Intelligent traffic lane changing supervision system based on big data and VR

Abstract

The invention discloses an intelligent traffic lane changing supervision system based on big data and VR, and relates to the technical field of traffic supervision. According to the invention, the road side acquisition unit acquires road data through the road side equipment, and generates the road intersection supervision subsystem according to the road data; acquiring driving traffic data corresponding to road vehicles through a vehicle-mounted sensor; the lane changing behavior analysis unit is used for obtaining a multi-source abnormal lane changing behavior based on big data to analyze and construct an abnormal lane changing model; the real-time data analysis unit constructs a vehicle driving data VR model according to the road data and the driving traffic data; real-time monitoring is carried out on driving traffic data corresponding to the vehicle through the vehicle driving data VR model, and abnormal lane changing behaviors are automatically identified; generating corresponding early warning information according to the abnormal lane changing behavior; and obtaining historical abnormal lane changing events of the corresponding vehicle according to the early warning information, and performing data fusion on the historical abnormal lane changing events through a vehicle driving data VR model to obtain a VR abnormal trajectory.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic supervision, and specifically to an intelligent traffic lane-changing supervision system based on big data and VR. Background Art

[0002] VR, as the name implies, is the combination of virtual and reality (using specific technologies to generate a virtual scenario, but being perceived by people as reality); theoretically speaking, virtual reality technology (VR) is a computer simulation system that can create and experience virtual worlds. It uses a computer to generate a simulated environment and enables users to immerse themselves in this environment; virtual reality technology uses data in real life, generates electronic signals through computer technology, and combines them with various output devices to transform them into phenomena that people can feel. These phenomena can be real objects in reality or substances that we cannot see with the naked eye, and are presented through three-dimensional models. Since these phenomena are not directly visible to us, but are a simulated real world through computer technology, it is called virtual reality; In urban roads, vehicle lane-changing behaviors are frequent, and unreasonable lane-changing is one of the important factors leading to traffic accidents and traffic jams. According to statistics, traffic accidents caused by illegal vehicle lane-changing account for a considerable proportion in various traffic accidents, seriously threatening road traffic safety. Traditional traffic supervision methods are no longer able to meet the needs of modern traffic management. Therefore, it is extremely urgent to develop an efficient and intelligent traffic lane-changing supervision system; The layout of urban roads is complex, with numerous intersections, and the traffic flow varies greatly in different sections. During the traffic peak period, the vehicle density is high and the driving speed is slow. In order to reach the destination as soon as possible, it is common for drivers to change lanes frequently. At the same time, some drivers have weak traffic safety awareness and do not abide by traffic rules. Behaviors such as random lane-changing and forced lane-changing occur from time to time. This not only increases the risk of vehicle collisions, but also easily leads to traffic jams and reduces the road traffic efficiency. For example, on the main roads of some big cities, traffic accidents caused by illegal vehicle lane-changing often result in traffic paralysis, extending the traffic congestion time for several hours and bringing great inconvenience to the travel of citizens; therefore, abnormal lane-changing behaviors are likely to occur during the lane-changing process at road intersections, but in the existing technology, it is impossible to timely and accurately identify abnormal lane-changing behavior patterns to provide strong data support for traffic supervision; and it is impossible to quickly determine whether a vehicle has an illegal lane-changing behavior; therefore, in order to solve the above technical problems, the present invention provides an intelligent traffic lane-changing supervision system based on big data and VR. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides an intelligent traffic lane-changing supervision system based on big data and VR; The object of the present invention can be achieved by the following technical solutions: A smart traffic lane-changing supervision system based on big data and VR, including a monitoring center. The system includes a traffic data collection model, a data analysis module, a supervision module, an early warning module, and a traceability module; The traffic data collection module is provided with a roadside collection unit and a vehicle-mounted collection unit; the roadside collection unit collects road data through roadside devices and generates a road intersection supervision subsystem according to the road data; the vehicle-mounted collection unit is used to collect the driving traffic data corresponding to road vehicles through vehicle-mounted sensors; The data analysis module is provided with a lane-changing behavior analysis unit and a real-time data analysis unit; the lane-changing behavior analysis unit is used to analyze multi-source abnormal lane-changing behaviors based on big data to construct an abnormal lane-changing model; the real-time data analysis unit constructs a vehicle driving data VR model according to the road data and the driving traffic data; The supervision module is used to monitor the driving traffic data corresponding to the vehicle in real time through the vehicle driving data VR model and automatically identify abnormal lane-changing behaviors; The early warning module is used to generate corresponding early warning information for the abnormal lane-changing behaviors; The traceability module is used to obtain the historical abnormal lane-changing events of the corresponding vehicle according to the early warning information, and perform data fusion on the historical abnormal lane-changing events through the vehicle driving data VR model to obtain a VR abnormal trajectory.

