Data processing method and device, electronic equipment and storage medium

Abstract

The invention relates to a data processing method and device, electronic equipment and a storage medium. The method comprises the steps that vehicle driving state information collected in real time in the driving process of multiple target vehicles in a target area is acquired; based on the positioning information, obtaining a position point matched with the positioning information in a target map; wherein the position point has corresponding driving route information; and fusing the driving route information with the vehicle driving state information to obtain the driving speed of the vehicle in each driving route in the target area, and determining whether the target area has a slow-down slow-down area based on the driving speed. Therefore, by acquiring the real-time driving information of each vehicle on the road, the service requirements of the whole scene in the road can be fully covered, the sensing efficiency in the road scene can be improved to a great extent, and the slow-down slow-driving area in the road can be quickly and accurately positioned.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and particularly relates to a data processing method and device, electronic equipment and storage medium. BACKGROUND

[0002] In the process of driving on the road, the related art usually relies on sensors such as vehicle-mounted cameras, laser radars or millimeter wave radars to identify the target around the vehicle, including the identification of static targets and dynamic targets, to perform scene perception on the current road.

[0003] However, the related art can only perform scene perception on the surrounding environment of the vehicle through the sensors arranged on the vehicle, and cannot perceive the road scene beyond the perception range of the vehicle, thereby failing to cover the business needs of the full scene on the road, and resulting in low scene perception efficiency of the related art. SUMMARY

[0004] The present disclosure provides a data processing method and device, electronic equipment and storage medium.

[0005] According to a first aspect of the present disclosure, a data processing method is provided, and the method comprises:

[0006] obtaining vehicle driving state information collected in real time by a plurality of target vehicles in a target area in a driving process, wherein the vehicle driving state information comprises positioning information;

[0007] based on the positioning information, obtaining a position point in a target map that matches the positioning information; wherein the position point has corresponding driving route information;

[0008] fusing the driving route information and the vehicle driving state information to obtain a driving speed of the vehicle in each driving route in the target area, and determining whether there is a slow driving area for deceleration and slow driving in the target area based on the driving speed.

[0009] According to a second aspect of the present disclosure, a data processing device is provided, and the device comprises:

[0010] an information acquisition module configured to obtain vehicle driving state information collected in real time by a plurality of target vehicles in a target area in a driving process, wherein the vehicle driving state information comprises positioning information;

[0011] a position point determination module configured to obtain a position point in a target map that matches the positioning information based on the positioning information; wherein the position point has corresponding driving route information;

[0012] The slow driving area determination module is configured to fuse the driving route information and the vehicle driving state information, obtain a driving speed of the vehicle driving in each driving route in the target area, and determine whether the target area has a slow driving area based on the driving speed.

[0013] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method as described above when executing the program.

[0014] According to a fourth aspect of the present disclosure, a computer readable storage medium is provided, which stores a computer program, and the program is executed by a processor to implement the method as described above.

[0015] The data processing method, device, electronic device and storage medium provided by the embodiments of the present disclosure can obtain the driving speed of the vehicle driving in each driving route in the target area by fusing the driving route information and the vehicle driving state information, and determine whether the target area has a slow driving area based on the driving speed. In this way, by obtaining the real-time driving information of each vehicle on the road, the business needs of all scenarios in the road can be fully covered, and the perception efficiency in the road scene can be improved to a great extent, and the slow driving area in the road can be quickly and accurately located. BRIEF DESCRIPTION OF DRAWINGS

[0016] In the following description of exemplary embodiments in conjunction with the accompanying drawings, more details, features and advantages of the present disclosure are disclosed, in which:

[0017] Figure 1 The scene schematic diagram provided by an exemplary embodiment of the present disclosure;

[0018] Figure 2 The flowchart of the data processing method provided by an exemplary embodiment of the present disclosure;

[0019] Figure 3 The functional module schematic block diagram of the data processing device provided by an exemplary embodiment of the present disclosure;

[0020] Figure 4 The structural block diagram of the electronic device provided by an exemplary embodiment of the present disclosure;

[0021] Figure 5 The structural block diagram of the computer system provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein; rather, these embodiments are provided so as to more completely and thoroughly understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of protection of the present disclosure.

[0023] It should be understood that each of the steps recited in the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0024] The term "comprising" and variations thereof as used herein are used inclusively, i.e., "comprising, but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Related definitions are given throughout the description below. It should be noted that the concepts mentioned in the present disclosure are merely used for distinguishing different apparatuses, modules or units and are not intended to limit the functions of the apparatuses, modules or units.

[0025] It should be noted that the terms "one", "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise explicitly stated in the context, it should be understood as "one or more".

[0026] The names of the messages or information exchanged between the plurality of apparatuses in the embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of the messages or information.

[0027] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the type, use range, use scenario, etc. of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0028] For example, when responding to the active request of the user, a prompt information is sent to the user to explicitly prompt the user that the operation requested to be performed will require obtaining and using the personal information of the user. Thus, the user can voluntarily choose whether to provide the personal information to the software or hardware such as electronic device, application program, server or storage medium, etc. performing the operation of the technical solutions of the present disclosure according to the prompt information.

