Method, System, Device and Medium for Urban Traffic Violation Recognition and Voice Notification

Abstract

The present invention relates to data analysis technology, and discloses a method and system for urban traffic violation recognition and voice notification, including: performing violation detection based on multi-modal driving parameters of a target vehicle to obtain a violation detection result, judging whether the target vehicle violates the regulations according to the violation detection result, if it violates the regulations, generating a violation notification voice according to the result, and playing the violation notification voice by using a preset speaker, otherwise, performing violation probability recognition on the target vehicle, judging whether the violation probability is greater than a preset threshold, if it is less than or equal to the threshold, selecting a new vehicle as the target vehicle and re-obtaining the multi-modal 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 the speaker. The present invention can improve the efficiency and accuracy of violation recognition, as well as identify vehicles with a tendency to violate the regulations and give warnings.

Description

Technical Field

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

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

[0003] Traditional violation detection systems mostly rely on single cameras or radars, resulting in limited accuracy of violation detection. After a violation occurs, the driver cannot obtain warning information immediately and needs to rely on post-event law enforcement. Moreover, existing violation detection systems cannot identify vehicles with a tendency to violate in advance and give warnings. Summary of the Invention

[0004] The present invention provides a method and system for identifying urban traffic violations and voice notification, and its main purpose is to solve the problems that existing violation detection methods are inefficient, inaccurate, and cannot identify vehicles with a tendency to violate in advance and give warnings.

[0005] To achieve the above object, a method for identifying urban traffic violations and voice notification provided by the present invention includes:

[0006] Obtain multi-modal driving parameters of a target vehicle in a monitored section;

[0007] Perform violation detection on the target vehicle according to the multi-modal driving parameters to obtain a violation detection result;

[0008] Judge whether the target vehicle has violated according to the violation detection result;

[0009] If the target vehicle has violated, generate a violation notification voice according to the violation detection result, and play the violation notification voice by using a preset speaker in the monitored section;

[0010] If the target vehicle has not violated, perform violation probability identification on the target vehicle, where the calculation formula of the violation probability is as follows:

[0011]

[0012] where G is the violation probability, K is the red-light running discrimination coefficient, T is the historical number of violations, v0 is the rated speed limit data of the monitored section, v is the speed data included in the multi-modal driving data, a is the acceleration data included in the multi-modal driving data, W is the distance data, L is the lateral displacement data included in the multi-modal driving data, ω2 is a preset balance coefficient, Tavg is the pre-acquired average number of violations;

[0013] Determine whether the violation probability is greater than a preset probability threshold;

[0014] If the violation probability is less than or equal to the probability threshold, select a new vehicle as the target vehicle and return to the step of obtaining the multimodal driving parameters of the target vehicle in the monitored section;

[0015] If the violation probability is greater than the probability threshold, generate a warning voice according to the vehicle information of the target vehicle and play the warning voice using the speaker.

[0016] Optionally, the performing a violation detection on the target vehicle according to the multimodal driving parameters to obtain a violation detection result includes:

[0017] Obtain image data within a preset time period in the monitored section;

[0018] Perform road marking recognition according to the image data to obtain marking data;

[0019] Obtain the speed data and driving direction data included in the multimodal driving parameters;

[0020] Judge whether the target vehicle is driving in reverse according to the marking data and the driving direction data;

[0021] If the target vehicle is driving in reverse, the violation detection result is that the vehicle is driving in reverse;

[0022] If the target vehicle is not driving in reverse, perform a red light running detection on the target vehicle to obtain a red light running detection result, perform a speeding detection on the target vehicle according to the speed data to obtain a speeding detection result, and perform a lane crossing driving detection on the target vehicle to obtain a lane crossing driving detection result;

[0023] Summarize the red light running detection result, the speeding detection result, and the lane crossing driving detection result to obtain the violation detection result.

[0024] Optionally, the performing a red light running detection on the target vehicle to obtain a red light running detection result includes:

[0025] Perform target vehicle trajectory recognition according to the image data to obtain a vehicle trajectory;

[0026] Perform signal recognition on the image data to obtain signal light image data;

[0027] Use the traffic signal controller to obtain the signal light digital data of the monitored section;

[0028] Synchronize timestamps for 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;

[0029] Perform double signal verification on the synchronized signal light image data and the synchronized signal light digital data to obtain a verification result;

[0030] Judge whether the synchronized signal light image data is synchronized with the synchronized signal light digital data according to the verification result;

[0031] If the verification result determines that the synchronized signal light image data is not synchronized with the synchronized signal light digital data, after adjusting the preset time delay, return to the step of synchronizing timestamps for the signal light image data and the signal light digital data based on the preset time delay to obtain synchronized signal light image data and synchronized signal light digital data;

[0032] If the verification result determines that the synchronized signal light image data is synchronized with the synchronized signal light digital data, perform red light running detection based on the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result.

