Traffic event reporting method, device, equipment, medium and program product

Abstract

The embodiment of the invention provides a traffic incident reporting method and device, equipment, a medium and a program product, and can relate to the field of intelligent traffic, the field of automatic driving and the like, and the method comprises the steps: obtaining sensor data collected by N sensors for M vehicles on a target road section; based on sensor data collected by each sensor for the M vehicles on the target road section, position information of the M vehicles on the target road section under a map coordinate system is determined; determining to-be-merged position information of the same vehicle on the target road section based on the position information of the M vehicles on the target road section under the map coordinate system; merging the to-be-merged position information of the same vehicle on the target road section; based on the combined position information of the M vehicles on the target road section under the map coordinate system, determining a violation traffic event corresponding to the violation vehicle on the target road section; and a violation traffic event corresponding to the violation vehicle is reported. Therefore, the condition of repeated reporting can be avoided.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation, and more particularly to a method, apparatus, device, medium, and program product for reporting traffic incidents. Background Technology

[0002] The construction and transformation of intelligent and information-based transportation systems have been booming in recent years, and among them, the reporting of traffic violations is one of the most important business applications.

[0003] To enable the reporting of traffic violations, sensors such as radar and cameras, as well as mobile edge computing (MEC) devices, are currently installed on various road sections. The sensors in each road section collect sensor data from the vehicles on that road section and transmit it to the MEC device on that road section. Furthermore, the MEC device can analyze whether there are any traffic violations based on the acquired sensor data. If so, it reports the traffic violation to the business center.

[0004] However, there are currently road sections that are covered by multiple sensors simultaneously. These sensors can collect sensor data on vehicles on that road section, and this sensor data is transmitted to the MEC devices corresponding to each sensor. These MEC devices analyze the acquired sensor data to determine if there are any traffic violations. If so, they report the traffic violation, which may result in duplicate reporting of traffic violations. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and program product for reporting traffic incidents, which can avoid the repeated reporting of traffic violations.

[0006] In a first aspect, embodiments of this application provide a traffic incident reporting method, comprising: acquiring sensor data collected by N sensors for M vehicles on a target road segment; wherein N is an integer greater than 1 and M is a positive integer; for each of the N sensors, determining the location information of the M vehicles on the target road segment in a map coordinate system based on the sensor data collected by the sensor for the M vehicles on the target road segment; determining the merged location information of the same vehicle on the target road segment based on the location information of the M vehicles on the target road segment in the map coordinate system; merging the merged location information of the same vehicle on the target road segment to obtain merged location information of the M vehicles on the target road segment in the map coordinate system; determining the traffic violation incident corresponding to the violating vehicle on the target road segment based on the merged location information of the M vehicles on the target road segment in the map coordinate system; and reporting the traffic violation incident corresponding to the violating vehicle.

[0007] Secondly, embodiments of this application provide a traffic incident reporting device, comprising: an acquisition module, a determination module, a merging module, and a reporting module; wherein, the acquisition module is used to acquire sensor data collected by N sensors for M vehicles on a target road segment; wherein N is an integer greater than 1, and M is a positive integer; the determination module is used to determine the position information of the M vehicles on the target road segment in a map coordinate system based on the sensor data collected by the sensor for the M vehicles on the target road segment for each of the N sensors; the determination module is also used to determine the position information to be merged for the same vehicle on the target road segment based on the position information of the M vehicles on the target road segment in the map coordinate system; the merging module is used to merge the position information to be merged for the same vehicle on the target road segment to obtain the merged position information of the M vehicles on the target road segment in the map coordinate system; the determination module is also used to determine the traffic violation incident corresponding to the violating vehicle on the target road segment based on the merged position information of the M vehicles on the target road segment in the map coordinate system; and the reporting module is used to report the traffic violation incident corresponding to the violating vehicle.

[0008] In some possible implementations, the determining module is specifically used to: determine the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information; wherein the first location information and the second location information are sensor data converted from any two sensors, and are any two location information on the target road segment in the map coordinate system; and determine the location information to be merged for the same vehicle on the target road segment based on the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information.

[0009] In some implementation methods, the determining module is specifically used to: if the distance between the map coordinate point corresponding to the first location information and the map coordinate point corresponding to the second location information is less than a preset distance, then determine that the first location information and the second location information are the location information to be merged of the same vehicle on the target road segment.

[0010] In some implementation methods, the merging module is specifically used to: merge the location information of the same vehicle on the target road segment into one location information of the location information to be merged, so as to obtain the merged location information of M vehicles on the target road segment in the map coordinate system.

[0011] In some implementations, the determination module is specifically used to: use the K-means clustering algorithm to obtain K clusters based on the location information of M vehicles on the target road segment in the map coordinate system; and for each of the K clusters, determine the location information of all vehicles in the cluster as the location information to be merged for the same vehicle on the target road segment.

[0012] In some implementations, the merging module is specifically used to: determine the average position information of the same vehicle on the target road segment to be merged; and use the average position information of each of the M vehicles on the target road segment as the merged position information of the M vehicles in the map coordinate system.

[0013] In some implementations, the determination module is specifically used to: determine the lane type of M vehicles based on the merged location information of M vehicles on the target road segment in the map coordinate system; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles.

[0014] In some implementations, the determination module is specifically used to: if the lane type occupied by the first vehicle among M vehicles is the emergency lane type, then determine that the first vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the first vehicle is an application lane occupation event.

[0015] In some implementations, before the determining module determines the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles, the determining module is also used to: determine the speed of the M vehicles on the target road segment in the map coordinate system based on the merged position information of the M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged position information; correspondingly, the determining module is specifically used to: determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles and the speed of the M vehicles on the target road segment in the map coordinate system.

[0016] In some implementations, the determining module is specifically used to: determine the speed limit range corresponding to the lane type where the M vehicles are located; if the speed of the second vehicle among the M vehicles exceeds the corresponding speed limit range, then the second vehicle is determined to be a violating vehicle, and the traffic violation event corresponding to the second vehicle is determined to be a violation event that exceeds the speed limit range.

[0017] In some implementations, the determining module is specifically used to: determine the speed of the M vehicles on the target road segment in the map coordinate system based on the merged location information of the M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged location information; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the speed of the M vehicles on the target road segment in the map coordinate system.

[0018] In some implementations, the determination module is specifically used to: if the speed of the third vehicle among M vehicles is greater than a preset speed, then determine that the third vehicle is a violating vehicle, and determine the traffic violation event corresponding to the third vehicle as a speeding event.

[0019] In some implementations, the determination module is specifically used to: determine the traffic violation events corresponding to the violating vehicles on the target road segment based on the road segment type of the target road segment and the speeds of M vehicles on the target road segment in the map coordinate system.

[0020] In some implementations, the determination module is specifically used to: if the target road segment is a highway segment, and the speed of the fourth vehicle among the M vehicles is 0 in the map coordinate system, then determine that the fourth vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the fourth vehicle is an illegal parking event.

[0021] In some implementations, the determination module is specifically used to: determine the driving direction of each of the M vehicles based on the merged position information of the M vehicles on the target road segment in the map coordinate system; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the driving direction of each of the M vehicles and the direction of the target road segment.

[0022] In some implementations, the determination module is specifically used to: if the driving direction of the fifth vehicle among the M vehicles is inconsistent with the direction of the target road segment, then determine that the fifth vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the fifth vehicle is a wrong-way driving violation event.

[0023] Thirdly, an electronic device is provided, comprising: a processor and a memory for storing a computer program, the processor for calling and running the computer program stored in the memory, and performing the methods as described in the first aspect or its various implementations.

[0024] Fourthly, a computer-readable storage medium is provided for storing a computer program that causes a computer to perform the methods described in the first aspect or its various implementations.

[0025] Fifthly, a computer program product is provided, including computer program instructions that cause a computer to perform the methods as described in the first aspect or its various implementations.

[0026] Sixthly, a computer program is provided that causes a computer to perform the methods described in the first aspect or its various implementations.

