TW201822168A - Vehicle moving direction predicting system and method using digital image recognition in combination with moving trace computation technology with application of big data computation technology - Google Patents

Vehicle moving direction predicting system and method using digital image recognition in combination with moving trace computation technology with application of big data computation technology Download PDF

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TW201822168A
TW201822168A TW105141167A TW105141167A TW201822168A TW 201822168 A TW201822168 A TW 201822168A TW 105141167 A TW105141167 A TW 105141167A TW 105141167 A TW105141167 A TW 105141167A TW 201822168 A TW201822168 A TW 201822168A
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vehicle
intersection
target vehicle
driving direction
time
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TWI624819B (en
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杜宛玲
林佳興
陳伯錚
葉雲兆
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中華電信股份有限公司
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Abstract

The present invention provides a vehicle moving direction predicting system and method, which uses digital image recognition in combination with moving trace computation technology, with application of big data computation technology, to provide an accurate moving direction predicting system. The present invention has three units including a road condition weight control unit, time and traffic condition weight control unit, and vehicle moving trace weight control unit, which carry out the calculation of weights according to the environmental factors including the person, event, time, place, and object of a vehicle and uses the statistic result of previous vehicle moving trace data as a basis for prediction, also involving the instantaneously traffic condition, traffic flow, and road condition, to provide a dynamic and instantaneous prediction of a potential moving direction of a target vehicle, in order to facilitate police to track a suspect vehicle.

Description

車輛行駛方向預測系統及方法    Vehicle driving direction prediction system and method   

本發明係提取人、事、時、地、物等環境因子進行權重控制之以分析判斷之車輛行駛方向預測系統及方法。 The invention is a system and method for predicting a vehicle's driving direction by extracting environmental factors such as people, events, times, places, and things for weight control to analyze and judge.

隨著交通流量逐年成長,交通事故日漸增加,警方利用智慧型路口影像監控及車牌辨識系統協助辦案,查找失竊車牌、警政署通報查緝車輛、肇事逃逸車及圍捕車輛的需求越來越高,以往雖已有車輛即時影像軌跡追蹤系統可提供方向預測供員警參考,但預測結果仍不夠準確,因此為了在圍捕犯罪車輛時能有效改善勤務佈署與警力運用之方式,如何能夠透過智慧型通報警車攔截系統整合雲龍軌跡系統與警車定位派遣系統的整合,達成即時掌握犯罪車輛的位置與附近的警力資源,參考車輛行駛方向預系統分析後的預測結果,進一步適當調配勤務佈署,使相同的警力資源能達到最好的追緝效率,為各方努力研究之課題。 As the traffic flow grows year by year and traffic accidents are increasing, the police use smart intersection image monitoring and license plate recognition systems to assist in handling cases, find stolen license plates, the Police Department has reported increasing demand for seized vehicles, accident escape cars and rounded up vehicles. In the past, although the vehicle real-time image trajectory tracking system can provide direction prediction for the reference of the police, the prediction result is still not accurate. Therefore, in order to effectively improve the way of service deployment and police use when rounding up criminal vehicles, how can we use intelligent The alarm vehicle interception system is integrated with the integration of the Yunlong trajectory system and the police vehicle positioning dispatch system to achieve real-time grasp of the location of the criminal vehicle and the nearby police resources. With reference to the prediction results of the vehicle driving direction pre-analysis system, it is necessary to further appropriately deploy service deployment to make the same The police resources can achieve the best hunting efficiency, which is a subject for all parties to study hard.

