Urban traffic violation recognition and voice notification method, system and device and medium

Abstract

The invention relates to a data analysis technology, and discloses an urban traffic violation identification and voice notification method and system, and the method comprises the steps: carrying out the violation detection according to the multi-mode driving parameters of a target vehicle, obtaining a violation detection result, judging whether the target vehicle violates a violation or not according to the violation detection result, and if the target vehicle violates the violation, generating a violation notification voice, and playing the violation notification voice by using a preset loudspeaker, otherwise, carrying out violation probability identification on the target vehicle, judging whether the violation probability is greater than a preset threshold value, if the violation probability is less than or equal to the threshold value, selecting a new vehicle as the target vehicle, and re-obtaining the multi-mode driving parameters of the target vehicle. Otherwise, generating a warning voice according to the vehicle information of the target vehicle, and playing the warning voice by using a loudspeaker. According to the invention, the efficiency and accuracy of violation identification can be improved, and vehicles with violation tendencies can be identified and warned.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for identifying urban traffic violations and providing voice notification. Background Art

[0002] With the development of intelligent transportation technology, urban traffic violation recognition systems are evolving from single functions to multi-modal fusion. Existing technologies mainly rely on traditional equipment such as cameras and single radars, which have limitations in complex traffic scenarios.

[0003] Traditional violation detection systems rely on a single camera or radar, which limits the accuracy of violation detection. After a violation occurs, the driver cannot get warning information immediately and must rely on post-incident law enforcement. In addition, existing violation detection systems cannot identify vehicles with a tendency to violate traffic rules in advance and issue warnings. Summary of the invention

[0004] The present invention provides a method and system for identifying and voice-notifying urban traffic violations, the main purpose of which is to solve the problems of low efficiency and low accuracy of existing violation detection methods, and the inability to identify and warn vehicles with violation tendencies in advance.

[0005] To achieve the above purpose, the present invention provides a method for identifying and announcing urban traffic violations, comprising: Obtain multimodal driving parameters of target vehicles in the monitored road section; Performing a traffic violation detection on the target vehicle according to the multimodal driving parameters to obtain a traffic violation detection result; Determine whether the target vehicle has violated a traffic violation according to the traffic violation detection result; If the target vehicle has violated traffic rules, generating a traffic violation notification voice according to the traffic violation detection result, and playing the traffic violation notification voice using a preset speaker in the monitored road section; If the target vehicle has not violated any traffic rules, then the target vehicle is subjected to a traffic violation probability identification; Determining whether the probability of violation is greater than a preset probability threshold; If the violation probability is less than or equal to the probability threshold, a new vehicle is selected as the target vehicle, and the process returns to the step of obtaining the multimodal driving parameters of the target vehicle in the monitored road section; If the traffic violation probability is greater than the probability threshold, a warning voice is generated according to the vehicle information of the target vehicle, and the warning voice is played by the speaker.

[0006] Optionally, performing violation detection on the target vehicle according to the multimodal driving parameter to obtain a violation detection result includes: Acquire image data of the monitored road section within a preset time period; Perform road marking recognition according to the image data to obtain road marking data; Acquiring speed data and driving direction data included in the multimodal driving parameters; Determining whether the target vehicle is traveling in the wrong direction according to the line marking data and the driving direction data; If the target vehicle is driving in the wrong direction, the violation detection result is that the vehicle is driving in the wrong direction; If the target vehicle is not traveling in the wrong direction, a red light running detection is performed on the target vehicle to obtain a red light running detection result, a speeding detection is performed on the target vehicle according to the speed data to obtain a speeding detection result, and a line crossing detection is performed on the target vehicle to obtain a line crossing detection result; The red light running detection result, the speeding detection result and the crossing-line driving detection result are summarized to obtain the traffic violation detection result.

[0007] Optionally, performing a red light running detection on the target vehicle to obtain a red light running detection result includes: Performing target vehicle trajectory recognition according to the image data to obtain a vehicle trajectory; Performing signal recognition on the image data to obtain signal light image data; Using a traffic signal controller to obtain the digital data of the traffic lights on the monitored road section; Performing time stamp synchronization on the signal light image data and the signal light digital data based on a preset time delay to obtain synchronized signal light image data and synchronized signal light digital data; Performing double signal verification on the synchronized signal light image data and the synchronized signal light digital data to obtain a verification result; Determine whether the synchronized signal light image data and the synchronized signal light digital data are synchronized according to the verification result; If the verification result determines that the synchronized signal light image data and the synchronized signal light digital data are not synchronized, then after adjusting the preset time delay, return to the step of performing timestamp synchronization on the signal light image data and the signal light digital data based on the preset time delay to obtain the synchronized signal light image data and the synchronized signal light digital data; If the verification result determines that the synchronized signal light image data is synchronized with the synchronized signal light digital data, a red light running detection is performed according to the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result.

