CN-121998778-A - Vehicle commercial premium pricing method, system, device, storage medium and product

CN121998778ACN 121998778 ACN121998778 ACN 121998778ACN-121998778-A

Abstract

The application discloses a vehicle commercial premium pricing method, a system, equipment, a storage medium and a product, relating to the technical field of automatic vehicle insurance, comprising the steps of matching basic rates corresponding to an automatic driving vehicle from an insurance contract library; obtaining SOTIF data of the automatic driving vehicle, determining a responsibility coefficient and a residual risk coefficient of a host factory through a responsibility computation engine, calculating a total loss measurement according to SOTIF data, and calculating to obtain commercial insurance expense of the automatic driving vehicle based on the basic rate, the responsibility coefficient of the host factory, the total loss measurement and the residual risk coefficient. According to the method, through basic rate, main machine factory responsibility coefficient, total loss measurement and residual risk coefficient, an automatic driving business premium accurate calculation model based on SOTIF is constructed, accident responsibility can be accurately defined, multidimensional loss can be quantized, matching precision of premium and real risk is remarkably improved, and accurate pricing is achieved.

Inventors

WENG RENHONG
YI WEI
XIONG WEIMING

Assignees

浙江吉利控股集团有限公司
吉利汽车研究院（宁波）有限公司

Dates

Publication Date: 20260508
Application Date: 20260123

Claims (10)

1. A vehicle commercial premium pricing method, the vehicle commercial premium pricing method comprising: matching a base rate corresponding to the autonomous vehicle from an insurance contract library; acquiring expected functional safety SOTIF data of the autonomous vehicle; Determining a host factory responsibility coefficient and a residual risk coefficient through a pre-constructed responsibility calculation engine based on SOTIF data; Calculating a total loss metric due to the autonomous vehicle from the SOTIF data; calculating a commercial insurance charge for the autonomous vehicle based on the base tariff, the host factory liability coefficient, the total loss metric, and the residual risk coefficient.
2. The vehicle commercial premium pricing method of claim 1, wherein the step of matching a base rate corresponding to the autonomous vehicle from an insurance contract library comprises: Determining an initial basic rate in the insurance contract library according to the vehicle value of the automatic driving vehicle, the risk coefficient corresponding to the operation area, the historical reference rate and the expected insurance policy; The vehicle value is determined based on the cost of sensors and automatic driving hardware in vehicle configuration, and the regional risk coefficient is determined according to the traffic complexity of an operation region.
3. The vehicle commercial premium pricing method of claim 2, wherein the step of determining an initial base rate in the insurance contract library based on the vehicle value of the autonomous vehicle, the risk coefficient corresponding to the operating region, the historical base rate, and the anticipated premium further comprises: acquiring actual odds, kilokilometer accident rate and SOTIF scene passing rate of the automatic driving vehicle in the operation process of a motorcade belonging to the automatic driving vehicle; determining a odds ratio change coefficient based on the deviation of the actual odds ratio and the target odds ratio, the change trend of the thousand kilometer accident rate and the passing rate of SOTIF scenes; And adjusting the initial basic rate according to the odds ratio change coefficient to obtain an updated basic rate.
4. The vehicle commercial premium pricing method of claim 1, wherein the step of determining, based on the SOTIF data, host factory liability coefficients and residual risk coefficients by a pre-built liability calculation engine comprises: judging whether the automatic driving vehicle is in a preset operation domain ODD range or not when an accident occurs based on the SOTIF data; determining a host factory responsibility coefficient by the pre-constructed responsibility calculation engine based on the judging result, the host factory responsibility weight and the user/third party responsibility weight; And calculating to obtain a residual risk coefficient based on the effectiveness coefficient of the design safeguard measure for the preset unsafe scene in SOTIF data, the verification coverage coefficient, the confirmation activity sufficiency coefficient and the scene exposure intensity.
5. The vehicle commercial premium pricing method of claim 4, wherein the step of determining, by the pre-built responsibility calculation engine, the host factory responsibility coefficients based on the determination, the host factory responsibility weights, and the user/third party responsibility weights comprises: If the automatic driving vehicle is in the ODD range, acquiring a deviation design domain ratio ODR, a safety takeover request frequency SOR, a remote takeover response timeliness RTR and a vehicle failure rate VFR of the automatic driving vehicle, and calculating a host factory responsibility coefficient based on ODR, SOR, RTR and the VFR; If the automatic driving vehicle is judged to exceed the ODD range, determining the responsibility coefficient of the host factory as the responsibility weight of a user or a third party according to whether an effective take-over prompt and a user operation record are sent out before an accident.
6. The vehicle commercial premium pricing method of claim 1, wherein the step of calculating a total loss metric due to the autonomous vehicle based on the SOTIF data comprises: Under the accident scene represented by SOTIF data, carrying out multidimensional monetization evaluation on loss caused by the accident to obtain property loss, personal injury loss, data and privacy loss and brand reputation loss, wherein the property loss is determined based on vehicle maintenance cost and third party damage, the personal injury is calculated according to preset personal injury compensation rules and injury parameters, the data and the privacy loss are determined according to the sensitivity type and quantity of the leaked data in combination with preset compensation unit price, and the brand reputation loss is estimated by analyzing the influence caused by the accident and combining with a preset enterprise market value fluctuation model; summing the property loss, personal injury loss, data and privacy loss and brand reputation loss to obtain the total loss metric.
7. A vehicle commercial premium pricing system, the vehicle commercial premium pricing system comprising: the first parameter determining module is used for matching basic rates corresponding to the automatic driving vehicles from the insurance contract library; the data acquisition module is used for acquiring the expected functional safety SOTIF data of the automatic driving vehicle; The second parameter determining module is used for determining a responsibility coefficient and a residual risk coefficient of the host factory through a pre-constructed responsibility calculation engine based on SOTIF data; A third parameter determination module for calculating a total loss metric due to the autonomous vehicle based on the SOTIF data; and the premium calculation module is used for calculating the commercial insurance premium of the automatic driving vehicle based on the basic rate, the responsibility coefficient of the host factory, the total loss measurement and the residual risk coefficient.
8. A vehicle commercial premium pricing apparatus, the apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle commercial premium pricing method of any one of claims 1 to 6.
9. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the vehicle commercial premium pricing method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program which when executed by a processor performs the steps of the vehicle commercial premium pricing method of any one of claims 1 to 6.

