Data extraction method and computing device for data storage system for autonomous driving

Abstract

A data extraction method and computing device for a data storage system for autonomous driving are provided. A DSSAD data extraction system can send an event data recorder (EDR) data request to an EDR data storage device and receive EDR data from the EDR data storage device; send a DSSAD data request and receive DSSAD data from a DSSAD data storage device; specify DSSAD data to be analyzed from the received DSSAD data; determine whether an event corresponding to the EDR data exists in the DSSAD data to be analyzed; and if it is determined that the event corresponding to the EDR data exists in the DSSAD data to be analyzed, extract from the DSSAD data to be analyzed DSSAD data whose recording time can be included in a predetermined time range based on an event occurrence time of the EDR data.

Description

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2024-0180516, filed with the Korean Intellectual Property Office on December 6, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to a data extraction method for a data storage system (DSSAD) used for autonomous driving. Background Technology

[0004] The Data Storage System for Automated Driving (DSSAD) is a data storage system for autonomous vehicles that ensures the safety and reliability of automated driving technology. DSSAD records critical events and data that occur during vehicle operation and is used for accident investigations, legal liability determination, and system improvement. Specifically, DSSAD can be configured to meet the legal and technical standards of each country, and for this purpose, it may be necessary to store and manage accurate and reliable data. Furthermore, DSSAD data needs to be extracted together with existing Event Data Recorder (EDR) data. This allows for comprehensive analysis of the interactions between the automated driving system and the driver, as well as environmental information at the time of an accident, providing a more complete picture. Summary of the Invention

[0005] The task to be solved is to provide a method for extracting DSSAD data that can be extracted together with EDR data extraction.

[0006] A DSSAD data extraction method according to an embodiment, executed in a DSSAD data extraction system, may include, for example, a data extraction device, an Event Data Recorder (EDR) data storage device, and / or a DSSAD data storage device. The method may include: the data extraction device sending an EDR data request to the EDR data storage device and receiving EDR data from the EDR data storage device; the data extraction device sending a DSSAD data request to the DSSAD data storage device and receiving DSSAD data from the DSSAD data storage device; the data extraction device specifying DSSAD data to be analyzed from the received DSSAD data; the data extraction device determining whether an event corresponding to the EDR data exists in the DSSAD data to be analyzed; and if it is determined that an event corresponding to the EDR data exists in the DSSAD data to be analyzed, the data extraction device extracting the DSSAD data from the DSSAD data to be analyzed, wherein the recording time is included within a predetermined time range based on the event occurrence time of the EDR data.

[0007] In some embodiments, the method may further include, if it is determined that there is no event in the DSSAD data to be analyzed that corresponds to the EDR data, then the data extraction device extracts the DSSAD data from the DSSAD data to be analyzed that is closest in time to a point before a predetermined time range.

[0008] In some embodiments, the received DSSAD may include DSSAD data with different regional attributes for each occurrence region, and the DSSAD data to be analyzed may include DSSAD data with the same regional attributes as the event occurrence region of the EDR data as specified by the data extraction device from the received DSSAD data, as the DSSAD data to be analyzed.

[0009] In some embodiments, the regional attribute can be determined based on the country where the event occurred in the EDR data.

[0010] In some embodiments, sending a DSSAD data request to a DSSAD data storage device and receiving DSSAD data from the DSSAD data storage device by the data extraction device may include: sending the DSSAD data request along with information about the same region attributes as the event occurrence region of the EDR data to the DSSAD data storage device by the data extraction device; and receiving DSSAD data from the DSSAD that is limited to the same region attributes as the event occurrence region of the EDR data by the data extraction device.

[0011] In some embodiments, the method may further include generating a DSSAD report linked to EDR data based on the extracted DSSAD data by a data extraction device.

[0012] In some embodiments, the recording time of the DSSAD data displayed in the DSSAD report may be expressed as the difference between the event occurrence time and that of the EDR data.

[0013] According to an embodiment, the DSSAD data extraction method, executed in a DSSAD data extraction system including a data extraction device, an EDR data storage device, and a DSSAD data storage device, may include: the data extraction device sending an EDR data request to the EDR data storage device and receiving EDR data from the EDR data storage device; the data extraction device confirming the event occurrence time of the EDR data; the data extraction device sending the DSSAD data request and information related to the event occurrence time of the EDR data to the DSSAD data storage device; the DSSAD data storage device specifying DSSAD data to be analyzed from the stored DSSAD data; the DSSAD data storage device determining whether an event corresponding to the EDR data exists in the DSSAD data to be analyzed; if it is determined that EDR data exists in the DSSAD data to be analyzed, the DSSAD data storage device extracting DSSAD data from the DSSAD data to be analyzed whose record time is within a predetermined time range based on the event occurrence time of the EDR data; and the DSSAD data storage device sending the extracted DSSAD data to the data extraction device.

[0014] In some embodiments, the method may further include, if it is determined that there is no event in the DSSAD data to be analyzed that corresponds to EDR data, then the DSSAD data storage device extracts the DSSAD data from the DSSAD data to be analyzed that is closest in time to a point before a predetermined time range.