[0008] Optionally, performing red light running detection according to the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result includes: Acquire the stop line data contained in the marking line data; confirming the red light time period according to the synchronized signal light image data; Confirming the vehicle trajectory during the red light period according to the vehicle trajectory during the red light time; Determine whether the target vehicle has crossed the stop line according to the vehicle trajectory during the red light period and the stop line data; If the target vehicle crosses the stop line, the red light running detection result is that the vehicle ran a red light; If the target vehicle has not crossed the stop line, the red light running detection result is that the vehicle has not run a red light.

[0009] Optionally, the identifying the probability of violation of the target vehicle includes: Identify the turn signal data of the target vehicle, and identify the target driving direction of the target vehicle according to the turn signal data Confirm the target driving direction according to the synchronized signal light image data to perform red light running judgment, and confirm a red light running judgment coefficient according to the judgment result; Acquire the distance data between the target vehicle and the solid line of the road according to the line marking data; Identify the license plate number of the target vehicle, and obtain the number of historical traffic violations of the target vehicle according to the license plate number; The probability of a traffic violation is calculated according to the red light running discrimination coefficient, the distance data, the number of historical traffic violations, and the multimodal driving data.

[0010] Optionally, determining the red light running discrimination coefficient according to the judgment result includes: Determine whether the target driving direction is a red light state according to the judgment result; If the target driving direction is not a red light state, confirming that the red light running discrimination coefficient is 0.1; If the target driving direction is in a red light state, obtaining the remaining time of the red light in the target driving direction; A red light running discrimination coefficient is calculated according to the remaining time of the red light and the speed data and the acceleration data included in the multimodal driving data.

[0011] Optionally, the calculation formula of the red light running discrimination coefficient is as follows: in, is the red light running discrimination coefficient, is the preset scale factor, is the remaining time of the red light, is the speed data, is the acceleration data.

[0012] Optionally, the calculating of the red light running discrimination coefficient according to the remaining time of the red light and the speed data and acceleration data included in the multimodal driving data includes: Performing exponential calculation according to the red light running discrimination coefficient and the number of historical traffic violations to obtain a red light running index item; Performing a logarithmic operation on the ratio of the preset rated speed limit data to the speed data and the acceleration data to obtain a speed ratio logarithmic term; Performing an exponential operation on the speed ratio logarithmic term and the number of historical traffic violations to obtain a historical traffic violation weight term; The acceleration data is multiplied by a preset balance coefficient and then added to the lateral displacement data included in the multimodal driving data to obtain an acceleration factor, and a ratio of the distance data to the acceleration factor is calculated to obtain a distance ratio item; Performing an exponential operation on a preset base of a natural logarithm with the distance ratio term to obtain a base exponential term; Calculating the sum of the historical number of traffic violations and a preset average number of traffic violations to obtain a total number of traffic violations, and calculating the ratio of the historical number of traffic violations to the total number of traffic violations to obtain a number ratio item; Calculate the inverse of the logarithmic operation of the frequency ratio term to obtain the frequency weight term; The ratio of the sum of the red light running index item, the historical violation weight item, and the base index item to the number weight item is calculated to obtain the violation probability.

[0013] Optionally, the calculation formula of the violation probability is as follows: in, is the violation probability, is the red light running discrimination coefficient, is the number of historical violations, is the rated speed limit data of the monitored road section, is the speed data included in the multimodal driving data, is the acceleration data included in the multimodal driving data, is the distance data, is the lateral displacement data included in the multi-modal driving data, is the preset balance coefficient, is the average number of violations obtained in advance.

