Traffic heat map generation method and device, equipment, storage medium and program product

Abstract

This application discloses a method, apparatus, device, storage medium, and program product for generating traffic heat maps, belonging to the field of autonomous driving technology. The method includes: receiving a data filtering instruction, the data filtering instruction including target data tags; filtering target traffic flow data from a preset database according to the target data tags, the preset database storing multiple traffic flow data collected by different data collection vehicles, the traffic flow data representing the number of traffic participants, and the traffic flow data corresponding to multiple data tags, including road segment tags and traffic participant tags, the road segment tags representing the road segment where the traffic participant is located, and the traffic participant tags representing the characteristics of the traffic participant; and generating a traffic heat map based on the target traffic flow data. The technical solution provided in this application can improve the flexibility and richness of traffic heat map generation.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method, apparatus, device, storage medium, and program product for generating traffic heat maps. Background Technology

[0002] In the field of autonomous driving, evaluating the test performance of autonomous driving and attributing the causes of test problems are very important aspects. Traffic heat maps can generally be used to evaluate the test performance of autonomous driving and attribute the causes of test problems.

[0003] However, existing methods for generating traffic heat maps suffer from poor flexibility and produce relatively simplistic traffic heat maps. Summary of the Invention

[0004] Therefore, it is necessary to provide a traffic heatmap generation method, apparatus, equipment, storage medium, and program product that can improve the flexibility and richness of traffic heatmap generation, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a method for generating traffic heat maps. The method includes:

[0006] The system receives a data filtering instruction, which includes a target data label. Based on this target data label, it filters target traffic flow data from a preset database. This database stores multiple traffic flow data points collected by different data collection vehicles. This traffic flow data represents the number of traffic participants and corresponds to multiple data labels, including road segment labels and traffic participant labels. The road segment labels represent the road segment where the traffic participant is located, and the traffic participant labels represent the characteristics of the traffic participant. A traffic heatmap is generated based on the target traffic flow data.

[0007] In one embodiment, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle; the relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0008] In one embodiment, the plurality of data tags also includes acquisition feature tags, which are used to characterize the data acquisition features of traffic flow data.

[0009] In one embodiment, the process of constructing the preset database includes:

[0010] The system acquires initial traffic flow data for each road segment collected by the target data collection vehicle while it travels along the target route. This initial traffic flow data includes the types of traffic participants, their location information, and the location information of the target data collection vehicle. For each road segment, the system determines the relative positional relationship between the traffic participants and the target data collection vehicle based on the location information of the traffic participants and the target data collection vehicle. Multiple traffic flow data points are generated based on the traffic participants included in the initial traffic flow data. Data tags are generated for each traffic flow data point based on the road segment, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle. Finally, a pre-defined database is constructed based on the traffic flow data points and their corresponding data tags.

[0011] In one embodiment, data labels corresponding to each traffic flow data are generated based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle, including:

[0012] The data collection features are determined based on the target data collection vehicle and its driving characteristics on the target route. Data labels corresponding to each traffic flow data are generated based on the data collection features, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0013] In one embodiment, the data acquisition features include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0014] In one embodiment, determining the relative positional relationship between the traffic participants and the target data collection vehicle on the road segment based on the location information of traffic participants collected on the road segment and the location information of the target data collection vehicle includes:

[0015] Based on the location information of traffic participants and the target data collection vehicle collected on the road segment, the lanes of each traffic participant, the lane of the target data collection vehicle, and the front and rear position information of each traffic participant are determined. The front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle. Based on the lanes of each traffic participant, the lane of the target data collection vehicle, and the front and rear position information of each traffic participant, the relative positional relationship between the traffic participants and the target data collection vehicle on the road segment is determined.

[0016] In one embodiment, generating a traffic heat map based on the target traffic flow data includes: determining the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; and generating the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0017] Secondly, this application also provides a traffic heat map generation device. The device includes:

[0018] The receiving module is used to receive data filtering instructions, which include target data tags.

[0019] The filtering module is used to filter target traffic flow data from a preset database based on the target data label. The preset database stores multiple traffic flow data collected by different data collection vehicles. The traffic flow data is used to represent the number of traffic participants. The traffic flow data corresponds to multiple data labels, including road segment labels and traffic participant labels. The road segment labels are used to represent the road segment where the traffic participant is located, and the traffic participant labels are used to represent the characteristics of the traffic participant.

[0020] The generation module is used to generate a traffic heat map based on the target traffic flow data.

[0021] In one embodiment, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle; the relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0022] In one embodiment, the plurality of data tags also includes acquisition feature tags, which are used to characterize the data acquisition features of traffic flow data.

[0023] In one embodiment, the device further includes a construction module, configured to: acquire initial traffic flow data for each road segment collected by the target data collection vehicle while it travels on the target route, the initial traffic flow data including the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle; for each road segment, determine the relative positional relationship between the traffic participants on that road segment and the target data collection vehicle based on the location information of the traffic participants collected on that road segment and the location information of the target data collection vehicle; generate multiple traffic flow data based on each traffic participant included in the initial traffic flow data, and generate data tags corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle; and construct the preset database based on each traffic flow data and the data tags corresponding to each traffic flow data.

[0024] In one embodiment, the construction module is specifically used to: determine data collection features based on the target data collection vehicle and the driving characteristics of the target data collection vehicle on the target route; and generate data labels corresponding to each traffic flow data based on the data collection features, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0025] In one embodiment, the data acquisition features include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0026] In one embodiment, the construction module is specifically used to: determine the lane where each traffic participant is located, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant based on the location information of traffic participants collected on the road segment and the location information of the target data collection vehicle, wherein the front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle; and determine the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle based on the lane where each traffic participant is located, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant.

[0027] In one embodiment, the generation module is specifically configured to: determine the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; and generate the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0028] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement any of the steps described in the first aspect above.