[0029] As an optional but non-limiting implementation method, in response to receiving the user's active request, the method of sending a prompt message to the user can be, for example, a pop-up window, and the prompt message can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation method of the present disclosure. Other methods that meet relevant laws and regulations can also be applied to the implementation method of the present disclosure.

[0030] In order to improve the efficiency of scene perception, the embodiment of the present disclosure obtains traffic big data, performs real-time data analysis on the traffic big data, and sends prompt information to vehicles based on the analysis results, thereby improving the vehicle's traffic efficiency.

[0031] In an embodiment, the vehicle BSM (Basic Safety Message) information can be collected and uploaded in real time through the on-board OBU (On board Unit) device. The BSM information contains key data such as timestamp, location longitude and latitude, vehicle speed, heading angle, vehicle body size, gear position, steering wheel steering and axis acceleration. The BSM information is transmitted to the edge cloud through the RSU (Road Side Unit). The big data traffic analysis subsystem on the edge cloud side performs real-time data analysis and mining on the BSM information to obtain real-time mining of road deceleration and slow-moving areas under vehicle group behavior. Analyzing from a new perspective can greatly reduce costs and improve efficiency and accuracy.

[0032] like Figure 1 As shown, Figure 1 The vehicle 11 can collect the vehicle's BSM information in real time through the onboard OBU device, including data collected by various sensors such as positioning information, IMU (Inertial Measurement Unit), wheel speed meter or laser radar, etc.

[0033] Vehicle 11 can send collected vehicle information to server 16 via 5G base station 13. Vehicle 11 can also send vehicle information to edge cloud 15 via access gateway 14 using the 5G wireless installation mode of RSU 12. The edge cloud can perform data analysis on the vehicle information and perform high-precision vehicle positioning. In this embodiment, access gateway 14 can also send vehicle information to server 16 in the core computer room. At the same time, vehicle 11 can also receive data analysis results in real time to select the appropriate road or lane for passage. For example, RSU 12 can send current road information to vehicle 11 via broadcast.

[0034] Specifically, in the embodiments provided by the present disclosure, the vehicle-mounted OBU can collect vehicle information in real time and upload the collected vehicle information to the RSU, and the RSU can upload the received vehicle information to the server in the edge cloud. At the same time, the high-definition map can also collect and process data, and upload the collected and processed high-definition map data to the server in the edge cloud for storage.

[0035] In the embodiments, the high-definition map obtains fused vehicle information by analyzing and obtaining real-time vehicle road, intersection and lane line information of the vehicle. The edge cloud can mine and analyze traffic data of the vehicle in real time, and can also distribute the analysis result to the vehicle, so that the vehicle can take timely measures to avoid entering a congested road section, thereby improving the traffic efficiency. The analysis result can include information such as the current road deceleration slow-moving area.

[0036] In the embodiments, in the process of collecting data of the vehicle, SHP data can be produced based on the original data of the vehicle. The SHP data is a vector data format used to store and edit various geographic information data such as points, lines and surfaces. The SHP file is a binary-based format containing geometric information and attribute information of geographic entities.

[0037] However, since the SHP only contains the original data of points, lines and surfaces, and does not contain road, intersection and lane line information, it belongs to a vector graphics format and cannot store the topological relationship of geographic data.

[0038] Therefore, the SHP data can be converted into OpenDrive format data by WorldEditor in the embodiments of the present disclosure. OpenDrive is used to describe the logic of road grid and contains road information, intersection information, lane line information (length and width), and the correlation between intersections, lane lines and roads, such as the road to which the lane line belongs, the predecessor and successor of the road, the incoming road and connected road of the intersection, etc. It is complete continuous data information and contains a large amount of data.

[0039] In addition, the OpenDirve data can be converted into the high-definition map format required by the embodiments. The format only contains a certain amount of point set, extracts the lane line, intersection and road line in OpenDrive, such as extracting a point every meter, and saves the sparse data without losing information, and can also add lane center point information.

[0040] In the embodiments provided herein, vehicle BSM data can be collected using an onboard OBU (Operating Bus Unit) and transmitted to an RSU (Remote State Unit). BSM data includes information such as timestamp, location latitude and longitude, vehicle speed, heading angle, vehicle size, gear position, steering wheel direction, and axis acceleration. Therefore, it can be used in V2V communication scenarios such as lane change warning, blind spot warning, and intersection collision warning.

[0041] In addition, the RSU receives BSM data uploaded by vehicles and forwards it to the edge cloud. One RSU device can cover a 300-meter radius, and BSM messages from vehicles within this radius can be uploaded to the edge cloud for real-time computing and analysis.

