馬斯克:特斯拉已經非常接近 Level 5 完 - 特斯拉
By Lucy
at 2020-07-10T09:16
at 2020-07-10T09:16
Table of Contents
推 Scape: 有些人還拿那些Lv4載客服務的試驗車說嘴,表示根本看不懂07/09 17:21
→ Scape: 人家在做什麼。絕大多數的無人駕駛試驗車都是依靠昂貴的07/09 17:22
→ Scape: 感應器跟安全人員去慢慢推進算法,用很慢的速度累積里程07/09 17:23
→ Scape: Tesla早就放棄了這一套做法,路上有超過百萬輛Tesla跟他們07/09 17:23
→ Scape: 蒐集大量的資訊,同時這些資訊到中心後提供Tesla做學習訓練07/09 17:25
→ Scape: 然後改進,最後反饋到車主的車輛上用陰影模式大量的測試07/09 17:25
→ Scape: 測試結果安全了、成功了才會放出來給車主使用。07/09 17:26
→ Scape: 跟那些原型車拿牌照在路上跑根本不是一回事,竟然還有人07/09 17:27
→ Scape: 拿這來反對,人家在做什麼事情,其本質就那麼難看懂?07/09 17:28
→ Scape: 上百萬輛車在全世界各地供你用影子模式做測試,為何還要回07/09 17:29
→ Scape: 過頭去搞那些又貴數量又少的原型車?07/09 17:29
就算有影子模式訓練,也絕對無法取代自駕車實際路試
自駕車路試的重點
1. 環境(號誌/他車/行人) -> 自駕電腦
感測器從環境收集數據,由自駕電腦運算和決策
2. 自駕車輛 -> 環境
自駕電腦的決策(例如變換車道),進而影響環境(他車/行人)
也就是說
不只環境會影響自駕決策/行為,自駕決策/行為也會影響到環境
被自駕決策/行為影響後的環境,又再進一步影響自駕決策/行為
影響並非單邊而是雙向溝通,且形成迴圏
因此也考驗自駕電腦即時(real time)運算的能力
Tesla所謂的影子模式本身只做到第1點
用"已經蒐集好"的場景環境訓練自駕電腦
場景是不會變動的既定pattern
一旦自駕電腦做了任何跟原駕駛不同的決策
由於當下沒有實際行為(介入駕駛),因此自駕電腦的決策並沒有實際反饋到環境
所以就算Tesla數據量較Waymo龐大許多
但前者本質上仍然是AP和車主的駕駛數據
雖有大量數據供自駕電腦訓練,但自駕電腦決策和行為並不影響到數據
而後者則是由自駕電腦與環境實際互動的純自駕數據
並不是說影子模式沒有意義
但你說有了影子模式就不需要昂貴數量少的原型車上路試驗
標準外行講的話
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