[0033] Optionally, the performing red light running detection based on the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result includes:

[0034] Obtain the stop line data included in the marking data;

[0035] Confirm the red light time period according to the synchronized signal light image data;

[0036] Confirm the vehicle trajectory during the red light according to the vehicle trajectory and the red light time;

[0037] Judge whether the target vehicle crosses the stop line according to the vehicle trajectory during the red light and the stop line data;

[0038] If the target vehicle crosses the stop line, the red light running detection result is that the vehicle runs a red light;

[0039] If the target vehicle does not cross the stop line, the red light running detection result is that the vehicle does not run a red light.

[0040] Optionally, the identifying the violation probability of the target vehicle includes:

[0041] 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

[0042] Based on the synchronized signal light image data, confirm the target driving direction for red light running judgment, and confirm the red light running discrimination coefficient according to the judgment result;

[0043] Obtain the distance data between the target vehicle and the road solid line according to the marking line data;

[0044] Identify the license plate number of the target vehicle, and obtain the historical violation times of the target vehicle according to the license plate number;

[0045] Calculate the violation probability according to the red light running discrimination coefficient, the distance data, the historical violation times, and the multi-modal driving data.

[0046] Optionally, the confirming the red light running discrimination coefficient according to the judgment result includes:

[0047] Judge whether the target driving direction is in the red light state according to the judgment result;

[0048] If the target driving direction is not in the red light state, confirm that the red light running discrimination coefficient is 0.1;

[0049] If the target driving direction is in the red light state, obtain the remaining red light time of the target driving direction;

[0050] Calculate the red light running discrimination coefficient according to the remaining red light time, the speed data and the acceleration data included in the multi-modal driving data.

[0051] Optionally, the calculation formula of the red light running discrimination coefficient is as follows:

[0052]

[0053] Among them, K is the red light running discrimination coefficient, ω1 is a preset proportional coefficient, T k is the remaining red light time, v is the speed data, and a is the acceleration data.

[0054] Optionally, the calculating the red light running discrimination coefficient according to the remaining red light time, the speed data and the acceleration data included in the multi-modal driving data includes:

[0055] Perform exponential operation according to the red light running discrimination coefficient and the historical violation times to obtain the red light running index term;

[0056] Perform logarithmic operation according to the ratio of the preset rated speed limit data to the speed data and the acceleration data to obtain the speed ratio logarithm term;

[0057] Perform exponential operation according to the speed ratio logarithm term and the historical violation times to obtain the historical violation weight term;

[0058] Multiply the acceleration data by a preset balance coefficient and then add it to the lateral displacement data included in the multi-modal driving data to obtain an acceleration factor, calculate the ratio of the distance data to the acceleration factor to obtain a distance ratio term;

[0059] Perform an exponential operation on the distance ratio term with respect to the base of the preset natural logarithm to obtain a base exponential term;

[0060] Calculate the sum of the historical number of violations and the preset average number of violations to obtain a sum of violation times, calculate the ratio of the historical number of violations to the sum of violation times to obtain a times ratio term;

[0061] Calculate the negative value after the logarithmic operation of the times ratio term to obtain a times weight term;

[0062] Calculate the ratio of the sum of the red-light running index term, the historical violation weight term, and the base exponential term to the times weight term to obtain the violation probability.