Description

Vehicle commercial premium pricing method, system, device, storage medium and product Technical Field The application relates to the technical field of automatic driving vehicle insurance, in particular to a vehicle commercial premium pricing method, a system, equipment, a storage medium and a product. Background The existing automobile insurance system exposes triple structural defects in the L4 grade Robotaxi (unmanned taxi) commercialization process, and is difficult to adapt to novel responsibility attribution logic: The responsibility definition failure is that the traditional vehicle insurance takes 'driver responsibility' as the core, but the human beings have no operation obligation in the L4 level automatic driving operation, and the accident responsibility is substantially transferred to a host factory or a system provider. The existing insurance clauses do not establish a clear compensation mechanism for damage caused by the system's intended functional limitations (e.g., SOTIF unknown scene collisions), creating a responsible vacuum. Loss assessment standard is missing, namely the current loss assessment still adopts a physical collision damage cost model based on NCAP (NEW CAR ASSESSMENT Program, new vehicle evaluation procedure), only the vehicle maintenance cost is evaluated, and the special ethical decision results of automatic driving (such as secondary accidents caused by malicious avoidance) are not covered. About 32% of L4 related incidents, according to ISO 21448 SOTIF case library statistics, raise ethical disputed claims, but lack corresponding damage quantification and payment basis. And the risk fine calculation is disjoint, namely the traditional fine calculation model highly depends on historical human accident data, and cannot capture dynamic risk changes (such as exposure of a new version to more border scenes) caused by continuous iteration of an automatic driving system through OTA. Munich reinsurance research indicates that the resulting premium pricing deviation can exceed 300% severely impacting product feasibility and market fairness. In summary, the existing insurance system cannot support the safety control and commercial landing of L4 Robotaxi in three links of responsibility identification, loss evaluation and risk pricing, so that accurate pricing of insurance cost of L4 grade Robotaxi cannot be realized. Disclosure of Invention The application mainly aims to provide a vehicle commercial premium pricing method, a system, equipment, a storage medium and a product, and aims to solve the technical problem that an existing insurance system cannot realize accurate premium pricing for an L4 automatic driving vehicle. In order to achieve the above object, the present application provides a vehicle commercial premium pricing method, comprising: Matching a base rate corresponding to the autonomous vehicle from an insurance contract library; Acquiring expected functional safety SOTIF data of an automatic driving vehicle; Determining a host factory responsibility coefficient and a residual risk coefficient through a pre-constructed responsibility calculation engine based on SOTIF data; Calculating a total loss metric due to the autonomous vehicle from the SOTIF data; a business insurance of the autonomous vehicle is calculated based on the base tariff, the host factory liability coefficient, the total loss metric, and the residual risk coefficient. In one embodiment, the step of matching a base rate corresponding to the autonomous vehicle from an insurance contract library includes: Determining an initial basic rate in the insurance contract library according to the vehicle value of the automatic driving vehicle, the risk coefficient corresponding to the operation area, the historical reference rate and the expected insurance policy; The vehicle value is determined based on the cost of sensors and automatic driving hardware in vehicle configuration, and the regional risk coefficient is determined according to the traffic complexity of an operation region. In an embodiment, after the step of determining the initial base rate in the insurance contract library according to the vehicle value of the automatic driving vehicle, the risk coefficient corresponding to the operation area, the historical reference rate and the expected insurance policy, the method further includes: acquiring actual odds and thousands of kilometers accident rate and SOTIF scene passing rate in the operation process of a motorcade; determining a odds ratio change coefficient based on the deviation of the actual odds ratio and the target odds ratio, the change trend of the thousand kilometer accident rate and the passing rate of SOTIF scenes; And adjusting the initial basic rate according to the odds ratio change coefficient to obtain an updated basic rate. In one embodiment, the step of determining, based on the SOTIF data, the host plant liability coefficient and the residual risk coefficient by a pre-built