TW202246085A - 車輛後視警示系統 - Google Patents
車輛後視警示系統 Download PDFInfo
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Abstract
一種車輛後視警示系統,包含一後視鏡顯示器、一前取像裝置、一後取像裝置與一邊緣運算裝置,該前取像裝置用以輸出一前行車影像,該後取像裝置用以輸出一後行車影像,該邊緣運算裝置訊號連接該後視鏡顯示器、該前取像裝置與該後取像裝置,該邊緣運算裝置分別接收該前行車影像和該後行車影像,並從該前行車影像或該後行車影像之中辨識別出一車輛物件;該處理器根據該車輛物件判斷是否驅動該後視鏡顯示器顯示一警示畫面,為駕駛人提供直觀的警示效果。
Description
本發明係有關一種警示系統,特別是指車輛後視警示系統。
駕駛人在駕駛車輛時,通常是將注意力放在前方路況,並搭配觀看後視鏡所反映的景物,以掌握車輛後方是否有其他來車,另一方面,道路組成環境複雜,包含例如車輛、行人、動物、紅綠燈、交通標誌、分隔島、車道線…等,故道路實際路況相當多元,駕駛人常常要決定下一步行車動作,例如是否要超車、是否要減速、在綠燈倒數秒數剩幾秒時是否要穿越路口…等。
由此可見,駕駛人的行車決定與交通安全息息相關,若駕駛人所作的決定大都即時且適宜,那麼對於整體交通安全必然有莫大幫助;相對的,若駕駛人的行車心態猶豫不決或警覺性不足,往往錯失最佳決定時機,導致提升發生交通意外的機率。
舉例來說,騎士騎乘機車在抵達路口之前,雖有看到巴士在路口處停等紅燈,但騎士仍繼續前行而停在巴士旁,該機車可能剛好位在巴士駕駛人的視線死角,即位在盲區(Blind Spot)的位置,基於巴士有內輪差之因素,當紅燈切換為綠燈且巴士要轉彎,巴士側面可能與機車發生擦撞,導致交通意外的發生。
有鑒於此,本發明的主要目的是提供一種車輛後視警示系統,能適時發出警示以輔助駕駛人行車,讓駕駛人察覺本發明提供的警示後,能提高警覺並預先採取相關動作,以期降低發生交通意外的風險,並改善交通安全。
本發明車輛後視警示系統供設置在一車輛,包含:
一後視鏡顯示器;
一前取像裝置,輸出一前行車影像;
一後取像裝置,輸出一後行車影像
一邊緣運算裝置,訊號連接該後視鏡顯示器、該前取像裝置與該後取像裝置,該邊緣運算裝置分別接收該前行車影像和該後行車影像,並從該前行車影像或該後行車影像之中辨識別出一車輛物件;該處理器根據該車輛物件判斷是否驅動該後視鏡顯示器顯示一警示畫面。
根據本發明車輛後視警示系統,該前取像裝置與該後取像裝置能拍攝車前與車後之影像,能兼顧車前與車後路況,此外,該邊緣運算裝置較佳的可透過人工智慧(AI)影像辨識技術在該前行車影像或該後行車影像之中辨識別出車輛物件,具有準確的辨識能力。本發明透過在該後視鏡顯示器顯示該警示畫面的方式,提供駕駛人最直觀的警示效果,也就是說,只要駕駛人看到該後視鏡顯示器的警示畫面,即可立即採取相關動作,以在最佳的時機決定是否要加速前進、超車、減速、停靠路邊…等,藉此落實行車安全。
舉例來說,當機車騎士看到該後視鏡顯示器的警示畫面時,機車尚未進入巴士的盲區,故機車騎士可即時決定不進入巴士盲區,例如將機車停靠路邊或騎在巴士後方,機車騎士可等到巴士完成轉彎後再繼續前進,藉此有效避免與巴士發生交通事故。
本發明車輛後視警示系統供設置在一車輛,該車輛為駕駛人駕駛的車輛,本發明的實施例以駕駛人為第一人稱的角度敘事,故該車輛亦可稱之為本車。本發明能透過影像辨識手段判斷是否有其他車輛接近本車,並據以適時發出警示給駕駛人。本發明所應用的該車輛可為電動車、油車(以汽油或柴油作為燃料的車輛)或油電混合車,亦可為機車、重型機車、轎車、休旅車、貨車、巴士、工程車…等,本發明所應用的車輛不以前述舉例為限。
請參考圖1,本發明車輛後視警示系統的實施例包含一後視鏡顯示器10、一前取像裝置20、一後取像裝置30與一邊緣運算(edge computing)裝置40,其中,本發明所需的工作電源可取自該車輛的電池50。