[0004] Further, the process that the roadside collection unit collects road data through roadside devices and generates a road intersection supervision subsystem according to the road data includes: Mark the position corresponding to the road intersection as a red node in the high-precision map, set the supervision range threshold corresponding to the road intersection, extract the road range corresponding to the road intersection from the high-precision map through the supervision range threshold to obtain the road intersection supervision range, and then connect the red nodes corresponding to the adjacent positions of each road intersection supervision range to generate a road intersection supervision subsystem; The road data includes road traffic flow lines, traffic sign states, and vehicle trajectories.

[0005] Further, the process that the vehicle-mounted collection unit collects the driving traffic data corresponding to road vehicles through vehicle-mounted sensors includes: The driving traffic data includes vehicle speed, turn signal state, lane deviation angle, and distance to surrounding vehicles; Obtain the license plate numbers of the vehicles corresponding to each road intersection supervision subsystem, and connect them with the corresponding driving traffic data to generate a vehicle driving traffic data set; Generate a vehicle speed vector with the vehicle speed starting from the corresponding vehicle position; perform data fusion on the lane deviation angle with the vehicle speed vector to obtain real-time vehicle data.

[0006] Furthermore, the process of the lane-changing behavior analysis unit obtaining multi-source abnormal lane-changing behaviors based on big data and constructing an abnormal lane-changing model includes: Extracting abnormal behavior characteristics from multi-source abnormal lane-changing behaviors through traffic rules, integrating each abnormal behavior characteristic to generate an abnormal behavior characteristic database; setting abnormal behavior lane-changing types, then classifying the abnormal type characteristics in the abnormal characteristic database by abnormal behavior lane-changing types, obtaining each abnormal behavior characteristic corresponding to each abnormal behavior characteristic type, and performing data connection to generate an abnormal lane-changing model.

[0007] Furthermore, the process of the real-time data analysis unit constructing a vehicle driving data VR model according to road data and driving traffic data includes: Mapping each corresponding road traffic flow line to the corresponding road in the road intersection supervision subsystem; then performing three-dimensional simulation on the corresponding traffic signs, vehicles and pedestrians and mapping them on the road intersection supervision subsystem to obtain a three-dimensional supervision model of the road intersection; then synchronizing the traffic sign status corresponding to the traffic signs, the vehicle positions corresponding to the vehicles and the pedestrian positions corresponding to the pedestrians in real time in the three-dimensional supervision model of the road intersection, and connecting with the abnormal lane-changing model to construct a vehicle driving data VR model.

[0008] Furthermore, the process of the supervision module performing real-time monitoring on the driving traffic data corresponding to the vehicle through the vehicle driving data VR model and automatically identifying the abnormal behavior lane-changing type includes: Performing real-time AI active identification on the abnormal behavior characteristics in the vehicle driving data VR model according to the abnormal lane-changing model, marking the identified vehicle as an abnormal vehicle, then obtaining the abnormal behavior lane-changing type corresponding to the abnormally identified vehicle through the abnormal lane-changing model, further obtaining the license plate number of the abnormal vehicle, and obtaining the vehicle driver information according to the license plate number; sending the abnormal behavior lane-changing type to the mobile terminal corresponding to the vehicle driver information according to the vehicle driver information.

