989 results about "Driving test" patented technology

The invention discloses an AR-fused remote driving vehicle virtual-real interaction hardware-in-the-loop test system, particularly relates to the technical field of automatic driving test, and is used for solving the problems of inaccurate coupling between a virtual scene and a real vehicle behavior and lack of AR prompt response evaluation. The method comprises the following steps: firstly, constructing a dynamic obstacle intention-driven prediction model based on time series data of a multi-modal sensor, and generating a trajectory probability distribution and risk thermodynamic diagram; then, space-time alignment of the virtual accident scene and the real environment is achieved through a dynamic binding algorithm, and the virtual-real shielding priority of an AR interface is dynamically adjusted; by simulating abnormal disturbance of a vehicle actuator, synchronously collecting control and watching responses of a driver, and extracting obstacle avoidance path deviation degree and takeover timeliness parameters; and finally, separating and compensating virtual and actual residual errors based on a path deviation index, realizing online correction of a virtual scene attitude and a dynamic trajectory, constructing a closed-loop optimization mechanism, and improving the precision and stability of a test system.

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.

An autonomous driving test platform based on virtual-real integration technology, which platform belongs to the technical field of autonomous car testing. On the basis of cloud-edge-end three-level linkage, the platform combines real environment testing and sand table environment testing with virtual simulation technology. A vehicle-end real scene is an actual environment in which an autonomous vehicle is applied; and environmental information is collected, and feedback is provided for the environmental information and virtual-real fusion information. In a cloud-end virtual environment, in addition to building an autonomous driving virtual scene, data fusion is also performed on information from a real scene and a sand table scene, so as to generate virtual-real fusion data. A sand table environment is used as an edge portion to provide a relay space, which is used for bridging the cloud-end virtual environment to a vehicle-end real environment, digitally twinning the autonomous vehicle in the actual environment to a virtual simulation environment, and transmitting the virtual-real fusion information to the autonomous vehicle. The sand table environment, the virtual environment and the real environment fully interact with one another to implement data enhancement, thereby obtaining an optimal test effect.

The invention discloses an automatic driving test scene generation framework and generation method, and the method comprises the steps: a road network generator is used for obtaining a node file and an edge file generated by a multi-modal large language model based on prompt word engineering and scene description, inputting the node file and the edge file into a traffic simulation tool, and obtaining a road network file synthesized by the traffic simulation tool; the vehicle generator is used for acquiring a plurality of initial vehicle models generated by the multi-modal large language model based on prompt word engineering and scene description, inputting the initial vehicle models into a traffic simulation tool, and acquiring a vehicle model generated after the traffic simulation tool adds a path file for each initial vehicle model; and the scene generation module is used for integrating the road network file and the plurality of vehicle models, obtaining an automatic driving test scene, building a bridge for interactive use of the multi-modal large language model and a traffic simulation tool, and constructing a traffic simulation mechanism of the multi-modal large language model based on a prompt project. A customized, real, reliable and targeted automatic driving test scene can be generated.

The invention belongs to the technical field of automatic driving test, and particularly relates to an intelligent automobile edge scene generation method based on a hierarchical reinforcement framework. Comprising the following steps: 1, processing natural driving data, and constructing an initial test environment; 2, establishing a risk conflict prediction method; step 3, establishing a test scene evaluation method including collision rationality evaluation and space-time similarity evaluation; 4, constructing a scene library based on index management; 5, constructing a basic reinforcement learning environment, and constructing a static environment and a basic interface; step 6, dividing a scene hierarchical scheduling period, and dividing a test period based on scene features; step 7, optimizing a reward mechanism and an action strategy, and performing adjustment according to a scene period and a vehicle behavior; and step 8, model training application and scene data output. Finally, the diversity generation of the edge scene is realized, the local convergence problem is broken through, and the edge scene mining efficiency is improved.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an accident scene generation method based on a scene knowledge graph and considering accident causes. Comprising the following steps: step 1, modeling a scene knowledge graph; 2, modeling the scene graph time sequence prediction model; 3, modeling the time sequence causal inference model; step 4, modeling the scene graph time sequence decision generation model so as to generate an accident scene; according to the method, the accident scene database with high authenticity, high diversity and accident cause consistency can be constructed under the condition that the accident scene sample data size is limited, the number of accident scenes of the same type in the automatic driving algorithm closed-loop self-evolution cloud database is efficiently expanded, the constructed scene database is used for training the automatic driving algorithm, and the accuracy of the automatic driving algorithm is improved. The adaptive capacity of the self-driving automobile to scenes with the same type of accidents can be effectively enhanced, and safe and reliable operation of the self-driving automobile in the real world is guaranteed.

