Video analysis based warning-responsiveness linked risk-rating index computation system, server, apparatus and metho

Abstract

The present invention relates to a system, server, device, and method for calculating risk assessment indicators, wherein a wearable image analysis device worn by a user of a means of transportation or mounted to be synchronized with the user's gaze direction, or an image-based risk assessment device linked with a vehicle camera, black box, vehicle-mounted ADAS camera, mobile device, on-board unit (OBU), vehicle-to-everything (V2X) terminal, etc., identifies a driving context from a front image, location information, sensor information, or vehicle status information, determines a risk event according to differential safety standards for each context and outputs a warning, and measures response data including whether to return to a normal state, the time required for return, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change based on the time of warning output or the time of risk event confirmation. The above system generates risk assessment indicators, behavior-based safety scores, or premium rate calculation factors by cumulatively analyzing risk event data and response data, and reflects warning responsiveness independently of the frequency of risk event occurrences. Furthermore, it can enhance the precision and fairness of risk assessment by utilizing warning-free driving indicators, Time to Collision (TTC) correction models, multimodal dynamic weights, adverse weather preprocessing, individual baseline correction, and user-level continuous responsiveness profiles. In addition, the present invention can reduce data tampering and personal information exposure through the calculation of complete indicators within the device, the output of interaction data packets, verification of event packet integrity based on a Trusted Execution Environment (TEE), dual trajectory cross-verification, and on-device anonymization, and depending on the embodiment, it can also be applied to the calculation of risk assessment indicators based on wearable devices, mobile devices, or automotive devices.

Description

Technology Field

[0001] The present invention relates to Behavior-Based Insurance (BBI), analysis of mobility user behavior, and technology for calculating risk assessment indicators. More specifically, the invention relates to a system, server, device, and method in which a wearable video analysis device, which is worn on the body or clothing of a mobility user or mounted to be linked with the user's gaze direction, analyzes a first-person viewpoint front video, location information, and sensor information to identify driving contexts such as road type, driving lane, signal status, and weather conditions, determines a risk event according to safety standards set differentially for each context, provides a warning, and quantitatively measures and accumulates response data including whether the user returns to a normal state, the time required for return, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change based on the warning output time or the risk event confirmation time, and generates a risk assessment indicator or an insurance premium rate calculation factor.

[0003] In addition, according to an embodiment, the present invention includes cases in which the wearable image analysis device is implemented as smart glasses, sunglasses, a helmet-type camera, a body cam, a clip-type camera, or a clothing-attached camera, or is linked with a vehicle front camera, a black box, a vehicle-mounted ADAS camera, a surround-view camera, a perception camera of an autonomous vehicle, a vehicle-attached video device, an OBU, a V2X terminal, or a vehicle navigation terminal to generate and supplement risk event data and reaction data.

[0005] Furthermore, the present invention relates to a technology for calculating a risk assessment indicator based on warning responsiveness regardless of the form of the image acquisition means or computation node, through a Time to Collision (TTC) correction model, calculation of a complete risk assessment indicator within the device, output of interaction data packets, verification of event packet integrity based on a Trusted Execution Environment (TEE), on-device anonymization, and continuous responsiveness profile management at the user level. Background Technology

[0007] Conventional Usage-Based Insurance (UBI) collects driving patterns such as sudden acceleration, sudden braking, and speeding based on vehicle-mounted OBD terminals, vehicle sensors, accelerometers, or smartphone GPS trajectories, and reflects this in insurance premium rates. However, this method has limitations in that it is difficult to identify what judgments a user made in a given visual situation, that is, the visual context of the behavior and the risk response process.

[0009] Korean registered patent No. 10-2241734 discloses a configuration for calculating insurance rates by calculating the risk of movement (D), accident prevention participation (P), and safety index (S=D+P) based on the GPS, movement speed, vehicle ID, gyro sudden stop signal, and public transportation payment status of a mobile terminal; registered patent No. 10-1769102 discloses OBD and smartphone driving record analysis; and registered patent No. 10-1823015 discloses the calculation of UBI insurance rates based on navigation GPS trajectories.

[0011] However, the aforementioned conventional technologies rely merely on location information, inertial sensor information, or on-board device information, and fail to sufficiently disclose a configuration that identifies a driving context from a forward image corresponding to the user's viewpoint or the direction of travel of the means of transportation, quantifies responsiveness based on the warning output time or the risk event confirmation time, and reflects this in the risk assessment.

[0013] While vehicle-mounted Driver Monitoring Systems (DMS) can detect drowsiness, distraction, or eye deviation using interior cameras, they are dependent on a specific vehicle, making it difficult to obtain continuous behavioral data from the same user when switching vehicles, using rental cars or shared vehicles, riding motorcycles, or using personal mobility devices.

[0015] Furthermore, since DMS primarily focuses on observing the driver's state inside the vehicle, it has limitations in combining and analyzing external hazards such as forward objects, road type, lane condition, signal condition, weather conditions, and road surface hazards from the user's perspective, and in converting the process of returning to a normal state after a hazard warning into risk assessment indicators.

[0017] Conventional navigation or ADAS systems can warn of speeding, distance between vehicles, lane departure, or forward collision risks based on a registered speed limit database, map data, vehicle sensors, or a front camera. However, while these systems focus only on warning of risks or assisting in vehicle control, they do not sufficiently provide a configuration that accumulates the user's return time, warning repetition count, ignore duration, deceleration reaction time, steering reaction time, or safe distance recovery time based on the warning output time or the point at which a risk event is confirmed, and converts this into user-specific risk assessment indicators or insurance premium rate calculation factors.

[0019] Conventional vehicle cameras, black boxes, ADAS cameras, OBUs, or V2X terminals can be utilized for accident video recording, surrounding object recognition, vehicle status collection, or communication with road infrastructure; however, they have limitations in calculating warning responsiveness as a factor independent of the number of risk event occurrences and managing it as a continuous responsiveness profile at the user level by aligning data between wearable devices, mobile devices, vehicle devices, and servers according to the same reference point timestamp.

[0021] Conventional wearable technologies often remain at the level of detecting drowsiness using an inward-facing camera, collecting biosignals, or providing simple forward hazard alerts. For example, while wearable hazard warning technology may partially disclose configurations that transmit vehicle ADAS warnings to a wearable device or recognize and warn of forward hazards on smart glasses or health platforms, it does not sufficiently present an integrated configuration that identifies driving context using outward-facing camera images, determines hazard events based on differential safety standards for each context, and quantifies whether the vehicle returns to a normal state, the time required for return, the number of repetitions, and the ignore rate based on the warning output time or the time the hazard event is confirmed, converting these into risk assessment indicators or insurance premium rate calculation factors.

[0023] Furthermore, conventional technology lacks a structure to output the internal computation results of an AI model, warning output logs, response measurement logs, and metric calculation results as externally verifiable interaction data packets, or to verify forgery in a Trusted Execution Environment (TEE) or an equivalent security area by adding sequence numbers, session identifiers, hash values, digital signature values, and packet status values ​​to event packets.

[0025] Accordingly, there are limitations in verifying whether warning outputs, response measurements, and metric calculations are performed, or in systematically detecting packet loss, order reversal, duplicate transmissions, replay attacks, and position spoofing, without directly examining the source code or internal model structure.

[0027] Therefore, a technology is required that is not dependent on a specific vehicle, specific on-board device, or specific mobility platform, identifies driving context based on image acquisition means that follow an individual user or respond to the user's forward driving judgment, determines and warns of risk events based on differential safety standards for each context, and quantitatively measures responsiveness to warnings to reflect them in risk assessment indicators.

[0029] Furthermore, there is a need for risk assessment indicator calculation technology capable of providing in-device complete indicator calculation, Time to Collision (TTC) correction models, multimodal dynamic weights, interaction data packets, Trusted Execution Environment (TEE)-based event packet integrity verification, and user-level continuous reactivity profile management. Prior art literature

[0031] Republic of Korea Registered Patent 10-2241734 Republic of Korea Registered Patent 10-1769102 Republic of Korea Registered Patent 10-1823015 US 10,351,058 B2 US 9,870,716 B1 EP 3 067 827 A1 US 9,481,326 B2

[0032] SAE International, Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, SAE J3016.ISO, Road vehicles - Functional safety, ISO 26262.ISO / SAE, Road vehicles - Cybersecurity engineering, ISO / SAE 21434.UNECE, UN Regulation No. 157 - Automated Lane Keeping Systems (ALKS) The problem to be solved

[0033] The present invention aims to provide a system, server, device, and method that enables precise behavior-based risk assessment at the individual user level by using a wearable video analysis device that is not dependent on a specific vehicle, a specific on-board device, or a specific mobility platform, which is worn on the user's body or clothing or mounted to be linked with the user's gaze direction, identifies driving context from a first-person perspective, determines and warns of risk events based on differential safety standards for each context, and quantitatively measures the response to the warning and converts it into a risk assessment indicator or an insurance premium rate calculation factor.

[0035] The present invention aims to provide a risk assessment index capable of evaluating a user's actual risk response ability by not only evaluating the simple number of occurrences of risk events or driving trajectories, but also quantifying at least one of whether to return to a normal state, the time required for return, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change based on the warning output time or the time when the risk event is confirmed as a reference point, and reflecting this as a reactivity factor independent of the number of occurrences of risk events.

[0037] The present invention aims to provide both an in-device complete embodiment in which a wearable video analysis device calculates, stores, and displays behavioral risk indicators or Behavior-Based Insurance (BBI) scores within the device without requiring integration with an external server, and a server-side embodiment in which behavioral data is received from the wearable video analysis device or a vehicle device to calculate risk assessment indicators or insurance premium rate calculation factors. Accordingly, a risk assessment structure applicable to different implementing entities, such as terminal manufacturers, server operators, mobility platforms, or insurance companies, can be provided.

[0039] The present invention aims to provide an expandable structure capable of calculating at least one of a deceleration reaction time, steering reaction time, safe distance recovery time, lane return time, and warning ignore rate based on a warning output time or a risk event confirmation time, and reflecting it in a risk assessment index, even when linked with or utilizing at least one of a vehicle front camera, black box, vehicle built-in ADAS camera, vehicle-mounted perception camera, vehicle-attached video device, OBU, V2X terminal, or vehicle navigation terminal, in addition to a wearable image analysis device.

[0041] The present invention aims to calculate the estimated time to collision (TTC) from a front image based on a monocular camera and to dynamically adjust the weights of a risk event determination criterion, a warning criterion, a reactivity evaluation criterion, or a risk evaluation indicator using a TTC correction model or a TTC mapping table that is corrected according to at least one of a speed segment, a relative speed segment, a front object distance segment, a road type, a lane type, an object type, camera parameters, weather conditions, or illumination conditions.

[0043] The present invention aims to prevent or suppress data falsification, proxy wearing, driving without wearing, location spoofing, and exposure of personal information by verifying data reliability using at least one of a wearing state, device posture, camera obstruction, user authentication, whether the wearer matches the actual driver, pairing information with a mobile device, event hash value, and electronic signature value, and by excluding or restricting the constant external transmission of the original video.

[0045] The present invention aims to enable an external server, an integrated application, or a measurement device to verify whether warning output, response measurement, and indicator calculation have been performed without directly receiving internal source code or an artificial intelligence model structure, by generating and outputting an interaction data packet comprising at least one of a reference point timestamp message corresponding to the warning output time or the risk event confirmation time, a warning output log, a response measurement log, a normal state return log, a no-warning section summary log, and an indicator calculation result log.

[0047] The present invention aims to provide an integrity verification structure capable of detecting packet loss, reverse order, duplicate transmission, replay attacks, and data corruption in communication interruption sections by adding at least one of a sequence number, session identifier, event identifier, timestamp, previous packet hash value, current packet hash value, digital signature value, and packet status value to an event packet, storing this in a Trusted Execution Environment (TEE) or security area, or verifying it on a server, and performing retransmission requests, exclusion from calculation, or assignment of low reliability weights.

[0049] The present invention aims to perform on-device artificial intelligence image analysis using a low-power multi-stage trigger structure, improve the precision of risk determination by combining differential criteria based on road type, lane type, signal status, and weather conditions with image-based distance estimation or TTC calculation, and enable the detection of rear approach risks even in vehicles without rear cameras, motorcycles, or personal mobility devices through mirror area analysis.

[0051] Furthermore, due to the nature of wearable image analysis devices being worn on a user's head or body, they have limitations regarding battery capacity, heat generation, comfort, and long-term operation. Therefore, the present invention aims to provide sustainable risk assessment even in a wearable device environment by switching to an image analysis mode when at least one of driving conditions, risk context, sensor changes, or user input is detected, rather than performing image analysis at high power at all times, and by dynamically adjusting the image computation cycle, model complexity, sensor fusion weights, or computation nodes according to image reliability, power status, temperature status, computational load, or communication status.

[0053] The present invention aims to accumulate and update risk event data and reaction data in units of user identifiers or non-identifiable user identifiers, manage a continuous responsiveness profile of the same user even when using at least two modes of transportation or usage situations alternately among private cars, rental cars, shared vehicles, motorcycles, bicycles, personal mobility devices, delivery vehicles, or public transportation, and generate at least one of a Behavior-Based Insurance (BBI) indicator for calculating insurance premium rates, a UBI correction indicator, a fleet safety management indicator, a mobility platform matching indicator, a risk information reliability indicator for control, or an autonomous driving control transfer responsiveness indicator from said profile.

[0055] The problems that the present invention aims to solve are not limited to those specified above, and a person skilled in the art can understand other problems included in the technical concept of the present invention from the entire description of this specification. means of solving the problem

[0057] A risk assessment indicator calculation system according to one aspect comprises: a wearable image analysis device having an outward-facing camera that is worn on the body or clothing of a user of a means of transportation or mounted to be linked with the user's gaze direction and captures the front corresponding to the user's viewpoint, a position positioning unit, and an output unit; a context identification unit that identifies a driving context including at least one of a road type, a driving lane, a signal status, and a weather condition from the front image and location information of the outward-facing camera; a risk determination unit that determines a risk event based on safety standards differentially set for each identified driving context; a warning output unit that outputs a warning as at least one of a screen display, vibration, sound, voice guidance, and augmented reality display when the risk event is determined; a response measurement unit that measures response data including at least one of whether to return to a normal state, the time required for return, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change based on the time of output of the warning or the time of confirmation of the risk event; and an indicator calculation linkage unit that generates a risk assessment indicator or an insurance premium rate calculation factor by cumulatively analyzing the risk event data and the response data.

