Method for predicting traffic accident, computer device and readable recording medium

Abstract

A method for predicting a traffic accident, a computer device and a readable recording medium, the computer device analyzing a large number of traffic scene videos to classify the traffic scene videos in which the accident occurs, and finding out possible factors that cause the accident from each class of accident videos, according to the possible factors of each type of accidents, all factors of the accident environment in each accident film are integrated, various factor combinations in the same accident film are summarized, and a report is generated according to the summarized various factor combination results; the report shows the various factor combinations of the various accident films occurring at different road sections or intersections.

Description

Technical Field

[0001] The present invention relates to a method for finding accident factors from traffic scene videos, and more particularly to a method for predicting traffic accidents, specifically a method for automatically sorting and summarizing accident factors based on traffic scene videos using artificial intelligence. Background Art

[0002] Nowadays, cars are usually equipped with driving recorders. Because the driver may not have time to pay attention to other road details while driving, the driving recorder plays an auxiliary recording function, recording all road conditions while driving. In addition, the monitors installed at various intersections or sections of the road will also take traffic videos. In the event of a traffic accident, the video footage of the driving recorder and / or the traffic videos taken by the monitors at various intersections or sections of the road can help determine who is responsible for the accident.

[0003] However, when people need to review a large number of traffic scene videos and find out the possible factors related to each accident to clarify the responsibility, it will undoubtedly greatly increase the burden of relevant personnel to watch the video. It is also easy to miss or ignore some small factors or details that may be related to the accident because of the need to quickly browse the video, so that the cause of the accident may be simplified and some of the causes may be hidden, and the real factors or details of these accidents cannot be further used for predicting intersection accidents in the future. Summary of the invention

[0004] Therefore, the purpose of the present invention is to provide a method for predicting traffic accidents, specifically a method for automatically sorting out accident factors and predicting road accidents based on traffic scene videos, as well as a computer device and a computer-readable recording medium for implementing the method, which can automatically and quickly review a large number of traffic scene videos through artificial intelligence to find all possible factors for the occurrence of accidents, and use a combination of these factors to predict possible upcoming intersection traffic accidents and provide early warnings, actively eliminate some factors, and reduce the risk of traffic accidents.

[0005] Therefore, the present invention provides a method for predicting traffic accidents, comprising the following steps.

[0006] (A) When an accident classification module of a computer device pre-stored with a large number of traffic scene videos determines that a collision has occurred in the traffic scene videos and confirms the existence of an accident, the traffic scene videos in which the accident occurs are classified into at least three types of accident videos, namely, vehicle-to-person, vehicle-to-vehicle, and self-collision, according to the objects of the collision, and stored in a storage unit of the computer device; and the accident classification module obtains a first factor data from a time point when the accident occurs to a fixed time before the time point in the various accident videos and records it in the storage unit; the first factor data includes vehicles, pedestrians, and traffic signal equipment (traffic lights).

[0007] (B) An accident factor discovery module of the computer device reads the various accident videos from the storage unit, and obtains a second factor data other than the first factor data within a fixed time period from the time point when the accident occurred to before the time point in the various accident videos; the second factor data includes geographic information and weather information.

[0008] (C) An accident summarization module of the computer device integrates all factors of the accident environment in each accident video based on the first factor data and the second factor data obtained by the accident classification module and the accident factor discovery module, and further summarizes various factor combinations in the same accident video, and generates a report based on the above-summarized various factor combination results, which presents the various factor combinations of the various accident videos occurring at different road sections or intersections.

[0009] In some embodiments of the present invention, in step (A), the accident classification module also determines that when a vehicle in one of the traffic scene videos brakes suddenly or turns sharply, it is determined that a similar accident has occurred in the traffic scene video, and the traffic scene video in which the similar accident has occurred is classified as a similar accident video; and in step (B), the accident factor discovery module also summarizes the similar accident videos, and finds the second factor data in the accident environment in addition to the first factor data from the similar accident video; and the various accidents in step (C) also include similar accidents.

[0010] In some embodiments of the present invention, the method also includes an accident prediction module of the computer device detecting multiple factors appearing in an input traffic scene video to be predicted, and comparing the factors in the traffic scene video to be predicted with the factor combinations corresponding to the various accidents summarized in the report to generate a similarity value, and when it is determined that the similarity value meets a similarity threshold, generating and outputting an accident warning notification.

[0011] In some embodiments of the present invention, the method further includes the following steps.

[0012] (D) A road monitoring system receives a video of an intersection taken by a monitor installed at an intersection and provides it to the computer device. The accident prediction module of the computer device detects multiple factors appearing in the intersection video and compares the factors in the intersection video with the factor combinations corresponding to various accidents to generate a similarity value. When the accident prediction module confirms that the similarity value associated with the intersection video is less than the similarity threshold but greater than a first default value, a continuous intersection warning module is activated. The continuous intersection warning module determines whether a repeated portion of a factor combination in the intersection video and a factor combination corresponding to various accidents includes a high-risk driving factor related to a vehicle passing through the intersection. If so, the continuous intersection warning module generates a warning message and provides the warning message to the road monitoring system. The warning message includes the direction of travel of the vehicle.