The invention discloses a safety device for reducing the back-up collision risk in the automatic driving test process, and belongs to the technical field of automatic driving test safety. Aiming at the collision risk problem caused by rapid movement of dynamic obstacles and communication delay when a vehicle backs up in a closed test site, the device obtains millimeter-level position coordinates and course angles through positioning units deployed on a test vehicle and a target object; a C-V2X protocol is utilized to construct a real-time data link to share vehicle position, speed, backing track prediction data and target object outer contour feature point coordinates, potential conflicts are identified based on future minimum spacing calculation and dynamic threshold, and a cooperative collision avoidance path is generated in combination with a distributed optimization algorithm. And the high-risk vehicle preferentially preempts a communication channel to issue deceleration, course adjustment and braking instructions to surrounding vehicles. The device can effectively improve the reversing safety in a multi-vehicle cooperative test scene, and is suitable for collision risk real-time early warning and active intervention of an automatic driving vehicle closed test site.

The invention discloses an HIL simulation method for an underground mining whole process, and relates to the field of automatic driving simulation testing, and the method comprises the steps: collecting point cloud data of an underground scene, and generating a simulation scene in a CARLA format; compiling and importing the generated simulation scene in the CARLA format through a Unreal engine, building a vehicle model and a laser radar sensor model in a Carla simulator, and collecting a point cloud data packet of the simulation scene; according to the point cloud data packet, mapping is carried out through an SLAM algorithm, and a point cloud map in a PCD format is generated; constructing a map of the simulation scene according to the PCD point cloud map; building a hardware-in-the-loop HIL simulation platform comprising an upper computer, a lower computer and an execution mechanism; and loading the constructed map of the simulation scene to the constructed hardware-in-the-loop HIL simulation platform, simulating a real well working condition scene through the simulation platform, and verifying a well working condition unmanned driving algorithm. In order to solve the problem that unmanned driving tests are difficult to comprehensively evaluate in the prior art, the coverage rate of the tests is increased through a hardware integrated full-process closed loop and the like.

The invention belongs to the field of data processing, and discloses a test method for pedestrian trajectory prediction and emergency braking in an automatic driving platform. Comprising the steps of segmenting a real road environment in advance, obtaining grades of small road segments according to calculation of segmentation coefficients, carrying out resource allocation on the small segments according to the grades, and collecting multi-source heterogeneous data; importance optimization is introduced into a Transform model, possible prediction trajectories of individuals are generated, graph data are constructed by using the possible prediction trajectories, an Autoencoder anomaly detection mechanism is introduced, high-risk trajectories are screened out, and high-risk scenes are output; combining the road geometric model with the high-risk scene to form a scene library, loading the scene in a simulation platform, carrying out automatic driving test, carrying out virtual-real data cross validation in combination with real vehicle test data, and obtaining performance evaluation of an algorithm; pedestrian track prediction and emergency braking in the automatic driving process are achieved.

The invention relates to an online multi-dimensional comprehensive test evaluation method for an automatic driving test. The method comprises the following steps: constructing a test scene library; constructing a test scene complexity model; building an intelligent driving joint simulation test platform, and embedding the intelligent driving algorithm into the intelligent driving joint simulation test platform; constructing an automatic driving test multi-dimensional evaluation index system for performing multi-dimensional performance evaluation on the automatic driving vehicle; an intelligent driving joint simulation test platform is utilized to obtain vehicle test data, an evaluation index system and a test scene complexity model are combined to calculate objective evaluation index weights, then comprehensive performance quantitative scores are calculated, and finally an automatic evaluation report is output and obtained. Compared with the prior art, the subjectivity of manual evaluation can be reduced, and the evaluation efficiency of the automatic driving vehicle is improved; comprehensive and objective quantitative scoring can be carried out on the multi-dimensional performance of the automatic driving vehicle, and the influence of different test scenes on the scoring difference of the automatic driving vehicle is reduced.