[0059] In one embodiment, the indicator calculation linkage unit calculates a risk assessment indicator by not only using the number of risk event occurrences, but also reflecting the time required for recovery, the number of warning repetitions, the duration of ignoring, or the amount of behavioral change based on the warning output time or the risk event confirmation time as reference points as reactivity factors independent of the number of risk event occurrences.

[0061] The above risk assessment indicators can be converted into at least one of an insurance premium rate calculation factor, a behavior-based safety score, a reward calculation value, a fare discount / surcharge calculation value, a driver training score, a fleet safety management score, or a mobility platform matching indicator.

[0063] In one embodiment, the indicator calculation linkage unit is a risk assessment indicator

[0064] BBI_score = Σ ( Wi × Context_Riski × f(T_responsei) × γ) can be calculated.

[0066] Here, Wi is a weight for each risk event type, Context_Riski is a risk constant for each context, T_responsei is the time taken to return for the i-th risk event, f(·) is a function that increases as the time taken to return increases, and γ may be a surcharge factor based on the number of warning repetitions or the warning ignore rate.

[0068] In one embodiment, the risk determination unit or indicator calculation linkage unit calculates the estimated time to collision (TTC) using at least one of the rate of change in the bounding box size of a front object, the bottom position of the object, the vanishing point, the distance converted value based on lane width, light flow, the rate of change in relative size between frames, vehicle speed, relative speed, and camera parameters from a front image based on a monocular camera, and can adjust the weights of the risk event determination criteria, warning criteria, reactivity evaluation criteria, or risk evaluation indicators using a TTC correction model or a TTC mapping table that is corrected according to at least one of the speed range, relative speed range, front object distance range, road type, lane type, object type, weather conditions, or illumination conditions.

[0070] A risk assessment indicator calculation server according to another aspect comprises: a communication unit that receives behavioral data including driving context, risk events, warning output time or risk event confirmation time, reaction data, and reliability information from a wearable video analysis device or a vehicle device; a reliability verification unit that verifies at least one of the integrity of the behavioral data, wearing status, user authentication, whether the wearer and the driving entity match, hash value, or electronic signature value; and a calculation unit that accumulates the verified behavioral data to calculate cumulative statistics including at least one of the number of risk events by type, average return time, warning ignore rate, and reactivity profile, and calculates a risk assessment indicator or a discount / surcharge factor for the insurance premium rate based on warning reactivity.

[0072] A wearable image analysis device according to another aspect includes an external camera, a positioning unit, an output unit, a communication unit, a storage unit, and a processor equipped with an on-device artificial intelligence model.

[0074] The processor identifies a driving context based on front image and location information, determines a risk event according to differential safety standards for each context and outputs a warning, measures reaction data based on a timestamp corresponding to the time of warning output or the time of risk event confirmation, and generates a behavioral risk index from the risk event data and the reaction data.

[0076] The generation of the above behavioral risk indicator can be completed within the device without necessarily requiring real-time linkage with an external insurer server or rate calculation server.

[0078] In one embodiment, the wearable image analysis device operates with a multi-stage trigger structure that switches from a low-power standby mode to an image analysis mode triggered by at least one of a driving state, a danger context, a sensor change, or a user input.

[0080] In addition, the wearable image analysis device can dynamically adjust the image analysis frame rate, artificial intelligence model complexity, sensor fusion weights, or computation nodes according to at least one of the battery level, power consumption, processor temperature, external surface temperature, image analysis latency, or frame throughput.

[0082] In one embodiment, the wearable image analysis device, vehicle device, or server may generate or receive an interaction data packet comprising at least one of a reference point timestamp message corresponding to the warning output time or the risk event confirmation time, a warning output log, a response measurement start log, a response measurement end log, a normal state return log, a no-warning interval summary log, and an indicator calculation result log.

[0084] The above interaction data packet may be output through at least one interface among BLE, Wi-Fi, USB, NFC, CAN, OBD-II, V2X, SDK API, application programming interface, or local log file.

[0086] In one embodiment, the wearable image analysis device, vehicle device, or server may add at least one of a sequence number, session identifier, event identifier, timestamp, previous packet hash value, current packet hash value, digital signature value, and packet status value to an event packet and store it in a trusted execution environment (TEE), a secure area, or a tamper-proof storage unit.

[0088] The above server verifies the sequence number, session identifier, timestamp, and hash chain continuity of the received event packet to determine packet loss, reordering, duplicate transmission, or replay attack, and can perform retransmission requests, exclusion from calculation, or assignment of low confidence weights.

[0090] In one embodiment, the system may be linked with or utilize at least one of a vehicle front camera, a black box, a vehicle-mounted ADAS camera, a vehicle-mounted recognition camera, a vehicle-attached video device, an OBU, a V2X terminal, or a vehicle navigation terminal, in addition to a wearable video analysis device.

[0092] In this case, the system can identify driving context and danger events using at least one of a front image, vehicle status information, GNSS information, IMU information, CAN information, OBD-II information, or V2X message, and measure deceleration response time, steering response time, safety distance recovery time, lane return time, or warning ignore rate based on the warning output time or danger event confirmation time, and reflect this in the risk assessment indicator.

[0094] In one embodiment, the indicator calculation linkage unit or the risk assessment indicator calculation server can generate a continuous responsiveness profile of the same user by accumulating and updating risk assessment indicators and response data in units of user identifiers or non-identifiable user identifiers, rather than specific vehicles, specific on-board devices, or specific mobility platforms.

[0096] The above continuous responsiveness profile can be converted into at least one service domain-specific derived indicator among a Behavior-Based Insurance (BBI) indicator for calculating insurance premium rates, a UBI correction indicator for driver habit-based insurance, a safety management indicator for motorcycles or delivery riders, a rating indicator for rental car / shared vehicles, a fleet safety management indicator, a mobility platform matching indicator, a risk information reliability indicator for control, or a responsiveness indicator for the transfer of autonomous driving control. Effects of the invention

[0098] According to the present invention, since a wearable image analysis device is worn on a user's body or clothing or mounted to be linked with the user's gaze direction and can move along with the individual user, continuous behavioral data of a user of a means of transportation can be secured without being dependent on a specific vehicle, a specific on-board device, or a specific mobility platform. Accordingly, responsive profiles for the same user unit can be generated and updated even in different environments of using means of transportation, such as private cars, rental cars, shared vehicles, motorcycles, bicycles, personal mobility devices, or public transportation, thereby increasing the personalization precision of risk assessment indicators or insurance premium rate calculation factors.

[0100] In addition, the present invention does not merely accumulate the number of occurrences of a risk event or the driving trajectory, but rather quantitatively measures at least one of the following as response data: whether to return to a normal state, the time required for return, the deceleration reaction time, the steering reaction time, the time to recover safe distance, the time to return to the lane, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change, based on the time of warning output or the time of confirmation of the risk event. Accordingly, even when the same risk event occurs, it is possible to distinguish and evaluate users who respond quickly to the warning and return to a normal state from users who repeatedly ignore the warning or delay the return, thereby more precisely reflecting the actual risk response characteristics of the user than an evaluation method that uses only the number of occurrences of the risk event.

[0102] Furthermore, the present invention identifies a driving context including at least one of a road type, driving lane, signal status, weather status, road surface status, or forward object status, and can apply safety standards set differentially for each context. Accordingly, even for the same behaviors such as deceleration, lane deviation, insufficient safe distance, or looking at a smartphone, the risk can be calculated differently depending on different driving environments, such as highways, city roads, exit ramps, merging lanes, congested lanes, rain, night, or low-light conditions, thereby enabling a risk assessment that reflects the driving context.

[0104] In addition, the present invention may utilize a Time To Collision (TTC) correction model or a TTC mapping table that calculates the estimated time to collision (TTC) from a front image based on a monocular camera and is corrected according to at least one of a speed range, a relative speed range, a distance range to a front object, a road type, a lane type, an object type, camera parameters, weather conditions, or illumination conditions. Accordingly, since at least one of the change in distance to a front object, the minimum TTC value after a warning, the TTC value at the time of returning to a normal state, or the TTC recovery speed can be reflected as a reactivity evaluation factor, the degree to which the collision risk is actually mitigated after a warning, rather than simply whether a warning occurs, can be reflected in the risk evaluation indicator.

[0106] Furthermore, the present invention enables a wearable video analysis device to calculate, store, and display behavioral risk indicators or safety behavior profiles based on risk event data and response data within the device, without requiring real-time linkage with an external insurance company server or rate calculation server. Accordingly, complete risk assessment within the device is possible even in environments where communication is limited, such as motorcycles, personal mobility devices, delivery operations, rental cars, shared vehicles, or public safety management environments, and non-identifiable metadata or interaction data packets can be synchronized with an external server if necessary.

[0108] In addition, the present invention can generate and output an interaction data packet comprising at least one of a reference point timestamp message corresponding to the time of warning output or the time of risk event confirmation, a warning output log, a response measurement log, a normal state return log, a no-warning interval summary log, and an indicator calculation result log. Accordingly, an insurer server, a control server, a mobility platform server, a verification server, or an integrated application can verify whether warning output, response measurement, and risk assessment indicator calculation have been performed without directly receiving the original video or the internal structure of the artificial intelligence model.

[0110] In addition, the present invention adds at least one of a sequence number, session identifier, event identifier, timestamp, previous packet hash value, current packet hash value, digital signature value, and packet status value to an event packet, and can store this in a Trusted Execution Environment (TEE) or a secure area, or verify it on a server. Accordingly, it is possible to detect packet loss, reverse order, duplicate transmission, replay attacks, location spoofing, or data corruption in communication interruption sections, and can enhance the reliability of risk assessment indicators by performing retransmission requests, exclusion from calculation, or assignment of low reliability weights based on the verification results.

[0112] In addition, the present invention does not constantly transmit the original image externally, but can transmit at least one of the risk event type, time, location, response time, number of warning repetitions, duration of ignoring, reliability, indicator score, metadata, or non-identifiable feature value. Furthermore, since personal information areas such as faces, vehicle license plates, and smartphone screens can be masked, blurred, feature extracted, or hashed on-device, data usability and personal information protection can be achieved simultaneously in fields such as insurance, mobility, control, and public safety management where personal information protection is required.

[0114] Furthermore, the present invention may be linked with or utilize at least one of a vehicle front camera, a black box, a vehicle-mounted ADAS camera, a vehicle-mounted perception camera, a vehicle-attached video device, an OBU, a V2X terminal, or a vehicle navigation terminal, in addition to a wearable video analysis device. Accordingly, warning responsiveness or responsiveness after the confirmation of a risk event can be utilized as an independent factor of the risk assessment indicator not only in embodiments based on a wearable device but also in embodiments based on a vehicle-mounted / attached device or a vehicle communication terminal.

[0116] Furthermore, the present invention does not require linkage with a road boundary zone identifier, control server, CCTV, road sensor, or external traffic information as a mandatory component, and a wearable video analysis device or a video-based risk assessment device can calculate a risk assessment index based on front video or front video-based data and response data. However, if a road boundary zone identifier, control server, CCTV, road sensor, or external traffic information is available, it can be combined as an auxiliary input to improve the reliability of identifying driving context, determining risk events, or calculating risk assessment indexes. Brief explanation of the drawing

[0118] Figure 1 is an overall configuration diagram of the risk assessment indicator calculation system of the present invention. Figure 2 is a block diagram of a wearable image analysis device. Figure 3 is a flowchart of driving context identification and differential safety standard application. Figure 4 is a flowchart of the warning output and response data measurement. Figure 5 is a flowchart of the generation and transmission of risk assessment indicators. Figure 6 is a diagram showing the dual trajectory cross-verification and offline synchronization structure of a wearable device and a mobile device. Figure 7 is a diagram showing the calculation of server-side risk assessment indicators and the structure for linking with an external server. Figure 8 is a diagram showing the structure linking the TTC correction model and the risk assessment indicator weights. Figure 9 is a diagram showing the structure for calculating a complete risk assessment indicator within the device and outputting interaction data packets. Figure 10 is a diagram showing the TEE-based event packet integrity verification and missing recovery structure. Figure 11 is a diagram showing the structure for calculating risk assessment indicators based on a vehicle device, OBU, or V2X terminal. Figure 12 is a diagram showing a risk reliability calculation structure that combines multiple report data with CCTV or road sensor information. Figure 13 is a diagram showing the structure for calculating risk assessment indicators for vehicle control linkage and autonomous driving hybrid. Figure 14 is a diagram showing the structure for generating user-unit continuous reactivity profiles and service domain-specific derived indicators. Specific details for implementing the invention

[0119] 1. Overall System Configuration and Higher-Level Concepts

[0120] Referring to FIG. 1, the present system includes a wearable video analysis device (100), a mobile device (200), a vehicle device (300), a control server (400), and a risk assessment index calculation server (500). Users are not limited to drivers but include drivers of personal mobility devices such as two-wheeled vehicles, bicycles, and electric scooters, delivery riders, public transportation passengers, and co-passengers. The wearable video analysis device (100) is preferably a body-worn type such as smart glasses, sunglasses, helmets, body cams, clips, or clothing attachments, but any personal outward-facing camera device mounted on a helmet, handlebars, dashboard, etc., to synchronize with the user's gaze and to film the front corresponding to the user's viewpoint is acceptable, and is distinguished from a vehicle-mounted black box that is not synchronized with the user's gaze.

[0122] The wearable video analysis device of the present invention differs in technical configuration and operational effects from a black box or dash cam that is completely fixed to the vehicle regardless of the user's gaze and only records a fixed field of view, in that it films the front from a first-person perspective linked to the user's gaze, and cannot be replaced by a simple design change.

[0124] In one embodiment, the wearable image analysis device (100) can be linked with a vehicle's black box, a vehicle-mounted ADAS camera, a surround view camera, a rear camera, or an autonomous driving perception camera to mutually utilize front, rear, and side images, and since the risk assessment of the present invention is characterized by the quantification of warning responsiveness, it can be applied regardless of whether the image acquisition means is wearable, vehicle itself, or attached.

[0126] Additionally, in this specification, an image-based risk assessment device or a forward image acquisition means may refer to a device or module that directly acquires a forward image corresponding to the user's viewpoint or the viewpoint in the direction of travel of a means of transportation, or receives image feature values, object recognition results, risk event metadata, or warning output logs generated from the forward image and uses them to calculate risk assessment indicators.