[0027] The technical solution provided in this application allows an electronic device to merge sensor data converted from different sensors and the location information of the same vehicle, i.e., deduplication, and then report traffic violations based on the merged location information, thereby avoiding duplicate reporting. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of a system architecture according to an embodiment of this application;

[0030] Figure 2 This is a schematic diagram of another system architecture involved in an embodiment of this application;

[0031] Figure 3 This is a schematic diagram of another system architecture involved in the embodiments of this application;

[0032] Figure 4 A flowchart of a traffic incident reporting method provided in this application embodiment;

[0033] Figure 5 A schematic diagram of a target road segment provided in an embodiment of this application;

[0034] Figure 6 This is a schematic diagram of image and map joint calibration provided in the embodiments of this application;

[0035] Figure 7 This is a schematic diagram of radar and map joint calibration provided in an embodiment of this application;

[0036] Figure 8 This is a schematic diagram of the vehicle's driving trajectory;

[0037] Figure 9 This application provides a schematic diagram of a traffic incident reporting device 900.

[0038] Figure 10 This is a schematic block diagram of the electronic device 1000 provided in the embodiments of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0041] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0042] The embodiments of this application may relate to the fields of intelligent transportation, autonomous driving, etc., but are not limited thereto.

[0043] Intelligent Traffic Systems (ITS), also known as Intelligent Transportation Systems, effectively integrate advanced technologies (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) into transportation, service control, and vehicle manufacturing. This strengthens the connection between vehicles, roads, and users, forming a comprehensive transportation system that ensures safety, improves efficiency, enhances the environment, and conserves energy. In this embodiment, sensor technology is primarily used to collect sensor data, which can be converted into vehicle location information in a map coordinate system. Computer technology is then used to merge sensor data converted from different sensors, and the location information of the same vehicle is merged (i.e., deduplication). Finally, traffic violations are reported based on the merged location information.

[0044] Autonomous driving is a rapidly developing technological field that aims to enable vehicles to drive autonomously without human intervention through advanced sensors, algorithms, and control systems. The fundamental principle of autonomous driving is to control the vehicle's movement based on its operating status using a series of sensors and electronic devices. These sensors include LiDAR, millimeter-wave radar, and cameras, which can accurately identify the vehicle's surroundings, autonomously avoid obstacles, and perform automatic steering.

[0045] The four key technologies in the field of autonomous driving include environmental perception and sensor fusion, intelligent connectivity (V2X), high-precision maps, and human-machine interaction (HMI). Environmental perception is the foundation of autonomous driving, collecting information about the vehicle's surroundings through various sensors to provide accurate and real-time data for the autonomous driving system. V2X technology enables intelligent interconnection between vehicles, roads, people, and networks. High-precision maps provide accurate positioning and navigation services for autonomous driving, containing rich road element data. HMI technology plays a crucial role in autonomous driving, ensuring effective communication between people and vehicles.

[0046] Autonomous driving levels are divided into six levels, ranging from zero to full automation. These levels are classified based on the degree of control the autonomous driving system has over the driving task and the level of driver involvement, as detailed below:

[0047] Level 0 autonomous driving (no automation): At Level 0, the driver must have full control over all driving tasks, including the operation of the accelerator, brake and steering wheel.

[0048] Level 1 autonomous driving (driving assistance): At this level, the system can assist the driver in certain driving tasks, but the driver still needs to bear the primary responsibility for driving.

[0049] Level 2 Automated Driving (Partial Automation): At Level 2, the automated driving system can automatically perform certain driving tasks, such as lane keeping or adaptive cruise control. However, the driver still needs to remain focused on the road conditions and be prepared to take over driving at any time.

[0050] Level 3 Automated Driving (Conditional Automation): At Level 3, the automated driving system can automatically control the vehicle under specific environmental and conditions. At this level, the driver does not need to directly participate in driving, but still needs to monitor the vehicle's operation and take over control when necessary.

[0051] Level 4 Automated Driving (High Automation): Level 4 automated driving systems can control the vehicle completely autonomously and complete all driving tasks without driver intervention. However, this level usually still requires the retention of interfaces such as a steering wheel so that the driver can take over control when needed.

[0052] Level 5 Autonomous Driving (Fully Automated): At Level 5, the autonomous driving system can fully control the vehicle in any environment and under any conditions, without any driver intervention. This level represents the highest level of autonomous driving technology, enabling the vehicle to drive autonomously in all weather conditions and locations, adapting to various environmental, climatic, and geographical changes.

[0053] The relevant knowledge involved in this application will be explained below:

[0054] I. Bird's Eye View (BEV), also known as a bird's-eye view or aerial view, is an image of a scene viewed from an aerial perspective, presented from a bird's-eye viewpoint. This perspective allows us to gain a wider field of vision and a more comprehensive understanding of the geographical location, topographical structure, and building layout of the observed area. In the field of autonomous driving, BEV technology is a vehicle environmental perception technology that provides a more comprehensive scene perception by projecting information about the vehicle's surroundings onto an aerial view. This technology uses multiple cameras to capture environmental information and convert it into a bird's-eye view, thereby assisting autonomous driving and safe driving systems.

[0055] II. Sensor coordinate system is a three-dimensional coordinate system centered on the sensor. For example, the camera coordinate system is a three-dimensional Cartesian coordinate system established with the camera's focal center as the origin and the optical axis as the Z-axis. The X and Y axes are perpendicular to the camera's optical axis, and the intersection of the X-axis and the camera's optical axis is located at the center of the camera's imaging plane. Another example is the radar coordinate system, which is typically a right-handed coordinate system. The X-axis usually points forward towards the radar; when the laser beam is emitted directly forward, the distance measurement in that direction will produce a positive value on the X-axis. The Y-axis usually points to the left of the radar; when the laser beam is emitted directly to the left, the distance measurement in that direction will produce a positive value on the Y-axis. The Z-axis usually points upward towards the radar and is perpendicular to the X and Y axes. Height measurements are usually taken along the Z-axis; a positive value indicates that the object is above the radar equipment, while a negative value indicates that the object is below the radar equipment.

[0056] III. Map coordinate systems are used to describe the location of any point on Earth, allowing that point to be accurately located on a map. Map coordinate systems are typically established based on a specific reference surface and projection method; different map coordinate systems may use different parameters and transformation methods.

[0057] IV. The image coordinate system describes the position of each point in an image. It typically consists of mathematical coordinates in two directions: the x-coordinate and the y-coordinate. The origin of the coordinate system is usually located at the center of the image. The x-coordinate represents the horizontal position of the point, and the y-coordinate represents the vertical position of the point. In image processing and analysis, this coordinate system is the fundamental reference frame.

[0058] V. Pixel coordinates, which use pixels as the unit of measurement in an image, are typically used for discrete image processing and analysis. Its origin is usually set at the top left corner of the image, and the u and v axes are parallel to the x and y axes of the image coordinate system, respectively. Unlike the image coordinate system, the coordinate values ​​in the pixel coordinate system directly correspond to the pixel positions in the image.

[0059] The technical problems to be solved, the inventive concept and the system architecture of the embodiments of this application will be described below:

[0060] As mentioned above, there are currently road sections that are repeatedly covered by multiple sensors. These sensors can collect sensor data on vehicles on the road section, and this sensor data is transmitted to the MEC devices corresponding to each sensor. These MEC devices analyze whether there are traffic violations based on the acquired sensor data. If there are, they report the traffic violation, which may lead to duplicate reporting of traffic violations.

[0061] To solve the above technical problems, an electronic device merges sensor data converted from different sensors and the location information of the same vehicle, i.e., deduplication, and then reports traffic violations based on the merged location information, thereby avoiding duplicate reporting.

[0062] In some possible implementations, the system architecture of embodiments of this application is as follows: Figure 1 As shown.

[0063] Figure 1 This is a schematic diagram of a system architecture according to an embodiment of this application. The system architecture includes: sensors 110 on each road segment and MEC devices 120 on each road segment.

