1747 results about "Test scene" patented technology

The invention relates to the field of chip verification, and provides a chip verification method, device and equipment, a medium and a chip. The method comprises the following steps: establishing a dynamic simulation verification environment of a to-be-verified design, and generating a plurality of test scenes and corresponding test excitation according to a design specification definition; sending the test excitation to a driver through a sequencer, converting the test excitation into a signal conforming to a to-be-verified design interface protocol by the driver, and transmitting the signal to the to-be-verified design; the monitor collects an input signal and an output signal of a to-be-verified design in real time, converts the input signal and the output signal into transactions, and transmits the transactions to the scoreboard; comparing the reference model with an output signal of the to-be-verified design to generate a dynamic simulation verification result; according to the dynamic simulation result, the state space is reduced, state space traversal is carried out on a design part which is not covered by dynamic simulation in the design to be verified, and a formal verification analysis result is generated; and evaluating the to-be-verified design according to the formal verification analysis result.

The invention relates to an automatic derivation method, system and device for an automatic driving test scene and a medium, and the method comprises the steps: analyzing a test demand of an automatic driving system, and mapping the test demand to a multi-dimensional scene element hierarchical architecture, so as to construct a multi-layer road scene; reasoning a scene condition dependency relationship by using a dual conditional probability, and generating an initial scene fragment set in combination with probability distribution sampling; establishing a multi-objective optimization function based on the scene type distribution weight, the element redundancy penalty factor, the physical consistency constraint and the security rule, screening and recombining the multiple initial scene fragment sets, and outputting an optimal scene set; extracting a causal logical relationship of the optimal scene set according to the directed acyclic graph, and generating a scene evolution path in combination with topological sorting; and executing multiple safety verification on the scene evolution path, and determining the optimal scene passing the verification as the automatic driving test scene. The technical problems that an existing test scene is low in generation efficiency, poor in authenticity and unreasonable in event chain logic are solved.

The invention relates to a server testing method, electronic equipment, a storage medium and a program product, which are applied to the technical field of computer testing, and the method comprises the following steps: in the process of executing a pressure test on a tested server in a composite testing scene, based on the standard exceeding condition of the performance index of the tested server, according to a degradation strategy matched with the standard exceeding condition, determining the performance index of the tested server according to the degradation strategy matched with the standard exceeding condition; the access traffic injected into the tested server is adjusted, and the degradation strategy is used for performing overload protection on the tested server; converting the acquired running state data of the tested server into multi-dimensional feature data according to a plurality of performance dimensions; determining weights of different performance dimensions based on a test mode of a pressure test; and determining a test result of the tested server according to the weight and the multi-dimensional feature data.

The invention discloses an automatic driving test scene set optimization method, apparatus and device, and a storage medium. The method comprises the steps of generating a simulation scene file in an OpenSCENARIO format through preprocessing data; analyzing the simulation scene file through a teacher model, outputting a risk description text, receiving a scene feature vector and the risk description text through a student model, and outputting a failure probability; determining a comprehensive value index according to the failure probability, dynamically updating a scene library of automatic driving test scenes, collecting failure data in an AUT test, performing incremental fine tuning on the student model, and obtaining an optimized target test scene set; the problem of gradient estimation variance explosion caused by sparseness disasters can be effectively solved, the stability of model training is improved, the recognition capability of a rare long-tail failure scene is enhanced, the accuracy of failure probability prediction is improved, the judgment accuracy of the model to a boundary scene is improved, and the accuracy and comprehensiveness of scene value quantification are improved.

The invention discloses an automatic driving test case self-evolution repairing method based on a large language model, and belongs to the technical field of testing and performance optimization of an automatic driving system. According to the automatic driving test case self-evolution repairing method based on the large language model, deep semantic mining is conducted on a performance log of the model by means of semantic comprehension and recommendation capacity of the large language model, and a high-quality repairing scene is recommended from a structured test scene library. The method comprises the steps of performance log collection and pre-evaluation execution, failure mode semantic extraction, a scene semantic retrieval mechanism, initial recommendation construction, reflection type semantic optimization, few-sample fine adjustment adaptation and a closed-loop self-evolution mechanism. The method has obvious advantages in the aspects of automatic driving test case recommendation, failure mode recognition, rapid model capability adaptation and system closed-loop self-evolution, and the safety, generalization capability and test maintainability of the automatic driving system can be remarkably improved.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention provides a scene simulation optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: constructing a test scene with emergency characteristics by adopting reinforcement learning, wherein the test scene comprises a modeling module, an environment disturbance module and a behavior body configuration module; if the input automatic driving algorithm is valid in the risk of the test scene, evolving and updating a trajectory disturbance strategy of the adversarial behavior body; and if the automatic driving algorithm fails in the test scene or the evolved and updated trajectory disturbance strategy, performing continuous pressure measurement and depth verification on the decision vulnerability of the automatic driving algorithm based on a vulnerability amplification mechanism. A good theoretical basis method is provided for testing and verifying decision robustness and limit safety performance of an automatic driving system in a complex urban traffic environment.