[0128] The above image-based risk assessment device or front image acquisition means may include or be linked with at least one of a wearable image analysis device, smart glasses, sunglasses, a helmet-type camera, a body cam, a clip-type camera, a clothing-attached camera, a mobile device, a vehicle front camera, a black box, a vehicle-mounted ADAS camera, a surround view camera, a vehicle-mounted perception camera, a vehicle-attached image device, an on-board unit (OBU), a vehicle-to-everything (V2X) terminal, a vehicle navigation terminal, or a vehicle communication terminal.

[0130] However, depending on the embodiment of each claim, embodiments in which a wearable image analysis device is an essential component and embodiments utilizing a vehicle device, mobile device, OBU, or V2X terminal may be distinguished, and the common technical feature of the present invention is to identify driving context and risk events using front image or front image-based data, and to reflect response data based on the warning output time or the risk event confirmation time in a risk assessment index.

[0132] In one embodiment, the vehicle device (300) may include at least one of a vehicle front camera, a black box, a vehicle built-in ADAS camera, a surround view camera, a rear camera, a vehicle-mounted recognition camera, an OBU (On-Board Unit), a V2X terminal, a navigation terminal, a vehicle communication terminal, or a vehicle controller.

[0134] When the vehicle device (300) is equipped with a video recording function, the vehicle device (300) can acquire a forward image corresponding to the point in time of the direction of travel of the vehicle and calculate driving context, risk event, and reaction data based on the forward image and location and vehicle status information.

[0136] Even if the vehicle device (300) does not directly have a video recording function such as an OBU or V2X terminal, it may receive video feature values, risk event metadata, or warning output logs from a vehicle camera, black box, ADAS camera, or mobile device, and combine them with GNSS, IMU, CAN, V2X messages, or vehicle status data to be used for calculating risk assessment indicators.

[0138] Risk assessment indicators are not limited to factors for calculating insurance premium rates but include behavior-based safety scores, cashback and rewards, rental deposits, rate discounts and surcharges, driver coaching scores, etc.

[0140] In addition, the above indicators may be used for at least one of the following, in addition to calculating insurance premium rates: grading of rental car and shared vehicle users, safety scores for corporate vehicles and fleets, grading of delivery and motorcycle riders, mobility platform matching, driver education and coaching, and operational safety control. The external server includes at least one of an insurance company, rate calculation, rental, mobility platform, and control server.

[0142] In one embodiment, the risk assessment indicator is not dependent on a specific vehicle, a specific on-board device, or a specific mobility platform, and can constitute a continuous safety behavior profile that is accumulated and updated in units of user identifiers or non-identifiable user identifiers.

[0144] The above-described continuous safety behavior profile manages risk event data and response data collected during the use of at least one mode of transportation among private cars, rental cars, shared vehicles, motorcycles, bicycles, personal mobility devices, delivery operations, public transportation use, or passenger transport by normalizing them at the same user level. Accordingly, even when data from the OBD, black box, or ADAS camera installed in a specific vehicle is unavailable, the risk response propensity at the user level can be continuously evaluated.

[0146] In one embodiment, the indicator calculation linkage unit or the risk assessment indicator calculation server can generate service domain-specific derived indicators from the continuous safety behavior profile.

[0148] The above service domain-specific derived indicators may include at least one of the following: a behavior-based insurance (BBI) indicator for calculating insurance premium rates, a UBI adjustment indicator for driver habit-based insurance, a safety management indicator for two-wheeled vehicle or delivery riders, a user rating indicator for rental cars or shared vehicles, a safety management indicator for corporate vehicles or fleets, a mobility platform matching indicator, a driver education and coaching indicator, a road hazard reporting reliability indicator, an autonomous driving control transfer responsiveness indicator, and an OEM safety evaluation indicator.

[0150] In one embodiment, even if the service domain-specific derived indicators are generated from the same source response data, they may be converted into different forms according to at least one of the request format, evaluation purpose, personal information processing policy, statistical unit, driving distance normalization standard, risk event weight, and reliability standard of each external server.

[0152] For example, premium discount / surcharge calculation factors may be provided to insurance company servers, non-identifiable risk information including location, time, risk type, and reliability may be provided to control servers, user safety ratings or operational suitability indicators may be provided to mobility platform servers, and control transfer response times or combined system responsiveness indicators may be provided to OEM or autonomous driving-related servers.

[0154] The wearable video analysis device (100) includes an outward-facing camera (110), a positioning unit (120), an inertial sensor unit (130), an environment sensor unit (135), an output unit (140), a communication unit (150), a low-power context-aware processor (160), and a storage unit (170). The processor (160) is equipped with an on-device artificial intelligence model to perform video analysis without a constant server connection and operates with a multi-stage trigger structure that switches to a high-power mode only when a trigger occurs, such as driving detection.

[0156] 2. On-device AI video analysis

[0157] The context identification unit and the risk determination unit are implemented as on-device artificial intelligence models.

[0158] In one embodiment, the system is trained with front images labeled by road, lane, signal, and weather, as well as images of speed limit signs, front vehicle boundary boxes, brake lights, road surface hazards, and construction signs; preprocessing including frame extraction, resolution normalization, region of interest segmentation, light flow calculation, and illuminance correction is performed on the input images; and context identification values, risk event type values, and confidence values ​​are calculated through an object detection model and a classification model.

[0160] The correlation between input features and output risk events is defined by at least one of monocular distance estimation, object size change rate, lane width-based distance conversion, and time-to-time (TTC).

[0162] In one embodiment, the risk determination unit calculates the estimated time to collision (TTC) using at least one of the following: the rate of change in the bounding box size of a front object, the bottom position of the object, the vanishing point, the distance converted based on lane width, the light flow, the rate of change in relative size between frames, the vehicle speed, the relative speed, and the camera installation height, angle of view, and focal length from a front image based on a monocular camera.

[0164] At this time, TTC is not limited to a single formula and can be calculated using a TTC correction model or mapping table that is corrected according to at least one of speed range, road type, lane type, object type, camera parameters, image resolution, weather conditions, and illumination conditions.

[0166] The above TTC correction model or mapping table may include coefficients, thresholds, reliability correction values, or risk grade values ​​for correcting TTC calculated values ​​by vehicle speed range, relative speed range, front object distance range, road context, or camera installation conditions.

[0168] In one embodiment, the TTC correction model or TTC mapping table may be implemented as a multidimensional mapping table having a plurality of input axes and a plurality of output values. The input axes may include at least one of a vehicle speed range, a relative speed range, a forward object distance range, a road type, a lane type, an object type, a camera installation height, a camera field of view, an image resolution, weather conditions, and illumination conditions, and the output values ​​may include at least one of a TTC correction coefficient, a TTC threshold value, a warning output reference value, a deceleration recommendation reference value, a reliability correction value, or a risk grade value.

[0170] Speed ​​zone Road type Weather and light conditions Forward object distance section TTC correction factor Warning TTC Standard Risk level low-speed section city ​​roads Weekly · Clear short distance 1 First reference value commonly Medium speed section national road night or rain mid-range 1.2 Value greater than the first threshold height High-speed section Expressway or motorway Weekly · Clear mid-range 1.3 Value greater than the first threshold height High-speed section Exit or merging lane Rain, Fog, Low Light short distance 1.5 or higher Value greater than the first threshold Very high Arbitrary speed range Tunnels and sections at risk of freezing Low light or freezing risk temperature short distance 1.5 or higher Value greater than the first threshold Very high

[0172] The figures, intervals, and grades in the above table are merely examples for illustrative purposes and may be updated in actual implementation based on at least one of accident statistics, insurance claim data, frequency of risk events, secondary accident data, vehicle type, camera type, camera installation conditions, or user response data.

[0174] In one embodiment, if the TTC correction factor is greater than 1, the risk determination unit may advance the warning output time, increase the warning intensity, shorten the responsiveness evaluation standard time, or increase the weight of the risk evaluation indicator, even if the calculated TTC is the same. Conversely, if the TTC correction factor is less than 1 or the risk reliability is low, the risk determination unit may withhold the warning output, lower the warning intensity, or assign a low reliability weight to the event.

[0176] In one embodiment, the TTC correction model may be implemented as at least one of a basic table stored at the time of initial shipment, an update table received from an insurance company server or a control server, an adaptive table updated according to actual driving data, a calibration table by vehicle / camera type, or a learning-based regression model. In this case, the wearable image analysis device, the vehicle device, or the server may update the TTC correction model or the TTC mapping table by comparing the calculated TTC value with the actual deceleration response time, steering response time, distance recovery time, TTC recovery speed, or accident / near-miss occurrence results.

[0178] For example, even if the same rate of change in object size in an image is detected, the TTC threshold or warning output criteria may be set differently depending on whether it is a highway, city road, exit ramp, junction, rain, night, or tunnel section.

[0180] In one embodiment, the TTC correction model or mapping table includes at least one of a basic table stored at the time of initial shipment, an update table received from an insurer or control server, an adaptive table learned and updated using actual driving data or accident / secondary accident data, or a calibration table for each vehicle / camera type.

[0182] A wearable video analysis device, a vehicle device, or a server can update the TTC correction model or mapping table by comparing the calculated TTC value with the actual driver's deceleration reaction time, steering reaction time, distance recovery time, or risk event occurrence result.

[0184] In one embodiment, the artificial intelligence model includes a CNN-based object detection and segmentation model for image object and context recognition, and an RNN / LSTM or Transformer-based sequence model for time-series tracking of response data after the warning output point.

[0186] The input is a preprocessed forward image frame sequence (e.g., H×W×C tensor) and position and inertia time series, and the output is a context identification value, a risk event type value, a confidence value, and an estimate of the time to return to a normal state t_recovery.

[0188] The sequence model tracks frames after to to calculate T_response = t_recovery - to. This model is intended to recognize time-series changes in driving environment and behavior from video and is unrelated to neural decoding of biosignals.

[0190] In one embodiment, the response measurement unit improves the accuracy of recovery detection compared to a single modality by fusion of lane deviation and changes in distance to a front object in a front image with steering, braking, and deceleration inputs from an inertial sensor to detect the point of return to a normal state t_recovery. Additionally, the context identification unit maintains the reliability of context identification and risk determination by performing preprocessing including multi-frame fusion, exposure and white balance correction, and temporal smoothing under low light and adverse weather conditions such as night, backlight, tunnel, and rain.

[0192] In one embodiment, the response measurement unit and the context identification unit calculate the reliability of image-based features and the reliability of inertial sensor-based features, respectively, and dynamically assign reliability-based weights to the two modalities to fuse them.

[0194] When image reliability drops below a threshold due to nighttime, backlight, heavy rain, fog, etc., the weights of the inertial sensors (steering, braking, acceleration) are automatically increased to detect the point of return to a normal state and response data; once image reliability is restored, the image weights are increased again. This maintains the continuity and accuracy of responsiveness measurements even in adverse weather and low light conditions compared to a single modality.

[0196] In one embodiment, the processor (160) monitors at least one of the battery level, power consumption, processor temperature, external surface temperature, image analysis latency, and frame throughput of the wearable image analysis device (100), and if the value deviates from a reference range, it lowers the image analysis frame rate, switches the high-resolution object detection model to a low-resolution or lightweight model, or increases the weight of the inertial sensor-based response detection mode.

[0198] In addition, in a stable driving section where no dangerous events exist, it operates in a low-power standby mode or a low-period sampling mode, and switches to a high-precision image analysis mode only when at least one trigger is detected among sudden deceleration, lane deviation, insufficient safe distance, smartphone screen detection, and approach to a dangerous object.

[0200] In another embodiment, when the device temperature or power status of the wearable image analysis device (100) rises above a reference value, it transfers part of the image analysis to a mobile device, vehicle device, edge computing node, or cloud server, or transmits only feature vectors, object bounding boxes, and risk event metadata instead of the original image to distribute the computational load and communication load. At this time, the timestamp of the warning output time or the risk event confirmation time, which is the reference point of the response data, is fixed within the device, so that the calculation criteria for the return time and warning ignore rate are maintained even if the computation node is changed.

[0202] The risk assessment unit restricts warning output or indicator reflection if the calculated reliability is below the threshold value.

[0204] 3. Differential safety standards by context

[0205] The context identification unit identifies at least one of the road type, driving lane (main lane, exit, deceleration, merging, congestion), signal status, and weather status from the forward image and location information.

[0207] The risk assessment division sets safety distance standards differently for each road type, such as 100m for highways and 20m for city roads, and applies different standards for standard speed, deceleration patterns, safety distance, and lane change allowance criteria depending on the lane context.

[0209] Even in non-navigation sections, it independently determines speeding based on video-recognized speed limit signs and road types, and lowers the standard speed when heavy snow or rain is identified or when the temperature is below the risk of freezing.

[0211] Meanwhile, if a road boundary zone identifier (a separate identifier including a layer, driving direction, zone type, and pair identifier) ​​is available, the context identification unit may combine it as an auxiliary input for image-based context determination or as a basis for cross-verification, but the determination of driving road conditions and selective risk information propagation itself by the road boundary zone identifier may be implemented as a separate technical configuration distinct from the present application, and the present application is characterized by context identification and warning responsiveness quantification based on a first-person front image.

[0213] 4. Risk Event Types and Mirror Area Rear Detection

[0214] The risk events determined by the risk determination unit include, but are not limited to: (a) repeated detection of the smartphone screen, (b) speeding, (c) distinction between passing yellow / green, (d) distinction between lane change being allowed / prohibited, (e) operation of the turn signal, (f) lanes where driving is prohibited, (g) insufficient safe distance, (h) road surface hazard, (i) hazard lights, brake lights, or sudden deceleration of the vehicle ahead, (j) cutting in, (k) sudden acceleration approach from behind in the mirror area, and (l) identification of construction, accident, or breakdown vehicle types.

[0216] (c) Passing a yellow signal is not determined as a standalone danger event, but rather, at least one of the following is considered together to distinguish between passing with a stopable line and passing with a difficult stop: the estimated time to reach the stop line, the braking distance, the current speed, the approach of a vehicle behind, and the road surface condition.

[0218] (k) The outward-facing camera detects entry into the rearview mirror and side mirror areas within the field of view based on the user's head rotation, detects the mirror area using mirror frame shape, reflection area boundaries, rear vehicle objects, and head rotation information, and determines the approach of a rear vehicle with rapid acceleration by analyzing the mirror area images at regular intervals separately from eye tracking. This detects rear hazards even in vehicles without a rear camera or on two-wheeled vehicles.

[0220] 5. Warning output, reaction data measurement, and reactivity quantification

[0221] The warning output unit outputs at least one of a screen, vibration, voice, or sound depending on the type and severity.