本發明提供一種車輛行駛方向預測系統,包括:一路口監控攝影模組,係擷取一路口的一即時車流影像及一目標車輛之一車牌資訊,其中路口監控攝影模組係包含複數個監控攝影機。一智慧型影 像管理模組,係透過路口車流影像及車牌資訊,判斷路口之一車輛流速,並記錄目標車輛的車牌資訊所對應之習慣行經的時間及路線,其中智慧型影像管理模組更包含。一歷史軌跡分析單元,係記錄目標車輛的車牌資訊所對應之習慣行經的時間及路線。一即時路況分析單元,係透過路口車流影像分析目前車輛流速。一環境因子提取單元,係依據一環境及路況資料,產生一環境變數,其中環境變數為道路種類、目標車輛種類與行駛情境,即時車流量以及習慣路徑。一車輛行駛方向預測計算模組,係透過環境變數、目前車輛流速、習慣行經的時間及路線,進行目標車輛之行駛方向預測。一網路通訊界面模組,係提供路口監控攝影模組、智慧型影像管理模組、歷史軌跡分析單元、即時路況分析單元、環境因子提取單元及車輛行駛方向預測計算模組之間之傳輸。 The invention provides a vehicle driving direction prediction system, including: an intersection surveillance camera module, which captures an instant traffic image of an intersection and license plate information of a target vehicle, wherein the intersection surveillance camera module includes a plurality of surveillance cameras . A smart image management module is based on the intersection traffic image and license plate information to determine the flow velocity of a vehicle at an intersection, and records the customary travel time and route corresponding to the license plate information of the target vehicle. The smart image management module also includes . A historical trajectory analysis unit records the time and route of the habitual travel corresponding to the license plate information of the target vehicle. A real-time road condition analysis unit is used to analyze the current vehicle flow rate through the intersection traffic image. An environmental factor extraction unit generates an environmental variable based on an environmental and road condition data, wherein the environmental variable is a road type, a target vehicle type and a driving situation, real-time traffic flow and a habitual path. A vehicle driving direction prediction calculation module is used to predict the driving direction of a target vehicle through environmental variables, the current vehicle speed, the time and route used to travel. A network communication interface module provides transmission between intersection surveillance camera modules, intelligent image management modules, historical trajectory analysis units, real-time road condition analysis units, environmental factor extraction units, and vehicle driving direction prediction calculation modules.

其中車輛行駛方向預測計算模組係透過一行車軌跡權重控制單元,根據目標車輛的車牌資訊所對應之習慣行經的時間及路線,計算目標車輛行經路口之機率。 The vehicle driving direction prediction calculation module calculates the probability of the target vehicle passing through the intersection according to the customary travel time and route corresponding to the license plate information of the target vehicle through a line of vehicle trajectory weight control units.

其中車輛行駛方向預測計算模組係透過一時間與路況權重控制單元,根據目前車輛流速,計算目標車輛行經路口之機率。 The vehicle driving direction prediction calculation module calculates the probability of the target vehicle passing through the intersection through a time and road condition weight control unit based on the current vehicle flow rate.

其中車輛行駛方向預測計算模組係透過一道路條件權重控制單元,根據路口之道路寬度及速限,計算目標車輛行經路口之機率。 The vehicle driving direction prediction calculation module uses a road condition weight control unit to calculate the probability of the target vehicle passing through the intersection according to the road width and speed limit of the intersection.

本發明提供一種車輛行駛方向預測方法,其步驟包含:一路口監控攝影模組擷取一路口的一即時車流影像及一目標車輛之一車牌資訊並送至一智慧型影像管理模組;智慧型影像管理模組係透過路口車流影像及車牌資訊,判斷路口之一車輛 流速及目標車輛之車牌資訊,包含目標車輛之車牌、車型、車色,並儲存目標車輛的車牌資訊所對應之習慣行經的時間及路線;利用一即時路況分析單元分析目前車輛流速,一歷史軌跡分析單元分係目標車輛所對應之習慣行經的時間及路線,一環境因子提取單元依據一環境及路況資料產生一環境變數;以及一車輛行駛方向預測計算模組係透過環境變數、目前車輛流速、習慣行經的時間及路線,進行目標車輛之行駛方向預測。 The invention provides a method for predicting a vehicle's driving direction. The steps include: an intersection monitoring camera module captures an instant traffic image of an intersection and a license plate information of a target vehicle and sends the information to an intelligent image management module; the intelligent image management module; The image management module judges the flow rate of a vehicle at an intersection and the license plate information of the target vehicle through the traffic image and license plate information at the intersection, including the license plate, model, and color of the target vehicle, and stores the customary travel corresponding to the license plate information of the target vehicle. Time and route; An instantaneous road condition analysis unit is used to analyze the current vehicle flow rate, a historical trajectory analysis unit is used to analyze the time and route of the habitual travel of the target vehicle, and an environmental factor extraction unit generates an environmental variable based on environmental and road condition data; And a vehicle driving direction prediction calculation module performs prediction of the driving direction of the target vehicle through environmental variables, current vehicle flow velocity, time and route used to travel.