[0015] In some embodiments, the retained DSSAD may include DSSAD data with different regional attributes for each occurrence region, and the DSSAD data to be analyzed may include DSSAD data with the same regional attributes as the event occurrence region of the EDR data, specified by the DSSAD data storage device from the retained DSSAD data, as the DSSAD data to be analyzed.

[0016] In some embodiments, the regional attribute may be determined based on the country where the event occurred in the EDR data.

[0017] In some embodiments, sending a DSSAD data request along with information about the event occurrence time of the EDR data to the DSSAD data storage device by the data extraction device may include: sending the DSSAD data request along with information about the event occurrence time of the EDR data and information about the same region attribute as the event occurrence region of the EDR data to the DSSAD data storage device by the data extraction device.

[0018] In some embodiments, the method may further include generating a DSSAD report linked to EDR data by a data extraction device based on DSSAD data received from a DSSAD data storage device.

[0019] In some embodiments, the recording time of the DSSAD data displayed in the DSSAD report may be expressed as the difference between the event occurrence time and that of the EDR data.

[0020] A DSSAD data extraction system may include a data extraction device, an Event Data Recorder (EDR), and a DSSAD data storage device. Methods performed by this system may include: the data extraction device sending an EDR data request to the EDR and DSSAD data storage devices; receiving EDR data from the EDR and DSSAD data storage devices; the data extraction device confirming the event occurrence time of the EDR data; the data extraction device sending a DSSAD data request and information related to the occurrence time of the EDR data to the EDR and DSSAD data storage devices; the EDR and DSSAD data storage devices specifying DSSAD data to be analyzed from stored DSSAD data; the EDR and DSSAD data storage devices determining whether the event corresponding to the EDR data exists in the DSSAD data to be analyzed; if the EDR data is determined to exist in the DSSAD data to be analyzed, the EDR and DSSAD data storage devices extracting DSSAD data from the DSSAD data to be analyzed whose recording time includes a predetermined time range based on the event occurrence time of the EDR data; and the EDR and DSSAD data storage devices sending the extracted DSSAD data to the data extraction device.

[0021] In some embodiments, the method may further include: if it is determined that there is no event corresponding to EDR data in the DSSAD data to be analyzed, then the EDR and DSSAD data storage device extracts the DSSAD data from the DSSAD data to be analyzed that is closest in time to a point before a predetermined time range.

[0022] In some embodiments, the retained DSSAD may include DSSAD data with different regional attributes for each occurrence region, and the DSSAD data to be analyzed may include DSSAD data in the retained DSSAD data that have the same regional attributes as the event occurrence regions of the EDR data, as specified by the EDR and DSSAD data storage devices, as the DSSAD data to be analyzed.

[0023] In some embodiments, the regional attribute may be determined based on the country where the event occurred in the EDR data.

[0024] In some embodiments, sending a DSSAD data request along with information related to the event occurrence time of the EDR data to the EDR and DSSAD data storage device may include: the EDR and DSSAD data storage device sending the DSSAD data request along with information related to the event occurrence time of the EDR data and information related to the same region attribute as the event occurrence region of the EDR data to the EDR and DSSAD data storage device.

[0025] In some embodiments, the method may further include generating a DSSAD report linked to EDR data based on DSSAD data received from the EDR and DSSAD data storage devices, wherein the recording time of the DSSAD data displayed in the DSSAD report may be expressed as the difference between the event occurrence time and that of the EDR data.

[0026] For example, a computing device can be configured to manage data during autonomous driving of a vehicle. The computing device can receive event data corresponding to vehicle events from an event data recorder associated with the vehicle. The computing device can then receive DSSAD data from a Data Storage System for Automated Driving (DSSAD) system, which indicates one or more measurements of autonomous driving of the vehicle, and identify timing information of the event data recorder triggers based on the event data. The timing information can be associated with at least one of a start time corresponding to a vehicle event or an end time corresponding to the vehicle event. The computing device can extract a portion of the DSSAD data based on the timing information of the event data recorder triggers and based on the end time; and based on said portion of the DSSAD data, send information indicating one or more measurements of autonomous driving of the vehicle during the vehicle event. Attached Figure Description

[0027] Figure 1 This is a diagram illustrating a data retrieval system used in a data storage system for autonomous driving (DSSAD).

[0028] Figure 2 This is a diagram illustrating the DSSAD data extraction system.

[0029] Figure 3 This is a diagram illustrating the DSSAD data extraction system.

[0030] Figure 4 and Figure 5 This is a view used to illustrate the operation of the DSSAD data extraction system.

[0031] Figure 6 This is a diagram illustrating the DSSAD data extraction system.

[0032] Figure 7 This is a diagram used to illustrate the DSSAD data extraction method.

[0033] Figure 8 This is a diagram used to illustrate the DSSAD data extraction method.

[0034] Figure 9 This is a diagram used to illustrate the DSSAD data extraction method.

[0035] Figure 10 It is a view used to explain a computing device. Detailed Implementation

[0036] In the following, the present disclosure will be described more fully with reference to the accompanying drawings, in which embodiments of the present disclosure are illustrated. As those skilled in the art will recognize, the described embodiments may be modified in various ways without departing from the spirit or scope of the present disclosure. Therefore, the drawings and description are to be considered illustrative rather than restrictive in nature. Throughout the description, the same reference numerals denote the same elements.

