1665 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.

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 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 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 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.

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.

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.

The invention discloses an intelligent logistics transportation operation system and a method thereof, and relates to the technical field of logistics transportation intelligent management. The whole logistics transportation process is comprehensively optimized through an intelligent means, in the aspect of Internet of Things equipment monitoring, equipment state data are collected in real time, the health condition is evaluated through machine learning, stable data transmission is ensured through a standby channel or local cache, an unmanned vehicle positioning module is combined with a multi-source fusion algorithm, the positioning precision in a complex environment is remarkably improved, and the positioning accuracy is improved. The distribution path is optimized, a multi-band RFID scanning technology and a deep learning algorithm are adopted, goods classification and storage layout are optimized, the storage efficiency is improved, the comprehensive optimization module globally adjusts a transportation route, a distribution task and a storage strategy through a multi-target optimization algorithm, optimal resource configuration is achieved, and the storage efficiency is improved. The problems of unstable dynamic data, large positioning error, low storage efficiency and the like are effectively solved, and the intelligent level and the overall operation efficiency of a logistics system are remarkably improved.

The invention relates to the technical field of navigation, in particular to a high-positioning-precision vehicle self-adaptive navigation method and a vehicle-mounted navigator, and the method comprises the following steps: obtaining real-time vehicle positioning data, real-time event information and weather data, carrying out association matching with geographical road sections, and establishing a data record for each road section. According to the invention, quantitative evaluation is carried out on each road segment factor based on the preset reference, and a road segment navigation risk score reflecting a real-time risk level is generated in combination with a dynamic weight distribution mechanism of events and weather, so that the response sensitivity to emergencies is enhanced. By analyzing the vehicle track speed deviation value and correcting the comprehensive traffic cost of the road section, the historical traffic flow density mean value is introduced to be compared with real-time data, and it is ensured that cost calculation gives consideration to both stability and timeliness. In the path planning stage, traffic cost and risk scores are synchronously fused, a screening model of a product of geometric average risk indexes and total cost is adopted, a dual-objective optimization function is constructed, and efficiency and safety requirements are effectively balanced.

The invention discloses a vehicle positioning method and system, and relates to unmanned driving. According to IMU data and laser radar data, a fast-lio algorithm is used to construct a global prior map; constructing a local point cloud map according to laser radar data collected in real time; recognizing and rejecting dynamic point clouds, and carrying out NDT registration on residual static point clouds and the global prior map to obtain a laser odometer pose; extracting feature points of the camera data by adopting a shii-tomasi algorithm; a Lucas-Kanade optical flow method is used to acquire motion vectors of the feature points; calculating the speed of the feature points according to the motion vector, removing the dynamic feature points with the speed exceeding a preset threshold value, and obtaining the pose of the visual odometer; reckoning the position and course of the vehicle through a CTRV model to obtain a track reckoning result; and carrying out multi-source heterogeneous data fusion by adopting a non-destructive Kalman filtering algorithm. Aiming at the defects of dynamic environment interference and non-linear motion trail modeling existing in traditional open air and mines, the positioning stability of the unmanned vehicle is improved.

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

The invention discloses an unmanned vehicle environment perception and obstacle avoidance control method in a complex dynamic scene, relates to the technical field of unmanned vehicle positioning and navigation, and is used for solving the problems of low positioning accuracy, low stability, poor real-time response capability and the like in a dynamic complex environment in an existing method. The method is specifically achieved through an unmanned vehicle environment sensing and obstacle avoidance control system, the unmanned vehicle environment sensing and obstacle avoidance control system is provided with a sensor module, a vehicle-mounted computer module and a chassis control module, and the vehicle-mounted computer module is connected with the sensor module and the chassis control module. The sensor module is provided with a solid-state laser radar, a multi-line mechanical laser radar and an inertial measurement unit which are installed on the unmanned vehicle, and the chassis control module is provided with an accelerator and brake calibration meter and a driving execution unit. The method has the advantages that the positioning accuracy, stability and real-time response capability of the unmanned vehicle in a dynamic complex environment are effectively improved.

