Zero knowledge proof-based traffic accident liability affirmation method and device

Abstract

The invention relates to a traffic accident liability affirmation method and device based on zero knowledge proof, and the method comprises the steps: obtaining the vehicle driving data of an accident vehicle under the condition of a traffic accident; logic verification is conducted on the vehicle driving data through an accident judgment circuit model in the nearest node of the accident vehicle, a circuit compiling result is output, and the accident judgment circuit model is used for mapping a correct decision of an automatic driving system in the face of a traffic accident into a circuit logic constraint; the circuit compilation result is used for indicating whether the automatic driving system of the accident vehicle undertakes the main accident responsibility; processing the vehicle driving data and the circuit logic constraint of the accident judgment circuit model through a zero-knowledge proof generator to generate a proof object; and after the certification object, the vehicle driving data and the circuit compilation result are linked, the linked data are verified by using an intelligent contract generated by the circuit compilation result. According to the invention, the credibility of the accident liability affirmation result is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchains, and in particular to a traffic accident liability identification method and device based on zero-knowledge proof. BACKGROUND

[0002] In a smart traffic system, especially when a traffic accident occurs, accident liability identification is very important, which is related to a series of subsequent insurance claims and other matters.

[0003] At present, in the field of traffic data security and accident liability identification, a common method is to chain the key data of the accident scene, thereby proving the non-tamperability of the data in the storage process, but this method cannot verify the correctness of the complex calculations involved in the accident liability identification process, and it is also difficult to prove whether the decision-making process within the automatic driving system strictly follows the predetermined algorithm, which results in low credibility of accident liability identification. SUMMARY

[0004] The present application provides a traffic accident liability identification method and device based on zero-knowledge proof to solve the problem of low credibility of accident liability identification.

[0005] In a first aspect, the present application provides a traffic accident liability identification method based on zero-knowledge proof, which comprises:

[0006] In the case of a traffic accident, obtaining vehicle driving data of an accident vehicle;

[0007] Performing logical verification on the vehicle driving data by an accident determination circuit model in the nearest node of the accident vehicle, and outputting a circuit compilation result, wherein the accident determination circuit model is used to map the correct decision of an automatic driving system in the face of a traffic accident into a circuit logic constraint, and the circuit compilation result is used to indicate whether the automatic driving system of the accident vehicle is responsible for the main accident;

[0008] Processing the vehicle driving data and the circuit logic constraint of the accident determination circuit model by a zero-knowledge proof generator to generate a proof object, wherein the proof object is used to prove the correctness of the circuit calculation process;

[0009] After chaining the proof object, the vehicle driving data, and the circuit compilation result, verifying the chained data by using a smart contract generated by the circuit compilation result.

[0010] Optionally, performing logical verification on the vehicle driving data by an accident determination circuit model in the nearest node of the accident vehicle, and outputting a circuit compilation result comprises:

[0011] vehicle driving data from a plurality of sources into an accident determination circuit model in a node closest to the accident vehicle, wherein the node is a vehicle terminal or a roadside device, and the vehicle driving data from a plurality of sources is from a vehicle terminal, a roadside device, and a cloud;

[0012] standardizing and fusing the vehicle driving data from a plurality of sources through the accident determination circuit model;

[0013] processing the fused data according to each constraint condition in the accident determination circuit model to obtain a verification result of each constraint condition, wherein the circuit logic constraint includes at least one constraint condition, and each constraint condition is used to determine accident liability from different dimensions;

[0014] comprehensively analyzing at least one verification result based on a preset rule to obtain the circuit compilation result.

[0015] Optionally, the vehicle driving data and the circuit logic constraint of the accident determination circuit model are processed by a zero-knowledge proof generator to generate a proof object, which includes:

[0016] generating a proof key of the zero-knowledge proof generator according to the circuit logic constraint in the accident determination circuit model;

[0017] processing the proof key, the vehicle driving data, and the verification result of each constraint condition by the zero-knowledge proof generator to generate a proof object.

[0018] Optionally, after the proof object, the vehicle driving data, and the circuit compilation result are chained, a smart contract generated by the circuit compilation result is used to verify the chained data, which includes:

[0019] After generating the smart contract according to the circuit compilation result, the verification key corresponding to the circuit logic constraint and the proof key is embedded in the smart contract, and the smart contract is deployed to a blockchain;

[0020] After the proof object, the vehicle driving data, and the circuit compilation result are chained, the verification key in the smart contract is called to verify the chained data.

[0021] Optionally, obtaining the vehicle driving data of the accident vehicle includes: obtaining public data and initial private data of the accident vehicle; encrypting, desensitizing, and setting a storage index for the initial private data to obtain target private data;

[0022] Chaining the vehicle driving data includes: uploading the public data to a blockchain.

[0023] Optionally, after the data uploaded after the verification of the smart contract generated by the circuit compilation result, the method further comprises:

[0024] The verification result generated by the smart contract is uploaded and registered, wherein the verification result is used to indicate the credibility of the proof object;

[0025] According to the proof object, the public data, the circuit compilation result and the verification result uploaded and registered, a complete electronic audit evidence chain is generated.

[0026] Optionally, after the complete electronic audit evidence chain is generated, the method further comprises:

[0027] In the case of detecting that the accident determination circuit model in the node changes, a new proof key and a new verification key are generated by the node;

[0028] According to the new proof key, the vehicle driving data and the verification result of each constraint condition, a new proof object is generated;

[0029] The new proof object, the public data and the circuit compilation result are uploaded;

[0030] According to the new verification key, the data uploaded after the re-uploading is verified, and the verification result is uploaded and registered.

[0031] In a second aspect, the present application provides a traffic accident liability determination device based on zero-knowledge proof, the device comprises:

[0032] The acquisition module is used to acquire the vehicle driving data of the accident vehicle in the case of a traffic accident;

[0033] The logical verification module is used to perform logical verification on the vehicle driving data by the accident determination circuit model in the nearest node of the accident vehicle, and output a circuit compilation result, wherein the accident determination circuit model is used to map the correct decision of the automatic driving system in the face of a traffic accident into a circuit logic constraint, and the circuit compilation result is used to indicate whether the automatic driving system of the accident vehicle assumes the main accident responsibility;

[0034] The generation module is used to process the vehicle driving data and the circuit logic constraint of the accident determination circuit model by a zero-knowledge proof generator, and generate a proof object, wherein the proof object is used to prove the correctness of the circuit calculation process;

[0035] The uploading and verification module is used to upload the proof object, the vehicle driving data and the circuit compilation result, and then verify the data uploaded after the uploading by a smart contract generated by the circuit compilation result.

[0036] In a third aspect, the present application provides an electronic device, comprising: at least one communication interface; at least one bus connected with the at least one communication interface; at least one processor connected with the at least one bus; and at least one memory connected with the at least one bus.