[0013] (E) After receiving the warning message, the road monitoring system transmits a warning instruction to a warning device installed at the next intersection that the vehicle is about to arrive at based on the direction of travel of the vehicle contained in the warning message, so that the warning device outputs a warning message based on the warning instruction, and transmits a monitoring instruction to a monitor installed at the next intersection, so that the monitor at the next intersection takes a video of the intersection and provides it to the computer device.

[0014] (F) When the continuous intersection warning module of the computer device determines that the similarity value associated with the intersection video of step (E) is still less than the similarity threshold but greater than the first default value, and a factor combination of the intersection video of step (E) and the repeated part of each factor combination corresponding to various accidents still include the high-risk driving factor related to the vehicle, the continuous intersection warning module generates a warning message and provides it to the road monitoring system, and the warning message includes the direction of travel of the vehicle.

[0015] (G) Repeat steps (E) and (F) until the continuous intersection warning module determines that the similarity value associated with the intersection video of step (E) is less than the first default value, or the repeated part of the factor combination of the intersection video of step (E) and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle.

[0016] In some embodiments of the present invention, in step (G), when the similarity value associated with the intersection video analyzed by the continuous intersection warning module in step (E) is less than the first default value, or the repeated part of the factor combination of the intersection video in step (E) and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle, the continuous intersection warning module generates a monitoring message and provides it to the road monitoring system, and the monitoring message includes the direction of travel of the vehicle; and the method also includes the following steps after step (G).

[0017] (H) The road monitoring system transmits a monitoring instruction to a monitor installed at the next intersection that the vehicle is about to arrive at based on the vehicle's travel direction contained in the monitoring message of step (G), so that the monitor at the next intersection that the vehicle is about to arrive at takes a video of the intersection and provides it to the computer device.

[0018] (I) Repeat steps (G) and (H) until the continuous intersection warning module determines that the similarity value associated with N consecutive (N≥2) intersection videos is less than the first default value, or the repeated part of the factor combination of the N consecutive intersection videos and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle.

[0019] In some embodiments of the present invention, the accident warning notification is also transmitted through Internet of Vehicles related equipment to a vehicle-mounted device on a vehicle that is about to pass through the traffic section displayed in the traffic scene video to be predicted.

[0020] In addition, the present invention implements a computer device for the above method, including a storage unit in which a large number of traffic scene videos are pre-stored and a processing unit, the processing unit is electrically connected to the storage unit to access the storage unit, and the processing unit includes an accident classification module, an accident factor discovery module, an accident summary module, an accident prediction module and a continuous intersection warning module, and the processing unit executes the above modules to complete the method of automatically sorting out accident factors and predicting road section accidents based on traffic scene videos as described above.

[0021] Furthermore, the present invention implements a computer-readable recording medium for the above method, which stores a software program including an accident classification module, an accident factor discovery module, an accident summary module, an accident prediction module and a continuous intersection warning module, and when the software program is loaded and executed by a computer device that pre-stores a large number of traffic scene videos, the computer device can complete the method of automatically sorting out accident factors and predicting road accidents based on traffic scene videos as described above.

[0022] Compared with the prior art, the present invention is a method for predicting traffic accidents, a computer device, and a readable recording medium. The computer device determines whether an accident occurs in a traffic scene video by means of the accident classification module, and classifies the traffic scene video where the accident occurs. The above-mentioned multiple accident factor discovery modules find all possible factors that lead to the accident from accident videos of different categories and perform statistics. Then, the accident summary module summarizes the correlation of all current factor combinations that appear in the video in a fixed time period before the accident occurs based on the possible factors of various accidents that have been statistically analyzed. In this way, it is possible to judge whether the probability of an accident occurring in a road section in the newly input traffic scene video is high from the combination of various factors that appear in the newly input traffic scene video that change dynamically, and then give an early warning to eliminate or destroy the factor combination that causes the accident. At the same time, the method automatically and quickly examines a large number of traffic scene videos, which not only reduces the burden of relevant personnel to find accident-related factors from a large number of videos, but also makes it easier to find some small factors or details in the video that may be related to the accident. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features and effects of the present invention will be clearly presented in the embodiments with reference to the accompanying drawings, in which:

[0024] Figure 1 The main process steps of an embodiment of the method for predicting traffic accidents, a computer device and a readable recording medium of the present invention;

[0025] Figure 2 is realized Figure 1 A schematic diagram of the software and hardware blocks mainly included in the computer device of the method flow;

[0026] Figure 3 is a schematic diagram of the computer device of this embodiment being used in conjunction with the road monitoring system for road intersection monitoring and warning; and

[0027] Figure 4 and Figure 5 This is the process step of the computer device of this embodiment cooperating with the road monitoring system to monitor and warn road intersections. DETAILED DESCRIPTION

[0028] Before the present invention is described in detail, it should be noted that similar elements are denoted by the same reference numerals in the following description.

