特斯拉FSD beta 來比眼力有沒有比AI好 - 汽車
By Iris
at 2021-04-08T19:39
at 2021-04-08T19:39
Table of Contents
這個youtuber將FSD AI看到的畫面結合實際行車畫面
https://www.youtube.com/watch?v=qRjY55ELYnc
有幾個亮點, 你去觀察對向車道,或是更遠處都可以發現AI都可以清楚知道車子位置在哪
換句話說,FSD用了鏡頭就可以模擬光達的距離偵測了
這個技術已經被特斯拉提出為專利
https://uspto.report/patent/app/20200265247
特斯拉一直在訓練深度學習神經網絡,該神經網絡可以說是“偽LiDAR”所以馬斯克
認為根本不需要光達.靠鏡頭模擬即可,不服的人去看看AI所看到的畫面跟實際差異
再來. 要問大家在1分27秒處, AI感應到馬路中間有行人, 我想問看看多少人能在影片中
看到.如果看不到就代表AI的感知能力已經大於人類
如果還找不到 請看這張圖
https://i.imgur.com/khEAyN8.jpg
AI看到行人,並且以藍色框表示
我相信隨著數據繼續增加,AI深度學習,最後特斯拉仍然在自動駕駛處於統治地位
其他關於特斯拉這項技術的解釋
https://tinyurl.com/ssrktpeh
機器翻譯
特斯拉發布專利:“使用視覺圖像數據評估對象特性”以增強自動駕駛系統
為了操作自動駕駛系統,通常在汽車上安裝大量各種昂貴的傳感器。但是,特斯拉決定避
免這種情況,並使用攝像頭構建自己的自動駕駛系統。這使AI學習過程變得複雜,但是它
具有許多關鍵優勢,並且可以為駕駛提供真正完整的自主權,被歸類為5級自主權。
特斯拉已經發布了一項專利“使用視覺圖像數據評估對象屬性”。公開的發明有助於基於
由車輛攝像機捕獲的圖像來接收數據,以便部分地識別物體與車輛的距離。
自主駕駛系統通常依賴於安裝眾多傳感器,包括視覺和發射距離傳感器的集合(例如,雷
達,激光雷達,超聲波等)。通過收集每個傳感器捕獲的數據,系統可以了解車輛的環境
並確定如何控制車輛。但是,隨著傳感器數量和類型的增加,系統的複雜性和成本也隨之
增加。
例如,將發射距離傳感器(例如激光雷達)包括在大眾市場的車輛中通常是昂貴的。此外
,每個附加傳感器都增加了自動駕駛系統的輸入帶寬要求。因此,特斯拉開始尋找車輛上
傳感器的最佳配置。理想的配置應限制傳感器的總數,而不限制捕獲的數據的數量和類型
,以準確描述周圍環境並安全地控制車輛。
該專利中描述的系統包括一個或多個耦合到存儲器的處理器。一個或多個處理器被配置為
基於由車輛攝像機捕獲的圖像來接收圖像數據。然後,目標是利用此數據(作為對經過訓
練的機器學習模型的輸入的基礎),以至少部分地識別物體與車輛的距離。機器學習模型
已經使用訓練圖像和發射距離傳感器的相關輸出進行了訓練。
該專利描述了一種用於從視覺數據生成高度準確的機器學習結果的訓練技術。使用輔助傳
感器數據(例如雷達和激光雷達結果),輔助數據與從視覺數據中識別出的對象相關聯,
以準確估算對象屬性(例如距離)。在各個部分中,輔助數據與視覺數據的收集和關聯都
是自動完成的,幾乎不需要人工干預。
--
https://www.youtube.com/watch?v=qRjY55ELYnc
有幾個亮點, 你去觀察對向車道,或是更遠處都可以發現AI都可以清楚知道車子位置在哪
換句話說,FSD用了鏡頭就可以模擬光達的距離偵測了
這個技術已經被特斯拉提出為專利
https://uspto.report/patent/app/20200265247
特斯拉一直在訓練深度學習神經網絡,該神經網絡可以說是“偽LiDAR”所以馬斯克
認為根本不需要光達.靠鏡頭模擬即可,不服的人去看看AI所看到的畫面跟實際差異
再來. 要問大家在1分27秒處, AI感應到馬路中間有行人, 我想問看看多少人能在影片中
看到.如果看不到就代表AI的感知能力已經大於人類
如果還找不到 請看這張圖
https://i.imgur.com/khEAyN8.jpg
AI看到行人,並且以藍色框表示
我相信隨著數據繼續增加,AI深度學習,最後特斯拉仍然在自動駕駛處於統治地位
其他關於特斯拉這項技術的解釋
https://tinyurl.com/ssrktpeh
機器翻譯
特斯拉發布專利:“使用視覺圖像數據評估對象特性”以增強自動駕駛系統
為了操作自動駕駛系統,通常在汽車上安裝大量各種昂貴的傳感器。但是,特斯拉決定避
免這種情況,並使用攝像頭構建自己的自動駕駛系統。這使AI學習過程變得複雜,但是它
具有許多關鍵優勢,並且可以為駕駛提供真正完整的自主權,被歸類為5級自主權。
特斯拉已經發布了一項專利“使用視覺圖像數據評估對象屬性”。公開的發明有助於基於
由車輛攝像機捕獲的圖像來接收數據,以便部分地識別物體與車輛的距離。
自主駕駛系統通常依賴於安裝眾多傳感器,包括視覺和發射距離傳感器的集合(例如,雷
達,激光雷達,超聲波等)。通過收集每個傳感器捕獲的數據,系統可以了解車輛的環境
並確定如何控制車輛。但是,隨著傳感器數量和類型的增加,系統的複雜性和成本也隨之
增加。
例如,將發射距離傳感器(例如激光雷達)包括在大眾市場的車輛中通常是昂貴的。此外
,每個附加傳感器都增加了自動駕駛系統的輸入帶寬要求。因此,特斯拉開始尋找車輛上
傳感器的最佳配置。理想的配置應限制傳感器的總數,而不限制捕獲的數據的數量和類型
,以準確描述周圍環境並安全地控制車輛。
該專利中描述的系統包括一個或多個耦合到存儲器的處理器。一個或多個處理器被配置為
基於由車輛攝像機捕獲的圖像來接收圖像數據。然後,目標是利用此數據(作為對經過訓
練的機器學習模型的輸入的基礎),以至少部分地識別物體與車輛的距離。機器學習模型
已經使用訓練圖像和發射距離傳感器的相關輸出進行了訓練。
該專利描述了一種用於從視覺數據生成高度準確的機器學習結果的訓練技術。使用輔助傳
感器數據(例如雷達和激光雷達結果),輔助數據與從視覺數據中識別出的對象相關聯,
以準確估算對象屬性(例如距離)。在各個部分中,輔助數據與視覺數據的收集和關聯都
是自動完成的,幾乎不需要人工干預。
--
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