Data acquisition method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of vehicle data processing, in particular to a data acquisition method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a vehicle driving condition of a target vehicle; verifying whether the vehicle driving condition meets a data acquisition triggering condition; and under the condition that the vehicle driving condition meets the data acquisition triggering condition, performing data acquisition on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle. According to the invention, when the target vehicle triggers the key event, data acquisition operation can be carried out in time, and a data basis is provided for subsequent analysis of the vehicle driving state and the vehicle driving environment of the target vehicle after key information of the target vehicle is prevented.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle data processing, and in particular to a data acquisition method, device, computer equipment and storage medium. Background Art

[0002] With the rapid development of autonomous driving and intelligent transportation systems, the real-time perception and analysis of environmental information by vehicles during driving has become increasingly important. Currently, many intelligent driving systems rely on various sensors (such as lidar, cameras, ultrasonic sensors, etc.) to collect data about the surrounding environment. These data include not only static information (such as road signs, lane lines, etc.), but also dynamic information (such as the speed and movement trajectory of other vehicles).

[0003] Existing data collection methods have limitations. Currently, most intelligent driving systems use fixed time intervals to collect data, which makes it difficult to capture critical events that change instantaneously. Summary of the invention

[0004] Based on this, it is necessary to provide a data collection method, device, computer equipment and storage medium that can capture key data in response to the above technical problems.

[0005] In a first aspect, the present application provides a data collection method. The method comprises:

[0006] Obtaining the vehicle driving status of the target vehicle;

[0007] Verifying whether the vehicle driving condition meets the data collection triggering condition;

[0008] When the vehicle driving condition meets the data collection triggering condition, data collection is performed on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle.

[0009] In one embodiment, the data collection trigger includes: event trigger conditions, traffic sign trigger conditions, semantic information trigger conditions and driver operation trigger conditions; the verification of whether the vehicle driving condition meets the data collection trigger conditions includes:

[0010] Determining whether the target vehicle has a trigger event recorded in the event trigger condition according to the vehicle driving condition;

[0011] Determining whether there is a triggering traffic sign recorded in the traffic sign triggering condition in the driving environment of the target vehicle according to the driving condition of the vehicle;

[0012] Determining the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verifying whether the driving environment semantics meets the semantic information triggering condition;

[0013] The driver operation information of the target vehicle is determined according to the vehicle driving condition, and it is verified whether the driver operation information meets the driver operation trigger condition.

[0014] In one embodiment, determining the driving environment semantic information of the target vehicle according to the driving condition of the vehicle, and verifying whether the driving environment semantics meets the semantic information triggering condition, includes:

[0015] Based on the deep learning model and the vehicle driving condition, the driving environment semantic information of the target vehicle is determined.

[0016] In one embodiment, collecting the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle includes:

[0017] Collecting the vehicle driving data of the target vehicle to obtain initial driving data;

[0018] Determining a data label corresponding to the initial driving data;

[0019] The vehicle driving data of the target vehicle is determined according to the initial driving data and the data label.

[0020] In one embodiment, determining the data label corresponding to the initial driving data includes:

[0021] According to the data collection triggering condition triggered by the vehicle driving condition, a data label corresponding to the initial driving data is determined.

[0022] In one embodiment, the initial driving data includes vehicle static data and vehicle dynamic data; wherein the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; and the vehicle static data includes at least one of road signs and lane lines.

[0023] In a second aspect, the present application also provides a data acquisition device. The device comprises:

[0024] An acquisition module, used to acquire the vehicle driving status of the target vehicle;

[0025] A verification module, used to verify whether the vehicle driving condition meets the data collection triggering condition;

[0026] The acquisition module is used to acquire the vehicle driving data of the target vehicle when the vehicle driving condition meets the data acquisition trigger condition, so as to obtain the vehicle driving data of the target vehicle.

[0027] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0028] Obtaining the vehicle driving status of the target vehicle;

[0029] Verifying whether the vehicle driving condition meets the data collection triggering condition;

[0030] When the vehicle driving condition meets the data collection triggering condition, data collection is performed on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0032] Obtaining the vehicle driving status of the target vehicle;

[0033] Verifying whether the vehicle driving condition meets the data collection triggering condition;

[0034] When the vehicle driving condition meets the data collection triggering condition, data collection is performed on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle.

