Patent Publication Number: US-2019172345-A1

Title: System and method for detecting dangerous vehicle

Description:
RELATED APPLICATIONS 
     This application claims priority to Taiwan Patent Application No. 106142420, filed Dec. 4, 2017, the entirety of which is herein incorporated by reference. 
     BACKGROUND 
     Field of Invention 
     The present disclosure relates to the apparatuses and methods, and more particularly, systems and methods for detecting dangerous vehicles. 
     Description of Related Art 
     The motor vehicle is provided by an engine or motor, usually by an internal combustion engine. The motor vehicle mainly refers to the vehicles on the road. Motor vehicles move fast, they are important regulatory traffic objects in the world. 
     However, in the past, the evaluation of the driving behavior is only focused on the characteristics of a single vehicle, but the characteristics of the road are varied, it was easy to make a mistake in evaluation considering only the characteristics of the single vehicle. Moreover, in the past, it was a one-time monitoring of all vehicles and therefore a system overload problem occurs. 
     SUMMARY 
     The following presents a simplified summary of the disclosure in order to provide a basic understanding to the reader. This summary is not an extensive overview of the disclosure and it does not identify key/critical elements of the present invention or delineate the scope of the present invention. Its sole purpose is to present some concepts disclosed herein in a simplified form as a prelude to the more detailed description that is presented later. 
     In one or more various aspects, the present disclosure is directed to systems and methods for detecting dangerous vehicles. 
     An embodiment of the present disclosure is related to a system includes a plurality of vehicle detectors and a server, and the server is communicated with the vehicle detectors. The vehicle detectors are spaced apart from each other, and each vehicle detector configured to obtain a traffic image. The server is configured to infer an interaction among vehicles in the traffic image according to a car-following theory, so as to find at least one outlier vehicle from the vehicles and to select the outlier vehicle as a focus vehicle to be tracked, and the server configured to determine whether a driving behavior of the focus vehicle falls into an abnormal behavior model. 
     In one embodiment, the server determines a size and a moving direction of focus area surroundings the focus vehicle required to be detected according to a direction, a speed and a position of the focus vehicle. 
     In one embodiment, the server recognizes types of the vehicles  141 ,  142  and  143  from the traffic image. 
     In one embodiment, the server collects driving track data of the vehicles from the traffic image. 
     In one embodiment, the abnormal behavior model comprises a violation condition of a plurality of traffic rules, and when the server determines that the driving behavior of the focus vehicle violates at least one of the traffic rules, the server determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model comprises at least one abnormal track, when the server determines that a driving track of the focus vehicle is different from driving tracks of others of the vehicles, and when the driving track of the focus vehicle meets the at least one abnormal track, the server determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model includes at least one abnormal speed difference range, the server compares a speed of the focus vehicle with an average speed of others of the vehicles, and when a speed difference between the driving speed of the focus vehicle and the average driving speed falls within the at least one abnormal speed difference range, the server determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model includes at least one abnormal distance, and when the server determines that a distance between the focus vehicle and any of others of the vehicles is less than the at least one abnormality distance, the server determines that the focus vehicle is abnormal. 
     In one embodiment, the server performs an alert processing procedure after the driving behavior of the focus vehicle has fallen into the abnormal behavior model. 
     In one embodiment, wherein each of the vehicle detectors is a roadside camera. 
     Another embodiment of the present disclosure is related to a method for detecting dangerous vehicle includes steps of: providing a plurality of vehicle detectors spaced apart from each other, and each vehicle detector configured to obtain a traffic image; using a server configured to infer an interaction among vehicles in the traffic image according to a car-following theory, so as to find at least one outlier vehicle from the vehicles and to select the outlier vehicle as a focus vehicle to be tracked, and the server configured to determine whether a driving behavior of the focus vehicle falls into an abnormal behavior model. 
     In one embodiment, the method further includes steps of: using the server to determine a size and a moving direction of a focus area surroundings the focus vehicle required to be detected according to a direction, a speed and a position of the focus vehicle. 
