An automatic driving data recording method, device, equipment and storage medium

Abstract

Embodiments of the present application disclose an automatic driving data recording method, device, equipment and storage medium, comprising: in response to a trigger event of automatic driving data recording, obtaining a plurality of driving description information; wherein the type of driving description information includes: vehicle basic information, vehicle state information, automatic driving system running information, driving environment information and driver operation information; constructing a column vector corresponding to each type of driving description information, and generating a vehicle data matrix according to each column vector; constructing label information for describing the column vector position of each type of driving description information in the vehicle data matrix; according to the type of the trigger event, the vehicle data matrix and the label information are associated and stored in the target partition of the target database, so as to be read and analyzed by the user, the data acquisition range can be expanded, the data transmission efficiency is improved, and a data basis is provided for vehicle accident cause analysis and responsibility definition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, and in particular to an automatic driving data recording method, device, equipment and storage medium. BACKGROUND

[0002] At present, while the vehicle intelligent technology is developing, the number of automatic driving accidents is also gradually increasing. Since the automatic driving vehicle generates massive data at all times, it is difficult to quickly locate the data at the time of the accident, thereby making the accident analysis very difficult.

[0003] In the prior art, driving description information is usually collected through a driving recorder or an event data recording system, and the driving description information is directly uploaded to a data receiving end.

[0004] However, since the driving recorder can only collect the video outside the vehicle and the vehicle positioning information, and the event data recording system can only collect the key running information of the vehicle, the data range collected by the prior art is small, thereby it is difficult to accurately determine the accident liability. Secondly, since the collected driving description information is usually very large, directly uploading the driving description information to the data receiving end will result in a slow transmission rate of the driving description information. SUMMARY

[0005] The present application provides an automatic driving data recording method, device, equipment and storage medium, which can expand the data collection range, improve the data transmission efficiency, and provide a data basis for vehicle accident cause analysis and liability definition.

[0006] In a first aspect, an embodiment of the present application provides an automatic driving data recording method, which comprises:

[0007] In response to a trigger event of automatic driving data recording, a plurality of driving description information is obtained;

[0008] The type of driving description information includes vehicle basic information, vehicle state information, automatic driving system running information, driving environment information and driver operation information;

[0009] A column vector corresponding to each type of driving description information is constructed, and a vehicle data matrix is generated according to each column vector;

[0010] Label information is constructed for describing the column vector position of each type of driving description information in the vehicle data matrix;

[0011] According to the type of the trigger event, the vehicle data matrix and the label information are associated and stored in a target partition of a target database for user reading and analysis.

[0012] Optionally, after generating the vehicle data matrix according to the column vectors, the method further comprises: fitting a weight matrix between two column vectors in the vehicle data matrix, wherein the weight matrix is used to describe the data correlation between the elements of the two column vectors; and storing the vehicle data matrix into a target partition of a target database according to the type of the trigger event, further comprising: storing the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix into the target partition of the target database according to the type of the trigger event.

[0013] Optionally, storing the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix into the target partition of the target database according to the type of the trigger event further comprises: encrypting the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix respectively according to a pre-generated symmetric key to obtain a content encryption result; encrypting the symmetric key according to a pre-generated asymmetric public key to obtain a key encryption result; and storing the content encryption result and the key encryption result into the target partition of the target database.

[0014] Optionally, the method further comprises: storing event data corresponding to the trigger event into an event database; and storing the symmetric key, the asymmetric public key, and an asymmetric key corresponding to the asymmetric public key into a security database.

[0015] Optionally, storing the vehicle data matrix into the target partition of the target database according to the type of the trigger event comprises: if a storage amount in the target partition is greater than or equal to a preset storage amount threshold, storing the vehicle data matrix into the target partition of the target database after uploading data stored in the target partition to a target storage space; or storing the vehicle data matrix into the target partition of the target database after deleting historical data in the target partition that is farthest from a storage time of the vehicle data matrix.

[0016] In a second aspect, an embodiment of the present application provides an automatic driving data reading method, which comprises: acquiring the vehicle data matrix of any embodiment of the present application from a target partition of a target database; and reading column vectors corresponding to each type of driving description information from the vehicle data matrix according to label information associated with the vehicle data matrix.