[0222] The response measurement unit measures at least one of (i) whether the state returns to normal, (ii) the time required for return (seconds), (iii) the number of repetitions, (iv) the duration of the ignore, and (v) the amount of behavior change and the re-violation interval based on the warning output point.

[0224] A normal state means a state in which at least one of the driving speed, distance from the vehicle ahead, lane deviation, and degree of visual deviation has returned to within the standard safety range corresponding to the context in which the dangerous event occurred.

[0226] The warning ignore rate refers to the ratio of warning events in which a change in state or a change in steering, braking, or deceleration input is not detected within a preset time after the warning output, and the recovery time (T_response) refers to the time from to to t_recovery.

[0228] For example, the return to a normal state for a safety distance insufficient event may include a state in which the distance to a forward object or the time to collision (TTC) is recovered to a value greater than a reference value; the return to a normal state for a smartphone gaze event may include a state in which the smartphone screen area is not detected or the gaze direction is returned to the forward; the return to a normal state for a lane deviation event may include a state in which the deviation amount relative to the lane centerline is recovered to within an allowable range; the return to a normal state for a road surface hazard event may include a state in which at least one of deceleration, avoidance trajectory, or lane keeping is detected; and the return to a normal state for a signal-related event may include at least one of stopping before the stop line, decelerating, or passing difficult stopping.

[0230] In one embodiment, since the user's perception and reaction delays vary depending on the warning output format (sound, vibration, augmented reality display, etc.), the reaction measurement unit normalizes the return time based on the standard reaction time for each format. Additionally, the indicator calculation linkage unit establishes a personal baseline including the user's age group, driving experience, and past average reaction time, and calculates a risk assessment indicator based on the deviation from the baseline rather than the absolute return time, thereby ensuring fairness among users.

[0232] In one embodiment, the reference point of the response data is not limited to the time of warning output, but may be at least one of the time when a risk event is confirmed to be above a reference reliability level, the time when a warning is generated, the time when a warning output command is given, or the time when an output recognizable to the user is initiated.

[0234] Accordingly, even in cases where the term 'warning' is not used in the embodiment and a danger notification, attention induction, request for transfer of control, route modification suggestion, or danger event confirmation signal is used, at least one of the user's deceleration, steering, lane return, gaze return, safe distance recovery, or ignore duration after the above reference point can be measured and calculated as a reactive factor.

[0236] In one embodiment, the individual baseline is variably set according to the time of day (day, night, or late night), accumulated driving time, and the elapsed time since the previous rest. During night or long-duration continuous driving sections, the baseline is adjusted upward to reflect a trend of increasing average reaction time, and if a trend of decreasing responsiveness (gradual increase in the time required to return to continuous dangerous events) is detected, it is estimated to be in a fatigue state, and an increase in warning intensity or a rest recommendation may be output. The estimation of the fatigue state is based on outward-looking image-based responsiveness and driving behavior trends, and does not require inward-looking bio-observation.

[0238] The warning output unit records the success or failure of the warning output, the output format, output intensity, output duration, and the user's perceptibility status, and the response measurement unit excludes events where the warning was not actually delivered or perceived by the user from the reactivity evaluation or classifies them as a separate status value, thereby preventing the misevaluation of non-delivery of warnings as response delays and contributing to the fairness of insurance premium rate calculation and the prevention of disputes.

[0240] In one embodiment, the warning output unit gradually adjusts the warning intensity and warning form according to at least one of the severity of the danger event, the estimated time of collision, the driving speed, the road type, and the user's past reactivity profile.

[0242] For example, in the first stage, a screen display or weak vibration is output, in the second stage, voice guidance or strong vibration is output, and in the third stage, repetitive sound, augmented reality highlighting, a rest recommendation, or a request to transfer control can be output.

[0244] In one embodiment, the response measuring unit detects at least one of the user's deceleration, steering, lane return, gaze return, recovery of distance to a forward object, warning confirmation input, or voice response after the warning output, and estimates whether the warning is recognized or whether the warning is complied with.

[0246] If a recognition or compliance signal is not detected within a preset time after the warning is output, the warning output unit may re-output the same warning or change the warning format, and the indicator calculation linkage unit may distinguish and store the initial warning time, the re-warning time, and the final return time.

[0248] The indicator calculation integration unit combines response data with risk event data to calculate cumulative statistics (annual frequency by type, average return time, ignore rate), converts them into risk assessment indicators (BBI indicators), and transmits them to an external server.

[0250] In one embodiment, the indicator can be calculated using the following formula.

[0251] BBI_score = Σ ( Wi × Context_Riski × f(T_responsei) × γ

[0252] Wi is the type-specific weight, Context_Riski is the context-specific risk constant, T_responsei is the time taken for the i-th return, f(·) is a monotonically increasing function, and γ is the repetition / ignore rate surcharge factor. A lower Behavior-Based Insurance (BBI)_score indicates better responsiveness, resulting in a discount, while a higher score results in a surcharge.

[0254] The time series processing flow is explained with reference to Fig. 5.

[0255] In step S100, first-person forward video (e.g., 30 to 60 frames per second) is acquired with an external camera, and position and inertial data are visually synchronized.

[0256] In step S200, the context identification unit recognizes road type, lane characteristics, signals, and weather in real time.

[0257] In step S300, the risk determination unit determines a risk event (E_risk) by applying a context-specific variable threshold.

[0258] In step S400, the warning output unit issues a warning via screen, bone conduction sound, or vibration, and the device's internal timer fixes the warning time to a timestamp.

[0259] In step S500, the response measurement unit tracks the image sensor frame by frame after to detect t_recovery, calculates T_response=t_recovery-to, and accumulates N_repeat·T_ignore.

[0260] In step S600, the indicator calculation integration unit inputs this into a formula model to generate a Behavior-Based Insurance (BBI) score and selectively transmits the indicator and metadata to an external server without transmitting the original image.

[0262] In one embodiment, f(·) is defined as an exponential penalty model as f(T_responsei) = exp( βi × max( T_responsei - T_threshold,i , 0 ) where T_threshold,i is the allowable return time per road type (e.g., highway 4 seconds, city road 3 to 6 seconds), and βi is a risk weighting constant based on accident statistics (e.g., 0.2 to 0.5).

[0264] If the return time is within the threshold, the penalty is not applied as f=1 due to max, and it increases exponentially only when the threshold is exceeded; this mathematically reflects the critical effect of road driving, where delays of even seconds can lead directly to major accidents.

[0266] In another embodiment of the additive method, f = exp(βi × max(T_responsei - T_threshold,i, 0)) - 1, it can also be implemented in the logistic form 1 / (1+exp(-k(T-To))).

[0268] In one embodiment, T_threshold,i, βi, Context_Riski, or Wi may be dynamically determined based on a TTC correction model or a speed-based TTC mapping table.

[0270] For example, even with the same return time, a higher risk weight may be assigned in sections where the TTC is short or the relative speed is high, while a lower risk weight may be assigned in sections where the TTC is sufficiently long or a deceleration distance is secured.

[0272] In one embodiment, the indicator calculation linkage unit uses at least one of the TTC value at the time of the occurrence of a risk event, the TTC value at the time of the warning output, the minimum TTC value after the warning, the TTC value at the time of returning to a normal state, and the TTC recovery speed as a reactivity evaluation factor. Accordingly, not only how much time has passed since the warning, but also how quickly the collision risk was actually mitigated after the warning can be reflected in the risk evaluation indicator.

[0274] In other embodiments, the risk assessment indicators are

[0275] BBI_score = Σ_{k=1}^{M} ( W_context(k) × S_event(k) × F_response(k), where S_event(k) is a unique severity constant for each risk event type (e.g., lane deviation 1.0, forward gaze inattention 2.5), and F_response = exp( α_r · ( max( T_response - T_threshold , 0 ) / T_max ) × ( 1 + β_r · N_repeat ) + γ_r · ( T_ignore / T_max ).

[0277] T_max normalizes the reactivity penalty to a dimensionless normalized maximum allowable standby time per risk event. α_r, β_r, and γ_r in this paragraph are intrinsic constants of this model, distinct from the EWMA smoothing coefficients or surcharge coefficients.

[0279] The surcharge factor γ can be specified as γ = 1 + δ × ( R / R_threshold ) + ε × Ignore_rate. R is the number of repetitions, R_threshold is the threshold number of repetitions, Ignore_rate is the ratio of the ignore duration to the total warning time, and δ·ε is the surcharge weighting constant. Independent of the number of risk events, the surcharge is increased for users who repeatedly ignore warnings.

[0281] The indicator calculation linkage unit normalizes the Behavior-Based Insurance (BBI)_score by the total driving distance or time (BBI_score ÷ D) to prevent disadvantages for low-driving users, attenuates the impact of low-reliability data by multiplying by a reliability coefficient (0.6~1.0), and cumulatively updates the exponentially weighted moving average BBI_EWMA(t) = α × BBI_instant(t) + (1 - α) × BBI_EWMA(t-1) (α is 0.15~0.5).

[0283] Examples of weights and context risk constants by risk event type are shown in the table below, and their values ​​may be adjusted based on accident statistics or insurer loss ratio data.

[0285] A method for calculating a Behavior-Based Insurance (BBI) score according to one embodiment can be implemented to receive a plurality of risk events and a total distance as input and calculate a BBI score.

[0287] More specifically, for each risk event e_i, the BBI_score calculation unit can calculate the risk contribution per event using a weight W_i corresponding to the type of the risk event, a context risk constant C_r,i corresponding to the context of the risk event, a response time T_response,i, a repeat occurrence degree repeat_i, a ignore rate ignore_rate_i, and a confidence coefficient rel_i.

[0289] At this time, the reaction time coefficient f_T,i can be calculated according to the following mathematical formula 1.

[0290] (Mathematical Formula 1)

[0291] f_T,i = exp( β_i × max( T_response,i - θ(context_i), 0 ) )

[0293] Here, β_i is a reaction time sensitivity coefficient for a risk event type or context, and θ(context_i) may be a threshold time set according to the corresponding context. The threshold time may be variably set according to at least one of road type, lane type, driving speed, weather condition, illumination condition, time to collision (TTC), or forward object type.

[0295] In addition, the repetition / ignorance correction factor γ_i can be calculated according to the following mathematical formula 2.

[0296] (Mathematical Formula 2)

[0297] γ_i = 1 + δ × (repeat_i / R_th) + ε × ignore_rate_i

[0299] Here, δ is the repetition correction factor, ε is the neglect rate correction factor, and R_th may be the repetition reference value.

[0300] The risk contribution score_i for each risk event e_i can be calculated according to the following mathematical formula 3.

[0302] (Mathematical Formula 3)

[0303] score_i = W_i × C_r,i × f_T,i × γ_i × rel_i

[0304] And the total risk score can be calculated by summing the risk contribution score_i for each of the multiple risk events, which can be expressed as Equation 4 below.

[0306] (Mathematical Formula 4)

[0307] score = Σ_i ( W_i × C_r,i × f_T,i × γ_i × rel_i )

[0308] Subsequently, the BBI_score calculation unit can calculate the instantaneous score inst by normalizing the above total risk score score by the total distance total_distance or total driving time. At this time, to prevent the denominator from becoming zero, if the total distance or total driving time is less than the preset minimum normalization threshold D_min, D_min may be applied. Accordingly, the instantaneous score inst can be calculated according to the following mathematical formula 5.

[0310] (Mathematical Formula 5)

[0311] inst = score / max(total_distance, D_min)

[0312] Here, D_min may be a minimum normalization threshold value that is set to a non-zero positive value.

[0314] Subsequently, the BBI_score calculation unit can calculate the BBI_score_t at time t by applying an Exponentially Weighted Moving Average (EWMA) to the instantaneous score inst. For example, if the adaptation factor α is set to 0.3, the BBI_score_t can be calculated according to the following Equation 6.

[0316] (Mathematical Formula 6)

[0317] BBI_score_t = α × inst_t + ( 1 - α ) × BBI_score_t-1

[0318] Here, α may be an adaptation factor greater than 0 and less than or equal to 1, and may be set in the range of, for example, 0.15 to 0.5.

[0320] Types of risk events Wi / S_event context Risk Speeding 1.0 highway 0.8 Insufficient safety distance 1.2~1.5 city ​​roads 1.3 Traffic violation 1.5~2.0 Exit / Merging 1.6 Turn signal not working when changing lanes 1.3 lucidity 1.0 staring at the smartphone 1.8~2.5 rain 1.4 Failure to respond to road surface hazards 1.7 Heavy snow and fog 2.0

[0322] Accordingly, the BBI_score according to one embodiment can be calculated as an adaptive risk assessment indicator that is normalized based on travel distance or driving time and smoothed in a time series, while comprehensively reflecting the importance of risk events by type, the risk of the occurrence context, reaction time delay, recurrence characteristics, the ignore rate, and event reliability.

[0324] Meanwhile, the above mathematical formulas 1 to 6 are merely examples of BBI_score calculation methods, and the BBI_score calculation unit can calculate a reactivity-based risk assessment indicator using a logistic function, a segment-by-segment linear function, an exponential penalty function, a rule-based table, a learning-based model, a Time to Collision (TTC) correction model, or a combination thereof.

[0326] A BBI_score calculation method according to one embodiment may include steps of receiving a plurality of risk events and a total distance and calculating a risk assessment indicator.

[0328] In step S610, the BBI_score calculation unit may receive or acquire a plurality of risk events and a total distance traveled. The plurality of risk events may include at least one of speeding, insufficient safe distance, signal-related events, lane deviation, smartphone viewing, failure to respond to road surface hazards, sudden deceleration of a vehicle ahead, cutting in, or rear approach hazards.

[0330] In step S620, the BBI_score calculation unit can obtain or calculate, for each risk event e_i, a weight W_i corresponding to the event type, a context risk constant C_r,i corresponding to the event context, a reaction time coefficient f_T,i based on the reaction time, a repetition / ignorance correction coefficient γ_i based on the degree of repetition and ignorance rate, and an event reliability coefficient rel_i.

[0332] In step S630, the BBI_score calculation unit can calculate the event-specific risk contribution score_i for each risk event e_i. In one embodiment, the event-specific risk contribution score_i can be calculated according to the following mathematical formula.

[0334] score_i = W_i × C_r,i × f_T,i × γ_i × rel_i

[0335] Here, W_i is a weight for each risk event type, C_r,i is a risk constant for each context, f_T,i is a reaction time coefficient, γ_i is a repetition / ignore correction coefficient, and rel_i may be an event reliability coefficient.

[0337] In step S640, the BBI_score calculation unit can calculate the total risk score by cumulatively summing the event-specific risk contribution score_i for each of the plurality of risk events. In one embodiment, the total risk score can be calculated according to the following mathematical formula.