[0037] Furthermore, unless explicitly stated otherwise, the word "comprising" and variations such as "including" or "containing" will be understood to imply inclusion of the stated elements, but not to exclude any other elements. Terms including serial numbers such as first, second, etc., will be used only to describe the various components and will not be construed as limiting those components. These terms are used only to distinguish one component from others.

[0038] In this disclosure, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” and “at least one of A, B, and C” may include any or all possible combinations of the items listed together in the corresponding phrase. For the purposes of this specification and claims, the exemplary phrases “at least one: A; B; or C” or “at least one A, B, or C” are used, which means “any combination of at least one A, or at least one B, or at least one C, or at least one A, at least one B, and at least one C.” Furthermore, the exemplary phrases used herein (e.g., “A, B, or C,” “at least one A, B, and C,” “at least one A, B, or C,” etc.) may refer to each listed item or all possible combinations of listed items. For example, “at least one of A or B” may mean (1) at least one A; (2) at least one B; or (3) at least one A and at least one B.

[0039] As used in this specification, the terms "module" or "unit" refer to software and / or hardware components, and a "module" or "unit" performs certain operations / functions / roles. However, a "module" or "unit" is not to be construed as limited to software or hardware. A "module" or "unit" may be configured to reside in addressable storage media or to execute on one or more processors. Thus, by way of example, a "module" or "unit" may include at least one of the following components: software components, object-oriented software components, class components and task components, processes, functions, attributes, programs, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The functionality provided in a component, "module," or "unit" may be combined into a smaller number of components, "modules," or "units" or further divided into additional components, "modules," or "units."

[0040] In this disclosure, a “module” or “unit” can be implemented as a processor and memory. “Processor” should be broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), microcontrollers, state machines, etc. In some contexts, “processor” can refer to application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or field-programmable gate arrays (FPGAs). For example, “processor” can refer to a combination of processing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other such combination. Furthermore, “memory” should be broadly interpreted to include any electronic component capable of storing electronic information. “Memory” can refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage devices, and registers. The memory can be in a state of electronic communication with the processor when the processor can read information from the memory and / or record information in the memory. Memory integrated into the processor is in a state of electronic communication with the processor.

[0041] One or more features described herein can be provided as a computer program stored in a computer-readable recording medium for execution on a computer. The medium may continuously store a computer-executable program or temporarily store a program for execution or download. Furthermore, the medium can be a variety of recording or storage devices in the form of a single hardware device or multiple combined hardware devices, and is not limited to media directly connected to certain computer systems, but may also be distributed across a network. Examples of such media include magnetic media such as hard disks, floppy disks, or magnetic tapes; optical recording media such as CD-ROMs or DVDs; magneto-optical media such as floppy disks; and ROMs, RAMs, or flash memory configured to store program instructions. Additional embodiments of such media include media or storage media managed by application stores that distribute applications or by different other sites or servers that provide or distribute software.

[0042] In a hardware implementation, the processing unit for performing these techniques may be implemented within one or more ASICs, DSPs, digital signal processing devices, programmable logic devices, field-programmable gate arrays, processors, controllers, microcontrollers, microprocessors, electronic devices, or computers or combinations thereof, designed to perform the functions described in this disclosure.

[0043] According to the Society of Automotive Engineers (SAE), the automation levels of autonomous vehicles can be categorized as follows: At Level 0 of Automated Driving, the SAE classification corresponds to "No Automation," where the automated driving system temporarily handles emergency situations (e.g., automatic emergency braking) and / or only provides warnings (e.g., blind spot warning, lane departure warning, etc.) and expects the driver to operate the vehicle. At Level 1 of Automated Driving, the SAE classification corresponds to "Driver Assistance," where the system performs some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.) when the driver operates the vehicle in normal operating conditions, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. At Level 2 of Automated Driving, the SAE classification corresponds to "Partial Automation," where the system performs steering, acceleration, and / or braking under driver supervision, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and handle (e.g., resolve) emergency situations. At Level 3 of autonomous driving, the SAE classification standard can correspond to "conditional automation," where the system drives the vehicle under constrained conditions (e.g., performing driving functions such as steering, acceleration, and / or braking), but transfers driving control to the driver when the desired conditions are not met. The driver is expected to determine the system's operating state and / or timing and take over control in emergency situations, but not otherwise operate the vehicle (e.g., steering, acceleration, and / or braking). At Level 4 of autonomous driving, the SAE classification standard can correspond to "high automation," where the system performs all driving functions and the driver is expected to control the vehicle only in emergency situations. At Level 5 of autonomous driving, the SAE classification standard can correspond to "full automation," where the system performs all driving functions without any assistance from the driver, including in emergency situations, and the driver is not expected to perform any driving functions other than determining the system's operating state. While this disclosure applies the SAE classification standard to autonomous driving classification, other classification methods and / or algorithms can be used in one or more configurations described herein.