[0017] Optionally, after reading the column vectors corresponding to each type of driving description information from the vehicle data matrix respectively according to the label information associated with the vehicle data matrix, the method further comprises: if it is determined that a target column vector corresponding to a target type of driving description information is missing in the vehicle data matrix, detecting at least one auxiliary recovery column vector in the vehicle data matrix; combining the target column vector with each auxiliary recovery column vector to obtain a column vector pair, and obtaining a target weight matrix corresponding to each column vector pair from a target partition of the target database; and recovering the target column vector according to each target weight matrix and the auxiliary recovery column vector matched with each target weight matrix.

[0018] In a third aspect, an embodiment of the present application further provides an automatic driving data recording device, which comprises:

[0019] a driving information acquisition module, configured to acquire a plurality of driving description information in response to a trigger event of automatic driving data recording;

[0020] wherein the types of driving description information include vehicle basic information, vehicle state information, automatic driving system operation information, driving environment information and driver operation information;

[0021] a data matrix generation module, configured to construct column vectors corresponding to each type of driving description information respectively, and generate a vehicle data matrix according to the column vectors;

[0022] a label information construction module, configured to construct label information for describing the column vector positions of each type of driving description information in the vehicle data matrix;

[0023] a data storage module, configured to store the vehicle data matrix and the label information in a target partition of a target database according to the type of the trigger event, for being read and parsed by a user.

[0024] In a fourth aspect, an embodiment of the present application further provides an electronic device, which comprises:

[0025] at least one processor; and

[0026] a memory in communication connection with the at least one processor; wherein

[0027] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the automatic driving data recording method or the automatic driving data reading method provided by any one of the embodiments of the present application.

[0028] In a fifth aspect, the embodiments of the present application further provide a computer readable storage medium storing computer instructions for causing a processor to implement the automatic driving data recording method or the automatic driving data reading method provided by any of the embodiments of the present application.

[0029] The technical scheme of the embodiments of the present application solves the problem of small data range collected by the prior art by acquiring vehicle basic information, vehicle state information, automatic driving system running information, driving environment information and driver operation information, and can realize complete acquisition of data required for accident cause analysis. In addition, the data transmission efficiency is improved by constructing column vectors corresponding to each type of driving description information and generating a vehicle data matrix for data transmission according to each column vector.

[0030] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0032] Figure 1 is a flowchart of an automatic driving data recording method according to the first embodiment of the present application;

[0033] Figure 2 is a flowchart of another automatic driving data recording method according to the second embodiment of the present application;

[0034] Figure 3 is a flowchart of a data encryption method according to the embodiments of the present application;

[0035] Figure 4 is a flowchart of an automatic driving data reading method according to the third embodiment of the present application;

[0036] Figure 5 is a structural schematic diagram of an automatic driving data recording device according to the fourth embodiment of the present application;

[0037] Figure 6 is a structural schematic diagram of an automatic driving data reading device according to the fifth embodiment of the present application;

[0038] Figure 7is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0040] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0041] Embodiment one

[0042] Figure 1 is a flowchart of an automatic driving data recording method according to an embodiment of the present application. The embodiment can be applicable to the case of recording automatic driving data. The method can be executed by an automatic driving data recording device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device.

[0043] As shown in Figure 1 , the automatic driving data recording method disclosed in the embodiment includes:

[0044] S110, in response to a triggering event of automatic driving data recording, acquiring a plurality of driving description information.

[0045] In the embodiment, the triggering event can be an event that can trigger the automatic driving description information acquisition operation. There can be many triggering events, such as collision event, collision risk event, automatic driving system state change event and system failure event, etc.

[0046] At this step, specifically, the vehicle basic information, the vehicle state information collected in real time through the vehicle sensors and the bus network signals, the automatic driving system running information collected in real time through the vehicle automatic driving domain control system, the driving environment information collected in real time through the vehicle external camera, millimeter wave radar and other sensors, and the driver operation information collected in real time through the vehicle internal sensors or bus network signals can be acquired after receiving the trigger event.