[0035] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0036] Obtaining the vehicle driving status of the target vehicle;

[0037] Verifying whether the vehicle driving condition meets the data collection triggering condition;

[0038] When the vehicle driving condition meets the data collection triggering condition, data collection is performed on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle.

[0039] The above-mentioned data collection method, device, computer equipment and storage medium obtain the vehicle driving condition of the target vehicle; and then verify whether the vehicle driving condition meets the data collection trigger condition; when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle; according to the above content, it can be seen that when the vehicle driving data of the target vehicle is collected, the present application does not adopt a periodic data collection method, but judges whether the vehicle driving condition of the target vehicle meets the data collection trigger condition, and then, when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected, ensuring that when the target vehicle triggers a key event, the data collection operation can be performed in time to prevent the key information of the target vehicle from being lost, and providing a data basis for the subsequent analysis of the vehicle driving status and vehicle driving environment of the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 An application environment diagram of a data collection method provided in an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a first data collection method provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of a second data collection method provided in an embodiment of the present application;

[0043] Figure 4 A schematic diagram of a third data collection method provided in an embodiment of the present application;

[0044] Figure 5 A structural block diagram of a data acquisition device provided in an embodiment of the present application;

[0045] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] The data collection method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. By obtaining the vehicle driving condition of the target vehicle; then verifying whether the vehicle driving condition meets the data collection trigger condition; when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0048] In one embodiment, Figure 2 As shown, a data acquisition method is provided, which is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0049] S201, obtaining the vehicle driving condition of the target vehicle.

[0050] It should be noted that the operation of obtaining the driving condition of the target vehicle can be achieved through the vehicle sensors of the target vehicle, where the vehicle sensors of the target vehicle may include but are not limited to: lidar, high-precision camera, millimeter-wave radar, smart camera, combined inertial navigation, etc.; each sensor has the ability to work independently and can cooperate with each other to form a comprehensive information network.

[0051] It is further explained that the vehicle driving condition can be used to characterize vehicle events, traffic signs, semantic information, driver operation signals, vehicle motion signals, vehicle control signals, perception algorithm signals, etc.; by triggering different vehicle sensors and the mutual cooperation between different vehicle sensors, the vehicle driving condition can be acquired.

[0052] S202, verifying whether the vehicle driving condition meets the data collection triggering condition.

[0053] It should be noted that in order to further ensure the accuracy of the verification of the vehicle's driving status, multiple trigger conditions can be verified for the vehicle's driving status respectively, so as to ensure that the technical data collection operation can be realized for a variety of key data. Among them, the data collection trigger conditions include: event trigger conditions, traffic sign trigger conditions, semantic information trigger conditions and driver operation trigger conditions. Furthermore, there are many data collection trigger conditions, and the data collection trigger conditions are not explained one by one here.

[0054] Specifically, when it is necessary to verify whether the vehicle driving condition meets the data collection trigger conditions, the following contents may be included: judging whether a trigger event recorded in the event trigger conditions occurs on the target vehicle based on the vehicle driving condition; judging whether a trigger traffic sign recorded in the traffic sign trigger conditions exists in the driving environment of the target vehicle based on the vehicle driving condition; determining the driving environment semantic information of the target vehicle based on the vehicle driving condition, and verifying whether the driving environment semantics meets the semantic information trigger conditions; determining the driver operation information of the target vehicle based on the vehicle driving condition, and verifying whether the driver operation information meets the driver operation trigger conditions.

[0055] Among them, when determining the driving environment semantic information of the target vehicle according to the vehicle driving condition and verifying whether the driving environment semantics meets the semantic information triggering conditions, the driving environment semantic information of the target vehicle can be determined based on the deep learning model and the vehicle driving condition.

[0056] In one embodiment of the present application, a deep learning model for driving environment semantics can be pre-trained, and then the vehicle driving conditions are input into the deep learning model, and the output result of the deep learning model is obtained. The output result is the driving environment semantic information of the target vehicle.

[0057] The target detection accuracy can be improved through deep learning models by fusing various information contained in the vehicle's driving conditions; for example, preprocessing the camera image, such as scaling and normalization; using CNN to extract features and combining them with the lidar point cloud.