[0064] The number of sensors 110 on each road segment can be one or more, and the sensor type on each road segment can include at least one of radar, camera, and video camera types. This application embodiment does not limit the number of sensors or the sensor type on each road segment.

[0065] It should be understood that each sensor 110 can be used to collect sensor data of vehicles on its covered road segment.

[0066] In this application, the sensor 110 on each road segment and the MEC device 120 on that road segment can be directly or indirectly connected via wired or wireless communication.

[0067] The MEC device 120 on each road segment can obtain sensor data from the sensor 110 on that road segment.

[0068] The MEC devices 120 on two adjacent road segments can be directly or indirectly connected via wired or wireless communication. One of the MEC devices 120 is used to execute the traffic event reporting method provided in the embodiments of this application, and it can obtain sensor data collected by the sensor corresponding to the other MEC device 120.

[0069] In some possible implementations, embodiments of this application may default to one of the MEC devices 120 on two adjacent road segments being used to execute the traffic incident reporting method provided in embodiments of this application.

[0070] In some other possible implementations, MEC devices 120 on two adjacent road segments can negotiate to determine the MEC device 120 used to perform the traffic incident reporting method provided in the embodiments of this application.

[0071] It should be understood that MEC devices can also be understood as industrial control computers.

[0072] In some possible implementations, the system architecture of embodiments of this application is as follows: Figure 2 As shown.

[0073] Figure 2 This is a schematic diagram of another system architecture involved in the embodiments of this application. The system architecture includes: sensors 210 on each road segment and MEC devices 220 on each road segment, and also includes: electronic devices 230 for executing the traffic incident reporting method provided in the embodiments of this application.

[0074] The number of sensors 210 on each road segment can be one or more, and the sensor type on each road segment can include at least one of radar, camera, and video camera types. This application embodiment does not limit the number of sensors or the sensor type on each road segment.

[0075] It should be understood that each sensor 110 can be used to collect sensor data of vehicles on its covered road segment.

[0076] The sensors 210 on each road segment and the MEC devices 220 on that road segment can be directly or indirectly connected via wired or wireless communication, and this application does not impose any restrictions on this.

[0077] The MEC device 120 on each road segment can obtain sensor data from the sensor 110 on that road segment.

[0078] The MEC devices 220 and electronic devices 230 on each road segment can be directly or indirectly connected via wired or wireless communication, and this application does not impose any restrictions on this.

[0079] Electronic device 230 can acquire sensor data from MEC device 120 on each road segment.

[0080] In some possible implementations, electronic device 230 can be a traditional device or a cloud device, but is not limited to these. For example, electronic device 230 can be a terminal device or a server.

[0081] In some possible implementations, the terminal device can be a smartphone, tablet, smartwatch, virtual reality (VR) device, augmented reality (AR) device, etc., but is not limited to these.

[0082] In some implementations, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, 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 communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0083] In some possible implementations, the system architecture of embodiments of this application is as follows: Figure 3 As shown.

[0084] Figure 3 This is a schematic diagram of another system architecture involved in the embodiments of this application. The system architecture includes: sensors 310 on each road segment, and also includes: electronic equipment 320 for performing the traffic incident reporting method provided in the embodiments of this application.

[0085] The number of sensors 310 on each road segment can be one or more, and the sensor type can include at least one of radar, camera, and video camera types. This application embodiment does not limit the number of sensors or the sensor type on each road segment.

[0086] It should be understood that each sensor 310 can be used to collect sensor data of vehicles on its covered road segment.

[0087] The sensors 310 and electronic devices 320 on each road segment can be directly or indirectly connected via wired or wireless communication, and this application does not impose any restrictions on this.

[0088] Among them, electronic device 320 can acquire sensor data from sensor 310 on each road segment.

[0089] In some possible implementations, electronic device 320 can be a traditional device or a cloud device, but is not limited to these. For example, electronic device 320 can be a terminal device or a server.

[0090] In some possible implementations, the terminal device can be a smartphone, tablet, smartwatch, VR device, AR device, etc., but is not limited to these.

[0091] In some implementations, the server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, 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 communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0092] It should be noted that, Figure 1 , Figure 2 and Figure 3 This is merely a schematic diagram of a system architecture provided in this application embodiment; the system architecture involved in this application embodiment is not limited to... Figure 1 , Figure 2 and Figure 3 The system architecture is illustrated. For example, MEC devices can be installed on all road segments except the first one, but not on the first one. Based on this, the MEC device on the latter of two adjacent road segments can acquire sensor data collected by sensors on those two road segments, and then execute the traffic event reporting method provided in this embodiment based on that sensor data. As another example, MEC devices can be installed on all road segments except the last one, but not on the last one. Based on this, the MEC device on the former of two adjacent road segments can acquire sensor data collected by sensors on those two road segments, and then execute the traffic event reporting method provided in this embodiment based on that sensor data.

[0093] The embodiments of this application will be described in detail below:

[0094] Figure 4 A flowchart illustrating a traffic incident reporting method provided in this application embodiment. This method can be executed by an electronic device, for example, the electronic device may be... Figure 1One of the MEC devices 120, or it can be Figure 2 The electronic device 230 in the middle can also be Figure 3 The electronic device 320 in this embodiment is not limited thereto. Figure 4 As shown, the method may include:

[0095] S410: Obtain sensor data collected by N sensors for M vehicles on the target road segment; where N is an integer greater than 1 and M is a positive integer;

[0096] It should be understood that the target road segment refers to the road segment simultaneously covered by the N sensors. For example, Figure 5 This is a schematic diagram of a target road segment provided in an embodiment of this application. The road segment is the intersection of the acquisition ranges of a sensor on the left and a sensor on the right.

[0097] It should be understood that the M vehicles can be all vehicles on the target road segment. For example, Figure 5 The target road segment shown includes one vehicle.

[0098] In some implementations, the sensor types of the N sensors may include at least one of radar, camera, and video camera types; this application embodiment does not limit this. The radar may be millimeter-wave radar, lidar, microwave radar, etc., and this application embodiment does not limit this as well.

[0099] In some implementations, for a radar, the sensor data collected from M vehicles on a target road segment includes: the position, velocity, and angle information of each vehicle in the radar coordinate system, where the radar coordinate system is a three-dimensional coordinate system with the center point of the radar as the origin. For example, for a millimeter-wave radar, the sensor data collected from a vehicle includes its position information (x_radar, y_radar, z_radar), velocity (v_radar), and angle (yaw_radar) in the radar coordinate system.

[0100] In some implementations, for a visual sensor such as a camera or webcam, the sensor data collected for M vehicles on a target road segment includes: images collected by the visual sensor, wherein each frame of the image collected by the visual sensor may include one or more vehicle targets.

[0101] In some implementations, the image acquired by the vision sensor can be in any of the following modes, but not limited to: Red Green Blue (RGB) mode and grayscale mode.

[0102] In some implementations, the images acquired by the vision sensor can be in any of the following formats, but not limited to: Joint Photographic Experts Group (JPEG) format, Graphics Interchange Format (GIF), Tagged Image File Format (TIFF), and Portable Network Graphics (PNG).

[0103] In some implementations, the image acquired by the vision sensor is an image in a pixel coordinate system, where the pixel coordinate system is a two-dimensional UV coordinate system of the image, and the origin of the pixel coordinate system is the top-left pixel of the image.

[0104] The following is an example illustrating how sensor data is acquired:

[0105] by Figure 1 Taking the system architecture shown as an example, assuming that the MEC device 120 used to execute the traffic event reporting method provided in the embodiments of this application is called the first MEC device, and the other MEC devices 120 are called the second MEC devices, then for the road segment under the responsibility of the first MEC device, the first MEC device can directly obtain the sensor data collected by the sensor 110 on that road segment, and for the road segment under the responsibility of the second MEC device, the second MEC device can directly obtain the sensor data collected by the sensor 110 on that road segment and send the sensor data to the first MEC device.

[0106] by Figure 2 Taking the system architecture shown as an example, for each road segment under the responsibility of MEC device 220, the MEC device 220 can directly obtain the sensor data collected by the sensor 210 on that road segment and send the sensor data to the electronic device 230.