[0047] The vehicle basic information can include a vehicle identification code (Vehicle Identification Number, VIN), a vehicle software version number, a vehicle hardware version number, a serial number, a type code of the trigger event, a complete flag of the trigger event, time, latitude and longitude, cumulative mileage and heading angle, etc. The vehicle state information can include dynamic information such as vehicle speed, lateral acceleration, longitudinal angular velocity, yaw angular velocity and roll angular velocity. The automatic driving system running information can include lateral control decision information of the automatic driving system, longitudinal control decision information of the automatic driving system, light and wiper control decision information of the automatic driving system, etc. The driving environment information can include related information of the perceived target objects such as the type, relative position and relative speed of the perceived target objects, and external image or video data such as the images or videos in front, left, right and back of the vehicle. The driver operation information can include the driver's operation information, the driver's safety state, the driver's takeover ability, whether the driver wears a seat belt, whether the driver is in the driving seat, etc. The driver's operation information can include the accelerator pedal opening degree, the brake pedal opening degree, the brake pedal state, the steering wheel angle and the steering torque, etc.

[0048] The advantage of such a setting is that the event trigger mechanism can reduce the frequency of data transmission, reduce data redundancy, and ensure that the data at critical moments are accurately recorded.

[0049] Optionally, after acquiring the multiple pieces of driving description information, the multiple pieces of driving description information can be stored in a preset interaction information template in a JavaScript Object Notation (JSON) format to obtain a vehicle interaction report, and the vehicle data report can be stored in an interaction report table of the event database.

[0050] S120, column vectors corresponding to each type of driving description information are constructed, and a vehicle data matrix is generated according to each column vector.

[0051] In this embodiment, each piece of driving description information corresponds to at least one dimension, for example, the vehicle state information can include dimensions such as position, speed and acceleration.

[0052] In this step, specifically, a multi-dimensional vector data chain including multiple nodes can be constructed according to the number of types of driving description information for each trigger event. Each node is used to process one item of driving description information of a corresponding type. Then, a data vectorization method can be used to construct a column vector corresponding to each type of driving description information. The data vectorization method can be various, such as principal component analysis and singular value decomposition. Finally, a vehicle data matrix can be generated according to each column vector and the processing priority of the node corresponding to each column vector. Optionally, the processing priority of the node can be automatically adjusted according to the node load and processing speed by a PID control algorithm (Proportion Integral Differential, PID), and an optimization algorithm is used to make the multi-dimensional vector data chain achieve the optimal effect in different states.

[0053] For example, assuming that there are N nodes, and each node contains M-dimensional data, an MxN vehicle data matrix can be generated. The specific formula of the vehicle data matrix is as follows:

[0054]

[0055] Wherein, A is the vehicle data matrix, [a1n, a2n, …, amn] T is the column vector of the nth column.

[0056] Optionally, a node can be added in the multi-dimensional vector data chain by adding a column vector in the vehicle data matrix, and the node can be deleted in the multi-dimensional vector data chain by deleting a column vector in the vehicle data matrix.

[0057] S130, constructing label information for describing the column vector position of each type of driving description information in the vehicle data matrix.

[0058] In this step, specifically, the label information corresponding to each column vector can be generated after each column vector is added to the vehicle data matrix.

[0059] S140, according to the type of trigger event, the vehicle data matrix and the label information are associated and stored in the target partition of the target database for users to read and analyze.

[0060] In this step, specifically, the type of the triggering event can be determined by identifying the identifier of the triggering event. Then, after obtaining the target database corresponding to the vehicle data matrix, the target partition corresponding to the type of the triggering event can be obtained, and the vehicle data matrix and the label information can be stored in the target partition of the target database. For example, the target partitions corresponding to the collision event and the collision risk event are different. Since the database is a non-volatile memory, by storing the vehicle data matrix and the label information in the target database, the data can be stored in order while avoiding data loss after power failure.

[0061] In an optional embodiment of the present application, according to the type of the triggering event, the vehicle data matrix is stored in the target partition of the target database, which includes: if the storage amount in the target partition is greater than or equal to the preset storage amount threshold, the data stored in the target partition is uploaded to the target storage space, and then the vehicle data matrix is stored in the target partition of the target database; or, after deleting the historical data in the target partition farthest from the storage time of the vehicle data matrix, the vehicle data matrix is stored in the target partition of the target database.

[0062] In a specific embodiment, when the storage amount in the target partition is greater than or equal to the preset storage amount threshold, the type of the data stored in the target partition can be determined. If all are lock event data, all the lock event data is uploaded to the target storage space.

[0063] The advantage of such a setting is that by uploading the data stored in the target partition to the target storage space, the flexible application of the storage space can be realized, the stability and reliability of the system using the target database can be ensured, and the basis for accident cause analysis and responsibility determination is improved. Secondly, by using the first-in-first-out principle to cover data in the same target partition, the latest data can be ensured to be retained.