[0009] Furthermore, the process of the warning module generating corresponding warning information for the abnormal lane-changing behavior includes: Obtaining the real-time abnormal data corresponding to the abnormal vehicle, where the real-time abnormal data includes location information, abnormal behavior lane-changing type and occurrence time; connecting the real-time abnormal data with the license plate number of the corresponding vehicle to generate warning information.

[0010] Furthermore, the process of the tracing module obtaining the historical abnormal lane-changing events of the corresponding vehicle according to the warning information and performing data fusion on the historical abnormal lane-changing events through the vehicle driving data VR model to obtain the VR abnormal trajectory includes: Set a trace period, obtain the warning information corresponding to the occurrence time of abnormal vehicles during the trace period, and connect each warning information to generate the historical abnormal lane-changing events of abnormal vehicles during the trace period; furthermore, through the vehicle driving data VR model, the historical abnormal lane-changing events corresponding to the position information of the abnormal vehicles are data-fused in chronological order of occurrence time to obtain the VR abnormal trajectory.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: The roadside acquisition unit of the present invention collects road data through roadside devices and generates a road intersection supervision subsystem based on the road data; collects the driving traffic data corresponding to road vehicles through in-vehicle sensors; the lane-changing behavior analysis unit is used to analyze and construct an abnormal lane-changing model based on big data to obtain multi-source abnormal lane-changing behaviors; the real-time data analysis unit constructs a vehicle driving data VR model according to the road data and the driving traffic data; furthermore, through the vehicle driving data VR model, the driving traffic data corresponding to the vehicle is monitored in real time, and abnormal lane-changing behaviors are automatically identified; the abnormal lane-changing behaviors are generated into corresponding warning information; the historical abnormal lane-changing events of the corresponding vehicles are obtained according to the warning information, and the historical abnormal lane-changing events are data-fused through the vehicle driving data VR model to obtain the VR abnormal trajectory; effectively improving the recognition accuracy of abnormal lane-changing at intersections; Applying big data and VR technologies to the field of traffic lane-changing supervision can make up for the deficiencies of traditional supervision methods and improve the intelligent level and efficiency of traffic supervision. Based on this, the present invention proposes a smart traffic lane-changing supervision system based on big data and VR. The system collects road data and vehicle driving traffic data through a traffic data acquisition model, and uses a data analysis module to construct an abnormal lane-changing model and a vehicle driving data VR model to achieve precise analysis and real-time monitoring of vehicle lane-changing behaviors. The supervision module automatically identifies abnormal lane-changing behaviors with the help of the vehicle driving data VR model, the warning module issues warning information in a timely manner, and the trace module can trace the historical behaviors of abnormal lane-changing vehicles, thus forming a complete traffic lane-changing supervision system, effectively improving road traffic safety and traffic efficiency; BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0013] Figure 1 It is the system schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0015] As Figure 1 shown, a smart traffic lane-changing supervision system based on big data and VR, the system includes a traffic data collection model, a data analysis module, a supervision module, an early warning module, and a traceability module; The traffic data collection module is provided with a roadside collection unit and a vehicle-mounted collection unit; the roadside collection unit collects road data through roadside devices and generates a road intersection supervision subsystem according to the road data; the vehicle-mounted collection unit is used to collect the driving traffic data corresponding to the road vehicles through vehicle-mounted sensors; The data analysis module is provided with a lane-changing behavior analysis unit and a real-time data analysis unit; the lane-changing behavior analysis unit is used to analyze multi-source abnormal lane-changing behaviors based on big data to construct an abnormal lane-changing model; the real-time data analysis unit constructs a vehicle driving data VR model according to the road data and the driving traffic data; The supervision module monitors the driving traffic data corresponding to the vehicle in real time through the vehicle driving data VR model and automatically identifies the abnormal behavior lane-changing type; The early warning module is used to generate corresponding early warning information for the abnormal lane-changing behavior; The traceability module is used to obtain the historical abnormal lane-changing events of the corresponding vehicle according to the early warning information, and fuse the historical abnormal lane-changing events through the vehicle driving data VR model to obtain the VR abnormal trajectory.