[0037] In a fourth aspect, the present application further provides a computer storage medium storing computer executable instructions for executing the zero-knowledge proof based traffic accident liability determination method according to any one of the above aspects.

[0038] The above technical solution provided by the embodiments of the present application has the following advantages compared with the prior art: the accident determination circuit model converts the abstract automatic driving decision logic into specific circuit logic constraints, and when an accident occurs, the circuit model performs programmatic verification on the vehicle driving data according to these logic constraints, so as to determine whether the accident vehicle bears the main responsibility. In order to further determine whether the circuit model operates and processes strictly according to the circuit logic constraints, a zero-knowledge proof generator is used to perform encryption verification on the circuit calculation process, and the generated proof object can prove to the outside world that the circuit calculation process is performed according to the correct logic constraints and no rule bypassing or error calculation occurs, so as to ensure that the obtained circuit compilation result has credibility. Finally, a smart contract is generated by using the circuit compilation result, and the smart contract further verifies the data on the chain. Only when all the verifications pass, the smart contract confirms that the liability determination result is valid. This automatic verification mechanism, under the support of the block chain non-tamperable characteristics, ensures that the whole process from data processing to on-chain storage is strictly verified, especially the algorithm of the automatic driving system is accurately verified, which improves the credibility of the accident liability determination result. BRIEF DESCRIPTION OF DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application together with the specification.

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced here. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0041] One or more embodiments are illustrated by way of example in the drawings that are for illustrative purposes only, and are not construed to limit the embodiments, and elements having the same reference numerals in the drawings represent similar elements, unless otherwise specified, and the drawings do not constitute a proportional limitation.

[0042] Figure 1 A traffic accident responsibility identification system block diagram based on zero-knowledge proof is provided for the embodiments of the present application;

[0043] Figure 2 A traffic accident responsibility identification method flow chart based on zero-knowledge proof is provided for the embodiments of the present application;

[0044] Figure 3 A structural schematic diagram of a traffic accident responsibility identification device based on zero-knowledge proof is provided for the embodiments of the present application;

[0045] Figure 4 A structural schematic diagram of an electronic device is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0047] The following disclosure provides many different embodiments, or examples, for implementing different structures of the present application. For the purpose of simplicity and clarity, the description in the following text describes the components and settings of specific examples. Of course, they are only examples, and the purpose is not to limit the present application. In addition, reference numerals and / or letters can be repeated in different examples in the present application. Such repetition is for the purpose of simplification and clarity, and does not in itself indicate a relationship between the various embodiments and / or settings being discussed.

[0048] To solve the problem of low credibility in accident responsibility identification mentioned in the background, the embodiments of the present application convert abstract autonomous driving decision logic into specific circuit logic constraints, programmatic verification of vehicle driving data according to these logic constraints, and further verification of circuit compilation results and proof objects to improve the credibility of accident responsibility identification.

[0049] Optionally, in the embodiments of the present application, the traffic accident responsibility identification method based on zero-knowledge proof described above can be applied to the hardware environment composed of a vehicle terminal 101 and a roadside device 103 as shown in Figure 1 As shown in Figure 1 The vehicle terminal 101 and the roadside device 103 can collect and store vehicle driving data in normal circumstances, and after a traffic accident occurs, the vehicle terminal 101 and the roadside device 103 transmit the vehicle driving data of the accident vehicle to the nearest node, and each vehicle terminal and roadside device can serve as a node.

[0050] The traffic accident responsibility identification method based on zero-knowledge proof provided by the embodiment of the application will be described in detail below in combination with the specific implementation process. As shown in the following specific steps: Figure 2

[0051] Step 201: In the case of a traffic accident, obtaining vehicle driving data of the accident vehicle;

[0052] Step 202: performing logical verification on the vehicle driving data by an accident determination circuit model in the nearest node of the accident vehicle, and outputting a circuit compilation result, wherein the accident determination circuit model is used to map the correct decision of the automatic driving system in the face of a traffic accident into circuit logic constraints, and the circuit compilation result is used to indicate whether the automatic driving system of the accident vehicle assumes the main accident responsibility;

[0053] Step 203: processing the vehicle driving data and the circuit logic constraints of the accident determination circuit model by a zero-knowledge proof generator to generate a proof object, wherein the proof object is used to prove the correctness of the circuit calculation process;

[0054] Step 204: after the proof object, the vehicle driving data and the circuit compilation result are chained, verifying the chained data by using the smart contract generated by the circuit compilation result.

[0055] Firstly, some terms mentioned in the embodiment of the application are explained.

[0056] Roadside device: various devices installed on both sides of the road or other specific locations for monitoring and collecting traffic-related information. Common roadside devices include traffic cameras, intelligent roadside units (RSU, Road Side Unit), geomagnetic sensors, laser radars, millimeter wave radars, etc.

[0057] Vehicle driving data: covers various information generated by the accident vehicle during driving, including but not limited to speed, acceleration, steering angle, braking state, sensor data (such as surrounding environment information captured by radar and camera), decision instructions and execution of the automatic driving system, etc.

[0058] Accident determination circuit model: a pre-defined special circuit model that converts the correct decision that the automatic driving system should make when facing a traffic accident into verifiable circuit logic constraints. These constraints are based on traffic regulations, safety standards and design specifications of the automatic driving system.

[0059] Circuit compilation result: the result output after logical verification on the vehicle driving data, used to determine whether the automatic driving system of the accident vehicle assumes the main accident responsibility, which is the key basis for responsibility identification. ​

[0060] Zero-knowledge proof generator: A tool that uses zero-knowledge proof algorithms to encrypt the calculation process of vehicle driving data and circuit logic constraints without revealing the specific content of the data.

[0061] Proof object: An encrypted data structure generated by the zero-knowledge proof generator, used to prove that the circuit calculation process follows the pre-set logic constraints, ensuring the correctness of the calculation results.

[0062] Smart contract: An automatic execution program deployed on the blockchain, generated based on the circuit compilation results, used to verify the proof object, vehicle driving data, and circuit compilation results that have been chained.

[0063] In step 201, during the daily driving of the vehicle, the system collects and stores vehicle driving data in real time through vehicle-mounted sensors (such as speed sensors, steering sensors, radars, cameras, etc.) and roadside devices (such as traffic cameras, intelligent roadside units). These data include vehicle speed, acceleration, steering angle, braking state, environmental perception information, automatic driving system decision instructions, etc., and are backed up to the cloud or local storage devices regularly.