The present disclosure provides an automatic driving test method. The method includes: acquiring test configuration information input by a user; generating test cases according to the test configuration information, and allocating the test cases to corresponding test types, wherein test types comprise a virtual simulation test, a whole vehicle in-loop test, a closed site test, and an open road test; and under each test type, testing a to be tested vehicle based on the corresponding test cases to obtain test results corresponding to each of the test type, and the test results comprise a simulation test result, a whole vehicle in-loop test result, a closed road test result, and an open road test result. Thus obtaining rich comprehensive test evaluation results makes the test results for autonomous vehicles more accurate.

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 a multi-modal driving test scene generation method and system, and the method comprises the steps: obtaining a plurality of groups of model training samples, each group of model training samples comprising a user natural language description, a driving scene visual image, a risk point thermodynamic diagram, and a safety key scene description code; constructing a driving test scene generation framework, and training the driving test scene generation framework by using the multiple groups of model training samples to obtain a trained driving test scene generation framework; wherein the driving test scene generation framework comprises an image content coding model, a risk point mining model, a thermodynamic diagram coding model and a large language model, the risk point mining model and the thermodynamic diagram coding model are connected in series, and the output end of the image content coding model and the output end of the thermodynamic diagram coding model are connected with the input end of the large language model; and generating a multi-modal driving test scene according to the input user natural language description and the driving scene visual image by using the trained driving test scene generation architecture.

The invention belongs to the field of data analysis and processing, and discloses a virtuality and reality combined automobile automatic driving test method. Comprising the following steps: connecting a sensor suite and an automatic driving system of an automobile with a virtual twin center by using a CAN bus or an Ethernet interface, operating the automatic driving system, and generating a performance report; the method comprises the following steps: initializing raindrop attributes, setting the transparency of raindrops based on real environment data, introducing a complex wind field model and a surface ponding dynamic evolution mechanism, carrying out collision detection on all raindrop pairs by using a space segmentation algorithm, and simulating dynamic behaviors of the raindrops in the air based on a discrete element method and a smoothed particle fluid dynamics method. Visual rendering is carried out on the water spray effect; under the influence of raindrop physical characteristic modeling, output of laser radar point cloud scattering modeling, camera image fuzzy modeling, millimeter wave radar signal attenuation and scattering modeling and ultrasonic sensor interference modeling is iteratively updated, and automatic driving testing of an automobile under virtual-real combination is achieved.

The invention discloses an automatic driving multi-vehicle interactive test scene generation method, and the method comprises the steps: carrying out the decomposition of a scene according to the design requirements of an automatic driving test scene, and extracting necessary static environment information and the dynamic parameter distribution of traffic participants; static environment information and dynamic parameters of traffic participants are sampled, a scene is extracted into a plurality of scene sections, and the scene sections are divided into test scene samples and verification scene samples according to a preset proportion; inputting the test scene sample and the verification scene sample into a multi-agent model for training to obtain a trained multi-agent model; and deploying the trained multi-agent model to a test scene sample, carrying out an interaction test with the tested automatic driving system, and obtaining an automatic driving multi-vehicle interactive test scene, so that the target vehicle is controlled through the multi-agent model, and interaction with the automatic driving system is carried out. Therefore, the performance of the automatic driving system in a high-risk environment can be reflected more truly, and the safety and robustness evaluation of the automatic driving system are ensured to be more comprehensive.

The invention relates to the technical field of battery testing, in particular to an automatic BMS function testing system and method. The method comprises the following steps: acquiring BMS working parameters; performing short-circuit working condition configuration processing according to the BMS working parameters to generate short-circuit working condition configuration data; performing short-circuit signal simulation based on the short-circuit working condition configuration data to generate short-circuit simulation control signal time sequence data; based on the short-circuit analog control signal time sequence data, a battery pack current short-circuit driving test is carried out through a BMS monitoring system, and BMS monitoring response data is generated; and performing short-circuit current monitoring protection evaluation according to the BMS monitoring response data, and generating BMS function test level data. According to the invention, the automatic test of the BMS short-circuit protection function is realized by intelligently configuring the short-circuit working condition, and the efficiency, accuracy and repeatability of the test are remarkably improved.