[0016] It should be further noted that the roadside collection unit collects road data through roadside devices and generates a road intersection supervision subsystem according to the road data; including: Mark the position corresponding to the road intersection as a red node in the high-precision map, set the supervision range threshold corresponding to the road intersection, extract the road range corresponding to the road intersection from the high-precision map through the supervision range threshold to obtain the road intersection supervision range, and then connect the red nodes corresponding to the adjacent positions of each road intersection supervision range to generate a road intersection supervision subsystem; The road data includes road traffic flow lines, traffic sign states, and vehicle trajectories; In the above embodiment, it should be further noted that the roadside devices include, but are not limited to, cameras, millimeter-wave radars, lidar and other devices on both sides of the road; In the above embodiments, it should be further noted that lane changes at road intersections are the most likely to be dangerous, so it is necessary to supervise lane changes at road intersections.

[0017] It should be further noted that the vehicle-mounted acquisition unit collects the driving traffic data corresponding to road vehicles through vehicle-mounted sensors, including: The driving traffic data includes vehicle speed, turn signal status, lane deviation angle, and the distance to surrounding vehicles; Obtain the license plate numbers of each vehicle corresponding to the road intersection supervision subsystem, and connect them with the corresponding driving traffic data to generate a vehicle driving traffic data set; Generate a vehicle speed vector with the vehicle speed starting from the corresponding vehicle position; use the vehicle speed vector to fuse the lane deviation angle to obtain real-time vehicle data.

[0018] It should be further noted that the lane change behavior analysis unit analyzes multi-source abnormal lane change behaviors based on big data to construct an abnormal lane change model, including: Extract abnormal behavior characteristics from multi-source abnormal lane change behaviors through traffic rules, and integrate each abnormal behavior characteristic to generate an abnormal behavior characteristic database; set abnormal behavior lane change types, and then classify the abnormal type characteristics in the abnormal characteristic database by abnormal behavior lane change types, obtain each abnormal behavior characteristic corresponding to each abnormal behavior characteristic type, and perform data connection to generate an abnormal lane change model; In the above embodiments, it should be further noted that the abnormal behavior lane change types include but are not limited to crossing the solid line, continuous lane change, etc.; obtain the abnormal behavior characteristics corresponding to the abnormal lane change behavior through traffic rules, and classify the multi-source abnormal lane change behaviors through the abnormal behavior characteristics, so as to better analyze the abnormal lane change behaviors and improve the accuracy of the data.

[0019] It should be further noted that the real-time data analysis unit constructs a vehicle driving data VR model based on road data and driving traffic data, including: Map each corresponding road traffic streamline to the road corresponding to the road intersection supervision subsystem; then perform three-dimensional simulation on the corresponding traffic signs, vehicles, and pedestrians and map them on the road intersection supervision subsystem to obtain a three-dimensional supervision model of the road intersection; then synchronize the traffic sign status corresponding to the traffic signs, the vehicle positions corresponding to the vehicles, and the pedestrian positions corresponding to the pedestrians in real time in the three-dimensional supervision model of the road intersection, and connect them with the abnormal lane change model to construct a vehicle driving data VR model.

[0020] In the above embodiments, it should be further noted that the real-time data of each road intersection is simulated in three dimensions, and the vehicle data is fused to generate a VR model. Through the VR model, the vehicle driving data at the road intersection can be clearly understood.

[0021] It should be further noted that the supervision module monitors the corresponding driving traffic data of the vehicle in real time through the vehicle driving data VR model and automatically identifies the abnormal behavior lane change type; including: According to the abnormal lane change model, actively identify the abnormal behavior characteristics in the vehicle driving data VR model in real-time by AI, mark the identified vehicle as an abnormal vehicle, and then obtain the abnormal behavior lane change type corresponding to the abnormally identified vehicle through the abnormal lane change model, and then obtain the license plate number corresponding to the abnormal vehicle, and obtain the vehicle driver information according to the license plate number; send the abnormal behavior lane change type to the mobile terminal corresponding to the vehicle driver information according to the vehicle driver information; In the above embodiments, it should be further noted that the vehicle driver information includes the driver's name, contact information, etc.

