411 results about "Driving test" patented technology

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 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 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.

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.

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.

The invention discloses an automatic driving acceleration test method and device and a storage medium, and the method comprises the steps: carrying out the deconstruction of a scene according to the construction demands of an automatic driving test scene, and extracting the static environment information needed by the test scene and the dynamic parameter distribution of traffic participants; static environment information and dynamic parameters of traffic participants are sampled, and a scene is extracted into a plurality of scene sections; scene section generalization generation is realized by using a conditional table adversarial generative network; through each generated scene section, according to scene variable parameter distribution, behavior uncertainty is captured by using a triangular fuzzy number, and a complete fuzzy track sequence is expanded; constructing an automatic driving high-risk fuzzy test scene in combination with the static environment information and the fuzzy trajectory sequence; therefore, not only is the coverage rate of the test scene improved, but also the pertinence and authenticity of the test are enhanced, and more effective support is provided for safety performance evaluation of the automatic driving system in a complex environment.

The invention provides a user portrait method and device for driving training learning, electronic equipment and a medium, and belongs to the technical field of information recommendation, and the method comprises the steps: collecting multi-source heterogeneous data of a student, carrying out the data preprocessing, and obtaining a student data file, the multi-source heterogeneous data comprises learning stage data, capability evaluation data and basic attribute data; based on the trainee data file, extracting two-dimensional features of trainees, including obtaining learning stage features according to division of passing states and learning progress completion rates of driving test subjects, and quantifying the mastering degree of subdivided test points under weak subjects based on a weighted scoring algorithm to obtain weak subject features; and fusing the two-dimensional features and the basic attribute data, and constructing a student portrait including a learning stage label and a weak subject label. According to the invention, the accuracy of the user portrait is improved.

The invention discloses an automatic driving test and evaluation method and device based on a large language model, and solves the problems that the pertinence of a test scene is insufficient, the result understanding depends on manpower, the evaluation interpretation is weak and the like in a traditional method. By means of semantic analysis, logical reasoning and domain knowledge support of a large language model and in combination with an automatic driving test domain knowledge graph, generation of a structured test portrait of a tested system, construction of a test demand list, generation of a semantic test scene description file, interpretable test result attribution, multi-dimensional comprehensive evaluation and report output are completed in sequence. And a test portrait is updated through report feedback to form a closed loop. The method is suitable for function verification and performance evaluation of an automatic driving simulation test, can also be used for post-processing analysis of real vehicle test data, realizes automation of a whole test process and interpretability of an evaluation result, and improves test precision and efficiency.

The invention discloses an automatic driving test scene generation method and device based on a text collision report, equipment and a medium, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining an unstructured text collision report, converting the unstructured text collision report into a high-dimensional numerical vector through preprocessing and feature extraction, and obtaining a high-dimensional numerical vector; a typical collision mode is identified and classified by using an unsupervised clustering algorithm, a logic scene is further constructed in a parameterized manner, physical feasibility is verified through a kinematics verification module, risk levels are divided, and a risk scene library is formed. And finally, generating an automatic driving test case set by adopting a stratified sampling strategy. By using the collision text data, real, diversified and physically feasible test scenes are generated, and the efficiency and accuracy of automatic driving safety assessment are improved.

The invention discloses an automatic testing method and system for energy consumption and driving of a pure electric vehicle, and belongs to the technical field of vehicle testing, and the method comprises the steps: obtaining a theoretical working condition curve of the energy consumption and driving testing of the pure electric vehicle; according to the theoretical working condition curve, carrying out energy consumption and driving initial test on the vehicle; obtaining the electric energy consumption and the driving range of the vehicle in the CSSM stage, the electric energy consumption and the driving range of the vehicle in the CSSE stage and the total electric energy consumption of the initial test vehicle in the initial test process; determining a target driving mileage of the vehicle in the CSSM stage according to the electric energy consumption, the driving mileage, the electric energy consumption of the vehicle in the CSSE stage, the driving mileage and the total electric energy consumption of the vehicle tested for the first time; updating the theoretical working condition curve; and the updated working condition curve is used for carrying out energy consumption and continuous driving re-testing on the vehicle. Therefore, the vehicle energy consumption and driving test are ensured to meet related regulations, and the efficiency and accuracy of working condition curve determination are improved.

The invention provides an interference injection system for an automobile driving test, which comprises a test platform, an uphill plate, a downhill plate, a light-transmitting plate and an identification arrangement mechanism, and is characterized in that the identification arrangement mechanism comprises a lifting plate, an electric push rod, magnetorheological fluid, an electromagnetic coil, a reserve adjusting mechanism and a main control unit; and the main control unit can control the electromagnetic coils at different positions in the lifting plate to be electrified through the upper part of the light-transmitting plate, and the road sign is formed after the magnetorheological fluid at the upper part is gathered through the generated magnetic field, so that the identification capability of the automobile on the road sign can be tested, and the main control unit can test the identification capability of the automobile on the road sign by regulating and controlling the electrification of the electromagnetic coils. According to the invention, instantaneous switching of road sign interference is realized, and after the storage amount of the magnetorheological fluid is adjusted through the storage amount adjusting mechanism, the color of the road sign can be lightened when the road sign is generated, and the road sign worn by an external environment on a real road can be simulated, so that the performance of an automobile can be tested in an omnibearing manner.