該後視鏡顯示器10例如可為液晶顯示器(LCD),但不以此為限,該後視鏡顯示器10能受驅動而顯示靜態畫面或動畫。一般而言,該後視鏡顯示器10能結合於該車輛之後視鏡裝置的鏡片,請參考圖2與圖3,為該後視鏡顯示器10應用在機車70之後視鏡裝置60的示意圖;該後視鏡裝置60基本上可包含一鏡片座61與一鏡片62,該鏡片座61的前側610朝向機車70的前方,該鏡片座61的後側611朝向機車70的後方,該鏡片62設置在該鏡片座61的後側611,該鏡片座61與該鏡片62之間形成一容置空間63,該後視鏡顯示器10設置在該容置空間63中,該鏡片62例如可為具透光性的反射鏡片,該後視鏡顯示器10的螢幕表面能貼合於該鏡片62,故該後視鏡顯示器10的顯示畫面能穿透該鏡片62。當人員目視該鏡片62時,除了看到該鏡片62反映的景物,亦能看到該後視鏡顯示器10所顯示的畫面。另請參考圖4,為該後視鏡顯示器10應用在汽車71之後視鏡裝置60的示意圖。
該前取像裝置20設置在該車輛的前側以拍攝該車輛的前方,該後取像裝置30設置在該車輛的後側以拍攝該車輛的後方,其中,該前取像裝置20與該後取像裝置30可為攝影機,例如可為行車(影像)記錄器,該前取像裝置20輸出的影像定義為一前行車影像IM_f,該後取像裝置30輸出的影像定義為一後行車影像IM_r。請參考圖5為例,為該前取像裝置20與該後取像裝置30應用在機車70的示意圖,該前取像裝置20可設置在機車70的前面板700處,該後取像裝置30可設置在機車70的後擋泥板701處;另請參考圖6為例,為該前取像裝置20與該後取像裝置30應用在汽車71的示意圖,該前取像裝置20可設置在汽車71的前擋風玻璃710的內側,該後取像裝置30可設置在汽車71的車尾711處,例如後保險桿或車牌附近。較佳的,該前取像裝置20與該後取像裝置30可配備廣角鏡頭21、31,請配合參考圖7,從俯視來看,該前取像裝置20與該後取像裝置30透過廣角鏡頭21、31能實施130度的取像範圍。
該邊緣運算裝置40供安裝在該車輛的車身內,並訊號連接該後視鏡顯示器10、該前取像裝置20與該後取像裝置30,該邊緣運算裝置40具有資料傳輸、收集、處理及儲存等功能。本發明的實施例中,該邊緣運算裝置40包含一處理器41與電連接該處理器41的一儲存元件42、一第一傳輸介面43、一第二傳輸介面44、一第三傳輸介面45。
該處理器41可為積體電路晶片(IC chip),其可為但不限於中央處理器(CPU)、圖形處理器(GPU)、數位信號處理器(DSP)或電子控制單元(ECU),該儲存元件42可為記憶體或記憶卡。請參考圖1與圖8,該處理器41執行一影像辨識模型410,該影像辨識模型410為已完成訓練的模型(pre-trained model),其程式資料可儲存在該儲存元件42,以供該處理器41存取。該第一傳輸介面43訊號連接該前取像裝置20,該第二傳輸介面44訊號連接該後取像裝置30,該第三傳輸介面45訊號連接該後視鏡顯示器10。該第一傳輸介面43、該第二傳輸介面44與該第三傳輸介面45可為有線傳輸介面,而該後視鏡顯示器10、該前取像裝置20與該後取像裝置30內建或外接相對應的有線傳輸介面,所述有線傳輸介面例如可為但不限於高畫質多媒體介面(High Definition Multimedia Interface, HDMI)或通用序列匯流排(Universal Serial Bus, USB)介面;或者,該第一傳輸介面43、該第二傳輸介面44與該第三傳輸介面45亦可為無線傳輸介面,而該後視鏡顯示器10、該前取像裝置20與該後取像裝置30內建或外接相對應的無線傳輸介面,所述無線傳輸介面例如可為但不限於藍牙(Bluetooth)介面或藍牙低耗能(Bluetooth Low Energy, BLE)介面。