[0064] When a traffic accident occurs, the terminal device (such as the vehicle-mounted terminal, the vehicle-mounted unit of the nearby vehicle) or the roadside device (such as the edge computing node) deployed near the accident scene immediately triggers the data acquisition mechanism. By interacting with the vehicle-mounted storage system or the cloud data center, the complete vehicle driving data of the accident vehicle within a certain period of time before and after the accident is quickly retrieved, including the vehicle driving speed at the moment of the accident, the braking time, the collision angle, the vehicle position information, and the on-site environmental parameters, etc., and then the acquired vehicle driving data is transmitted to the nearest node to the accident vehicle, wherein the terminal device and the roadside node can serve as the node.

[0065] In step 202, each node is built-in with an accident determination circuit model, which performs logical verification on the data and outputs the circuit compilation results. The specific process is as follows: the accident determination circuit model maps the correct decisions of the automatic driving system in the face of traffic accidents into circuit logic constraints. These correct decisions come from traffic regulations, safe driving standards, and various accident scene response strategies, such as the deceleration or avoidance actions of the vehicle when detecting obstacles in front, and the response mode to traffic signals when passing through intersections, etc. The circuit model converts these decisions into "if…then…" form of logical expressions and rules, constructing circuit logic constraints as the benchmark for subsequent data verification.

[0066] When the vehicle driving data is input into the circuit model, the circuit will systematically check the data according to these logical constraints, compare the actual driving state of the vehicle with the requirements of the logical constraints, and verify whether the obstacle distance and speed change in the vehicle driving data meet the condition of the constraint "the vehicle speed needs to be reduced within the specified time when the distance to the obstacle in front is less than the safe distance". After completing the verification of all logical constraints, the circuit will comprehensively process the verification results of each constraint condition according to the preset algorithm and rules, and finally generate the circuit compilation result. This result determines whether the automatic driving system of the accident vehicle is responsible for the main accident in a clear and explicit manner.

[0067] In actual operation, the automatic driving system algorithm may cause verification results to be inaccurate due to program bugs, parameter deviations, or external interference, and its logical rules are relatively flexible, allowing for the possibility of human intervention or adjustment, making it difficult to ensure the absolute objectivity of the verification process. The accident determination circuit model solidifies correct decisions such as traffic regulations and safety standards into explicit circuit logical constraints, and implements logical verification in the form of hardware circuit. Once the circuit design is completed, the verification rules and processes cannot be changed at will, and the determination standard can be strictly and consistently executed to avoid verification deviations caused by unstable algorithms or human factors, ensuring the objectivity of responsibility determination.

[0068] In addition, the automatic driving system algorithm is usually complex, and its internal operation logic and verification process are like a black box, making it difficult to understand and review directly. When there is a dispute over the verification result, it is difficult to trace the root cause of the problem. The logical constraints and verification process of the accident determination circuit model are based on clear circuit design principles and have high transparency. Each data verification step, constraint condition judgment, and final compilation result generation have clear circuit signal transmission and logical operation processes, making it easy to check and trace. Once there is a question about the responsibility determination result, the problem can be quickly located and analyzed.

[0069] In step 203, when the vehicle driving data of the accident vehicle is verified by the logic of the accident determination circuit model, and the output circuit compiles the results, the zero-knowledge proof generator uses cryptographic algorithms to encrypt the entire circuit calculation process based on the vehicle driving data and the circuit logic constraints of the accident determination circuit model. It will verify whether the vehicle driving data strictly follows the circuit logic constraints, that is, whether the decision and operation of the autonomous driving system comply with the pre-set rules. In this process, the zero-knowledge proof generator will generate a series of complex mathematical proofs, which constitute the proof object. The proof object is a digitally encrypted information that can prove to the outside world that the circuit calculation process is carried out according to the correct logic constraints, that is, it proves to the outside world that the circuit model indeed operates according to the pre-set logic constraints when processing data, without rule bypassing or incorrect calculation, ensuring the credibility of the circuit compilation results and providing a solid technical endorsement for the accuracy of responsibility identification. In addition, the zero-knowledge proof generator will not leak sensitive information in the vehicle driving data, such as the personal privacy of the driver and the business secrets of the vehicle.

[0070] In step 204, after successfully generating the proof object, the system uploads the proof object, vehicle driving data and circuit compilation results to the blockchain network for storage. The non-tamperable nature of the blockchain ensures that the data cannot be tampered with once it is uploaded. Once the data is successfully uploaded, the system will automatically generate a smart contract based on the circuit compilation results. The smart contract will automatically call the proof object, vehicle driving data and circuit compilation results from the blockchain and verify these data according to the pre-set verification rules.

[0071] Specifically, it will verify whether the proof object is authentic and valid, that is, whether the circuit calculation process proved by the proof object complies with the circuit logic constraints; at the same time, it will check whether the vehicle driving data and circuit compilation results are complete and accurate, and consistent with the content proved by the proof object. If all the verification conditions are met, the smart contract will output the result of passing the verification; if any rule-inconsistent situation is found, the smart contract will output the result of failing the verification and record detailed error information. In this way, the smart contract can provide objective and accurate verification results for accident responsibility identification, ensuring the fairness and credibility of the entire responsibility identification process.

[0072] Exemplarily, an autonomous vehicle rear-ends a front vehicle on a road with a speed limit of 60 km / h. After the accident, the vehicle-mounted sensors and roadside cameras quickly collect data, including the vehicle's speed before the collision (80 km / h), brake activation time, distance from the front vehicle, and other information, and transmit them to the nearest edge computing node. The accident determination circuit model of the edge computing node starts, verifies through logical constraints such as "overspeed and too close to the front vehicle need to brake immediately", and after verification, the vehicle is overspeed and does not brake in time, violating the constraint conditions, outputs the circuit compilation result, and determines that the autonomous driving system is mainly responsible. The system inputs the vehicle driving data and circuit logical constraints into the zero-knowledge proof generator, generates a proof object to prove the correctness of the calculation process without revealing sensitive information. The system uploads the proof object, original data, and compilation result to the blockchain, and the smart contract generated based on the compilation result automatically verifies and confirms that the responsibility determination result is correct.

[0073] In this application, the accident determination circuit model converts abstract autonomous driving decision logic into specific circuit logic constraints. When an accident occurs, the circuit model verifies the vehicle driving data according to these logical constraints to determine whether the accident vehicle is mainly responsible. To further determine whether the circuit model operates strictly according to the circuit logic constraints, a zero-knowledge proof generator is used to encrypt and verify the circuit calculation process. The generated proof object can prove to the outside world that the circuit calculation process is conducted according to the correct logical constraints without rule bypassing or incorrect calculation, ensuring that the circuit compilation result is reliable. Finally, a smart contract is generated using the circuit compilation result, which further verifies the data uploaded to the chain. Only when all verifications pass, the smart contract confirms that the responsibility determination result is valid. This automated verification mechanism, combined with the non-tamperable characteristics of the blockchain, ensures that the entire process from data processing to chain storage is strictly verified, especially the accurate verification of the autonomous driving system's algorithm, which improves the credibility of the accident responsibility determination result.