本發明提出一種車輛行駛方向預測系統,將車輛行駛的環境變異參數,並參考車輛過往習慣選擇的路徑,經過權重計算後可得到更智慧化的預測結果,有助警方在圍捕的過程中能夠更精準地掌握犯罪車輛的路線,順利完成逮捕。本發明相較於現有技術,特徵在於能夠提取人、事、時、地、物等環境因子進行權重控制,環境因子簡述如下: The invention proposes a vehicle driving direction prediction system, which uses the vehicle's driving environment to mutate parameters and refer to the vehicle's past habitually selected path. After weight calculation, a more intelligent prediction result can be obtained, which helps the police to be more informed during the round-up process. Accurately grasp the route of the criminal vehicle and successfully complete the arrest. Compared with the prior art, the present invention is characterized in that it can extract environmental factors such as people, events, times, places and things for weight control. The environmental factors are briefly described as follows:

1.「人」的因子指的是目標車輛行駛習慣,根據資料庫中所紀錄過去各天的各個時間的歷史軌跡資料,基於過去資料中,分析目標車輛的行駛習慣,找出過往習慣選擇的路線,以作為車輛行駛方向預測參考。 1. The "person" factor refers to the driving habits of the target vehicle. Based on the historical track data of the past days and times recorded in the database, based on the past data, analyze the driving habits of the target vehicle to find out the choice of past habits. Route as a reference for predicting the direction of travel of the vehicle.

2.「事」的因子指的是目標車輛的行駛情境,正在逃逸警方追捕的現行逃逸車輛與通報查緝的問題車輛,兩者所選擇的行駛路線依照過去的經驗統計結果表示,會有一些差異,亦會影響車輛行駛方向預測結果。 2. The "event" factor refers to the driving situation of the target vehicle, the current escaped vehicle being chased by the police and the problem vehicle notified for detection. The driving route chosen by the two is based on past experience and statistics. There are some differences. , Will also affect the prediction result of the vehicle driving direction.

3.「時」的因子指的是目標車輛的行駛時間,即時車流量是影響車輛行駛方向預測的因素,尖峰時間主要幹道的車流量較大,現行逃逸車輛選擇塞車路段的可能性較低,除此之外,號誌狀態亦會間接影響車輛行使方向預測,當路上的車輛依照號誌停紅燈時,對目標車輛而言就 如同塞車,若路口可左轉或右轉,目標車輛選擇左、右轉的機率便會提高。 3. The "time" factor refers to the travel time of the target vehicle. The real-time traffic volume is a factor that affects the prediction of the vehicle's driving direction. The rush hour traffic volume on the main road is relatively large. The current escape vehicles are less likely to choose traffic jam sections. In addition, the status of the sign will also indirectly affect the prediction of the direction of the vehicle. When a vehicle on the road stops at the red light according to the sign, it is like a traffic jam to the target vehicle. If the intersection can turn left or right, the target vehicle chooses The chance of turning left and right will increase.