[0014] In order to solve the above problems, the present invention also provides a city traffic violation identification and voice notification system, the system comprising: A data acquisition module is used to obtain multimodal driving parameters of target vehicles in the monitored road section; A traffic violation detection module, used to perform traffic violation detection on the target vehicle according to the multi-modal driving parameters to obtain a traffic violation detection result; A traffic violation notification module, used to determine whether the target vehicle has violated the traffic rules according to the traffic violation detection result, and if the target vehicle has violated the traffic rules, generate a traffic violation notification voice according to the traffic violation detection result, and play the traffic violation notification voice using a preset speaker in the monitored road section; A traffic violation prediction module, used to determine whether the target vehicle has violated the traffic rules according to the traffic violation detection result, and if the target vehicle has not violated the traffic rules, then identify the traffic violation probability of the target vehicle; The threshold judgment module is used to judge whether the probability of violation is greater than a preset probability threshold. If the probability of violation is less than or equal to the probability threshold, a new vehicle is selected as the target vehicle, and the step of obtaining the multimodal driving parameters of the target vehicle in the monitored section is returned. If the probability of violation is greater than the probability threshold, a warning voice is generated according to the vehicle information of the target vehicle, and the warning voice is played using the speaker.

[0015] The embodiment of the present invention obtains the multimodal driving parameters of the target vehicle in the monitoring section, performs violation detection on the target vehicle according to the multimodal driving parameters, obtains the violation detection result, determines whether the target vehicle has violated the traffic rules according to the violation detection result, generates a violation notification voice according to the violation detection result if the target vehicle has violated the traffic rules, and uses a preset speaker in the monitoring section to play the violation notification voice, if the target vehicle has not violated the traffic rules, then performs violation probability identification on the target vehicle, determines whether the violation probability is greater than a preset probability threshold, if the violation probability is less than or equal to the probability threshold, selects a new vehicle as the target vehicle, returns to the step of obtaining the multimodal driving parameters of the target vehicle in the monitoring section, if the violation probability is greater than the probability threshold, generates a warning voice according to the vehicle information of the target vehicle, and uses the speaker to play the warning voice. Therefore, the urban traffic violation identification and voice notification method and system proposed by the present invention can solve the problems of low efficiency and low accuracy of the existing violation detection method, and the inability to identify and warn vehicles with violation tendencies in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for identifying and announcing urban traffic violations and voice notification provided by an embodiment of the present invention; Figure 2 A functional module diagram of a city traffic violation identification and voice notification system provided in one embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides a method for urban traffic violation identification and voice notification. The execution subject of the urban traffic violation identification and voice notification method includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the urban traffic violation identification and voice notification method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 FIG. 1 is a flow chart of a method for identifying and announcing urban traffic violations and voice notification provided by an embodiment of the present invention. In this embodiment, the method for identifying and announcing urban traffic violations and voice notification includes: S1. Obtain multimodal driving parameters of the target vehicle in the monitored road section.

[0021] In the embodiment of the present invention, the obtaining of multimodal driving parameters of the target vehicle in the monitored section refers to obtaining the target vehicle's speed, acceleration, driving direction, lateral displacement and other motion parameters in real time using a preset millimeter-wave radar matrix.

[0022] In the embodiment of the present invention, the millimeter wave radar matrix is ​​a radar matrix arranged at preset intervals, each radar operates in the millimeter wave frequency band, has a short wavelength, and has high accuracy, and can obtain vehicle speed, distance and other parameters. Since the coverage range and coverage angle of a single radar are limited, by setting up a millimeter wave radar matrix, multiple radars can work together to obtain multi-angle speed data and distance data.

[0023] In detail, the millimeter wave radar matrix adopts a three-dimensional matrix architecture. The matrix architecture is composed of multiple groups of millimeter wave radar units arranged in a diamond shape with an interval of 5-10 meters. Each group of radar units contains 3 independently working radar modules, covering fan-shaped ranges of different preset angles.

[0024] In the embodiment of the present invention, by acquiring the multimodal driving parameters of the target vehicle in the monitored road section, the accuracy of subsequent violation detection of the target vehicle can be improved.

[0025] S2. Performing a traffic violation detection on the target vehicle according to the multimodal driving parameters to obtain a traffic violation detection result.

[0026] In the embodiment of the present invention, the detection of traffic violations on the target vehicle according to the multimodal driving parameters may include detection of wrong-way driving, red light running, speeding and crossing-line driving on the target vehicle.