[0074] As an optional implementation, in step 202, the vehicle driving data is logically verified by the accident determination circuit model in the nearest node of the accident vehicle, and the circuit compilation result includes the following contents:

[0075] Step S11: input the multi-source vehicle driving data into the accident determination circuit model in the node closest to the accident vehicle, wherein the multi-source vehicle driving data comes from the vehicle terminal, roadside equipment, and cloud, and the node is the vehicle terminal or roadside equipment;

[0076] Step S12: standardize and fuse the multi-source vehicle driving data through the accident determination circuit model;

[0077] Step S13: Process the corresponding fusion data according to each constraint condition in the accident determination circuit model, and obtain the verification result of each constraint condition, wherein the circuit logic constraint includes at least one constraint condition, and each constraint condition is used to determine the accident responsibility from different dimensions;

[0078] Step S14: Comprehensive analysis of at least one verification result based on pre-set rules to obtain circuit compilation results.

[0079] In step S11, the vehicle terminal collects real-time driving state information of the vehicle through various sensors and data recording devices installed, such as vehicle speed, acceleration, steering angle, and braking state, etc. Roadside equipment is a facility deployed around the road, such as traffic cameras, intelligent roadside units, etc., which can obtain the vehicle's driving trajectory, relative position with other vehicles, road environment information, etc. Cloud data is the data uploaded to the cloud server by the vehicle during daily driving, which may include the vehicle's historical driving record, annual inspection record, etc.

[0080] After the system aggregates these multi-source data, it will be transmitted to the node closest to the accident vehicle. This node can be the vehicle terminal itself or the nearby roadside equipment, depending on the actual network connection and device performance, and a pre-deployed accident determination circuit model in this node will serve as the core tool for subsequent data processing and responsibility determination.

[0081] In step S12, after the accident determination circuit model receives multi-source vehicle driving data, it first needs to standardize these data. Since different data sources may use different data formats, sampling frequencies and measurement units, which will bring difficulties to subsequent analysis and processing. Therefore, the circuit model will convert these data into the same format and standard, such as converting speed data collected by different sensors into a unified unit (such as kilometers per hour), and converting timestamps into the same precision, etc.

[0082] After completing the standardization process, the circuit model will perform data fusion. Data fusion is the integration of related data from different data sources to obtain more comprehensive and accurate information. For example, combining the vehicle speed data collected by the vehicle terminal with the vehicle trajectory data recorded by the roadside equipment can more accurately understand the actual driving situation of the vehicle at the time of the accident. Through data fusion, redundancies and contradictions in the data can be eliminated, improving the quality and usability of the data.

[0083] In step S13, the circuit logic constraints in the accident determination circuit model contain a series of constraint conditions, which are formulated according to traffic laws, safety standards, and design specifications of the autonomous driving system. Each constraint condition determines the accident liability from different dimensions, so different types of fusion data need to be called for targeted processing. For example, the "overspeed determination constraint" needs to extract the real-time vehicle speed recorded by the vehicle speed sensor and the road section speed limit identification data labeled by the roadside device from the fusion data; the "safe distance constraint" needs to call the front vehicle distance perceived by the vehicle millimeter wave radar and the brake response time recorded by the vehicle acceleration sensor.

[0084] In specific implementation, the circuit model accurately filters and extracts the corresponding data subset from the fusion data according to the data requirements of each constraint condition, then substitutes the extracted data into the logical expression of the corresponding constraint condition for calculation and judgment, and finally outputs the explicit verification result of each constraint condition "pass" or "not pass".

[0085] For example, for the "intersection yielding rule" constraint, the circuit model preferentially selects data such as traffic signal state collected by roadside devices and entering intersection timestamp recorded by vehicle terminal, then compares vehicle speed and speed limit value, calculates whether the actual distance is less than the safety threshold, etc., to obtain the explicit verification result of "pass" or "not pass".

[0086] In step S14, after obtaining the verification result of each constraint condition, the circuit model will comprehensively analyze these verification results based on the preset rules. The preset rules are formulated according to the principles and methods of accident liability determination, which considers the importance and mutual relationship of each constraint condition. For example, some constraint conditions may have a decisive role in determining the accident liability, while other constraint conditions may only be auxiliary references. The circuit model will perform weighted calculation and logical judgment on the verification results according to these rules, and finally obtain a comprehensive circuit compilation result. The circuit compilation result indicates whether the autonomous driving system of the accident vehicle bears the main accident liability in an explicit manner.

[0087] For example, if there is only one constraint condition, such as proving whether the autonomous driving system correctly activated the predetermined emergency response in an emergency situation, the circuit will design a function f(S, D) as follows.

[0088] f(S, D) = a max(0, d safe -d fused ) + b (1-D)

[0089] Where d safe is the safety distance; d fused is the obstacle distance after data fusion; D is the decision output (1 indicates that the emergency response has been activated, and 0 indicates that the emergency response has not been activated); a and b are weighting coefficients.

[0090] When d fused If the distance between the vehicle and the obstacle is less than the safety distance d safe and the emergency response is not activated, the function value will increase, if the value reaches the preset threshold T, it proves that the system should trigger the emergency response, but it is not actually triggered, that is, it is considered as a decision failure, and finally a judgment result f(S, D) is output. If f(S, D) is equal to 1, it means that the automatic driving system fails to give the correct response according to the predetermined algorithm when the accident occurs, and the accident responsibility lies in the automatic driving system. If f(S, D) is equal to 0, it means that the decision-making process of the automatic driving system is correct, and the accident responsibility does not lie in the automatic driving system.

[0091] In this application, by collecting multi-source vehicle driving data including vehicle terminal, roadside equipment and cloud data, the cross-domain trusted computing is realized, and at the same time, the driving state and surrounding environment information of the vehicle at the time of the accident can be comprehensively and in detail. In the accident determination circuit model, the standardization processing and data fusion improve the quality and availability of data, reduce the error and inconsistency of data, and provide a solid data foundation for accurate determination of accident liability. Each constraint condition determines the accident liability from different dimensions, and through the verification and comprehensive analysis of these constraint conditions, the accident liability can be determined comprehensively and objectively. In addition, the verification results of each constraint condition can be recorded and traced, which makes the process of accident liability identification have high transparency, and enhances the public's trust in the results of accident liability identification.