[0063] To solve the above problems, the present invention also provides an urban traffic violation recognition and voice notification system, the system includes:

[0064] A data acquisition module for acquiring multi-modal driving parameters of a target vehicle in a monitored section;

[0065] A violation detection module for performing violation detection on the target vehicle according to the multi-modal driving parameters to obtain a violation detection result;

[0066] A violation notification module for judging whether the target vehicle has violated the regulations according to the violation detection result. If the target vehicle has violated the regulations, a violation notification voice is generated according to the violation detection result, and the violation notification voice is played by a preset loudspeaker in the monitored section;

[0067] A violation prediction module for judging whether the target vehicle has violated the regulations according to the violation detection result. If the target vehicle has not violated the regulations, the violation probability of the target vehicle is identified, where the calculation formula of the violation probability is as follows:

[0068]

[0069] Where G is the violation probability, K is the red-light running discrimination coefficient, T is the historical number of violations, v0 is the rated speed limit data of the monitored section, v is the speed data included in the multi-modal driving data, a is the acceleration data included in the multi-modal driving data, W is the distance data, L is the lateral displacement data included in the multi-modal driving data, ω2 is a preset balance coefficient, T avg Is the pre-acquired average number of violations;

[0070] A threshold judgment module, configured 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 step of obtaining the multimodal driving parameters of the target vehicle in the monitored section is returned. 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 by using the speaker.

[0071] In the embodiment of the present invention, by obtaining the multimodal driving parameters of the target vehicle in the monitored section, performing violation detection on the target vehicle according to the multimodal driving parameters to obtain a violation detection result, and judging whether the target vehicle violates the regulations according to the violation detection result. If the target vehicle has violated the regulations, a violation notice voice is generated according to the violation detection result, and the violation notice voice is played by using the preset speaker in the monitored section. If the target vehicle has not violated the regulations, the violation probability of the target vehicle is identified, and it is judged 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 step of obtaining the multimodal driving parameters of the target vehicle in the monitored section is returned. 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 by using the speaker. Therefore, the urban traffic violation recognition 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 methods, and the inability to identify vehicles with a tendency to violate the regulations in advance and give warnings. Brief Description of the Drawings

[0072] Figure 1 It is a schematic flowchart of the urban traffic violation recognition and voice notification method provided by an embodiment of the present invention;

[0073] Figure 2 It is a functional module diagram of the urban traffic violation recognition and voice notification system provided by an embodiment of the present invention.

[0074] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0075] 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.

[0076] An embodiment of the present application provides a method for identifying urban traffic violations 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 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 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 Network (CDN), and big data and artificial intelligence platforms.

[0077] Referring to Figure 1 As shown, it is a schematic flowchart of the urban traffic violation identification and voice notification method provided by an embodiment of the present invention. In this embodiment, the urban traffic violation identification and voice notification method includes:

[0078] S1. Obtain multi-modal driving parameters of a target vehicle in a monitored section.

[0079] In the embodiment of the present invention, obtaining the multi-modal driving parameters of the target vehicle in the monitored section means using a preset millimeter-wave radar matrix to continuously obtain motion parameters of the target vehicle such as speed, acceleration, driving direction, and lateral displacement.

[0080] In the embodiment of the present invention, the millimeter-wave radar matrix is a radar matrix arranged at a preset interval. Each radar operates in the millimeter-wave band, has a short wavelength and high precision, and can obtain parameters such as the speed and distance of the vehicle. 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 speed data and distance data from multiple angles.

[0081] Specifically, the millimeter-wave radar matrix adopts a three-dimensional stereo matrix architecture. This matrix architecture is composed of multiple groups of millimeter-wave radar units arranged in a rhombus shape at intervals of 5-10 meters. Each group of radar units contains 3 independently operating radar modules, which respectively cover fan-shaped ranges with different preset angles.

[0082] In the embodiment of the present invention, by obtaining the multi-modal driving parameters of the target vehicle in the monitored section, the accuracy of subsequent violation detection of the target vehicle can be improved.

[0083] S2. Perform violation detection on the target vehicle according to the multi-modal driving parameters to obtain a violation detection result.

[0084] In an embodiment of the present invention, the illegal detection of the target vehicle according to the multimodal driving parameters may include reverse driving detection, red light running detection, speeding detection, and lane crossing driving detection of the target vehicle.

[0085] In an embodiment of the present invention, the illegal detection of the target vehicle according to the multimodal driving parameters to obtain an illegal detection result includes:

[0086] Obtain image data within a preset time period of the monitored section;

[0087] Perform road marking recognition according to the image data to obtain marking data;

[0088] Obtain the speed data and driving direction data included in the multimodal driving parameters;

[0089] Judge whether the target vehicle is driving in reverse according to the marking data and the driving direction data;

[0090] If the target vehicle is driving in reverse, the illegal detection result is that the vehicle is driving in reverse;

[0091] If the target vehicle is not driving in reverse, perform red light running detection on the target vehicle to obtain a red light running detection result, perform speeding detection on the target vehicle according to the speed data to obtain a speeding detection result, and perform lane crossing driving detection on the target vehicle to obtain a lane crossing driving detection result;

[0092] Summarize the red light running detection result, the speeding detection result, and the lane crossing driving detection result to obtain the illegal detection result.