[0029] Fourthly, this application also provides a computer-readable storage medium. This computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements any of the steps described in the first aspect above.

[0030] Fifthly, this application also provides a computer program product. This computer program product includes a computer program that, when executed by a processor, implements any of the steps described in the first aspect above.

[0031] The beneficial effects of the technical solutions provided in this application include at least the following:

[0032] Upon receiving a data filtering instruction, the system uses target data tags to filter multiple traffic flow data stored in a preset database to obtain target traffic flow data. Based on this target traffic flow data, a traffic heatmap is generated. This heatmap can then be used to evaluate the performance of autonomous driving tests and attribute problems in autonomous driving testing. Compared to existing methods of generating traffic heatmaps, which suffer from poor flexibility and a limited variety of generated heatmaps, this embodiment of the application, during the filtering process, can select target traffic flow data from the preset database based on the actual required target data tags, thereby improving the flexibility and richness of traffic heatmap generation. Attached Figure Description

[0033] Figure 1 This is an application environment diagram of the traffic heatmap generation method in one embodiment;

[0034] Figure 2 This is a flowchart illustrating a traffic heatmap generation method in one embodiment;

[0035] Figure 3 This is an exemplary traffic heatmap in one embodiment;

[0036] Figure 4 This is an exemplary traffic heatmap in one embodiment;

[0037] Figure 5 This is a flowchart illustrating the steps of constructing a preset database in one embodiment;

[0038] Figure 6 This is a schematic diagram illustrating that traffic participants and target data collection vehicles are located in different lanes in one embodiment;

[0039] Figure 7 This is a schematic diagram illustrating different relative positional relationships between traffic participants and target data collection vehicles in one embodiment;

[0040] Figure 8 This is a schematic diagram illustrating the statistical analysis of the number of traffic participants of the same type based on road segments as the quantity fusion dimension in one embodiment;

[0041] Figure 9 This is a flowchart illustrating the steps of a method for generating a traffic heatmap in one embodiment.

[0042] Figure 10 This is a flowchart illustrating a traffic heatmap generation method in one embodiment;

[0043] Figure 11 This is a flowchart illustrating a traffic heatmap generation method in one embodiment;

[0044] Figure 12 This is a structural block diagram of a traffic heatmap generation device in one embodiment;

[0045] Figure 13 This is a structural block diagram of a traffic heatmap generation device in one embodiment;

[0046] Figure 14 A block diagram of a computer device in one embodiment;

[0047] Figure 15 This is a block diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] Level 4 autonomous driving is a complex engineering project integrating vehicle systems, sensors, mapping and localization, perception, planning, control, scene understanding and evaluation, and simulation. The iterative updates of Level 4 autonomous driving systems involve algorithm development, simulation testing, real-world road testing, test problem analysis, and algorithm iteration. In real-world road testing, weather conditions, autonomous vehicles, road characteristics of the autonomous vehicle's location, and other vehicles collectively constitute the autonomous driving test environment. Traffic flow conditions in the autonomous vehicle's location, as a crucial road characteristic, significantly influence the autonomous vehicle's decision-making and performance. Furthermore, in test problem analysis, traffic flow conditions in the autonomous vehicle's location become an important means of attributing problems. In other words, evaluating traffic flow conditions in the autonomous vehicle's location is a vital step in evaluating autonomous driving test performance and analyzing the causes of autonomous driving test problems. Traffic flow assessment is a technique integrating mathematics, physics, and computer science, which can help us better understand traffic phenomena and their essence, predict traffic trends, and optimize urban road design and travel strategies. Generally speaking, traffic heat maps can be used to reflect traffic flow conditions on road segments. However, existing methods for generating traffic heat maps suffer from poor flexibility and produce relatively simplistic results.

[0050] This application provides a traffic heatmap generation method. Upon receiving a data filtering instruction, it filters target traffic flow data from multiple traffic flow data stored in a preset database using target data tags. A traffic heatmap is then generated based on the obtained target traffic flow data. This heatmap can then be used to evaluate the performance of autonomous driving tests and attribute problems in autonomous driving testing. Compared to existing methods of generating traffic heatmaps, which suffer from poor flexibility and a limited variety of generated heatmaps, this application embodiment allows for the filtering of target traffic flow data from a preset database based on the actual required target data tags, thereby improving the flexibility and richness of traffic heatmap generation.

[0051] The traffic heatmap generation method provided in this application can be applied to, for example... Figure 1 The application environment shown.

[0052] Please refer to Figure 1 The application environment may include server 101 and terminal 102. Optionally, the application environment may include only server 101, or only terminal 102. When the application environment includes both server 101 and terminal 102, server 101 may deploy the preset database and can act as the execution entity to implement the traffic heat map generation method provided in this application embodiment through interaction with terminal 102. When the application environment includes only server 101, server 101 may deploy the preset database and can act as the execution entity to implement the traffic heat map generation method provided in this application embodiment. When the application environment includes only terminal 102, terminal 102 may deploy the preset database and can act as the execution entity to implement the traffic heat map generation method provided in this application embodiment.

[0053] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can be smart in-vehicle devices, and portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The server 101 can be a single server or a server cluster consisting of multiple servers. This application does not limit the specific types of terminals and servers, as long as they can perform the corresponding processing and ultimately achieve the goal of generating a traffic heat map.

[0054] In one embodiment, such as Figure 2 As shown, a method for generating traffic heat maps is provided. As mentioned above, the execution subject of this traffic heat map generation method can be a server or a terminal. For ease of explanation, the execution subject will be referred to as a computer device below. Figure 2 As shown, the method for generating traffic heatmaps includes the following steps:

[0055] Step 201: The computer device receives the data filtering instruction.

[0056] The data filtering instruction includes the target data label.