[0027] In the embodiment of the present invention, the step of performing violation detection on the target vehicle according to the multimodal driving parameters to obtain a violation detection result includes: Acquire image data of the monitored road section within a preset time period; Perform road marking recognition according to the image data to obtain road marking data; Acquiring speed data and driving direction data included in the multimodal driving parameters; Determining whether the target vehicle is traveling in the wrong direction according to the line marking data and the driving direction data; If the target vehicle is driving in the wrong direction, the violation detection result is that the vehicle is driving in the wrong direction; If the target vehicle is not traveling in the wrong direction, a red light running detection is performed on the target vehicle to obtain a red light running detection result, a speeding detection is performed on the target vehicle according to the speed data to obtain a speeding detection result, and a line crossing detection is performed on the target vehicle to obtain a line crossing detection result; The red light running detection result, the speeding detection result and the crossing-line driving detection result are summarized to obtain the traffic violation detection result.

[0028] In detail, the acquiring of the image data within a preset time period of the monitored section may be performed by acquiring the image data using a camera preset on the monitored section.

[0029] In detail, the road marking data is obtained by performing road marking recognition based on the image data, and the road marking data is obtained by graying the image data to obtain a grayscale image, identifying the parts with lower grayscale values ​​in the grayscale image based on threshold segmentation, and then identifying the straight line and curve parts based on edge detection to obtain the road marking data.

[0030] In detail, judging whether the target vehicle is traveling in the wrong direction based on the line marking data and the driving direction data is performed by judging whether the direction of the arrow line marking in the line marking data is the same as the driving direction data, and if they are not the same, it is judged to be traveling in the wrong direction.

[0031] In detail, the speeding detection of the target vehicle according to the speed data is performed by judging whether the speed data is greater than a preset speed limit data, and if so, it is determined to be speeding.

[0032] In the embodiment of the present invention, the red light running detection is performed on the target vehicle to obtain the red light running detection result, including: Performing target vehicle trajectory recognition according to the image data to obtain a vehicle trajectory; Performing signal recognition on the image data to obtain signal light image data; Using a traffic signal controller to obtain the digital data of the traffic lights on the monitored road section; Performing time stamp synchronization on the signal light image data and the signal light digital data based on a preset time delay to obtain synchronized signal light image data and synchronized signal light digital data; Performing double signal verification on the synchronized signal light image data and the synchronized signal light digital data to obtain a verification result; Determine whether the synchronized signal light image data and the synchronized signal light digital data are synchronized according to the verification result; If the verification result determines that the synchronized signal light image data and the synchronized signal light digital data are not synchronized, then after adjusting the preset time delay, return to the step of performing timestamp synchronization on the signal light image data and the signal light digital data based on the preset time delay to obtain the synchronized signal light image data and the synchronized signal light digital data; If the verification result determines that the synchronized signal light image data is synchronized with the synchronized signal light digital data, a red light running detection is performed according to the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result.

[0033] In detail, the target vehicle trajectory is identified according to the image data to obtain the vehicle trajectory, and the vehicle trajectory is obtained by identifying the position of the target vehicle in continuous images.

[0034] In detail, the traffic signal controller is a device that cyclically switches traffic lights according to preset time periods and fixed durations, and contains detailed data for controlling traffic lights.

[0035] In detail, the time stamp synchronization of the signal light image data and the signal light digital data based on the preset time delay is to use the preset camera and the traffic signal controller to achieve microsecond time synchronization through the PTP protocol. Due to the delay in communication, a time delay needs to be set in advance.

[0036] In detail, the PTP protocol is a high-precision time synchronization protocol that achieves time synchronization between a master clock and a slave clock through a combination of hardware and software.

[0037] In detail, the double signal verification of the synchronized signal light image data and the synchronized signal light digital data refers to determining whether the synchronized signal light image data and the synchronized signal light digital data have the same signal representation at the same time point, and if they are the same, the verification is successful.

[0038] In the embodiment of the present invention, performing red light running detection according to the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result includes: Acquire the stop line data contained in the marking line data; confirming the red light time period according to the synchronized signal light image data; Confirming the vehicle trajectory during the red light period according to the vehicle trajectory during the red light time; Determine whether the target vehicle has crossed the stop line according to the vehicle trajectory during the red light period and the stop line data; If the target vehicle crosses the stop line, the red light running detection result is that the vehicle ran a red light; If the target vehicle has not crossed the stop line, the red light running detection result is that the vehicle has not run a red light.

[0039] In the embodiment of the present invention, the cross-line driving detection is performed on the target vehicle to obtain the cross-line driving detection result by identifying the solid line position data contained in the line marking data, and then judging whether the target vehicle crosses the solid line based on the vehicle trajectory and the solid line position data.