藉此,請參考圖8,該邊緣運算裝置40的處理器41能即時的(real-time)分別從該前取像裝置20與該後取像裝置30接收該前行車影像IM_f和該後行車影像IM_r,並即時的將該前行車影像IM_f和該後行車影像IM_r分別輸入至該影像辨識模型410,而能透過該影像辨識模型410從該前行車影像IM_f或該後行車影像IM_r之中辨識別出一車輛物件Obj,並根據該車輛物件Obj即時判斷是否驅動該後視鏡顯示器10顯示一警示畫面,較佳的,該警示畫面可包含一光圈。是以,透過該影像辨識模型410,該邊緣運算裝置40能實施人工智慧(AI)的即時車輛偵測與即時警示。
該影像辨識模型410可採用現有技術,容不詳述模型運作原理,舉例而言,「YOLO」是一種用於物件偵測的卷積神經網路(Convolutional Neural Network, CNN)模型,參考網址:https://pjreddie.com。在進行模型的訓練時,將複數訓練圖片樣本以及車輛的特徵標籤(label)提供給未經訓練的影像辨識模型進行訓練,其中,車輛的特徵標籤較佳的可包含大型車輛(例如貨車、巴士、卡車、工程車…等),或進一步可包含一般中小型車輛(例如機車、轎車、休旅車…等),而用於建立所述特徵標籤的工具例如可採用LabelImg,參考網址:https://github.com/tzutalin/labelImg。請參考圖9所示的訓練圖片樣本80的範例,其為拍攝車輛前方影像的樣本,使用者可在LabelImg工具框選該訓練圖片樣本80中的車輛圖案800並建立其特徵標籤;另請參考圖10所示另一訓練圖片樣本81的範例,其為拍攝車輛後方影像的樣本,使用者可在LabelImg工具框選該訓練圖片樣本81中的車輛圖案810並建立其特徵標籤。需說明的是,物件偵測的卷積神經網路模型是現有技術,前述模型的說明僅是舉例而已,不用以限制本發明所欲請求保護之範圍。
是以,經過多幅訓練圖片樣本以及多樣車輛特徵標籤的訓練後,成為已完成訓練的該影像辨識模型410。另一方面,前述訓練過程可先在個人電腦(PC)實施,再將訓練完成之該影像辨識模型410的程式資料存入到該儲存元件42,以供該處理器41存取與執行。
關於是否驅動該後視鏡顯示器10顯示該警示畫面的時機,首先,若該處理器41是在該前行車影像IM_f辨識出該車輛物件Obj,該處理器41可計算該車輛物件Obj在該前行車影像IM_f的比例大小,舉例而言,請參考圖11所示的一幅前行車影像IM_f,該處理器41可將該前行車影像IM_f劃分為m×n個方格90,m與n為正整數,故以圖11為例,該前行車影像IM_f被劃分為104個方格90,該處理器41計算與該車輛物件Obj重疊的方格91的一重疊數量,該重疊數量即反映該車輛物件Obj在該前行車影像IM_f的比例大小,也就是說,當該重疊數量越多(即重疊的方格91),代表該車輛物件Obj在該前行車影像IM_f的比例越大,被偵測到的車輛離本車的實際距離越近;反之,當該重疊數量越少,代表該車輛物件Obj在該前行車影像IM_f的比例越小,被偵測到的車輛離本車的實際距離越遠。該處理器41根據該車輛物件Obj於該前行車影像IM_f中的比例大小控制該警示畫面,說明如下。
當該邊緣運算裝置40的處理器41判斷出該重疊數量大於或等於一預警門檻值,請參考圖12,該處理器41驅動該後視鏡顯示器10顯示一警示畫面100,該警示畫面100可包含一光圈101。是以,當駕駛人看到該光圈101,就可知道本車前方有一大型車輛,此時,駕駛人可預先提高警覺並可視情況採取相關動作,例如預先減速行駛。
請參考圖13,隨著本車接近該大型車輛,該車輛物件Obj重疊該前行車影像IM_f的方格91的重疊數量越多,當該邊緣運算裝置40的處理器41判斷出該重疊數量大於或等於一警告門檻值,該處理器41控制該警示畫面100的光圈101產生變化,例如色彩的變化,將圖12與圖14的光圈101相比,本發明以灑點密度反映光圈101的色彩變化,圖14所示的光圈101的灑點密度較高,舉例來說,該光圈101可由圖12的淺色變成圖14的深色,或於其他實施例中,該光圈101可由圖12的橘色變成圖14的紅色。故當駕駛人看到該光圈101的色彩發生變化,代表本車已相當接近該大型車輛,提醒駕駛人必需採取相關動作,例如將本車停靠路邊。