[0092] As an optional implementation, in step 203, the vehicle driving data and the circuit logic constraints of the accident determination circuit model are processed by the zero-knowledge proof generator to generate a proof object including the following contents:

[0093] Step S21: generating a proof key of the zero-knowledge proof generator according to the circuit logic constraints in the accident determination circuit model;

[0094] Step S22: processing the proof key, the vehicle driving data and the verification result of each constraint condition by the zero-knowledge proof generator to generate a proof object.

[0095] In step S21, after the accident determination circuit model output circuit compiles the results, the system deeply analyzes a series of constraint conditions contained in the accident determination circuit model, and then processes these constraint conditions mathematically and algorithmically to convert them into a form that can be understood and processed by the zero-knowledge proof algorithm. Subsequently, based on the specific rules and mechanisms of the zero-knowledge proof algorithm, the system generates a proof key using the converted constraint conditions. The proof key is a series of complex encrypted and encoded digital information that contains key parameters and algorithm instructions related to circuit logic constraints. The proof key is not only closely related to the logic of the accident determination circuit model, but also has unique uniqueness and security. Only through the proof key can subsequent data be effectively processed for zero-knowledge proof, ensuring the accuracy and reliability of the entire verification process.

[0096] In step S22, after obtaining the proof key, the system inputs the proof key, vehicle driving data, and verification results of each constraint condition into the zero-knowledge proof generator. The zero-knowledge proof generator, based on the received proof key, calls the pre-set zero-knowledge proof algorithm to encrypt the vehicle driving data and verification results. In this process, the zero-knowledge proof generator does not disclose sensitive information in the vehicle driving data, such as the driver's identity and vehicle internal technical parameters, but through complex cryptographic operations, it verifies whether the vehicle driving data complies with the circuit logic constraints of the accident determination circuit model and whether the verification results of each constraint condition are true and valid under the premise of protecting data privacy.

[0097] Specifically, the zero-knowledge proof generator will verify and encrypt each key information item in the vehicle driving data one by one, combining the proof key and circuit logic constraints. At the same time, it will also check the verification results of each constraint condition to ensure that these results are based on correct logic and data. After a series of complex calculations and verification processes, the zero-knowledge proof generator finally outputs a proof object. The proof object is a digital collection containing encrypted verification information and related proof parameters, which can prove to the outside world that the entire circuit calculation process is strictly in accordance with the logic constraints of the accident determination circuit model, and that the verification results of each constraint condition are accurate. When viewing the proof object, the outside world cannot obtain the specific content of the vehicle driving data, but can confirm the authenticity and reliability of the content proved by the proof object through specific verification mechanisms, thereby achieving the core goal of zero-knowledge proof, i.e., proving the correctness of the calculation process and results without revealing sensitive information.

[0098] In addition, a verification key is generated throughout the process, which is correlated with the proof key but functions differently. The proof key is mainly used for the zero-knowledge proof generator to encrypt and generate proofs for the data, while the verification key is a key tool for verifying the validity of the proof object in the subsequent steps. The verification key also undergoes strict encryption and generation process, and it contains specific verification algorithms and parameters. Only by using the correct verification key can the proof object be decrypted and verified, ensuring that the content proved by the proof object is authentic and reliable. In the subsequent steps, when the responsibility identification result needs to be verified, the verification key will be used to verify the proof object to ensure the effectiveness of the proof object and the reliability of the entire responsibility identification process.

[0099] In this application, the zero-knowledge proof generator generates a proof key based on the logical constraints of the accident judgment circuit model and processes the data to generate a proof object, which can prove to the outside world that the circuit calculation process is strictly in accordance with the pre-set logical constraints, so that relevant parties can be confident that the basis for responsibility identification is authentic and reliable, and the public credibility of the entire responsibility identification process is enhanced.

[0100] In addition, in the verification process of the traditional responsibility identification method, the specific content of the vehicle driving data may need to be disclosed, which can easily lead to the leakage of sensitive information such as driver privacy and vehicle business secrets. By processing data through the zero-knowledge proof generator, the specific content of the vehicle driving data does not need to be disclosed in the process of generating the proof object, and the correctness of the circuit calculation process can be proved. For example, when sensitive information such as personal travel trajectory and internal data collected by vehicle sensors is involved, the zero-knowledge proof technology can effectively prevent third parties from obtaining such information, ensuring data security and user privacy.

[0101] As an optional implementation, in step 204, after the proof object, vehicle driving data, and circuit compilation result are chained, the smart contract generated by the circuit compilation result is used to verify the chained data, including the following contents:

[0102] Step S31: After generating the smart contract according to the circuit compilation result, the circuit logical constraints and the verification key corresponding to the proof key are embedded in the smart contract, and the smart contract is deployed to the blockchain;

[0103] Step S32: After the proof object, vehicle driving data, and circuit compilation result are chained, the verification key in the smart contract is called to verify the chained data.

[0104] In step S31, when the accident determination circuit model completes the logical verification of the vehicle driving data and outputs the circuit compilation result, the system generates a smart contract based on the compilation result. To ensure that the smart contract can accurately verify the data, key information needs to be embedded in it. First, the circuit logic constraints of the accident determination circuit model, which are the core basis for liability determination, are embedded in the smart contract, so that the contract can refer to these standards during the verification process to determine whether the data meets the requirements. At the same time, the verification key corresponding to the proof key in the zero-knowledge proof generator is also embedded in the smart contract. The verification key is a key tool for verifying the validity of the proof object. Only by using the correct verification key can the proof object be decrypted and verified to ensure that the content proved by the proof object is true and reliable. Embedding the verification key in the smart contract provides the necessary conditions for subsequent verification of the proof object.

[0105] After the above embedding operation is completed, the smart contract will be deployed to the blockchain. The blockchain is a distributed ledger system with characteristics such as decentralization and tamper resistance. After the smart contract is deployed to the blockchain, its code and execution process will be recorded and supervised by multiple nodes, ensuring the fairness and security of the contract and preventing it from being tampered with or manipulated maliciously.

[0106] In step S32, after the smart contract is successfully deployed to the blockchain, the proof object, vehicle driving data, and circuit compilation result will be uploaded to the blockchain for storage. The distributed storage mechanism of the blockchain ensures that these data are stored on multiple nodes and are difficult to tamper with once they are chained, ensuring the integrity and authenticity of the data.

[0107] After the data is chained, the system automatically calls the smart contract that has been deployed in the blockchain. After the smart contract is called, it will first use the verification key embedded in it to verify the proof object. During the verification process, the smart contract will check whether the proof object is valid, i.e., whether it can prove that the circuit calculation process strictly follows the pre-set circuit logic constraints and that the verification results of each constraint condition are accurate. After verifying the proof object, the smart contract will also conduct a comprehensive verification of the vehicle driving data and the circuit compilation result. It will compare the vehicle driving data with the circuit logic constraints again to check whether the data has any contradictions or abnormalities; at the same time, it will confirm whether the circuit compilation result matches the actual verification process and data. Through comprehensive verification of these data, the smart contract can finally determine the reliability and accuracy of the liability determination result.