The invention relates to an automatic driving key test scene generation method fusing a visual language large model and a diffusion model, and the method comprises the steps: constructing a diffusion model which is used for learning the data distribution of natural driving; aiming at an original scene, utilizing a visual language large model to analyze and form a guide strategy; and based on a guiding strategy and a guiding diffusion algorithm, guiding intervention is carried out on the diffusion model in a simulation environment so as to calculate a background vehicle track and generate a key scene. Compared with the prior art, the method has the advantages that the original scene is automatically analyzed and potential risks are inferred by utilizing the visual language large model, so that the generation process of the bottom diffusion model is guided, intervened and corrected, and the key test scene with high interactivity, high risk and authenticity can be generated in closed-loop simulation.

The invention discloses an automatic driving test scene library construction method based on real traffic data, and relates to the technical field of automatic driving, and the method comprises the following steps: based on a selective sensor fusion framework, dynamically adjusting a fusion strategy of a multi-modal sensor according to a current driving environment, and obtaining corresponding scene elements; performing hierarchical classification on scene elements, constructing a risk assessment model, calculating a comprehensive risk score, and preliminarily dividing risk levels; constructing a rule-based classifier by adopting an association rule mining technology on the basis of results of hierarchical classification and preliminary risk grading, and carrying out risk grading on the scene to be evaluated; and the scene elements and the risk levels are stored in a structured manner, and an automatic driving scene library supporting multi-dimensional query is constructed. According to the method, the characteristics of the traffic scene can be captured more comprehensively, scene elements can be identified more accurately, the risk levels of the scene can be divided scientifically, and the scene library is constructed by combining the scene elements and the risk levels, so that the diversity and pertinence of the test scene are improved.

The invention belongs to the technical field of charging detection, and particularly relates to a charging pile extreme environment test method based on composite stress simulation, a terminal and a storage medium, and the method comprises the steps: inputting an identifier corresponding to a target test scene through a human-computer interaction interface of a test cabin, and executing a query operation based on the input identifier, retrieving a preset parameter combination from a preset scenarized stress library by using the mapping function; in a closed test cabin, environment parameter data are collected in real time through a distributed sensor array and fed back to a central controller, and the central controller drives an execution component to cooperatively regulate and control parameters such as temperature and humidity, so that the environment parameters are stabilized in a preset range; a sequential stress scheme is generated based on a pre-stored dynamic loading algorithm, an execution component is controlled to realize stress superposition or stress alternation, and a charging load is synchronously adjusted to simulate a composite stress effect under an actual working condition; simulation of multi-factor combined stress and dynamic loading is realized, an actual use fault can be found in advance, and the matching degree of a test result and a real environment is remarkably improved.

The invention provides an anthropomorphic automatic driving simulation test scene construction method. The method comprises the steps that all vehicles in a traffic scene are divided into two types of intelligent agents including a test vehicle and an environment vehicle, the traffic scene is modeled into a Markov decision process, the test vehicle is a vehicle controlled by an automatic driving algorithm, and the environment vehicle is a vehicle controlled by a GAIL-GRU driving strategy model; a Markov decision process is utilized to extract a driving track from the human driving data set, and an expert track data set is generated; and training a GAIL-GRU driving strategy model by using the expert track data set, realizing interaction between an environment vehicle and a test vehicle by using the trained GAIL-GRU driving strategy model, and constructing a simulation test scene. The automatic driving simulation test scene constructed by the invention can effectively expose decision defects of an automatic driving algorithm in a complex interaction situation, has good anthropomorphism and relatively high risk scene coverage, and provides support for a decision control simulation test of a high-level automatic driving vehicle.

