294 results about "Map matching" patented technology

The invention aims to provide a speed anomaly detection and fraud identification method and system based on trajectory data, and belongs to the technical field of road traffic safety, the method realizes abnormal trajectory detection and fraud identification through multi-stage data processing: firstly, preprocessing original trajectory data, and removing invalid data; noise points are filtered based on a DBSCAN algorithm; track segments are divided according to vehicle speed changes; matching the moving track segment to a map road through a map matching algorithm; calculating an error ratio between the calculation speed and the equipment uploading speed, and identifying an abnormal track segment; and finally, analyzing the speed distribution of the abnormal track section, and identifying a counterfeit behavior. The system comprises a data preprocessing module, a trajectory noise filtering module, a trajectory division module, a map matching module, an abnormal speed detection module and a speed verification and forgery identification module. According to the method, the accuracy of speed anomaly detection is improved, the recognition capability of a hidden speed forgery behavior is enhanced, and the authenticity and credibility of trajectory data are improved.

The invention relates to a dynamic visual map matching autonomous optimization method based on IMU sensing, and belongs to the technical field of positioning and navigation of autonomous mobile equipment. The method comprises the steps of obtaining IMU high-frequency motion data and visual image data, performing noise reduction to obtain carrier short-time attitude change information, performing semantic segmentation on an image to recognize a dynamic target, extracting feature points and performing dynamic target weight evaluation; calculating confidence degree scores of the feature points, and mapping fusion weights of the IMU and the vision through a self-adaptive credibility distribution mechanism; s3, predicting a trajectory and a potential matching area based on carrier short-time attitude change information, incrementally updating a local map, and matching by taking an IMU trajectory as a constraint to obtain an initial result; and detecting the matching validity, reducing the re-matching range by using an IMU short-time track during mismatching, and correcting an initial value to obtain a final result. The map matching precision and stability in a complex dynamic scene are improved, the real-time performance and continuity are balanced, and the problems that a traditional scheme is redundant in calculation power and slow in mismatch recovery are solved.

The invention discloses a multi-target track association method and system based on interactive attention map matching and a storage medium, and the method comprises the steps: preprocessing track data, and obtaining an input track; utilizing a track historical feature extraction network to obtain embedded features of each track; a multi-graph structure is utilized to model tracks, nodes represent one track, edges represent message passing between the tracks, and the number of the edges is limited through graph cutting; features of neighbor nodes are aggregated by using a self-attention mechanism and a cross attention mechanism, and finally embedded representation of the nodes is obtained; and constructing a score matrix, and performing a Sinkhorn algorithm on the matrix to obtain an association probability matrix. And track association is carried out by using the multi-target track association model based on interaction attention map matching, and an association task is completed. According to the method, the spatial-temporal characteristics of the track can be fully mined, the track association accuracy is improved, and the track association error rate is reduced.

Proposed is a mobile robot utilizing the uncertainty of map matching poses for localization. The mobile robot includes a point cloud sensor which acquires point cloud data through scanning, a matching pose calculation part which applies a pre-registered point cloud map and the point cloud data to a pre-registered scan-map matching algorithm to calculate a map-matching pose of the mobile robot, and an uncertainty estimation part which estimates uncertainty of the map-matching pose on the basis of a probability distribution of a preset pose space around the map-matching pose. Through this, the uncertainty of the map-matching pose is estimated to evaluate the reliability of the map-matching pose, enabling more precise localization.

The invention discloses an industrial automobile crane positioning and mapping method and device based on multi-sensing fusion, and relates to the field of machine sensing, and the method comprises the following steps: initializing a pose and offset of an inertial measurement unit by using an input laser point cloud and a pre-integration result of the inertial measurement unit; correcting laser point cloud motion distortion by using an inertial measurement unit, extracting a dynamic object point cloud, and synchronously extracting laser features and visual features with depth; calculating a key frame pose in real time by minimizing a visual re-projection error and an inertial measurement unit pre-integration error, and performing frame-to-map matching by taking the key frame pose as an initial value to further optimize the pose; a visual word bag model is utilized to quickly retrieve a closed-loop candidate frame, after laser passes geometric matching to verify a closed loop, a laser radar factor, a visual factor, an inertial measurement unit factor and a closed-loop factor are fused, a global factor graph is constructed and optimized, and a pose and point cloud map is updated. According to the method, the environment sensing capability of the industrial automobile crane can be improved, and the sensing accuracy and robustness are improved.