[0029] See also Figure 1 FIG. 1 is a diagram showing the main process steps of an embodiment of a method for predicting traffic accidents, a computer device, and a readable recording medium of the present invention. Figure 2The computer device 1 shown in the figure is pre-stored with a large number of traffic scene videos, and these traffic scene videos can be driving recorders installed on many vehicles and / or traffic videos taken by monitors at various road sections (including: intersections). These vehicles may belong to one or more public transportation fleets or transportation companies, such as but not limited to bus operating companies, taxi fleets (car dealerships), tour bus companies, gravel truck (or truck) transportation companies, etc.

[0030] The computer device 1 includes a storage unit 11, such as a memory module, and a processing unit 12, such as a central processing unit, which is electrically connected to the storage unit 11 to access the storage unit 11. The processing unit 12 includes an accident classification module 121, an accident factor discovery module 122, and an accident summary module 123. The modules 121-123 can be software programs pre-stored in the storage unit 11 and can be loaded and executed by the processing unit 12 to complete the Figure 1 The method flow is shown.

[0031] Alternatively, the modules 121-123 may also be integrated into one or more application-specific integrated circuits (ASIC) chips or a programmable logic device (PLD) of the computer device 1, and the processing unit 12 is the application-specific integrated circuit chip or the programmable logic device and can complete Figure 1 Alternatively, the modules 121-125 may be firmware recorded in a microprocessor of the computer device 1, and the processing unit 122 is the microprocessor and can execute the firmware to complete the operation. Figure 1 The method flow is shown.

[0032] By this, Figure 1In step S1, the accident classification module 121 reads the traffic scene videos from the storage unit 11 and recognizes each of the traffic scene videos through images to determine whether there is a traffic accident in the traffic scene video, and identifies the accident as a vehicle-to-person, vehicle-to-vehicle, or self-collision accident type, thereby classifying the traffic scene video where the accident occurs into a vehicle-to-person accident video, a vehicle-to-vehicle accident video, or a self-collision accident video and storing them in the storage unit 11 according to the classification; specifically, the accident classification module 121 uses a trained artificial intelligence algorithm to detect objects in the traffic scene video, and the artificial intelligence algorithm can be, for example, but not limited to, a YOLOv4 object detection model, or other object detection models, such as, but not limited to, YOLOv1, YOLOv2, YOLOv3, CNN, R-CNN, Fast R-CNN, Faster R-CNN, and other artificial intelligence models with deep learning.

[0033] Therefore, the accident classification module 121 can detect vehicles and pedestrians (animals) and their moving trajectories, average or single vehicle speeds, surrounding fixed objects (traffic lights, dividing islands, road trees, etc.), road markings, abnormal smoke, etc. in the traffic scene video. Next, the accident classification module 121 determines whether the vehicles in the video have collided, for example but not limited to detecting that the vehicle has decelerated significantly, the driving trajectory has deviated from the lane and has come into contact with other objects to become stationary, etc., and uses different recognition conditions that are sufficient to reflect the collision to determine whether there is an accident in the traffic scene video. If there is an accident, and the accident classification module 121 further confirms that the vehicle collided with a person (or animal), the accident classification module 121 will classify the traffic scene video from the time when the accident occurred to before that time. All objects detected within a fixed time (e.g., 30 seconds) are taken as a first factor data, and the traffic video is classified as a vehicle-to-person accident video according to the collision relationship result, and is stored together with the first factor data in the corresponding folder of the storage unit 11; similarly, the accident classification module 121 classifies the traffic scene video with an accident into a vehicle-to-vehicle accident video or a self-collision accident video in the same manner as described above, and stores these videos and the corresponding first factor data in the storage unit 11 according to the classification. It is worth mentioning that the first factor data can be used to infer data such as road conditions (e.g., whether there is a traffic jam, the current traffic flow status, vehicle or pedestrian violation status), etc., changes in vehicle type distribution (e.g., large vehicles, trucks, buses, articulated vehicles, motorcycles, etc.), etc.

[0034] Then, if Figure 1In step S2, the processing unit 12 executes the accident factor mining module 122. The accident factor mining module 122 reads the various accident videos from the storage unit 11, and also uses a trained artificial intelligence algorithm to search for second factor data (other factors) other than the first factor data within the fixed time from the time point when the accident occurred to the time point before the various accident videos, such as geographic information (such as but not limited to location, road type, road attribute, etc.), traffic sign content (such as no entry, speed limit, etc.), weather information (such as sunny, cloudy, overcast, drizzle, heavy rain, fog, etc.), special conditions (such as lane reduction, diversion and closure, etc.), and the artificial intelligence algorithm is such as but not limited to the YOLOv4 object detection model, and can also be other object detection models, such as but not limited to YOLOv1, YOLOv2, YOLOv3, CNN, R-CNN, Fast R-CNN, Faster R-CNN and other artificial intelligence models with deep learning.

[0035] Thus, as in step S2, the accident factor discovery module 122 reads the traffic scene videos (hereinafter referred to as accident videos) and the first factor data that have been classified and have occurred in the accident from the storage unit 11, identifies the accident videos, and combines the first factor data of the accident classification module 121 to sort out the factor combination states of the first factor data and the second factor data within the fixed time before the accident from the accident videos; specifically, taking the vehicle-to-person accident video as an example, the accident factor discovery module 122 will identify the second factor data in each vehicle-to-person accident video from 30 seconds before the vehicle collision to the moment of the collision, and then integrate the known first factor data to compile it into a factor combination corresponding to the vehicle-to-person accident video and store it in the storage unit 11.