[0064] The technical scheme of the embodiment is that, in response to a trigger event of automatic driving data recording, a plurality of driving description information is acquired, wherein the types of the driving description information include vehicle basic information, vehicle state information, automatic driving system running information, driving environment information and driver operation information; column vectors corresponding to each type of driving description information are constructed, and a vehicle data matrix is generated according to the column vectors; label information for describing the column vector positions of each type of driving description information in the vehicle data matrix is constructed; and the vehicle data matrix and the label information are stored in a target partition of a target database in association according to the type of the trigger event, so as to provide a technical means for users to read and analyze, solve the problem that the data acquisition range is small and the data transmission efficiency is low caused by the data acquisition and transmission of the prior art, expand the data acquisition range, improve the data transmission efficiency, and provide a data basis for vehicle accident cause analysis and responsibility definition.

[0065] Embodiment two

[0066] Figure 2 is a flowchart of another automatic driving data recording method according to the embodiment two of the present application, and the embodiment is further optimized and expanded based on the above-mentioned embodiments, and can be combined with each optional technical scheme in the above-mentioned embodiments.

[0067] As shown in Figure 2 , the automatic driving data recording method disclosed by the embodiment comprises:

[0068] S210, in response to a trigger event of automatic driving data recording, a plurality of driving description information is acquired.

[0069] The types of the driving description information include vehicle basic information, vehicle state information, automatic driving system running information, driving environment information and driver operation information.

[0070] Optionally, when the plurality of driving description information is acquired, for sensors that need to collect data at high frequency, such as laser radar and camera, a high-speed data bus can be used for transmission to ensure high-precision collection of the sensors that need to collect data at high frequency.

[0071] Optionally, after the plurality of driving description information is acquired, the collected data can be temporarily stored, so that when network transmission problems or delays occur, the data can still be recorded, thereby avoiding data loss.

[0072] Optionally, according to the severity level of the trigger event, the plurality of driving description information corresponding to the trigger event can be uploaded to a remote monitoring center, so as to respond and dispatch in real time.

[0073] S220, construct column vectors corresponding to each type of driving description information respectively, and generate a vehicle data matrix according to each column vector.

[0074] Optionally, the similarity or difference of different driving description information in each dimension can be measured by a multi-dimensional Euclidean distance formula.

[0075] Optionally, the acquisition operation of multiple driving description information can be executed synchronously with the vehicle data matrix generation operation, that is, the vehicle data matrix corresponding to another trigger event can be generated while collecting the driving description information corresponding to the current trigger event.

[0076] S230, fitting a weight matrix between each two column vectors in the vehicle data matrix, wherein the weight matrix is used to describe the data correlation between each vector element in the two column vectors.

[0077] In this step, specifically, two non-repeated column vectors in the vehicle data matrix can be obtained, and the weight matrix between the two column vectors can be determined. It is judged whether the processing of any two column vectors in the vehicle data matrix is completed, if not, the operation of obtaining two non-repeated column vectors in the vehicle data matrix is executed, until the processing of any two column vectors in the vehicle data matrix is completed.

[0078] For example, assuming that there are N column vectors, and the dimension of each column vector is 3, the following vehicle data matrix can be generated:

[0079]

[0080] Wherein, A is the vehicle data matrix.

[0081] Then, the weight matrix between the two column vectors can be determined according to the following specific calculation formula:

[0082] Ai=W·Aj

[0083] Wherein, Ai is the i-th column vector in the vehicle data matrix, Aj is the j-th column vector in the vehicle data matrix, and W is the weight matrix between the i-th column vector and the j-th column vector.

[0084] Specifically, W is a 3x3 matrix, and the specific formula of W is as follows:

[0085]

[0086] S240, constructing label information for describing the column vector position of each type of driving description information in the vehicle data matrix.

[0087] S250, according to the type of trigger event, the vehicle data matrix, and the weight matrix between each two column vectors in the vehicle data matrix are stored in the target partition of the target database for users to read and analyze.

[0088] In this step, specifically, after obtaining the target database corresponding to the vehicle data matrix, the target partition corresponding to the type of trigger event is obtained, and the vehicle data matrix, label information and each weight matrix are stored in the target partition of the target database.

[0089] Figure 3 is a flowchart of a data encryption method according to an embodiment of the application.