[0093] Specifically, the obtaining of the image data within a preset time period of the monitored section may be to obtain the image data by using a camera preset in the monitored section.

[0094] Specifically, the performing of road marking recognition according to the image data to obtain marking data is to perform grayscale processing on the image data to obtain a grayscale image, identify the part with a lower grayscale value in the grayscale image based on threshold segmentation, and then identify the straight line and curve parts based on edge detection to obtain the marking data.

[0095] Specifically, the judging of whether the target vehicle is driving in reverse according to the marking data and the driving direction data is to judge whether the arrow marking direction in the marking data is the same as the driving direction data, and if they are not the same, it is determined that the vehicle is driving in reverse.

[0096] Specifically, the overspeed detection of the target vehicle based on the speed data is performed by determining whether the speed data is greater than a preset speed limit data. If it is greater, it is determined as overspeed.

[0097] In an embodiment of the present invention, the red light running detection of the target vehicle to obtain a red light running detection result includes:

[0098] Performing target vehicle trajectory recognition on the image data to obtain a vehicle trajectory;

[0099] Performing signal recognition on the image data to obtain signal light image data;

[0100] Using a traffic signal controller to obtain the signal light digital data of the monitored section;

[0101] Based on a preset time delay, performing timestamp synchronization on the signal light image data and the signal light digital data to obtain synchronized signal light image data and synchronized signal light digital data;

[0102] Performing dual signal verification on the synchronized signal light image data and the synchronized signal light digital data to obtain a verification result;

[0103] Based on the verification result, determining whether the synchronized signal light image data and the synchronized signal light digital data are synchronized;

[0104] If the verification result determines that the synchronized signal light image data and the synchronized signal light digital data are not synchronized, 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 synchronized signal light image data and synchronized signal light digital data;

[0105] If the verification result determines that the synchronized signal light image data and the synchronized signal light digital data are synchronized, perform red light running detection based on the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result.

[0106] Specifically, the performing target vehicle trajectory recognition on the image data to obtain a vehicle trajectory is performed by recognizing the positions of the target vehicle in consecutive images to obtain the vehicle trajectory.

[0107] Specifically, the traffic signal controller is a device that cycles through signal lights according to a preset time period and a fixed duration, and contains detailed data for controlling traffic signal lights.

[0108] Specifically, the timestamp synchronization of the traffic signal lamp image data and the traffic signal lamp digital data based on a preset time delay is achieved by using a preset camera and the traffic signal controller to perform microsecond-level time synchronization through the PTP protocol. Since there is a communication delay, a time delay needs to be set in advance.

[0109] Specifically, the PTP protocol is a high-precision time synchronization protocol. It realizes the time synchronization between the master clock and the slave clock through the combination of hardware and software.

[0110] Specifically, the double signal verification of the synchronized traffic signal lamp image data and the synchronized traffic signal lamp digital data means judging whether the synchronized traffic signal lamp image data and the synchronized traffic signal lamp digital data have the same signal representation at the same time point. If they are the same, the verification is successful.

[0111] In the embodiment of the present invention, the red light running detection is performed according to the vehicle trajectory and the synchronized traffic signal lamp image data to obtain a red light running detection result, including:

[0112] Obtain the stop line data included in the marking data;

[0113] Confirm the red light time period according to the synchronized traffic signal lamp image data;

[0114] Confirm the vehicle trajectory during the red light according to the red light time and the vehicle trajectory;

[0115] Judge whether the target vehicle crosses the stop line according to the vehicle trajectory during the red light and the stop line data;

[0116] If the target vehicle crosses the stop line, the red light running detection result is that the vehicle runs a red light;

[0117] If the target vehicle does not cross the stop line, the red light running detection result is that the vehicle does not run a red light.

[0118] In the embodiment of the present invention, the lane violation detection of the target vehicle to obtain a lane violation detection result is to identify the solid line position data included in the marking data, and then judge whether the target vehicle crosses the solid line according to the vehicle trajectory and the solid line position data.