[0036] Then, the accident factor mining module 122 collects the combination of factors in the collision scene from the aforementioned various accident videos and stores them in the storage unit 11 .

[0037] Finally, if Figure 1 In step S3, the accident summarizing module 123 summarizes the data of multiple factor combinations of the same type of accident videos (e.g., vehicle-to-personnel) stored in the storage unit 11 with a specific factor, and unites the factor combinations with the same specific factor. For example, the factor combinations of 100 vehicle-to-personnel accident videos are summarized with the road type in factor 1, and the corresponding factor combinations are united to form a summary factor combination: a fork in the road, an urban road, 7 to 9 p.m., a medium traffic volume, an average speed of a relatively fast vehicle, heavy rain, a large number of pedestrians, and a situation where pedestrians illegally cross the intersection. When all the factors in the above factor combination are fully satisfied, it means that the probability of collision risk in the traffic scene is the highest.

[0038] In addition, in the above step S1, the accident classification module 121 of the present embodiment can further identify whether the vehicle in the traffic scene video has an emergency situation such as sudden braking, sudden turning or vehicle skidding when identifying each of the traffic scene videos. If so, it is determined that a similar accident has occurred in the traffic scene video, and the traffic scene video in which the similar accident has occurred is further classified as a similar accident video and stored in the storage unit 11; and as mentioned above, the accident factor mining module 122 identifies the similar accident videos to find the second factor in the similar accident video from the similar accident videos. The data is used to count the factor combinations of the first factor data and the second factor data in a period of time before the sudden braking, sudden turning and other actions of the vehicle in those similar accident videos, and store them in the storage unit 11. Similarly, the accident induction module 123 summarizes with a specific factor, and unites the factor combinations with the same specific factor. For example, it is summarized from multiple similar accident videos to find out that if the vehicle is prone to sudden braking or skidding on a downhill curve when the combination of factors such as dusk, drizzle, average speed is fast, traffic volume is low, acceleration before entering a curve or improper deceleration is met.

[0039] Furthermore, the accident summary module 123 can generate a report based on the above-mentioned summary results, which presents various combinations of factors in various accident environments occurring at different road sections (intersections), and various accidents include the above-mentioned vehicle-to-person, vehicle-to-vehicle, self-collision and similar accidents, etc., whereby the above-mentioned public transportation fleet or transportation company can refer to the report to educate and advocate its internal vehicle drivers, or provide the report to a back-end traffic warning system for application. For example, when a vehicle travels to a certain road section that meets a certain combination of factors presented in the above-mentioned report, the traffic warning system can send a warning notification to the vehicle-mounted device in advance through the Internet of Vehicles related equipment or wireless network communication method to remind the vehicle driver to pay attention to the possible factors that are prone to accidents.

[0040] In addition, the processing unit 12 of the present embodiment may also include an accident prediction module 124, which can detect (recognize) various factors appearing in an input traffic scene video to be predicted (e.g., a traffic video of a certain road section (entrance)) through the above-mentioned object detection model, such as geographic information (e.g., but not limited to location, road type), road conditions (e.g., many cars, few cars, traffic jam, etc.), changes in the proportion of vehicle types (e.g., large trucks, trucks, buses, articulated vehicles, motorcycles, etc. in the lane), objects or signs (e.g., traffic rules, traffic signs, vehicles, pedestrians, density, animals, etc.), weather (e.g., sunny, cloudy, overcast, drizzle, heavy rain, fog, etc.). ), then the accident prediction module 124 compares the factor combination in the current traffic scene with the factor combinations corresponding to the various accidents (vehicle-to-person, vehicle-to-vehicle, self-collision and similar accidents) summarized in the report of the above-mentioned accident summary module 123 to generate a similarity value, and when the similarity value of the factor combination of the traffic scene video to be predicted and the factor combination corresponding to the above-mentioned various types of accidents meets a similarity threshold (for example, the factor combinations are highly overlapped (for example, repetition is more than 90%) or completely overlapped (complete repetition is 100%) with the factor combinations in the vehicle-to-person accident video), the accident prediction module 124 generates and outputs an accident warning notification.

[0041] In addition, if the accident prediction module 124 finds a plurality of identical highest similarity values, the accident warning notification corresponding to the accident type may be outputted respectively according to the accident type.

[0042] The above-mentioned (vehicle-to-person, vehicle-to-vehicle, self-collision, and similar accidents) accident warning notices can be output to a traffic control center of a relevant government unit, so that relevant personnel can be mobilized to the scene of a possible traffic accident to direct and guide traffic, control illegal behaviors of road users, etc., to eliminate some of the factors of traffic accidents, thereby reducing the similarity value of the overall factor combination, or display the accident warning notices on the roadside or on the road, such as electronic billboards, to remind vehicle drivers to drive carefully; or, transmit the accident warning notices to the on-board device of the vehicle that is about to pass through the traffic section shown in the traffic scene video to be predicted through vehicle networking related equipment or wireless network communication methods, so as to remind the vehicle driver to pay attention to the factors that may cause accidents.