[0022] It should be further noted that the warning module generates corresponding warning information for the abnormal lane change behavior; including: Obtain the real-time abnormal data corresponding to the abnormal vehicle, where the real-time abnormal data includes location information, abnormal behavior lane change type, and occurrence time; connect the real-time abnormal data with the license plate number of the corresponding vehicle to generate warning information.

[0023] It should be further noted that the traceability module obtains the historical abnormal lane change events of the corresponding vehicle according to the warning information, and fuses the historical abnormal lane change events through the vehicle driving data VR model to obtain the VR abnormal trajectory; including: Set a traceability period, obtain the warning information corresponding to the occurrence time of the abnormal vehicle in the traceability period, connect each warning information to generate the historical abnormal lane change events of the abnormal vehicle within the traceability period; then, through the vehicle driving data VR model, fuse the historical abnormal lane change events of the corresponding location information of the abnormal vehicle in chronological order of occurrence time to obtain the VR abnormal trajectory.

[0024] Working principle: In the present invention, the roadside acquisition unit acquires road data through roadside devices and generates a road intersection supervision subsystem based on the road data; the on-vehicle sensors acquire the driving traffic data corresponding to road vehicles; the lane-changing behavior analysis unit is used to analyze and construct an abnormal lane-changing model for multi-source abnormal lane-changing behaviors based on big data; the real-time data analysis unit constructs a vehicle driving data VR model according to the road data and the driving traffic data; and then, the driving traffic data corresponding to the vehicle is monitored in real time through the vehicle driving data VR model, and abnormal lane-changing behaviors are automatically identified; the abnormal lane-changing behaviors are generated into corresponding warning information; the historical abnormal lane-changing events of the corresponding vehicle are obtained according to the warning information, and the historical abnormal lane-changing events are subjected to data fusion through the vehicle driving data VR model to obtain a VR abnormal trajectory.

[0025] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments; it should be understood that the specific embodiments described herein are only intended to explain the present application, rather than limiting the present application; for those skilled in the art, the present application can be implemented without some of these specific details; the above description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0026] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A smart traffic lane change supervision system based on big data and VR, including a monitoring center, characterized in that: The system includes a traffic data collection model, a data analysis module, a supervision module, an early warning module and a tracing module; The traffic data collection module is provided with a roadside collection unit and a vehicle-mounted collection unit; the roadside collection unit collects road data through roadside equipment and generates a road intersection supervision subsystem based on the road data; the vehicle-mounted collection unit is used to collect the driving traffic data corresponding to the road vehicles through the vehicle-mounted sensors; The data analysis module is provided with a lane change behavior analysis unit and a real-time data analysis unit; the lane change behavior analysis unit is used to obtain multi-source abnormal lane change behaviors based on big data to analyze and construct an abnormal lane change model; the real-time data analysis unit constructs a vehicle driving data VR model based on road data and driving traffic data; The supervision module is used to monitor the driving traffic data corresponding to the vehicle in real time through the vehicle driving data VR model, and automatically identify abnormal lane changing behavior; The warning module is used to generate corresponding warning information for abnormal lane changing behavior; The tracing module is used to obtain historical abnormal lane change events of the corresponding vehicle according to the warning information, and to obtain VR abnormal trajectories by fusing the historical abnormal lane change events through the vehicle driving data VR model.

2. According to claim 1, a smart traffic lane change supervision system based on big data and VR is characterized in that: The process of the roadside collection unit collecting road data through roadside equipment and generating a road intersection supervision subsystem according to the road data includes: Mark the position corresponding to the road intersection as a red node on the high-precision map, set the supervision range threshold corresponding to the road intersection, extract the road range corresponding to the road intersection from the high-precision map through the supervision range threshold, obtain the road intersection supervision range, and then connect the red nodes corresponding to the adjacent positions of each road intersection supervision range to generate a road intersection supervision subsystem; The road data includes road traffic flow lines, traffic sign status and vehicle trajectories.