[0058] Therefore, the process of processing and real-time analysis of input data using algorithms such as CNN and LSTM includes: 1. CNN is used for object detection, such as identifying traffic lights, pedestrians, and traffic signs. In this process, a pre-trained model is used, and further fine-tuned for specific urban environments through transfer learning to improve the recognition rate; 2. LSTM analyzes time series data to predict what may happen in the next few seconds, such as a sudden deceleration of the vehicle ahead. In this link, the data trend in the past 5 seconds will be considered, and the corresponding prediction threshold will be set. For example, if it is found that the vehicle ahead has decelerated by more than 20% in the past 3 seconds, it will be judged as an emergency situation that meets the data collection trigger conditions.

[0059] Further explanation is given that data thresholds for different data collection trigger conditions may also be preset, and then, based on the different data thresholds, it is verified whether the vehicle driving condition meets the data collection trigger conditions.

[0060] For example: setting a distance threshold, if it is determined based on the vehicle driving condition that the distance between the target vehicle and the obstacle is less than the distance threshold, then it is determined that the vehicle driving state meets the event triggering condition; setting a speed threshold, when it is determined based on the vehicle driving condition that the driving speed of the target vehicle exceeds the speed threshold, then it is determined that the vehicle driving state meets the event triggering condition.

[0061] S203, when the vehicle driving condition meets the data collection triggering condition, data collection is performed on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle.

[0062] In one embodiment of the present application, during the process of collecting data from the target vehicle, in order to ensure the validity and information integrity of subsequent data, the label of the data can also be determined, so as to achieve further precision of the data.

[0063] Specifically, collecting vehicle driving data of the target vehicle may include: collecting vehicle driving data of the target vehicle to obtain initial driving data; determining a data label corresponding to the initial driving data; and determining the vehicle driving data of the target vehicle based on the initial driving data and the data label.

[0064] The above-mentioned data collection method obtains the vehicle driving condition of the target vehicle; and then verifies whether the vehicle driving condition meets the data collection trigger condition; when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle; according to the above content, it can be seen that when the vehicle driving data of the target vehicle is collected, the present application does not adopt a periodic data collection method, but judges whether the vehicle driving condition of the target vehicle meets the data collection trigger condition, and then, when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected, which ensures that when the target vehicle triggers a key event, the data collection operation can be performed in time to prevent the key information of the target vehicle from being lost, and provides a data basis for the subsequent analysis of the vehicle driving status and vehicle driving environment of the target vehicle.

[0065] In one embodiment, if Figure 3 As shown, when it is necessary to collect the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle, the following contents may be specifically included:

[0066] S301, collecting vehicle driving data of a target vehicle to obtain initial driving data.

[0067] The initial driving data includes vehicle static data and vehicle dynamic data; the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; the vehicle static data includes at least one of road signs and lane lines.

[0068] S302, determining a data label corresponding to the initial driving data.

[0069] It should be noted that when it is necessary to determine the data label corresponding to the initial driving data, the following contents may be specifically included: determining the data label corresponding to the initial driving data according to the data collection triggering condition triggered by the vehicle driving condition.

[0070] S303, determining the vehicle driving data of the target vehicle according to the initial driving data and the data label.

[0071] In one embodiment of the present application, when it is necessary to determine the vehicle driving data of the target vehicle, the following contents may be included: 1. Based on event triggering, when a pedestrian crossing or an obstacle is detected, the tagging action is immediately started. This process is adjusted by setting the sensitivity parameter. For example, setting the sensitivity to "high" allows the system to start an alarm at a distance of 15 meters, while "low" is 10 meters; 2. Traffic sign triggering, once a new traffic sign (such as speed limit) is identified, the vehicle behavior is adjusted accordingly and the tagging action is started. If the speed limit is reduced from 60km / h to 40km / h, the speed is immediately reduced, and the speed reduction and surrounding environment data, as well as the corresponding timestamp, are collected during the tagging time; 3. Semantic information triggering, the deep learning model analyzes the current road conditions, and if a complex situation (such as an intersection or a school area) is found, the tagging action is started, and the sampling frequency of the relevant parameters is increased from every 100 milliseconds to 50 milliseconds to obtain a more detailed data stream; 4. Driver operation signal monitoring, collecting the steering wheel rotation angle and the use of the accelerator and brake pedals, judging the driver's intention and responding accordingly. If the driver brakes suddenly and the speed drops rapidly, the labeling action is initiated. At the same time, an operation threshold can be set. For example, if the braking force exceeds 70%, it is considered an emergency operation. For example: in an urban intersection scenario, when encountering a red light and there are pedestrians crossing the road in front, the driver presses the brake pedal to 80% of the force, then the system immediately confirms that this is a potentially dangerous situation, and executes the labeling action to completely save the current state and surrounding environment information. 5. Vehicle motion event trigger conditions, including event trigger conditions such as speed, acceleration and angular velocity, provide a basis for subsequent decision-making. This information can be used to predict upcoming situations, such as early warning of collision risks. For example: in a highway driving scenario, when it detects that an obstacle is approaching quickly in front and its own acceleration reaches 6m / s 2At this time, the system will execute the following measures to start the labeling action and completely save the sudden driving trajectory, including the timestamp at that time and the location data of other surrounding models.