The invention discloses a violation person identity recognition method for gas turbine power plant on-site safety supervision, relates to the technical field of person identity recognition, and aims at recognizing specific violation behaviors, positioning the occurrence time and space of the violation behaviors and marking corresponding behavior subjects. The method comprises the following steps: acquiring operation image data, extracting behavior subject image features and equipment acquisition data, generating fusion identity information, matching violation identities, outputting structured records, evaluating violation risk levels, verifying the identities of persons in charge and filing violation records, and automatically identifying dangerous behaviors in a gas turbine power plant through the operation image data acquisition and violation behavior identification model. And in combination with the dynamic attitude vector sequence and equipment acquisition data, generating identity feature fusion data, performing map matching, identifying the identity of a violation person, labeling through a risk level label, pushing to a responsible person terminal, completing identity confirmation and violation behavior recording and archiving, and realizing whole-process supervision and closed-loop management.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

A vehicle localization system and method includes acquiring, from a first sensor, first data indicative of a first navigation state of a vehicle; acquiring, from a second sensor, second data indicative of an environment in the vicinity of the vehicle; generating, using a processor communicatively coupled to the first sensor and the second sensor, a second navigation state based on matching the second data to a known map of the environment; and, generating, using the processor, a current navigation state based on the first navigation state and the second navigation state.

The invention relates to an unmanned aerial vehicle navigation decoy defense method based on visual map matching, and solves the technical problems that a traditional relative positioning mode is easy to cause positioning drift and an unmanned aerial vehicle is easy to decoy and interfere under long-term and large-scene conditions. The absolute positioning method based on the scene coordinate regression network is adopted, positioning drift is avoided in mechanism, and the scene coordinate regression network based on deep learning is used for performing positioning information mapping on the visual image and the scene coordinates. And inputting a visual image shot by the unmanned aerial vehicle into the optimized scene coordinate regression network to obtain a scene coordinate predicted value, and calculating the scene coordinate predicted value by using an RANSAC algorithm to obtain a pose estimation result. And comparing the pose estimation result with global navigation satellite system (GPS) positioning information received by the unmanned aerial vehicle to complete positioning signal decoy recognition, so that the unmanned aerial vehicle can effectively defend against GPS decoy signals.

The invention discloses an end-to-end automatic driving long tail scene data acquisition and automatic labeling method. The method comprises the following steps: firstly, collecting multi-modal time series data including images, point cloud and odometer information; thirdly, calculating a comprehensive long-tail scene judgment coefficient by constructing an occupation map and fusing perception uncertainty and prediction uncertainty, thereby automatically identifying a high-risk and high-uncertainty long-tail scene; then, only for the data judged as the long-tail scene, starting a particle filtering-based track truth value generation process, fusing the motion speculation of the odometer and the map matching of the laser radar point cloud in the process, and carrying out state prediction, observation updating, resampling and weighted averaging to obtain a track truth value of the long-tail scene; and generating a high-quality self-vehicle track point sequence conforming to the vehicle kinematics constraint as a truth value label. According to the method, accurate screening and true value automatic generation of the long-tail scene are realized, the data acquisition efficiency and the labeling quality are improved, and the safety and generalization ability of an end-to-end automatic driving model in a complex scene are enhanced.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention discloses a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) / map tightly integrated navigation positioning method and system based on road marking matching, and belongs to the technical field of navigation positioning, and the method comprises the following steps: acquiring priori road map data, foresight road image data, a GNSS original observation value and an INS original observation value; a semantic segmentation network is adopted to extract road marking semantic features in the foresight road image data, and a road marking feature instance is obtained; performing nearest neighbor search on the road marking feature instance and map elements in the prior road map data, and obtaining corresponding map matching observation based on a nearest neighbor search result; and coupling the GNSS original observation value, the INS original observation value and map matching observation to obtain a navigation positioning result. According to the method, a tight combination model based on GNSS / INS and map matching original observation values is provided, the positioning performance of a navigation system in a complex city scene is improved, and omni-directional pose constraint is provided for a vehicle.