[0338] score = Σ_i ( W_i × C_r,i × f_T,i × γ_i × rel_i )

[0340] In step S650, the BBI_score calculation unit can calculate an instantaneous score inst by normalizing the total risk score based on the total travel distance or total travel time.

[0342] At this time, to prevent the denominator from becoming zero, if the total travel distance or total driving time is less than the preset minimum normalization threshold value D_min, D_min may be applied. In one embodiment, the instantaneous score inst may be calculated according to the following mathematical formula.

[0343] inst = score / max(total_distance, D_min)

[0345] Here, D_min may be a minimum normalization threshold value that is set to a non-zero positive value.

[0347] In step S660, the BBI_score calculation unit can calculate the final BBI_score_t at time t by applying an Exponentially Weighted Moving Average (EWMA) to the instantaneous score inst.

[0348] In one embodiment, BBI_score_t can be calculated according to the following mathematical formula.

[0349] BBI_score_t = α × inst_t + ( 1 - α ) × BBI_score_t-1

[0351] Here, α may be an adaptation factor greater than 0 and less than or equal to 1, and may be set in the range of, for example, 0.15 to 0.5.

[0353] In step S670, the BBI_score calculation unit may use the calculated BBI_score for at least one of driver behavior risk assessment, insurance premium rate calculation, insurance premium discount / surcharge, safety rating calculation, safe driving reward, driver coaching, mobility platform matching, fleet safety management, or customized warning provision, or transmit it to an external server.

[0355] Meanwhile, steps S610 to S670 above are merely examples for convenience of explanation, and the order of each step may be changed, and at least some steps may be performed in parallel or omitted. In addition, the BBI_score calculation unit is not limited to the above mathematical formula and may calculate a reactivity-based risk assessment indicator using a logistic function, a segment-by-segment linear function, an exponential penalty function, a rule-based table, a learning-based model, a Time To Collision (TTC) correction model, or a combination thereof.

[0357] In one embodiment, if the image recognition reliability, positioning reliability, sensor time synchronization reliability, warning output success, or user recognition status of a risk event is below a threshold value, the event is excluded from the calculation of risk evaluation indicators or is assigned a low reliability weight.

[0359] In addition, events determined to be false warnings through post-verification or user cancellation / correction input, or events for which warning delivery failed due to communication delays or output unit errors, may be excluded from the responsiveness evaluation or stored as separate exception status values.

[0361] In one embodiment, the indicator calculation linkage unit utilizes the warning-free driving section itself, where no risk event or warning occurs, as an inverse indicator of safety (inverse-responsiveness). For users whose risk event or warning occurrence per unit driving distance or hour remains below a standard, even in cases where responsiveness data is insufficient, the risk assessment indicator is lowered (discounted) based on the warning-free driving indicator. Thus, the present invention reflects not only responsiveness when a warning occurs but also safety in a state where no warning occurs, and the scope of rights is not limited to the form of warning occurrence implementation.

[0363] In one embodiment, the warning-free driving indicator is not immediately reflected in the calculation of insurance premium rates or precision responsiveness evaluation, and may first be used as at least one lightweight reward factor among safe driving rewards, cashback, points, driver coaching, encouragement of continued wearing, or low-risk driving certification during the initial service phase.

[0365] Subsequently, when warning-free driving data, risk event data, and reaction data are accumulated for a certain period or a certain driving distance or longer, the indicator calculation linkage unit can combine the warning-free driving indicator and the warning responsiveness indicator to convert them into an insurance premium rate calculation factor or an advanced BBI_score.

[0367] In one embodiment, the indicator calculation linkage unit calculates a reward indicator based on warning-free driving distance or warning-free driving time in the first step according to a policy for each user or insurer, reflects whether the system returns to a normal state after a warning when a risk event occurs in the second step, and calculates a precise BBI_score in the third step by combining at least one of the time required for return, the number of warning repetitions, the ignore rate, the recovery speed of Time To Collision (TTC), and the deviation from an individual baseline. Accordingly, the risk assessment service can be operated in stages even in the initial introduction phase where field data is insufficient.

[0369] 6. Calculation of Complete Indicators within Insurance-Integrated Non-Dependent Mechanisms

[0370] In one embodiment, the wearable device (100) does not require linkage with an external insurer or rate calculation server, and directly calculates a behavioral risk index from risk event and reaction data in the processor (160) within the device and stores it in the storage unit (170) or displays it through the output unit (140). This complete embodiment can be implemented so that operation can be verified through externally measurable logs or interaction data packets.

[0372] In one embodiment, the wearable video analysis device (100) can calculate a BBI_score or behavioral risk indicator within the device using risk event data, warning output time, risk event confirmation time, and reaction data without real-time communication with an external server, and can display the calculated indicator in at least one form among a score, grade, color, icon, voice guidance, or rewardable status through an output unit (140).

[0374] Additionally, the wearable video analysis device (100) can store at least one of a reference point timestamp, a warning output log, a reaction measurement start / end log, a normal state return log, and a warning-free driving log generated during the indicator calculation process in a local storage unit (170).

[0376] In one embodiment, the wearable video analysis device (100) can generate a complete safety behavior profile within the device by calculating the number of risk events per unit driving section, the average time to return, the warning ignore rate, the warning-free driving distance, or the warning-free driving time, even when there is no external server or the network connection is blocked.

[0378] The complete safety behavior profile within the above device can be synchronized in the form of non-identifiable metadata when subsequently connected to a mobile device, an insurer server, a mobility platform server, or a control server.

[0380] In one embodiment, the wearable image analysis device (100) generates an interaction data packet including at least one of a reference point timestamp message, an event identifier, a session identifier, a warning type, whether the warning output was successful, a response measurement start message, a response measurement end message, a normal state return message, a no-warning section summary message, and an indicator calculation result message corresponding to the warning output time or the risk event confirmation time, so that external measurement or interlocking device verification is possible, and can output this through at least one interface among BLE, Wi-Fi, USB, NFC, CAN, OBD-II, V2X, SDK API, application programming interface, or local log file.

[0382] The above interaction data packet does not include the original image and may include at least one of an event identifier, a session identifier, a timestamp, an event type, a warning format, a response time, whether the warning is ignored, a warning-free driving distance, a warning-free driving time, an indicator score, a confidence value, a hash value, and a digital signature value.

[0384] Accordingly, even without directly analyzing the source code or internal operations of the artificial intelligence model, an external measurement device, an integrated application, an insurance company server, a control server, or a verification server can verify whether to perform warning output, response measurement, and indicator calculation based on the occurrence time, sequence, content, and integrity value of the interaction data packet.

[0386] 7. Prevention of Insurance Fraud and Data Integrity

[0387] A wearable device (100) or an external server secures data reliability using at least one of the following: wearing status, device posture, camera field of view validity, user authentication, whether the wearer matches the driving entity, pairing information, event hash value, and digital signature value.

[0389] When camera obstruction, non-wearing of the device, wearing by another person, positional trajectory discrepancy, or duplicate transmission of the same event is detected, the data is excluded from evaluation or assigned a low reliability to prevent inaccurate metric calculation or fraudulent use caused by proxy wearing, non-wearing, or manipulation.

[0391] The wearable device (100) wakes up the mobile device (200) when the driving state is detected and cross-verifies the first trajectory and the second trajectory to prevent distortion caused by single device manipulation or falsification and to ensure integrity.

[0393] In one embodiment, a wearable device or an external server generates an event log including a risk event identifier, a warning output time, a risk event confirmation time, a response measurement start / end time, a device identifier, a user authentication value, a summary value of location / inertial data, and an indicator calculation result, and adds at least one of a timestamp, a hash value, a hash chain, and a digital signature value to each log and stores it in a Trusted Execution Environment (TEE) or a secure area.

[0395] The above digital signature value is generated as a device unique key within a Trusted Execution Environment (TEE) and combined with timestamp and location information to prove the integrity of the time and place of data creation, and the server verifies the consistency of the digital signature, timestamp, and location, as well as the physical validity of satellite navigation signal characteristics and movement trajectory, to determine whether there is tampering such as satellite navigation spoofing or log replay, and excludes verification failure events from evaluation or assigns a low reliability weight.

[0397] In one embodiment, a wearable device (100), a vehicle device (300), or an OBU generates event packets within a Trusted Execution Environment (TEE) or a secure area, and adds at least one of a sequence number, a session identifier, an event identifier, a timestamp, a hash value of the previous packet, a hash value of the current packet, a digital signature value, and a packet status value to each event packet.

[0399] The above packet status value may include at least one of creation complete, temporary storage, waiting for transmission, transmission complete, server receipt acknowledgment, waiting for retransmission, verification failure, and discard.

[0401] In one embodiment, the Trusted Execution Environment (TEE) or security area does not store event packets solely in volatile memory, but stores them in a non-volatile secure storage unit or a local circular buffer using an atomic write method. Here, the atomic write method includes marking a packet as a valid packet only when both the packet body and the packet integrity value have been written, and marking the packet as an incomplete packet to make it subject to subsequent verification or regeneration if a power interruption, communication disconnection, or process interruption occurs during writing.

[0403] In one embodiment, the server verifies the sequence number, session identifier, timestamp, and hash chain continuity of the received event packet to determine packet loss, reordering, duplicate transmission, or replay attack. If packet loss is detected, the server may request the device to retransmit the missing section or request a summary packet, hash proof value, or sensor summary value stored in the device's local circular buffer.

[0404] Sections that fail retransmission or recovery verification may be excluded from the calculation of risk assessment indicators or assigned a low reliability weight.

[0406] In one embodiment, even when a failure in verifying hash chain continuity is detected, the server does not uniformly discard the entire corresponding driving section, and can calculate a reliability grade based on at least one of the number of missing packets, the ratio of missing packets, the time length of the missing section, the driving distance of the missing section, the type of missing packet, whether retransmission was successful, whether a summary packet exists in a local circular buffer, the consistency of the sensor summary value, and the result of cross-verification with other device or server logs.

[0408] For example, if a missing packet is a critical packet that directly affects the baseline or result of a reactivity evaluation, such as an alert output packet or a response start / end packet, the server may significantly lower the reliability weight of the event or exclude it from the calculation. Conversely, if the missing packet is a device status packet or a duplicate summary packet, and the continuity of the baseline timestamp, alert output log, response measurement log, and normal state return log is confirmed by other logs, the server may apply only a small reduction in reliability.

[0410] In one embodiment, the server calculates a packet reliability value by scoring hash chain continuity, digital signature value, timestamp consistency, location trajectory consistency, sensor summary value consistency, and retransmission success, respectively, and can reflect the packet reliability value as at least one of a multiplicative reliability coefficient, an additive deduction value, or a graded filter when calculating a risk evaluation index.

[0412] Accordingly, the present invention prevents the excessive discarding of entire driving data due to technical failures not directly related to the user's risky behavior, such as communication interruption, temporary battery shortage, or network delay, while excluding the relevant event or driving section from the calculation or assigning a very low reliability weight to intentional falsification attempts such as location spoofing, log replay attacks, time reversal settings, or event packet manipulation.

[0414] In one embodiment, the event packet is classified into at least one of a risk event packet, a warning output packet, a response start packet, a response end packet, a normal state return packet, a warning ignore packet, a user authentication packet, a device state packet, and a de-identification processing packet. The packets are connected by a hash chain with the same session identifier, so that the entire flow from the occurrence of a risk event to warning output, response measurement, and metric calculation can be verified without omission.

[0416] In one embodiment, when the wearable device and the mobile device are physically separated or the real-time connection is temporarily interrupted due to BLE, Wi-Fi interference, or shadowing, each device independently records the trajectory and event logs along with timestamps and hash values ​​in a local circular buffer, and when the connection is restored, they exchange logs with each other to sort and cross-verify based on a common time standard.

[0418] At this time, the integrity of dual-loop cross-verification is maintained even in environments where real-time communication is not guaranteed by verifying hash chain continuity and timestamp consistency to determine whether there is data omission, duplication, or falsification in the disconnected sections, and by excluding sections that fail to match from the risk assessment or assigning a low confidence weight.

[0420] In one embodiment, a server or a wearable video analysis device (100) detects at least one abnormal behavior among proxy wearing, device separation operation, location spoofing, log replay attack, time reversal setting, repeated transmission of the same event, and injection of low-reliability sensor values. For example, if the trajectory of the wearable device and the trajectory of the mobile device are separated for a certain period of time or longer, if the satellite navigation position change is physically inconsistent with the inertial sensor-based movement amount, or if the hash chain order of the event log is discontinuous, the weight of the risk assessment indicator calculation for the corresponding section may be lowered or excluded from the calculation target.

[0422] In one embodiment, the wearable image analysis device (100) performs user authentication at at least one of the time of driving start, the time of a risk event occurrence, or the time of the start of a driving section subject to insurance premium rate calculation, and calculates the reliability of the driving subject by combining the authentication result with the wearing status, device posture, camera field of view validity, and mobile device pairing status. If the reliability of the driving subject is less than a reference value, the indicator calculation linkage unit may exclude the corresponding driving section from the insurance premium rate calculation target or assign a low reliability weight.

[0424] 8. Advanced Personal Information Protection and De-identification

[0425] The wearable device (100) does not always transmit the original video to the outside, but transmits only at least one metadata or non-identifiable feature value among the risk event type, time, location, response time, number of repetitions, reliability, and index score.

[0427] Faces, vehicle license plates, smartphone screens, etc. are masked, blurred, feature extracted, and hashed on-device, then stored and transmitted.

[0429] On-device anonymization can be enhanced by precisely extracting and masking only personal information regions using object detection models specifically trained for faces, license plates, and smartphone screens, performing anonymization operations and integrity processing within a Trusted Execution Environment (TEE), adding hashes and digital signatures to each processing event, and injecting statistical noise using differential privacy techniques into the anonymized data to counter re-identification attacks.

[0431] 9. Expansion of Vehicle Control, Management, Non-driving, and Autonomous Driving

[0432] The risk assessment indicator calculation server (500) receives behavioral data from a wearable device (100) or a mobile device (200), calculates cumulative statistics based on warning responsiveness, and calculates premium discount / surcharge factors. It can also be implemented in a server-side form that calculates rates by receiving only data without directly manufacturing or selling the wearable terminal.