[0043] It is worth mentioning that the traffic control center can allocate weights of importance based on different types of traffic accidents, such as vehicle-to-person, vehicle-to-vehicle, self-collision or similar accidents. That is, when there are high accident risks at multiple intersections at the same time, the order of priority warning notifications or measures to eliminate the factors can be further determined based on the importance of the types. For example, the weight distribution can be used to determine the intersections or sections where personnel are given priority for on-site control and guidance.

[0044] In addition, if Figure 3 As shown, the computer device 1 of this embodiment can also be connected to a road monitoring system 2, wherein the computer device 1 is integrated into a monitor 31, and can also be directly integrated into the road monitoring system 2 to connect multiple external monitors. Through the computer device 1, a specific vehicle 4 can be selectively continuously monitored passing through the continuous intersections. If the similarity value generated by the current factor combination of the intersections does not meet the similarity threshold, but an abnormal driving factor is detected in the factor combination of the intersections, a continuous intersection monitoring function is triggered to start, and the vehicles and / or pedestrians about to pass through the intersections are reminded to pay attention to the possible safety concerns of the vehicle 4. The specific implementation process is as follows: Figure 4 and Figure 5 As shown, first, Figure 4 In step S41, the road monitoring system 2 receives a signal set at an intersection, such as Figure 3 The monitor 31 of the intersection A shown in FIG. 1 captures a video of the intersection A and provides the video of the intersection A to the computer device 1. Then, as shown in FIG. Figure 4 In step S42, when the computer device 1 performs the above-mentioned method through the accident prediction module 124 to confirm that the similarity value generated by comparing a factor combination of the current intersection A with the factor combinations corresponding to various accidents (vehicle-to-person, vehicle-to-vehicle, self-collision and similar accidents) summarized in the report is less than the similarity threshold but greater than a first default value, the computer device 1 activates a continuous intersection warning module 125, wherein the first default value is, for example but not limited to, 20%. For example, the possible factors corresponding to the vehicle-to-person accident are daytime, rain, many pedestrians, crossroads, heavy traffic, etc., and the factor combinations of the current intersection A video are nighttime, rain, many pedestrians, crossroads and the vehicle 4 speeding, then the similarity value generated by comparing the factors appearing in the intersection A video with the factor combinations corresponding to the vehicle-to-person accident is 60% and is less than the similarity threshold but greater than the first default value.

[0045] Therefore, when the judgment result of step S42 is yes, Figure 4 As shown in step S43, the continuous intersection warning module 125 then determines whether the repeated part of the factor combination of the intersection A and the factor combinations corresponding to various accidents (raining, many pedestrians, crossroads and the vehicle 4 speeding) contains a high-risk driving factor related to the vehicle 4. In this embodiment, the high-risk driving factor is defined as but not limited to severe speeding (for example, exceeding the speed limit of the road section by more than 40 kilometers), and when the judgment result of step S43 is yes, as shown in step S43, Figure 4 In step S44, the continuous intersection warning module 125 generates a warning message and transmits the warning message to the road monitoring system 2. The warning message includes the direction of travel, license plate information, vehicle color and model, etc. of the vehicle 4.

[0046] Then, if Figure 4 In step S45, after receiving the warning message, the road monitoring system 2 sends a warning instruction to the next intersection (e.g. Figure 3 A warning device 32 is set at the intersection B shown in the figure, so that the warning device 32 outputs a warning message according to the warning instruction. The warning device 32 is, for example, an electronic signboard and / or a voice output device set at the intersection B, and the warning message is displayed on the electronic signboard to remind the driver of the vehicle about to pass the intersection to slow down and pay attention to the oncoming vehicle, or the warning message is output through the voice output device to remind the pedestrians to pay attention to the vehicle. At the same time, the road monitoring system 2 sends a monitoring instruction to a monitor 33 set at the next intersection (i.e., intersection B), so that the monitor 33 at the next intersection (intersection B) takes a video of the current intersection B and provides it to the computer device 1, wherein the next intersection can also be a plurality of possible intersections predicted by the road monitoring system 2 through the image, and may not only be intersection B.

[0047] Then, if Figure 5 In step S46, the continuous intersection warning module 125 of the computer device 1 further confirms whether the similarity value generated by comparing the factor combination of the intersection B with the factor combinations corresponding to various accidents continues to be lower than the similarity threshold but greater than the first default value, and if the judgment result of step S46 is yes, for example, the factor combination of the intersection B is reduced to pedestrians, crossroads and the vehicle 4 is speeding, then the similarity value generated by comparing the factor combination of the intersection B with the factor combination of the corresponding vehicle-to-pedestrian accident is reduced to 40% and is less than the similarity threshold but still greater than the first default value, then the Figure 5 In step S47, the continuous intersection warning module 125 determines whether the repeated factors (many pedestrians, intersections and the vehicle 4 overspeeding) include the high-risk driving factor generated by the vehicle, that is, the above-mentioned overspeeding. If so, Figure 5 In step S48, the continuous intersection warning module 125 generates a warning message and provides it to the road monitoring system 2. The warning message includes the traveling direction of the vehicle 4, that is, the continuous intersection monitoring is performed on the vehicle 4.