The invention discloses a quadruped robot space precise positioning method and system based on deep learning. The quadruped robot space precise positioning method specifically comprises the steps that S1, a laser radar data stream and an inertial measurement unit data stream are aligned to generate a synchronous sensing data sequence in a unified mode; s2, determining a mapping pose sequence through consistency evaluation of poses output by the offline point cloud registration algorithm pool; s3, performing parallel loopback detection to generate a unified loopback constraint set; s4, the mapping pose sequence, the loopback constraint set and the pre-integration constraint are written into the factor graph model to generate a three-dimensional point cloud reference map; s5, the local point cloud frame and the three-dimensional point cloud reference map are registered, and a map matching pose is output; s6, the map matching pose and the pre-integration result are written into a factor graph model to continuously solve and output a continuous-time six-degree-of-freedom pose result; and S7, outputting a six-degree-of-freedom pose result in continuous time. According to the invention, direction constraint consistency point cloud registration and dual-channel loopback optimization are introduced, and stable and high-precision positioning of the closed space is realized.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

The invention discloses an unplanned operation early warning method and system fused with multi-source data mining. According to the method, vehicle tracks, operation plans and electronic fence data are collected, cleaning and map matching preprocessing are carried out on the tracks, and then track sequences are mapped into low-dimensional embedded vectors through a track representation learning model; recognizing an abnormal behavior mode through clustering based on the vector, and constructing a dynamic risk index and predicting the future state of the vehicle in combination with real-time data; inputting the abnormal mode, the risk index and the prediction result into a fuzzy logic decision maker for multi-source fusion, outputting a comprehensive risk level and triggering graded early warning; a reinforcement learning mechanism is adopted to dynamically optimize decision maker parameters according to the early warning effect, and finally early warning information is pushed to a management platform and feedback is received to form closed-loop management. According to the invention, accurate and adaptive early warning of unplanned operation behaviors is realized, and the intelligent level of operation safety supervision is significantly improved.

The invention belongs to the field of tourism data processing, and provides a GIS-based culture and tourism spatio-temporal feature fusion system, which comprises a data acquisition module used for acquiring culture and tourism spatio-temporal data; the data fusion module is used for establishing semantic association between the cultural tourism data based on the knowledge graph; the GIS topological structure construction module is used for constructing a topological graph of the culture and tourism data and constructing a topological relation between the culture and tourism data based on the similarity of the topological graph and the semantic association relation; the culture and tourism time-space feature fusion module is used for constructing a culture and tourism fusion map; matching the tourist trajectory data with the cultural tourism resources; determining an influence range of the cultural activity; the prediction module is used for predicting a tourist diffusion thermodynamic diagram of a preset destination in a preset time; and the visual display module is used for displaying the cultural tourism fusion map and dynamically displaying the tourist diffusion thermodynamic diagram on the cultural tourism fusion map.

The invention provides a map matching method and device, equipment, a medium and a product, and relates to artificial intelligence, in particular to the field of automatic driving and the field of map navigation. The specific implementation scheme is as follows: for a track point of a to-be-matched track, determining the emission probability of the track point relative to each road section in a target road section combination in a map; wherein the target road section combination is a combination formed by all road sections meeting a preset rule in the initial candidate road sections of the track point, and the combination formed by all the road sections meeting the preset rule comprises a continuous road section in which the tail part of the previous road section is connected with the head part of the next road section; determining candidate road sections of the road section combination according to the emission probability; and according to the candidate road sections, determining a matched road section sequence corresponding to the to-be-matched track in a map. Through the method, the map matching efficiency can be effectively improved, and the matching calculation amount is reduced.

The invention discloses a sparse GPS map matching method based on confidence and intelligent fragment retrieval, relates to the technical field of map data processing, and realizes real-time adaptive adjustment of weights along with scenes by constructing a three-dimensional dynamic confidence model fusing road complexity, track point quality and historical matching popularity; by constructing a quaternary composite trigger logic of a confidence threshold value + track point spacing + road section topology complexity + GPS signal quality, false triggering or missing triggering caused by a single threshold value is avoided; data reliability is complemented by establishing a credibility scoring system (fusing matching success rate, use frequency and time freshness) of historical track fragments and performing two-dimensional sorting according to similarity and credibility during retrieval; the method does not need multi-sensor fusion and GPU computing power support, only depends on GPS single-sensor data, realizes balance of high-precision completion and low-redundancy calculation through a dynamic strategy, and adapts to resource-limited scenes such as vehicle-mounted edge equipment.

The invention relates to the technical field of fabricated building cost estimation, and discloses an indoor fabricated design parameter intelligent matching cost estimation method. The method comprises the following steps: constructing a dynamic coupling fabricated component spatio-temporal evolution deduction network for bearing and simulating an interaction and transition process of design parameters in a complete assembly period; based on the network, spatio-temporal trajectory simulation is carried out on historical project data, and a component evolution path sequence containing cost correlation is generated. And establishing a semantic mapping channel from the evolution path sequence to the cost element, and analyzing the sequence into a structured cost influence factor map. And for a design scheme input in real time, the description of the design scheme is injected into the network for deduction, and a target component evolution path is obtained. And carrying out map matching and factor mapping on a target path by utilizing the cost influence factor map, and automatically outputting a refined cost estimation entry. According to the method, accurate and automatic estimation of the dynamic influence cost of the design parameters is realized.