[0042] In an embodiment, the received BSM data can be mined through the edge cloud. Since the deceleration area section can be caused by many factors, such as road damage, obstacles, cargo drops, water accumulation, ice, speed limits and other static scenes or dynamic scenes, etc. Therefore, in an embodiment, the deceleration area can be defined: in a lane, at least 3 consecutive vehicles are recorded to have deceleration behavior in a certain section of the road, and their deceleration ranges have an intersection, and the intersection range is not less than 50m, then this intersection range is defined as the range that needs to be decelerated, that is, the deceleration area.

[0043] It should be noted that the definition of vehicle deceleration can be understood as follows: in the lane in the same direction (excluding intersections and the 20m area associated with intersections), the real-time speed of the vehicle is V, and the average speed of all vehicles passing through this lane during the historical period is V0. If the real-time speed of the vehicle continues for a period of time, The vehicle decelerates and moves slowly.

[0044] In the embodiment, the starting point (S) of the deceleration zone is: the vehicle continues to have a real-time speed The initial position when Then restart from Then start calculating the starting point.

[0045] In the embodiment, the deceleration end point (F) is: the real-time speed of the vehicle After that, the first appearance The position at the time of tracking is 1 minute at most. If it exceeds 1 minute, F is calculated based on the position at the end of 1 minute. If the vehicle changes lanes, it is calculated based on the lane change position.

[0046] In the embodiment, the area where the vehicle needs to slow down is the longitudinal range between point S and point F in the same lane.

[0047] It should be noted that when the vehicle has abnormal behaviors such as overspeeding, reverse driving, abnormally low speed, sudden braking, fast and slow driving alternately, or frequent lane changing, the deceleration behavior of the vehicle is not counted, but the original count can not be cleared.

[0048] In the embodiment, the deceleration slow driving area to be released is: if a vehicle passes through the deceleration slow driving area, if the deceleration slow driving area is released.

[0049] Based on the above embodiment, the judgment method of the deceleration slow driving area can include the following steps:

[0050] In step S11, the real-time lane line information and road information of the vehicle are judged according to the latitude and longitude information of the original vehicle data combined with the high-precision map data.

[0051] Specifically, a), analyze the high-precision map data. By analyzing the high-precision map data, the intersection information in the high-precision map data is analyzed, and the association relationship between the intersection and the road information is generated. And analyze the road information in the high-precision map data, and then analyze the lane line information in the corresponding road, generate the association relationship of the intersection, road, lane line and the corresponding lane center line point and center line information.

[0052] Based on the analysis of the parsed high-precision map data, a high-precision map weighted topological relationship diagram is generated. For example, a directed graph can be initialized based on NetworkX, and points and lines in the topological graph are generated, where each point represents a lane in the road. NetworkX is a software package written in python language, which can store networks in standardized and non-standardized data formats, generate various random networks and classic networks, analyze network structure, build network models, design new network algorithms and draw networks.

[0053] b) Generate a high-precision map weighted topological relationship diagram. In the embodiment, when the road topological relationship diagram is initialized for the first time, the weights can be set to be the same. The road and lane information where the current vehicle is located is calculated, since the vehicle travels according to the topological connection of the road, therefore the next latitude and longitude coordinate point only needs to be judged on the road and the associated road, the weight of the current road and the directly connected road can be set to be the highest, which is one degree of connection road; The second is the two-degree connected road; In addition to the road and lane information where the vehicle's latitude and longitude is located for the first time, subsequent search is carried out in one-degree connected road, and when the nearest distance from the one-degree connected road is large, two-degree connected road is searched. After each judgment, the one-degree and two-degree connected roads are updated in real time, that is, different road weights are set, which belong to one-degree road and which belong to two-degree road.

[0054] c) Constructing kd-tree (k-dimensional tree). The kd-tree structure can be constructed using the center line points of the latitude and longitude coordinates of the whole map and the label of each point, such as 'road id, lane id and point index'.

[0055] The construction of the kd-tree includes selecting a division dimension (which can be based on the latitude and longitude coordinates, and can also be based on the longitude or latitude), selecting a division element (a latitude and longitude point on the center line), or dividing the entire latitude and longitude data set into different subsets by recursion, to construct the storage structure of the kd-tree.

[0056] d) Finding the nearest point of the whole map. The latitude and longitude point of the input vehicle is used to find the nearest point information of the whole map based on the kd-tree. According to the nearest point information, such as including latitude and longitude, the corresponding lane and road information of the vehicle at this moment is obtained by taking out the corresponding lane and road information, which is the lane and road to which the vehicle belongs at this moment.

[0057] e) Fusing vehicle information. Based on the above vehicle road and lane line information, the existing vehicle BSM information is fused to form complete vehicle traffic information, which can be used to determine the deceleration and slow-moving area of the vehicle group behavior.

[0058] Step S12, grid the road and calculate the average speed.

[0059] Specifically, the road can be logically processed by gridding, that is, the road is divided into grids at intervals of every 20 meters. Based on the historical driving records of the group vehicles, the average speed V0 in the free flow condition on the road, lane and grid is calculated by using big data distributed computing.