[0048] Then, the road monitoring system 2 repeats the above step S45, and the road monitoring system 2 sends a warning instruction to the next intersection (e.g. Figure 3The continuous intersection warning module 125 then repeats the above steps S46 to S48; and the above steps S45 to S48 will be repeatedly executed until the judgment result of step S47 is no, that is, the continuous intersection warning module 125 determines that the repeated factors and the factor combination no longer include the high-risk driving factor (i.e., the speeding mentioned above) generated by the vehicle 4, or although they still include the high-risk driving factor generated by the vehicle, the similarity value of the factor combination is lower than the first default value, the process enters Figure 5 's A node and ends the monitoring process of the vehicle 4.

[0049] Or, if Figure 5 In steps S49 and S50, the continuous intersection warning module 125 starts to accumulate the number of times N (N=1), and determines whether the accumulated number of times N reaches a default number, such as 2. If not (N is less than 2), Figure 5 In step S51, the continuous intersection warning module 125 generates a monitoring message and provides it to the road monitoring system 2, wherein the monitoring message includes the traveling direction of the vehicle 4, wherein the default number of times may also be set to any other positive integer.

[0050] Then, if Figure 5 In step S52, the road monitoring system sends a monitoring instruction to the next intersection (e.g., the intersection) that the vehicle 4 is about to reach or may reach according to the direction of travel of the vehicle included in the warning message provided in step S51. Figure 3 A monitor 36 is arranged at the intersection D) shown in the figure, so that the monitor 36 at the next intersection (intersection D) where the vehicle 4 is about to arrive or may arrive takes an intersection video and provides it to the computer device 1.

[0051] Then, repeat the above steps S46 to S47. If the result of the judgment in step S47 is no, and the cumulative number N reaches 2 in step S49, and in step S50, it is judged that N=2, then enter the Figure 5 Node A of the vehicle 4 and the monitoring process of the vehicle 4 ends. On the contrary, if the result of the judgment in step S47 is yes (indicating that the vehicle 4 is still speeding at the intersection D), the continuous intersection warning module 125 resets the accumulated number N to zero and repeats steps S48, S45 to S47 to continuously monitor each intersection that the vehicle 4 may pass through next and issue a warning in advance.

[0052] Returning to step S42, when the judgment result of step S42 is no, it means that the similarity value generated by comparing the combination of multiple factors appearing in the intersection video with the combination of factors of the vehicle-to-pedestrian accident (or other types of accidents) is less than the similarity threshold and also less than the first default value, indicating that the intersection environment does not have the conditions for causing the driving of the vehicle 4 to cause major casualties. At this time, other reporting and punishment measures should be used to curb such behavior, and the monitoring and warning related to the vehicle 4 can be stopped.

[0053] Returning to step S43 , when the judgment result of step S43 is no, it indicates that the vehicle 4 does not exhibit high-risk driving behavior, and the monitoring and warning related to the vehicle 4 can be stopped.

[0054] In summary, the above embodiment determines whether an accident occurs in a traffic scene video by means of the accident classification module 121 based on the artificial intelligence algorithm, and classifies the traffic scene video where an accident occurs, and the above multiple accident factor discovery modules find all the factors that cause the accident from accident videos of different categories and perform statistics, and then the accident summary module 125 summarizes the correlation between certain factors and various accidents based on the factors of various accidents that have been statistically analyzed. In this way, in addition to being able to determine whether the probability of an accident occurring on the road section displayed in the traffic scene video is high based on the correlation between certain factors and various accidents, from various factors appearing in the newly input traffic scene video, and then issue an early warning to eliminate or reduce the factors causing the accident, and can automatically and quickly review a large number of traffic scene videos and find out the possible factors that cause the accident, which not only reduces the burden of relevant personnel in finding accident-related factors from a large number of videos, but also increases the efficiency of relevant personnel in finding accident factors, and makes it easier to find some small factors or details in the video that may be related to the accident, thereby achieving the efficacy and purpose of the present invention.

[0055] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting traffic accidents, characterized in that: include: (A) When an accident classification module of a computer device pre-stored with a large number of traffic scene videos determines that a collision has occurred in the traffic scene videos and confirms that an accident exists, the traffic scene videos in which the accident exists are classified into at least three types of accident videos, namely, vehicle-to-person, vehicle-to-vehicle, and self-collision, according to the objects of the collision, and stored in a storage unit of the computer device; and the accident classification module obtains a first factor data from a time point of the accident occurrence to a fixed time before the time point in the various accident videos and records it in the storage unit; the first factor data includes vehicles, pedestrians, and traffic signal equipment; (B) an accident factor discovery module of the computer device reads the various accident videos from the storage unit, and obtains second factor data other than the first factor data within a fixed time period from the time point when the accident occurred to before the time point, from the various accident videos; the second factor data includes geographic information and weather information; and (C) An accident summarization module of the computer device integrates all factors of the accident environment in each accident video based on the first factor data and the second factor data obtained by the accident classification module and the accident factor discovery module, and further summarizes various factor combinations in the same accident video, and generates a report based on the above-summarized various factor combination results, which presents the various factor combinations of the various accident videos occurring at different road sections or intersections.