[0119] S3. Judge whether the target vehicle is in violation according to the violation detection result.

[0120] If the target vehicle has violated the regulations, execute S4. Generate a violation notice voice according to the violation detection result, and play the violation notice voice by using a preset speaker in the monitored section.

[0121] In the embodiment of the present invention, generating a traffic violation notice voice according to the traffic violation detection result is to identify the license plate number of the target vehicle, and generate a traffic violation notice voice according to the license plate number and the traffic violation content in the traffic violation detection result. For example, assuming that the license plate number of the target vehicle is A and the traffic violation detection result is running a red light, the traffic violation notice voice can be "The owner of the vehicle with license plate number A, you have run a red light."

[0122] If the target vehicle has no traffic violation, then execute S5 to identify the traffic violation probability of the target vehicle.

[0123] In the embodiment of the present invention, when the target vehicle has no traffic violation, the traffic violation probability of the target vehicle can be identified to predict in advance the rated traffic violation behavior of the target vehicle and give an early warning to the target vehicle.

[0124] In the embodiment of the present invention, the identifying the traffic violation probability of the target vehicle includes:

[0125] Identifying the turn signal data of the target vehicle, and identifying the target driving direction of the target vehicle according to the turn signal data

[0126] Judging whether to run a red light according to the synchronized signal light image data for the target driving direction, and confirming a red light running discrimination coefficient according to the judgment result;

[0127] Obtaining the distance data between the target vehicle and the road solid line according to the marking data;

[0128] Identifying the license plate number of the target vehicle, and obtaining the historical traffic violation times of the target vehicle according to the license plate number;

[0129] Calculating the traffic violation probability according to the red light running discrimination coefficient, the distance data, the historical traffic violation times, and the multi-modal driving data.

[0130] Specifically, the confirming the red light running discrimination coefficient according to the judgment result includes:

[0131] Judging whether the target driving direction is in a red light state according to the judgment result;

[0132] If the target driving direction is not in a red light state, then confirm that the red light running discrimination coefficient is 0.1;

[0133] If the target driving direction is in a red light state, then obtain the remaining red light time of the target driving direction;

[0134] Calculating the red light running discrimination coefficient according to the remaining red light time, the speed data and the acceleration data included in the multi-modal driving data.

[0135] Specifically, to 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, an image of the target vehicle is obtained from the image data, and the turn signal direction of the target vehicle is identified based on a pre-trained target detection model.

[0136] Specifically, when the left turn signal of the target vehicle flashes, it indicates that the target driving direction of the target vehicle is to the left. When the right turn signal of the target vehicle flashes, it indicates that the target driving direction of the target vehicle is to the right. When the left and right turn signals of the target vehicle both flash, it indicates that the target driving direction of the target vehicle is straight ahead. The turn signal data can be "L (left)", "R (right)", or "0 (no turn signal)".

[0137] In an 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.

[0138] In an embodiment of the present invention, to identify the license plate number of the target vehicle, character recognition is performed on the license plate of the target vehicle through a neural network model to obtain the license plate number.

[0139] Specifically, obtaining the historical violation times of the target vehicle according to the license plate number means querying the violation times corresponding to the license plate number in a preset database according to the license plate number.

[0140] Specifically, the calculation formula of the red light running discrimination coefficient is as follows:

[0141]

[0142] Where K is the red light running discrimination coefficient, ω1 is a preset proportionality coefficient, T k is the remaining red light time, v is the speed data, and a is the acceleration data.

[0143] In an embodiment of the present invention, calculating the red light running discrimination coefficient according to the remaining red light time and the speed data and acceleration data included in the multi-modal driving data includes:

[0144] Performing an exponential operation according to the red light running discrimination coefficient and the historical violation times to obtain a red light running index term;

[0145] Performing a logarithmic operation according to the ratio of the preset rated speed limit data to the speed data and the acceleration data to obtain a speed ratio logarithmic term;

[0146] Performing an exponential operation according to the speed ratio logarithmic term and the historical violation times to obtain a historical violation weight term;

[0147] Multiply the acceleration data by a preset balance coefficient and then add it to the lateral displacement data included in the multimodal driving data to obtain an acceleration factor, calculate the ratio of the distance data to the acceleration factor to obtain a distance ratio term;

[0148] Perform an exponential operation on the distance ratio term with respect to the base of the preset natural logarithm to obtain a base exponential term;

[0149] Calculate the sum of the historical number of violations and the preset average number of violations to obtain a sum of violation times, and calculate the ratio of the historical number of violations to the sum of violation times to obtain a times ratio term;

[0150] Calculate the negative value after logarithmic operation of the times ratio term to obtain a times weight term;

[0151] Calculate the ratio of the sum of the red light running index term, the historical violation weight term, and the base exponential term to the times weight term to obtain the violation probability.