[0040] S3. Determine whether the target vehicle has violated a traffic violation based on the traffic violation detection result.

[0041] If the target vehicle has violated traffic rules, S4 is executed to generate a traffic violation notification voice according to the traffic violation detection result, and the traffic violation notification voice is played by a speaker preset in the monitored road section.

[0042] In the embodiment of the present invention, the generation of the violation notification voice according to the violation detection result is to generate the violation notification voice according to the license plate number of the target vehicle and the violation content in the violation detection result by identifying the license plate number of the target vehicle. For example, assuming that the license plate number of the target vehicle is A and the violation detection result is running a red light, the violation notification voice can be "Owner of the license plate number A, you have run a red light".

[0043] If the target vehicle has not violated any traffic rules, then S5 is executed to identify the probability of violation of the target vehicle.

[0044] In the embodiment of the present invention, when the target vehicle has not violated any traffic rules, the probability of violation of the target vehicle can be identified to predict the target vehicle's traffic violation behavior in advance and warn the target vehicle in advance.

[0045] In the embodiment of the present invention, the step of identifying the probability of violation of the target vehicle includes: Identify the turn signal data of the target vehicle, and identify the target driving direction of the target vehicle according to the turn signal data Confirm the target driving direction according to the synchronized signal light image data to perform red light running judgment, and confirm a red light running judgment coefficient according to the judgment result; Acquire the distance data between the target vehicle and the solid line of the road according to the line marking data; Identify the license plate number of the target vehicle, and obtain the number of historical traffic violations of the target vehicle according to the license plate number; The probability of a traffic violation is calculated according to the red light running discrimination coefficient, the distance data, the number of historical traffic violations, and the multimodal driving data.

[0046] In detail, the step of determining the red light running discrimination coefficient according to the judgment result includes: Determine whether the target driving direction is a red light state according to the judgment result; If the target driving direction is not a red light state, confirming that the red light running discrimination coefficient is 0.1; If the target driving direction is in a red light state, obtaining the remaining time of the red light in the target driving direction; A red light running discrimination coefficient is calculated according to the remaining time of the red light and the speed data and the acceleration data included in the multimodal driving data.

[0047] In detail, the identifying of the turn signal data of the target vehicle and identifying the target driving direction of the target vehicle based on the turn signal data is performed by acquiring the image of the target vehicle through the image data and identifying the turn signal direction of the target vehicle based on a pre-trained target detection model.

[0048] Specifically, when the left turn signal of the target vehicle flashes, it indicates that the target driving direction of the target vehicle is the left side; when the right turn signal of the target vehicle flashes, it indicates that the target driving direction of the target vehicle is the right side; when both the left and right turn signals of the target vehicle flash, it indicates that the target driving direction of the target vehicle is going straight. The turn signal signal data can be "L (left)", "R (right)" or "0 (turn signal not on)". In the embodiment of the present invention, the red light running discrimination coefficient is a coefficient representing the probability of the target vehicle running a red light.

[0049] In the embodiment of the present invention, the identifying of the license plate number of the target vehicle is to perform character recognition on the license plate of the target vehicle through a neural network model to obtain the license plate number.

[0050] In detail, obtaining the historical number of traffic violations of the target vehicle according to the license plate number refers to querying the number of traffic violations corresponding to the license plate number in a preset database according to the license plate number.

[0051] In detail, the calculation formula of the red light running discrimination coefficient is as follows: in, is the red light running discrimination coefficient, is the preset scale factor, is the remaining time of the red light, is the speed data, is the acceleration data.

[0052] In the embodiment of the present invention, the red light running discrimination coefficient is calculated according to the remaining time of the red light and the speed data and acceleration data included in the multimodal driving data, including: Performing exponential calculation according to the red light running discrimination coefficient and the number of historical traffic violations to obtain a red light running index item; Performing a logarithmic operation on the ratio of the preset rated speed limit data to the speed data and the acceleration data to obtain a speed ratio logarithmic term; Performing exponential operation on the speed ratio logarithmic term and the number of historical traffic violations to obtain a historical traffic violation weight term; The acceleration data is multiplied by a preset balance coefficient and then added to the lateral displacement data included in the multimodal driving data to obtain an acceleration factor, and a ratio of the distance data to the acceleration factor is calculated to obtain a distance ratio item; Performing an exponential operation on a preset base of a natural logarithm with the distance ratio term to obtain a base exponential term; Calculating the sum of the historical number of traffic violations and a preset average number of traffic violations to obtain a total number of traffic violations, and calculating the ratio of the historical number of traffic violations to the total number of traffic violations to obtain a number ratio item; Calculate the inverse of the logarithmic operation of the frequency ratio term to obtain the frequency weight term; The ratio of the sum of the red light running index item, the historical violation weight item, and the base index item to the number weight item is calculated to obtain the violation probability.