3. According to claim 2, a smart traffic lane change supervision system based on big data and VR is characterized in that: The process of the vehicle-mounted collection unit collecting the driving traffic data corresponding to the road vehicle through the vehicle-mounted sensor includes: The driving traffic data includes vehicle speed, turn signal status, lane deviation angle, and distance to surrounding vehicles; Obtain the license plate number corresponding to each vehicle in the road intersection supervision subsystem, and connect it with the corresponding driving traffic data to generate a vehicle driving traffic data set; The vehicle speed is used to generate a vehicle speed vector with the corresponding vehicle position as the starting point; the lane deviation angle is fused with the vehicle speed vector to obtain real-time vehicle data.

4. According to claim 3, a smart traffic lane change supervision system based on big data and VR is characterized in that: The process of the lane changing behavior analysis unit acquiring multi-source abnormal lane changing behaviors based on big data and analyzing and constructing an abnormal lane changing model includes: Abnormal behavior features of multi-source abnormal lane changing behaviors are extracted through traffic rules, and various abnormal behavior features are integrated to generate an abnormal behavior feature database; abnormal behavior lane changing types are set, and then abnormal behavior lane changing types are classified according to abnormal type features in the abnormal feature database, and each abnormal behavior feature corresponding to each abnormal behavior feature type is obtained, and data connection is performed to generate an abnormal lane changing model.

5. According to claim 4, a smart traffic lane change supervision system based on big data and VR is characterized in that: The process of constructing a vehicle driving data VR model according to the road data and driving traffic data by the real-time data analysis unit includes: Map each corresponding road traffic flow line onto the road corresponding to the road intersection supervision subsystem; then perform three-dimensional simulation on the corresponding traffic signs, vehicles and pedestrians and map them on the road intersection supervision subsystem to obtain a three-dimensional supervision model of the road intersection; then synchronize the traffic sign status corresponding to the traffic sign, the vehicle position corresponding to the vehicle and the pedestrian position corresponding to the pedestrian in real time on the three-dimensional supervision model of the road intersection and connect with the abnormal lane change model to construct a VR model of vehicle driving data.

6. According to claim 5, a smart traffic lane change supervision system based on big data and VR is characterized in that: The supervision module monitors the driving traffic data corresponding to the vehicle in real time through the vehicle driving data VR model, and the process of automatically identifying the abnormal behavior lane change type includes: According to the abnormal lane change model, real-time AI actively identifies abnormal behavior features in the VR model of vehicle driving data, marks the identified vehicle as an abnormal vehicle, and then obtains the abnormal lane change type corresponding to the abnormal identified vehicle through the abnormal lane change model, and then obtains the license plate number corresponding to the abnormal vehicle, and obtains the vehicle driver information based on the license plate number; based on the vehicle driver information, the abnormal lane change type is sent to the mobile terminal corresponding to the vehicle driver information.

7. The intelligent traffic lane change supervision system based on big data and VR according to claim 6 is characterized in that: The process of generating corresponding warning information from abnormal lane-changing behavior by the warning module includes: Acquire real-time abnormal data corresponding to the abnormal vehicle, wherein the real-time abnormal data includes location information, abnormal behavior lane change type and occurrence time; connect the real-time abnormal data with the license plate number of the corresponding vehicle to generate warning information.

8. The intelligent traffic lane change supervision system based on big data and VR according to claim 7 is characterized in that: The tracing module obtains the historical abnormal lane change events of the corresponding vehicle according to the warning information, and the process of fusing the historical abnormal lane change events through the vehicle driving data VR model to obtain the VR abnormal trajectory includes: Set a tracing period, obtain the warning information of the abnormal vehicle corresponding to the occurrence time in the tracing period, connect each warning information to generate the historical abnormal lane change events of the abnormal vehicle in the tracing period; then use the vehicle driving data VR model to fuse the historical abnormal lane change events of the abnormal vehicle according to the chronological order of occurrence and the corresponding position information to obtain the VR abnormal trajectory.

Images