2764 results about "Vehicle positioning" patented technology

The invention belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle flight path positioning method and system based on multi-source information fusion. The method comprises the following steps: acquiring an original observation data stream of an unmanned aerial vehicle in real time through a sensor array, and preprocessing to output a multi-source data sample set; calculating relative motion increment between adjacent key frames through an IMU pre-integration model, and extracting local pose estimation through a visual geometric calculation method; evaluating the confidence of each sensor in real time to dynamically generate an adaptive weight coefficient; and constructing a tight coupling fusion filter taking position, speed and attitude errors as core state quantities, and outputting a global optimal trajectory of the unmanned aerial vehicle. According to the invention, the weight is dynamically distributed through the real-time confidence of the sensor, the multi-source data association is fully mined, and the flight path positioning precision and environmental adaptability of the unmanned aerial vehicle are improved.

The invention discloses an unmanned aerial vehicle positioning system and method based on multi-source position signal fusion, and relates to the technical field of unmanned aerial vehicle positioning, and the system comprises a plurality of positioning data collection units which are carried on an unmanned aerial vehicle, and each type is specially used for collecting positioning data of a single source; the positioning data processing unit is suitable for calculating a confidence coefficient value based on the attribute parameter of each positioning data, and determining a weight of each positioning data according to the confidence coefficient value; and the positioning data fusion unit is suitable for fusing all the positioning data by applying a fusion algorithm and combining the weights of the positioning data to generate fused positioning data. According to the system, the confidence of each source positioning data is evaluated in real time, and the fusion weight is dynamically optimized according to the confidence, so that the dynamic evaluation and optimal fusion of the multi-source positioning data are realized, and the positioning precision and reliability of the unmanned aerial vehicle in a complex environment are improved.

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention discloses a container wharf hazardous chemical transport vehicle intelligent tracking and positioning system comprising a data acquisition module used for acquiring vehicle positioning, driving behaviors and environment state data; the data fusion and time synchronization module is used for performing time alignment on the multi-source data; the behavior modeling and trajectory prediction module is used for extracting driving behavior characteristics and predicting a driving trajectory; the risk assessment and anomaly detection module is used for calculating an anomaly score and predicting a driving risk; the security strategy optimization module is used for constructing a zero-sum game security control model and optimizing speed limit, path and early warning strategies; the remote control and intelligent intervention module is used for executing remote speed regulation, emergency braking, path optimization and alarm pushing in the intelligent dispatching system; and the cloud monitoring and data analysis module is used for storing a track, an environment, a driving score and a safety strategy and predicting a long-term risk trend. According to the invention, based on intelligent game and multi-modal fusion, accurate tracking and safety management and control of the hazardous chemical substance transport vehicle are realized.

Processing circuitry may configure a system to implement a CAR-OnMap (“CAROM”) air framework for vehicle localization and traffic scene reconstruction using the aerial video of the traffic scene. Such a system may obtain aerial video of a traffic scene including vehicles that traverse the traffic scene and a satellite map image of the traffic scene as a distinct reference image. In such an example, processing circuitry may determine aerial image reference points within the aerial image which correspond to reference points in the satellite map image of the traffic scene. Processing circuitry may responsively generate calibrated images of the traffic scene from individual frames of the aerial video and determine unique keypoints on the vehicles in the traffic scene. In such an example, processing circuitry may track the vehicles across the individual frames of the aerial video utilizing the unique keypoints. Processing circuitry may output vehicle metrics for the vehicles.

The invention provides a high-precision perception-driven automatic driving positioning method and system, and relates to the technical field of automatic driving, and the method comprises the steps: collecting vehicle environment data in real time through a multi-mode sensor array, and generating a multi-source perception data set; performing time-space synchronization processing; performing motion distortion correction on the fused sensing data stream to generate dynamic compensation sensing data; inputting a multi-source fusion positioning model, and performing three-dimensional space matching in combination with high-precision map data to obtain real-time positioning coordinates; and performing iterative optimization on the real-time positioning coordinates through an adaptive error correction module to generate a vehicle positioning result. According to the method and the device, the technical problem that the positioning precision is affected due to data distortion caused by the influence of the vehicle motion on the sensor data is solved, the distortion caused by the vehicle motion is eliminated through motion distortion correction and compensation, and the positioning precision and stability are improved.