[0057] As described above, the executing entity can be a server. When the executing entity is a server, the server can receive the data filtering instruction sent by the terminal. The terminal can provide a data filtering interface containing multiple data tag input fields, allowing the user to input the corresponding data tag values ​​according to their needs, thereby obtaining the target data tag based on the user's input. After obtaining the target data tag, the terminal can send a data filtering instruction carrying that target data tag to the server.

[0058] As mentioned above, the executing entity can be a terminal. In the case of a terminal, similar to the above, the terminal can provide a data filtering interface. The data filtering interface can contain multiple data label input items, allowing users to input the corresponding data labels according to their own needs. Based on the user's input, the target data label is obtained. After obtaining the target data label, the terminal can obtain the data filtering instruction.

[0059] Step 202: The computer equipment filters and obtains the target traffic flow data from the preset database based on the target data label.

[0060] The preset database stores multiple traffic flow data points collected by different data collection vehicles. These traffic flow data points are used to represent the number of traffic participants, and each traffic flow data point corresponds to multiple data tags.

[0061] In an optional embodiment of this application, the data collection vehicle can be an autonomous vehicle. Different data collection vehicles can collect data related to driving (referred to as initial traffic flow data in this embodiment of the application) during the driving process, thereby obtaining multiple traffic flow data and data labels corresponding to each traffic flow data based on the initial traffic flow data.

[0062] Optionally, the data collection vehicle may be equipped with sensing devices, which can be used to collect initial traffic flow data. These sensing devices may include radar (e.g., lidar, millimeter-wave radar), cameras (e.g., color cameras, depth cameras), positioning components (e.g., GPS), etc. In addition, in optional embodiments of this application, the data collection vehicle may also be equipped with communication devices, which can be used to interact with servers, other vehicles, or roadside equipment to obtain initial traffic flow data.

[0063] Optionally, the initial traffic flow data may include, for example, the location information of traffic participants, the type of traffic participants, and the location information of the data collection vehicle, etc., which are not specifically limited in this embodiment. In the optional embodiments of this application, traffic participants refer to moving and non-moving objects in the road segment. For example, the traffic participants can be cars, trucks, bicycles, electric vehicles, pedestrians, roadblocks, cones, etc., which are not specifically limited in this embodiment. Correspondingly, the type of traffic participant can include car type, truck type, bicycle type, electric vehicle type, pedestrian type, roadblock type, cone type, etc., or the type of traffic participant can include motor vehicle type, non-motor vehicle type, etc., which are also not specifically limited in this embodiment.

[0064] It should be noted that, in the optional embodiments of this application, the multiple traffic flow data and the corresponding data tags of each traffic flow data can be obtained not only from the initial traffic flow data collected by the data collection vehicle, but also from other data. These other data can be, for example, data characterizing the data collection features. For instance, the data characterizing the data collection features can be the identifier of the data collection vehicle, the version of the autonomous driving system installed in the data collection vehicle, the identifier of the route traveled by the data collection vehicle, and the time information of the data collection vehicle traveling on that route, etc. This application embodiment does not specifically limit this. Optionally, the time information can include a period of time with two specific moments as two endpoints. The time information can also include information reflecting the characteristics of that period of time, such as whether the period of time is a busy traffic period, etc. This application embodiment does not specifically limit this.

[0065] In an optional embodiment of this application, the multiple data tags corresponding to traffic flow data may include at least road segment tags and traffic participant tags. The road segment tag is used to characterize the road segment where the traffic participant is located, and the traffic participant tag is used to characterize the characteristics of the traffic participant. In this embodiment, a road segment refers to a section of road containing at least one lane that is accessible for traffic participants to drive on or to carry traffic participants.

[0066] In an optional embodiment of this application, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle. The type of traffic participant has been described above and will not be repeated here. The relative positional relationship between the traffic participant and the data collection vehicle may include the front-rear relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0067] For example, the relative positional relationship between a traffic participant and a data-collecting vehicle can include: directly in front of the data-collecting vehicle, directly behind the data-collecting vehicle, behind the data-collecting vehicle in the left lane, in front of the data-collecting vehicle in the left lane, behind the data-collecting vehicle in the right lane, and in front of the data-collecting vehicle in the right lane. Specifically, being directly in front of or directly behind the data-collecting vehicle indicates that the traffic participant and the data-collecting vehicle are in the same lane. As explained above, the relative positional relationship represented by the traffic participant label is a lane-level relative positional relationship.

[0068] In an optional embodiment of this application, the multiple data tags corresponding to traffic flow data may further include collection feature tags, which are used to characterize the data collection features of traffic flow data. As described above, the data collection features may include the identifier of the data collection vehicle, the version of the autonomous driving system installed in the data collection vehicle, the identifier of the route traveled by the data collection vehicle, and the time information of the data collection vehicle traveling on the route, etc.

[0069] To facilitate the reader's understanding of the preset database provided in the embodiments of this application, please refer to Table 1, which illustrates a schematic diagram of traffic flow data stored in an exemplary preset database.

[0070] Table 1

[0071]

[0072] Among them, feature a may include, for example, the identifier of the data collection vehicle being #1, the version of the autonomous driving system installed in the data collection vehicle being #1, the identifier of the route traveled by the data collection vehicle being #1, and the time information of the data collection vehicle traveling on the route being normal hours; feature b may include, for example, the identifier of the data collection vehicle being #2, the version of the autonomous driving system installed in the data collection vehicle being #2, the identifier of the route traveled by the data collection vehicle being #2, and the time information of the data collection vehicle traveling on the route being morning hours; feature c may include, for example, the identifier of the data collection vehicle being #3, the version of the autonomous driving system installed in the data collection vehicle being #3, the identifier of the route traveled by the data collection vehicle being #3, and the time information of the data collection vehicle traveling on the route being evening hours.