     In one embodiment, the method further includes steps of: using the server to recognize types of the vehicles from the traffic image. 
     In one embodiment, the method further includes steps of: using the server to collect driving track data of the vehicles from the traffic image. 
     In one embodiment, the abnormal behavior model comprises a violation condition of a plurality of traffic rules, and the method further includes steps of: when the server determines that the driving behavior of the focus vehicle violates at least one of the traffic rules, determining that the focus vehicle is abnormal by using the server. 
     In one embodiment, the abnormal behavior model comprises at least one abnormal track, and the method further includes steps of: when the server determines that a driving track of the focus vehicle is different from driving tracks of others of the vehicles, and when the driving track of the focus vehicle meets the at least one abnormal track, determining that the focus vehicle is abnormal by using the server. 
     In one embodiment, the abnormal behavior model includes at least one abnormal speed difference range, and the method further includes steps of: using the server compares a speed of the focus vehicle with an average speed of others of the vehicles; when a speed difference between the driving speed of the focus vehicle and the average driving speed falls within the at least one abnormal speed difference range, determining that the focus vehicle is abnormal by using the server. 
     In one embodiment, the abnormal behavior model includes at least one abnormal distance, and the method further includes steps of: when the server determines that a distance between the focus vehicle and any of others of the vehicles is less than the at least one abnormality distance, the server determines that the focus vehicle is abnormal. 
     In one embodiment, the method further includes steps of: using the server performs an alert processing procedure after the driving behavior of the focus vehicle has fallen into the abnormal behavior model. 
     In one embodiment, each of the vehicle detectors is a roadside camera. 
     Technical advantages are generally achieved, by embodiments of the present invention. The system and the method for detecting the dangerous vehicle provide the vehicle dynamic focus image recognition, so as to accomplish accurate and comprehensive consideration of the warning mode. 
     Many of the attendant features will be more readily appreciated, as the same becomes better understood by reference to the following detailed description considered in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention can be more fully understood by reading the following detailed description of the embodiment, with reference made to the accompanying drawings as follows: 
         FIG. 1A  is a block diagram of a system for detecting a dangerous vehicle according to one embodiment of the present disclosure; 
         FIG. 1B  is a block diagram of a system for detecting a dangerous vehicle according to another embodiment of the present disclosure; 
         FIG. 2  is a schematic diagram of a focus area according to one embodiment of the present disclosure; and 
         FIG. 3  is a flow chart of a method for detecting a dangerous vehicle according to one embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to the present embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the description to refer to the same or like parts. 
     As used in the description herein and throughout the claims that follow, the meaning of “a”, “an”, and “the” includes reference to the plural unless the context clearly dictates otherwise. Also, as used in the description herein and throughout the claims that follow, the terms “comprise or comprising”, “include or including”, “have or having”, “contain or containing” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. As used in the description herein and throughout the claims that follow, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise. 
     It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the embodiments. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items. 
     It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being “directly connected” or “directly coupled” to another element, there are no intervening elements present. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein. 
       FIG. 1A  is a block diagram of a system  100 A for detecting a dangerous vehicle according to one embodiment of the present disclosure. As shown in  FIG. 1A , the system  100 A includes a plurality of vehicle detectors  101 ,  102  and  103  and a server  120 . In structure, the server  120  is communicated with the vehicle detectors  101 ,  102  and  103 . In another embodiment, the system  100  further includes an alert platform  130 , and the server  120  is communicated with the alert platform  130 . In one embodiment, the communication  140  is established among the system  100 , vehicle detectors  101 ,  102  and  103  and/or the alert platform  130  in a wired or wireless manner, such as Wi-Fi wireless communication or wired network communication. 