[0107] by Figure 3 Taking the system architecture shown as an example, the electronic device 320 can directly acquire the sensor data collected by each sensor 310 on each road segment.

[0108] S420: For each of the N sensors, based on the sensor data collected by that sensor for the M vehicles on the target road segment, determine the position information of the M vehicles on the target road segment in the map coordinate system;

[0109] It should be understood that for each of the N sensors, the sensor data collected is in that sensor's coordinate system. The electronic device needs to convert this sensor data in its own coordinate system into location information in the map coordinate system. Specifically, during this conversion, if the sensor data in that coordinate system is target-level data, the electronic device can convert it directly into location information in the map coordinate system. If the sensor data in that coordinate system is not target-level data, the electronic device first needs to convert it to target-level data, and then convert it back to location information in the map coordinate system.

[0110] It should be understood that target-level data refers to data used to describe vehicle targets. For example, radar-collected sensor data includes the vehicle's position, speed, and angle information in the radar coordinate system, which can be used to describe vehicle targets; therefore, radar-collected sensor data is target-level data. On the other hand, images collected by vision sensors cannot describe vehicle targets; therefore, images collected by vision sensors are not target-level data.

[0111] In some possible implementations, for each of the N sensors, if the sensor data collected by that sensor is not target-level data, the electronic device converts the sensor data in the sensor coordinate system into target-level data. This includes: the electronic device can input the sensor data in the sensor coordinate system into the target model to output target-level data.

[0112] For example, for an image captured by a camera, an electronic device can input it into a You Only Look Once-Smallversion (YOLO-S) network, and the output is the object recognition result bounding box in the image coordinate system, including: x_image, y_image, w_image, h_image, where (x_image, y_image) represents the top left corner of the object bounding box, w_image represents the width of the object bounding box, and h_image represents the height of the object bounding box.

[0113] It should be understood that the YOLO-S network is a lightweight, accurate YOLO-like network for small object detection. It utilizes a small feature extractor and promotes feature reuse across the network through bypasses, cascaded skip connections, and a reshaped pass-through layer, combining low-level location information with more meaningful high-level information. YOLO-S's simplicity, speed, and efficiency give it a significant advantage in handling such tasks. Furthermore, YOLO-S inherits some fundamental advantages of the YOLO series of algorithms. For example, it is fast, requiring only one forward pass to complete the detection of the entire image, enabling real-time processing of video stream data; it is accurate, employing global and cross-loss functions to perform detection on feature maps of different scales, simultaneously predicting the object's category and location, thus improving detection accuracy; and it performs well in object detection, capable of detecting objects of various sizes, shapes, and rotation angles, and maintaining good detection performance even in complex backgrounds.

[0114] It should be noted that the embodiments of this application do not limit the conversion method for converting sensor data into target-level data.

[0115] It should be understood that, in this embodiment of the application, the map coordinate system is also referred to as the BEV map coordinate system, and the map data under this map coordinate system is a vector diagram composed of strings of points in the BEV space. Each marking line on the road, such as lane lines and boundary lines, is composed of strings of points with coordinate positions and directions.

[0116] It should be understood that the reason why electronic devices convert target-level data from or converted from different sensors into location information in a map coordinate system is that electronic devices need to uniformly map these target data into the BEV space, thereby merging the location information of the same vehicle, i.e. deduplication, and then reporting traffic violations based on the merged location information.

[0117] The following is an example illustrating how to convert target-level data into location information in a map coordinate system:

[0118] For example, the images captured by a camera are not target-level data. Therefore, an electronic device can first input this image into a YOLO-S network to obtain object recognition results. These results are in the image coordinate system. Thus, the electronic device needs to obtain the translation and rotation parameters for transforming from the image coordinate system to the map coordinate system, or in other words, the translation and rotation parameters for transforming from the image coordinate system to the camera coordinate system, and vice versa. Based on this, the electronic device can project the camera coordinate system onto the map coordinate system, achieving projection calibration from the camera coordinate system to the map coordinate system. This projection calibration is achieved by adjusting the translation and rotation parameters in the calibration matrices of both systems, so that the map coordinate system... Figure 3 A string of points can be accurately projected onto lane lines on the image plane, such as... Figure 6 As shown, the white dot string represents the ground. Figure 3 A series of points, which can be projected accurately onto lane lines on the image plane. Figure 6 The x-axis, y-axis, and z-axis offsets on the right are translation parameters involved in the projection calibration from the camera coordinate system to the map coordinate system. The pitch, roll, and yaw angles are rotation parameters involved in the projection calibration from the camera coordinate system to the map coordinate system. The projection calibration from the image coordinate system to the camera coordinate system can be achieved based on camera intrinsic parameters. Finally, after the electronic device obtains the translation and rotation parameters from the image coordinate system to the camera coordinate system, and from the camera coordinate system to the map coordinate system, it can first use the translation and rotation parameters from the image coordinate system to the camera coordinate system to convert the object recognition results to the camera coordinate system, and then use the translation and rotation parameters from the camera coordinate system to the map coordinate system to convert the object recognition results in the camera coordinate system to position information in the map coordinate system.

[0119] For example, radar collects target-level data in its own coordinate system. Therefore, electronic equipment needs to acquire the translation and rotation parameters for transforming the radar coordinate system to the map coordinate system. Based on this, the electronic equipment can project the radar coordinate system onto the map coordinate system, achieving projection calibration from the radar coordinate system to the map coordinate system. This projection calibration adjusts the translation and rotation parameters in the calibration matrices of both systems, ensuring that the target-level data output by the radar fits well within the map's lane markings, thus preventing collisions with guardrails, abnormal traffic flow, and other issues. Figure 7 As shown, white dots represent target-level data or target points output by the radar, while lines represent lane lines, guardrails, etc. Figure 7The x-axis, y-axis, and z-axis offsets on the right are translation parameters involved in the projection calibration from the radar coordinate system to the map coordinate system. The pitch, roll, and yaw angles are rotation parameters involved in the projection calibration from the radar coordinate system to the map coordinate system. Finally, after the electronic equipment acquires the translation and rotation parameters from the radar coordinate system to the map coordinate system, it can use these parameters to convert the target-level data in the radar coordinate system into position information in the map coordinate system.

[0120] S430: Based on the position information of M vehicles on the target road segment in the map coordinate system, determine the position information of the same vehicle on the target road segment to be merged; and merge the position information of the same vehicle on the target road segment to obtain the merged position information of M vehicles on the target road segment in the map coordinate system.

[0121] It should be understood that when multiple sensors simultaneously observe the same vehicle in physical space, after BEV projection, the same vehicle target will appear as multiple observed targets in the BEV map space. Therefore, deduplication of the BEV space is necessary. In other words, the electronic equipment needs to determine the merged location information of the same vehicle on the target road segment and merge the merged location information of the same vehicle on the target road segment.

[0122] It should be understood that location information of the same vehicle to be merged is considered duplicate location information of that vehicle. Therefore, it is necessary to merge the location information to achieve the purpose of deduplication. For example, Figure 8 A schematic diagram of the vehicle's driving trajectory, such as Figure 8 As shown, white dots constitute one driving trajectory of a vehicle, and black dots constitute another driving trajectory of the same vehicle. These two driving trajectories are generated from sensor data collected by two sensors. In order to avoid duplicate reporting of traffic violations, it is necessary to ensure the uniqueness of the vehicle's driving trajectory. Based on this, the location information corresponding to the nearest black and white dot pair is the location information to be merged.

[0123] The method for determining the location information of the same vehicle to be merged is explained below:

[0124] In some possible implementations, the electronic device can determine the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information; and based on the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information, determine the location information to be merged for the same vehicle on the target road segment; wherein the first location information and the second location information are sensor data converted from any two sensors, and are any two location information on the target road segment in the map coordinate system.