The invention relates to a point cloud map construction method, device and system, engineering machinery and a program product. The method comprises the following steps: acquiring IMU data and laser radar point cloud data; performing forward propagation integration on the IMU data, performing timestamp accumulation on the laser radar point cloud data, and generating an IMU data integration result and a point cloud data sequence which are aligned in time sequence; correcting the distortion of the point cloud data according to the back propagation of the IMU data integration result, extracting geometric features from the distortion-removed point cloud data, calculating the point-surface residual error, and constructing a state estimation constraint; aiming at a map matching failure scene, optimizing the contour residual error; fusing multiple types of residual errors to construct a residual error set; and iteratively correcting the system state through a first predetermined algorithm, and synchronously generating an odometer and a map of the underground roadway. According to the method, the IMU and laser radar data can be cooperatively processed in stages, and the forward and back propagation algorithms and multi-residual joint optimization are combined, so that high-precision state estimation and map construction are realized.

The invention relates to the technical field of non-networked indoor autonomous positioning and navigation, in particular to a smart phone positioning and navigation method and system in a non-networked indoor scene, and the method comprises the steps that a smart phone receives data returned by a Bluetooth AOA positioning base station through point-to-point communication, and an AOA position estimation result is obtained through calculation of a single-base-station AOA fusion positioning model; the smart phone collects real-time data of an IMU and a magnetometer, and user coordinates and course angles are recursively estimated through a pedestrian dead reckoning (PDR) method; according to an AOA base station deployment condition of an actual indoor space, multi-base station centroid positioning, AOA fusion positioning and PDR positioning are automatically switched, so that a position result can effectively cover a whole space scene; the method comprises the following steps: constructing an obtained binary grid map through an indoor map model, carrying out position post-processing based on map matching on positioning coordinates to obtain pedestrian coordinates, and carrying out digitization on a map model to realize map display and finally realize pedestrian position display. The method can be reliably operated in a scene lacking network coverage.

The invention discloses a 5G and Beidou fused vehicle positioning navigation system. The system comprises a multi-source heterogeneous positioning module; a high-precision map and a dynamic traffic information base; a self-adaptive multi-layer map matching module; the dynamic path planning module considers steering limitation and real-time position feedback; and a central information fusion and decision unit. According to the invention, multi-source heterogeneous positioning data such as Beidou / GPS, 5G positioning, high-precision IMU, a vehicle-mounted atomic clock and a barometric altimeter are fused, and a tight combination algorithm based on ESKF or a factor graph is adopted, so that the system can realize accurate positioning in an environment with good satellite signals, partial shielding and even serious loss. Continuous, smooth and high-precision vehicle state estimation can be output, particularly when the number of visible satellites is insufficient, double-satellite positioning can be achieved by using the vehicle-mounted atomic clock to predict the clock error and combining the elevation constraint of the barometric altimeter, and the usability and robustness of positioning are improved.

The invention provides a prototype prior and closed-loop condition augmented diffusion model-based vehicle trajectory completion method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the map matching and standardization processing of a sparse trajectory and a road network, and constructing a multi-subspace trajectory product manifold to form a unified geometric measurement; constructing a prototype priori library based on the historical trajectory, and obtaining hybrid prototype priori through retrieval fusion; a closed-loop condition augmentation diffusion model is utilized, observation masks, intermediate prediction and prototype prior are fused in reverse denoising, a track is recovered step by step, and error accumulation is suppressed through unidirectional information flow constraint and data consistency projection; and performing evaluation and rearrangement on the candidate trajectories based on unified measurement, and generating a high-precision complemented trajectory which is reachable in topology, reasonable in direction and consistent in scale. The method effectively relieves the problems of geometric distortion, lack of travel prior, condition diffusion and stiffness and the like, remarkably improves the track recovery precision, stability and diversity in a complex road network, and is suitable for the fields of intelligent transportation, urban calculation and the like.

The invention discloses a vehicle body positioning method and system of a rock drilling robot based on visual positioning. According to the method, a multi-view visual sensor is used for collecting an iron mine roadway three-dimensional environment image, rock wall feature point cloud data are extracted and matched with a reference map, inertial navigation data are combined, and position parameters are fused and optimized through a Kalman filtering algorithm; establishing an environment interference model for dust and illumination interference, and correcting position parameters by switching an enhanced positioning mode; and generating a target pose matrix according to the correction parameters, and controlling the vehicle body to move to form closed-loop control. The system comprises a visual perception module, a reference map construction module, a position calculation module, an error compensation module and a motion control module. Multi-dimensional information of the roadway rock wall is obtained through multiple cameras, visual positioning accidental errors and inertial navigation accumulative errors are effectively eliminated, the system can adapt to the complex environment, the positioning reliability is improved, and the system is suitable for precise positioning operation of the rock drilling robot in the iron ore roadway.