     In practice, the server  120  may be a cloud server. The alert platform  130  may be a host computer of local unit, a traffic control unit, or the police. The plurality of vehicle detectors  101 ,  102  and  103  are arranged at a fixed spacing from each other or non-fixed spacing from each other. For example, the vehicle detectors  101 ,  102 , and  103  are all roadside cameras and are spaced apart from each other on a street lamp, a dividing island or a roadside of a sidewalk, or the vehicle detectors  101 ,  102 , and  103  are all aerial cameras. Alternatively, one or more of the vehicle detectors  101 ,  102  and  103  may be roadside cameras, and the other may be aerial cameras. Those with ordinary skill in the art may flexibly design the vehicle detectors depending on the desired application. 
     In  FIG. 1A , the server  120  may include a communication device  121 , a processor  122 , and a storage device  123 . The communication device  121  (e.g., a wired or wireless network device) to establish communications  140  with the vehicle detectors  101 ,  102  and  103  and/or the alert platform  130 . The storage device  123  (e.g., a hard disk) can be used to preload an abnormal behavior model of the vehicle and record traffic images acquired by the vehicle detectors  101 ,  102  and  103 . Accordingly, the processor  122  (e.g., a central processing unit) can detect a dangerous vehicle. 
     Specifically, in system  100 A, each vehicle detector (e.g., the vehicle detector  101 ) configured to obtain the traffic image. The server  120  is configured to infer an interaction among vehicles  141 ,  142  and  143  in the traffic image according to a car-following theory, so as to find at least one outlier vehicle (e.g., the vehicle  142 ) from the vehicles  141 ,  142  and  143  and to select the outlier vehicle as the focus vehicle  142  to be tracked. It should be noted that when the focus vehicle  142  is out of the detection range of the vehicle detector  101 , the server  120  automatically calls the next vehicle detector  102  to keep tracking the focus vehicle  142  according to the traveling direction of the focus vehicle  142 . 
     Moreover, the server  120  can recognize types of the vehicles from the traffic image. The server  120  also can collect driving track data of the vehicles from the traffic image. 
     Then, the server  120  is configured to determine whether a driving behavior of the focus vehicle  142  falls into the abnormal behavior model. When the driving behavior of the focus vehicle  142  falls into the abnormal behavior model as determined, the server  120  determines the focus vehicle  142  is abnormal. 
     In one embodiment, the abnormal behavior model comprises a violation condition of a plurality of traffic rules. When the server  120  determines that the driving behavior of the focus vehicle  142  violates at least one of the traffic rules (e.g., speeding), the server  120  determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model comprises at least one abnormal track. When the server  120  determines that a driving track of the focus vehicle  142  is different from driving tracks of others of the vehicles, and when the driving track of the focus vehicle  142  meets the at least one abnormal track (e.g., driving in a zigzag pattern), the server  120  determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model includes at least one abnormal speed difference range (e.g., a speed difference over 30 kilometers per hour). The server  120  compares a speed of the focus vehicle  142  with an average speed of others of the vehicles. When a speed difference between the driving speed of the focus vehicle  142  and the average driving speed falls within the at least one abnormal speed difference range, the server  120  determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model includes at least one abnormal distance (e.g., spacing of less than 50 meters). When the server  120  determines that a distance between the focus vehicle  142  and any (e.g., the vehicle  143 ) of others of the vehicles is less than the at least one abnormality distance, the server  120  determines that the focus vehicle  142  is abnormal. 
     After the driving behavior of the focus vehicle has fallen into the abnormal behavior model, the server  120  performs an alert processing procedure. For example, after the driving behavior of the focus vehicle  142  falls into the abnormal behavior model, the server  120  performs path prediction on the focus vehicle  142  on the basis of the historical track, the current speed and direction of focus vehicle  142 , so as to determine whether the focus vehicle  142  is dangerous to any other vehicle; if so, the server  120  performs the alert processing procedure. 