[0125] It should be understood that the reason why the distance between the map coordinates corresponding to any two location information can be used to determine whether two locations are to be merged is as follows: For the same vehicle, location information obtained from sensor data collected by different sensors at the same time is very close, which means the distance between the map coordinates corresponding to these two locations is very close. However, for different vehicles, location information obtained from sensor data collected by different sensors at the same time differs significantly, which means the distance between the map coordinates corresponding to these two locations is far. Similarly, for the same vehicle, location information obtained from sensor data collected by the same sensor at different times differs significantly, which means the distance between the map coordinates corresponding to these two locations is far. In other words, for any two location information, the following situations exist:

[0126] Scenario 1: These two location information are obtained from sensor data collected by different sensors on the same vehicle at the same time.

[0127] Scenario 2: These two location information are obtained from sensor data collected by different vehicles based on different sensors at the same time.

[0128] Scenario 3: These two location information are obtained from sensor data collected by the same vehicle at different times based on the same sensor.

[0129] Scenario 4: These two location information are obtained from sensor data collected by different sensors at different times by the same vehicle.

[0130] Scenario 5: These two location information are obtained from sensor data collected by different vehicles based on different sensors at different times.

[0131] The purpose of this application embodiment is to find two location information that meet condition one, and to determine these two location information as the location information to be merged.

[0132] In some possible implementations, the location information to be merged for the same vehicle on the target road segment is determined based on the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information. This includes: if the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information is less than a preset distance, the electronic device determines that the first location information and the second location information are location information to be merged for the same vehicle on the target road segment.

[0133] In some possible implementations, the preset distance can be 0.1 meters, 0.2 meters, 0.3 meters, 0.4 meters, 0.5 meters, etc., and this application embodiment does not limit this.

[0134] In some feasible methods, for the case where the location information of the same vehicle on the target road segment to be merged is determined based on distance, the location information of the same vehicle on the target road segment to be merged is merged to obtain the merged location information of M vehicles on the target road segment in the map coordinate system. This includes: electronic devices can merge the location information of the same vehicle on the target road segment to be merged into one location information to be merged, thereby obtaining the merged location information of M vehicles on the target road segment in the map coordinate system.

[0135] For example, suppose a vehicle's location information to be merged includes: first location information and second location information. Then, the electronic device can use either the first location information or the second location information as the merged location information of the vehicle in the map coordinate system.

[0136] In some feasible implementations, for cases where the location information of the same vehicle on the target road segment to be merged is determined based on distance, the location information of the same vehicle on the target road segment to be merged is merged to obtain the merged location information of M vehicles on the target road segment in the map coordinate system. This includes: the average location information of the location information of the same vehicle on the target road segment to be merged is determined by electronic devices; and the average location information of each of the M vehicles on the target road segment is used as the merged location information of the M vehicles in the map coordinate system.

[0137] For example, suppose the location information to be merged for a vehicle includes: first location information (x1, y1, z1) and second location information (x2, y2, z2). Then the electronic device can use the location information ((x1+x2) / 2, (y1+y2) / 2, (z1+z2) / 2) as the merged location information of the vehicle in the map coordinate system.

[0138] It should be noted that when calculating average information, electronic devices may use either geometric mean or arithmetic mean, and this application embodiment does not impose any restrictions on this.

[0139] It should be noted that the embodiments of this application are for the case of determining the location information of the same vehicle on the target road segment based on distance, and do not limit the merging method of the location information to be merged.

[0140] In some feasible implementations, based on the location information of M vehicles on the target road segment in the map coordinate system, the location information of the same vehicle on the target road segment to be merged is determined, including: the electronic device uses the K-means clustering algorithm to obtain K clusters based on the location information of the M vehicles on the target road segment in the map coordinate system; for each of the K clusters, the location information of all vehicles in the cluster is determined as the location information of the same vehicle on the target road segment to be merged.

[0141] The specific steps of K-means clustering are as follows:

[0142] S1: Initialize K cluster centers. You can randomly select K map coordinate points corresponding to location information as cluster centers.

[0143] S2: For each location information corresponding to a coordinate point, calculate its distance to the K cluster centers and assign it to the cluster containing the nearest cluster center.

[0144] S3: For each cluster, recalculate its cluster center, that is, take the average of the location information of all map coordinate points in the cluster as the new cluster center.

[0145] Repeat steps 2 and 3 until the cluster centers no longer change or the preset number of iterations is reached.

[0146] In some feasible implementations, for cases where the merged location information of the same vehicle on a target road segment is determined based on the K-means clustering algorithm, the merged location information of the same vehicle on the target road segment is combined to obtain the merged location information of M vehicles on the target road segment in the map coordinate system. This includes: the average location information of the merged location information of the same vehicle on the target road segment determined by electronic devices; and using the average location information of each of the M vehicles on the target road segment as the merged location information of the M vehicles in the map coordinate system. It should be understood that, in this case, the average location information is also the location information of the cluster center of a cluster.

[0147] It should be noted that the embodiments of this application are for the case of determining the location information to be merged of the same vehicle on the target road segment based on the K-means clustering algorithm, and do not restrict the merging method of the location information to be merged.

[0148] S440: Based on the merged location information of M vehicles on the target road segment in the map coordinate system, determine the traffic violation incidents corresponding to the violating vehicles on the target road segment; and report the traffic violation incidents corresponding to the violating vehicles.

[0149] In some feasible implementations, based on the merged location information of M vehicles on the target road segment in the map coordinate system, the traffic violation event corresponding to the violating vehicle on the target road segment is determined, including: electronic devices can determine the lane type of the M vehicles based on the merged location information of the M vehicles on the target road segment in the map coordinate system; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles.

[0150] In some possible implementations, for each of the M vehicles, the electronic device can read map information using the merged location information of that vehicle in the map coordinate system to obtain the lane type of that vehicle.

[0151] In some feasible implementations, the types of lanes included may differ depending on the road segment type. For highway segments, the lane types include: main roads, ramps, and auxiliary lanes. Main roads include: overtaking lanes, fast lanes, and slow lanes. Ramps include: interchange ramps, acceleration lanes, deceleration lanes, approach ramps, collector / distributor ramps, and turning ramps. Auxiliary lanes include: emergency lanes, U-turn lanes, climbing lanes, escape lanes, and cooling pool lanes.

[0152] In some possible implementations, based on the lane types of the M vehicles, the traffic violation event corresponding to the violating vehicle on the target road segment is determined, including: if the first vehicle among the M vehicles is in the emergency lane, the electronic device determines that the first vehicle is a violating vehicle, and determines that the traffic violation event corresponding to the first vehicle is an event of occupying the application lane.

[0153] In some possible implementations, before determining the traffic violation event corresponding to the offending vehicle on the target road segment based on the lane type of the M vehicles, the electronic device further includes: determining the speed of the M vehicles on the target road segment in the map coordinate system based on the merged location information of the M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged location information; correspondingly, the electronic device can determine the traffic violation event corresponding to the offending vehicle on the target road segment based on the lane type of the M vehicles and the speed of the M vehicles on the target road segment in the map coordinate system.

[0154] In some feasible implementations, if the merged location information is one of the two location information to be merged, then the time information corresponding to the merged location information can be the generation time of the sensor data corresponding to that location information to be merged.

[0155] In some feasible implementations, if the average location information of the two location information to be merged is the merged location information, then the time information corresponding to the merged location information can be the average of the generation times of the sensor data corresponding to the two location information to be merged.

[0156] It should be understood that the embodiments of this application do not limit the calculation method of the time information corresponding to the merged location information.

[0157] The following explains how the speed is calculated for each of the M vehicles:

[0158] In some possible implementations, for each of the M vehicles, the electronic device can determine the distance between the map coordinates corresponding to any two merged location information of the vehicle, and determine the duration between the time information corresponding to the two merged location information. Furthermore, the electronic device can calculate the ratio of the distance to the duration to obtain the speed of the vehicle.