4.「地」的因子指的是目標車輛所在的路口可能選擇行駛的道路種類,如:主要幹道、支線道路、巷弄道路、單行道、無尾巷...等,一般而言,行駛車輛選擇筆直的主要幹道的機率比其它道路的機率高。 4. The "ground" factor refers to the types of roads that the target vehicle may choose to drive at the intersection, such as: main roads, branch roads, lanes, one-way streets, tailless lanes, etc. Generally speaking, driving Vehicles are more likely to choose straight main roads than other roads.

5.「物」的因子指的是目標車輛的種類(如:大型車、小型車、機踏車...等),車輛種類可用來過濾無法通行的路段,排除不合理的預測判斷,提高預測結果的準確性。如:大型車無法進入狹小的巷弄道路、車流量大的單行道,逆向行駛的可能性很低。 5. The "material" factor refers to the type of target vehicle (such as large cars, small cars, treadmills, etc.). Vehicle types can be used to filter inaccessible road sections, exclude unreasonable predictions, and improve predictions. The accuracy of the results. For example, large cars cannot enter narrow alley roads and single-lane roads with high traffic volume, and the possibility of reverse driving is very low.

上列詳細說明係針對本發明之一可行實施例之具體說明,惟該實施例並非用以限制本發明之專利範圍,凡未脫離本發明技藝精神所為之等效實施或變更,均應包含於本案之專利範圍中。 The above detailed description is a specific description of a feasible embodiment of the present invention, but this embodiment is not intended to limit the patent scope of the present invention. Any equivalent implementation or change that does not depart from the technical spirit of the present invention should be included in Within the scope of the patent in this case.

綜上所述,本案不但在空間型態上確屬創新,並能較習用物品增進上述多項功效,應已充分符合新穎性及進步性之法定發明專利要件,爰依法提出申請,懇請 貴局核准本件發明專利申請案,以勵發明,至感德便。 To sum up, this case is not only innovative in terms of space type, but also enhances the above-mentioned multiple effects over conventional items. It should have fully met the requirements for statutory invention patents that are novel and progressive. Apply for it in accordance with the law and ask your office for approval. This invention patent application is designed to encourage inventions, and it is a matter of virtue.

100‧‧‧路口監控攝影模組 100‧‧‧Intersection surveillance camera module

200‧‧‧智慧型影像管理模組 200‧‧‧Smart Image Management Module

300‧‧‧歷史軌跡分析單元 300‧‧‧History Track Analysis Unit

400‧‧‧即時路況分析單元 400‧‧‧Real-time traffic analysis unit

500‧‧‧環境因子提取單元 500‧‧‧Environmental factor extraction unit

600‧‧‧車輛行駛方向預測計算模組 600‧‧‧Vehicle driving direction prediction calculation module

700‧‧‧網路通訊界面模組 700‧‧‧ network communication interface module

110‧‧‧監控攝影機 110‧‧‧ Surveillance Camera

610‧‧‧行車軌跡權重控制單元 610‧‧‧travel track weight control unit

620‧‧‧時間與路況權重控制單元 620‧‧‧time and road weight control unit

630‧‧‧道路條件權重控制單元 630‧‧‧ road condition weight control unit

S201~S204‧‧‧步驟流程 S201 ~ S204‧‧‧step flow

圖1為本發明之車輛行駛方向預測系統之架構圖。 FIG. 1 is a structural diagram of a vehicle driving direction prediction system of the present invention.

圖2為本發明之車輛行駛方向預測方法之流程圖。 FIG. 2 is a flowchart of a method for predicting a driving direction of a vehicle according to the present invention.

為利 貴審查委員了解本發明之技術特徵、內容與優點及其所能達到之功效,茲將本發明配合附圖,並以實施例之表達形式詳細說明如下,而其中所使用之圖式,其主旨僅為示意及輔助說明書之用,未必為本發明實施後之真實比例與精準配置,故不應就所附之圖式的比例與配置關係解讀、侷限本發明於實際實施上的權利範圍,合先敘明。 In order for the reviewing committee members to understand the technical features, contents and advantages of the present invention and the effects that can be achieved, the present invention is described in detail with the accompanying drawings in the form of embodiments, and the drawings used therein are The main purpose is only for the purpose of illustration and supplementary description. It may not be the actual proportion and precise configuration after the implementation of the invention. Therefore, the attached drawings should not be interpreted and limited to the scope of rights of the present invention in actual implementation. He Xianming.