[0434] In another embodiment, the risk assessment index calculation server (500) may receive behavioral data from the vehicle device (300) and calculate a risk assessment index. In this case, the vehicle device (300) includes at least one of a vehicle front camera, a black box, a vehicle built-in ADAS camera, an OBU, a V2X terminal, or a vehicle navigation terminal, identifies driving context and risk events from front video or vehicle status data, and can calculate at least one of whether the driver or vehicle returns to a normal state, the time required for return, the deceleration reaction time, the steering reaction time, the time to recover the distance between vehicles, the time to return to the lane, and the warning ignore rate based on the warning output time or the time to confirm the risk event.

[0436] In one embodiment, the behavioral data received by the risk assessment indicator calculation server (500) does not, in principle, include the original front image, and may include at least one of a driving context, a risk event identifier, risk event metadata, image feature value, object recognition result, warning output log, response measurement log, normal state return log, no-warning section summary information, confidence value, hash value, or digital signature value.

[0438] Even if the above-mentioned risk assessment indicator calculation server (500) does not directly receive or analyze the original front video, it can verify whether warning output, reaction measurement, and risk assessment indicator calculation are performed based on at least one of the reference point timestamp, warning output log, reaction measurement start log, reaction measurement end log, normal state return log, and indicator calculation result log included in the above-mentioned behavior data and interaction data packets.

[0440] Accordingly, the present invention enables an insurance company server, control server, mobility platform server, or verification server to secure data reliability and verifiability necessary for calculating risk assessment indicators without requiring constant external transmission of the original video.

[0442] As such, the present invention can utilize warning responsiveness or responsiveness after a risk event is confirmed as an independent factor of the risk assessment index not only in embodiments using a wearable device but also in vehicle-mounted / attached devices or OBU-based embodiments.

[0444] In one embodiment, the vehicle device (300) outputs a warning through at least one of a vehicle instrument panel, a head-up display, a navigation screen, an audio output unit, a vibration device, a smartphone app, or a wearable device, and measures the driver's braking, steering, release of acceleration, return to lane, restoration of safe distance, or return of gaze after the warning output.

[0446] Additionally, the vehicle device (300) may generate response data by combining at least one of CAN data, OBD-II data, ADAS event log, V2X message, GNSS position, IMU data and image recognition result, and transmit the response data to a server by adding a timestamp, hash value, or digital signature value.

[0448] In one embodiment, the wearable device (100) further includes a vehicle control linkage unit that is linked to a vehicle control system including the vehicle's ECU and ACC, as well as OBD-II, a CAN gateway, BLE, etc. When the risk determination unit detects the illumination of the emergency lights or brake lights of the vehicle ahead or sudden deceleration via video, it calculates the estimated time to collision (TTC: Time To Collision) and the current speed, generates a deceleration request signal (including a target deceleration rate or a target distance between vehicles), and transmits it through the vehicle control linkage unit.

[0450] If the Time to Collision (TTC) is below the first threshold, a warning linkage signal is output in stages, and if it is below the second threshold, a deceleration request signal is output in stages, but the request is released when driver braking input is detected. In this way, even in vehicles lacking front and rear sensors, pre-deceleration is induced using only first-person video to prevent collisions.

[0452] This vehicle control interlocking configuration can be implemented in combination with the risk event judgment and responsiveness evaluation structure of the present invention, and can also be implemented as an independent accident prevention or driver assistance control system.

[0454] In another embodiment, the wearable device (100) identifies traffic congestion, road surface hazards, and construction zones using a first-person front view video to generate a non-identifiable report dataset including an event identifier, time, location, type of hazard, reliability, and hash value, and calculates the risk reliability by combining reports from multiple wearable devices and CCTV analysis results.

[0456] This embodiment is characterized by the generation of report data via wearable first-person video and the calculation of reliability through the combination of multiple reports and CCTV. When selectively transmitting to subsequent users, road boundary zone identifiers (layer, driving direction, zone type, and pair identifiers) may be combined to transmit only to subsequent users in the same layer and driving direction; however, the configuration of the road boundary zone identifiers and the selective transmission of risk information by layer and driving direction based thereon may be implemented as a separate technical configuration distinct from the present application. Therefore, the claims of the present application do not require the selective transmission itself as an essential component.

[0458] This reporting and reliability calculation configuration can be implemented in combination with the warning responsiveness-based risk assessment structure of the present invention, and can also be implemented as an independent driving risk reporting or traffic control data generation system.

[0460] This risk assessment system can be designed with different application methods depending on the autonomous driving level (SAE J3016).

[0462] At levels 0 to 2, user alert responsiveness (T_response, repetition / ignorance rate) is a key factor.

[0463] At Level 3, the user response time to the point of the take-over request is measured and combined into the hybrid metric BBI_hybrid = w_h × BBI_driver + w_s × BBI_system (w_h + w_s = 1, e.g., L3 w_h=0.7·w_s=0.3, L4 w_h=0.3·w_s=0.7).

[0464] At levels 4 and 5, system responsiveness (TTC-based response time, fail-operational success rate) is the primary factor, and the wearable device serves as an auxiliary monitoring tool.

[0466] These autonomous driving level and hybrid configurations can be implemented as a risk assessment method that considers both driver responsiveness and system responsiveness, and can be applied to the assessment of behavioral risk during autonomous driving transitions or control transfer situations.

[0468] In another embodiment, the wearable device (100) detects a person or moving object approaching from behind in a non-driving (walking) state using an object size change rate and light flow, provides a preliminary warning using vibration and sound, and when a protected person wears the device, if a pattern of sudden image change, forced boarding, restraint, or resistance is detected, it determines it to be an emergency situation and transmits a safety protection dataset including location, time, and non-identifiable feature values ​​to the guardian terminal and control center.

[0469] This non-driving safety protection configuration can be implemented as a safety protection system that collects and evaluates risk response data in pedestrian or daily life safety situations.

[0471] In one embodiment, risk assessment indicators and response data are accumulated and updated at the level of user identifiers rather than specific vehicles, so that even when a user alternately uses different modes of transportation such as private cars, rental cars, shared vehicles, and motorcycles, they are managed as a continuous response profile of the same user.

[0473] Furthermore, the above-mentioned reactivity profile can be integrated with risk response data collected during non-driving states, such as walking, to derive the user's general risk propensity; however, depending on the embodiment, the risk verification and safety protection configuration in the above-mentioned non-driving state may be combined with the user-unit reactivity profile of the present invention or implemented as an independent life safety risk assessment structure.

[0475] In one embodiment, the user-unit reactivity profile may be generated by mapping at least one of a non-identifiable user identifier, a device identifier, a mobile device identifier, a vehicle identifier, a mobility platform account identifier, or hash values ​​thereof, rather than account information dependent on a single service provider.

[0477] The above mapping information may be stored separately on the server side or processed in a trusted execution environment or secure area in accordance with the privacy policy, and only non-identifiable behavioral datasets or derived indicators, rather than original images or direct identifiable information, may be provided to external servers.

[0479] In one embodiment, the risk assessment indicator calculation server may manage the reactivity profile of the same user by separating it according to the type of transportation, service domain, or purpose of use. For example, private car driving profiles, motorcycle driving profiles, delivery driving profiles, rental car usage profiles, shared vehicle usage profiles, personal mobility device usage profiles, and non-driving pedestrian safety profiles may be managed as separate sub-profiles, but can also be updated as an integrated safety behavior profile at the overall user level.

[0481] The above embodiments are merely illustrative, and a person skilled in the art can make various modifications and equivalent implementations within the scope of the technical spirit of the present invention. The scope of the rights of the present invention is defined by the claims. Industrial applicability

[0483] The present invention can be applied to wearable image analysis devices, smart glasses, sunglasses, helmet-type terminals, body cams, clothing-attached cameras, smartphones, automotive electronic devices, black boxes, ADAS devices, navigation devices, vehicle front cameras, vehicle-mounted recognition cameras, OBUs, V2X terminals, vehicle communication terminals, mobility platforms, insurance premium rate calculation servers, control servers, and driver safety management servers.

[0484] No content

[0485] According to the present invention, a driving context such as road type, driving lane, signal status, and weather condition can be identified by using a forward image and location / sensor information corresponding to the viewpoint of a user of a means of transportation, and a risk event can be determined according to safety standards that are differentially set for each context. In addition, since the user's return to a normal state, the time required for return, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change can be quantitatively measured based on the warning output time or the time when the risk event is confirmed, the user's actual risk response ability, which is difficult to evaluate based only on a simple driving trajectory or the number of sudden accelerations and sudden brakings, can be utilized industrially.

[0486] No content

[0487] In addition, the present invention calculates the Time to Collision (TTC) from a front image based on a monocular camera and utilizes a Time to Collision (TTC) correction model or a Time to Collision (TTC) mapping table that is corrected according to speed ranges, relative speed ranges, front object distance ranges, road types, lane types, object types, camera parameters, weather conditions, or illumination conditions, thereby increasing the precision of risk event detection, warning output, and responsiveness evaluation even in various modes of transportation and image acquisition environments.

[0488] No content

[0489] The present invention can be used for behavior-based insurance (BBI), usage-based insurance (UBI), safety management for motorcycles and delivery riders, user grading for rental cars and shared vehicles, safety management for corporate vehicles and fleets, driver education and coaching, safe driving rewards, cashback, insurance premium discounts and surcharges, mobility platform matching, traffic control, and accident prevention services.

[0490] No content

[0491] In addition, since the present invention allows for the use of metadata or non-identifiable feature values ​​such as risk event type, time, location, response time, reliability, and indicator score without constantly transmitting the original image externally, it can be utilized in the insurance, mobility, and control industries where personal information protection is required. Furthermore, by masking, blurring, extracting feature values, or hashing personal information areas such as faces, vehicle license plates, and smartphone screens on-device, and adding hash values ​​or digital signature values ​​to the results of the de-identification process, both data usability and personal information protection can be achieved.

[0492] No content

[0493] Furthermore, since the present invention can be implemented not only as a system linked with an external server but also as an in-device structure that directly calculates risk assessment indicators within the wearable video analysis device, it has high industrial applicability in fields such as motorcycles, personal mobility devices, delivery and logistics sites, rental cars, shared vehicles, and public safety management where communication environments are limited.

[0494] No content

[0495] In addition, the present invention can generate or output an interaction data packet comprising at least one of a reference point timestamp message corresponding to the time of warning output or the time of confirmation of a risk event, a warning output log, a response measurement log, a normal state return log, a no-warning section summary log, and an indicator calculation result log. Accordingly, an insurer server, a control server, a mobility platform server, a verification server, or an integrated application can verify whether warning output, response measurement, and indicator calculation have been performed without directly checking the original video or the internal structure of the artificial intelligence model.

[0496] No content

[0497] Furthermore, the present invention adds at least one of a sequence number, session identifier, timestamp, hash value, digital signature value, and packet status value to an event packet and stores it in a Trusted Execution Environment (TEE) or a secure area, or verifies it on a server, thereby enabling the detection of packet loss, reverse order, duplicate transmission, replay attacks, location spoofing, or data corruption in communication interruption sections. Accordingly, data integrity and reliability can be enhanced in the processes of calculating insurance premium rates, paying rewards, assigning driver safety ratings, and assessing the reliability of risk information for control purposes.

[0498] No content

[0499] Furthermore, according to the embodiments, the present invention can be linked with a vehicle control system, an autonomous driving system, a traffic control system, a risk information reporting system, and a non-driving safety protection system, so it can be applied to the automotive industry, insurance industry, smart mobility industry, traffic safety industry, public safety industry, and the entire on-device artificial intelligence-based wearable industry.

[0500] No content

[0501] In addition, the present invention may combine at least one of a front image, vehicle status information, GNSS information, IMU information, CAN information, OBD-II information, or V2X message by linking with or utilizing a vehicle front camera, a black box, a vehicle built-in ADAS camera, a vehicle-mounted perception camera, an OBU, a V2X terminal, or a vehicle navigation terminal. Accordingly, it can be applied not only to embodiments using a wearable device but also to risk assessment services based on a vehicle-mounted / attachable device or a vehicle communication terminal.

[0502] No content

[0503] In addition, since the present invention can generate continuous safety behavior profiles in units of user identifiers or non-identifiable user identifiers, it can be used for mobility-independent risk assessment services that are not dependent on a specific vehicle, specific OBD terminal, specific black box, or specific mobility platform. Accordingly, risk event data and response data collected during the use of private cars, rental cars, shared vehicles, motorcycles, bicycles, personal mobility devices, delivery operations, and public transportation can be normalized in units of the same user and applied to insurance, mobility, fleet management, control, driver training, and public safety services.

[0504] No content

[0505] Furthermore, the above-mentioned continuous safety behavior profile can be converted into service domain-specific derived indicators. The above-mentioned service domain-specific derived indicators may include at least one of the following: a behavior-based insurance (BBI) indicator for calculating insurance premium rates, a UBI adjustment indicator, a safety management indicator for motorcycles or delivery riders, a user rating indicator for rental cars or shared vehicles, a safety management indicator for corporate vehicles or fleets, a mobility platform matching indicator, a driver education / coaching indicator, a road hazard reporting reliability indicator, an autonomous driving control transfer responsiveness indicator, and an OEM safety evaluation indicator.

[0506] No content

[0507] The present invention is not limited to the industrial fields described above and can be applied to various safety management, insurance, mobility, control, logistics, education, and public service fields where image-based risk recognition, warning output, response data measurement, risk assessment indicator calculation, and anonymized data verification are required.