[0159] In some feasible implementations, based on the lane types of the M vehicles and the speeds of the M vehicles on the target road segment in the map coordinate system, the traffic violation events corresponding to the violating vehicles on the target road segment are determined, including: electronic devices determining the speed limit range corresponding to the lane types of the M vehicles; if the speed of the second vehicle among the M vehicles exceeds the corresponding speed limit range, then the second vehicle is determined to be a violating vehicle, and the traffic violation event corresponding to the second vehicle is determined to be a violation event exceeding the speed limit range.

[0160] In some feasible implementations, this speed limit range includes a lower speed limit and / or a higher speed limit. Based on this, a second vehicle exceeding the corresponding speed limit range falls into three categories:

[0161] Scenario 1: If the speed limit range includes the lower speed limit, then the second vehicle's speed exceeding the corresponding speed limit range means that the second vehicle's speed is less than the lower speed limit.

[0162] Scenario 2: If the speed limit range includes the upper speed limit, then the second vehicle's speed exceeding the corresponding speed limit range means that the second vehicle's speed is greater than the upper speed limit.

[0163] Scenario 3: If the speed limit range includes a lower speed limit and a higher speed limit, then the second vehicle's speed exceeding the corresponding speed limit range means that the second vehicle's speed is less than the lower speed limit, or that the second vehicle's speed is greater than the higher speed limit.

[0164] In some feasible implementations, based on the lane types of the M vehicles and the speeds of the M vehicles on the target road segment in the map coordinate system, the traffic violation events corresponding to the violating vehicles on the target road segment are determined. This includes: electronic devices determining the speed limit range and speed limit time corresponding to the lane types of the M vehicles; if the speed of one of the M vehicles exceeds the speed limit range corresponding to the speed limit time, then the vehicle is determined to be a violating vehicle, and the traffic violation event corresponding to the vehicle is determined to be a violation event exceeding the speed limit range.

[0165] It should be understood that the explanation of the speed limit range can be found above, and will not be repeated in this embodiment. The difference between this implementation method and the previous implementation method is that in this implementation method, the speed limit range is considered to be related to the speed limit time, while in the previous implementation method, the speed limit range is considered to be unrelated to the speed limit time.

[0166] For example, suppose the speed limit for the fast lane on a highway is 60 km / h to 100 km / h during the morning rush hour (8:00-9:30) and the evening rush hour (17:00-19:00). If a vehicle is traveling at 70 km / h in the fast lane at 9:00 AM, then the vehicle is not in violation. However, suppose the speed limit for the fast lane on the same highway is 90 km / h to 100 km / h during other times outside of the morning and evening rush hours. If a vehicle is traveling at 70 km / h in the fast lane at 10:00 AM, then the vehicle is in violation, and the corresponding traffic violation is exceeding the speed limit.

[0167] In some feasible implementations, based on the merged location information of M vehicles on the target road segment in the map coordinate system, the traffic violation event corresponding to the violating vehicle on the target road segment is determined. This includes: electronic devices determining the speed of the M vehicles on the target road segment in the map coordinate system based on the merged location information of the M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged location information; and determining the traffic violation event corresponding to the violating vehicle on the target road segment based on the speed of the M vehicles on the target road segment in the map coordinate system.

[0168] It should be understood that the method for calculating vehicle speed can be referred to above, and the embodiments of this application do not limit it in this way.

[0169] In some feasible implementations, based on the speeds of M vehicles on the target road segment in the map coordinate system, the traffic violation events corresponding to the violating vehicles on the target road segment are determined, including: if the speed of the third vehicle among the M vehicles is greater than a preset speed, the electronic device determines that the second vehicle is a violating vehicle, and determines that the traffic violation event corresponding to the third vehicle is a speeding event.

[0170] In some possible implementations, the preset speed can be 80km / h, 90km / h, 1000km / h, 110km / h, 120km / h, etc., and the embodiments of this application do not limit this.

[0171] In some feasible implementations, traffic violations are determined based on the speeds of M vehicles on a target road segment in a map coordinate system. This includes: electronic devices determining traffic violations based on the road segment type and the speeds of the M vehicles on the target road segment in a map coordinate system.

[0172] In some feasible ways, road segment types include, but are not limited to: highway segments, urban segments, rural segments, etc.

[0173] In some feasible implementations, the traffic violation event corresponding to the violating vehicle on the target road segment is determined based on the road segment type and the speeds of the M vehicles on the target road segment in the map coordinate system. This includes: if the road segment type of the target road segment is a highway segment, and the speed of the fourth vehicle among the M vehicles in the map coordinate system is 0, then the electronic device determines that the fourth vehicle is a violating vehicle, and determines that the traffic violation event corresponding to the fourth vehicle is an illegal parking event.

[0174] It should be understood that since parking is not allowed on highways, if a vehicle's speed is 0, it means that the vehicle is illegally parked.

[0175] In some feasible implementations, based on the road segment type of the target road segment and the speeds of M vehicles on the target road segment in the map coordinate system, the traffic violation event corresponding to the violating vehicle on the target road segment is determined. This includes: if the road segment type of the target road segment is a highway segment, and the speed of one of the M vehicles is 0 in multiple consecutive time windows in the map coordinate system, then the electronic device determines that the vehicle is a violating vehicle, and determines that the traffic violation event corresponding to the fourth vehicle is an illegal parking event.

[0176] It should be understood that the difference between this feasible method and the previous feasible method is that in this feasible method, the electronic device needs to calculate the vehicle's speed over multiple time windows, while in the previous feasible method, the electronic device only needs to calculate the vehicle's speed over a single time window. By calculating the vehicle's speed over multiple time windows, it is possible to better determine whether the vehicle is illegally parked.

[0177] In some feasible implementations, based on the merged location information of M vehicles on the target road segment in the map coordinate system, the traffic violation event corresponding to the violating vehicle on the target road segment is determined. This includes: electronic devices can determine the driving direction of each of the M vehicles based on the merged location information of the M vehicles on the target road segment in the map coordinate system; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the driving direction of each of the M vehicles and the direction of the target road segment.

[0178] In some feasible implementations, based on the respective driving directions of the M vehicles and the direction of the target road segment, the traffic violation event corresponding to the violating vehicle on the target road segment is determined, including: if the driving direction of the fifth vehicle among the M vehicles is inconsistent with the direction of the target road segment, the electronic device determines that the fifth vehicle is a violating vehicle, and determines that the traffic violation event corresponding to the fifth vehicle is a wrong-way driving violation event.

[0179] In some feasible implementations, based on the merged location information of M vehicles on the target road segment in the map coordinate system, the traffic violation event corresponding to the violating vehicle on the target road segment is determined. This includes: the electronic device can determine the driving trajectory of each of the M vehicles based on the merged location information of the M vehicles on the target road segment in the map coordinate system; if the driving trajectory of the sixth vehicle among the M vehicles shows that the vehicle overtakes on the right, the electronic device determines that the sixth vehicle is a violating vehicle, and determines that the traffic violation event corresponding to the fifth vehicle is a right-overtaking event.

[0180] It should be noted that this application does not impose any restrictions on how to determine the traffic violations corresponding to the vehicles violating the target road segment.

[0181] This application provides a traffic incident reporting method, including: acquiring sensor data collected by N sensors for M vehicles on a target road segment; where N is an integer greater than 1 and M is a positive integer; for each of the N sensors, determining the location information of the M vehicles on the target road segment in a map coordinate system based on the sensor data collected by the sensor for the M vehicles on the target road segment; determining the merged location information of the same vehicle on the target road segment based on the location information of the M vehicles on the target road segment in the map coordinate system; merging the merged location information of the same vehicle on the target road segment to obtain merged location information of the M vehicles on the target road segment in the map coordinate system; determining the traffic violation incident corresponding to the violating vehicle on the target road segment based on the merged location information of the M vehicles on the target road segment in the map coordinate system; and reporting the traffic violation incident corresponding to the violating vehicle. In this application embodiment, an electronic device merges the sensor data converted from different sensors and the location information of the same vehicle (i.e., deduplication), and then reports the traffic violation incident based on the merged location information, thereby avoiding duplicate reporting.