The invention relates to the technical field of traffic monitoring, in particular to a Beidou-based traffic flow real-time monitoring method and system, and the method comprises the following steps: receiving signal data and analyzing a signal source based on a Beidou short message communication terminal, extracting vehicle positioning information and road occupation information in vehicle operation state data, and transmitting the vehicle positioning information and the road occupation information to a Beidou short message communication terminal; and analyzing the vehicle speed and the traffic flow density, and carrying out matching operation on the analysis result and the road section number to obtain traffic flow state data. According to the method, vehicle positioning and road occupation information is extracted through signal analysis, efficient acquisition and integration of traffic state data are realized, road section numbers are matched to improve data space precision, traffic pressure is reflected, dynamic flow analysis is supported, adjacent road section data are integrated, abnormal areas are positioned, shunting paths and proportions are optimized, and emergency response efficiency is improved. Real-time regulation and control of traffic flow are realized through dynamic pushing, the flexibility and responsiveness of traffic operation are enhanced, and the monitoring precision and the road network operation efficiency are comprehensively improved.

The invention discloses an unmanned aerial vehicle positioning navigation and path planning optimization method based on machine learning, and the method comprises the steps: carrying out the three-dimensional grid division of an unmanned aerial vehicle activity region based on a sensor coverage range, obtaining the obstacle layout condition of each grid, calculating the environment complexity of each grid based on the obstacle layout condition, and carrying out the calculation of the environment complexity. Setting response time for the grid according to the environmental complexity, constructing an obstacle avoidance model, obtaining an obstacle avoidance strategy according to the obstacle avoidance model, constructing a way-finding algorithm based on the obstacle avoidance strategy, taking the response time as interval search time of the way-finding algorithm, and obtaining a feasible path set based on the way-finding algorithm in an unmanned aerial vehicle activity area, and constructing a multi-objective function to optimize the feasible path set to obtain an optimal path. The method adopts a machine learning method, can adapt to the requirements of different environments and tasks, effectively enhances the obstacle avoidance capability and optimizes the path, improves the autonomy and intelligence level of the unmanned aerial vehicle, and has good interpretability.

The invention discloses a Bayesian data fusion vehicle positioning method and system based on a Kalman filter. The method comprises the following steps: firstly, preprocessing positioning data acquired by a vehicle sensor positioning system; carrying out forward filtering on the preprocessed data through a Kalman filter; inputting the filtered data into a Bayesian fusion module for data fusion; and finally, carrying out backward filtering output on the fused data through a Kalman filter. By combining the Bayesian estimation with the Kalman filter, the method can dynamically process the data abnormity while denoising and estimating the sensor data, and improves the precision and robustness of vehicle positioning.

The invention discloses a substation route inspection planning method based on deep learning. The method comprises the following steps: acquiring substation equipment state data and environment monitoring data; performing multi-dimensional feature analysis on the equipment state data based on a preset depth feature extraction model to generate an equipment health degree evaluation vector; based on the environment monitoring data and the equipment health degree evaluation vector, multiple candidate paths are generated through a reinforcement learning algorithm, an initial inspection path is obtained through screening in the multiple candidate paths, and the initial inspection path is dynamically optimized by fusing geographic information in real time; according to unmanned aerial vehicle positioning data and equipment state feedback, if it is detected that the health degree is lower than a threshold value or the environmental risk exceeds the limit, local path re-planning is triggered, and navigation to high-risk equipment is preferentially carried out. The method has the following advantages and effects that the intelligence, accuracy and safety of routing inspection path planning can be improved, and it is ensured that the unmanned aerial vehicle can efficiently and reliably execute the substation equipment routing inspection task.