[0060] Step S13, judging the deceleration and slow-moving grid.

[0061] Specifically, the data can be grouped according to the road and lane, and it is judged whether the real-time speed V of the vehicle satisfies

[0062] If the above vehicle speed condition is met, the road, lane and corresponding grid can be saved as the key, and the number of occurrences is the value, which can be accumulated.

[0063] If the value is greater than or equal to 3, the grid is a deceleration and slow-moving grid. If the speed is greater than or equal to 30 km / h, the saved value is reset, that is, it is reset to 0, which represents that the deceleration and slow-moving area grid disappears, and the vehicle passing through the grid is re-judged and analyzed, and the occurrence of the deceleration and slow-moving behavior is accumulated.

[0064] Step S14, judging the deceleration and slow-moving area S.

[0065] Specifically, in the embodiment, by judging the grid that occurs deceleration slow running, it is judged whether it is a continuous grid, if it is continuous, the larger grid is merged, and the merged grid can be taken as S.

[0066] If the grid is not a continuous region, the grid merging is not performed, which represents that there is no deceleration slow running region between the grids. By analyzing whether the grid needs to be merged, the maximum boundary range of the deceleration slow running region is judged, and there can be multiple deceleration slow running regions.

[0067] Step S15, release the deceleration region.

[0068] Specifically, for the deceleration slow running region that has been judged, in the process of the cycle detection of step S13, if the vehicle that occurs through the deceleration grid The vehicle means that the deceleration slow running region of the grid disappears, the whole deceleration slow running region warning is released, and all key-value pairs related to the deceleration slow running region are deleted. However, this does not mean that there is no deceleration slow running region, for example, a region can be composed of multiple grids, only one grid is invalid, but there is still a deceleration slow running region. Since the reset in step S13 is to the grid level, the logic of continuously judging the deceleration slow running region through real-time cycle can still find a new deceleration slow running region that removes the grid. Based on the invalid grid position, there can be one or more new deceleration slow running regions.

[0069] Step S16, issue the deceleration slow running region message.

[0070] Finally, the dynamic traffic information analyzed in real time, that is, the deceleration slow running region, contains specific road, lane, range (start and end longitude and latitude) and time information, etc. Through the edge cloud, it is issued to the corresponding RSU device, the RSU device broadcasts and sends to the subsequent vehicle for traffic reminding and traffic guiding. Since the OBU receiver is installed on the vehicle, the vehicle in the RSU coverage range can receive the information issued by the RSU in real time. The information received by the OBU is received through the Bluetooth receiver, and finally presented on the HMI (Human Machine Interface) interface of the vehicle terminal. Real-time information such as deceleration slow running region and its coverage range can be seen. At the same time, it can also be sent to the relevant departments for event response through the service of the edge cloud.

[0071] Therefore, the embodiment of the present disclosure can find the nearest point in the full map through the KD-Tree structure of the edge cloud according to the latitude and longitude point of the vehicle included in the lane information, fuse the nearest point information including the road and lane line information of the vehicle with the vehicle BSM information, determine the real-time lane information and road information of the vehicle, determine the average speed of the vehicle based on the real-time lane information and road information of the vehicle, group the vehicles according to the road and lane, determine whether the real-time speed of the vehicle in each grid meets the speed condition corresponding to the average speed, determine whether the grid where the deceleration and slow driving occurs meets the deceleration and slow driving condition, and if so, determine the deceleration and slow driving area in the grid road in the edge cloud in real time.

[0072] The embodiment of the present disclosure provides lane information of a high-precision map, and the identified deceleration and slow driving area has high map accuracy, and multiple deceleration and slow driving areas can be distinguished in the same road section. Compared with the way of statistically determining deceleration of vehicles based on the whole road section in the related art, the embodiment of the present disclosure has finer judgment ability. At the same time, the embodiment of the present disclosure can exclude personal overtaking and driving jitter, and can more stably and objectively output the deceleration and slow driving area recognition result. Further, the embodiment of the present disclosure can realize deceleration and slow driving area detection of a panoramic road, and has an important role in real-time traffic information presentation, road anomaly maintenance, accident diversion, and path planning. In this way, by solving the instantaneousness of vehicle perception and by mining the behavior of past group vehicles to find rules, recognition errors and recognition omissions caused by the instantaneousness of perception can be avoided, thereby improving the accuracy of determining the deceleration and slow driving area.

[0073] Based on the above embodiment, the embodiment of the present disclosure further provides a data processing method, as shown in Figure 2 The method can include the following steps:

[0074] In step S210, vehicle driving state information collected in real time by a plurality of target vehicles in a target area during driving is obtained. The vehicle driving state includes positioning information.

[0075] In the embodiment, the vehicle driving state information can be the vehicle information in the above embodiment, which can include the current positioning information of the vehicle. The target area can include a plurality of target vehicles, and the plurality of target vehicles can drive on one or several roads. The plurality of target vehicles can be part of the vehicles on the one or several roads, or can be all the vehicles, and the embodiment is not limited thereto.

