584 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.

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 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 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 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 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 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 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 technical field of automatic driving, in particular to a multi-view-angle perception-driven automatic driving test scene twin reconstruction method, which comprises the following steps of: S1, acquiring and preprocessing multi-view-angle image perception data to form a multi-view-angle image sequence under a unified coordinate system; s2, spatial coding and modeling based on an orthogonal three-plane structure; s3, on the basis of a view perception-space interaction encoder of a Transform architecture, completing space coding processing represented by an orthogonal three-plane structure; s4, structure and semantics combined twinborn reconstruction decoding is carried out; and S5, generating a simulation compatible format and deploying a platform. According to the method, the terrain, facilities, traffic elements and other contents of a real road scene can be automatically restored, the storage and calculation cost is remarkably reduced while the spatial information integrity is ensured, various simulation platform support formats can be flexibly output, the method serves for closed-loop testing of automatic driving, and the reconstruction efficiency and the system suitability are improved.

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 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.

The invention discloses a vehicle driving simulation test method and system based on digital twinning, belongs to the technical field of vehicle driving, and realizes real interaction of a driving behavior model and a control algorithm in a virtual scene by constructing a digital twinning body highly corresponding to an actual vehicle and a driving environment. Time stamps and coordinate information of all the modules are collected, time differences and space residual errors are extracted, a space-time semantic consistency index model is constructed, and the loading process is intelligently judged and optimized; when inconsistency is detected, adjustment is performed through clock synchronization and coordinate recalibration, a closed-loop optimization mechanism is formed, and high collaboration of the model and the environment is ensured, so that the accuracy, the stability and the credibility of the simulation system are improved, and the method is suitable for complex and changeable automatic driving test scenes.

The application provides a test method for intelligent auxiliary driving test, constructs a test problem description grabbing model, directly collects signals uploaded by an intelligent vehicle in real time through a vehicle-mounted Ethernet and a CAN FD direct connection interface without using other data interfaces, triggers a test problem description reporting process once abnormal signals are monitored, and ensures that the vehicle meets safety requirements of real vehicle road test.

The invention discloses an automatic driving test scene generation method based on a large-scale language model and a probabilistic scene generation language. The automatic driving test scene generation method comprises the following steps: S1, analyzing a natural language instruction input by a user by using the large-scale language model; s2, performing structured decomposition on the scene description; s3, generating a code snippet of a probabilistic scene generation language; and S4, assembling and compiling a script of the probabilistic scene generation language. The invention further discloses an automatic driving test scene generation device based on the large language model and the probabilistic scene generation language. According to the method, direct, efficient and reliable conversion from the natural language intention to the complex dynamic simulation scene is realized, and the method can be widely applied to the technical field of simulation testing and artificial intelligence.

Provided in the present invention are a comprehensive evaluation method for an autonomous driving test scenario and a related device. The method comprises: acquiring accident data from historical traffic accidents, extracting pre-collision trajectories of accident participants, and determining test scenario elements on the basis of the pre-collision trajectories; performing weighting calculation on each test scenario element to obtain the weighting values of each test scenario element under a scenario complexity degree and a scenario risk degree; by means of the weighting values of each test scenario element under the scenario complexity degree and scenario risk degree, quantifying a risk index and a complexity degree index of each test scenario; and finally, using the complexity degree index and the risk index to perform comprehensive evaluation on each test scenario, thereby improving the effectiveness and accuracy of evaluation.

The invention discloses a driving test timing anti-cheating system and method, and the system comprises a timing card punching device, a signal detection module, and a central processor, and the timing card punching device comprises a Bluetooth communication module; the signal detection module comprises a frame number reading unit, a vehicle state acquisition unit and a gyroscope sensor which are integrated on the same circuit board, the frame number reading unit acquires a VIN code, the vehicle state acquisition unit acquires a vehicle speed, a rotating speed and an ignition key state, and the gyroscope sensor detects X / Y / Z axis acceleration; the central processing unit binds a vehicle VIN code to a cloud background, compares the current VIN code with the bound VIN code when the vehicle is started, and performs class hour validity verification according to the vehicle state and data detected by the gyroscope sensor; the driving test cheating behavior is thoroughly solved through triple dynamic verification chains including vehicle frame number lifelong locking, vehicle signal-gyroscope fusion analysis and timing and card punching equipment anti-disassembly monitoring.

The invention belongs to the technical field of automatic driving test, and particularly relates to a multi-mode accident scene library construction method for end-to-end automatic driving test. The method specifically comprises the following steps: step 1, extracting text information based on an MCAT-BiLSTM-CRF algorithm; 2, designing the ontology architecture, and storing accident scene information by using a knowledge graph to obtain an accident text knowledge base; step 3, derivative expansion is carried out on the accident scene element combination based on a HyCon-Sg-Net algorithm; step 4, performing enhanced fine tuning on the Open-Sora model to realize modal conversion from the text knowledge base to the accident video database so as to construct a multi-modal accident scene library; the method can be used for testing the performance of an end-to-end automatic driving automobile in an extreme accident scene, and the adaptive capacity of an automatic driving algorithm to the extreme accident scene is remarkably improved.

The invention relates to an automatic driving test scene simulation generalization generation method and system based on knowledge distillation. The method comprises the following steps: acquiring real trajectory data, constructing a scene-level multi-agent trajectory generation framework based on a denoising diffusion probability model, and generating diversified and vivid agent trajectories through an iterative denoising process so as to realize modeling of multi-agent interaction behaviors in a complex traffic environment; a mixed knowledge self-distillation framework is further proposed, multi-level knowledge of a teacher model is migrated in a student model, and task loss and distillation loss are dynamically balanced in combination with a simulated annealing strategy, so that the cross-scene adaptability and generation stability of the model are improved; simulation verification shows that the generated intelligent agent track can effectively reduce the collision event rate and the retrograde event rate, and interaction coordination and traffic rule constraints are ensured. According to the system, the authenticity, diversity and cross-scene adaptive capacity of an automatic driving simulation scene can be improved.