The invention discloses a vehicle navigation positioning method and system when a navigation signal is lost, and relates to the technical field of vehicle autonomous localization, and the method comprises the steps: carrying out the tight coupling matching based on a geomagnetic feature map, obtaining the geomagnetic sequence similarity through a sliding window dynamic time warping algorithm, and generating a fusion positioning track; extracting a road network topology connection relation from the high-precision map, constructing a road connection graph Laplacian matrix with a weight, and obtaining a topology constraint correction coefficient through a graph convolutional network in combination with the fusion positioning track; and constructing a joint optimization function based on the topological constraint correction coefficient, and solving an optimal positioning coordinate by adopting an asymmetric Gaussian-Newton iterative algorithm. According to the method, the defects of topology modeling stiffness and non-linear optimization instability of a traditional method are overcome, so that high-precision and high-robustness vehicle continuous positioning is realized in a GNSS signal loss scene.

The invention relates to the technical field of highway tunnel safety monitoring, and discloses a highway tunnel intelligent fire safety early warning system, which comprises a data acquisition and preprocessing module used for acquiring video monitoring data, environment sensor data and vehicle positioning data in a tunnel in real time; the dynamic risk assessment module is used for constructing a risk field model based on vehicle trajectory and smoke diffusion space-time coupling; the emergency resource elastic adaptation module is used for automatically adjusting the induction screen and the fire fighting equipment according to the risk intensity; according to the method, the limitation of traditional threshold alarm is avoided, fire early warning nodes are greatly advanced through multi-source data dynamic fusion, long-term stability of a monitoring benchmark is guaranteed by utilizing an optical flow compensation and pollution self-repairing mechanism, and the self-adaptive migration of a calculation task is realized by utilizing the optical flow compensation and pollution self-repairing mechanism. And the initiative and reliability of tunnel safety prevention and control are obviously improved.

The invention discloses a vehicle ad hoc network system and method based on deep reinforcement learning, and relates to the technical field of vehicle networking communication. The system comprises a dynamic routing module which adjusts a relay path in real time by using a deep reinforcement learning algorithm and optimizes a network load; the relay signal transmission module is used for constructing a multi-hop relay link by means of network signals of other vehicles when the signals are weak; the multimode communication module is used for intelligently switching a V2X communication protocol and a Wi-Fi 6 communication protocol according to the signal strength and the network environment; the positioning cooperation module fuses UWB and IMU sensor data to realize high-precision inter-vehicle positioning; the security verification module is used for ensuring data security and identity credibility by adopting a national cipher SM4 encryption algorithm and a block chain smart contract; and the communication control module is used for coordinating data transmission, path selection and protocol switching. According to the invention, the problems of signal attenuation, protocol switching and safety in the dynamic environment of the Internet of Vehicles are effectively solved, and the communication capability and the system stability of the intelligent networked vehicle are improved.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

The invention provides a rail car positioning method and system based on information fusion of a laser radar and a sensor. The method comprises the steps that S1, off-line calibration of the laser radar and inertial navigation is carried out; s2, establishing vehicle pose calculation based on point cloud features according to the laser radar point cloud and inertial navigation data; according to an RFID coordinate observation solution graph optimization problem, constructing a globally optimized point cloud map; s3, based on the point cloud map, matching the map with the real-time laser radar data to obtain a vehicle positioning result; and performing pose optimization by using an error state Kalman filtering method and fusing inertial navigation data to obtain an accurate positioning result of the vehicle. Compared with a pure sensor data fusion positioning algorithm, the positioning method combining high-precision point cloud map data and fusing other sensor information has better adaptability and precision.

The embodiment of the invention discloses an agricultural vehicle positioning method and device, electronic equipment and a storage medium, and the method comprises the steps: determining the posture, position and speed of an agricultural vehicle under a geographic coordinate system according to inertial navigation data obtained by an inertial navigation system; establishing an initial observation equation according to a satellite observation equation corresponding to a global satellite positioning system and a visual observation equation corresponding to a visual sensor; when it is detected that satellite data collected by a global satellite positioning system is valid, the initial observation equation serves as a target observation equation; when it is detected that the satellite data collected by the global satellite positioning system is invalid, the initial observation equation with the satellite observation equations removed serves as a target observation equation; the current error state quantity of the inertial navigation system is corrected according to the target observation equation, and the attitude, position and speed of the agricultural vehicle are corrected according to the updated error state quantity, so that the actual positioning information of the agricultural vehicle is obtained, and the robustness of the positioning result of the agricultural vehicle is improved.