[0090] In an optional implementation method, as shown in Figure 3 S250 specifically can include:

[0091] S251, according to the pre-generated symmetric key, the vehicle data matrix and the weight matrix between each two column vectors in the vehicle data matrix are encrypted respectively to obtain the content encryption result.

[0092] In this embodiment, the pre-generated symmetric key can be updated regularly according to user demand.

[0093] In this step, specifically, a symmetric key can be generated by a symmetric encryption algorithm (Advanced Encryption Standard, AES), and the vehicle data matrix and each weight matrix corresponding to the vehicle data matrix are encrypted according to the symmetric key to obtain the content encryption result. Optionally, when abnormal activities or malicious intrusion are detected, the data encryption operation can be interrupted in time to avoid sensitive information leakage.

[0094] S252, according to the pre-generated asymmetric public key, the symmetric key is encrypted to obtain the key encryption result, and the content encryption result and the key encryption result are stored in the target partition of the target database for users to read and analyze.

[0095] In this embodiment, the pre-generated asymmetric public key can be updated regularly according to user demand.

[0096] In this step, specifically, the asymmetric public key and the asymmetric private key can be generated by an elliptic curve encryption algorithm. Then, the symmetric key can be encrypted by using the asymmetric public key to obtain a key encryption result. Optionally, after transmitting the content encryption result and the key encryption result to the target database, the key encryption result can be decrypted by using the asymmetric private key to obtain the symmetric key. The asymmetric private key can be updated regularly according to user demand. Then, the content encryption result can be decrypted according to the symmetric key, and the decrypted driving description information can be stored in different data tables of the target database. For example, the vehicle basic information can be stored in the vehicle basic information table, and the vehicle state information can be stored in the vehicle state information table.

[0097] The advantage of such a setting is that, since the symmetric encryption is faster, encrypting the vehicle data matrix and the weight matrix, which are large amounts of data, by using the symmetric key can improve the data encryption efficiency. Secondly, after encrypting the vehicle data matrix and the weight matrix by using the symmetric key, encrypting the symmetric key by using the asymmetric key can improve the security of the data while ensuring the data encryption efficiency.

[0098] In an optional embodiment, the event data corresponding to the trigger event can be stored in the event database. The event data can include event number, vehicle identification code, event type, event data, and timestamp. The symmetric key, the asymmetric public key, and the asymmetric key corresponding to the asymmetric public key are stored in the security database, and the related information of the multi-dimensional vector data chain is stored in the link database. The related information of the multi-dimensional vector data chain can include vehicle identification code, data chain identifier, and data chain information.

[0099] In a specific embodiment, the event data can be stored in the event data table of the event database. The symmetric key, the asymmetric public key, and the asymmetric key corresponding to the asymmetric public key are stored in the security information table of the security database. The related information of the multi-dimensional vector data chain is stored in the data chain information table of the link database. The target database is the first layer database, the event database is the second layer database, the security database is the third layer database, and the link database is the fourth layer database.

[0100] The advantage of such a setting is that, by storing different types of data in different databases, the data can be stored and isolated in a classified manner, improving the stability and maintainability of the system. Secondly, by setting different purposes for the databases of different layers, the efficient management and secure storage of data are ensured.

[0101] The technical scheme of the embodiment determines the weight matrix between two column vectors in the vehicle data matrix through fitting, wherein the weight matrix is used to describe the data correlation between the elements of the two column vectors; label information is constructed to describe the column vector positions of each type of driving description information in the vehicle data matrix; and the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix are stored in the target partition of the target database according to the type of the trigger event, so as to provide a technical means for users to read and analyze, thereby solving the problem of small data collection range and low data transmission efficiency caused by data collection and transmission by the prior art. The weight matrix between two column vectors in the vehicle data matrix is determined through fitting, which facilitates subsequent recovery of lost column vectors.

[0102] Embodiment three

[0103] Figure 4 A flowchart of an automatic driving data reading method according to Embodiment three of the present application is shown in FIG. 3. The embodiment can be applied to the case of reading recorded automatic driving data. The method can be executed by an automatic driving data reading device, which can be realized in the form of hardware and / or software and can be configured in an electronic device.

[0104] As shown in FIG. 3, the automatic driving data reading method disclosed in the embodiment includes the following steps. Figure 4

[0105] S310, obtaining the vehicle data matrix of any embodiment of the present application from the target partition of the target database.