[0076] In step S220, based on the positioning information, a position point in a target map that matches the positioning information is obtained.

[0077] The position point has corresponding driving route information, and the position point can be generated based on a lane point in a target map. The lane point has corresponding road information and lane information, and the road information and lane information can be used as driving route information, i.e., the specific road on which the vehicle travels and the lane in which the vehicle travels on the road.

[0078] In the embodiment, the target map can be the high-definition map in the above embodiment. By obtaining the positioning information of the vehicle, such as the latitude and longitude, the nearest point information in the high-definition map can be found based on the pre-constructed kd-tree. According to the nearest point information, such as the latitude and longitude, the road and lane information contained in the nearest point information, the lane information and road information of the vehicle at the current time can be determined. For example, the road information and lane information corresponding to the nearest point information can be directly used as the road information and lane information of the vehicle at the current time.

[0079] In the embodiment, the positioning information can be input into the search tree (i.e., kd-tree) pre-constructed in the target map. The search tree includes a set of corresponding relationships between road information, lane information and position points. In this way, the position points in the set of relationships that match the positioning information can be obtained.

[0080] Specifically, the distance between the positioning information and each position point in the set of relationships can be obtained, and the position point in the target map that matches the positioning information can be determined based on the distance. For example, the latitude and longitude point of the vehicle can be input, and the nearest point information in the entire map can be found based on the kd-tree. According to the nearest point information, such as the latitude and longitude, the road and lane information, the corresponding lane and road information can be obtained, which are the lane and road to which the vehicle belongs at the current time.

[0081] The lane in the road includes lane lines, and the lane lines can be composed of a plurality of lane points. The lane points have certain attributes, i.e., each lane point has corresponding lane information and road information. Since the positioning information of the vehicle generally has certain errors, the nearest position point on the map to the positioning information of the vehicle can be found according to the positioning information of the vehicle. Since the position point is generated based on the lane point, the road information and lane information of the vehicle at the current time can be determined.

[0082] In step S230, the driving route information and the vehicle driving state information are fused to obtain the driving speed of the vehicle in each driving route in the target area, and whether there is a slow driving area in the target area is determined based on the driving speed.

[0083] In the embodiment, by fusing the road information, lane information and vehicle driving state information of each vehicle on the road, the driving state of each vehicle on the road can be comprehensively perceived. And by the driving speed of the vehicle driving on each driving route in the target area, such as the average speed of the vehicle, the number of vehicles greater than the speed threshold or the number of vehicles not greater than the speed threshold, etc., whether there is a slow-moving area in the target area can be determined according to the driving speed, so as to facilitate the vehicle to be ready in advance or to bypass the slow-moving area in advance, so as to improve the traffic efficiency.

[0084] For example, if the average speed of the target driving route in the target area is less than a certain speed threshold, it can be determined that there is a slow-moving area in the target area, the road information in the slow-moving area can be specifically determined, and the lane information in the road information can be specifically determined, that is, not only which road in the target area appears slow-moving, but also which lane in the road appears slow-moving, so as to more accurately determine the slow-moving area in the target area.

[0085] The data processing method provided by the embodiment of the present disclosure can obtain the driving speed of the vehicle driving on each driving route in the target area by acquiring the vehicle driving state information collected in real time by the plurality of target vehicles in the driving process and acquiring the position point in the target map matched with the positioning information, and determine whether there is a slow-moving area in the target area based on the driving speed. In this way, by acquiring the real-time driving information of each vehicle on the road, the business needs of all scenes in the road can be comprehensively covered, and the perception efficiency in the road scene can be improved to a great extent, and the slow-moving area in the road can be quickly and accurately located.

[0086] Based on the above embodiment, in another embodiment provided by the present disclosure, the method can further include the following steps:

[0087] In step S240, the intersection information, road information and lane information in the preset map are acquired, and the association relationship between the intersection information, road information and lane information is established.

[0088] In step S250, a topological relationship graph in the preset map is generated based on the association relationship.

[0089] The topological relationship graph includes position points and lines, and the position points and lines respectively represent roads and lane lines in the roads. Different roads in the topological relationship correspond to different weights, and the weight is positively correlated with the association degree between the roads.

[0090] In step S260, a target map is generated based on the topological relationship graph.

[0091] In the embodiment, the preset map can be a basic map, i.e., a map used in daily life. By extracting the intersection information, road information and lane information in the preset map, the association relationship between the intersection information, road information and lane information can be established, and then the topological relationship map can be generated based on the management relationship.

[0092] In the embodiment, the same weight can be set for the road when the road topological relationship map is initialized. When the road and lane information where the current vehicle is located is calculated by the latitude and longitude (positioning information) of the current vehicle, since the vehicle travels according to the topological connection of the road, the next latitude and longitude coordinate point only needs to be judged on the road and the associated road, and the weight of the current road and the directly connected road can be set to be the highest, i.e., one-degree connected road, and the weight of the two-degree connected road is the second, which can be referred to the description of the above embodiment and will not be described here. In this way, the high-precision map, i.e., the target map, can be generated based on the topological relationship map with weights.