請參閱圖1,如圖所示,為本發明之車輛行駛方向預測系統之架構圖,其中包含路口監控攝影模組100、智慧型影像管理模組200、歷史軌跡分析單元300、即時路況分析單元400、環境因子提取單元500、車輛行駛方向預測計算模組600以及網路通訊界面模組700,其中各模組間透過網路通訊界面模組700傳輸,其中路口監控攝影模組100的利用多個監控攝影機110將影像透過網路通訊界面模組700傳送到智慧型影像管理模組200,經過智慧型影像管理模組200辨識目標車輛之特徵如,車牌、車型、車色),並將辨識結果儲存於資料庫中。辨識結果經歷史軌跡分析單元300及即時路況分析單元400進行大資料運算,環境因子提取單元500可由分析結果中從中取出道路種類、目標車輛種類與行駛情境、即時車流量及目標車輛的習慣路徑等環境因子,再分別經過行車軌跡權重控制單元610、時間與路況權重控制單元620、道路條件權重控制單元630計算權重,接著由車輛行駛方向預測計算模組600進行加權計算,最後可得到目標車輛行駛方向預測結果。 Please refer to FIG. 1. As shown in the figure, it is a structural diagram of a vehicle driving direction prediction system according to the present invention, which includes an intersection monitoring camera module 100, an intelligent image management module 200, a historical trajectory analysis unit 300, and a real-time road condition analysis unit. 400, an environmental factor extraction unit 500, a vehicle driving direction prediction calculation module 600, and a network communication interface module 700, among which each module is transmitted through the network communication interface module 700, and the use of the intersection monitoring camera module 100 is more The surveillance cameras 110 transmit the image to the intelligent image management module 200 through the network communication interface module 700. The intelligent image management module 200 identifies the characteristics of the target vehicle (such as license plate, vehicle model, and vehicle color), and recognizes The results are stored in a database. The recognition result is calculated by the historical trajectory analysis unit 300 and the real-time road condition analysis unit 400. The environmental factor extraction unit 500 can extract the road type, target vehicle type and driving situation, real-time traffic volume, and habitual path of the target vehicle from the analysis results. The environmental factors are calculated by the trajectory weight control unit 610, time and road condition weight control unit 620, and road condition weight control unit 630, and then weighted by the vehicle driving direction prediction calculation module 600. Finally, the target vehicle can be obtained. Direction prediction results.

請參閱圖2,如圖所示,車輛行駛方向預測方法之流程圖,其步驟包含:S201:路口監控攝影模組擷取路口的即時車流影像及目標車輛之車牌資訊 並送至智慧型影像管理模組;S202:智慧型影像管理模組係透過路口車流影像及車牌資訊,判斷路口之車輛流速及目標車輛之車牌資訊,並儲存目標車輛的車牌資訊所對應之習慣行經的時間及路線;S203:利用即時路況分析單元分析目前車輛流速,歷史軌跡分析單元分係目標車輛所對應之習慣行經的時間及路線,環境因子提取單元依據環境及路況資料產生環境變數;以及S204:車輛行駛方向預測計算模組係透過環境變數、目前車輛流速、習慣行經的時間及路線,進行目標車輛之行駛方向預測。 Please refer to FIG. 2. As shown in the figure, the flowchart of the method for predicting the direction of vehicle travel includes the following steps: S201: The intersection monitoring camera module captures the real-time traffic image of the intersection and the license plate information of the target vehicle and sends it to the intelligent image management. Module; S202: The intelligent image management module judges the vehicle flow rate at the intersection and the license plate information of the target vehicle through the traffic image and license plate information at the intersection, and stores the time and route of the customary travel corresponding to the license plate information of the target vehicle; S203 : Using the real-time road condition analysis unit to analyze the current vehicle flow rate, the historical trajectory analysis unit is used to analyze the time and route of the habitual travel of the target vehicle, and the environmental factor extraction unit generates environmental variables based on the environment and road condition data; and S204: the vehicle direction prediction calculation The module uses the environment variables, the current vehicle flow rate, the time and route used to travel to predict the driving direction of the target vehicle.