This document describes methods and systems for vehicle localization based on radar detections. Radar localization starts with building a radar reference map. The radar reference map may be generated and updated using different techniques as described herein. Once a radar reference map is available, real-time localization may be achieved with inexpensive radar sensors and navigation systems. Using the techniques described in this document, the data from the radar sensors and the navigation systems may be processed to identify stationary localization objects, or landmarks, in the vicinity of the vehicle. Comparing the landmark data originating from the onboard sensors and systems of the vehicle with landmark data detailed in the radar reference map may generate an accurate pose of the vehicle in its environment. By using inexpensive radar systems and lower quality navigation systems, a highly accurate vehicle pose may be obtained in a cost-effective manner.

The invention provides a positioning method and system based on a laser point cloud map. The method comprises the following steps: acquiring laser point cloud sequence data, IMU sequence data and RTK pose sequence data of an actual site based on a laser radar sensor, an IMU sensor and an RTK moving station; the laser point cloud sequence data, the IMU sequence data and the RTK pose sequence data are fused, and a laser point cloud map is constructed through multi-round optimization; marking and segmenting the laser point cloud map; and according to the segmented laser point cloud map, fusing the real-time laser point cloud data of the vehicle, the IMU data and the RTK pose data to obtain real-time positioning information of the vehicle. The method comprises the steps of laser radar point cloud collection, mapping based on the SLAM technology, laser point cloud marking, site high-precision map generation and U3D modeling of the site high-precision map, and high-precision positioning of a vehicle in a scene without RTK signals is achieved.

The invention discloses an underground vehicle positioning method based on visual target information fusion, and relates to underground vehicle positioning. The method comprises the steps that visual targets are arranged on the side wall and the top of an underground roadway at preset intervals; collecting multi-source sensor data; extracting a target ID (Identity) and a pixel coordinate # imgabs0 # of the target in an image coordinate system; according to the vehicle-mounted visual camera, converting the extracted pixel coordinate # imgabs2 # of the target into a three-dimensional coordinate # imgabs3 # under a camera coordinate system by using a pre-calibrated camera internal reference matrix K and an external reference matrix # imgabs1 #; querying target coordinates of the corresponding target in a world coordinate system from a database according to the target ID; according to the target coordinate and the three-dimensional coordinate # imgabs4 #, solving the position and attitude of the vehicle relative to a world coordinate system by solving an inverse projection problem, and obtaining a visual target positioning result; and performing multi-sensor fusion according to the recognition state of the visual target to obtain a vehicle positioning result. According to the method, the problems of large positioning accumulative error of the underground long and straight roadway and positioning sudden change caused by visual target misrecognition are solved.

A system and method for aligning a target to a vehicle for calibration of a vehicle sensor includes positioning a target adjustment stand relative to a vehicle, where the target stand has a base and a target mount moveably mounted on the target stand with the target mount configured to support a target, and with the target stand including actuators configured to selectively move the target mount relative to the base. The system further includes a pair of distance targets and distance sensors configured for use in measuring the distances between respective ones of aligned distance targets and sensors. The distance sensors and targets are arranged for measuring the distances from the target stand to either side of a vehicle for use in adjusting the position of the target mount. The target mount is vertically and laterally movable on a tower of the target stand, with the tower configured to rotate about a vertical axis.

The invention relates to the technical field of edge computing, in particular to an intelligent parking unattended vehicle access management system and method based on edge computing. The method comprises the following steps: a vehicle detection positioning unit obtains multi-modal data based on a multi-modal sensor array module, and realizes real-time positioning of a vehicle and real-time updating of a parking space state through a Kalman filtering algorithm in combination with a QBCN information interference variable; the license plate recognition unit completes license plate feature extraction and recognition on the basis of a multispectral imaging technology and a lightweight CRNN model under the condition of abnormal illumination. The edge calculation decision unit combines a real-time rule engine and a space-time anomaly detection algorithm, optimizes a resource allocation strategy, and realizes low-delay decision and localization control; the payment authentication unit adopts a block chain intelligent contract, dynamic rate calculation, multi-factor identity authentication and abnormal payment fusing protection; and the edge cloud collaboration unit realizes model differential updating, cross-domain task scheduling and energy efficiency optimization through federated learning and space-time data compression technologies.