前述中,該預警門檻值與該警告門檻值是儲存在該處理器41或該儲存元件42的可調整預設值,且該警告門檻值大於該預警門檻值,舉例來說,圖11與圖13所示的該前行車影像IM_f被劃分為104個方格,該預警門檻值可設定為30,該警告門檻值可設定為40。實際上,該警告門檻值與該預警門檻值可隨著該前行車影像IM_f及該後行車影像IM_r被劃分的方格總數量而對應調整,或根據該車輛物件Obj的車型而對應調整。
類似的,該處理器41對於該後行車影像IM_r的車輛物件辨識手段、預警手段與警告手段可依前述類推,容不重複詳述。換言之,因為該處理器41係同時接收該前行車影像IM_f與該後行車影像IM_r,只要該處理器41是在該前行車影像IM_f與該後行車影像IM_r中的任一影像辨識出該車輛物件Obj,都能實施如前所述的預警與警告手段。
綜上所述,本發明能實施人工智慧(AI)的車輛偵測與即時警示,不論本發明係應用在汽車或機車,尤其是對於機車而言,當駕駛人看到如圖12所示預警的光圈101時,代表本車的前方或後方可能有大型車輛,此時,駕駛人可預先提高警覺。再以圖13所示的情境為例,在圖13所示的情境下,若本車繼續前進可能進入該大型車輛的盲區,是以,當駕駛人看到如圖14所示警告的光圈101時,本車尚未進入該大型車輛的盲區,故駕駛人可在進入該大型車輛的盲區之前就將本車停靠路邊或行駛在該大型車輛後方,因此不會進入該大型車輛的盲區,等該大型車輛轉彎後再繼續前進,藉此有效避免與該大型車輛發生交通事故。
10:後視鏡顯示器
100:警示畫面
101:光圈
20:前取像裝置
21:廣角鏡頭
30:後取像裝置
31:廣角鏡頭
40:邊緣運算裝置
41:處理器
410:影像辨識模型
42:儲存元件
43:第一傳輸介面
44:第二傳輸介面
45:第三傳輸介面
50:電池
60:後視鏡裝置
61:鏡片座
610:前側
611:後側
62:鏡片
63:容置空間
70:機車
700:前面板
701:後擋泥板
71:汽車
710:前擋風玻璃
711:車尾
80,81:訓練圖片樣本
800,810:車輛圖案
90,91:方格
IM_f:前行車影像
IM_r:後行車影像
Obj:車輛物件
圖1:本發明車輛後視警示系統的實施例的方塊示意圖。
圖2:本發明的後視鏡顯示器應用在機車的示意圖。
圖3:本發明的後視鏡顯示器應用在後視鏡裝置的剖面示意圖。
圖4:本發明的後視鏡顯示器應用在汽車的示意圖。
圖5:本發明的後視鏡顯示器、前取像裝置和後取像裝置應用在機車的示意圖。
圖6:本發明的後視鏡顯示器、前取像裝置和後取像裝置應用在汽車的示意圖。
圖7:本發明的前取像裝置和後取像裝置具有130度取像範圍的示意圖。
圖8:本發明的影像辨識模型根據前行車影像或後行車影像辨識出車輛物件的示意圖。
圖9:本發明的影像辨識模型的訓練圖片樣本的示意圖。
圖10:本發明的影像辨識模型的訓練圖片樣本的示意圖。
圖11:本發明的前取像裝置所輸出的一幅前行車影像的示意圖。
圖12:本發明的後視鏡顯示器顯示警示畫面的示意圖。
圖13:本發明的前取像裝置所輸出的另一前行車影像的示意圖。
圖14:本發明的後視鏡顯示器顯示另一警示畫面的示意圖。
10:後視鏡顯示器
100:警示畫面
20:前取像裝置
21:廣角鏡頭
30:後取像裝置
31:廣角鏡頭
40:邊緣運算裝置
41:處理器
42:儲存元件
43:第一傳輸介面
44:第二傳輸介面
45:第三傳輸介面
50:電池
IM_f:前行車影像
IM_r:後行車影像
Claims (8)
- 一種車輛後視警示系統,供設置在一車輛,包含: 一後視鏡顯示器; 一前取像裝置,輸出一前行車影像; 一後取像裝置,輸出一後行車影像 一邊緣運算裝置,訊號連接該後視鏡顯示器、該前取像裝置與該後取像裝置,該邊緣運算裝置分別接收該前行車影像和該後行車影像,並從該前行車影像或該後行車影像之中辨識別出一車輛物件;該處理器根據該車輛物件判斷是否驅動該後視鏡顯示器顯示一警示畫面。