[0073] In an optional embodiment of this application, after receiving a data filtering instruction, the computer device can extract the target data tag contained in the data filtering instruction. Then, the computer device can match the target data tag with the data tag corresponding to each traffic flow data in the preset database, and obtain the successfully matched traffic flow data as the target traffic flow data.

[0074] It should be noted that, in the optional embodiments of this application, a successful match means that the target data label is completely consistent with some of the data labels among the multiple data labels corresponding to a certain traffic flow data, or that the target data label is completely consistent with all of the data labels among the multiple data labels corresponding to a certain traffic flow data.

[0075] For example, suppose the target data label is segment C, pedestrian, located in front of the left lane of the data collection vehicle, the data collection vehicle is identified as #3, the version of the autonomous driving system installed in the data collection vehicle is #3, the route traveled by the data collection vehicle is identified as #3, and the time information of the data collection vehicle traveling on this route is evening. Then, it is completely consistent with all the data labels corresponding to the traffic flow data in row 3 of Table 1. Therefore, the traffic flow data "30" in row 3 is the target traffic flow data that has been matched.

[0076] For another example, suppose the target data label is C segment, pedestrian, located in front of the left lane of the data collection vehicle, the data collection vehicle is identified as #3, the version of the autonomous driving system installed in the data collection vehicle is #3, and the identification of the route traveled by the data collection vehicle is #3. It can be seen that the target data label is missing the time information label. In this case, since the target data label is completely consistent with the partial data label (label without time information) corresponding to the traffic flow data in row 3 of Table 1, the traffic flow data "30" in row 3 is the matched target traffic flow data.

[0077] Step 203: The computer equipment generates a traffic heat map based on the target traffic flow data.

[0078] In an optional embodiment of this application, the traffic heat map can be a fusion of lines and a static traffic map, wherein the lines overlap with road segments in the static traffic map to reflect the number of traffic participants in the corresponding road segments.

[0079] Please refer to Figure 3 It shows an exemplary traffic heatmap, such as Figure 3 As shown, the target traffic flow data used to generate this traffic heatmap is obtained from a preset database based on target data labels containing the following information: 1. Date: YYY-MM-DD; 2. Identifier of the data collection vehicle: #n; 3. Version of the autonomous driving system installed in the data collection vehicle: #n; 4. Time information: morning, noon, evening, and weekday periods; 5. Identifier of the route traveled by the data collection vehicle: fixed route #n; 6. Type of traffic participant: four-wheeled vehicle, pedestrian, bicycle, cone, and roadblock. Figure 3 As shown, the traffic heat map includes a line that overlaps with multiple road segments in the static traffic map, and the color of the line is used to reflect the number of traffic participants in the corresponding road segment.

[0080] In an optional embodiment of this application, after obtaining the traffic heat map, the performance evaluation of autonomous driving tests and the attribution analysis of autonomous driving test problems can be performed based on the traffic heat map.

[0081] In the context of attribution analysis for autonomous driving test problems, the relationship between traffic flow and autonomous driving problems can be quickly identified by comprehensively examining the number of traffic participants around an autonomous vehicle (i.e., a data collection vehicle) while it is driving on a certain road segment and the problems generated by the autonomous driving system on that road segment, thereby achieving the goal of attributing autonomous driving test problems.

[0082] For evaluating the performance of autonomous driving tests, please refer to... Figure 4 The graph shows traffic heatmaps for a fixed route in a certain location at different times (midday and regular times). The graph shows that there are more traffic participants of different vehicle types on the same road segment during midday than during regular times. Based on this information, it can be predicted that autonomous vehicles may encounter more problems when driving on this road segment during midday, while fewer problems may occur during regular times. Therefore, more autonomous driving system tests can be conducted on this road segment during midday to collect more test questions.

[0083] The traffic heatmap generation method provided in this embodiment, upon receiving a data filtering instruction, uses target data labels to filter target traffic flow data from multiple traffic flow data stored in a preset database. Based on the obtained target traffic flow data, a traffic heatmap is generated. This traffic heatmap can then be used to evaluate the performance of autonomous driving tests and attribute problems in autonomous driving testing. Compared to existing methods of generating traffic heatmaps, which suffer from poor flexibility and a limited variety of generated heatmaps, this embodiment, during the filtering process, can select target traffic flow data from the preset database based on the actual required target data labels, thereby improving the flexibility and richness of traffic heatmap generation.

[0084] In one embodiment, such as Figure 5 As shown, an optional preset database construction process is provided, wherein the construction process includes the following steps:

[0085] Step 501: The computer equipment acquires the initial traffic flow data of each road segment collected when the target data collection vehicle travels on the target route.

[0086] In an optional embodiment of this application, the target route may include multiple road segments. For example, the target route #n may include road segment A, road segment B, and road segment C. The target data collection vehicle can collect the initial traffic flow data corresponding to each road segment while driving on the target route. Optionally, the target data collection vehicle can periodically collect data.

[0087] It should be noted that the number of target data collection vehicles is not limited in this application embodiment. In other words, in this application embodiment, there can be multiple target data collection vehicles. In this case, the target routes traveled by different target data collection vehicles can be different. Each target data collection vehicle can collect the initial traffic flow data corresponding to each road segment when it travels on its corresponding target route. The computer device can acquire the initial traffic flow data collected by each target data collection vehicle.

[0088] Similarly, as described above, the initial traffic flow data in step 501 may include the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle. As also described above, the target data collection vehicle may be an autonomous vehicle equipped with sensing and / or communication devices. This vehicle can collect the initial traffic flow data corresponding to each road segment based on these sensing and / or communication devices.

[0089] It should be noted that the accuracy of the location information of traffic participants collected by the target data collection vehicle and the location information of the target data collection vehicle can be at the centimeter level.

[0090] Step 502: For each road segment, the computer equipment determines the relative positional relationship between the traffic participants and the target data collection vehicle based on the location information of the traffic participants collected on that road segment and the location information of the target data collection vehicle.