[0182] Furthermore, in current related technologies, the detection and reporting of traffic violations are achieved through artificial intelligence (AI). AI methods typically rely on deep neural networks, which have large network parameters. Therefore, the detection of traffic violations is complex and the reporting latency is high. This application embodiment does not use AI. Instead, it determines the traffic violations corresponding to the violating vehicles on the target road segment based on the merged location information of M vehicles on the target road segment in the map coordinate system, and reports the traffic violations corresponding to the violating vehicles. This reduces the complexity of traffic violation detection and the reporting latency.

[0183] The preferred embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solutions of this application, and these simple modifications all fall within the protection scope of this application. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not describe the various possible combinations separately. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the spirit of this application, they should also be considered as the content disclosed in this application.

[0184] It should also be understood that, in the various method embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0185] The method provided in the embodiments of this application has been described above. The traffic incident reporting device provided in the embodiments of this application will be described below.

[0186] Figure 9 This application provides a schematic diagram of a traffic incident reporting device 900, as shown in the embodiment. Figure 9 As shown, the traffic incident reporting device 900 includes: an acquisition module 910, a determination module 920, a merging module 930, and a reporting module 940; wherein, the acquisition module 910 is used to acquire sensor data collected by N sensors for M vehicles on a target road segment respectively; where N is an integer greater than 1, and M is a positive integer; the determination module 920 is used to determine the position information of the M vehicles on the target road segment in the map coordinate system based on the sensor data collected by each of the N sensors for the M vehicles on the target road segment; the determination module 920 also... The module 920 is used to determine the merged location information of the same vehicle on the target road segment based on the location information of M vehicles on the target road segment in the map coordinate system; the merging module 930 is used to merge the merged location information of the same vehicle on the target road segment to obtain the merged location information of M vehicles on the target road segment in the map coordinate system; the determining module 920 is also used to determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the merged location information of M vehicles on the target road segment in the map coordinate system; the reporting module 940 is used to report the traffic violation event corresponding to the violating vehicle.

[0187] In some implementations, the determining module 920 is specifically used to: determine the distance between the map coordinate points corresponding to the first location information and the map coordinate points corresponding to the second location information; wherein the first location information and the second location information are sensor data converted from any two sensors, and are any two location information in the map coordinate system on the target road segment; and determine the location information to be merged for the same vehicle on the target road segment based on the distance between the map coordinate points corresponding to the first location information and the map coordinate points corresponding to the second location information.

[0188] In some implementations, the determining module 920 is specifically used to: if the distance between the map coordinate point corresponding to the first location information and the map coordinate point corresponding to the second location information is less than a preset distance, then determine that the first location information and the second location information are the location information to be merged of the same vehicle on the target road segment.

[0189] In some possible implementations, the merging module 930 is specifically used to: merge the location information of the same vehicle on the target road segment into one location information of the location information to be merged, so as to obtain the merged location information of M vehicles on the target road segment in the map coordinate system.

[0190] In some possible implementations, the determining module 920 is specifically used to: use the K-means clustering algorithm to obtain K clusters for the location information of M vehicles on the target road segment in the map coordinate system; and for each of the K clusters, determine the location information of all vehicles in the cluster as the location information to be merged for the same vehicle on the target road segment.

[0191] In some possible implementations, the merging module 930 is specifically used to: determine the average position information of the same vehicle to be merged on the target road segment; and use the average position information of each of the M vehicles on the target road segment as the merged position information of the M vehicles in the map coordinate system.

[0192] In some implementations, the determining module 920 is specifically used to: determine the lane type of the M vehicles based on the merged location information of the M vehicles on the target road segment in the map coordinate system; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles.

[0193] In some implementations, the determination module 920 is specifically used to: if the lane type occupied by the first vehicle among the M vehicles is the emergency lane type, then determine that the first vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the first vehicle is an application lane occupation event.

[0194] In some implementations, before determining the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles, the determining module 920 is also used to: determine the speed of the M vehicles on the target road segment in the map coordinate system based on the merged position information of the M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged position information; correspondingly, the determining module 920 is specifically used to: determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles and the speed of the M vehicles on the target road segment in the map coordinate system.

[0195] In some possible implementations, the determining module 920 is specifically used to: determine the speed limit range corresponding to the lane type where the M vehicles are located; if the speed of the second vehicle among the M vehicles exceeds the corresponding speed limit range, then determine that the second vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the second vehicle is a violation event that exceeds the speed limit range.

[0196] In some possible implementations, the determining module 920 is specifically used to: determine the speed of the M vehicles on the target road segment in the map coordinate system based on the merged location information of the M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged location information; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the speed of the M vehicles on the target road segment in the map coordinate system.

[0197] In some possible implementations, the determination module 920 is specifically used to: if the speed of the third vehicle among the M vehicles is greater than a preset speed, then determine that the third vehicle is a violating vehicle, and determine the traffic violation event corresponding to the third vehicle as a speeding event.

[0198] In some implementations, the determination module 920 is specifically used to: determine the traffic violation events corresponding to the violating vehicles on the target road segment based on the road segment type of the target road segment and the speeds of the M vehicles on the target road segment in the map coordinate system.

[0199] In some possible implementations, the determination module 920 is specifically used to: if the target road segment is a highway segment, and the speed of the fourth vehicle among the M vehicles is 0 in the map coordinate system, then determine that the fourth vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the fourth vehicle is an illegal parking event.

[0200] In some possible implementations, the determining module 920 is specifically used to: determine the driving direction of each of the M vehicles based on the merged position information of the M vehicles on the target road segment in the map coordinate system; and determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the driving direction of each of the M vehicles and the direction of the target road segment.

[0201] In some possible implementations, the determination module 920 is specifically used to: if the driving direction of the fifth vehicle among the M vehicles is inconsistent with the direction of the target road segment, then determine that the fifth vehicle is a violating vehicle, and determine that the traffic violation event corresponding to the fifth vehicle is a wrong-way driving violation event.

[0202] It should be understood that the embodiments of the traffic incident reporting device and the method can correspond to each other, and similar descriptions can be found in the method embodiments. To avoid repetition, further details are omitted here. Specifically, Figure 9 The traffic incident reporting device 900 shown can perform... Figure 4 The corresponding method embodiments, and the foregoing and other operations and / or functions of each module in the traffic incident reporting device 900 are respectively for implementing Figure 4 For the sake of brevity, the corresponding processes in each method are not described in detail here.

[0203] The traffic incident reporting device 900 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by the integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly manifested as execution by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0204] Figure 10 This is a schematic block diagram of the electronic device 1000 provided in an embodiment of this application. The electronic device can be an MEC device or other electronic devices, such as cloud devices; this embodiment of the application does not impose any limitations on this.

[0205] like Figure 10 As shown, the electronic device 1000 may include:

[0206] The system includes a memory 1010 and a processor 1020. The memory 1010 stores a computer program 1030 and transfers the computer program 1030 to the processor 1020. In other words, the processor 1020 can retrieve and run the computer program 1030 from the memory 1010 to implement the methods described in the embodiments of this application.

[0207] For example, the processor 1020 can be used to execute the steps in the above method according to the instructions in the computer program 1030.

[0208] In some embodiments of this application, the processor 1020 may include, but is not limited to:

[0209] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0210] In some embodiments of this application, the memory 1010 includes, but is not limited to:

[0211] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0212] In some embodiments of this application, the computer program 1030 may be divided into one or more modules, which are stored in the memory 1010 and executed by the processor 1020 to complete the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 1030 in the electronic device.

[0213] like Figure 10 As shown, the electronic device 1000 may further include:

[0214] Transceiver 1040, which can be connected to processor 1020 or memory 1010.

[0215] The processor 1020 can control the transceiver 1040 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 1040 may include a transmitter and a receiver. The transceiver 1040 may further include antennas, and the number of antennas may be one or more.

[0216] It should be understood that the various components in the electronic device 1000 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0217] According to one aspect of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0218] According to another aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method described in the above-described method embodiments.