- 如請求項1所述之車輛後視警示系統,其中,該警示畫面包含一光圈。
- 如請求項1所述之車輛後視警示系統,其中,該前取像裝置與該後取像裝置配備廣角鏡頭。
- 如請求項1所述之車輛後視警示系統,其中,該邊緣運算裝置根據該車輛物件於該前行車影像或該後行車影像中的比例大小控制該警示畫面。
- 如請求項4所述之車輛後視警示系統,其中,該邊緣運算裝置將該前行車影像或該後行車影像劃分為m×n個方格,m與n為正整數,該邊緣運算裝置計算與該車輛物件重疊的方格的一重疊數量; 當該邊緣運算裝置判斷出該重疊數量大於或等於一預警門檻值,驅動該後視鏡顯示器顯示該警示畫面,該警示畫面包含一光圈; 當該邊緣運算裝置判斷出該重疊數量大於或等於一警告門檻值,控制該警示畫面的該光圈產生變化,其中,該警告門檻值大於該預警門檻值。
- 如請求項5所述之車輛後視警示系統,其中,該預警門檻值與該警告門檻值是可調整預設值。
- 如請求項1所述之車輛後視警示系統,其中,該邊緣運算裝置有線連接該後視鏡顯示器、該前取像裝置與該後取像裝置。
- 如請求項1所述之車輛後視警示系統,其中,該邊緣運算裝置無線連接該後視鏡顯示器、該前取像裝置與該後取像裝置。
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TWI726278B (zh) * | 2019-01-30 | 2021-05-01 | 宏碁股份有限公司 | 行車偵測方法、車輛及行車處理裝置 |
JP7205768B2 (ja) * | 2019-03-08 | 2023-01-17 | スズキ株式会社 | 車両の走行制御装置 |
TWI717102B (zh) * | 2019-11-14 | 2021-01-21 | 黃玄 | 基於影像辨識之交通路況車聯網系統 |
JP2021093628A (ja) * | 2019-12-10 | 2021-06-17 | トヨタ自動車株式会社 | 車両用周辺表示装置 |
-
2021
- 2021-05-24 TW TW110118695A patent/TWI809401B/zh active
- 2021-07-23 CN CN202110836562.XA patent/CN115465191A/zh active Pending
- 2021-11-30 US US17/457,026 patent/US11618379B2/en active Active
- 2021-12-01 EP EP21211741.0A patent/EP4095813A1/en not_active Withdrawn
- 2021-12-16 JP JP2021204240A patent/JP2022180281A/ja active Pending
- 2021-12-17 KR KR1020210181750A patent/KR20220158594A/ko unknown
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CN115465191A (zh) | 2022-12-13 |
US20220371510A1 (en) | 2022-11-24 |
KR20220158594A (ko) | 2022-12-01 |
US11618379B2 (en) | 2023-04-04 |
JP2022180281A (ja) | 2022-12-06 |
TWI809401B (zh) | 2023-07-21 |
EP4095813A1 (en) | 2022-11-30 |
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