以一實際案例說明,中華東路、小東路口位於台南市東區,鄰近大灣交流道,中華東路為連結台南市永康區與東區之南北向外環幹道,屬主要幹道等級,而小東路為台南市東區東西向主要幹道;假設警方正在追緝一現行逃逸的車輛,此目標車輛即將由南往北經過中華東路、小東路口,車輛行駛方向的樣本空間S={left,straight,right},P(left)為車輛向左轉向機率、P(right)為車輛向右轉向機率以及P(straight)為車輛直行的機率,並依據下列公式進行權重計算: 其中行駛情境調整因素fs,如果fs=現行逃逸,則增加大條路的權重。經統計得到由南往北經過此路口的車輛轉向機率P(left)=9%、P(straight)=72%、P(right)=19%,追緝時間為平日下午18:30,正逢下班交通尖峰時間,由歷史資料統計結果顯示該時段的車輛轉向機率為P(left)=7%、 P(straight)=80%、P(right)=13%,依據過去此目標車輛的行車軌跡紀錄的統計結果,發現該目標車輛行由南往北行經此路口所選擇的路徑為P(left)=6%、P(straight)=42%、P(right)=52%,經權重控制單元計算,可得到預測結果如下: Take a practical case to illustrate, the intersection of Zhonghua East Road and Xiaodong Road is located in the East District of Tainan City, adjacent to the Dawan Interchange. Zhonghua East Road is the main arterial road linking the north-south ring road connecting Yongkang District and Tainan District in Tainan City. The east-west main arterial road in Shidong District; suppose the police are hunting for a currently escaping vehicle. This target vehicle is about to pass from Zhonghua East Road and Xiaodong Intersection from south to north. The sample space S = {left, straight, right} for the direction of the vehicle. P (left) is the probability of the vehicle turning to the left, P (right) is the probability of the vehicle turning to the right, and P (straight) is the probability of the vehicle going straight, and the weight is calculated according to the following formula: The driving situation adjustment factor fs, if fs = current escape, increase the weight of the large road. According to statistics, the turning probability of vehicles passing this intersection from south to north is P (left) = 9%, P (straight) = 72%, and P (right) = 19%. The hunting time is 18:30 in the afternoon on weekdays. The rush hour of off-duty traffic, the historical data statistics show that the vehicle steering probability for this period is P (left) = 7%, P (straight) = 80%, P (right) = 13%, based on the driving trajectory of this target vehicle in the past According to the recorded statistical results, it was found that the route selected by the target vehicle from south to north via this intersection was P (left) = 6%, P (straight) = 42%, P (right) = 52%, and passed the weight control unit. After calculation, the prediction results are as follows:

綜上所述,本案不僅於技術思想上確屬創新,並具備習用之傳統方法所不及之上述多項功效,已充分符合新穎性及進步性之法定發明專利要件,爰依法提出申請,懇請 貴局核准本件發明專利申請案,以勵發明,至感德便。 To sum up, this case is not only innovative in terms of technical ideas, but also has many of the above-mentioned effects that are not used by traditional methods. It has fully met the requirements of statutory invention patents that are novel and progressive. To approve this invention patent application, to encourage invention, to the utmost convenience.