[0093] Based on the above embodiment, in another embodiment provided by the present disclosure, the step S230 can further include the following steps:

[0094] In step S231, the target map is divided into grids to obtain a plurality of grids. Each grid represents a corresponding region.

[0095] In step S232, the current driving speed of each vehicle in the target grid in the target lane in the target map is obtained.

[0096] In step S233, if there is a slow vehicle with a driving speed less than a speed threshold in the target grid in the target lane, and the number of the slow vehicle is greater than a preset number, it is determined that the target region has a slow region, and the region corresponding to the target grid is taken as the slow region.

[0097] In the embodiment, during the grid logical processing of the road, the grid of the road can be divided every 20 meters. By sensing the real-time driving information of each vehicle on the road, the real-time driving information can include the real-time driving speed of the vehicle on each lane in each road. For the current speed of the vehicle in the target grid in the target lane, if the current speed of the vehicle in the target grid in the target lane is less than the speed threshold, the target region corresponding to the target grid can be taken as the slow region. Similarly, if the current speed of the vehicle in the target grid in the target lane is not less than the speed threshold, the target region corresponding to the target grid can be taken as the smooth region. The slow region in the embodiment can be a slow-down region.

[0098] In the embodiment, the historical driving speeds of the plurality of vehicles in the target lane are also acquired, the historical average driving speed of the target lane is acquired based on the historical driving speeds, and the speed threshold is determined based on the historical average driving speed.

[0099] Specifically, the speed threshold can be calculated based on the historical driving records of the group of vehicles, the average speed of the grid under the road and lane in the free flow condition is calculated and recorded as V0, the data can be grouped according to the road and lane, and it is judged whether the real-time speed V of the vehicle meets

[0100] If the vehicle speed condition is met, the road, lane and corresponding grid can be saved as a key, and the number of occurrences is Value. The number of occurrences is the number of vehicles with a speed less than the speed threshold. If the Value is greater than or equal to 3, the grid is a deceleration and slow driving grid. If the speed is less than or equal to V0, the saved Value is reset, that is, reset to 0, which represents that the grid of the deceleration and slow driving area disappears, and the vehicle passing through the grid is reanalyzed and the occurrence of the deceleration and slow driving behavior is accumulated.

[0101] In the embodiment, the grids where deceleration and slow driving occurs are judged to determine whether they are continuous grids. If they are continuous, the grids are merged into larger grids. If the grids are not continuous, the grids are not merged, which represents that there is no deceleration and slow driving area between the grids. By analyzing whether the grids need to be merged, the maximum boundary range of the deceleration and slow driving area is determined, and there can be multiple deceleration and slow driving areas.

[0102] In the embodiment, traffic information including road information, lane information, area range information and time information corresponding to the slow driving area can be sent to the roadside equipment corresponding to the slow driving area, and the sending of the traffic information to the roadside equipment corresponding to the slow driving area is stopped when the slow driving area is removed. In this way, the related vehicles can be sent the traffic information in advance, so that the vehicles can make effective response in advance, thereby improving the traffic efficiency.

[0103] In the case of dividing each functional module corresponding to each function, the data processing apparatus provided by an example embodiment of the present disclosure can be a server, a terminal or a chip applied to a server. Figure 3 The functional module schematic diagram of the data processing apparatus provided by an example embodiment of the present disclosure is shown in FIG. 1. Figure 3 As shown in FIG. 1, the data processing apparatus includes:

[0104] The information acquisition module 10 is configured to acquire vehicle driving state information collected in real time by a plurality of target vehicles in a target area during driving, and the vehicle driving state information includes positioning information.

[0105] The position point determination module 20 is configured to acquire a position point in a target map that matches the positioning information based on the positioning information, wherein the position point has corresponding driving route information.

[0106] The slow driving area determination module 30 is configured to fuse the driving route information and the vehicle driving state information to obtain a driving speed of the vehicle in each driving route in the target area, and determine whether there is a slow driving area in the target area based on the driving speed.

[0107] In another embodiment provided by the present disclosure, the device further comprises:

[0108] The association relationship acquisition module is configured to acquire intersection information, road information and lane information in a preset map, and establish an association relationship among the intersection information, the road information and the lane information.

[0109] The topological relationship graph generation module is configured to generate a topological relationship graph in the preset map based on the association relationship, wherein the topological relationship graph comprises position points and lines, the position points and the lines represent roads and lane lines in the roads respectively, different roads in the topological relationship correspond to different weights respectively, and the weights are positively correlated with the association degrees between the roads.

[0110] The target map generation module is configured to generate the target map based on the topological relationship graph.