[0072] The above-mentioned data collection method obtains the initial driving data by collecting the vehicle driving data of the target vehicle, and determines the data label corresponding to the initial driving data, so as to determine the vehicle driving data of the target vehicle. This ensures that when the target vehicle triggers a key event, the data collection operation can be performed in time to prevent the key information of the target vehicle from being lost, thereby providing a data basis for the subsequent analysis of the vehicle driving status and vehicle driving environment of the target vehicle.

[0073] In one embodiment, if Figure 4 As shown, when it is necessary to collect the vehicle driving data of the target vehicle, the following contents may be included:

[0074] S401, obtaining the vehicle driving condition of the target vehicle.

[0075] S402, verifying whether the vehicle driving condition meets the data collection triggering condition.

[0076] S403, when the vehicle driving condition meets the data collection triggering condition, collecting the vehicle driving data of the target vehicle to obtain initial driving data.

[0077] S405, determining a data label corresponding to the initial driving data according to a data collection triggering condition triggered by the vehicle driving condition.

[0078] S406, determining the vehicle driving data of the target vehicle according to the initial driving data and the data label.

[0079] In one embodiment, the data acquisition method can be executed by a data acquisition system. Specifically, the data acquisition system can include sensors, controllers, storage modules and communication modules. Specifically, 1. Sensors, including laser radars, high-precision cameras, millimeter-wave radars, smart cameras, combined inertial navigation, etc., are used to collect data about the surrounding environment. Each sensor has the ability to work independently and can cooperate with each other to form a comprehensive information network; 2. The controller is based on a high-performance computing platform (such as GPU or FPGA), which is used to trigger data acquisition signals and run multiple machine learning algorithms, such as CNN, LSTM, SVM, etc. The controller continuously receives data streams from the sensor module and pre-processes and analyzes the data; 3. The storage module is divided into two layers: the high-speed cache is used to store data of recent important times, and the low-speed storage is used to store raw data and historical records for a long time; 4. The communication module supports the 5G / V2X protocol, realizes low-latency, high-bandwidth information exchange, and shares real-time information with the cloud server.

[0080] The above-mentioned data collection method obtains the vehicle driving condition of the target vehicle; and then verifies whether the vehicle driving condition meets the data collection trigger condition; when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle; according to the above content, it can be seen that when the vehicle driving data of the target vehicle is collected, the present application does not adopt a periodic data collection method, but judges whether the vehicle driving condition of the target vehicle meets the data collection trigger condition, and then, when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected, which ensures that when the target vehicle triggers a key event, the data collection operation can be performed in time to prevent the key information of the target vehicle from being lost, and provides a data basis for the subsequent analysis of the vehicle driving status and vehicle driving environment of the target vehicle.

[0081] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0082] Based on the same inventive concept, the embodiment of the present application also provides a data acquisition device for implementing the data acquisition method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more data acquisition device embodiments provided below can refer to the limitations on the data acquisition method above, and will not be repeated here.

[0083] In one embodiment, Figure 5 As shown, a data acquisition device is provided, including: an acquisition module 10, a verification module 20 and a collection module 30, wherein:

[0084] The acquisition module 10 is used to acquire the vehicle driving condition of the target vehicle.

[0085] The verification module 20 is used to verify whether the vehicle driving condition meets the data collection triggering condition.

[0086] The collection module 30 is used to collect the vehicle driving data of the target vehicle when the vehicle driving condition meets the data collection triggering condition, so as to obtain the vehicle driving data of the target vehicle.