[0106] In this step, specifically, the vehicle data matrix can be obtained from the target partition of the target database through a unified data reading port and protocol conforming to the light-duty vehicle on-board diagnostic system management technical specification. The data reading port conforms to the preset international standard rules, and the protocol is a diagnosis communication protocol based on IP (Diagnosis Over Internet Protocol, DoIP).

[0107] The advantage of such a setting is that by setting a unified data reading port and protocol, the data reading process can be independent of the vehicle manufacturer, that is, a third party can realize data extraction according to the standard without additional customized development, thereby improving the flexibility and portability of the system. Secondly, through the unified data extraction method, consistent extraction of whole vehicle and component level data can be realized, which reduces the dependence on vehicle manufacturers and makes the data reading and analysis more convenient and reliable.

[0108] ​S320, reading, from the vehicle data matrix, column vectors corresponding to each type of driving description information according to label information associated with the vehicle data matrix.

[0109] In an optional implementation of the embodiment of the present application, after reading, from the vehicle data matrix, column vectors corresponding to each type of driving description information according to label information associated with the vehicle data matrix, the method further comprises: if it is determined that a target column vector corresponding to a target type of driving description information is missing in the vehicle data matrix, detecting at least one auxiliary recovery column vector in the vehicle data matrix; combining the target column vector with each auxiliary recovery column vector to obtain a column vector pair, and obtaining a target weight matrix corresponding to each column vector pair from a target partition of the target database; and recovering the target column vector according to each target weight matrix and an auxiliary recovery column vector matched with each target weight matrix.

[0110] In one specific implementation, one of the target weight matrices corresponding to the target column vector can be selected as a current target weight matrix, and the target column vector is recovered according to the current target weight matrix and a current auxiliary recovery column vector matched with the current target weight matrix.

[0111] The advantage of this arrangement is that the vehicle data matrix can be accurately determined by recovering the target column vector through the target weight matrix, thereby improving the accuracy of the vehicle accident cause analysis and responsibility determination results.

[0112] In an optional implementation of the embodiment of the present application, after reading the vehicle data matrix, each item of driving description information can be parsed by a parser with a predefined data format to obtain driving description information corresponding to each column vector in the vehicle data matrix. For example, the real-time state of the vehicle can be obtained by parsing the column vector corresponding to the vehicle state information. The behavior of the driver and the driving state of the vehicle can be obtained by parsing the column vector corresponding to the driver operation information. Optionally, considering the dynamic changes of factors such as vehicle type and sensor configuration, the parameters of the parser can be dynamically adjusted according to the specific configuration of the current vehicle to adapt to the data parsing needs of different vehicle types.

[0113] The advantage of this arrangement is that by deeply parsing each item of driving description information, a high-quality data basis can be provided for subsequent data analysis and application, meeting the needs of different vehicles and application scenarios.

[0114] The technical scheme of the embodiment is that the vehicle data matrix of any embodiment of the application is acquired from a target partition of a target database, and the column vectors corresponding to each type of driving description information are read from the vehicle data matrix according to the label information associated with the vehicle data matrix, so that the vehicle accident cause can be analyzed and the responsibility can be defined according to each type of driving description information, and the accuracy of the vehicle accident cause analysis and the responsibility definition result is improved.

[0115] Based on the above embodiments, the application provides an automatic driving data management system configured with an automatic driving data recording device and an automatic driving data reading device, through which each type of driving description information can be collected, recorded, read and analyzed after a trigger event is received, so that the user can timely handle the traffic accident corresponding to the trigger event according to each type of driving description information.

[0116] Embodiment four

[0117] Figure 5 is a structural schematic diagram of an automatic driving data recording device provided by the embodiment four of the application, the embodiment can be applied to the case of recording automatic driving data, the automatic driving data recording device can be realized in the form of hardware and / or software, and can be configured in an electronic device.

[0118] As shown in Figure 5 , the automatic driving data recording device disclosed by the embodiment comprises:

[0119] The driving information acquisition module 51 is configured to acquire a plurality of driving description information in response to a trigger event of automatic driving data recording.

[0120] The type of driving description information includes vehicle basic information, vehicle state information, automatic driving system operation information, driving environment information and driver operation information.

[0121] The data matrix generation module 52 is configured to construct column vectors corresponding to each type of driving description information, and generate a vehicle data matrix according to each column vector.

[0122] The label information construction module 53 is configured to construct label information for describing the column vector position of each type of driving description information in the vehicle data matrix.