The invention provides a mountainous road driving assistance method and device and a vehicle, and relates to the technical field of vehicles. The method is based on a vehicle carrying an unmanned aerial vehicle. The method comprises the following steps: in response to a mountain road navigation instruction, controlling the unmanned aerial vehicle to acquire traffic information about a mountain road where the vehicle is located; constructing a three-dimensional terrain model about the mountain road according to the traffic information; and generating driving path suggestion information and / or early warning information according to vehicle positioning information and the three-dimensional terrain model. On the basis of the vehicle carrying the unmanned aerial vehicle, the traffic information of the mountain road where the vehicle is located is obtained timely and accurately, the three-dimensional terrain model about the mountain road is constructed, and the driving path suggestion information and / or early warning information are / is generated, so that active guidance and risk prompt for the driving behavior of a driver are achieved, and the driving safety of the driver is improved. And the driving safety of the vehicle on the mountainous road and the driving experience of a driver are improved.

The invention discloses a port internal and external fleet positioning system and method, and relates to the technical field of intelligent positioning. The method is used for solving the problems of frequent occurrence of positioning blind areas, sensitive dynamic interference, uncontrollable error accumulation and low anchor point resource scheduling efficiency in a complex scene of a port. Anchor point coverage is dynamically calculated through metal environment signal attenuation characteristics, blind area information is generated in combination with vehicle trajectory clustering, and signal resource allocation is optimized. Ground texture gray scale entropy segmentation and cross-frame Bayesian correlation are carried out in a blind area, interference is eliminated, synchronous pose data are generated, and visual positioning robustness is improved. An error propagation chain model is constructed based on communication topology among vehicles, accumulated errors are corrected by fusing confidence coefficients of multiple vehicles, and the motorcade positioning consistency is enhanced. And finally, analyzing a port scheduling plan and a vehicle motion state, constructing a space-time prediction model, dynamically generating an anchor point instruction, and realizing on-demand scheduling of anchor point resources. The port vehicle positioning precision and the resource utilization efficiency are remarkably improved, and reliable support is provided for port automatic operation.

The invention relates to the technical field of vehicle control, and discloses a vehicle positioning and control method and system based on machine vision, and the method comprises the steps: building a vehicle kinematics prediction model, and generating a reference pose sequence according to a driving instruction through the vehicle kinematics prediction model; recording a first reference pose during image acquisition; calculating a deviation vector between a positioning result and the first reference pose when positioning calculation is completed; determining a dynamic damping coefficient according to a direction included angle between the velocity vector and the deviation vector, generating a compensation gain sequence, and converting the compensation gain sequence into a compensation torque of a current loop given value; according to the method, the effective decoupling of the sensing time delay of the sensor and the dynamic response of the carrier is realized, the phase lag is reduced on the premise of ensuring the momentum continuity of the controlled object, the instantaneous control oscillation of positioning correction is eliminated, and the mechanical stress impact of an execution mechanism is inhibited.

The embodiment of the invention provides an unmanned aerial vehicle positioning method and device, computing equipment, a storage medium and a program product, and the method comprises the steps: carrying out the positioning based on the collected current motion state information and / or environment information, and determining the current reference position of an unmanned aerial vehicle; under the condition that the transponder receiving device receives a signal of a target transponder, the time point of passing through the target transponder and the target position of the target transponder are determined, and the target transponder is any one of trackside transponders laid along a track in the track traffic system; and correcting the current reference position based on the time point of passing through the target transponder and the target position of the target transponder to obtain the target position information of the unmanned aerial vehicle. By combining the motion state of the unmanned aerial vehicle, the environment information and the accurate position of the trackside transponder, the position information of the unmanned aerial vehicle is corrected in real time, the positioning precision and reliability are improved, and the method is particularly suitable for the rail transit tunnel environment where positioning signals are unavailable.