[0053] In the embodiment of the present invention, the calculation formula of the violation probability is as follows: in, is the violation probability, is the red light running discrimination coefficient, is the number of historical violations, is the rated speed limit data of the monitored road section, is the speed data included in the multimodal driving data, is the acceleration data included in the multimodal driving data, is the distance data, is the lateral displacement data included in the multi-modal driving data, is the preset balance coefficient, is the average number of violations obtained in advance.

[0054] S6. Determine whether the traffic violation probability is greater than a preset probability threshold.

[0055] If the violation probability is less than or equal to the probability threshold, execute S7, select a new vehicle as the target vehicle, return to S1, and obtain the multimodal driving parameters of the target vehicle in the monitored section.

[0056] In the embodiment of the present invention, when the violation probability is less than or equal to the probability threshold, it means that the target vehicle has not violated the traffic rules and has a low tendency to violate the traffic rules. Therefore, a new target vehicle can be selected for re-monitoring.

[0057] If the traffic violation probability is greater than the probability threshold, S8 is executed to generate a warning voice according to the vehicle information of the target vehicle, and the warning voice is played by the speaker.

[0058] However, when the violation probability is greater than the probability threshold, it means that the target vehicle has not violated the traffic rules at present, but has a high tendency to violate the traffic rules, so the target vehicle needs to be warned by voice.

[0059] In the embodiment of the present invention, the warning voice is generated according to the vehicle information of the target vehicle, and the warning voice can be generated according to the color, model and the last digit of the license plate number by identifying the license plate number, color and model of the target vehicle. For example, the warning voice can be "Owner of the white car with the last digit of the license plate number 9527, please drive in a standardized manner and do not violate traffic rules." While warning the target vehicle, the privacy of the target vehicle is also protected.

[0060] In the embodiment of the present invention, when the violation probability is greater than the probability threshold, a warning voice is generated according to the vehicle information of the target vehicle and the warning voice is played by the speaker, thereby reducing the violation probability of the target vehicle and improving traffic safety.

[0061] The present invention obtains vehicle speed, acceleration, driving direction and other motion parameters in real time through a millimeter-wave radar matrix, performs multi-dimensional violation detection based on the acquired parameters, issues voice notifications to vehicles that violate the law, calculates the probability of violation for vehicles that do not violate the law based on multimodal driving parameters, and issues warnings to vehicles whose probability of violation is greater than a preset threshold, thereby improving the efficiency of violation identification and notification.

[0062] like Figure 2 1 is a functional module diagram of a city traffic violation identification and voice notification system provided by an embodiment of the present invention.

[0063] The urban traffic violation identification and voice notification system 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the urban traffic violation identification and voice notification system 100 may include a data acquisition module 101, a violation detection module 102, a violation notification module 103, a violation prediction module 104 and a threshold judgment module 105. The module of the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0064] In this embodiment, the functions of each module / unit are as follows: The data acquisition module 101 is used to acquire multimodal driving parameters of the target vehicle in the monitored road section; The violation detection module 102 is used to perform violation detection on the target vehicle according to the multi-modal driving parameters to obtain a violation detection result; The traffic violation notification module 103 is used to determine whether the target vehicle has violated the traffic rules according to the traffic violation detection result, and if the target vehicle has violated the traffic rules, generate a traffic violation notification voice according to the traffic violation detection result, and play the traffic violation notification voice using a preset speaker in the monitored road section; The violation prediction module 104 is used to determine whether the target vehicle has violated the traffic rules according to the violation detection result, and if the target vehicle has not violated the traffic rules, then identify the probability of violation of the target vehicle; The threshold judgment module 105 is used to judge whether the violation probability is greater than a preset probability threshold. If the violation probability is less than or equal to the probability threshold, a new vehicle is selected as the target vehicle, and the process returns to the step of obtaining the multimodal driving parameters of the target vehicle in the monitored section. If the violation probability is greater than the probability threshold, a warning voice is generated according to the vehicle information of the target vehicle, and the warning voice is played using the speaker.