Disclosed herein is a method to determine a geolocation that includes receiving, by a processor, from a base station (BS), radio predictors, a user equipment (UE) location history, and a geolocation of a first UE for which a minimization of drive test (MDT) mode is activated, radio predictors and a UE location history of a second UE for which the MDT mode is not activated, and cell physical parameters. The method includes training, by the processor, a machine learning (ML) model at least based on the radio predictors, the UE location history, and the geolocation of the first UE, and the cell physical parameters. The method includes executing, by the processor, the ML model to determine the azimuth of the second UE and providing, by the processor, to a downstream application, a geolocation of the second UE at least based on the azimuth and the TA of the second UE.

The invention discloses an automatic driving scene library dynamic generation method based on multi-modal data fusion, belongs to the field of automatic driving testing, and solves the problems of insufficient coverage of extreme working conditions, data splitting and single evaluation of a static scene library. According to the technical scheme, the method comprises the following steps: establishing a distributed data acquisition layer, and accessing a vehicle-mounted terminal and a V2X roadside equipment data stream through a federated learning framework; constructing a multi-modal semantic coding layer, fusing data extraction features, converting laser radar point cloud data into a space-time constraint matrix, and outputting a scene DNA coding sequence; deploying a dynamic evolution engine, mapping the DNA coding sequence into a traffic flow state parameter, and calculating an entropy gradient field; and starting a virtual-real co-occurrence scene generator, receiving the coordinates of the high-entropy region and the physical interference intensity parameters, performing simulation to generate a controlled physical interference test scene, injecting a group interaction rule based on behavior uncertainty, and finally outputting a dynamically updated scene library and a risk report for automatic driving system test evaluation.

The invention belongs to the technical field of automatic driving, and provides a driving scene generation method based on multi-modal large language model driving and application, and the method comprises the steps: automatically intercepting a scene data packet of a specific time window from an original road test data stream; performing fusion perception and reasoning on the scene data packet based on a pre-trained multi-modal large language model to obtain a structured narrative text containing generalized parameters; automatically analyzing the structured narrative text, extracting a core behavior template from the structured narrative text, and obtaining a generalization range of each generalizable parameter; automatically generating a simulation scene file based on the core behavior template and a group of specific parameter values selected from the generalization range; and running the simulation scene file in the simulation environment, evaluating the scene value and storing the scene value into the hierarchical scene library. According to the method, real drive test data is inverted and generalized into a high-value simulation test scene based on semantic understanding of a multi-modal large language model, and the method has the advantages of high efficiency and unification of fidelity and value.

The invention discloses an automatic driving test scene generation method and system based on a hybrid expert model, and belongs to the technical field of automatic driving test verification and artificial intelligence cross, and the method comprises the following steps: obtaining a scene description file through the hybrid expert model; generating a first scene according to the scene description file, and collecting first scene data; and obtaining a sensitive area of the first scene according to the entropy of the first scene data and the gradient direction thereof. A scene description file is generated based on the hybrid expert model, and the scene generation efficiency is improved; by identifying the sensitive area of the scene, the scene is iterated based on the sensitive area, and a challenging scene is generated; closed-loop optimization is carried out on the hybrid expert model through test feedback and a reinforcement learning reward function, directional iteration is carried out based on the gradient direction of scene decision uncertainty, an automatic and directional iterative'generation-test-evaluation-iteration-optimization 'intelligent closed loop is formed, and the efficiency, coverage and pertinence of scene generation are remarkably improved.

The invention relates to the technical field of automatic driving test, and discloses a site test scene construction method based on feature matching and trajectory optimization, comprising the following steps: S1, extracting road topological features and motion topological features from a simulation scene, and constructing feature vectors of the simulation scene; s2, searching a candidate area in an actual site map based on the feature vector of the simulation scene; s3, for the screened candidate region, evaluating the geometric similarity between the simulation trajectory and the path of the candidate region, including using Procrustes to analyze the alignment trajectory and calculating the Frechet distance; and S4, on the basis of the candidate region with the highest geometric similarity, the simulation trajectory is adapted to an actual site through an optimization model, an actual test trajectory is generated, the optimization model takes feature similarity cost and deformation cost as optimization targets, it is ensured that the trajectory conforms to actual site constraints, and semantic features of an original scene are reserved. According to the method, high-efficiency, high-precision, automatic and semantic-preserving migration from a simulation test scene to a real field environment can be realized.

The invention discloses a multifunctional asphalt mixture rut test method which is implemented by using a device integrating loading and measurement, test piece supporting and temperature control drainage, environment simulation and optical measurement and a central control and data acquisition system. A test piece is arranged in the test piece groove, temperature control is realized through circulating a heat-conducting medium in the temperature-conducting cavity, different gradients are formed through the hoisting mechanism and the angle adjusting assembly, the test wheel is driven to perform one-way circulating loading along a closed track and snub at a preset moment, and rolling and sliding friction forces are synchronously collected; in the test process, rainfall is simulated by adopting the watering assembly, incoming water is recycled by the drainage tank, speckle images are acquired by the high-frequency industrial camera, and surface full-field displacement and strain are obtained through digital image correlation analysis. Through time registration and fusion of temperature, gradient, wheel speed, rainfall and friction-deformation data, rut deformation, skid resistance and apparent structure evolution laws of the asphalt mixture under multi-climate and multi-gradient conditions are obtained.