[0044] One or more features associated with autonomous driving control can be activated based on configured autonomous driving control settings (e.g., based on at least one of the following: autonomous driving classification, selection of the vehicle's autonomous driving level, etc.). Vehicle operation can be controlled based on one or more features described herein (e.g., features extracted from one or more portions of DSSAD data, EDR data, etc.). Vehicle control can include various operational controls associated with the vehicle (e.g., autonomous driving control, sensor control, braking control, braking time control, acceleration control, acceleration rate of change control, warning timing control, forward collision warning timing control, etc.).

[0045] One or more auxiliary devices (e.g., engine brakes, exhaust brakes, hydraulic reducers, electric reducers, regenerative brakes, etc.) may also be controlled, for example, based on one or more features described herein (e.g., features of one or more extracted portions of DSSAD data, EDR data, etc.).

[0046] For example, based on one or more features described herein (e.g., features extracted from DSSAD data, EDR data, etc.), one or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc.) capable of communicating via one or more wired or wireless communication protocols such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long Term Evolution (LTE), 5G New Radio (NR), Vehicle to Everything (V2X), etc., can also be controlled.

[0047] One or more Minimum Risk Maneuvering (MRM) operations can also be controlled, for example, based on one or more features described herein (e.g., features extracted from DSSAD data, EDR data, etc.). Minimum Risk Maneuvering operations (e.g., minimum risk maneuver, minimum risk operation) can be vehicle maneuvers designed to minimize (e.g., reduce) the risk of collisions with surrounding vehicles to achieve a reduced (e.g., minimum) risk state. Minimum Risk Maneuvering can be an operation activated during autonomous driving when the driver is unable to respond to an intervention request. During Minimum Risk Maneuvering, one or more processors in the vehicle can control the vehicle's driving operations for a set time period.

[0048] One or more biased driving operations can also be controlled, for example, based on one or more features described herein (e.g., features of one or more extracted portions of DSSAD data, EDR data, etc.). The driving control device can perform biased drive control. To perform biased driving, the driving control device can control the vehicle to travel within the lane by maintaining a lateral distance between the vehicle's center position and the center of the lane. For example, the driving control device can control the vehicle to remain in the lane but not in the center of the lane. The driving control device can identify or determine a target lateral distance for biased driving control. For example, the target lateral distance can include an intentionally adjusted lateral distance that the vehicle can deviate from during maneuvers such as lane changes from a reference point (such as the center of the lane or another vehicle). This adjustment can be made to improve the vehicle's stability, safety, and / or performance under changing driving conditions. For example, during lane changes, the driving control system can bias the lateral distance to maintain a safer clearance with adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0049] One or more sensors (e.g., IMU sensors, cameras, LiDAR, RADAR, blind spot monitoring sensors, lane departure warning sensors, parking sensors, light sensors, rain sensors, traction control sensors, anti-lock braking system sensors, tire pressure monitoring sensors, seat belt sensors, airbag sensors, fuel sensors, emission sensors, throttle position sensors, inverters, converters, motor controllers, power distribution units, high-voltage wiring and connectors, auxiliary power modules, charging interfaces, etc.) can also be used for control, for example, based on one or more features described herein (e.g., features of one or more extracted portions of DSSAD data, EDR data, etc.). Operational control for autonomous vehicles may include various driving controls of the vehicle by vehicle control equipment (e.g., acceleration, deceleration, steering control, gear shifting control, braking system control, traction control, stability control, cruise control, lane keeping assist control, collision avoidance system control, emergency braking assist control, traffic sign recognition control, adaptive headlight control, etc.).

[0050] The level of autonomous driving and / or activation / deactivation of autonomous driving can also be controlled, for example, based on one or more features described herein (e.g., features extracted from DSSAD data, EDR data, etc.). The driving control device can perform autonomous driving level control (e.g., changing the level of autonomous driving, changing the required user attention, etc.) or deactivate autonomous driving operation. For example, by changing the required user attention, the driver may be required to keep his / her hands on the steering wheel more frequently (e.g., at least once within a threshold time period, such as 5 seconds, 30 seconds, 1 minute, etc.). By changing the required user attention, the driver may be required to look forward more frequently (e.g., at least once within a threshold time period, such as 5 seconds, 30 seconds, 1 minute, etc.). By changing the level of autonomous driving, one or more video contents may not be displayed on the vehicle's displays.

[0051] Figure 1 This is a diagram illustrating a DSSAD data extraction system according to one embodiment.

[0052] refer to Figure 1The on-board diagnostic communication process in the DSSAD data extraction system can be as follows. First, when an Event Data Recorder (EDR) data extraction request (①) occurs in the data extraction device 10, the request can be sent to the communication gateway 20 (②), and an EDR report can be generated and sent from the EDR data storage device 11 (③) and sent (④). Simultaneously, a DSSAD data request linked to the EDR can be formed in the data extraction device 10 (⑤), and the corresponding request signal can be sent to the DSSAD data storage device 12 (⑥). Therefore, the EDR-linked DSSAD event data extracted from the DSSAD data storage device 12 can be sent (⑦) and finally sent (⑧) to the data extraction device 10. Through this process, EDR data and DSSAD data can be managed and provided in an integrated manner.

[0053] Figure 2 This is a diagram illustrating a DSSAD data extraction system according to one embodiment.