     With regard to the alert processing routine, for example, the server  120  may send alert information regarding the focus vehicle  142  to the alert platform  130 . Alternatively, the server  120  notifies the radio device and/or display device of the focus vehicle  142  and/or the other vehicles  141  and  143  through a broadcast system and/or display system (not shown) of the vehicle detector  101 , or notifies the radio device and/or display device of the focus vehicle  142  and/or the other vehicles  141  and  143  through via the nearest broadcast system and/or display system on the road (not shown). Those with ordinary skill in the art may flexibly design the alert manner depending on the desired application. 
       FIG. 1B  is a block diagram of a system  100 B for detecting a dangerous vehicle according to one embodiment of the present disclosure. The system  100 B in structure is substantially the same as the system  100 A except that  FIG. 1B  has no lane line  110  as shown in  FIG. 1A , thus, are not repeated herein. No matter whether the lane line exists, the system in the present disclosure can detect the dangerous vehicle. 
       FIG. 2  is a schematic diagram of a focus area according to one embodiment of the present disclosure. As shown in  FIGS. 1A and 2 , the server is configured to infer an interaction among vehicles in the traffic image according to a car-following theory, so as to find vehicles  241  and  242  and to select the outlier vehicle as focus vehicles to be tracked. For example, the vehicle  241  is the outlier at the vehicle speed (e.g., its vehicle speed dramatically over the average speed) and therefore the vehicle  242  is the outlier away from the track of the other vehicles. The server  120  determines the size and the moving direction of focus areas  211  and  212  surroundings the focus vehicle  241  and  242  required to be detected according to the direction, the speed and the positions of the focus vehicles  241  and  242 , so as to tracks focus vehicles  241  and  242  effectively through the dynamic focus areas  211  and  212 . 
     For example, the server  120  determines the dynamic movement directions of the focus areas  211  and  212  after determining the potential focus vehicles  241  and  242  according to the vehicle direction. The server  120  adjusts the sizes of the focus areas  211  and  212  according to the vehicle speed and the positions. 
     It should be understood that the above-mentioned car-following theory uses dynamic methods to study the vehicle lined up in the lane; the rear vehicle maintains a certain safety distance with the front vehicle, and often changes the driving speed as the front vehicle. The state of the rear vehicle following the front vehicle is expressed in mathematical terms and clarified as the car-following theory. 
     For a more complete understanding of a method performed by the system  100 A and/or  100 B, referring  FIGS. 1A, 1B, 2 and 3 ,  FIG. 3  is a flow chart of a method  300  for detecting a dangerous vehicle according to one embodiment of the present disclosure. As shown in  FIG. 3 , the method  300  includes operations S 301 -S 311 . However, as could be appreciated by persons having ordinary skill in the art, for the steps described in the present embodiment, the sequence in which these steps is performed, unless explicitly stated otherwise, can be altered depending on actual needs; in certain cases, all or some of these steps can be performed concurrently. For example, operations S 307 -S 309  may be regarded as optional steps, or operations S 310  and S 311  may be regarded as optional steps. 
     In the method  300 , the vehicle detectors  101 ,  102  and  103  (e.g., the roadside camera) spaced apart from each other are provided, and each vehicle detector configured to obtain a traffic image; the server  120  is configured to infer an interaction among vehicles  141 ,  142  and  143  in the traffic image according to a car-following theory, so as to find at least one outlier vehicle from the vehicles and to select the outlier vehicle as a focus vehicle (e.g., the vehicle  142 ) to be tracked, and the server  120  is configured to determine whether a driving behavior of the focus vehicle  142  falls into an abnormal behavior model. 
     Specifically, in operation S 301 , the server  120  determines a size and a moving direction of focus area surroundings the focus vehicle  142  required to be detected according to a direction, a speed and a position of the focus vehicle  142 . In operation S 302 , the server  120  recognizes types of the vehicles  141 ,  142  and  143  from the traffic image. In operation S 303 , the server  120  collects driving track data of the vehicles from the traffic image. In operation S 304 , the server  120  defines normal or abnormal behavior of vehicle as a basis of establishing an abnormal behavior model. 