The invention relates to the technical field of automobile testing equipment, and discloses a new energy automobile chassis load testing device which comprises a testing table, a ramp is connected to one side of the surface of the testing table, electric sliding rails are symmetrically installed on the two sides of the testing table, and walking frames are slidably arranged in the electric sliding rails. Telescopic rods are arranged on the two sides, close to each other, of the walking frame, pressure detection mechanisms are arranged at the tops of the two sides of each telescopic rod correspondingly, each pressure detection mechanism comprises a mounting plate, the mounting plates are fixedly connected with the two sides of the corresponding telescopic rod correspondingly, and hanging rods are fixedly mounted on the two sides of the center of the bottom of each mounting plate; the bottom of the hanging rod is hinged to a swing arm, an extension arm is movably installed in the swing arm, and the sides, close to each other, of the extension arm are rotationally connected with a gravity roller, the driving test of various different items can be simulated, continuous running is achieved, the integration degree is high, and the test time is highly saved.

The present disclosure is related to a RAN node, a management system, a CN node, and methods for UE selection for MDT. A method at a RAN node for selecting one or more UEs for MDT comprises: receiving UE selection information indicating one or more criteria for selecting UEs for MDT; and selecting the one or more UEs for MDT based on at least the received UE selection information.

The invention discloses a traffic scene simulation method based on an accident report, and aims to solve the problem of insufficient complex dynamic scene data in traffic simulation. According to the method, physical clues (such as vehicle speed, collision angle, quality and the like) and context information (such as road types and environmental conditions) are extracted from a real traffic accident report, and a high-fidelity traffic scene data set is generated. In foreground processing, the extracted physical clues are used for accurately constructing a vehicle motion trajectory, diversified dynamic traffic scenes are generated through a pre-collision trajectory planning algorithm, and trajectory prediction is optimized in combination with a large language model to ensure that the real physical law is met. In background processing, high-quality three-dimensional scene reconstruction is carried out according to environment information in an accident report, a vivid background image is generated, and the consistency of a visual angle and illumination is kept. And finally, through fusion of the foreground vehicle track and the background image, a visual and physical double real traffic simulation video is generated. The method significantly improves the diversity and authenticity of traffic scenes, reduces the dependence on real data collection, is widely suitable for high-fidelity traffic simulation scenes such as automatic driving test and traffic flow analysis, and effectively promotes the progress of the traffic simulation technology.

The invention provides a simulation method and device for an intelligent driving test, electronic equipment, a program product and a storage medium. The simulation method comprises the steps that a to-be-tested problem set and a traffic sign data set of a target area are acquired; according to each to-be-tested problem in the to-be-tested problem set, processing an acquired data set of a vehicle, and determining an acquired data subset corresponding to each to-be-tested problem; on the basis of the collected data subset corresponding to each to-be-tested problem, real vehicle simulation of each to-be-tested problem is generated; traversing and combining all traffic sign data in the traffic sign data set to generate corresponding road network simulation; and combining the real vehicle simulation of each to-be-tested problem with the road network simulation to obtain target simulation of the intelligent driving test. Therefore, integrated verification of high authenticity, high coverage, compliance and repeatability of intelligent driving is realized.

The application relates to a world model construction method, in particular to an end-to-end automatic driving test high-fidelity lightweight world model construction method, which constructs a high-fidelity world model, solves the problems of large world model parameters and low inference efficiency, performs knowledge distillation on the world model, reduces model parameters on the basis of retaining world model generation capacity, and improves inference efficiency; a CUDA operator is self-defined for a world model calculation bottleneck part, memory allocation is optimized, and a single-device multi-thread scheduling and multi-device cooperative calculation method are used to improve the inference efficiency of the high-fidelity world model. The application can construct an end-to-end automatic driving test high-fidelity lightweight world model, effectively solve the problems of low multi-modal information alignment accuracy, poor cross-view and cross-frame consistency, and low inference efficiency of the existing world model, improve the confidence of an end-to-end automatic driving system test process, and greatly accelerate the test efficiency of the end-to-end automatic driving system.

This invention discloses an autonomous driving testing method, apparatus, device, and storage medium, relating to the field of autonomous driving technology. The method includes: acquiring a scenario configuration file for autonomous driving testing; parsing behavior execution information from the scenario configuration file; performing content transformation on the behavior execution information to obtain target behavior execution information, wherein the depth of the behavior tree structure indicated by the target behavior execution information is less than the depth of the behavior tree structure indicated by the behavior execution information; constructing a scenario behavior tree for the test scenario according to the target behavior execution information; parsing the candidate parameter space of the tested object and the test scenario from the scenario configuration file, the candidate parameter space including all parameters used to implement autonomous driving testing; filtering parameters in the candidate parameter space based on the test task of autonomous driving testing to obtain a target parameter space; and executing autonomous driving testing according to the scenario behavior tree and the target parameter space. This invention can improve the efficiency of autonomous driving testing.