[0054] refer to Figure 2 The process for generating a DSSAD report linked to EDR can be as follows: When an EDR data request is sent from the data extraction device 10 to the EDR data storage device 11 (①), EDR data can be obtained from the EDR data storage device 11 (②). Next, DSSAD data linked to EDR can be requested from the data extraction device 10 (③) to the DSSAD data storage device 12, and DSSAD data can be sent from the DSSAD data storage device 12 to the data extraction device 10 according to the request (④). Thereafter, the data extraction device 10 can extract all DSSAD data within the first 30 seconds of the EDR occurrence time in the entire DSSAD data (⑤). However, if EDR trigger information may not exist in the DSSAD data, only the DSSAD data closest to the EDR reference time can be displayed.

[0055] Figure 3 This is a view used to explain a DSSAD data extraction system according to one embodiment, and Figure 4 and Figure 5 This is a view used to explain the operation of a DSSAD data extraction system according to one embodiment.

[0056] refer to Figure 3The process for generating a DSSAD report linked to EDR can be as follows. When an EDR data request is sent from the data extraction device 10 to the EDR data storage device 11 (①), the corresponding data can be sent from the EDR data storage device 11 (②) to the data extraction device 10. Thereafter, a request to send DSSAD data linked to EDR can be executed from the data extraction device 10 to the DSSAD data storage device 12 (③), and DSSAD data can be extracted from the DSSAD data storage device 12 (④). In this process, data can be extracted based on information from countries such as Germany 121, France 122, and the United Kingdom 123, as well as DSSAD events that occur at the same time as the EDR triggering in the DSSAD event that occurs. DSSAD events can be processed in two cases: (a) when an EDR event exists, such as Figure 4 As shown, based on the EDR occurrence time in the entire DSSAD data, all DSSAD events within the first 30 seconds are extracted, and the EDR event time can be set to 0 seconds. (b) If there is no EDR event, only the last event in the DSSAD event record can be extracted, such as... Figure 5 As shown. Finally, an EDRDSAD report (⑤) can be generated based on the linked DSSAD data.

[0057] Figure 6 This is a diagram illustrating a DSSAD data extraction system according to one embodiment.

[0058] refer to Figure 6 The process for generating a DSSAD report linked to EDR can be as follows: When an EDR data request is sent from the data extraction device 10 to the EDR and DSSAD data storage device 13 (①), EDR data can be obtained from the EDR and DSSAD data storage device 13 (②). Then, DSSAD data linked to EDR can be requested from the data extraction device 10 (③) to the EDR and DSSAD data storage device 13, and DSSAD data can be sent from the EDR and DSSAD data storage device 13 to the data extraction device 10 upon request (④). Thereafter, the data extraction device 10 extracts all DSSAD data within the 30 seconds prior to the EDR occurrence time from the entire DSSAD data (⑤). However, if EDR trigger information is not present in the DSSAD data, only the DSSAD data closest to the EDR reference time can be displayed.

[0059] Figure 7 This is a diagram used to explain a DSSAD data extraction method according to one embodiment.

[0060] refer to Figure 7The DSSAD data extraction method according to one embodiment can be executed in a DSSAD data extraction system including a data extraction device 10, an EDR data storage device 11, and a DSSAD data storage device 12.

[0061] The method includes sending an EDR data request to an EDR data storage device 11 by a data extraction device 10 (S701), receiving EDR data from the EDR data storage device 11 (S702), sending a DSSAD data request to a DSSAD data storage device 12 by the data extraction device 10 (S703), receiving DSSAD data from the DSSAD data storage device 12 by the data extraction device 10 (S704), specifying the DSSAD data to be analyzed from the received DSSAD data, determining whether the event corresponding to the EDR data exists in the DSSAD data analyzed by the data extraction device 10 (S705), when it is determined that there is an event corresponding to the EDR data in the DSSAD data to be analyzed by the data extraction device 10, recalculating the recording time of the DSSAD data using the event occurrence time of the EDR data as a reference value (S706), and extracting DSSAD data whose recording time includes a predetermined time range (e.g., the first 30 seconds) based on the event occurrence time of the EDR data in the DSSAD data to be analyzed by the data extraction device 10 (S707).

[0062] The method may further include: if it is determined that there is no event corresponding to EDR data in the DSSAD data to be analyzed, the data extraction device 10 extracts the DSSAD data that is closest in time to the point before the predetermined time range from the DSSAD data to be analyzed (S708).

[0063] In some embodiments, the received DSSAD includes DSSAD data with different regional attributes for each occurrence region, and specifying the DSSAD data to be analyzed may include: the data extraction device 10 designating DSSAD data having the same regional attributes as the event occurrence region of the EDR data in the received DSSAD data as the DSSAD data to be analyzed. In some embodiments, the regional attributes may be determined based on the country where the EDR data occurred.

[0064] In some embodiments, sending a DSSAD data request from the data extraction device 10 to the DSSAD data storage device 12 and receiving DSSAD data from the DSSAD data storage device 12 may include: the data extraction device 10 sending a DSSAD data request to the DSSAD data storage device 12, the DSSAD data request having information about regional attributes that are the same as the event occurrence area of ​​the EDR data, and the data extraction device 10 receiving DSSAD data from the DSSAD data storage device 12, the DSSAD data being restricted to regional attributes that are the same as the event occurrence area of ​​the EDR data.