[0091] Similarly, the relative positional relationship in step 502 can include the front-to-back relative positional relationship and the lane relative positional relationship between the traffic participant and the target data collection vehicle. For example, the relative positional relationship in step 502 can include: directly in front of the target data collection vehicle, directly behind the target data collection vehicle, behind the left lane of the target data collection vehicle, in front of the left lane of the target data collection vehicle, behind the right lane of the target data collection vehicle, and in front of the right lane of the target data collection vehicle. Among these, being directly in front of the target data collection vehicle and being directly behind the target data collection vehicle indicates that the traffic participant and the target data collection vehicle are in the same lane.

[0092] In an optional embodiment of this application, the computer device can determine the lane where the traffic participant is located, the lane where the target data collection vehicle is located, and the front and rear position information of the traffic participant based on the location information of the traffic participant and the location information of the target data collection vehicle. The front and rear position information is used to characterize the relative front and rear position relationship between the traffic participant and the target data collection vehicle, that is, the front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle.

[0093] Then, the computer equipment can determine the relative positional relationship between the traffic participant and the target data collection vehicle based on the lane where the traffic participant is located, the lane where the target data collection vehicle is located, and the front and rear position information of the traffic participant. Specifically, the computer equipment can determine the lane-relative positional relationship between the traffic participant and the target data collection vehicle based on the lane-relative positional relationship and the front and rear relative positional relationship indicated by the front and rear relative position information.

[0094] Optionally, the computer equipment can map the traffic participants and the target data collection vehicles onto a static traffic map based on the location information of the traffic participants and the location information of the target data collection vehicles. Optionally, the static traffic map can be a high-precision map at the centimeter level.

[0095] After mapping traffic participants and target data collection vehicles onto a static traffic map, computer equipment can determine the lanes of the traffic participants and target data collection vehicles based on the coordinate range of each lane in the static traffic map, the coordinates of the traffic participants mapped onto the static traffic map, and the coordinates of the target data collection vehicles mapped onto the static traffic map. Based on this, traffic participants can be divided into those in the same lane as the target data collection vehicle and those in a different lane.

[0096] Among them, traffic participants not located in the same lane as the target data collection vehicle can include traffic participants in the lane to the left of the target data collection vehicle and traffic participants in the lane to the right of the target data collection vehicle. Optionally, if there is more than one lane to the left of the target data collection vehicle, then traffic participants in the lane to the left of the target data collection vehicle can include traffic participants in the first lane to the left of the target data collection vehicle, traffic participants in the second lane to the left of the target data collection vehicle, and so on. Similarly, if there is more than one lane to the right of the target data collection vehicle, then traffic participants in the lane to the right of the target data collection vehicle can include traffic participants in the first lane to the right of the target data collection vehicle, traffic participants in the second lane to the right of the target data collection vehicle, and so on.

[0097] Based on the above process, the lane-relative positional relationship between the traffic participant and the target data collection vehicle can be determined. Furthermore, the computer equipment can determine the forward and backward position information of the traffic participant based on the coordinates of the traffic participant mapped onto the static traffic map and the coordinates of the target data collection vehicle mapped onto the static traffic map. This forward and backward position information indicates the relative forward and backward position of the traffic participant relative to the target data collection vehicle. Combining the lane-relative positional relationship and the forward and backward relative positional relationship, the computer equipment can obtain the relative positional relationship between the traffic participant and the target data collection vehicle.

[0098] Please refer to Figure 6 It shows a schematic diagram of traffic participants and target data collection vehicles located in different lanes, such as... Figure 6 As shown, traffic participant A is in the same lane as the target data collection vehicle S, traffic participant B is in the left lane of the target data collection vehicle S, and traffic participant C is in the right lane of the target data collection vehicle S.

[0099] Please refer to Figure 7 It shows the different relative positional relationships between traffic participants and the target data collection vehicle, such as Figure 7As shown, traffic participant A is located directly in front of the target data collection vehicle S, traffic participant B is located directly behind the target data collection vehicle S, traffic participant C is located in front of the left lane of the target data collection vehicle S, traffic participant D is located behind the left lane of the target data collection vehicle S, traffic participant E is located in front of the right lane of the target data collection vehicle S, and traffic participant F is located behind the right lane of the target data collection vehicle S.

[0100] Step 503: The computer device generates multiple traffic flow data based on each traffic participant included in the initial traffic flow data, and generates data labels corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0101] In an optional embodiment of this application, for each traffic participant, the computer device can generate a data tag for that traffic participant based on the road segment where the traffic participant is located, the type of the traffic participant, and the relative positional relationship between the traffic participant and the target data collection vehicle.

[0102] Of course, similarly to the above, in addition to generating data tags for traffic participants based on the road segment where the traffic participant is located, the type of traffic participant, and the relative positional relationship between the traffic participant and the target data collection vehicle, data collection features can also be determined based on the target data collection vehicle and its driving characteristics on the target route. Then, by combining these data collection features, the road segment where the traffic participant is located, the type of traffic participant, and the relative positional relationship between the traffic participant and the target data collection vehicle, data tags corresponding to the traffic participants can be generated. This enables the generation of data tags corresponding to traffic flow data in subsequent steps by combining data collection features, the road segment where the traffic participant is located, the type of traffic participant, and the relative positional relationship between the traffic participant and the target data collection vehicle.

[0103] Similar to the above, the data acquisition features here may include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0104] After obtaining the data tags of each traffic participant, the computer device can merge the traffic participants with the same data tags. In other words, the computer device can count the number of traffic participants with the same data tags. The number of traffic participants is a traffic flow data, and the data tags corresponding to this traffic flow data are the data tags of the traffic participants merged into the traffic flow data.