The invention discloses a metal material surface corrosion resistance testing device and method, and relates to a metal material corrosion resistance testing technology, the metal material surface corrosion resistance testing device comprises a testing machine body and a lifting stand column fixedly mounted at the rear of the top surface of the testing machine body; the test mechanism is arranged above the test machine body, the test mechanism comprises a test platform, test cone hoppers are arranged on the front side and the rear side of the interior of the test platform, and the test cone hoppers are used for limiting corrosive liquid to the interior so as to realize corrosion test; the bottom of the test platform is provided with a recovery mechanism, and the recovery mechanism comprises a recovery sliding plate. According to the device and the method for testing the surface corrosion resistance of the metal material, the double-station testing platform drives the testing cone hopper to perform corrosion testing on the metal material, and dust before testing and corrosive liquid after testing are collected, so that subsequent corrosion condition observation is facilitated; the convenience and the accuracy of the metal material in the testing process are improved.

A user equipment (UE) may receive, from a base station, configuration information that indicates one or more first measurement types. Accordingly, the UE may transmit, to the base station, periodically and while the UE is in an idle mode or an inactive state, first measurements based at least in part on the one or more first measurement types indicated in the configuration information. The UE may further transmit, to the base station, second measurements based at least in part on a second measurement type. The first measurement type(s) may be associated with a minimization of drive test (MDT). Similarly, the second measurement type may be associated with an MDT.

A mobile wireless communication device (mobile device) receives a Minimization of Drive Test (MDT) configuration comprising criteria for performing event-based logging. The mobile device determines if the criteria have been met and, in response to a determination that the criteria have been met, performs an MDT in accordance with the MDT configuration. The MDT results are logged in the memory as an MDT log. The mobile device transmits, to a base station, a log availability indicator indicating the availability of the event-based MDT log.

The embodiment of the present disclosure provides a kind of automatic driving test scene extraction method based on deep embedding clustering and medium, belong to data processing technical field, specifically include: to real traffic accident is deep accident deconstruction and obtains accident data, reconstructs the accident data collected, and extracts the pre-collision trajectory sequence of accident participant;Pre-collision trajectory sequence and static environment information are combined, and pre-collision trajectory matrix is created;With pre-collision trajectory matrix as input, dimension reduction is processed using heap auto-encoder, and low-dimensional embedding feature is obtained;Latent feature is used as input variable, clustering is carried out using k-mean algorithm, and multiple class feature cluster is obtained;According to the label in each feature cluster, backtrack pre-collision trajectory matrix, obtain the scene information of each cluster and summarize the scene commonality of each cluster, obtain the scene description corresponding to each typical accident class.By the scheme of the present disclosure, the implantability and adaptability of typical scene are increased.

The application discloses a kind of end disc type screw pump vertical test device, including test screw pump and test oil tank;The top of test screw pump is equipped with test motor for driving test screw pump to run;Test oil tank is also equipped with inlet pipeline, and inlet pipeline is connected with the oil inlet of test screw pump;Inlet pipeline is equipped with inlet pipeline adjusting mechanism, which can connect inlet pipeline with oil inlet at different positions;Test oil tank is also equipped with outlet pipeline, and outlet pipeline is connected with the oil outlet of test screw pump;Outlet pipeline is also equipped with outlet pipeline adjusting mechanism, which can connect outlet pipeline with oil outlet at different positions.The application can meet the test requirements of different specifications of end disc type screw pump, so that a set of test device can quickly meet the test work of various inlet and outlet direction combination modes of different specifications of end disc type screw pump, improve efficiency, save cost, better meet the efficient output of end disc type screw pump.

The present invention aims to improve the efficiency of automobile development by front loading the evaluation of an on-road driving test, and comprises a virtual evaluation step of simulating the on-road driving test using a driving environment model which models a driving environment, an operating state model which models an operating state, and a vehicle model which models the actual vehicle; a bench test evaluation step of imitating the on-road driving test by combining a part or all of the actual vehicle, a part or all of the vehicle model, a part or all of the driving environment model, and a part or all of the operating state model, and conducting a bench test of a part or all of the actual vehicle, and an on-road driving test evaluation step of conducting the on-road driving test of the actual vehicle in a real driving environment.

The invention provides an automobile automatic driving test method for simulating a complex environment, and the method comprises the following steps: constructing a simulation test scene and a test simulation body for testing an automobile, and mapping the test simulation body into the simulation test scene; querying accident-prone nodes in the simulation test scene, marking one of the accident-prone nodes as a false node, and marking the other nodes as accident nodes; common accidents are injected into the accident nodes for simulation, and false accident information of false nodes is generated at the same time; performing an automatic driving test on the test simulation body in the simulation test scene, and sending false accident information to the test simulation body; acquiring decision data passing through the false node and the accident node in the test process of the test simulation body, and acquiring a test result based on the decision data; by testing whether the tested vehicle blindly follows and identifying common accidents, the automatic driving performance of the vehicle can be fully tested, so that a decision strategy can be optimized in time.