[0065] In some embodiments, the method may further include generating a DSSAD report linked to EDR data based on the extracted DSSAD data by the data extraction device 10. In some embodiments, the recording time of the DSSAD data displayed in the DSSAD report may be expressed as the difference between the event occurrence time and that of the EDR data.

[0066] Figure 8 This is a diagram used to explain a DSSAD data extraction method according to one embodiment.

[0067] refer to Figure 8 The DSSAD data extraction method according to one embodiment can be executed in a DSSAD data extraction system including a data extraction device 10, an EDR data storage device 11, and a DSSAD data storage device 12.

[0068] The method may include: sending an EDR data request from a data extraction device 10 to an EDR data storage device 11 (S801); receiving EDR data from the EDR data storage device 11 (S802); confirming the event occurrence time of the EDR data by the data extraction device 10 (S803); sending a DSSAD data request and information related to the event occurrence time of the EDR data to a DSSAD data storage device 12 (S804); specifying the DSSAD data to be analyzed from the included DSSAD data; and determining whether an event corresponding to the EDR data exists. If it can be determined that EDR data exists in the DSSAD data to be analyzed (S805), the DSSAD data storage device 12 uses the event occurrence time of the EDR data as a reference value to recalculate the recording time of the DSSAD data (S806). The DSSAD data storage device 12 extracts DSSAD data from the DSSAD data to be analyzed whose recording time may include the DSSAD data within a predetermined time range (e.g., the first 30 seconds) based on the event occurrence time of the EDR data (S807), and the DSSAD data storage device 12 sends the extracted DSSAD data to the data extraction device 10 (S809).

[0069] The method may further include: if it is determined that there is no event corresponding to the EDR data in the DSSAD data to be analyzed, the DSSAD data storage device 12 extracts the DSSAD data from the DSSAD data to be analyzed that is closest in time to the point before a predetermined time range (S808).

[0070] In some embodiments, the retained DSSAD includes DSSAD data with different regional attributes for each occurrence region, and specifying the DSSAD data to be analyzed may include: specifying DSSAD data with the same regional attributes as the event occurrence region of the EDR data in the retained DSSAD data as the DSSAD data to be analyzed by the DSSAD data storage device 12. In some embodiments, the regional attributes may be determined based on the country where the EDR data occurred.

[0071] In some embodiments, sending a DSSAD data request along with information related to the event occurrence time of the EDR data to the DSSAD data storage device 12 by the data extraction device 10 may include: sending the DSSAD data request along with information related to the event occurrence time of the EDR data and information related to the same region attribute as the event occurrence region of the EDR data to the DSSAD data storage device 12 by the data extraction device 10.

[0072] In some embodiments, the method may further include generating a DSSAD report linked to EDR data based on DSSAD data received by the data extraction device 10 from the DSSAD data storage device 12 (S810). In some embodiments, the recording time of the DSSAD data displayed in the DSSAD report may be expressed as the difference between the recording time of the event and the recording time of the EDR data.

[0073] Figure 9 This is a diagram used to explain a DSSAD data extraction method according to one embodiment.

[0074] refer to Figure 9 The DSSAD data extraction method according to one embodiment can be executed in a DSSAD data extraction system including a data extraction device 10 and an EDR and DSSAD data storage device 13.

[0075] The method includes: sending an EDR data request from the data extraction device 10 to the EDR and DSSAD data storage device 13 (S901); receiving EDR data from the EDR and DSSAD data storage device 13 (S902); confirming the event occurrence time of the EDR data by the data extraction device 10 (S903); sending the DSSAD data request and information related to the event occurrence time of the EDR data to the EDR and DSSAD data storage device 13 (S904); specifying the DSSAD data to be analyzed from the stored DSSAD data by the EDR and DSSAD data storage device 13; and determining the corresponding EDR data. If the event exists in the DSSAD data to be analyzed (S905), and if it is determined that the EDR data exists in the DSSAD data to be analyzed, the EDR and DSSAD data storage device 13 recalculates the recording time of the DSSAD data based on the event occurrence time of the EDR data as a reference value (S906). The EDR and DSSAD data storage device 13 extracts the DSSAD data from the DSSAD data to be analyzed, which may include the DSSAD data within a predetermined time range (e.g., the first 30 seconds) based on the event occurrence time of the EDR data (S907). The EDR and DSSAD data storage device 13 then sends the extracted DSSAD data to the data extraction device 10 (S909).

[0076] The method may further include: if it is determined that there is no event corresponding to the EDR data in the DSSAD data to be analyzed, then the EDR and DSSAD data storage device 13 extracts the DSSAD data from the DSSAD data to be analyzed that is closest in time to the point before the predetermined time range (S908).

[0077] In some embodiments, the retained DSSAD includes DSSAD data with different regional attributes for each occurrence region, and the DSSAD data to be analyzed specified by the EDR and DSSAD data storage device 13 may include DSSAD data that specifies the same regional attributes as the event occurrence regions of the EDR data in the retained DSSAD data as the DSSAD data to be analyzed. In some embodiments, the regional attributes may be determined based on the country where the EDR data occurred.