[0108] After the smart contract completes the verification of the proof object, vehicle driving data, and circuit compilation result, a clear verification result, such as "pass" or "fail", is generated. The system will automatically start the on-chain registration process, and use the distributed ledger technology of the blockchain to synchronize the verification result to each node in the blockchain network. After the on-chain registration of the verification result is completed, the system generates a complete electronic audit evidence chain based on the proof object, vehicle driving data, circuit compilation result, and newly registered verification result stored in the blockchain.

[0109] The specific process of generating the electronic audit evidence chain is as follows: the system sorts and integrates the data after on-chain, and according to the time sequence and logical association, the proof information about the correctness of the circuit calculation process in the proof object, the environmental background at the time of the accident in the public data, the responsibility determination conclusion obtained by the circuit compilation result, and the verification of the credibility of the proof object by the verification result are sequentially connected. For example, starting from the accident time, the environmental information of the accident scene recorded in the public data is arranged in sequence, followed by the circuit compilation result output by the vehicle driving data processed by the accident determination circuit model, combined with the evidence of the proof object on the calculation process, and finally the verification result generated by the smart contract.

[0110] In this application, the system embeds the circuit logic constraints and verification keys into the smart contract, so that the smart contract has a clear standard and tool in the verification process. The circuit logic constraints ensure that the smart contract verifies according to the correct responsibility determination rules, and the verification keys ensure the effectiveness of the proof object. The combination of the two makes the verification process rigorous and accurate, effectively avoids the situation of false verification or false verification, and improves the credibility of the responsibility determination result. The code and execution process of the smart contract are open and transparent on the blockchain, which makes the responsibility determination process highly transparent. At the same time, the traceability feature of the blockchain makes all data operations and contract execution records traceable, so that the cause can be quickly and accurately found in case of disputes over the responsibility determination result, further ensuring the fairness of the responsibility determination. Finally, using a lightweight zero-knowledge proof algorithm (such as Groth16 or PLONK), the proof can be quickly generated after completing the complex calculation off-chain, and the smart contract can be verified in milliseconds on-chain, which is also highly time-efficient.

[0111] As an optional implementation, obtaining the vehicle driving data of the accident vehicle includes: obtaining public data and initial private data of the accident vehicle; encrypting, desensitizing, and setting a storage index for the initial private data to obtain target private data; and uploading the public data to the blockchain.

[0112] In the embodiments of the present application, when a traffic accident occurs, the system will immediately start the data collection program and comprehensively obtain the vehicle driving data of the accident vehicle, which is specifically divided into public data and initial private data.

[0113] Public data: The public data mainly comes from various types of roadside equipment deployed around the road and part of the public traffic information system. For example, the traffic camera on the road can record the driving track of the vehicle before and after the accident, the surrounding environment condition; the intelligent roadside unit can collect the speed, driving direction and other information when the vehicle passes; the traffic signal system of the traffic management department can provide the traffic signal state data at the time of the accident. These public data do not involve sensitive private information and can intuitively present the macro situation of the accident scene, providing basic background materials for responsibility identification.

[0114] Private data: The initial private data mainly comes from the vehicle-mounted sensors and data recording system of the accident vehicle, which contains a large amount of sensitive information related to the vehicle and the driver. For example, the driver's biological feature data recorded by the vehicle internal sensor (fingerprint, facial recognition information), vehicle control parameters (engine working state, automatic driving system internal algorithm parameters), detailed operation log during vehicle driving, etc. These data are crucial for restoring the internal running state of the vehicle at the time of the accident, but also involve privacy and business secrets, which need special treatment.

[0115] After obtaining the private data, the system will perform a series of processing on it: encryption processing, desensitization processing and setting storage index. The storage index enables quick positioning and extraction of related information when the data is needed, improving the data processing efficiency. After the above processing, the initial private data is converted into target private data, which together with the public data constitutes the complete vehicle driving data.

[0116] In the subsequent data chaining process, the public data does not involve sensitive information and needs to be widely shared for responsibility identification, so the system will directly upload it to the blockchain network, while the target private data is temporarily not stored on the chain due to encryption and desensitization processing, but is stored in a secure local server, encrypted database or cloud, only the hash value and other key identification information of the data are stored on the chain, which maximizes the protection of sensitive information security.

[0117] At this time, the circuit module binds the data storage location (such as a cloud storage path, a key-value index inside a database) with the circuit calculation process through an abstract data interface. The circuit does not directly obtain the specific content of the target private data, but rather calls the key feature value or the calculated summary information of the data as needed to participate in logical operation according to the bound data structure path. This design ensures that the private data provided by different subjects remains in an encrypted storage state when participating in circuit calculation for accident responsibility identification, and only outputs necessary verification information to the circuit in a safe and controllable manner.

[0118] In the present application, the initial private data is encrypted and desensitized, effectively preventing the leakage of driver privacy information and the theft of vehicle commercial secrets. The differentiated on-chain strategy, i.e., only uploading public data and keeping private data locally stored, further reduces the risk of sensitive information being attacked and leaked in the blockchain network, and complies with data security and privacy protection regulations. The present application protects private data and improves data security from data collection to data on-chain process.

[0119] As an optional implementation, after generating the complete electronic audit evidence chain, the method further includes the following contents: generating a new proof key and a new verification key by the node in the case of detecting a change in the accident determination circuit model in the node; generating a new proof object according to the new proof key, the vehicle driving data, and the verification result of each constraint condition; uploading the new proof object, the public data, and the circuit compilation result to the chain; verifying the data after re-uploading according to the new verification key, and uploading and registering the verification result.

[0120] Firstly, when the system detects a change in the accident determination circuit model in the node (such as adjustment of logical constraints due to update of traffic regulations or upgrade of autonomous driving technology), a new round of key generation and data verification process will be triggered. First, the node generates a new proof key based on the circuit logical constraints in the updated circuit model through a zero-knowledge proof algorithm. The new proof key is closely related to the updated logical constraints and contains the adjusted verification rules and parameter information, which is the core basis for generating a new proof object.

[0121] Next, the system uses the newly generated proof key, combines the existing vehicle driving data and the verification result of each constraint condition, and re-encrypts and logically verifies through a zero-knowledge proof generator to generate a new proof object. In this process, the new proof object will verify the compliance of the vehicle driving data again based on the updated logical constraints, ensuring that the data processing process meets the latest requirements of the circuit model, while continuing to protect data privacy and not leaking sensitive information.

[0122] Subsequently, the system uploads the newly generated proof object, public data, and circuit compilation result to the blockchain. The tamper-proof nature of the blockchain ensures the integrity and security of these data during transmission and storage, while facilitating subsequent tracing and verification.