[0123] The data storage module 54 is configured to store the vehicle data matrix and the label information in a target partition of a target database according to the type of the trigger event, so as to be read and analyzed by the user.

[0124] The technical scheme in the embodiment solves the problem of small data collection range and low data transmission efficiency caused by data collection and transmission through the prior art, expands the data collection range, improves the data transmission efficiency, and provides a data basis for vehicle accident cause analysis and responsibility definition through mutual cooperation of the driving information acquisition module, the data matrix generation module, the label information construction module, and the data storage module.

[0125] Optionally, the apparatus further includes a weight matrix determination module, specifically configured to:

[0126] fitting determines the weight matrix between two column vectors in the vehicle data matrix, wherein the weight matrix is used to describe the data correlation between the vector elements in the two column vectors.

[0127] Optionally, the data storage module 54 includes:

[0128] a matrix storage unit, configured to store the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix into a target partition of a target database according to the type of the triggering event;

[0129] a content encryption unit, configured to respectively encrypt the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix according to a pre-generated symmetric key to obtain a content encryption result;

[0130] a key encryption unit, configured to encrypt the symmetric key according to a pre-generated asymmetric public key to obtain a key encryption result, and store the content encryption result and the key encryption result into the target partition of the target database;

[0131] a storage space management unit, configured to, if the storage amount in the target partition is greater than or equal to a preset storage amount threshold, store the vehicle data matrix into the target partition of the target database after uploading the data stored in the target partition to a target storage space; or, delete the historical data in the target partition farthest from the storage time of the vehicle data matrix, and then store the vehicle data matrix into the target partition of the target database.

[0132] Optionally, the apparatus further includes a sub-database storage module, which includes:

[0133] an event data storage unit, configured to store event data corresponding to the triggering event into an event database;

[0134] a key data storage unit, configured to store the symmetric key, the asymmetric public key, and an asymmetric key corresponding to the asymmetric public key into a security database.

[0135] The automatic driving data recording device provided in the embodiments of the present application can execute the automatic driving data recording method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method. The contents not described in detail in the present embodiment can refer to the description in any of the method embodiments of the present application.

[0136] Embodiment five

[0137] Figure 6 is a structural schematic diagram of an automatic driving data reading device provided according to the embodiment five of the present application. The present embodiment can be applied to the case of reading the recorded automatic driving data. The automatic driving data reading device can be realized in the form of hardware and / or software, and can be configured in an electronic device.

[0138] As shown in Figure 6 , the automatic driving data reading device disclosed in the present embodiment comprises:

[0139] The data matrix acquisition module 61 is configured to acquire the vehicle data matrix of any of the embodiments of the present application from the target partition of the target database.

[0140] The column vector acquisition module 62 is configured to read the column vectors corresponding to each type of driving description information from the vehicle data matrix according to the label information associated with the vehicle data matrix.

[0141] The technical solution in the present embodiment can realize the analysis of vehicle accident causes and the definition of responsibilities according to each type of driving description information through the cooperation of the production load determination module and the production management prompting module, and improves the accuracy of the results of vehicle accident cause analysis and responsibility definition.

[0142] Optionally, the device further comprises a column vector recovery module, which is specifically configured to:

[0143] If it is determined that the target column vector corresponding to the target type of driving description information is missing in the vehicle data matrix, at least one auxiliary recovery column vector is acquired from the vehicle data matrix;

[0144] Each target column vector is combined with each auxiliary recovery column vector to obtain a column vector pair, and a target weight matrix corresponding to each column vector pair is acquired from the target partition of the target database.

[0145] The target column vector is recovered according to each target weight matrix and the auxiliary recovery column vector matched with each target weight matrix.

[0146] The autonomous driving data reading device provided in this embodiment of the invention can execute the autonomous driving data reading method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution. Content not described in detail in this embodiment can be referred to the description in any method embodiment of this application.

[0147] Example 6

[0148] Figure 7 A schematic diagram of the structure of an electronic device 10 that can be used to implement embodiments of the present invention is shown. For example... Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0149] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0150] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as autonomous driving data recording or autonomous driving data reading methods.

[0151] In some embodiments, the automated driving data recording method or the automated driving data reading method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, parts or all of the computer program can be loaded onto and / or installed in the electronic device 10 via the ROM 12 and / or the communication unit 19. One or more steps of the automated driving data recording method or the automated driving data reading method described above can be performed when the computer program is loaded onto the RAM 13 and executed by the processor 11. Alternatively, in other embodiments, the processor 11 can be configured to perform the automated driving data recording or the automated driving data reading method by any other suitable means, for example, by means of firmware.