[0087] In one embodiment, it is determined whether a triggering event recorded in an event triggering condition occurs in the target vehicle according to the vehicle driving condition;

[0088] Determine whether there is a triggering traffic sign recorded in the traffic sign triggering condition in the driving environment of the target vehicle according to the driving condition of the vehicle;

[0089] Determine the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verify whether the driving environment semantics meets the semantic information triggering conditions;

[0090] The driver operation information of the target vehicle is determined according to the vehicle driving condition, and it is verified whether the driver operation information meets the driver operation trigger condition.

[0091] In one embodiment, the driving environment semantic information of the target vehicle is determined based on the deep learning model and the vehicle driving condition.

[0092] In one embodiment, data collection is performed on vehicle driving data of the target vehicle to obtain initial driving data;

[0093] Determine the data label corresponding to the initial driving data;

[0094] The vehicle driving data of the target vehicle is determined based on the initial driving data and the data label.

[0095] In one embodiment, the data tag corresponding to the initial driving data is determined according to the data collection triggering condition triggered by the vehicle driving condition.

[0096] In one embodiment, the initial driving data includes vehicle static data and vehicle dynamic data; wherein the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; and the vehicle static data includes at least one of road signs and lane lines.

[0097] The above-mentioned data acquisition device obtains the vehicle driving condition of the target vehicle; and then verifies whether the vehicle driving condition meets the data acquisition trigger condition; when the vehicle driving condition meets the data acquisition trigger condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle; according to the above content, it can be seen that when the vehicle driving data of the target vehicle is collected, the present application does not adopt a periodic data collection method, but judges whether the vehicle driving condition of the target vehicle meets the data collection trigger condition, and then, when the vehicle driving condition meets the data collection trigger condition, the vehicle driving data of the target vehicle is collected, which ensures that when the target vehicle triggers a key event, the data collection operation can be carried out in time to prevent the key information of the target vehicle from being lost, and provides a data basis for the subsequent analysis of the vehicle driving state and vehicle driving environment of the target vehicle.

[0098] Each module in the above data acquisition device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0099] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data acquisition method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0100] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0101] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0102] Obtaining the vehicle driving status of the target vehicle;

[0103] Verify whether the vehicle's driving conditions meet the data collection trigger conditions;

[0104] When the vehicle driving condition meets the data collection triggering condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle.

[0105] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0106] Determine whether the target vehicle has a trigger event recorded in the event trigger condition according to the vehicle driving condition;

[0107] Determine whether there is a triggering traffic sign recorded in the traffic sign triggering condition in the driving environment of the target vehicle according to the driving condition of the vehicle;

[0108] Determine the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verify whether the driving environment semantics meets the semantic information triggering conditions;

[0109] The driver operation information of the target vehicle is determined according to the vehicle driving condition, and it is verified whether the driver operation information meets the driver operation trigger condition.

[0110] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0111] Based on the deep learning model and the vehicle's driving conditions, the semantic information of the target vehicle's driving environment is determined.

[0112] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0113] Collecting vehicle driving data of the target vehicle to obtain initial driving data;

[0114] Determine the data label corresponding to the initial driving data;

[0115] The vehicle driving data of the target vehicle is determined based on the initial driving data and the data label.

[0116] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0117] According to the data collection triggering conditions triggered by the vehicle driving condition, the data label corresponding to the initial driving data is determined.

[0118] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0119] The initial driving data includes vehicle static data and vehicle dynamic data; wherein the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; the vehicle static data includes at least one of road signs and lane lines.

[0120] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0121] Obtaining the vehicle driving status of the target vehicle;

[0122] Verify whether the vehicle's driving conditions meet the data collection trigger conditions;

[0123] When the vehicle driving condition meets the data collection triggering condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle.

[0124] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0125] Determine whether the target vehicle has a trigger event recorded in the event trigger condition according to the vehicle driving condition;

[0126] Determine whether there is a triggering traffic sign recorded in the traffic sign triggering condition in the driving environment of the target vehicle according to the driving condition of the vehicle;

[0127] Determine the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verify whether the driving environment semantics meets the semantic information triggering conditions;

[0128] The driver operation information of the target vehicle is determined according to the vehicle driving condition, and it is verified whether the driver operation information meets the driver operation trigger condition.

[0129] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0130] Based on the deep learning model and the vehicle's driving conditions, the semantic information of the target vehicle's driving environment is determined.

[0131] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0132] Collecting vehicle driving data of the target vehicle to obtain initial driving data;

[0133] Determine the data label corresponding to the initial driving data;

[0134] The vehicle driving data of the target vehicle is determined based on the initial driving data and the data label.