[0065] In detail, each module described in the urban traffic violation identification and voice notification system 100 in the embodiment of the present invention is used in the same manner as described above. Figure 1 The urban traffic violation identification and voice notification methods described in the text are the same technical means and can produce the same technical effects, so they will not be repeated here.

[0066] In the embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0067] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0068] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0069] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0070] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0071] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0072] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for identifying and announcing urban traffic violations, characterized in that: The method comprises: Obtain multimodal driving parameters of target vehicles in the monitored road section; Performing a traffic violation detection on the target vehicle according to the multimodal driving parameters to obtain a traffic violation detection result; Determine whether the target vehicle has violated a traffic violation according to the traffic violation detection result; If the target vehicle has violated traffic rules, generating a traffic violation notification voice according to the traffic violation detection result, and playing the traffic violation notification voice using a preset speaker in the monitored road section; If the target vehicle has not violated any traffic rules, then the target vehicle is subjected to a traffic violation probability identification; Determining whether the probability of violation is greater than a preset probability threshold; If the violation probability is less than or equal to the probability threshold, a new vehicle is selected as the target vehicle, and the process returns to the step of obtaining the multimodal driving parameters of the target vehicle in the monitored road section; If the traffic violation probability is greater than the probability threshold, a warning voice is generated according to the vehicle information of the target vehicle, and the warning voice is played by the speaker.

2. The urban traffic violation identification and voice notification method as claimed in claim 1, characterized in that: The step of performing a traffic violation detection on the target vehicle according to the multi-modal driving parameters to obtain a traffic violation detection result includes: Acquire image data of the monitored road section within a preset time period; Perform road marking recognition according to the image data to obtain road marking data; Acquiring speed data and driving direction data included in the multimodal driving parameters; Determining whether the target vehicle is traveling in the wrong direction according to the line marking data and the driving direction data; If the target vehicle is driving in the wrong direction, the violation detection result is that the vehicle is driving in the wrong direction; If the target vehicle is not traveling in the wrong direction, a red light running detection is performed on the target vehicle to obtain a red light running detection result, a speeding detection is performed on the target vehicle according to the speed data to obtain a speeding detection result, and a line crossing detection is performed on the target vehicle to obtain a line crossing detection result; The red light running detection result, the speeding detection result and the crossing-line driving detection result are summarized to obtain the traffic violation detection result.

3. The urban traffic violation identification and voice notification method as claimed in claim 2, characterized in that: The step of performing a red light running detection on the target vehicle to obtain a red light running detection result includes: Performing target vehicle trajectory recognition according to the image data to obtain a vehicle trajectory; Performing signal recognition on the image data to obtain signal light image data; Using a traffic signal controller to obtain the digital data of the traffic lights on the monitored road section; Performing time stamp synchronization on the signal light image data and the signal light digital data based on a preset time delay to obtain synchronized signal light image data and synchronized signal light digital data; Performing double signal verification on the synchronized signal light image data and the synchronized signal light digital data to obtain a verification result; Determine whether the synchronized signal light image data and the synchronized signal light digital data are synchronized according to the verification result; If the verification result determines that the synchronized signal light image data and the synchronized signal light digital data are not synchronized, then after adjusting the preset time delay, return to the step of performing timestamp synchronization on the signal light image data and the signal light digital data based on the preset time delay to obtain the synchronized signal light image data and the synchronized signal light digital data; If the verification result determines that the synchronized signal light image data is synchronized with the synchronized signal light digital data, a red light running detection is performed according to the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result.

4. The urban traffic violation identification and voice notification method as claimed in claim 3, characterized in that: The red light running detection is performed according to the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result, including: Acquire the stop line data contained in the marking line data; confirming the red light time period according to the synchronized signal light image data; Confirming the vehicle trajectory during the red light period according to the vehicle trajectory during the red light time; Determine whether the target vehicle has crossed the stop line according to the vehicle trajectory during the red light period and the stop line data; If the target vehicle crosses the stop line, the red light running detection result is that the vehicle ran a red light; If the target vehicle has not crossed the stop line, the red light running detection result is that the vehicle has not run a red light.