Methods and systems for assisting a vehicle to park using mixed-domain image data. Image-domain data is generated based on raw image data received from a plurality of cameras mounted on a vehicle. The raw image data is associated with a parking zone outside the vehicle, and the image-domain data is generated by a feature-detection machine learning model. A bird's-eye-view (BEV) image is generated based on the raw image data, wherein the BEV image is a projected image of the parking zone. BEV-domain data associated with the BEV image is generated. The BEV-domain data includes data associated with parking landmarks in the parking zone. A computing system localizes the vehicle within the parking zone based on the BEV-domain data and the image-domain data to generate localization data. The computing system performs mapping of the parking zone based on the BEV-domain data, the image-domain data, and the localization data.

A system tracks the position of a container handling vehicle on a grid, following a route relative to rails on a frame structure forming a storage grid. The vehicle has first and second sets of wheels connected to drives for moving the vehicle in corresponding x- and y-directions on the grid. The vehicle receives information of number of rails to pass between start and stop positions in x- and y-directions according to the route from a central computer system. The container handling vehicle has at least one camera mounted underneath to film downwards while the container handling vehicle is in motion, a computer system with an image processing algorithm to process the transferred images and detect the rails in the images, and the camera is positioned so as to be able to capture at least one rail in the x-direction and one rail in the y-direction while the container handling vehicle is positioned over a column.

The invention belongs to the technical field of vehicle positioning, particularly discloses an unmanned vehicle robust position identification method based on a look-around image, and provides a position identification model based on an improved Mama module in the method. In the aspect of model input, a 360-degree look-around image is introduced; in the aspect of a model structure, a look-around image feature coding module in the model adopts a multi-path image weight sharing encoder branch for capturing multi-level and multi-channel fine-grained features, each path of encoder adopts a DenseNet network to process image features, association among multi-view images is captured through an improved Mamba module, and the multi-view image feature coding is realized through the improved Mama module. According to the method provided by the invention, the target area can be fully covered, and the spatial information can be captured without omission, so that the problem of viewpoint difference in visual identification can be solved, and the robustness and the accuracy of the position identification can be improved.

A hitch point where a receiving vehicle is coupled to a following vehicle is located relative to a leading vehicle. The trajectory or route of the following vehicle is detected and a dynamic model estimates a location of a receiving area in the receiving vehicle relative to the leading vehicle based on the trajectory or route of the following vehicle and the location of the hitch point relative to the leading vehicle.

The invention discloses a method and system for cooperatively positioning a vehicle through a door lock, and the method comprises the steps: obtaining and dynamically updating a neighbor door lock list through the detected door lock of the vehicle according to the broadcast and scanning data of a Bluetooth tag on a moving vehicle; according to the periodic detection report of the door locks on the vehicle and the global analysis of the management system, the cooperative timers of the related door locks are synchronized, and timing management for preventing repeated triggering is implemented; according to the response of the obtained neighbor door lock which initiates multicast query after the collaborative timer expires, the door lock which initiates the query verifies whether all responders are in the neighbor list, so as to judge whether the vehicle is stably parked; and according to the vehicle parking information confirmed by the door lock, the management system updates the global state and sends a parking position notification to the owner. According to the embodiment of the invention, accurate and low-misjudgment judgment of the parking state of the vehicle can be realized, and the reliability and real-time performance of vehicle positioning management are improved by means of global state synchronization.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

Provided are a UWB-based method and apparatus for positioning a vehicle. The method includes: receiving, by the UWB module, UWB signals sent by UWB anchors on the vehicle, and acquiring a first relative distance parameter between the intelligent key and the vehicle based on the UWB signals; determining, when the first relative distance parameter is within a first preset distance range, direction information of the vehicle based on the UWB signals; determining, when the first relative distance parameter is within a second preset distance range, relative position information between the intelligent key and the vehicle based on the UWB signals; and outputting vehicle guidance information based on the direction information of the vehicle or the relative position information. A maximum distance parameter in the second preset distance range is less than a minimum distance parameter in the first preset distance range.