[0219] In other words, when implemented using software, it can be implemented wholly or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0220] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0221] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0222] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0223] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for reporting traffic incidents, characterized in that, include: Obtain sensor data collected by N sensors for M vehicles on the target road segment; where N is an integer greater than 1 and M is a positive integer; For each of the N sensors, based on the sensor data collected by the sensor for M vehicles on the target road segment, determine the position information of the M vehicles on the target road segment in the map coordinate system; Based on the location information of M vehicles on the target road segment in the map coordinate system, determine the location information of the same vehicle on the target road segment to be merged; and merge the location information of the same vehicle on the target road segment to obtain the merged location information of the M vehicles on the target road segment in the map coordinate system. Based on the merged location information of M vehicles on the target road segment in the map coordinate system, determine the traffic violation events corresponding to the violating vehicles on the target road segment; and report the traffic violation events corresponding to the violating vehicles.

2. The method according to claim 1, characterized in that, The step of determining the merged location information of the same vehicle on the target road segment based on the location information of M vehicles on the target road segment in the map coordinate system includes: Determine the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information; wherein, the first location information and the second location information are sensor data converted from any two sensors, and are any two location information on the target road segment in the map coordinate system; Based on the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information, the location information to be merged for the same vehicle on the target road segment is determined.

3. The method according to claim 2, characterized in that, The step of determining the merged location information of the same vehicle on the target road segment based on the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information includes: If the distance between the map coordinates corresponding to the first location information and the map coordinates corresponding to the second location information is less than a preset distance, then the first location information and the second location information are determined to be the location information to be merged for the same vehicle on the target road segment.

4. The method according to claim 2 or 3, characterized in that, The process of merging the location information of the same vehicle on the target road segment to obtain the merged location information of M vehicles on the target road segment in the map coordinate system includes: The location information of the same vehicle on the target road segment to be merged is merged into one location information in the location information to be merged, so as to obtain the merged location information of M vehicles on the target road segment in the map coordinate system.

5. The method according to claim 1, characterized in that, The step of determining the merged location information of the same vehicle on the target road segment based on the location information of M vehicles on the target road segment in the map coordinate system includes: The K-means clustering algorithm is used to obtain K clusters based on the location information of M vehicles on the target road segment in the map coordinate system. For each of the K clusters, the location information of all vehicles in the cluster is determined as the location information to be merged for the same vehicle on the target road segment.

6. The method according to claim 2, 3 or 5, characterized in that, The process of merging the location information of the same vehicle on the target road segment to obtain the merged location information of M vehicles on the target road segment in the map coordinate system includes: Determine the average location information of the same vehicle to be merged on the target road segment; The average position information of each of the M vehicles on the target road segment is used as the merged position information of the M vehicles in the map coordinate system.

7. The method according to any one of claims 1-3, characterized in that, The step of determining the traffic violation event corresponding to the violating vehicle on the target road segment based on the merged location information of M vehicles on the target road segment in the map coordinate system includes: Based on the merged position information of M vehicles on the target road segment in the map coordinate system, the lane type of the M vehicles is determined; Based on the lane type of the M vehicles, determine the traffic violation events corresponding to the vehicles violating the rules on the target road segment.

8. The method according to claim 7, characterized in that, The process of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the lane types of the M vehicles includes: If the first vehicle among the M vehicles is in an emergency lane, then the first vehicle is determined to be a violating vehicle, and the traffic violation event corresponding to the first vehicle is determined to be an event of occupying the application lane.

9. The method according to claim 7, characterized in that, Before determining the traffic violation event corresponding to the violating vehicle on the target road segment based on the lane type of the M vehicles, the method further includes: Based on the merged position information of M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged position information, the speed of the M vehicles on the target road segment in the map coordinate system is determined. The process of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the lane types of the M vehicles includes: Based on the lane type of the M vehicles and the speed of the M vehicles on the target road segment in the map coordinate system, determine the traffic violation events corresponding to the vehicles violating the rules on the target road segment.

10. The method according to claim 9, characterized in that, The process of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the lane type of the M vehicles and the speed of the M vehicles on the target road segment in the map coordinate system includes: Determine the speed limit range corresponding to the lane type of the M vehicles; If the speed of the second vehicle among the M vehicles exceeds the corresponding speed limit, then the second vehicle is determined to be a violating vehicle, and the traffic violation event corresponding to the second vehicle is determined to be a violation event exceeding the speed limit.

11. The method according to any one of claims 1-3, characterized in that, The step of determining the traffic violation event corresponding to the violating vehicle on the target road segment based on the merged location information of M vehicles on the target road segment in the map coordinate system includes: Based on the merged position information of M vehicles on the target road segment in the map coordinate system and the time information corresponding to each merged position information, the speed of the M vehicles on the target road segment in the map coordinate system is determined. Based on the speeds of M vehicles on the target road segment in the map coordinate system, determine the traffic violation events corresponding to the vehicles violating the rules on the target road segment.

12. The method according to claim 11, characterized in that, The process of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the speeds of M vehicles on the target road segment in the map coordinate system includes: If the speed of the third vehicle among the M vehicles is greater than the preset speed, then the third vehicle is determined to be a vehicle violating the traffic rules, and the traffic violation event corresponding to the third vehicle is determined to be a speeding event.

13. The method according to claim 11, characterized in that, The process of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the speeds of M vehicles on the target road segment in the map coordinate system includes: Based on the road segment type of the target road segment and the speeds of M vehicles on the target road segment in the map coordinate system, determine the traffic violation events corresponding to the violating vehicles on the target road segment.

14. The method according to claim 13, characterized in that, The method of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the road segment type and the speeds of M vehicles on the target road segment in the map coordinate system includes: If the target road segment is a highway and the speed of the fourth vehicle among the M vehicles is 0 in the map coordinate system, then the fourth vehicle is determined to be a violating vehicle, and the traffic violation event corresponding to the fourth vehicle is determined to be an illegal parking event.

15. The method according to any one of claims 1-3, characterized in that, The step of determining the traffic violation event corresponding to the violating vehicle on the target road segment based on the merged location information of M vehicles on the target road segment in the map coordinate system includes: Based on the merged position information of M vehicles on the target road segment in the map coordinate system, the driving direction of each of the M vehicles is determined. Based on the respective driving directions of the M vehicles and the direction of the target road segment, determine the traffic violation events corresponding to the vehicles violating the rules on the target road segment.

16. The method according to claim 15, characterized in that, The process of determining the traffic violation events corresponding to the violating vehicles on the target road segment based on the respective driving directions of the M vehicles and the direction of the target road segment includes: If the fifth vehicle among the M vehicles is traveling in a direction inconsistent with the direction of the target road segment, then the fifth vehicle is determined to be a violating vehicle, and the traffic violation corresponding to the fifth vehicle is determined to be a wrong-way driving violation.

17. A traffic incident reporting device, characterized in that, include: The modules include: acquisition module, determination module, merging module, and reporting module. The acquisition module is used to acquire sensor data collected by N sensors for M vehicles on the target road segment; where N is an integer greater than 1 and M is a positive integer; The determining module is used to determine the position information of the M vehicles on the target road segment in the map coordinate system based on the sensor data collected by the sensor for the M vehicles on the target road segment for each of the N sensors. The determining module is also used to determine the merged location information of the same vehicle on the target road segment based on the location information of M vehicles on the target road segment in the map coordinate system; The merging module is used to merge the location information of the same vehicle on the target road segment to obtain the merged location information of M vehicles on the target road segment in the map coordinate system. The determining module is also used to determine the traffic violation event corresponding to the violating vehicle on the target road segment based on the merged location information of M vehicles on the target road segment in the map coordinate system; The reporting module is used to report traffic violations corresponding to the violating vehicles.

18. An electronic device, characterized in that, include: A processor and a memory, the memory being used to store a computer program, the processor being used to invoke and run the computer program stored in the memory to perform the method of any one of claims 1 to 16.

19. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the method as described in any one of claims 1 to 16.

20. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method as described in any one of claims 1 to 16.

Images