[0152] In the embodiment of the present invention, the calculation formula of the violation probability is as follows:

[0153]

[0154] Where G is the violation probability, K is the red light running discrimination coefficient, T is the historical number of violations, v0 is the rated speed limit data of the monitored section, v is the speed data included in the multimodal driving data, a is the acceleration data included in the multimodal driving data, W is the distance data, L is the lateral displacement data included in the multimodal driving data, ω2 is the preset balance coefficient, and T avg is the average number of violations obtained in advance.

[0155] S6. Determine whether the violation probability is greater than a preset probability threshold.

[0156] If the violation probability is less than or equal to the probability threshold, then execute S7. Select a new vehicle as the target vehicle, and return to S1. Obtain the multimodal driving parameters of the target vehicle in the monitored section.

[0157] In the embodiment of the present invention, when the violation probability is less than or equal to the probability threshold, it indicates that the target vehicle is not currently violating the regulations and has a low tendency to violate the regulations. Therefore, a new target vehicle can be selected for re-monitoring.

[0158] If the violation probability is greater than the probability threshold, then execute S8. Generate a warning voice according to the vehicle information of the target vehicle, and play the warning voice using the speaker.

[0159] However, when the probability of violation is greater than the probability threshold, it indicates that the target vehicle is not currently violating the regulations but has a high tendency to violate. Therefore, it is necessary to warn the target vehicle through voice.

[0160] In an embodiment of the present invention, generating a warning voice according to the vehicle information of the target vehicle may be to identify the license plate number, color, and model of the target vehicle, and generate a warning voice according to the color, model, and the last digit of the license plate number. For example, the warning voice may be "The owner of the white car with license plate number ending 9527, please pay attention to driving in a standard manner and do not violate the regulations." While warning the target vehicle, the privacy of the target vehicle is also protected.

[0161] In an embodiment of the present invention, when the probability of violation is greater than the probability threshold, by generating a warning voice according to the vehicle information of the target vehicle and playing the warning voice using the speaker, the probability of violation of the target vehicle is reduced, and traffic safety is improved.

[0162] The present invention obtains real-time motion parameters such as vehicle speed, acceleration, and driving direction through a millimeter-wave radar matrix, performs multi-dimensional violation detection based on the obtained parameters, gives a voice notification to the violating vehicle, calculates the probability of violation for the non-violating vehicle according to multi-modal driving parameters, and warns the vehicle with a probability of violation greater than the preset threshold, which can improve the efficiency of violation recognition and the efficiency of notification.

[0163] As Figure 2 shown, it is a functional module diagram of an urban traffic violation recognition and voice notification system provided by an embodiment of the present invention.

[0164] The urban traffic violation recognition and voice notification system 100 of the present invention can be installed in an electronic device. According to the implemented functions, the urban traffic violation recognition 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 modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0165] In this embodiment, the functions of each module / unit are as follows:

[0166] The data acquisition module 101 is used to acquire multi-modal driving parameters of a target vehicle in a monitored section;

[0167] 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;

[0168] The violation notice module 103 is used to determine whether the target vehicle has violated the regulations according to the violation detection result. If the target vehicle has violated the regulations, a violation notice voice is generated according to the violation detection result, and the violation notice voice is played through a preset speaker in the monitored section;

[0169] The violation prediction module 104 is used to determine whether the target vehicle has violated the regulations according to the violation detection result. If the target vehicle has not violated the regulations, the violation probability of the target vehicle is identified. The calculation formula of the violation probability is as follows:

[0170]

[0171] where G is the violation probability, K is the red light running discrimination coefficient, T is the historical number of violations, v0 is the rated speed limit data of the monitored section, v is the speed data included in the multimodal driving data, a is the acceleration data included in the multimodal driving data, W is the distance data, L is the lateral displacement data included in the multimodal driving data, ω2 is a preset balance coefficient, and T avg is the average number of violations obtained in advance;

[0172] 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 step of obtaining the multimodal driving parameters of the target vehicle in the monitored section is returned. 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 through the speaker.