The invention belongs to the field of automatic driving test, and provides an end-to-end automatic driving system closed-loop confrontation test method based on a world model, and the method comprises the steps: building an end-to-end automatic driving system test platform based on the world model, providing end-to-end automatic driving system perception input information, and carrying out the modeling of a camera sensor, the accurate alignment among the multi-modal scene information is realized; constructing a traffic flow module, and providing map and scene global state information; a traffic vehicle multi-agent collaborative confrontation strategy balancing strategy convergence and strategy diversity is designed, a dense reward function is constructed and matched with an agent heterogeneous strategy adaptive adjustment method, a global exploration target is planned as a whole, and the test efficiency is improved; all the modules are integrated, an end-to-end automatic driving system to be tested is integrated, and efficient closed-loop testing is achieved. According to the method, efficient testing of the performance of the end-to-end automatic driving system can be achieved, the robustness and safety of the end-to-end automatic driving system can be evaluated, and functional defects of the system can be rapidly excavated.

The invention discloses an automatic driving test case generation method based on a large model retrieval enhancement technology, and aims to improve the generation efficiency of an automatic driving test scene. The method comprises the following steps: firstly, cutting a preprocessed data text into blocks, and vectorizing the text through an embedded model to construct an efficient vector database; secondly, preliminarily retrieving related text blocks by using an improved hybrid retrieval algorithm, and reordering by using a BERT cross encoder to improve the relevance of retrieval results; aiming at the complex task requirement in the automatic driving test, the method is divided into a plurality of sub-tasks to be executed in parallel, so that the retrieval efficiency and accuracy are improved. And then, a LoRA strategy is adopted to finely adjust the large language model so as to adapt to an automatic driving scene generation task. And finally, jointly inputting the cue word template and the reordering result into a fine tuning model to generate a test case. According to the invention, a brand new technical scheme is provided for automatic driving test scene generation, and the method is of great significance in improving the test evaluation level of automatic driving and accelerating the landing of the automatic driving technology.

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 relates to a world model construction method, in particular to a high-fidelity lightweight world model construction method for an end-to-end automatic driving test, which is used for constructing a high-fidelity world model and performing knowledge distillation on the world model to solve the problems of huge parameters, low reasoning efficiency and the like of the world model. Model parameters are reduced on the basis of reserving the world model generation capability, and the reasoning efficiency is improved; a CUDA operator is developed in a user-defined mode for the bottleneck part of world model calculation, video memory allocation is optimized, and the reasoning efficiency of a high-fidelity world model is improved based on a single-device multi-thread scheduling and multi-device cooperative calculation method. According to the method, the high-fidelity lightweight world model for the end-to-end automatic driving test can be constructed, the problems that an existing world model is low in multi-modal information alignment precision, poor in cross-view and cross-frame consistency, low in reasoning efficiency and the like are effectively solved, the confidence coefficient of the end-to-end automatic driving system test process is improved, and the test efficiency of the end-to-end automatic driving system is improved. And the testing efficiency of the end-to-end automatic driving system is greatly improved.

The invention discloses an automatic driving test scene multi-dimensional evaluation method, and the method comprises the steps: carrying out the decomposition of each automatic driving test scene based on an evaluation category, and obtaining a dynamic element and a static element corresponding to the evaluation category; the evaluation category comprises risk, complexity and rare degree; determining a subjective weight of each automatic driving test scene by using an analytic hierarchy process, determining an objective weight of each automatic driving test scene by using an entropy weight method, and fusing the subjective weight and the objective weight based on a game theory to obtain a comprehensive weight; inputting an evaluation model based on the comprehensive weight to obtain an evaluation grade corresponding to the evaluation category; the evaluation grades corresponding to the automatic driving scenes are classified based on the clustering algorithm to obtain the corresponding evaluation grades, so that the risk, the complexity and the rare degree of the scenes are comprehensively considered, and the performance of the automatic driving vehicle is comprehensively evaluated.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention relates to an automatic driving simulation test scene generation method based on a text traffic accident report, and aims to apply a generative language model technology in the field of natural language processing, a code generation technology in the field of program synthesis and a fuzzy test theory in the field of software test. The problem that diversified and standardized high-quality simulation test scenes are lacked in the current automatic driving test field is solved, and the method can generate diversified scene files in the OpenSCENARIO format on the basis of natural languages, and mutates seed scenes through multiple transformation operators so as to efficiently discover defects existing in an automatic driving system.