The invention relates to a test case generation method and system based on an industrial agent. The method comprises the steps that an industrial data set is acquired, and a system dependency model is constructed; generating a path coverage test sequence through the first agent; generating a boundary value test data set through the second agent; generating abnormal test scene information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test data set and the abnormal test scene information. In conclusion, by constructing the system dependency model and utilizing the multi-agent collaborative analysis process logic path, boundary condition and dependency relationship, the multi-surface coverage of the industrial control system test scene is realized, the automation of the test case generation is realized, the effects of improving the generation efficiency and enhancing the comprehensiveness of the test coverage are achieved, and the method is suitable for industrial control system test. Especially for the coverage of boundary conditions, abnormal scenes and concurrent paths, the manual dependence is reduced, and the reliability and consistency of the test are improved.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention provides an interactive system virtual test method and device based on multi-mode cognitive twin bodies. The method comprises the following steps: 1) constructing user cognitive twin: outputting a behavior decision tree with confidence through an adversarial generative network based on population attributes, cognitive preferences and behavior history; 2) creating a dynamic interaction environment: encoding a to-be-tested interface into a semantic graph structure which can be analyzed by a machine, and associating a physical operation constraint rule base; 3) running a multi-agent test: deploying a twin cluster in the virtual sandbox, and recording an operation path, an attention thermodynamic diagram and decision delay; and 4) generating a design defect map, namely, positioning an interface cognitive friction point by comparing the deviation degree of an expected interaction flow and an actual behavior track. According to the method, the problems that a traditional user test period is long and scene coverage is limited are solved, and early interception of interaction risks and design optimization preposition are achieved. The method can be widely applied to high-risk / high-cost / high-cycle test scenes of the man-machine interaction system.

The invention provides a simulation test system simulation degree evaluation method and device, equipment, a storage medium and a product. The method comprises the steps of determining a test scene corresponding to a to-be-tested vehicle according to a design operation range of the to-be-tested vehicle; according to the test scene, determining a corresponding evaluation item, and respectively testing in the simulation test system and the actual environment to obtain simulation test data corresponding to the simulation test system and actual test data of the to-be-tested vehicle in the actual environment; based on the simulation test data, obtaining a simulation evaluation index corresponding to each evaluation item; based on the actual test data, obtaining an actual evaluation index corresponding to each evaluation item; and obtaining a simulation degree evaluation result of the simulation test system based on the simulation evaluation index and the actual evaluation index. According to the method, the simulation data and the actual data are compared according to the scene characteristics and the evaluation indexes related to the driving scene, and targeted evaluation results can be accurately obtained in different driving scenes.

The invention provides an automatic driving safety test system and method, and relates to the technical field of automatic driving, and the system comprises a conventional traffic scene generation subsystem which is used for generating a conventional driving scene data set in a simulation platform; the safety key scene generation subsystem is used for generating a safety key scene by utilizing an automatic driving scene generation network SAGA based on adversarial disturbance according to the conventional driving scene data set; and the safety key scene clustering analysis subsystem is used for dynamically discovering a new category and optimizing a clustering structure by introducing a progressive unsupervised clustering method according to the generated safety key scene to obtain a scene analysis result. According to the method, the problems of insufficient test scene coverage, weak resistance test capability, low key scene analysis efficiency and the like in the safety verification of the existing automatic driving system are solved.

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

The invention provides a multi-step automatic testing method based on machine vision, and the method comprises the steps: capturing an original image sequence of a test scene through a camera, extracting the contour features of a test object in an image through an edge detection algorithm, and obtaining a preliminary positioning coordinate; if the dynamic position change trend exceeds a preset threshold value, adjusting an image enhancement parameter to suppress background noise, and obtaining an enhanced target image; feature matching is carried out through the enhanced target image, a robust identifier such as a texture mode is extracted, and an accurate three-dimensional position coordinate is obtained; according to the accurate three-dimensional position coordinates, calculating an execution deviation value of the current step, and judging whether the deviation value is within an allowable range or not; if the deviation value is within the allowable range, a control instruction sequence is generated and transmitted to the mechanical arm, and a step execution confirmation signal is obtained; comparing a confirmation signal with a next image sequence through the steps, updating positioning model parameters, and determining a continuous adjustment scheme of the whole test process.

The invention provides a test case library construction method and system based on a large model, and relates to the technical field of intelligent automobile tests.The method comprises the steps that semantic analysis of a tested function document is separated from test scene recognition, and an initial test case is generated in combination with a rule-guided large language model; systematization, intellectualization and standardization of a test case library construction process are realized; formats and content specifications output by the large model are effectively restrained through preset professional rules, and the professionality and structural consistency of the generated use cases are guaranteed; meanwhile, problems are found and fed back in time through a multi-dimensional verification and quality evaluation mechanism, self-adaptive optimization of a construction strategy is driven, and the quality and generation efficiency of the test cases are improved; finally, qualified cases are integrated into standardized records, a high-quality test case library with both manageability and technicality is formed, and efficient and automatic conversion from unstructured documents to reusable and maintainable test assets is achieved on the whole.