[0173] Specifically, each module in the urban traffic violation recognition and voice notification system 100 in the embodiment of the present invention adopts the same technical means as those Figure 1 described in the above-mentioned urban traffic violation recognition and voice notification method, and can produce the same technical effects, which will not be elaborated here.

[0174] 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 merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0175] The module described as a separation component may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, in each embodiment of the present invention, the functional modules can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional modules.

[0177] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0178] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed by the present invention. Any reference signs in the claims should not be regarded as limiting the claimed elements.

[0179] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

[0180] In addition, obviously, the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system claims can also be implemented by one unit or system through software or hardware. The terms such as first and second are used to represent names and do not represent any specific order.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for identifying urban traffic violations and voice notification, characterized in that, The method includes: Obtaining multi-modal driving parameters of a target vehicle in a monitored section; Performing violation detection on the target vehicle according to the multi-modal driving parameters to obtain a violation detection result; Judging whether the target vehicle violates the regulations according to the violation detection result; If the target vehicle has violated the regulations, generating a violation notice voice according to the violation detection result, and playing the violation notice voice by using a preset speaker in the monitored section; If the target vehicle has not violated the regulations, identifying the violation probability of the target vehicle, where the calculation formula of the violation probability is as follows: Among them, G is the violation probability, K is the red-light running discrimination coefficient, T is the historical violation times, v0 is the rated speed limit data of the monitored section, v is the speed data included in the multimodal driving data, a is the acceleration data included in the multimodal driving data, W is the distance data, L is the lateral displacement data included in the multimodal driving data, ω2 is the preset balance coefficient, and T avg is the average violation times obtained in advance; Judging whether the violation probability is greater than a preset probability threshold; If the violation probability is less than or equal to the probability threshold, selecting a new vehicle as the target vehicle, and returning to the step of obtaining the multi-modal driving parameters of the target vehicle in the monitored section; If the violation probability is greater than the probability threshold, generating a warning voice according to the vehicle information of the target vehicle, and playing the warning voice by using the speaker.

2. The method for identifying urban traffic violations and voice notification according to claim 1, characterized in that, The performing violation detection on the target vehicle according to the multi-modal driving parameters to obtain a violation detection result includes: Obtaining image data within a preset time period of the monitored section; Performing road marking recognition according to the image data to obtain marking data; Obtaining the speed data and driving direction data included in the multi-modal driving parameters; Judging whether the target vehicle is driving in reverse according to the marking data and the driving direction data; If the target vehicle is driving in reverse, the violation detection result is that the vehicle is driving in reverse; If the target vehicle is not driving in reverse, performing red light running detection on the target vehicle to obtain a red light running detection result, performing speeding detection on the target vehicle according to the speed data to obtain a speeding detection result, and performing lane crossing driving detection on the target vehicle to obtain a lane crossing driving detection result; Summarizing the red light running detection result, the speeding detection result and the lane crossing driving detection result to obtain the violation detection result.

3. The urban traffic violation recognition and voice notification method according to claim 2, wherein, The performing 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 lamp image data; Obtaining the signal lamp digital data of the monitored section by using a traffic signal controller; Performing timestamp synchronization on the signal lamp image data and the signal lamp digital data based on a preset time delay to obtain synchronized signal lamp image data and synchronized signal lamp digital data; Performing dual signal verification on the synchronized signal lamp image data and the synchronized signal lamp digital data to obtain a verification result; Judging whether the synchronized signal lamp image data is synchronized with the synchronized signal lamp digital data according to the verification result; If the verification result judges that the synchronized signal lamp image data is not synchronized with the synchronized signal lamp digital data, after adjusting the preset time delay, returning to the step of performing timestamp synchronization on the signal lamp image data and the signal lamp digital data based on the preset time delay to obtain synchronized signal lamp image data and synchronized signal lamp digital data; If the verification result determines that the synchronized signal light image data is synchronized with the synchronized signal light digital data, then perform red light running detection based on the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result.