[0123] Finally, the system calls the new verification key corresponding to the new proof key, and conducts a comprehensive verification of the re-chained data. During the verification process, the smart contract will use the new verification key to check the validity of the new proof object, ensuring that it accurately proves the correctness of the calculation process based on the updated circuit model. At the same time, consistency checks are performed on the public data and circuit compilation results to confirm that the logical relationships between the data meet expectations. After verification is complete, the verification results are chained and registered to form a new, complete verification record, keeping the entire responsibility determination process synchronized with the updated circuit model.

[0124] In this application, as traffic regulations are updated and autonomous driving technology develops, the accident determination circuit model needs to be adjusted in a timely manner, and new proof keys and verification keys are generated based on the updated circuit logic constraints, which enables data verification to adapt to changes in the circuit model, accurately reflects the latest responsibility determination logic, and avoids incorrect judgments due to outdated verification mechanisms after model updates, improving the accuracy and reliability of responsibility determination results. Even in the case of changes in the circuit model, the application of zero-knowledge proof technology still protects the privacy of vehicle driving data. The new proof object generation process does not disclose sensitive information, and the re-chained data and verification process are protected by the blockchain security mechanism, preventing data tampering and leakage, and continuously ensuring data privacy and security. Each re-verification and re-chaining operation after the circuit model changes will form new records on the blockchain, which are related to the original electronic audit evidence chain, forming a more complete and coherent evidence chain. When there is a dispute over responsibility determination, the impact of changes in the circuit model on responsibility determination can be clearly traced, further improving the traceability and credibility of the evidence chain, and enhancing the public credibility of the responsibility determination results.

[0125] The present application provides a zero-knowledge proof-based traffic accident responsibility determination method, including the following steps.

[0126] 1. Data collection and processing: Collect accident vehicle data from multiple subjects such as vehicle sensors, roadside devices, and regulatory platforms, and divide them into public data and initial private data. Encrypt, desensitize, and set storage index processing for the initial private data to convert it into target private data, and keep the public data in its original state. Circuit design reserves abstract data interfaces, binds off-chain data structure paths (such as cloud paths, database internal key-value indexes), and avoids direct upload of original private data.

[0127] 2. Circuit logic verification: The processed vehicle driving data (index information of public data and target private data, etc.) is input into the accident determination circuit model in the node closest to the accident vehicle (vehicle terminal or roadside device). The model first standardizes and fuses the multi-source data, and then according to each condition in the circuit logic constraint, it acquires and processes the corresponding fused data to obtain the verification result of each constraint condition. Finally, based on the preset rule, the verification result is comprehensively analyzed, and the circuit compilation result is output, which clearly shows whether the automatic driving system of the accident vehicle is responsible.

[0128] 3. Zero-knowledge proof generation: According to the circuit logic constraint in the accident determination circuit model, the proof key of the zero-knowledge proof generator is generated; through the zero-knowledge proof generator, the proof key, vehicle driving data and verification result of each constraint condition are processed, and under the premise of not leaking data privacy, the proof object is generated to prove the correctness of the circuit calculation process.

[0129] 4. Smart contract verification and data on-chain: According to the circuit compilation result, a smart contract is generated, the verification key corresponding to the circuit logic constraint and the proof key is embedded in the smart contract, and is deployed to the blockchain. The proof object, vehicle driving data and circuit compilation result are chained, and the verification key in the smart contract is called to verify the chained data, ensuring the credibility of the proof object, the accuracy and consistency of the data.

[0130] 5. Evidence chain generation and maintenance: The verification result generated by the smart contract is chained and registered, and the complete electronic audit evidence chain is generated by combining the chained proof object, public data, circuit compilation result and verification result.

[0131] 6. Circuit model update: When it is detected that the accident determination circuit model in the node changes, a new proof key and a new verification key are generated through the node; according to the new proof key, vehicle driving data and verification result of each constraint condition, a new proof object is generated; the new proof object, public data and circuit compilation result are chained; then the new verification key is used to verify the re-chained data, and the verification result is chained and registered, ensuring that the evidence chain always keeps consistent with the latest circuit model.

[0132] The present application proposes a solution based on "off-chain calculation-on-chain verification" for smart traffic, which combines circuit design in smart traffic scenarios, zero-knowledge proof technology and on-chain smart contract, and can credibly prove the correctness of multi-source data fusion and decision calculation at the accident scene without leaking detailed original data, thereby providing complete, transparent and tamper-proof electronic evidence for accident liability identification.

[0133] Based on the same technical concept, the application provides a traffic accident responsibility identification device based on zero-knowledge proof, as shown in the figure, the device comprises: Figure 3

[0134] The acquisition module 301 is configured to acquire vehicle driving data of an accident vehicle in the case of a traffic accident;

[0135] The logic verification module 302 is configured to perform logic verification on the vehicle driving data by an accident determination circuit model in the nearest node of the accident vehicle, and output a circuit compilation result, wherein the accident determination circuit model is used to map the correct decision of the autonomous driving system in the face of a traffic accident into a circuit logic constraint, and the circuit compilation result is used to indicate whether the autonomous driving system of the accident vehicle assumes the main accident responsibility;

[0136] The generation module 303 is configured to process the vehicle driving data and the circuit logic constraint of the accident determination circuit model by a zero-knowledge proof generator, and generate a proof object, wherein the proof object is used to prove the correctness of the circuit calculation process;

[0137] The on-chain and verification module 304 is configured to, after chaining the proof object, the vehicle driving data and the circuit compilation result, verify the chained data by using a smart contract generated by the circuit compilation result.

[0138] Optionally, the logic verification module 302 is configured to:

[0139] The multi-source vehicle driving data is transmitted into the accident determination circuit model in the nearest node of the accident vehicle, wherein the multi-source vehicle driving data comes from a vehicle terminal, a roadside device and a cloud, and the node is the vehicle terminal or the roadside device;

[0140] The multi-source vehicle driving data is standardized and fused by the accident determination circuit model;

[0141] Each constraint condition in the accident determination circuit model is used to process the corresponding fused data, and the verification result of each constraint condition is obtained, wherein the circuit logic constraint comprises at least one constraint condition, and each constraint condition is used to determine the accident responsibility from different dimensions;

[0142] At least one verification result is comprehensively analyzed based on a preset rule, and the circuit compilation result is obtained.

[0143] Optionally, the generation module 303 is configured to:

[0144] The proof key of the zero-knowledge proof generator is generated according to the circuit logic constraint in the accident determination circuit model;

[0145] ​The proof object is generated by processing the proof key, the vehicle driving data and the verification result of each constraint condition through the zero-knowledge proof generator.