[0508] No content Explanation of the symbols

[0509] 100: Wearable video analysis device 110: Extroverted camera 120: Positioning unit 130: Inertial sensor unit 135: Environmental sensor unit 140: Output section 150: Communications Department 160: Processor 170: Storage section 200: Mobile device 210: Mobile Communications Department 220: Mobile positioning unit 230: Mobile inertial sensor unit 240: Mobile Applications 300: Vehicle device 310: Vehicle Communications Unit 320: Vehicle control system 330: ECU 340: ACC 350: ADAS device 360: Dashcam or vehicle-mounted camera 400: Control Server 410: Risk Information Receiving Unit 420: Risk Information Verification Department 430: Risk Information Dissemination Division 500: Risk Assessment Indicator Calculation Server 510: Server Communications Unit 520: Behavior data receiver 530: Reliability Verification Department 540: Cumulative Statistics Calculation Department 550: Risk Assessment Indicator Calculation Department 560: Insurance premium rate calculation linkage unit 570: Database 600: External server 610: Insurance company server 620: Mobility Platform Server 630: Rental business operator server 640: Driver Coaching Server 700: TTC calibration model or TTC mapping table 710: TTC Calculation Department 720: TTC Calibration Information 800: Interaction data packet 810: Reference point timestamp message 820: Warning output log 830: Response measurement log 840: Normal state return log 850: No-warning 구간 Summary Log 860: Indicator Calculation Result Log 900: Event Packet 910: Sequence number 920: Session identifier 930: Hash value 940: Digital signature value 950: Packet status value 1000: Automotive Risk Assessment Module 1010: Vehicle front camera 1020: OBU or V2X terminal 1030: Vehicle status data 1040: CAN or OBD-II data 1050: Vehicle Risk Assessment Indicator Calculation Unit 1100: Tip-off Reliability Calculation Module 1110: De-identified tip dataset 1120: CCTV or road sensor analysis results 1130: Risk reliability 1200: Hybrid Risk Assessment Module 1210: Driver Responsiveness Indicator 1220: System responsiveness indicator 1230: Transfer of control response time 1240: Hybrid Risk Assessment Indicators 1300: User-level responsiveness profile 1310: Derived metrics by service domain E_risk: Risk event to: Time of warning output or time of risk event confirmation t_recovery: Point of return to normal state T_response: Return time N_repeat: Number of warning repetitions T_ignore: Ignore duration BBI_score: Behavior-based risk assessment metric Context_Risk: Context-specific risk constant Wi: Weights by Risk Event Type γ: Surcharge factor based on repetition or neglect rate