[0105] For example, if the data labels for traffic participants A, B, C, and D include: the road segment L1 they are on, the type of vehicle, and their relative position to the target data collection vehicle (directly in front of the target data collection vehicle), then the number 4 traffic participants A, B, C, and D can be considered as one traffic flow data point. The data labels for this traffic flow data point include: the road segment L1 they are on, the type of vehicle, and their relative position to the target data collection vehicle (directly in front of the target data collection vehicle).

[0106] To make the technical process of step 503 easier for readers to understand, please refer to... Figure 8 ,like Figure 8 As shown, as an optional implementation, computer equipment can count the number of traffic participants of the same type according to road segments as the quantity fusion dimension, thereby obtaining traffic flow data.

[0107] Step 504: The computer equipment constructs the preset database based on each traffic flow data and the corresponding data tags.

[0108] The preset database has been explained in detail above in the embodiments of this application, and will not be repeated here.

[0109] In one embodiment, such as Figure 9 As shown, an optional method for generating traffic heatmaps is provided, which includes the following steps:

[0110] Step 901: The computer equipment determines the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data.

[0111] As mentioned above, the data labels corresponding to traffic flow data include road segment labels. These road segment labels are used to characterize the road segment where traffic participants are located. Therefore, the data labels of the target traffic flow data obtained by the computer device from the preset database based on the target data labels should include road segment labels. Based on these road segment labels, the road segments related to the target traffic flow data can be obtained. In addition, the traffic flow data itself is used to characterize the number of traffic participants. Therefore, the number of traffic participants can be obtained based on the target traffic flow data. In this way, the number of traffic participants in each road segment related to the target traffic flow data can be determined based on the target traffic flow data.

[0112] For example, suppose there are 5 target traffic flow data points, where the first target traffic flow data point has 15 points and its corresponding road segment label indicates road segment A; the second target traffic flow data point has 20 points and its corresponding road segment label indicates road segment B; the third target traffic flow data point has 30 points and its corresponding road segment label indicates road segment C; the fourth target traffic flow data point has 25 points and its corresponding road segment label indicates road segment D; and the fifth target traffic flow data point has 19 points and its corresponding road segment label indicates road segment E. Then, in step 901, it can be determined that the number of traffic participants in road segment A is 15, the number of traffic participants in road segment B is 20, the number of traffic participants in road segment C is 30, the number of traffic participants in road segment D is 25, and the number of traffic participants in road segment E is 19.

[0113] Step 902: The computer device generates the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0114] As mentioned above, a traffic heat map can be a fusion of lines and a static traffic map. The lines overlap with road segments in the static traffic map to reflect the number of traffic participants in the corresponding road segments. After obtaining the number of traffic participants in each road segment related to the target traffic flow data, the computer device can generate lines based on the number of traffic participants in each road segment related to the target traffic flow data and fuse these lines with the static traffic map to obtain a traffic heat map.

[0115] In one embodiment, such as Figure 10 As shown, a method for generating traffic heatmaps is provided, which includes the following steps:

[0116] Step 1001: The computer equipment acquires the initial traffic flow data of each road segment collected when the target data collection vehicle travels on the target route.

[0117] Step 1002: For each road segment, the computer equipment determines the relative positional relationship between the traffic participants on that road segment and the target data collection vehicle.

[0118] Step 1003: The computer equipment determines the data collection characteristics based on the target data collection vehicle and the driving characteristics of the target data collection vehicle on the target route.

[0119] Step 1004: The computer equipment generates multiple traffic flow data and corresponding data tags for each traffic flow data based on the data collection characteristics, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0120] Step 1005: The computer equipment constructs a preset database based on each traffic flow data and the corresponding data tags.

[0121] Step 1006: The computer device receives a data filtering instruction, which includes a target data label.

[0122] Step 1007: The computer equipment filters and obtains the target traffic flow data from the preset database based on the target data label.

[0123] Step 1008: The computer equipment generates a traffic heat map based on the target traffic flow data.

[0124] To facilitate readers' understanding of the technical solutions provided in the embodiments of this application, please refer to... Figure 11 It illustrates a flowchart of the traffic heatmap generation method provided in an embodiment of this application, such as... Figure 11 As shown, autonomous driving test data can be acquired, which is the initial traffic flow data in this application embodiment. This initial traffic flow data can be processed periodically, with the processing interval being seconds or other automatic time intervals. After acquiring the autonomous driving test data, the location of the autonomous vehicle can be obtained from the autonomous driving test data, that is, the location information of the data collection vehicle described in this application embodiment. At the same time, the location of surrounding traffic participants can also be obtained, that is, the location information of traffic participants described in this application embodiment. Then, high-precision map lane information can be acquired, that is, the static traffic map described in this application embodiment. Next, the lane where the autonomous vehicle is located and the lanes where the surrounding traffic participants are located can be determined by combining the location of the autonomous vehicle, the location of the surrounding traffic participants, and the high-precision map lane information. Then, the relative positional relationship between the autonomous vehicle and the surrounding traffic participants can be determined based on the lane where the autonomous vehicle is located and the lanes where the surrounding traffic participants are located. Finally, a preset database is constructed based on the relative positional relationship to generate a traffic heat map based on the preset database.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] Based on the same inventive concept, this application also provides a traffic heat map generation apparatus for implementing the traffic heat map generation method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the traffic heat map generation apparatus provided below can be found in the limitations of the traffic heat map generation method described above, and will not be repeated here.

[0127] In one embodiment, such as Figure 12 As shown, a traffic heat map generation device 1200 is provided, including: a receiving module 1201, a filtering module 1202, and a generation module 1203, wherein:

[0128] The receiving module 1201 is used to receive a data filtering instruction, which includes a target data label.