[0146] Optionally, the upper chain and verification module 304 is configured to:

[0147] After the smart contract is generated according to the circuit compilation result, the circuit logic constraint and the verification key corresponding to the proof key are embedded into the smart contract, and the smart contract is deployed into the blockchain.

[0148] After the proof object, the vehicle driving data and the circuit compilation result are uploaded, the verification key in the smart contract is called to verify the uploaded data.

[0149] Optionally, the device is further configured to: obtain public data and initial private data of the accident vehicle; encrypt, desensitize and set a storage index for the initial private data to obtain target private data.

[0150] The public data is uploaded to the blockchain.

[0151] Optionally, the device is further configured to:

[0152] The verification result generated by the smart contract is uploaded and registered, wherein the verification result is used to indicate the credibility of the proof object.

[0153] According to the uploaded and registered proof object, the public data, the circuit compilation result and the verification result, a complete electronic audit evidence chain is generated.

[0154] Optionally, the device is further configured to:

[0155] In a case where it is detected that the accident determination circuit model in the node changes, a new proof key and a new verification key are generated by the node;

[0156] According to the new proof key, the vehicle driving data and the verification result of each constraint condition, a new proof object is generated;

[0157] The new proof object, the public data and the circuit compilation result are uploaded;

[0158] According to the new verification key, the re-uploaded data is verified, and the verification result is uploaded and registered.

[0159] As shown in Figure 4 The electronic device provided by the embodiment of the present application includes a processor 401, a communication interface 402, a memory 403 and a communication bus 404, wherein the processor 401, the communication interface 402 and the memory 403 complete mutual communication through the communication bus 404.

[0160] The memory 403 is used to store a computer program.

[0161] In one embodiment of this application, the processor 401, when executing the program stored in the memory 403, implements the traffic accident liability determination method based on zero-knowledge proof provided in any of the aforementioned method embodiments.

[0162] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the traffic accident liability determination method based on zero-knowledge proof as provided in any of the foregoing method embodiments.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0165] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0166] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for determining liability in traffic accidents based on zero-knowledge proofs, characterized in that, The method includes: In the event of a traffic accident, obtain the vehicle driving data of the vehicle involved in the accident; The vehicle driving data is logically verified by the accident determination circuit model in the nearest node of the accident vehicle, and the circuit compilation result is output. The accident determination circuit model is used to map the correct decision of the autonomous driving system in the face of traffic accidents into circuit logic constraints, and the circuit compilation result is used to indicate whether the autonomous driving system of the accident vehicle bears the main responsibility for the accident. The zero-knowledge proof generator processes the vehicle driving data and the circuit logic constraints of the accident determination circuit model to generate a proof object, wherein the proof object is used to prove the correctness of the circuit calculation process. After the proof object, the vehicle driving data, and the circuit compilation result are uploaded to the blockchain, the smart contract generated by the circuit compilation result is used to verify the data uploaded to the blockchain.

2. The method according to claim 1, characterized in that, The vehicle driving data is logically verified using the accident determination circuit model in the nearest node of the accident vehicle, and the output circuit compilation result includes: Multi-source vehicle driving data is transmitted to the accident determination circuit model in the node closest to the accident vehicle. The multi-source vehicle driving data comes from the vehicle terminal, roadside equipment and the cloud. The node is the vehicle terminal or the roadside equipment. The accident determination circuit model is used to standardize and fuse the multi-source vehicle driving data. The corresponding fused data is processed according to each constraint in the accident determination circuit model to obtain the verification result of each constraint. The circuit logic constraint includes at least one constraint, and each constraint is used to determine accident responsibility from different dimensions. The circuit compilation result is obtained by comprehensively analyzing at least one of the verification results based on preset rules.

3. The method according to claim 2, characterized in that, The zero-knowledge proof generator processes the vehicle driving data and the circuit logic constraints of the accident determination circuit model to generate proof objects including: The proof key for the zero-knowledge proof generator is generated based on the circuit logic constraints in the accident determination circuit model. A zero-knowledge proof generator is used to process the proof key, the vehicle driving data, and the verification results of each constraint to generate a proof object.

4. The method according to claim 3, characterized in that, After the proof object, the vehicle driving data, and the circuit compilation result are uploaded to the blockchain, the smart contract generated by the circuit compilation result verifies the uploaded data, including: After generating a smart contract based on the circuit compilation results, the circuit logic constraints and the verification key corresponding to the proof key are embedded in the smart contract, and the smart contract is deployed to the blockchain; After the proof object, the vehicle driving data, and the circuit compilation result are uploaded to the blockchain, the verification key in the smart contract is called to verify the uploaded data.

5. The method according to claim 1, characterized in that, Obtaining vehicle driving data of the accident vehicle includes: obtaining public data and initial private data of the accident vehicle; encrypting, de-identifying, and setting storage indexes for the initial private data to obtain target private data; Uploading the vehicle driving data to the blockchain includes uploading the public data to the blockchain.

6. The method according to claim 5, characterized in that, After the smart contract generated using the circuit compilation result verifies the on-chain data, the method further includes: The verification result generated by the smart contract is registered on the blockchain, wherein the verification result is used to indicate the credibility of the proof object; Based on the proof object, the public data, the circuit compilation result, and the verification result registered on the blockchain, a complete electronic audit evidence chain is generated.

7. The method according to claim 3, characterized in that, After generating a complete electronic audit evidence chain, the method further includes: If a change is detected in the accident determination circuit model in a node, a new proof key and a new verification key are generated through the node. A new proof object is generated based on the new proof key, the vehicle driving data, and the verification results of each constraint. The new proof object, public data, and circuit compilation results are uploaded to the blockchain. The data re-uploaded to the blockchain is verified using the new verification key, and the verification result is then registered on the blockchain.

8. A traffic accident liability determination device based on zero-knowledge proof, characterized in that, The device includes: The acquisition module is used to acquire vehicle driving data of the vehicles involved in a traffic accident. The logic verification module is used to perform logical verification on the vehicle driving data through the accident determination circuit model in the nearest node of the accident vehicle, and output the circuit compilation result. The accident determination circuit model is used to map the correct decision of the autonomous driving system in the face of traffic accidents into circuit logic constraints, and the circuit compilation result is used to indicate whether the autonomous driving system of the accident vehicle bears the main responsibility for the accident. The generation module is used to process the vehicle driving data and the circuit logic constraints of the accident determination circuit model through a zero-knowledge proof generator to generate a proof object, wherein the proof object is used to prove the correctness of the circuit calculation process. The on-chain and verification module is used to verify the on-chain data by using a smart contract generated from the circuit compilation results after the proof object, the vehicle driving data, and the circuit compilation results are uploaded to the blockchain.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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