The invention provides a charging pile testing method and device, a new energy automobile and electronic equipment, and the method comprises the steps: constructing a corresponding CAPL script according to the testing demands of a charging pile; performing version identification on the CAPL script to obtain a DBC file; defining message logic of the DBC file; dynamically configuring a protocol state machine corresponding to the DBC file after the message logic is defined; and after the dynamic configuration is completed, switching the configuration according to a test requirement, and generating a test report. The method can be applied to charging protocols of multiple versions, is higher in adaptability, improves the fault detection intensity, can be compatible with various different test scenes and test requirements, can dynamically adjust the test path according to the test requirements, and is higher in flexibility and higher in practicability.

The invention discloses a multi-modal data driven dynamic pressure test scene generation method and system, and the method comprises the steps: carrying out the event extraction and matching of a user operation log and a full-link API call sequence, and carrying out the feature enhancement of each alignment event pair, and obtaining a local feature vector; statistical features are extracted from each user session, a graph attention network is constructed, and a group feature matrix is obtained; obtaining a global incidence matrix according to the mapping between the service load and the resource consumption; and splicing the user session feature matrix and the group feature matrix to generate group enhancement features, performing matrix multiplication on the group enhancement features and the global incidence matrix to obtain system-level risk features, splicing local feature vectors and the encoded user behavior logs, splicing the system-level risk features and the encoded performance indexes, and obtaining the user behavior log. And finally, carrying out multi-source fusion to generate a pressure measurement scene. The simulation precision, the dynamic adaptive capacity, the abnormal reproduction capacity, the resource utilization rate and the like are remarkably improved.

The invention discloses a test method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: obtaining multi-source data of a distributed storage cluster, the multi-source data comprising hardware state data, service associated operation data and business dependence data; constructing a relation dependency graph among the hardware nodes, the service nodes and the service nodes based on the multi-source data; adjusting the chaos test script according to the real-time state of the distributed storage cluster and the dynamic adjustment strategy to obtain a target chaos test script; and executing the target chaos test script, determining a fault range and a recovery mechanism based on the relation dependency graph, and outputting a test report. According to the method, comprehensive and accurate presentation of the dependency relationship of each node is realized, and the chaos test script can adapt to the dynamic change of the cluster, so that the test scene is more practical, the invalid test is avoided, the effectiveness, reliability and efficiency of the cluster chaos test are remarkably improved, and the high availability and service continuity of the cluster are powerfully guaranteed.

The invention provides a vehicle auxiliary driving test scene generation method and device, equipment and a medium, and relates to the technical field of vehicles, and the method comprises the steps: obtaining the real-time state data and environment data of a vehicle; calculating a dynamic security threshold based on the real-time state data and the environmental data; the dynamic safety threshold value represents the safety risk of vehicle auxiliary driving in a vehicle real-time state dynamic change scene; under the condition that the dynamic safety threshold triggers and adjusts the complexity of the vehicle aided driving scene, guiding a preset test scene generation model to generate a test scene of vehicle aided driving based on the dynamic safety threshold; the test scene is matched with the safety risk. According to the invention, the safety risk can be fed back in real time based on the dynamic safety threshold so as to reduce the braking decision error, thereby improving the accuracy and comprehensiveness of verifying the safety of the auxiliary driving system.

The invention relates to the field of automatic driving test, in particular to an automatic driving panoramic test scene generation system based on a large language model, which mainly comprises a motion encoder used for encoding a vehicle motion parameter sequence into a low-dimensional latent feature by adopting a lightweight neural network motion encoder, injecting the U-Net network of the potential diffusion model through residual connection; the panoramic adapter is used for extracting and fusing multi-scale spatial-temporal features on four resolution levels by adopting spatial-temporal decoupling convolution processing scene overall motion conditions to obtain multi-modal features, and injecting the multi-modal features into a U-Net network of the potential diffusion model; and the multi-modal labeling system is used for realizing labeling of the panoramic image based on a large language model and generating a general scene description. According to the invention, high-fidelity generation and multi-dimensional verification of the automatic driving panoramic test scene can be realized, and the key problems of the existing scene generation technology in the aspects of physical rationality, space-time consistency and panoramic continuity are effectively solved.

The invention provides a performance testing method and device for a large language model and related equipment. The performance testing method comprises the steps that multiple testing platforms for testing, testing scene configuration parameters and a structured testing data set for testing are determined; on the basis of the test scene configuration parameters and the structured test data set, synchronous constraint parameters are generated and distributed to the multiple test platforms, and then a preset load generator is used for controlling the multiple test platforms to concurrently execute reasoning test tasks about the large language model; and when the test is executed, collecting multiple dimension performance indexes of each test platform in real time for data analysis, and generating a reasoning performance comparison evaluation report of the large language model in the multiple test platforms according to a data analysis result. According to the method, the cross-platform performance evaluation period is greatly shortened, and the waste of computing resources is reduced. Manual operation errors are avoided, comparability of multi-platform performance indexes is guaranteed, and more accurate and reliable multi-platform performance test results can be obtained.