5. The urban traffic violation identification and voice notification method as claimed in claim 3, characterized in that: The identifying the probability of violation of the target vehicle includes: Identify the turn signal data of the target vehicle, and identify the target driving direction of the target vehicle according to the turn signal data Confirm the target driving direction according to the synchronized signal light image data to perform red light running judgment, and confirm a red light running judgment coefficient according to the judgment result; Acquire the distance data between the target vehicle and the solid line of the road according to the line marking data; Identify the license plate number of the target vehicle, and obtain the number of historical traffic violations of the target vehicle according to the license plate number; The probability of a traffic violation is calculated according to the red light running discrimination coefficient, the distance data, the number of historical traffic violations, and the multimodal driving data.

6. The urban traffic violation identification and voice notification method as claimed in claim 5, characterized in that: Determining the red light running discrimination coefficient according to the judgment result includes: Determine whether the target driving direction is a red light state according to the judgment result; If the target driving direction is not a red light state, confirming that the red light running discrimination coefficient is 0.1; If the target driving direction is in a red light state, obtaining the remaining time of the red light in the target driving direction; A red light running discrimination coefficient is calculated according to the remaining time of the red light and the speed data and the acceleration data included in the multimodal driving data.

7. The urban traffic violation identification and voice notification method as claimed in claim 6, characterized in that: The calculation formula of the red light running discrimination coefficient is as follows: in, is the red light running discrimination coefficient, is the preset scale factor, is the remaining time of the red light, is the speed data, is the acceleration data.

8. The urban traffic violation identification and voice notification method as claimed in claim 5, characterized in that: The calculating of the red light running discrimination coefficient according to the remaining time of the red light and the speed data and acceleration data included in the multimodal driving data includes: Performing exponential calculation according to the red light running discrimination coefficient and the number of historical traffic violations to obtain a red light running index item; Performing a logarithmic operation on the ratio of the preset rated speed limit data to the speed data and the acceleration data to obtain a speed ratio logarithmic term; Performing exponential operation on the speed ratio logarithmic term and the number of historical traffic violations to obtain a historical traffic violation weight term; The acceleration data is multiplied by a preset balance coefficient and then added to the lateral displacement data included in the multimodal driving data to obtain an acceleration factor, and a ratio of the distance data to the acceleration factor is calculated to obtain a distance ratio item; Performing an exponential operation on a preset base of a natural logarithm with the distance ratio term to obtain a base exponential term; Calculating the sum of the historical number of traffic violations and a preset average number of traffic violations to obtain a total number of traffic violations, and calculating the ratio of the historical number of traffic violations to the total number of traffic violations to obtain a number ratio item; Calculate the inverse of the logarithmic operation of the frequency ratio term to obtain the frequency weight term; The ratio of the sum of the red light running index item, the historical violation weight item, and the base index item to the number weight item is calculated to obtain the violation probability.

9. The urban traffic violation identification and voice notification method as claimed in claim 5, characterized in that: The calculation formula of the violation probability is as follows: in, is the violation probability, is the red light running discrimination coefficient, is the number of historical violations, is the rated speed limit data of the monitored road section, is the speed data included in the multimodal driving data, is the acceleration data included in the multimodal driving data, is the distance data, is the lateral displacement data included in the multi-modal driving data, is the preset balance coefficient, is the average number of violations obtained in advance.

10. An urban traffic violation identification and voice notification system, characterized in that: The system comprises: A data acquisition module is used to obtain multimodal driving parameters of target vehicles in the monitored road section; A traffic violation detection module, used to perform traffic violation detection on the target vehicle according to the multimodal driving parameters to obtain a traffic violation detection result; A traffic violation notification module, used to determine whether the target vehicle has violated the traffic rules according to the traffic violation detection result, and if the target vehicle has violated the traffic rules, generate a traffic violation notification voice according to the traffic violation detection result, and play the traffic violation notification voice using a preset speaker in the monitored road section; A traffic violation prediction module, used to determine whether the target vehicle has violated the traffic rules according to the traffic violation detection result, and if the target vehicle has not violated the traffic rules, then identify the traffic violation probability of the target vehicle; The threshold judgment module is used to judge whether the probability of violation is greater than a preset probability threshold. If the probability of violation is less than or equal to the probability threshold, a new vehicle is selected as the target vehicle, and the step of obtaining the multimodal driving parameters of the target vehicle in the monitored section is returned. If the probability of violation is greater than the probability threshold, a warning voice is generated according to the vehicle information of the target vehicle, and the warning voice is played using the speaker.

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