The invention provides an unmanned aerial vehicle positioning method and system, an unmanned aerial vehicle and a medium, and the method comprises the steps: firstly, obtaining inertial motion data and visual perception data in real time, and continuously providing the inertial motion data through employing the characteristic that an inertial sensor is not limited by a signal environment, so as to avoid a positioning interruption problem when a positioning signal is weak; meanwhile, the spatial pose data is generated by performing pose analysis on the visual perception data, and the first position information is generated by combining the preset satellite map database in a matching manner, so that the defect of inertial navigation accumulative errors is made up. The inertial motion data, the spatial pose data and the first position information are fused and calculated to generate the second position information, and the second position information is converted into the GPS signal and accessed to the GPS system of the unmanned aerial vehicle, so that the error defect of a single sensor can be effectively inhibited, continuous and accurate positioning in a complex environment is realized, and the positioning accuracy is improved. The problem of application limitation of a traditional GPS positioning system in special environments with weak positioning signals and the like is fundamentally solved from the technical level.

Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used to in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior.

A system and method of calibrating an ADAS sensor of a vehicle by aligning a target with the sensor using a target adjustment stand that includes a base frame and a movable target mount configured to support a target, with the target adjustment stand including one or more actuators for adjusting the position of the target mount. The position of the target mount is adjusted based on the orientation of the vehicle relative to the target adjustment stand. Upon properly orienting the target mount, and the target supported thereon, a calibration routine is performed whereby the sensor is calibrated using the target.

The invention discloses a multi-scale map-based rapid relocation method for an unmanned logistics vehicle in a dynamic factory, and the method comprises the following steps: S1, filtering point cloud dynamic elements based on binary semantic segmentation of a deep convolutional neural network, S2, processing static point cloud, and S3, carrying out the rapid relocation of the unmanned logistics vehicle based on global and local map search. According to the method, movable elements in the cloud are filtered based on the deep convolutional neural network, and interference of a dynamic target on subsequent vehicle positioning is eliminated. The method is also combined with a GOD map descriptor to process the static point cloud, so that the subsequent positioning step is clear and rapid. According to the method, local map matching and global map matching are combined, and rapid relocation of the unmanned vehicle is achieved.

Various systems and methods are presented regarding utilizing technology onboard a vehicle to mitigate the effects of communication signals being deleteriously affected / lost when operational surroundings cause occlusion / loss of receipt of positioning / location signals at the vehicle. The vehicle can be operating non-autonomously, partially autonomously, or autonomously. To supplement the lost / occluded signals position information of other systems proximate to the vehicle can be obtained / utilized. The other systems can include another vehicle, a mobile device, an internet, and suchlike. Also, onboard devices can be utilized to determine the location, such as an onboard camera system providing information regarding street signs, building facades, and suchlike.

The invention relates to the technical field of vehicle positioning, and particularly discloses a Beidou + uwb indoor and outdoor fusion continuous positioning vehicle-mounted terminal, which comprises a vehicle-mounted power supply and a positioning terminal, the vehicle-mounted power supply provides stable power supply for an automobile, the positioning terminal is electrically connected with the vehicle-mounted power supply, and the positioning terminal comprises a Beidou positioning terminal, a UWB positioning terminal and a vehicle-mounted terminal; the Beidou positioning and the UWB positioning are subjected to position switching fusion, a position judgment rule is set, and switching is carried out between the Beidou positioning and the UWB positioning according to whether the position of a user is indoor or outdoor; by integrating Beidou positioning with UWB positioning, the Beidou positioning system can provide meter-level and even centimeter-level positioning precision in an open outdoor area, but the precision can be influenced in a complex environment. The UWB positioning technology has extremely strong anti-interference capability and ultrahigh positioning precision in indoor and short-distance ranges, and after the UWB positioning technology and the UWB positioning technology are fused, high-precision positioning in outdoor and indoor environments can be realized.