Claims (12)

一種車輛行駛方向預測系統,包括:一路口監控攝影模組,係擷取一路口的一即時車流影像及一目標車輛之一車牌資訊;一智慧型影像管理模組,係透過該路口車流影像及該車牌資訊,判斷該路口之一車輛流速,並記錄該目標車輛的該車牌資訊所對應之習慣行經的時間及路線,其中該智慧型影像管理模組更包含:一歷史軌跡分析單元,係記錄該目標車輛的該車牌資訊所對應之習慣行經的時間及路線;一即時路況分析單元,係透過該路口車流影像分析目前該車輛流速;一環境因子提取單元,係依據一環境及路況資料,產生一環境變數;一車輛行駛方向預測計算模組,係透過該環境變數、目前該車輛流速、習慣行經的時間及路線,進行該目標車輛之行駛方向預測;以及一網路通訊界面模組,係提供該路口監控攝影模組、該智慧型影像管理模組、該歷史軌跡分析單元、該即時路況分析單元、該環境因子提取單元及該車輛行駛方向預測計算模組之間之傳輸。     A vehicle driving direction prediction system includes: an intersection monitoring camera module, which captures an instant traffic image of an intersection and license plate information of a target vehicle; an intelligent image management module, which uses the intersection traffic image and The license plate information determines the flow velocity of a vehicle at the intersection, and records the time and route of customary travel corresponding to the license plate information of the target vehicle. The intelligent image management module further includes: a historical track analysis unit, which records The time and route of the habitual travel of the target vehicle corresponding to the license plate information; a real-time road condition analysis unit that analyzes the current vehicle flow rate through the traffic image at the intersection; an environmental factor extraction unit that generates data based on an environment and road condition data An environment variable; a vehicle driving direction prediction calculation module for predicting the driving direction of the target vehicle through the environmental variable, the current vehicle flow velocity, the time and route used to travel; and a network communication interface module, Provide the intersection surveillance camera module, the intelligent image management module, the history Trace analysis unit, the real-time traffic analysis unit, the extracting unit and the environmental factors traveling direction of the vehicle between the transmission of the prediction calculation module.     如申請專利範圍第1項所述之車輛行駛方向預測系統,其中該路口監控攝影模組係包含複數個監控攝影機。     The vehicle driving direction prediction system according to item 1 of the patent application scope, wherein the intersection surveillance camera module includes a plurality of surveillance cameras.     如申請專利範圍第1項所述之車輛行駛方向預測系統,其中該環境變數係為道路種類、目標車輛種類與行駛情境,即時車流量以及習慣路徑。     The vehicle driving direction prediction system according to item 1 of the scope of the patent application, wherein the environmental variables are road types, target vehicle types and driving situations, real-time traffic flow, and customary paths.     如申請專利範圍第1項所述之車輛行駛方向預測系統,其中該車輛行駛方向預測計算模組係透過一行車軌跡權重控制單元,根據該目標車輛的 該車牌資訊所對應之習慣行經的時間及路線,計算目標車輛行經該路口之機率。     The vehicle driving direction prediction system according to item 1 of the scope of the patent application, wherein the vehicle driving direction prediction calculation module is based on a line of trajectory weight control units, according to the time of customary travel corresponding to the license plate information of the target vehicle and Route, calculate the probability of the target vehicle passing through the intersection.     如申請專利範圍第1項所述之車輛行駛方向預測系統,其中該車輛行駛方向預測計算模組係透過一時間與路況權重控制單元,根據目前該車輛流速,計算目標車輛行經該路口之機率。     The vehicle driving direction prediction system according to item 1 of the patent application scope, wherein the vehicle driving direction prediction calculation module calculates the probability of the target vehicle passing the intersection through a time and road condition weight control unit based on the current vehicle flow rate.     如申請專利範圍第1項所述之車輛行駛方向預測系統,其中該車輛行駛方向預測計算模組係透過一道路條件權重控制單元,根據該路口之道路寬度及速限,計算目標車輛行經該路口之機率。     The vehicle driving direction prediction system according to item 1 of the scope of the patent application, wherein the vehicle driving direction prediction calculation module calculates a target vehicle passing through the intersection through a road condition weight control unit according to the road width and speed limit of the intersection The probability.     