[0129] The filtering module 1202 is used to filter target traffic flow data from a preset database according to the target data label. The preset database stores multiple traffic flow data collected by different data collection vehicles. The traffic flow data is used to represent the number of traffic participants. The traffic flow data corresponds to multiple data labels, including road segment labels and traffic participant labels. The road segment labels are used to represent the road segment where the traffic participant is located, and the traffic participant labels are used to represent the characteristics of the traffic participant.

[0130] The generation module 1203 is used to generate a traffic heat map based on the target traffic flow data.

[0131] In one embodiment, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle; the relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0132] In one embodiment, the plurality of data tags also includes acquisition feature tags, which are used to characterize the data acquisition features of traffic flow data.

[0133] In an optional embodiment of this application, the generation module 1203 is specifically used to: determine the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; and generate the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0134] This application embodiment also provides another traffic heat map generation device 1300, which, in addition to including the various modules included in the traffic heat map generation device 1200, also includes a construction module 1204.

[0135] The construction module 1204 is used to: acquire initial traffic flow data of each road segment collected by the target data collection vehicle when it travels on the target route, the initial traffic flow data including the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle; for each road segment, determine the relative positional relationship between the traffic participants and the target data collection vehicle based on the location information of the traffic participants and the location information of the target data collection vehicle collected on that road segment; generate multiple traffic flow data based on each traffic participant included in the initial traffic flow data, and generate data tags corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle; and construct the preset database based on each traffic flow data and the data tags corresponding to each traffic flow data.

[0136] In an optional embodiment of this application, the construction module 1204 is specifically used to: determine data collection features based on the target data collection vehicle and the driving characteristics of the target data collection vehicle on the target route; and generate data labels corresponding to each traffic flow data based on the data collection features, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0137] In an optional embodiment of this application, the data acquisition feature includes at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0138] In an optional embodiment of this application, the construction module 1204 is specifically used to: determine the lane where each traffic participant is located, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant based on the position information of traffic participants collected on the road segment and the position information of the target data collection vehicle, wherein the front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle; and determine the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle based on the lane where each traffic participant is located, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant.

[0139] The various modules in the aforementioned traffic heat map generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0140] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 14 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores target traffic flow data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a traffic heatmap generation method.

[0141] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 15As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for generating traffic heat maps. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0142] Those skilled in the art will understand that Figure 14 or Figure 15 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0143] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0144] The system receives a data filtering instruction, which includes a target data label. Based on this target data label, it filters target traffic flow data from a preset database. This database stores multiple traffic flow data points collected by different data collection vehicles. This traffic flow data represents the number of traffic participants and corresponds to multiple data labels, including road segment labels and traffic participant labels. The road segment labels represent the road segment where the traffic participant is located, and the traffic participant labels represent the characteristics of the traffic participant. A traffic heatmap is generated based on the target traffic flow data.

[0145] In one embodiment, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle; the relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0146] In one embodiment, the plurality of data labels further includes acquisition feature labels, which are used to characterize the data acquisition features of traffic flow data.

[0147] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring initial traffic flow data for each road segment collected by the target data collection vehicle while it is traveling on the target route, the initial traffic flow data including the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle; for each road segment, determining the relative positional relationship between the traffic participants on that road segment and the target data collection vehicle based on the location information of the traffic participants collected on that road segment and the location information of the target data collection vehicle; generating multiple traffic flow data based on each traffic participant included in the initial traffic flow data, and generating data tags corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle; and constructing the preset database based on each traffic flow data and the data tags corresponding to each traffic flow data.

[0148] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining data collection features based on the target data collection vehicle and the driving characteristics of the target data collection vehicle on the target route; generating data labels corresponding to each traffic flow data based on the data collection features, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0149] In one embodiment, the data acquisition features include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0150] In one embodiment, when the processor executes the computer program, it further implements the following steps: based on the location information of traffic participants collected on the road segment and the location information of the target data collection vehicle, it determines the lane where each traffic participant is located on the road segment, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant, wherein the front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle; based on the lane where each traffic participant is located on the road segment, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant, it determines the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle.

[0151] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; and generating the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0152] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0153] The system receives a data filtering instruction, which includes a target data label. Based on this target data label, it filters target traffic flow data from a preset database. This database stores multiple traffic flow data points collected by different data collection vehicles. This traffic flow data represents the number of traffic participants and corresponds to multiple data labels, including road segment labels and traffic participant labels. The road segment labels represent the road segment where the traffic participant is located, and the traffic participant labels represent the characteristics of the traffic participant. A traffic heatmap is generated based on the target traffic flow data.

[0154] In one embodiment, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle; the relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0155] In one embodiment, the plurality of data labels further includes acquisition feature labels, which are used to characterize the data acquisition features of traffic flow data.

[0156] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring initial traffic flow data for each road segment collected by the target data collection vehicle while it is traveling on the target route, the initial traffic flow data including the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle; for each road segment, determining the relative positional relationship between the traffic participants on that road segment and the target data collection vehicle based on the location information of the traffic participants collected on that road segment and the location information of the target data collection vehicle; generating multiple traffic flow data based on each traffic participant included in the initial traffic flow data, and generating data tags corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle; and constructing the preset database based on each traffic flow data and the data tags corresponding to each traffic flow data.

[0157] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining data collection features based on the target data collection vehicle and the driving characteristics of the target data collection vehicle on the target route; generating data labels corresponding to each traffic flow data based on the data collection features, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0158] In one embodiment, the data acquisition features include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0159] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the location information of traffic participants collected on the road segment and the location information of the target data collection vehicle, determining the lane where each traffic participant is located on the road segment, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant, wherein the front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle; and based on the lane where each traffic participant is located on the road segment, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant, determining the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle.

[0160] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; and generating the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0161] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0162] The system receives a data filtering instruction, which includes a target data label. Based on this target data label, it filters target traffic flow data from a preset database. This database stores multiple traffic flow data points collected by different data collection vehicles. This traffic flow data represents the number of traffic participants and corresponds to multiple data labels, including road segment labels and traffic participant labels. The road segment labels represent the road segment where the traffic participant is located, and the traffic participant labels represent the characteristics of the traffic participant. A traffic heatmap is generated based on the target traffic flow data.