4. The urban traffic violation recognition and voice notification method according to claim 3, characterized in that, The performing red light running detection based on the vehicle trajectory and the synchronized signal light image data to obtain a red light running detection result includes: Obtain the stop line data included in the marking data; Confirm the red light time period according to the synchronized signal light image data; Confirm the vehicle trajectory during the red light according to the vehicle trajectory during the red light time; Judge whether the target vehicle crosses the stop line according to the vehicle trajectory during the red light and the stop line data; If the target vehicle crosses the stop line, the red light running detection result is that the vehicle runs a red light; If the target vehicle does not cross the stop line, the red light running detection result is that the vehicle does not run a red light.

5. The urban traffic violation recognition and voice notification method according to claim 3, wherein The identifying the violation probability 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 Perform red light running judgment according to the target driving direction confirmed by the synchronized signal light image data, and confirm the red light running discrimination coefficient according to the judgment result; Obtain the distance data between the target vehicle and the road solid line according to the marking data; Identify the license plate number of the target vehicle, and obtain the historical violation times of the target vehicle according to the license plate number; Calculate the violation probability according to the red light running discrimination coefficient, the distance data, the historical violation times, and the multi-modal driving data.

6. The method for identifying urban traffic violations and voice notification according to claim 5, characterized in that, The confirming the red light running discrimination coefficient according to the judgment result includes: Judge whether the target driving direction is in the red light state according to the judgment result; If the target driving direction is not in the red light state, confirm the red light running discrimination coefficient to be 0.1; If the target driving direction is in the red light state, obtain the remaining red light time of the target driving direction; Calculate the red light running discrimination coefficient according to the remaining red light time, the speed data and the acceleration data included in the multi-modal driving data.

7. The method for identifying urban traffic violations and voice notification according to claim 6, characterized in that, The calculation formula of the red light running discrimination coefficient is as follows: where K is the red-light running discrimination coefficient, ω1 is a preset proportionality coefficient, T k is the remaining red-light time, v is the speed data, and a is the acceleration data.

8. The urban traffic violation recognition and voice notification method according to claim 5, wherein The calculating the red light running discrimination coefficient according to the remaining red light time, the speed data and the acceleration data included in the multi-modal driving data includes: Perform exponential operation according to the red light running discrimination coefficient and the historical violation times to obtain a red light running index term; Perform logarithmic operation according to the ratio of the preset rated speed limit data to the speed data and the acceleration data to obtain a speed ratio logarithmic term; Perform exponential operation according to the speed ratio logarithmic term and the historical violation times to obtain a historical violation weight term; Multiply the acceleration data by a preset balance coefficient and add the lateral displacement data included in the multi-modal driving data to obtain an acceleration factor, calculate the ratio of the distance data to the acceleration factor to obtain a distance ratio term; Perform exponential operation on the distance ratio term to the base of the preset natural logarithm to obtain a base exponential term; Calculate the sum of the historical number of violations and the preset average number of violations to obtain the sum of the number of violations, and calculate the ratio of the historical number of violations to the sum of the number of violations to obtain the ratio term of the number of times; Calculate the opposite number after the logarithmic operation of the ratio term of the number of times to obtain the weight term of the number of times; Calculate the ratio of the sum of the red-light running index term, the historical violation weight term, and the base index term to the weight term of the number of times to obtain the violation probability.

9. An urban traffic violation recognition and voice notification system, characterized in that, The system includes: A data acquisition module for acquiring multi-modal driving parameters of a target vehicle in a monitored section; A violation detection module for detecting violations of the target vehicle according to the multi-modal driving parameters to obtain a violation detection result; A violation notification module for judging whether the target vehicle has violated the regulations according to the violation detection result. If the target vehicle has violated the regulations, a violation notification voice is generated according to the violation detection result, and the violation notification voice is played by using a preset loudspeaker in the monitored section; A violation prediction module for judging whether the target vehicle has violated the regulations according to the violation detection result. If the target vehicle has not violated the regulations, the violation probability of the target vehicle is identified. The calculation formula of the violation probability is as follows: Wherein, G is the violation probability, K is the red-light running discrimination coefficient, T is the historical violation times, v0 is the rated speed limit data of the monitored section, v is the speed data included in the multi-modal driving data, a is the acceleration data included in the multi-modal driving data, W is the distance data, L is the lateral displacement data included in the multi-modal driving data, ω2 is a preset balance coefficient, and T avg is the average violation times obtained in advance; A threshold judgment module for judging 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 step of acquiring the multi-modal driving parameters of the target vehicle in the monitored section is returned. 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 by using the loudspeaker.

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