一種車輛行駛方向預測方法,其步驟包含:一路口監控攝影模組擷取一路口的一即時車流影像及一目標車輛之一車牌資訊並送至一智慧型影像管理模組;該智慧型影像管理模組係透過該路口車流影像及該車牌資訊,判斷該路口之一車輛流速及該目標車輛之該車牌資訊,並儲存該目標車輛的該車牌資訊所對應之習慣行經的時間及路線;利用一即時路況分析單元分析目前該車輛流速,一歷史軌跡分析單元分係該目標車輛所對應之習慣行經的時間及路線,一環境因子提取單元依據一環境及路況資料產生一環境變數;以及一車輛行駛方向預測計算模組係透過該環境變數、目前該車輛流速、習慣行經的時間及路線,進行該目標車輛之行駛方向預測。     A vehicle driving direction prediction method includes the steps of: an intersection monitoring camera module captures an instant traffic image of an intersection and a license plate information of a target vehicle and sends it to a smart image management module; the smart image management The module judges the flow velocity of a vehicle at the intersection and the license plate information of the target vehicle through the traffic image and the license plate information of the intersection, and stores the time and route of the customary travel corresponding to the license plate information of the target vehicle. The real-time road condition analysis unit analyzes the current vehicle flow rate, a historical trajectory analysis unit is divided into the habitual travel time and route corresponding to the target vehicle, an environmental factor extraction unit generates an environmental variable based on an environment and road condition data; and a vehicle travels The direction prediction calculation module performs prediction of the driving direction of the target vehicle through the environment variable, the current flow velocity of the vehicle, the time and route used to travel.     如申請專利範圍第7項所述之車輛行駛方向預測方法,其中該車牌資訊係包含該目標車輛之車牌、車型、車色。     The method for predicting the direction of travel of a vehicle as described in item 7 of the scope of patent application, wherein the license plate information includes the license plate, model, and color of the target vehicle.     如申請專利範圍第7項所述之車輛行駛方向預測方法,其中該環境變數 係為道路種類、目標車輛種類與行駛情境,即時車流量以及習慣路徑。     The method for predicting the driving direction of a vehicle as described in item 7 of the scope of the patent application, wherein the environmental variables are road types, target vehicle types and driving situations, real-time traffic flow, and customary paths.     如申請專利範圍第7項所述之車輛行駛方向預測方法,其中該車輛行駛方向預測計算模組係透過一行車軌跡權重控制單元,根據該目標車輛的該車牌資訊所對應之習慣行經的時間及路線,計算目標車輛行經該路口之機率。     The method for predicting the direction of travel of a vehicle according to item 7 in the scope of the patent application, wherein the calculation module of the direction of travel prediction of the vehicle is based on a line of trajectory weight control units, according to the time of customary travel corresponding to the license plate information of the target vehicle and Route, calculate the probability of the target vehicle passing through the intersection.     如申請專利範圍第7項所述之車輛行駛方向預測方法,其中該車輛行駛方向預測計算模組係透過一時間與路況權重控制單元,根據目前該車輛流速,計算目標車輛行經該路口之機率。     The method for predicting the driving direction of a vehicle according to item 7 in the scope of the patent application, wherein the vehicle driving direction prediction calculation module calculates the probability of the target vehicle passing the intersection through a time and road condition weight control unit based on the current vehicle flow rate.     如申請專利範圍第7項所述之車輛行駛方向預測方法,其中該車輛行駛方向預測計算模組係透過一道路條件權重控制單元,根據該路口之道路寬度及速限,計算目標車輛行經該路口之機率。     The method for predicting the driving direction of a vehicle according to item 7 in the scope of the patent application, wherein the vehicle driving direction prediction calculation module calculates the target vehicle passing through the intersection through a road condition weight control unit according to the road width and speed limit of the intersection The probability.    
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