[0163] In one embodiment, the traffic participant tag is specifically used to characterize the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle; the relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle and the lane relative positional relationship.

[0164] In one embodiment, the plurality of data labels further includes acquisition feature labels, which are used to characterize the data acquisition features of traffic flow data.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring initial traffic flow data for each road segment collected by the target data collection vehicle while it is traveling on the target route, the initial traffic flow data including the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle; for each road segment, determining the relative positional relationship between the traffic participants on that road segment and the target data collection vehicle based on the location information of the traffic participants collected on that road segment and the location information of the target data collection vehicle; generating multiple traffic flow data based on each traffic participant included in the initial traffic flow data, and generating data tags corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle; and constructing the preset database based on each traffic flow data and the data tags corresponding to each traffic flow data.

[0166] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining data collection features based on the target data collection vehicle and the driving characteristics of the target data collection vehicle on the target route; generating data labels corresponding to each traffic flow data based on the data collection features, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

[0167] In one embodiment, the data acquisition features include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

[0168] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the location information of traffic participants collected on the road segment and the location information of the target data collection vehicle, it determines the lane where each traffic participant is located on the road segment, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant, wherein the front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle; based on the lane where each traffic participant is located on the road segment and the front and rear position information of each traffic participant in the lane where the target data collection vehicle is located, it determines the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle.

[0169] In one embodiment, when the computer program is executed by a processor, it further performs the following steps: determining the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; and generating the traffic heat map based on the number of traffic participants in each road segment related to the target traffic flow data.

[0170] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0171] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0172] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A traffic heat map generation method, characterized by, The method includes: Receive a data filtering instruction, wherein the data filtering instruction includes target data tags; Target traffic flow data is obtained by filtering from a preset database based on the target data tags. The preset database stores multiple traffic flow data points collected by different data collection vehicles. The traffic flow data is used to represent the number of traffic participants, and the traffic flow data corresponds to multiple data tags, including road segment tags and traffic participant tags. The road segment tags are used to represent the road segment where the traffic participant is located, and the traffic participant tags are used to represent the characteristics of the traffic participant. Specifically, the traffic participant tags are used to represent the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicle. The relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicle, as well as the lane relative positional relationship. A traffic heat map is generated based on the target traffic flow data.

2. The method of claim 1, wherein, The plurality of data labels also include collection feature labels, which are used to characterize the data collection features of traffic flow data.

3. The method of claim 2, wherein, The process of constructing the preset database includes: Acquire initial traffic flow data for each road segment collected by the target data collection vehicle while it is traveling on the target route. The initial traffic flow data includes the type of traffic participants, the location information of the traffic participants, and the location information of the target data collection vehicle. For each of the aforementioned road segments, the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle is determined based on the location information of the traffic participants collected on the road segment and the location information of the target data collection vehicle. Multiple traffic flow data are generated based on each traffic participant included in the initial traffic flow data, and data labels corresponding to each traffic flow data are generated based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle. The preset database is constructed based on each of the traffic flow data and the corresponding data tags.

4. The method of claim 3, wherein, The process of generating data labels corresponding to each traffic flow data based on the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle includes: The data acquisition features are determined based on the target data acquisition vehicle and its driving characteristics on the target route. Data labels are generated for each traffic flow data based on the data collection characteristics, the road segment where each traffic participant is located, the type of each traffic participant, and the relative positional relationship between each traffic participant and the target data collection vehicle.

5. The method of claim 4, wherein, The data acquisition features include at least one of the following: the identifier of the target data acquisition vehicle, the version of the autonomous driving system installed in the target data acquisition vehicle, the time information of the target data acquisition vehicle traveling on the target route, and the identifier of the target route.

6. The method of claim 3, wherein, The step of determining the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle based on the position information of traffic participants collected on the road segment and the position information of the target data collection vehicle includes: Based on the location information of traffic participants collected on the road segment and the location information of the target data collection vehicle, the lane where each traffic participant is located on the road segment, the lane where the target data collection vehicle is located, and the front and rear position information of each traffic participant are determined respectively. The front and rear position information is used to indicate whether the traffic participant is in front of or behind the target data collection vehicle. Based on the lanes occupied by each traffic participant on the road segment, the lane occupied by the target data collection vehicle, and the front and rear position information of each traffic participant, the relative positional relationship between the traffic participants on the road segment and the target data collection vehicle is determined.

7. The method according to any one of claims 1 to 6, characterized in that, The step of generating a traffic heatmap based on the target traffic flow data includes: Determine the number of traffic participants in each road segment related to the target traffic flow data based on the target traffic flow data; The traffic heatmap is generated based on the number of traffic participants in each road segment associated with the target traffic flow data.

8. A traffic heat map generation apparatus characterized by comprising: The device includes: A receiving module is used to receive a data filtering instruction, wherein the data filtering instruction includes a target data tag; The filtering module is used to filter target traffic flow data from a preset database based on the target data tags. The preset database stores multiple traffic flow data points collected by different data collection vehicles. The traffic flow data is used to represent the number of traffic participants, and the traffic flow data corresponds to multiple data tags, including road segment tags and traffic participant tags. The road segment tags are used to represent the road segment where the traffic participant is located, and the traffic participant tags are used to represent the characteristics of the traffic participant. Specifically, the traffic participant tags are used to represent the type of traffic participant and the relative positional relationship between the traffic participant and the data collection vehicles. The relative positional relationship includes the front-to-back relative positional relationship between the traffic participant and the data collection vehicles, as well as the lane relative positional relationship. The generation module is used to generate a traffic heat map based on the target traffic flow data. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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