2. The method for predicting traffic accidents according to claim 1, characterized in that: In step (A), the accident classification module also determines that when a vehicle in one of the traffic scene videos brakes suddenly or turns sharply, it is determined that a similar accident has occurred in the traffic scene video, and classifies the traffic scene video in which the similar accident has occurred as a similar accident video; and in step (B), the accident factor discovery module also summarizes the similar accident videos, and finds the second factor data in the accident environment in addition to the first factor data from the similar accident video; and the various accidents in step (C) also include similar accidents.

3. The method for predicting traffic accidents according to claim 1, characterized in that: Also includes: An accident prediction module of the computer device detects multiple factors appearing in an input traffic scene video to be predicted, and compares the factors in the traffic scene video to be predicted with the factor combinations corresponding to the various accidents summarized in the report to generate a similarity value, and when it is determined that the similarity value meets a similarity threshold, an accident warning notification is generated and output.

4. The method for predicting traffic accidents according to claim 3, characterized in that: Also includes: (D) A road monitoring system receives a video of an intersection shot by a monitor installed at an intersection and provides it to the computer device, the accident prediction module of the computer device detects multiple factors appearing in the intersection video and compares the factors in the intersection video with various combinations of factors corresponding to various accidents to generate a similarity value, and when the accident prediction module confirms that the similarity value related to the intersection video is less than the similarity threshold but greater than a first default value, a continuous intersection warning module is activated, the continuous intersection warning module determines whether a repeated part of a combination of factors in the intersection video and a combination of factors corresponding to various accidents includes a high-risk driving factor related to a vehicle passing through the intersection, and if so, the continuous intersection warning module generates a warning message and provides the warning message to the road monitoring system, the warning message including the direction of travel of the vehicle; (E) after receiving the warning message, the road monitoring system transmits a warning instruction to a warning device installed at the next intersection that the vehicle is about to arrive at according to the travel direction of the vehicle contained in the warning message, so that the warning device outputs a warning message according to the warning instruction, and transmits a monitoring instruction to a monitor installed at the next intersection, so that the monitor at the next intersection takes a video of the intersection and provides it to the computer device; (F) when the continuous intersection warning module of the computer device determines that the similarity value associated with the intersection video of step (E) is still less than the similarity threshold but greater than the first default value, and a factor combination of the intersection video of step (E) and a repeated portion of each factor combination corresponding to various accidents still include the high-risk driving factor associated with the vehicle, the continuous intersection warning module generates a warning message and provides it to the road monitoring system, wherein the warning message includes the direction of travel of the vehicle; and (G) Repeat steps (E) and (F) until the continuous intersection warning module determines that the similarity value associated with the intersection video of step (E) is less than the first default value, or the repeated part of the factor combination of the intersection video of step (E) and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle.

5. The method for predicting traffic accidents according to claim 4, characterized in that: In step (G), when the similarity value related to the intersection video analyzed by the continuous intersection warning module in step (E) is less than the first default value, or the high-risk driving factor related to the vehicle is not included in the repeated part of the factor combination of the intersection video in step (E) and the factor combination corresponding to various accidents, the continuous intersection warning module generates a monitoring message and provides it to the road monitoring system, and the monitoring message includes the direction of travel of the vehicle; and the method further includes the following steps after step (G): (H) the road monitoring system transmits a monitoring instruction to a monitor installed at the next intersection that the vehicle is about to arrive at based on the travel direction of the vehicle included in the monitoring message of step (G), so that the monitor at the next intersection that the vehicle is about to arrive at takes a video of the intersection and provides it to the computer device; and (I) Repeat steps (G) and (H) until the continuous intersection warning module determines that the similarity value associated with N consecutive (N≥2) intersection videos is less than the first default value, or the repeated part of the factor combination of the N consecutive intersection videos and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle.

6. The method for predicting traffic accidents according to claim 3, characterized in that: The accident warning notification is also transmitted through vehicle networking related equipment to the on-board device of the vehicle that is about to pass through the traffic section shown in the predicted traffic scene video.

7. A computer device for predicting traffic accidents, characterized in that: include: A storage unit, in which a large number of traffic scene videos are pre-stored; and a processing unit, which is electrically connected to the storage unit to access the storage unit, and the processing unit includes an accident classification module, an accident factor discovery module and an accident summary module; wherein When the accident classification module determines that a collision occurs in the traffic scene videos and confirms that an accident exists, the traffic scene videos with an accident are classified into at least three types of accident videos, namely, vehicle-to-person, vehicle-to-vehicle, and self-collision, according to the objects of the collision, and stored in the storage unit; and the accident classification module obtains a first factor data from a time point of the accident occurrence to a fixed time before the time point in the various accident videos and records it in the storage unit; the first factor data includes vehicles, pedestrians, and traffic signal equipment; The accident factor mining module reads the various accident videos from the storage unit, and obtains second factor data other than the first factor data from the various accident videos within a fixed time period from the time point when the accident occurred to before the time point; the second factor data includes geographic information and weather information; The accident summarization module integrates all factors of the accident environment in each accident video based on the first factor data and the second factor data obtained by the accident classification module and the accident factor discovery module, and further summarizes various factor combinations in the same accident video, and generates a report based on the above-summarized various factor combination results, which presents the various factor combinations of the various accident videos occurring at different road sections or intersections.