[0152] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0153] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a separate software package, or entirely on a remote machine or server.

[0154] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0156] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0157] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0158] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0159] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. An automatic driving data recording method characterized by, The method comprises: in response to a trigger event of automatic driving data recording, obtaining a plurality of driving description information; wherein the type of driving description information includes: vehicle basic information, vehicle state information, automatic driving system operation information, driving environment information and driver operation information; constructing a column vector corresponding to each type of driving description information, and generating a vehicle data matrix according to each column vector; constructing label information for describing the column vector position of each type of driving description information in the vehicle data matrix; storing the vehicle data matrix and the label information in a target partition of a target database according to the type of trigger event for user reading and analysis; wherein, after generating a vehicle data matrix according to each column vector, further comprising: fitting to determine the weight matrix between two column vectors in the vehicle data matrix, wherein the weight matrix is used to describe the data correlation between the vector elements in the two column vectors; storing the vehicle data matrix in the target partition of the target database according to the type of trigger event, further comprising: according to the type of trigger event, storing the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix in the target partition of the target database.

2. The method of claim 1, wherein, storing the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix in the target partition of the target database according to the type of trigger event, further comprising: encrypting the vehicle data matrix and the weight matrix between two column vectors in the vehicle data matrix according to the pre-generated symmetric key to obtain a content encryption result; encrypting the symmetric key according to the pre-generated asymmetric public key to obtain a key encryption result, and storing the content encryption result and the key encryption result in the target partition of the target database.

3. The method of claim 2, wherein, The method further comprises: storing event data corresponding to the trigger event in an event database; storing the symmetric key, asymmetric public key, and asymmetric key corresponding to the asymmetric public key in a security database.

4. The method of claim 1, wherein, storing the vehicle data matrix in the target partition of the target database according to the type of trigger event, comprising: if the storage amount in the target partition is greater than or equal to the preset storage amount threshold, after uploading the data stored in the target partition to a target storage space, the vehicle data matrix is stored in the target partition of the target database; or, after deleting the historical data in the target partition farthest from the storage time of the vehicle data matrix, the vehicle data matrix is stored in the target partition of the target database.

5. An automatic driving data reading method characterized by comprising: The method comprises: obtaining a vehicle data matrix as claimed in any one of claims 1 to 4 from a target partition of a target database; according to the label information associated with the vehicle data matrix, reading the column vector corresponding to each type of driving description information from the vehicle data matrix respectively.

6. The method of claim 5, wherein, The method further comprises: if it is determined that a target column vector corresponding to a target type of driving description information is missing in the vehicle data matrix, detecting at least one auxiliary recovery column vector in the vehicle data matrix; combining the target column vector with each of the auxiliary recovery column vectors to obtain a column vector pair, and obtaining a target weight matrix corresponding to each column vector pair from a target partition of a target database; recovering the target column vector according to each target weight matrix and an auxiliary recovery column vector matched with each target weight matrix.

7. An automatic driving data recording apparatus characterized by comprising: The device comprises: a driving information acquisition module configured to acquire a plurality of driving description information in response to a trigger event of automatic driving data recording; wherein the types of driving description information include vehicle basic information, vehicle state information, automatic driving system operation information, driving environment information, and driver operation information; a data matrix generation module configured to construct column vectors corresponding to each type of driving description information, and generate a vehicle data matrix according to the column vectors; a label information construction module configured to construct label information describing the column vector positions of each type of driving description information in the vehicle data matrix; a data storage module configured to store the vehicle data matrix and the label information in a target partition of a target database according to the type of trigger event, for user reading and parsing; a weight matrix determination module configured to determine weight matrices between two column vectors in the vehicle data matrix, wherein the weight matrices are used to describe the data correlation between vector elements in two column vectors; the data storage module comprises: a matrix storage unit configured to store the vehicle data matrix and the weight matrices between two column vectors in the vehicle data matrix in a target partition of a target database according to the type of trigger event.

8. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the automatic driving data recording method of any one of claims 1-4 or the automatic driving data reading method of any one of claims 5-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing the processor to implement the automatic driving data recording method of any one of claims 1-4 or the automatic driving data reading method of any one of claims 5-6 when executed.

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