[0111] In another embodiment provided by the present disclosure, the position point determination module is specifically further configured to:

[0112] input the positioning information into a search tree pre-constructed in the target map, wherein the search tree comprises a corresponding relationship set among road information, lane information and position points;

[0113] acquire a position point in the relationship set that matches the positioning information.

[0114] In another embodiment provided by the present disclosure, the position point determination module is specifically further configured to:

[0115] acquire distances between the positioning information and each position point in the relationship set respectively;

[0116] determine a position point in the target map that matches the positioning information based on the distances.

[0117] In another embodiment provided by the present disclosure, the slow driving area determination module is specifically further configured to:

[0118] The target map is meshed to obtain a plurality of meshes; wherein each mesh represents a corresponding region;

[0119] The current driving speeds of the vehicles under the target mesh in the target lane in the target map are obtained;

[0120] In a case where there is a slow-moving vehicle with a driving speed less than the speed threshold under the target mesh, and the number of slow-moving vehicles is greater than a preset number, it is determined that the target region has a slow-moving region with deceleration and slow movement, and the region corresponding to the target mesh is taken as the slow-moving region.

[0121] In another embodiment provided by the present disclosure, the device further comprises:

[0122] a historical driving speed obtaining module configured to obtain historical driving speeds of a plurality of vehicles in the target lane;

[0123] a speed threshold determining module configured to obtain a historical average driving speed of the target lane based on the historical driving speeds, and determine the speed threshold based on the historical average driving speed.

[0124] In another embodiment provided by the present disclosure, the device further comprises:

[0125] an information sending module configured to send traffic information to a roadside device corresponding to the slow-moving region, and stop sending traffic information to the roadside device corresponding to the slow-moving region in a case where the slow-moving region is removed; wherein the traffic information includes road information, lane information, region range information and time information corresponding to the slow-moving region.

[0126] The data processing device provided by the embodiments of the present disclosure can obtain the driving speed of the vehicles in each driving route in the target region by obtaining the vehicle driving state information collected in real time by a plurality of target vehicles in the target region during driving, and obtaining the position points in the target map matched with the positioning information, and fusing the driving route information and the vehicle driving state information. The driving speed of the vehicles in each driving route in the target region is obtained, and whether the target region has a slow-moving region with deceleration and slow movement is determined based on the driving speed. In this way, by obtaining the real-time driving information of each vehicle on the road, the business needs of all scenes in the road can be fully covered, and the perception efficiency in the road scene can be greatly improved, and the slow-moving region with deceleration and slow movement in the road can be quickly and accurately located.

[0127] The embodiments of the present disclosure also provide an electronic device, which comprises at least one processor, a memory for storing instructions executable by the at least one processor, and wherein the at least one processor is configured to execute the instructions to implement the above-mentioned method disclosed by the embodiments of the present disclosure.

[0128] Figure 4A structural diagram of an electronic device is provided for an exemplary embodiment of the present disclosure. As shown in Figure 4 The electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801, which can perform corresponding steps in the above-described method disclosed by the embodiments of the present disclosure.

[0129] The processor 1801 can also be referred to as a central processing unit (CPU), which can be an integrated circuit chip having a processing capability of signals. Each step in the above-described method disclosed by the embodiments of the present disclosure can be completed by integrated logic circuits of hardware or instructions in the form of software in the processor 1801. The processor 1801 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware coding processor for execution, or a combination of hardware and software modules in the coding processor for execution. The software module can be located in the memory 1802, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, or other mature storage medium in the art. The processor 1801 reads information in the memory 1802 and completes the steps of the above-described method in conjunction with the hardware thereof.

[0130] In addition, various operations / processes according to the present disclosure are implemented by software and / or firmware, which can be installed in a computer system with a dedicated hardware structure, such as Figure 5 As shown in the computer system 1900, the computer system can perform various functions when various programs are installed, including functions such as those described above. Figure 5 A structural block diagram of a computer system is provided for an exemplary embodiment of the present disclosure.

[0131] The computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0132] As shown in Figure 5 The computer system 1900 includes a computing unit 1901 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 into a random access memory (RAM) 1903. Various programs and data required for the operation of the computer system 1900 can also be stored in the RAM 1903. The computing unit 1901, the ROM 1902, and the RAM 1903 are connected to each other through a bus 1904. An input / output (I / O) interface 1905 is also connected to the bus 1904.

[0133] Various components in the computer system 1900 are connected to the I / O interface 1905, including an input unit 1906, an output unit 1907, the storage unit 1908, and a communication unit 1909. The input unit 1906 can be any type of device that can input information to the computer system 1900, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls of the electronic device. The output unit 1907 can be any type of device that can present information, and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1908 can include, but is not limited to, a magnetic disk, an optical disk. The communication unit 1909 allows the computer system 1900 to exchange information / data with other devices through a network such as the Internet, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0134] The computing unit 1901 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 1901 performs various methods and processes described above. For example, in some embodiments, the above-described methods disclosed by the embodiments of the present disclosure can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1908. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device via the ROM 1902 and / or the communication unit 1909. In some embodiments, the computing unit 1901 can be configured to perform the above-described methods disclosed by the embodiments of the present disclosure by any other appropriate means (e.g., by means of firmware).