8. The computer device for predicting traffic accidents according to claim 7, characterized in that: The accident classification module also determines that when a vehicle in one of the traffic scene videos suddenly brakes or turns, it determines that a similar accident has occurred in the traffic scene video, and classifies the traffic scene video in which the similar accident has occurred as a similar accident video; and the accident factor discovery module also summarizes the similar accident videos, and finds the second factor data in the accident environment except the first factor data from the similar accident video; and the various accidents also include similar accidents.

9. The computer device for predicting traffic accidents according to claim 7, characterized in that: The processing unit also includes an accident prediction module, which detects multiple factors appearing in an input traffic scene video to be predicted, and compares the factors in the traffic scene video to be predicted with the factor combinations corresponding to the various accidents summarized in the report to generate a similarity value, and when it is determined that the similarity value meets a similarity threshold, generates and outputs an accident warning notification.

10. The computer device for predicting traffic accidents according to claim 9, characterized in that: The computer device is also electrically connected to a road monitoring system, and the processing unit further includes a continuous intersection warning module; and the computer device and the road monitoring system perform the following steps: (A) The road monitoring system receives a video of an intersection shot by a monitor installed at an intersection and provides it to the computer device, the accident prediction module of the computer device detects multiple factors appearing in the intersection video and compares the factors in the intersection video with various combinations of factors corresponding to various accidents to generate a similarity value, and when the accident prediction module confirms that the similarity value related to the intersection video is less than the similarity threshold but greater than a first default value, the continuous intersection warning module is activated, and the continuous intersection warning module determines whether a repeated part of a combination of factors in the intersection video and a combination of factors corresponding to various accidents includes a high-risk driving factor related to a vehicle passing through the intersection. If so, the continuous intersection warning module generates a warning message and provides the warning message to the road monitoring system, and the warning message includes the direction of travel of the vehicle; (B) after receiving the warning message, the road monitoring system transmits a warning instruction to a warning device installed at the next intersection that the vehicle is about to arrive at according to the travel direction of the vehicle contained in the warning message, so that the warning device outputs a warning message according to the warning instruction, and transmits a monitoring instruction to a monitor installed at the next intersection, so that the monitor at the next intersection takes a video of the intersection and provides it to the computer device; (C) when the continuous intersection warning module of the computer device determines that the similarity value associated with the intersection video of step (B) is still less than the similarity threshold but greater than the first default value, and the overlapping parts of a factor combination of the intersection video of step (B) and the factor combinations corresponding to various accidents still include the high-risk driving factor associated with the vehicle, the continuous intersection warning module generates a warning message and provides it to the road monitoring system, and the warning message includes the direction of travel of the vehicle; and (D) Repeat steps (B) and (C) until the continuous intersection warning module determines that the similarity value associated with the intersection video of step (E) is less than the first default value, or the repeated part of the factor combination of the intersection video of step (E) and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle.

11. The computer device for predicting traffic accidents according to claim 10, characterized in that: In step (D), when the similarity value related to the intersection video analyzed by the continuous intersection warning module in step (B) is less than the first default value, or the high-risk driving factor related to the vehicle is not included in the repeated part of the factor combination of the intersection video in step (B) and the factor combinations corresponding to various accidents, the continuous intersection warning module generates a monitoring message and provides it to the road monitoring system, and the monitoring message includes the direction of travel of the vehicle; and the computer device and the road monitoring system further perform the following steps after step (D): (E) the road monitoring system transmits a monitoring instruction to a monitor installed at the next intersection that the vehicle is about to arrive at based on the travel direction of the vehicle included in the monitoring message of step (D), so that the monitor at the next intersection that the vehicle is about to arrive at takes a video of the intersection and provides it to the computer device; and (F) Repeat steps (D) and (E) until the continuous intersection warning module determines that the similarity value associated with N consecutive (N≥2) intersection videos is less than the first default value, or the repeated part of the factor combination of the N consecutive intersection videos and the factor combinations corresponding to various accidents does not include the high-risk driving factor associated with the vehicle.

12. The computer device for predicting traffic accidents according to claim 9, characterized in that: The accident warning notification is also transmitted through vehicle networking related equipment to the on-board device of the vehicle that is about to pass through the traffic section shown in the predicted traffic scene video.

13. A computer-readable recording medium for predicting traffic accidents, characterized in that: A software program including an accident classification module, an accident factor discovery module, an accident summarization module, an accident prediction module and a continuous intersection warning module is stored therein, and when the software program is loaded and executed by a computer device pre-stored with a large number of traffic scene videos, the computer device can complete the method for predicting traffic accidents according to any one of claims 1 to 6.

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