The invention relates to the technical field of automatic driving test, and discloses an automatic driving network connection cloud control site test method fusing vehicle-road data, comprising the following steps: S1, constructing a dangerous scene set based on roadside sensing data; s2, selecting a test scene; constructing a continuous test task sequence, and solving an optimal solution of the scene risk degree and the coverage degree; executing target object process allocation and global trajectory planning based on the task sequence, and generating an automatic driving continuous test scheme; s3, establishing real-time communication connection between the controlled target object terminal and the cloud; receiving a planning track issued by the cloud at a controlled target object terminal, and constructing a system state model to realize tracking control; a global multi-target task state is monitored at a cloud end, a scene operation cost index is constructed, a multi-target trajectory planning scheme is dynamically adjusted, and a scene task execution state is researched and judged; and S4, constructing a digital twinborn model, and mapping and visualizing test data in real time. According to the invention, an optimization scheme is provided for the high-order intelligent driving function closed site test.

The invention belongs to the technical field of automatic driving test, and provides an automatic driving confrontation test scene generation method and system based on strategy switching, and the method comprises the steps: obtaining an automatic driving simulation scene; setting positions and initial speeds of a target vehicle and an intelligent body vehicle in the acquired simulation scene; in combination with the set position and initial speed of the target vehicle, the running state of the target vehicle is optimized by adopting a multi-agent near-end strategy, and the target vehicle is controlled; determining a current traffic state according to the control driving state of the target vehicle; in combination with the dynamic quantitative index of the artificial risk potential field, sensing the traffic flow risk of the intelligent body vehicle in the determined current traffic state in real time; and according to the obtained traffic flow risk level, automatically switching a defense strategy and an attack strategy of the agent vehicle, and driving the multi-agent vehicle to cooperatively generate an automatic driving confrontation test scene.

The invention discloses an automatic test case generation method and system based on a large model, and the method comprises the steps: multi-modal data fusion: receiving a natural language demand, a UML design drawing, an API document, a historical defect log and system monitoring data, aligning text and image features through a CLIP model, analyzing the interface constraint through CodeBERT, and carrying out the multi-modal data fusion; feature fusion is realized by adopting a cross-modal attention mechanism; a dynamic test scene modeling step: constructing an environment simulator based on reinforcement learning, and simulating high-concurrency, distributed abnormal and other complex scenes; and a layered test case generation step: generating a function test case, a boundary test case and an increment test case through GPT-4, GAN and LSTM respectively, and introducing an entropy evaluation model to quantify the effectiveness of the cases. According to the method, the multi-modal data is brought into the test case generation process for the first time, the test case generation time is shortened by 80%, the complex scene coverage rate is increased to 95%, the semantic matching degree reaches 92%, and the intelligent level and efficiency of software testing are remarkably improved.

The invention discloses a test case recommendation method and device, electronic equipment, a medium and a product. The method comprises the following steps: when it is monitored that a function demand is changed, obtaining change content; constructing a dynamic knowledge graph according to the change content; wherein the dynamic knowledge graph stores an association relationship among a function requirement, a test scene and a test case; collecting test environment state data; and outputting a to-be-recommended test case set according to the test environment state data and the dynamic knowledge graph. According to the method, the problems of low test case generation efficiency, incomplete coverage and inconsistency with requirements caused by frequent change of function requirements can be solved.

The invention relates to an automatic driving automobile test method based on multi-traffic-participant collaborative hunting confrontation, which comprises the following steps: dividing a test scene into a plurality of space-time resource grids based on scene information, and constructing a conflict event set according to tracks of a tested automobile and traffic participants; defining a conflict degree index, a safety index and passing time; a multi-target speed optimization search model is constructed based on the conflict degree index, the safety index and the passing time, a plurality of traffic participants are utilized to cooperatively surround the tested vehicle, the multi-target speed optimization search model is solved in the cooperative surrounding process, and a space-time resource occupation table is obtained; carrying out collision detection, and if no conflict exists, generating an optimal track of all traffic participants; otherwise, returning to execute collaborative hunting; and obtaining a comprehensive index of the tested vehicle when the traffic participant executes the optimal trajectory, and carrying out performance evaluation. Compared with the prior art, a more accurate automatic driving test is realized by constructing a complex scene considering the collaboration between traffic participants.