     Then, in operation S 305 , the server  120  determines whether a driving behavior of the focus vehicle  142  falls into the abnormal behavior model. When the driving behavior of the focus vehicle  142  does not fall into the abnormal behavior model, in operation S 306 , the server  120  executes the mechanical learning training of the normal/abnormal determination accordingly. 
     In one embodiment, the abnormal behavior model comprises a violation condition of a plurality of traffic rules. In operation S 305 , when the server  120  determines that the driving behavior of the focus vehicle  142  violates at least one of the traffic rules (e.g., speeding), the server  120  determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model comprises at least one abnormal track. In operation S 305 , when the server  120  determines that a driving track of the focus vehicle  142  is different from driving tracks of others of the vehicles, and when the driving track of the focus vehicle  142  meets the at least one abnormal track (e.g., driving in a zigzag pattern), the server  120  determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model includes at least one abnormal speed difference range (e.g., a speed difference over 30 kilometers per hour). In operation S 305 , the server  120  compares a speed of the focus vehicle  142  with an average speed of others of the vehicles. When a speed difference between the driving speed of the focus vehicle  142  and the average driving speed falls within the at least one abnormal speed difference range, the server  120  determines that the focus vehicle is abnormal. 
     In one embodiment, the abnormal behavior model includes at least one abnormal distance (e.g., spacing of less than 50 meters). In operation S 305 , when the server  120  determines that a distance between the focus vehicle  142  and any (e.g., the vehicle  143 ) of others of the vehicles is less than the at least one abnormality distance, the server  120  determines that the focus vehicle  142  is abnormal. 
     After the driving behavior of the focus vehicle  142  falls into the abnormal behavior model, in operation S 307 , the server  120  performs path prediction on the focus vehicle  142 . In operation S 308 , the server  120  uses dangerous values as a basis of determining the dangerous vehicle. Accordingly, in operation S 309 , the server  120  determines whether the focus vehicle  142  is dangerous to any other vehicle. 
     For an instance of the dangerous value, when that the driving behavior of the focus vehicle  142  violates at least one of the traffic rules (e.g., speeding), the dangerous value may be a speed limit plus 10 kilometers per hour. For another instance, when the driving track of the focus vehicle  142  meets the at least one abnormal track (e.g., driving in a zigzag pattern), the dangerous value may indicates the driving track across the lane line  110 . For yet another instance, when the speed difference between the driving speed of the focus vehicle  142  and the average driving speed falls within the abnormal speed difference range (e.g., a speed difference over 30 kilometers per hour), the dangerous value may be the speed difference over 40 kilometers per hour. For still yet another instance, When the server  120  determines that a distance between the focus vehicle  142  and any (e.g., the vehicle  143 ) of others of the vehicles is less than the (e.g., spacing of less than 50 meters) the dangerous value may be the spacing of less than 2 meters. In view of above, dangerous values can be upper/lower limits of the range of anomalies defined in the abnormal behavior model. Those with ordinary skill in the art may flexibly adjust dangerous values depending on the desired application. 
     When the focus vehicle  142  is not dangerous to other vehicles, in operation S 310 , the server  120  executes the mechanical learning training of the dangerous determination accordingly. On the contrary, when the focus vehicle  142  is not dangerous to any other vehicle, in operation S 311 , the server  120  performs an alert processing procedure. In another embodiment, operations S 307 -S 311  can be omitted, and therefore when the server  120  determines that the focus vehicle  142  is abnormal in operation S 305 , the server  120  performs the alert processing procedure directly in operation S 311 . Those with ordinary skill in the art may flexibly choose operations depending on the desired application. 
     In view of above, the system  100 A and  100 B and the method  300  for detecting the dangerous vehicle provide the vehicle dynamic focus image recognition, so as to accomplish accurate and comprehensive consideration of the warning mode. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present invention without departing from the scope or spirit of the invention. In view of the foregoing, it is intended that the present invention cover modifications and variations of this invention provided they fall within the scope of the following claims.