[0078] In some embodiments, sending a DSSAD data request along with information related to the event occurrence time of the EDR data to the EDR and DSSAD data storage device 13 by the data extraction device 10 may include: sending a DSSAD data request along with information related to the event occurrence time of the EDR data and information related to the same region attribute as the event occurrence region of the EDR data to the EDR and DSSAD data storage device 13 by the data extraction device 10.

[0079] In some embodiments, the method further includes generating a DSSAD report linked to the EDR data by the data extraction device 10 based on the DSSAD data received from the EDR and DSSAD data storage device 13 (S910), and the recording time of the DSSAD data displayed in the DSSAD report may be expressed as the difference between the event occurrence time of the EDR data and the event occurrence time of the DSSAD data.

[0080] Figure 10 This is a diagram used to explain a computing device according to one embodiment.

[0081] refer to Figure 10 The DSSAD data extraction method according to the embodiments can be implemented using computing devices 50. These computing devices 50 can be implemented as different types of electronic devices, servers or similar devices, and their functions can be implemented through a combination of software and hardware.

[0082] The computing device 50 may include at least one of a processor 510, a memory 530, a user interface input device 540, a user interface output device 550, and a storage device 560, which communicate via a bus 520. The computing device 50 may also include a network interface 570 electrically connected to the network 40. The network interface 570 can send or receive signals with other entities via the network 40.

[0083] Processor 510 can be implemented as various types of computing units, such as MCU (microcontroller unit), AP (application processor), CPU (central processing unit), GPU (graphics processing unit), NPU (neural processing unit), QPU (quantum processing unit), etc. Processor 510 can be a semiconductor device that executes instructions stored in memory 530 or storage device 560, and can play a key role in the system. The program code and data stored in memory 530 or storage device 560 instruct processor 510 to perform specific tasks, thereby enabling the overall operation of the system. Processor 510 can be configured to implement the above-mentioned references. Figures 1 to 9 The different functions and methods described.

[0084] Memory 530 and storage device 560 may include different forms of volatile or non-volatile storage media for storing and accessing data in the system. For example, memory 530 may include read-only memory (ROM) 531 and random access memory (RAM) 532. In some embodiments, memory 530 may be integrated into processor 510, in which case data transfer between memory 530 and processor 510 can be very fast. In some other embodiments, memory 530 may be located external to processor 510, in which case memory 530 may be connected to processor 510 via a different data bus or interface. This connection can be implemented by various known means, such as a peripheral component interconnect high-speed (PCIe) interface for high-speed data transfer or through a memory controller.

[0085] In some embodiments, at least some components or functions of the DSSAD data extraction method according to the example may be implemented as a program or software running on computing device 50, and the program or software may be stored on a computer-readable recording medium or storage medium. Specifically, the computer-readable recording medium or storage medium according to the example may be a computer having a program recorded thereon for causing a computer including processor 510 to perform the steps included in the implementation of the DSSAD data extraction method according to the example, the processor executing a program or command stored in memory 530 or storage device 560.

[0086] In some embodiments, at least some of the components or functions of the DSSAD data extraction method according to the example may be implemented using the hardware or circuitry of the computing device 50, or may be implemented as separate hardware or circuitry that can be electrically connected to the computing device 50.

[0087] According to an embodiment, DSSAD data can be linked with EDR data extraction and extracted together.

[0088] While this disclosure has been described in conjunction with what is now regarded as an actual exemplary embodiment, it is to be understood that this disclosure is not limited to the disclosed embodiments, but rather is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method performed by a device of a data retrieval system for a data storage system (DSSAD) used in autonomous driving, the method comprising: The processor of the data extraction system sends the Event Data Recorder (EDR) data request to the EDR data storage device of the data extraction system. Receive EDR data from the EDR data storage device; The processor sends a DSSAD data request to the DSSAD data storage device of the data extraction system. Receive DSSAD data from the DSSAD data storage device; The processor specifies the DSSAD data to be analyzed from the received DSSAD data; The processor determines whether the event corresponding to the EDR data exists in the DSSAD data to be analyzed; as well as The processor, based on the determination that an event corresponding to the EDR data exists in the DSSAD data to be analyzed, extracts a portion of the DSSAD data to be analyzed whose recording time is included within a predetermined time range, wherein the predetermined time range is based on the event occurrence time.

2. The method according to claim 1, further comprising: The processor extracts a portion of the DSSAD data to be analyzed from the DSSAD data based on the determination that the event corresponding to the EDR data does not exist in the DSSAD data to be analyzed, which is the point closest in time to a predetermined time range, wherein the predetermined time range is based on the event occurrence time of the event corresponding to the EDR data.

3. The method according to claim 1, wherein, The received DSSAD data includes DSSAD data with different regional attributes for each occurrence region, and The specified DSSAD data to be analyzed includes: a first portion of the DSSAD data that has the same regional attributes as the event occurrence region of the EDR data, as specified by the processor from the received DSSAD data, as the DSSAD data to be analyzed.