[0135] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0136] According to the data collection triggering conditions triggered by the vehicle driving condition, the data label corresponding to the initial driving data is determined.

[0137] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0138] The initial driving data includes vehicle static data and vehicle dynamic data; wherein the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; the vehicle static data includes at least one of road signs and lane lines.

[0139] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0140] Obtaining the vehicle driving status of the target vehicle;

[0141] Verify whether the vehicle's driving conditions meet the data collection trigger conditions;

[0142] When the vehicle driving condition meets the data collection triggering condition, the vehicle driving data of the target vehicle is collected to obtain the vehicle driving data of the target vehicle.

[0143] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0144] Determine whether the target vehicle has a trigger event recorded in the event trigger condition according to the vehicle driving condition;

[0145] Determine whether there is a triggering traffic sign recorded in the traffic sign triggering condition in the driving environment of the target vehicle according to the driving condition of the vehicle;

[0146] Determine the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verify whether the driving environment semantics meets the semantic information triggering conditions;

[0147] The driver operation information of the target vehicle is determined according to the vehicle driving condition, and it is verified whether the driver operation information meets the driver operation trigger condition.

[0148] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0149] Based on the deep learning model and the vehicle's driving conditions, the semantic information of the target vehicle's driving environment is determined.

[0150] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0151] Collecting vehicle driving data of the target vehicle to obtain initial driving data;

[0152] Determine the data label corresponding to the initial driving data;

[0153] The vehicle driving data of the target vehicle is determined based on the initial driving data and the data label.

[0154] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0155] According to the data collection triggering conditions triggered by the vehicle driving condition, the data label corresponding to the initial driving data is determined.

[0156] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0157] The initial driving data includes vehicle static data and vehicle dynamic data; wherein the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; the vehicle static data includes at least one of road signs and lane lines.

[0158] 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0159] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0160] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0161] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data collection method, characterized in that: The method comprises: Obtaining the vehicle driving status of the target vehicle; Verifying whether the vehicle driving condition meets the data collection triggering condition; When the vehicle driving condition meets the data collection triggering condition, data collection is performed on the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle.

2. The method according to claim 1, characterized in that The data collection triggering conditions include: event triggering conditions, traffic sign triggering conditions, semantic information triggering conditions and driver operation triggering conditions; the verification of whether the vehicle driving condition meets the data collection triggering conditions includes: Determining whether the target vehicle has a trigger event recorded in the event trigger condition according to the vehicle driving condition; Determining whether there is a triggering traffic sign recorded in the traffic sign triggering condition in the driving environment of the target vehicle according to the driving condition of the vehicle; Determining the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verifying whether the driving environment semantics meets the semantic information triggering condition; The driver operation information of the target vehicle is determined according to the vehicle driving condition, and it is verified whether the driver operation information meets the driver operation trigger condition.

3. The method according to claim 2, characterized in that Determining the driving environment semantic information of the target vehicle according to the vehicle driving condition, and verifying whether the driving environment semantics meets the semantic information triggering condition, includes: Based on the deep learning model and the vehicle driving condition, the driving environment semantic information of the target vehicle is determined.

4. The method according to claim 1, characterized in that: The step of collecting the vehicle driving data of the target vehicle to obtain the vehicle driving data of the target vehicle includes: Collecting the vehicle driving data of the target vehicle to obtain initial driving data; Determining a data label corresponding to the initial driving data; The vehicle driving data of the target vehicle is determined according to the initial driving data and the data label.

5. The method according to claim 4, characterized in that The determining of the data label corresponding to the initial driving data includes: According to the data collection triggering condition triggered by the vehicle driving condition, a data label corresponding to the initial driving data is determined.

6. The method according to claim 4, characterized in that The initial driving data includes vehicle static data and vehicle dynamic data; wherein the vehicle dynamic data includes at least one of vehicle driving speed, vehicle acceleration and vehicle angular velocity; and the vehicle static data includes at least one of road signs and lane lines.

7. A data acquisition device, characterized in that: The device comprises: An acquisition module, used to acquire the vehicle driving status of the target vehicle; A verification module, used to verify whether the vehicle driving condition meets the data collection triggering condition; The acquisition module is used to acquire the vehicle driving data of the target vehicle when the vehicle driving condition meets the data acquisition trigger condition, so as to obtain the vehicle driving data of the target vehicle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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