The invention relates to the technical field of automobile navigation, and provides a navigation driving control method and system and a storage medium. The method comprises the following steps: triggering a local subunit of a map matching module to perform matching calculation based on a target vehicle navigation starting position; if the matching calculation result of the local subunit is not consistent, the calculation result information is fed back to a map matching module, the map matching module is triggered to call a far-end calculation unit, and an initial lane-level driving path of the target vehicle is planned based on the navigation starting position of the target vehicle; road features corresponding to the initial lane level driving path are collected in real time, and the initial lane level driving path is adjusted through a dynamic compensation strategy, so that a target lane level driving path used for indicating driving of the target vehicle is formed. The technical problem that the driving experience of a user is poor due to the fact that existing road-level navigation cannot meet higher driving requirements of the user is solved.

The invention discloses a multi-source fusion continuous positioning method in combination with a prior feature map, and aims to solve the problem that an existing vision-inertial odometer easily generates accumulative errors in a GNSS denial environment, the method comprises the following steps: constructing a prior feature map comprising key frame poses, feature point descriptors and 3D coordinates based on an open source algorithm; a track displacement difference alignment strategy is adopted to complete visual-inertial system and GNSS joint initialization, and ESIKF is used to replace EKF to improve fusion precision; robust alignment of a local coordinate system and a map coordinate system is achieved through a single-frame geometric constraint accumulation-batch manifold alignment strategy; based on the reverse PnP, the current 3D point cloud and the historical 2D observation constraint are utilized to resolve the relative pose, the map matching result is used as observation to be fused with the GNSS or replace GNSS observation, the ESIKF loose coupling is utilized to eliminate accumulative errors, and continuous high-precision positioning in the GNSS failure scene is achieved.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

The application discloses a data-driven low-precision map matching method, and belongs to the field of map data processing. The method comprises the following steps: in each low-precision single package map, taking a randomly selected data element as a center point, a corresponding fixed range submap is generated; different kinds of data elements of multiple submaps are subjected to feature extraction to obtain feature information corresponding to the submaps; the feature information corresponding to two submaps corresponding to each center point in each two low-precision single package maps is subjected to automatic matching in sequence to obtain a matching score of different kinds of data elements between the two submaps, and an object corresponding to the most confident matching meeting geometric consistency is determined through the matching score; and according to the most confident matching, other objects corresponding to the most confident matching meeting geometric consistency adjacent to the most confident matching are obtained. The application guarantees the stability of matching, saves the equipment cost, and does not depend on the uniqueness of map elements.

The invention provides a method for identifying red light running of an electric bicycle at a two-phase intersection driven by trajectory data. The method comprises the following steps: 1) data acquisition: acquiring trajectory data of the electric bicycle, urban road network data and signal timing data of the two-phase intersection; 2) track data preprocessing and map matching: preprocessing the track data of the electric bicycle, and accurately mapping each track point to an urban road network; 3) red light running behavior identification: extracting the track chain passing through the target intersection, determining the steering direction of the track chain at the target intersection and the affiliated signal phase, and determining the red light running behavior according to the affiliated signal phase state; and 4) intersection red light running risk level division: counting the intersection red light running behavior frequency, selecting a proper frequency window to divide the intersection red light running behavior risk level, and visually displaying the intersection red light running behavior risk. The red light running behavior is identified through the space-time position relation between the track and the intersection, and the accuracy and efficiency of red light running behavior detection are effectively improved.

This application relates to an offline map matching method, apparatus, computer device, and storage medium. The method includes: starting from the starting position of the complete GPS trajectory to be matched, selecting a sliding window according to a set sliding window length, and searching for the shortest path set for each sliding window in the road network based on candidate starting and ending points; matching the local GPS trajectory corresponding to each shortest path to the corresponding road segment in the road network to generate the shortest matching path without backtracking, calculating the score of each matching path, and retaining the top k matching paths with the highest scores; performing local non-shortest path travel detection on the top k matching paths, and using a split matching algorithm to correct the travel trajectories of existing local non-shortest paths. The embodiments of this application effectively enhance the robustness of road network matching for local non-shortest path travel, improving the overall efficiency and trajectory matching accuracy of offline map matching.