4. The method according to claim 3, wherein: The regional attribute is determined based on the country where the event occurred in the EDR data.

5. The method according to claim 1, in, Sending the DSSAD data request to the DSSAD data storage device includes: the processor sending the DSSAD data request along with information about a region attribute that is the same as the event occurrence region of the EDR data to the DSSAD data storage device; and Receiving the DSSAD data from the DSSAD data storage device includes: the processor receiving DSSAD data from the DSSAD data storage device that is limited to the same region attributes as the event occurrence region of the EDR data.

6. The method according to claim 1, further comprising: The processor generates a DSSAD report linked to the EDR data based on the extracted DSSAD data.

7. The method according to claim 6, wherein: The recording time of the DSSAD data displayed in the DSSAD report is output as the difference between the event occurrence time and the event occurrence time of the EDR data.

8. A method performed by a device of a data retrieval system for a data storage system (DSSAD) used in autonomous driving, the method comprising: The processor of the data extraction system sends the Event Data Recorder (EDR) data request to the EDR data storage device of the data extraction system. Receive EDR data from the EDR data storage device; The processor confirms the event occurrence time of the EDR data; The processor sends the following to the DSSAD data storage device of the data extraction system: DSSAD data request, and Information related to the time of event occurrence in the EDR data; The DSSAD data to be analyzed is specified by the DSSAD data storage device from the stored DSSAD data; The DSSAD data storage device determines whether the event corresponding to the EDR data exists in the DSSAD data to be analyzed; The DSSAD data storage device determines that an event corresponding to the EDR data exists in the DSSAD data to be analyzed, and extracts a portion of the DSSAD data to be analyzed whose recording time is included within a predetermined time range, wherein the predetermined time range is based on the event occurrence time of the EDR data; as well as The extracted DSSAD data is sent to the processor by the DSSAD data storage device.

9. The method according to claim 8, further comprising: The DSSAD data storage device extracts a portion of the DSSAD data to be analyzed from the DSSAD data based on the determination that the event corresponding to the EDR data does not exist in the DSSAD data to be analyzed, which is the point in time closest to a predetermined time range, wherein the predetermined time range is based on the event occurrence time of the event corresponding to the EDR data.

10. The method according to claim 8, wherein, The retained DSSAD data includes different regional attributes for each occurrence area, and The DSSAD data to be analyzed includes: DSSAD data with the same regional attributes as the event occurrence region of the EDR data, which are specified by the DSSAD data storage device from the stored DSSAD data, as the DSSAD data to be analyzed.

11. The method of claim 10, wherein: The regional attribute is determined based on the country in which the event occurred in the EDR data.

12. The method according to claim 8, in, Sending the DSSAD data request to the DSSAD data storage device includes: the processor sending the DSSAD data request along with information about region attributes that are the same as the event occurrence region of the EDR data to the DSSAD data storage device; and The processor receives DSSAD data from the DSSAD data storage device that is limited to the same region attributes as the event occurrence region of the EDR data.

13. The method of claim 8, further comprising: The processor generates a DSSAD report linked to the EDR data based on the DSSAD data received from the DSSAD data storage device.

14. The method of claim 13, wherein: The recording time of the DSSAD data displayed in the DSSAD report is output as the difference between the event occurrence time and the event occurrence time of the EDR data.

15. A computing device configured to manage data during autonomous driving of a vehicle, the computing device comprising: One or more processors, and A memory storing instructions that, when executed by the one or more processors, cause the computing device to: Receive event data corresponding to vehicle events from the event data recorder associated with the vehicle; Receive DSSAD data from the data extraction system of the DSSAD data storage system for autonomous driving, the DSSAD data indicating one or more measurements of the vehicle's autonomous driving; Based on the event data, the time information of the event data logger trigger is identified, wherein the time information is associated with at least one of the following: Corresponding to the start time of the vehicle event; and Corresponding to the end time of the vehicle event; Based on the time information from the event data logger trigger, a portion of the DSSAD data is extracted from the DSSAD data; and Based on the portion of the DSSAD data, information indicating one or more measurements of the vehicle's autonomous driving during the vehicle event is transmitted.

16. The computing device according to claim 15, wherein, When executed by the one or more processors, the instructions cause the computing device to send information indicating the one or more measurements of the autonomous driving of the vehicle during the vehicle event by instructing the computing device to send an indication of the time difference between time information associated with the portion of the DSSAD data and time information of the event data recorder trigger.

17. The computing device according to claim 15, wherein, The one or more measurements of the vehicle's autonomous driving indicate the degree of autonomous driving of the vehicle.

18. The computing device according to claim 15, wherein, When executed by the one or more processors, the instructions cause the computing device to extract the portion of the DSSAD data by retrieving DSSAD data that begins within a predetermined time range prior to the time point corresponding to the time information of the event data logger trigger.

19. The computing device according to claim 15, wherein, When executed by the one or more processors, the instructions cause the computing device to: The vehicle event in the event data is identified by identifying the latest event in the event data.

20. The computing device according to claim 15, wherein, When executed by the one or more processors, the instructions cause the computing device to extract the portion of the DSSAD data based on the geographical location of the vehicle.

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