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 discloses a self-adaptive adjustment method based on driving behavior prediction, and relates to the technical field related to intelligent driving, and the method comprises the steps: carrying out the driving behavior data collection of a driver, and building a driving behavior data set; external environment data mapped with the driving behavior data set are obtained, and after data cleaning is conducted on the external environment data and the driving behavior data set, driving features and environment features are extracted; inputting into a deep learning prediction model, and executing deep learning prediction model training; and performing driving behavior prediction of the driver through the trained deep learning prediction model, and establishing feedback early warning according to a driving behavior prediction result. The technical problems that in the prior art, a training strategy cannot be dynamically adjusted according to individual behavior characteristics of a driver and environment data, so that the driving safety is not high, the driving behavior prediction precision is insufficient, and the examination passing rate is low are solved. The technical effects of improving the accuracy of driving behavior prediction, the driving safety and the driving test passing rate are achieved.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention provides a method for testing identification and response of an automatic driving vehicle to a signal lamp in a closed site, and belongs to the technical field of automatic driving testing. The method solves the problem that it is difficult to dynamically simulate a real signal lamp interaction scene and quantitatively evaluate the accuracy of a vehicle perception-decision chain in a closed test. According to the technical scheme, the method comprises the following steps: constructing a line segment topology network with directional attributes; deploying a three-dimensional adjustable signal lamp device at the node to dynamically adjust the pose; synchronously acquiring signal lamp identification result data and control response behavior data of the vehicle; calculating a time margin based on the vehicle positioning speed and the distance from the stop line, and triggering signal lamp state grading switching through comparison with a preset threshold value; a two-dimensional verification mechanism is adopted, and identification result data and control response behavior data are aligned through a time synchronization protocol to analyze causal relevance. The method is used for testing and verifying the recognition accuracy and response timeliness of an automatic driving vehicle on a signal lamp scene in a closed site.

The invention provides an automatic driving test system and method for a special vehicle, and the system comprises an environment simulation module which is used for constructing different simulation scenes required by the test of the special vehicle; the sensor simulation module is used for simulating sensor data collected by a sensor in the special vehicle based on the simulation scene; the controller is used for controlling the automatic driving system to perform automatic driving test on the special vehicle model in the same simulation scene according to the sensor data in the simulation scene; the monitoring module is used for monitoring operation data of the automatic driving system in the test in the simulation scene; and the data processing module is used for receiving test data generated by different simulation scenes in the test, performing performance analysis on the automatic driving system according to the test data and generating an optimization report of the automatic driving system based on an analysis result, and the test data comprises simulation scene data, sensor data and operation data. Therefore, the development efficiency and the test precision of the automatic driving technology of the special vehicle are remarkably improved.

Certain aspects of the present disclosure generally relate to methods and apparatus for performing minimization of drive test (MDT) operations. For example, certain aspects provide a method for wireless communication. The method generally includes receiving, at a radio access network (RAN), a measurement configuration to start a trace of a user-equipment (UE), determining a transition of the UE to an inactive state, and sending one or more messages to coordinate the trace of the UE or indicate that the trace has failed in response to the determination

The present invention relates to an autonomous driving test method based on multi-agent swarm adversarial, a device and a medium. The method includes: deducing a conflict topological relationship graph between a tested autonomous vehicle and an agent; deducing a feasible planning space of the tested autonomous vehicle according to the conflict topological relationship graph; establishing a multi-agent swarm adversarial model based on a potential game under the feasible planning space according to a correlation between an individual reward of the agent and a swarm adversarial test effect of a multi-agent system, and solving and obtaining an optimal adversarial strategy of the multi-agent system against the tested autonomous vehicle, where in the multi-agent swarm adversarial model, an adversarial intensity is introduced, and the adversarial intensity is adaptively adjusted according to an actual response of the tested autonomous vehicle; and repeatedly executing the S1-S3 until an adversarial task is completed.