this example, the scenario editor can immediately change a daytime scene into a nighttime scene, adjust the weather from sunny to rainy,
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or add agents to the scene.
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This greatly expands the range of real -world operational design domains, or ODDs, that AVs are exposed to during training.
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The responsiveness of these systems will be accelerated by the NVIDIA BlackWild GPU architecture,
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taking advantage of its expanded inference and next -generation transformer engines designed for generative AI workloads.
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When we combine the capabilities of MapLM for generating realistic environments,
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LCTGen for populating those environments with naturalistic agents, and the LM Power Scenario Editors for customizing simulation in all the verse using text,
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we can officially tap into Generative AI to create limitless ultra -realistic scenarios in new ways,
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as well as rigorous AV evaluation and training that accurately mirrors the real world.
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Stay tuned for these innovative capabilities to be integrated into NVIDIA Inference Microservices, or the NIMM roadmap.
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本課的詞彙與口語要點
這段影片共有 33 個句子、500 個單詞可供跟讀。 講話部分時長為 3:37。 說話人語速平穩，每分鐘約 138 個詞，很適合跟讀。 只有 67% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。
影片中的重點詞彙
影片中 15 個值得學習的單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| scenario 名詞 | /sɪˈnɛəɹioʊ/ | 指令碼 /指令碼, 場景 /場景 |
| realistic 形容詞 | /ˌɹɪəˈlɪs.tɪk/ | 現實 /現實, 栩栩如生 |
| simulation 名詞 | /ˌsɪm.jəˈleɪ.ʃn̩/ | 模擬 /模擬 |
| evaluation 名詞 | /ɪˌvæljuˈeɪʃn̩/ | 評價 /評價 |
| expand 動詞 | /ɪkˈspænd/ | 擴充套件 /擴充套件, 擴張 /擴張 |
| edit 動詞 | /ˈɛdɪt/ | 編輯 /編輯 |
| tune 名詞 | /tjuːn/ | 曲調 /曲調, 曲子 |
| rigorous 形容詞 | /ˈɹɪɡəɹəs/ | 嚴苛的 /嚴苛的, 嚴謹的 /嚴謹的 |
| mirror 名詞 | /ˈmiɹɚ/ | җинзы, 鏡 /鏡 |
| element 名詞 | /ˈɛləmənt/ | 要素 |
| architecture 名詞 | /ˈɑː.kɪˌtɛk.t͡ʃə/ | 建築 /建築, 建築學 /建築學 |
| input 名詞 | /ˈɪnpʊt/ | 輸入 /輸入 |
| greatly 副詞 | /ˈɡɹeɪ̯t.li/ | 大大 |
| component 名詞 | /kəmˈpoʊ.nənt/ | 元件, 部件 |
| diversity 名詞 | /daɪˈvɜː(ɹ)sɪti/ | 多樣性 /多樣性, 變化 /變化 |
需要注意的發音
- “sh” 和 “zh” 音: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, evaluation /ɪˌvæljuˈeɪʃn̩/, potentially /pəˈtɛnʃ(ə)li/, operational /ˌɑ.pəˈɹeɪ.ʃə.nəl/, variation /ˌvɛəɹiˈeɪʃn̩/
- 長單詞——注意重音位置: evaluation /ɪˌvæljuˈeɪʃn̩/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, accurately /ˈæk.jə.ɹɪt.li/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, potentially /pəˈtɛnʃ(ə)li/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 用正常速度逐句跟讀，每句重複到你的節奏與說話人一致為止。
- 錄下自己的聲音並與原聲對比，特別注意 scenario, realistic, simulation 這類單詞。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。