Claims

Claim 1 A risk assessment index calculation system comprising a wearable image analysis device having an outward-facing camera that is worn on the body or clothing of a user of a means of transportation or mounted in conjunction with the user's gaze direction to capture the front corresponding to the user's viewpoint, a position positioning unit, and an output unit, the system comprising: a context identification unit that identifies a driving context including at least one of a road type, a driving lane, a signal status, and a weather status from the front image and location information of the outward-facing camera; a risk determination unit that determines a risk event based on safety standards differentially set for each identified driving context; a warning output unit that outputs a warning as at least one of a screen display, vibration, sound, voice guidance, and augmented reality display upon determination of the risk event; and a response measurement unit that measures response data including at least one of whether the user returns to a normal state, the time required for return, the number of warning repetitions, the duration of ignoring, and the amount of behavioral change based on the time of output of the warning or the time of confirmation of the risk event. A risk assessment indicator calculation system comprising: an indicator calculation linkage unit that generates a risk assessment indicator or an insurance premium rate calculation factor by cumulatively analyzing the risk event data and the reaction data, and transmits it to an external server or stores it within a device; wherein the normal state includes at least one of a state indicating a return within a reference speed, recovery of a safe distance, return to a driving lane, a state where the smartphone screen is not detected, a state where the turn signal is activated, a state where the risk object is avoided, or a state indicating the resolution of the risk event, corresponding to the type of risk event. Claim 2 A system according to claim 1, wherein the external server comprises at least one of an insurance company server, an insurance premium rate calculation server, a mobility platform server, a rental business operator server, a control server, and a driver coaching server, and wherein the risk assessment indicator is converted into at least one of an insurance premium rate calculation factor, a behavior-based safety score, a cashback calculation value, a reward calculation value, a fare discount / surcharge calculation value, a driver education score, and a fleet safety management score. Claim 3 In claim 1, the indicator calculation linkage unit calculates the risk assessment indicator as BBI_score = Σ ( Wi × Context_Riski × f(T_responsei) × γ, where Wi is a weight for each risk event type, Context_Riski is a risk constant for each context, T_responsei is the time required for recovery for the i-th risk event, f(·) is a monotonically increasing function that increases as the time required for recovery increases, and γ is a surcharge coefficient based on the number of warning repetitions or the warning ignore rate, wherein f(T_responsei) is defined as f(T_responsei) = exp( βi × max( T_responsei - T_threshold,i, 0 ) such that no penalty is imposed if the time required for recovery is within the reference time, and increases if the reference time is exceeded, and γ is defined as γ = 1 + δ × ( R / R_threshold ) + ε × Ignore_rate, and the warning ignore rate or the time required for recovery is related to the number of risk event occurrences Reflected as independent factors, at least one of the above T_threshold, i, βi, Context_Riski, or Wi is determined based on a TTC correction model or TTC mapping table that is corrected according to at least one of vehicle speed range, relative speed range, front object distance range, road type, lane type, object type, camera parameters, image resolution, weather conditions, and illumination conditions, and the above TTC correction model or TTC mapping table includes a multidimensional table, a rule-based model, or a learning-based model that maps at least one of a TTC correction coefficient, TTC threshold, warning output reference value, deceleration recommendation reference value, reliability correction value, or risk grade value according to speed range, relative speed range, front object distance range, road type, lane type, object type, camera installation conditions, weather conditions, or illumination conditions, and the above indicator calculation linkage unit includes a TTC value at the time of occurrence of a risk event, a TTC value at the time of warning output, a minimum TTC value after the warning,A system characterized by further reflecting at least one of the TTC value at the time of return to normal state and the TTC recovery rate as a reactivity evaluation factor. Claim 4 A system characterized in that, in paragraph 3, the indicator calculation linkage unit calculates the driving distance or driving time of a warning-free driving section in which a risk event or warning occurs below a reference value as a warning-free driving indicator, and lowers the risk evaluation indicator as the warning-free driving indicator increases, thereby reflecting the safety of a state in which no warning occurs as an indirect indicator of safety, wherein the warning-free driving indicator is used as at least one lightweight reward factor among safe driving rewards, cashback, points, driver coaching, encouragement of continued wearing, or low-risk driving certification in the first operating mode, and in the second operating mode after risk event data and reaction data have been accumulated for a certain period or a certain driving distance or more, it is combined with at least one of the return time, number of warning repetitions, ignore rate, and deviation from individual baseline to be converted into an insurance premium rate calculation factor or an advanced BBI_score. Claim 5 A system according to claim 1, wherein the context identification unit and the risk determination unit are implemented as an on-device artificial intelligence model, wherein the on-device artificial intelligence model is trained with a forward image labeled with at least one of a road, lane, signal, weather, speed limit sign, vehicle ahead, braking light, road surface hazard, and construction sign, performs at least one preprocessing of frame extraction, resolution normalization, region of interest segmentation, light flow calculation, illuminance correction, and temporal smoothing on an input image, outputs at least one of a context identification value, a risk event type value, a confidence value, and an estimated value for the time to return to a normal state through an object detection model, a classification model, or a time series model, and restricts the output of a warning or the reflection of a risk assessment indicator when the confidence value is less than a reference value. Claim 6 A system according to claim 1, wherein the safety standard includes at least one of a safety distance standard with respect to a vehicle ahead, a standard speed, a deceleration standard, and a lane change permission standard; wherein the risk determination unit differentially applies the safety standard according to at least one driving context among an expressway, an automobile-only road, a national road, an urban road, a main road, an exit, a deceleration lane, a merging lane, and a congested lane; wherein, even in sections not covered by navigation, sections not registered in a map database, or sections with variable speed limits, a standard speed limit value is set based on at least one of an image-recognized speed limit sign, a road type, a lane type, a temporary speed limit display, or a variable speed limit sign; wherein the current driving speed calculated or received from at least one of the position change amount of a position positioning unit, a sensor value of an inertial sensor unit, a frame-to-frame change amount of a front image, location information of a mobile device, vehicle speed data, GNSS information, CAN information, OBD-II information, or a V2X message is compared with the standard speed limit value to determine whether the vehicle is overspeeding; and wherein the standard speed or safety distance standard is adjusted when conditions such as heavy snow, heavy rain, fog, freezing risk temperature, or low light are identified. Claim 7 A system according to claim 1, wherein the risk event determined by the risk determination unit includes at least one of the following: detection of a smartphone screen while driving, speeding, failure to maintain a safe distance, passing a yellow signal, changing lanes in a section where lane changes are possible or prohibited, operation of a turn signal, driving in a prohibited lane, road surface hazards including falling objects, black ice, and potholes, construction, accident, or breakdown vehicles caused by traffic cones, construction signs, safety triangles, or flares, emergency lights, brake lights, or sudden deceleration of a vehicle ahead, and cutting in by vehicles to the left or right, wherein the passing of the yellow signal is not determined as a risk event alone, but is distinguished and determined as passing with stopping possible or passing with difficulty by considering at least one of the following together: the estimated time to reach the stop line, the braking distance, the current speed, the state of approach of a rear vehicle, and the road surface condition. Claim 8 A system according to claim 1, wherein the risk determination unit detects that a rearview mirror or side mirror area enters the field of view of the outward-facing camera according to the head rotation of the user, detects the mirror area based on at least one of the mirror frame shape, reflection area boundary, rear vehicle object, and head rotation information, and determines the rapid acceleration approach of a rear vehicle by analyzing the mirror area image at regular intervals separately from eye tracking. Claim 9 A system according to claim 1, wherein the indicator calculation linkage unit calculates an indicator reliability based on at least one of a wearing state, device posture, camera field of view validity, user authentication, whether the wearer matches the actual driving entity, mobile device pairing information, event hash value, and electronic signature value, and excludes the data from evaluation or assigns a low reliability weight when camera obstruction, non-wearing of the device, wearing by another person, position trajectory mismatch, or duplicate transmission of the same event is detected, and when a driving state is detected, wakes up a mobile device linked via short-range wireless communication to cross-verify the first trajectory information of the wearable video analysis device and the second trajectory information of the mobile device. Claim 10 In claim 1, the wearable video analysis device or external server generates an event log including at least one of a risk event identifier, a warning output time, a risk event confirmation time, a response measurement start time, a response measurement end time, a device identifier, a user authentication value, a location / inertial data summary value, and an indicator calculation result; adds at least one of a timestamp, a hash value, a hash chain, and a digital signature value to each event log and stores it in a Trusted Execution Environment (TEE), a secure area, or a tamper-proof storage unit; the digital signature value is generated as a device unique key and combined with timestamp and location information to be used to verify the integrity of the time and place of data generation; the wearable video analysis device or external server detects at least one abnormal behavior among proxy wearing, device detachment operation, location spoofing, log replay attacks, time reversal setting, repeated transmission of the same event, and injection of low-trust sensor values; and if the trajectory of the wearable video analysis device and the trajectory of the mobile device are separated for a certain period of time or longer, or if the satellite navigation position change is physically inconsistent with the inertial sensor-based movement amount, or the event If the hash chain sequence of the log is discontinuous, the corresponding event or driving section is excluded from the calculation of risk assessment indicators or assigned a low reliability weight, and the wearable video analysis device or external server adds at least one of a sequence number, session identifier, event identifier, timestamp, previous packet hash value, current packet hash value, digital signature value, and packet status value to an event packet generated within a Trusted Execution Environment (TEE) or a secure area, and the packet status value includes at least one of creation complete, temporary storage, waiting for transmission, transmission complete, server receipt acknowledgment, waiting for retransmission, verification failure, and discard, and the external server verifies the sequence number, session identifier, timestamp, and hash chain continuity of the received event packet to check for packet omissions, reversed order,A system characterized by determining duplicate transmission or replay attacks, and if packet loss or order reversal is detected, requesting retransmission of the missing section or requesting a summary packet, hash proof value, or sensor summary value stored in a local circular buffer, and calculating a reliability weight for the corresponding event or driving section in stages based on at least one of the retransmission or recovery verification result, the number of missing packets, the missing packet ratio, the time length of the missing section, the type of missing packet, timestamp consistency, location trajectory consistency, and sensor summary value consistency, and excluding the corresponding event or driving section from the calculation of risk assessment indicators if the reliability weight is less than a reference value, and calculating the risk assessment indicators by reflecting the reliability weight if the reliability weight is greater than or equal to the reference value. Claim 11 A system according to claim 1, wherein the wearable image analysis device does not always transmit the original image externally but transmits at least one of the risk event type, time, location, response time, number of warning repetitions, duration of ignoring, reliability, index score, metadata, and non-identifiable feature value, and detects at least one personal information area among a face, vehicle license plate, and smartphone screen using an on-device object detection model to perform masking, blurring, feature value extraction, or hash processing, performs de-identification operations or integrity processing within a trusted execution environment, and is capable of injecting statistical noise of a differential privacy technique into the non-identifiable data. Claim 12 In claim 1, the response measuring unit calculates a responsiveness factor by tracking in a time series at least one of whether the dangerous behavior is resolved, the time required for resolution, the interval for recurrence after resolution, the degree of compliance, and the amount of change in behavior, using the time of output of the warning or the time of confirmation of the dangerous event as a reference point, and the response data is measured based on at least one of driving speed, distance to the vehicle ahead, amount of lane deviation, line of sight direction, steering input, braking input, change in acceleration, and change in evasion trajectory, and in determining whether a return to a normal state has occurred, the response measuring unit calculates the time of return to a normal state or the time required for return by using at least one of whether the distance to the forward object or the time to collision (TTC) has recovered above a reference value depending on the type of dangerous event, whether the amount of deviation from the lane centerline has recovered within an allowable range, whether the smartphone screen area has switched to an undetected state, whether the line of sight direction has returned to the forward direction, whether the driving speed has returned within a reference speed, or whether the steering input, braking input, deceleration, or change in evasion trajectory satisfies a reference condition, and the response measuring unit [determines] each modality based on the reliability of image-based features and the reliability of inertial sensor-based features Dynamically assigning weights, and detecting the point of return to a normal state by increasing the weight of inertial sensor-based features when image reliability drops below a threshold value; the processor of the wearable image analysis device or the response measurement unit monitors at least one of the battery level, power consumption, processor temperature, external surface temperature, image analysis latency, and frame throughput of the wearable image analysis device; and when the battery level, power consumption, processor temperature, external surface temperature, image analysis latency, or frame throughput deviates from a threshold range, the image analysis frame rate is lowered, the high-resolution object detection model is switched to a low-resolution or lightweight model, the weight of the inertial sensor-based response detection mode is increased, or a part of the image analysis is performed on a mobile device, a vehicle device,A system characterized by being transferred to an edge computing node or cloud server, wherein the timestamp of the warning output time or the risk event confirmation time is fixed in the wearable image analysis device, and the response data further includes at least one of the amount of TTC change before and after warning output, TTC recovery speed, minimum TTC value after warning, TTC value at the time of return to normal state, and amount of front object distance recovery, and wherein the response measurement unit prioritizes reflecting the monocular camera-based TTC calculation value when the image reliability is greater than or equal to a reference value, and corrects and determines whether TTC recovery or return to normal state is based on the inertial sensor-based deceleration, steering change amount, vehicle speed change, or mobile device sensor value when the image reliability is less than the reference value. Claim 13 In claim 1, the warning output unit records whether the warning output was successful, the output format, the output intensity, the output duration, and the user's perceptibility status; the response measurement unit excludes events determined to be warning output failures or user-unperceptible from the reactivity evaluation or classifies them as separate status values; the indicator calculation linkage unit excludes events where at least one of the image recognition reliability, location reliability, sensor synchronization reliability, and warning delivery success of the risk event is below a reference value from the risk evaluation indicator calculation or assigns a low reliability weight; the warning output unit gradually adjusts the warning intensity or warning format according to at least one of the severity of the risk event, estimated collision time, driving speed, road type, weather conditions, and the user's past reactivity profile; the response measurement unit detects at least one of the user's deceleration, steering, lane return, gaze return, recovery of distance to a forward object, warning confirmation input, and voice response after the warning output to estimate whether the warning is perceived or complied with; if a signal of perception or compliance is not detected within a preset time, the warning output unit re-outputs the same warning or changes the warning format; and the indicator calculation linkage unit, the initial warning time, A system characterized by storing the re-warning point and the final return point separately. Claim 14 In claim 1, the wearable image analysis device comprises at least one of smart glasses, sunglasses, a helmet, a body cam, a clip-on camera, and a clothing-attached camera, and the electronic device linked with the wearable image analysis device comprises at least one of a mobile device, a vehicle front camera, a black box, a vehicle built-in ADAS camera, a vehicle-mounted perception camera, a vehicle-mounted communication terminal, an OBU, a V2X terminal, a vehicle navigation terminal, an edge computing node, and a cloud server, and at least one of context identification, risk determination, warning output, response data measurement, and risk assessment index calculation is determined to be performed on which node among the wearable image analysis device, the mobile device, the vehicle device, the edge computing node, and the cloud server based on at least one of communication bandwidth, latency, computational load, power status, image reliability, sensor reliability, and privacy policy, and at least one function among the context identification unit, the risk determination unit, the response measurement unit, and the index calculation linkage unit is performed by an application, a software development kit (SDK), a library, or a framework executed on the wearable device or the linked electronic device, and the measurement of response data and the calculation of the risk assessment index are performed on different nodes A system characterized by calculating the return time or warning ignore rate based on the timestamp of the output time of the warning or the time of confirmation of the risk event, even when the above warning is executed. Claim 15 A system characterized in that, in paragraph 3, the indicator calculation linkage unit accumulates and updates the risk assessment indicator and response data in units of user identifiers or non-identifiable user identifiers rather than specific vehicles, specific on-board devices, or specific mobility platforms, manages the continuous responsiveness profile of the same user even when the user alternately uses at least two means of transportation or usage situations among private cars, rental cars, shared vehicles, motorcycles, bicycles, personal mobility devices, delivery means, and public transportation, and generates at least one service domain-specific derived indicator from the continuous responsiveness profile among a BBI indicator for calculating insurance premium rates, a UBI correction indicator for driver habit-based insurance, a safety management indicator for motorcycles or delivery riders, a user rating indicator for rental cars / shared vehicles, a safety management indicator for corporate vehicles or fleets, a mobility platform matching indicator, a driver education / coaching indicator, a risk information reliability indicator for control, and an autonomous driving control transfer responsiveness indicator. Claim 16 A wearable image analysis device that is worn on the body or clothing of a user of a means of transportation or mounted to be synchronized with the user's line of sight, comprising: an outward-facing camera that captures the front corresponding to the user's viewpoint; a positioning unit; an output unit capable of screen display, vibration, sound, or voice guidance; a communication unit; a storage unit; and a processor equipped with an on-device artificial intelligence model.It includes, wherein the processor identifies a driving context based on front video and location information, determines a risk event according to differential safety standards per context and outputs a warning, generates a timestamp, event identifier, and integrity verification value corresponding to the time of output of the warning or the time of confirmation of the risk event, measures reaction data based on the timestamp, and generates a behavioral risk indicator from the risk event data and reaction data, and operates with a multi-stage trigger structure that switches from a low-power standby mode to a video analysis mode triggered by the detection of a driving state, wherein the generation of the behavioral risk indicator is completed within the device without requiring linkage with an external insurance company server or rate calculation server, and the processor generates a complete safety behavior profile within the device by calculating at least one of the number of risk events per unit driving segment, average return time, warning ignore rate, warning-free driving distance, or warning-free driving time without real-time communication with an external server, and the output unit displays the behavioral risk indicator or safety behavior profile in at least one form among a score, grade, color, icon, voice guidance, vibration pattern, or rewardable status, and the communication unit or storage unit is a reference point A wearable video analysis device characterized by outputting or storing at least one of a timestamp message, a warning output log, a response measurement start log, a response measurement end log, a normal state return log, a no-warning interval summary log, and an indicator calculation result log as externally measurable data, and wherein the wearable video analysis device is linked with at least one of a mobile device, a vehicle front camera, a black box, a vehicle built-in ADAS camera, a vehicle-mounted perception camera, an OBU, a V2X terminal, a vehicle navigation terminal, or a vehicle communication terminal to transmit and receive at least one of vehicle status data, a warning output log, a risk event metadata, or response data. Claim 17 A server that calculates a risk assessment index based on behavior data not including an original image received from a video-based risk assessment device, comprising: a communication unit that receives behavior data including response data comprising at least one of a driving context identified within an on-device or linked device using at least one of a front image, image feature value, location information, sensor information, vehicle status information, GNSS information, IMU information, CAN information, OBD-II information, or V2X message by the video-based risk assessment device, a risk event determined according to differential safety standards per context, a warning output time or a risk event confirmation time, and whether to return to a normal state, time required for return, number of warning repetitions, warning ignore rate, and amount of behavior change measured based on the warning output time or risk event confirmation time; A calculation unit that accumulates the above behavioral data to calculate cumulative statistics including at least one of the number of times by risk event type, average return time, warning ignore rate, no-warning interval indicator, and responsiveness profile, and calculates at least one of a risk assessment indicator, a behavior-based safety score, an insurance premium rate calculation factor, a discount / surcharge factor for the insurance premium rate, a safe driving reward indicator, a driver coaching indicator, or a mobility platform matching indicator based on warning responsiveness;The behavior data includes, wherein the behavior data does not necessarily include the original front video, but includes at least one of a driving context, a risk event identifier, risk event metadata, video feature value, object recognition result, warning output log, response data, no-warning section summary information, confidence value, hash value, or digital signature value; the server does not necessarily include the outward camera, positioning unit, vehicle sensor, or output unit of the image-based risk assessment device as a required component, and is configured to calculate a risk assessment index based on the received behavior data and calculation result; the image-based risk assessment device includes or is linked with at least one of a wearable video analysis device, smart glasses, sunglasses, a helmet-type camera, a body cam, a clip-type camera, a clothing-attached camera, a mobile device, a vehicle front camera, a black box, a vehicle-mounted ADAS camera, a surround-view camera, a vehicle-mounted perception camera, a vehicle-attached video device, an OBU, a V2X terminal, a vehicle navigation terminal, and a vehicle communication terminal; and the server includes the wearing status, device status, user authentication, whether the wearer and the driver match, vehicle identification information, and terminal included in the behavior data. A risk assessment indicator calculation server characterized by verifying reliability using at least one of identification information, location trajectory information, hash value, or digital signature value to exclude data below a threshold from calculation or assign a low reliability weight, and the server receiving an interaction data packet from the image-based risk assessment device that includes at least one of a reference point timestamp message, a warning output log, a response measurement start log, a response measurement end log, a normal state return log, a no-warning interval summary message, and an indicator calculation result message, and verifying whether warning output, response measurement, and indicator calculation are performed without receiving the internal source code, original front-end image, or artificial intelligence model structure of the image-based risk assessment device. Claim 18 A method for calculating a risk assessment index using a wearable video analysis device, comprising: (a) a step in which a wearable or mounted wearable video analysis device detects a driving state using at least one of position information, inertial information, and a front image, and, if necessary, wakes up a mobile device linked via short-range wireless communication or cross-verifies a first trajectory of the wearable video analysis device and a second trajectory of the mobile device; (b) a step of identifying a driving context from at least one of the front image and position information; (c) a step of determining a risk event according to differential safety standards for each context; (d) a step of outputting a warning using at least one of a screen, vibration, sound, voice guidance, or augmented reality display, or generating a point in time when the risk event is confirmed; (e) a step of measuring at least one reaction data among whether to return to a normal state, time required for return, deceleration reaction time, steering reaction time, time to recover safe distance, time to return to lane, number of warning repetitions, duration of ignoring, warning ignoring rate, and amount of change in behavior based on the point in time when the warning is output or the point in time when the risk event is confirmed. and (f) a step of generating at least one of a risk assessment indicator, a behavior-based safety score, an insurance premium rate calculation factor, a discount / surcharge factor for the insurance premium rate, a safe driving reward indicator, or a driver coaching indicator by cumulatively analyzing risk event data and response data, wherein a low risk or discount factor is generated when the return time is short or the return to a normal state is fast, a high risk or surcharge factor is generated when the number of warning repetitions is high or the duration of ignoring is long or the warning ignoring rate is high, and a risk assessment indicator or an insurance premium rate calculation factor is generated by weighting a context-specific risk constant; comprising a method Claim 19 A risk assessment indicator or insurance premium rate calculation method performed by a risk assessment indicator calculation server, comprising: receiving behavioral data that does not include the original image, but includes reaction data measured based on a driving context identified within an on-device or linked device using at least one of a front image, image feature value, location information, sensor information, vehicle status information, GNSS information, IMU information, CAN information, OBD-II information, or V2X message, a determined risk event, a warning output time, or a risk event confirmation time; verifying at least one of the integrity of the behavioral data, device status, wearing status, user authentication, whether the wearer and the driving entity match, vehicle identification information, terminal identification information, location trajectory information, hash value, or digital signature value; accumulating the verified behavioral data to calculate cumulative statistics including at least one of the number of times by risk event type, average return time, warning ignore rate, no-warning interval indicator, and responsiveness profile; and based on warning responsiveness, a risk assessment indicator, a behavior-based safety score, an insurance premium rate calculation factor, a discount / surcharge factor for the insurance premium rate, a safe driving reward indicator, A method comprising: a step of calculating at least one of a driver coaching indicator or a mobility platform matching indicator; and a step of verifying whether the warning output, response measurement, and indicator calculation are performed without receiving the internal source code or artificial intelligence model structure of the image-based risk assessment device, based on an interaction data packet including at least one of a reference point timestamp message, a warning output log, a response measurement start log, a response measurement end log, a normal state return log, a no-warning interval summary message, and an indicator calculation result message received from the image-based risk assessment device. Claim 20 A method for calculating a risk assessment index using an image device that captures a front view corresponding to the viewpoint of a user of a means of transportation or the viewpoint of the direction of travel of said means of transportation, wherein the image device comprises at least one of a wearable image analysis device worn or mounted on a user, smart glasses, sunglasses, a helmet-type camera, a body cam, a clip-type camera, a clothing-attached camera, a mobile device, a vehicle front camera, a black box, a vehicle-mounted ADAS camera, a surround-view camera, a vehicle-mounted perception camera, a vehicle-attached image device, or an OBU, a V2X terminal, a vehicle navigation terminal, or a vehicle communication terminal linked with said image device, and (a) a step of identifying a driving context from at least one of a front image, an image feature value generated from said front image, a risk event metadata, a warning output log, location information, sensor information, vehicle status information, GNSS information, IMU information, CAN information, OBD-II information, and a V2X message; (b) a step of determining a risk event according to a differential safety standard for each context, and outputting a warning corresponding to said risk event or generating a risk event confirmation point; (c) the warning output point or risk event confirmation A step of quantitatively measuring response data including at least one of whether the state returns to normal, the time required to return to normal, the deceleration response time, the steering response time, the safety distance recovery time, the lane return time, the number of warning repetitions, the duration of disregard, the warning disregard rate, and the amount of change in behavior, based on a reference point;and (d) a step of calculating at least one of a risk assessment indicator, a behavior-based safety score, an insurance premium rate calculation factor, a safe driving reward indicator, a driver coaching indicator, or a mobility platform matching indicator by combining risk event data and response data, wherein the risk assessment indicator includes at least one of a monotonically increasing function of the time required to return to a normal state, a deceleration response time, a steering response time, a safe distance recovery time, a lane return time, a warning ignore rate, the number of warning repetitions, or the amount of behavioral change as a factor independent of the number of risk event occurrences, and such that a lower risk assessment indicator is calculated when responsiveness is superior even for the same context and the same number of risk events; wherein the determination of whether the normal state has been returned is made using at least one of whether the distance to a forward object or the time to collision (TTC) has recovered above a reference value according to the type of risk event, whether the lane centerline deviation has recovered within an allowable range, whether the smartphone screen area has switched to an undetected state, whether the gaze direction has returned to the forward direction, whether the driving speed has returned within a reference speed, or whether the steering input, braking input, deceleration rate, or change in evasion trajectory satisfies a reference condition, and the normal state A method characterized by setting the point at which a return is determined as the point of return to a normal state, and calculating the time from the point of output of the warning or the point at which a risk event is confirmed to the point of return to a normal state as the time required for return to a normal state. Claim 21 A method according to claim 20, wherein the above-mentioned imaging device is a wearable device, a mounted personal imaging device, or a mobile device that captures the front corresponding to the user's viewpoint in conjunction with the user's gaze direction. Claim 22 A non-transient computer-readable recording medium storing instructions for a computer to perform the method of any one of claims 18 to 20, wherein the instructions further include instructions for generating at least one of a reference point timestamp message, an event identifier, a session identifier, a warning type, whether the warning output was successful, a response measurement start message, a response measurement end message, a normal state return message, a no-warning interval summary message, a reliability value, and an indicator calculation result message corresponding to a warning output time or a risk event confirmation time, and for outputting the said messages through at least one interface among BLE, Wi-Fi, USB, NFC, CAN, OBD-II, V2X, SDK API, application programming interface, or local log file. Claim 23 A computer program stored on a non-transient computer-readable recording medium to execute a method of any one of claims 18 to 20 in combination with hardware, wherein the computer program generates at least one of a reference point timestamp message, an event identifier, a session identifier, a warning type, whether the warning output was successful, a response measurement start message, a response measurement end message, a normal state return message, a no-warning interval summary message, a reliability value, and an indicator calculation result message corresponding to a warning output time or a risk event confirmation time, and outputs said message through at least one interface among BLE, Wi-Fi, USB, NFC, CAN, OBD-II, V2X, SDK API, application programming interface, or local log file.

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