[0135] The embodiments of the present disclosure also provide a computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the above-described methods disclosed by the embodiments of the present disclosure.

[0136] The computer-readable storage medium in the embodiments of the present disclosure can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The above-described computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any appropriate combination thereof. More specifically, the above-described computer-readable storage medium can include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or a flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any appropriate combination thereof.

[0137] The above-described computer-readable medium can be contained in the above-described electronic device; or can exist separately without being assembled into the electronic device.

[0138] The embodiments of the present disclosure also provide a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the above-described methods disclosed by the embodiments of the present disclosure.

[0139] Computer program code for carrying out operations of the present disclosure can be written in any one or more of a variety of programming languages or combinations of languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0140] The flow diagrams and the block diagrams in the drawings are meant as possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0141] The modules, components or units described in the embodiments of the present disclosure can be implemented by software or by hardware. In some cases, the name of the module, component or unit does not constitute a limitation on the module, component or unit itself.

[0142] The functions described above in the detailed description of embodiments of the present disclosure can be implemented in one or more hardware logic components or by computer instructions that are executed in a hardware logic component. For example, and without limitation, illustrative hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-Chip (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0143] The above description is merely exemplary of some embodiments of the present disclosure and of the principles thereof. It is to be understood that the disclosure is not limited in scope to the particular embodiments described herein, which are intended as examples only, and that the scope of the disclosure is not limited to the particular arrangements and instrumentality described herein except as described by the appended claims. Thus, the embodiments are illustrative rather than limiting.

[0144] While some specific embodiments of the present disclosure have been described in detail, those skilled in the art should understand that the above examples are merely illustrative of some embodiments of the present disclosure and of the principles thereof. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A data processing method, characterized by, The method comprises: acquiring vehicle driving state information collected in real time by a plurality of target vehicles in a target area during driving, the vehicle driving state information comprising positioning information; based on the positioning information, acquiring a position point in a target map that matches the positioning information; wherein the position point has corresponding driving route information; fusing the driving route information and the vehicle driving state information to obtain a driving speed of the vehicles on each driving route in the target area, and determining whether the target area contains a slow-speed driving buffer area based on the driving speed.

2. The method of claim 1, wherein, The method further comprises: acquiring intersection information, road information and lane information in a preset map, and establishing an association relationship between the intersection information, the road information and the lane information; generating a topological relationship graph in the preset map based on the association relationship, and generating the target map based on the topological relationship graph; wherein the topological relationship graph comprises position points and lines, the position points and the lines respectively representing roads and lane lines in the roads, different roads in the topological relationship graph respectively corresponding to different weights, and the weights being positively correlated with the association degrees between the roads.

3. The method of claim 2, wherein, The method further comprises: inputting the positioning information into a search tree pre-constructed in the target map, the search tree comprising a corresponding relationship set between road information, lane information and position points; obtaining a position point in the relationship set that matches the positioning information.

4. The method of claim 3, wherein, The method further comprises: acquiring distances between the positioning information and each position point in the relationship set; determining a position point in the target map that matches the positioning information based on the distances.

5. The method of claim 1, wherein, The method further comprises: dividing the target map into a plurality of grids; wherein each grid represents a corresponding area; acquiring current driving speeds of vehicles under a target grid in a target lane in the target map; in a case where there are slow-speed driving vehicles under the target grid, and the number of the slow-speed driving vehicles is greater than a preset number, determining that the target area contains a slow-speed driving buffer area, and taking the area corresponding to the target grid as the slow-speed driving buffer area.

6. The method of claim 5, wherein, The method further comprises: acquiring historical driving speeds of a plurality of vehicles in the target lane; based on the historical driving speeds, acquiring a historical average driving speed of the target lane, and determining the speed threshold based on the historical average driving speed.

7. The method of claim 5, wherein, The method further comprises: sending traffic information to a roadside device corresponding to the slow-speed driving buffer area, and stopping sending the traffic information to the roadside device corresponding to the slow-speed driving buffer area in a case where the slow-speed driving buffer area is removed; wherein the traffic information comprises road information, lane information, area range information and time information corresponding to the slow-speed driving buffer area.

8. A data processing apparatus, characterized by, The device comprises: An information acquisition module is configured to acquire vehicle driving state information collected in real time by a plurality of target vehicles in a target area during driving, the vehicle driving state information including positioning information; A position point determination module is configured to acquire a position point in a target map that matches the positioning information based on the positioning information, wherein the position point has corresponding driving route information; A slow driving area determination module is configured to fuse the driving route information and the vehicle driving state information to obtain a driving speed of the vehicles on each driving route in the target area, and determine whether there is a slow driving area in the target area based on the driving speed.

9. An electronic device, comprising: comprise: at least one processor; a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the method of any one of claims 1-7.

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