591 results about "Smart vehicle" patented technology

The invention belongs to the field of intelligent vehicle-mounted technology, and particularly relates to a multifunctional vehicle-mounted sensing detection system based on multi-source information fusion, which comprises multi-source information acquisition, high-precision space-time synchronization and heterogeneous data preprocessing. A heterogeneous sensing system with feature level deep fusion, vehicle state dynamic performance evaluation and adaptive decision and risk evaluation capabilities is constructed, and a driving decision is dynamically adjusted through fusion perception and vehicle performance. The vehicle state and dynamic performance evaluation module is constructed by deeply integrating vehicle internal state data acquired by a vehicle-mounted diagnosis system into a perception fusion and decision planning process, and the core contradiction of disjunction of a perception result and a vehicle dynamic performance strategy is effectively solved. The system can evaluate key performance parameters such as power, braking, steering and the like of the vehicle and potential faults of the key performance parameters in real time, and the driving strategy and the safety margin are dynamically adjusted in the self-adaptive decision and risk evaluation module according to the actual physical limitation of the vehicle.

The invention discloses a multi-parameter self-adaptive optimization intelligent vehicle trajectory tracking control method in the technical field of intelligent vehicle control. The method comprises the following steps: estimating a vehicle motion state in real time according to a three-degree-of-freedom dynamic model and a sensor measurement value by adopting a self-adaptive extended Kalman filtering algorithm; based on the estimated vehicle motion state, designing and implementing a model prediction controller to perform trajectory tracking; calculating to obtain an optimal control increment sequence in a future control time domain; taking a first element of the optimal control increment sequence as an actual control instruction increment at the current moment to act on a vehicle actuator, and entering a next control period, so as to adaptively balance closed-loop trajectory tracking control of tracking precision and driving stability; according to the method, adaptive state estimation and fuzzy dynamic predictive control are fused, deep coupling of high-precision perception and intelligent decision making is achieved, and the trajectory tracking precision, the driving stability and the system robustness are remarkably improved under the complex working condition.

A high-order driving auxiliary lane changing trajectory planning method for an intelligent vehicle comprises the steps that laser radar and camera data are obtained, and feature vectors of obstacles are extracted after the data are processed by an ISP at the front end of a chip of the intelligent vehicle; constructing a spatio-temporal joint attention network model, inputting the feature vector of the obstacle into the spatio-temporal joint attention network model, calculating an interaction weight between the vehicle and the obstacle through a self-attention mechanism, and synchronously outputting predicted trajectory distribution of the obstacle and a candidate planning trajectory cluster of the vehicle; introducing a dynamic game in the game theory, and constructing a comprehensive cost function in combination with the interaction weight to obtain a screened track; and taking the screened trajectory as an initial value, inputting the initial value into a rear-end quadratic programming optimizer on the premise of meeting vehicle dynamics constraints, carrying out numerical optimization solution, searching an optimal programming sequence, and finally finishing lane changing trajectory planning. According to the invention, the interactive perception capability and riding comfort of the intelligent vehicle in a complex traffic scene can be improved.

The invention relates to the technical field of intelligent traffic and auxiliary travel, and discloses a multi-modal space-time fusion intelligent vehicle danger pre-judgment method and system, and the method comprises the steps: obtaining multi-modal sensing data around an intelligent vehicle, constructing a deep learning-based danger pre-judgment model which comprises a space-time fusion module and a risk evolution module; the space-time fusion module carries out space-time alignment and fusion on multi-modal sensing data to obtain unified space-time voxel features, and the space-time alignment and fusion processing process is focused on a high-uncertainty region through the risk attention gating module. The risk evolution module identifies objects around the intelligent vehicle according to the space-time voxel features, and predicts the motion trails and potential collision risks of the objects; and performing real-time danger judgment on the intelligent vehicle by using the world model distillation danger pre-judgment model and the post-distillation danger pre-judgment model. According to the invention, various sensing data can be effectively fused to carry out danger judgment in the vehicle driving process, and the judgment accuracy is improved.

The invention relates to the field of intelligent vehicles, and discloses a vehicle-mounted intelligent oxygen supply system and method, a vehicle and a storage medium. The system comprises a signal acquisition module used for acquiring an original respiration signal of a user; the AI processing module is used for preprocessing the original breathing signal to obtain a preprocessed breathing signal, extracting breathing characteristics of the user from the preprocessed breathing signal, learning the breathing characteristics of the user by utilizing a preset machine learning model, predicting a breathing mode of the user and generating a corresponding control signal; and the oxygen supply execution module is used for adjusting oxygen output parameters according to the control signal and outputting corresponding oxygen flow according to the oxygen output parameters. Through cooperation of all the modules, the technical effects that the oxygen utilization rate is greatly increased, the use comfort is remarkably improved, and breathing health is monitored in real time are achieved.

Systems and methods for implementing smart in-vehicle conferencing with user tracking and speaker prioritization. A controller system of a vehicle may determine, based at least in part on one or more cameras, a plurality of candidate best image sources, determine, from the plurality of candidate best image sources, a best image source for a speaker in the vehicle, determine, based at least in part on the best image source, a video feed, determine, based at least in part on one or more microphones, a plurality of candidate best audio sources, determine, from the plurality of candidate best audio sources, a best audio source for the speaker, determine, based at least in part on the best audio source, an audio feed, and provide, to a conference call line, the video feed and the audio feed.

The invention discloses a real-time cooperative task scheduling method and device for a vehicle-mounted operating system, and belongs to the field of intelligent vehicle-mounted systems and edge computing. The method comprises the following steps: collecting vehicle tracks and terminal and server state data in a vehicle-edge-cloud cooperative system in real time; using a PatchTST model to predict the future position of the vehicle based on the historical trajectory; constructing a collaborative scheduling model taking the vehicle and the edge server as intelligent agents; based on local observation, the vehicle agent generates an unloading decision, and the edge server agent generates a task migration decision; a multi-agent near-end strategy optimization algorithm for partial reward decoupling is adopted, and a strategy is optimized under a centralized training and distributed execution framework; task unloading, migration and dynamic result return are executed according to the decision; and calculating rewards based on the task completion condition and the system state, and continuously updating model parameters. According to the method, the task completion rate of vehicle-mounted task scheduling in a dynamic complex environment is effectively improved, and the average time delay is reduced.

The invention relates to the technical field of new energy intelligent vehicle energy management, and discloses an intelligent vehicle power supply energy management method based on deep learning, comprising the following steps: step A, collecting vehicle dynamic operation parameters, power battery electrochemical state parameters and external driving environment parameters, and constructing a multi-source heterogeneous data stream; and step B, inputting the multi-source heterogeneous data stream into a hierarchical deep learning architecture, wherein the hierarchical deep learning architecture comprises a convolution-long and short-term memory hybrid network for spatial-temporal feature extraction and an attention mechanism multi-task network for jointly estimating the state of charge and the state of health of the battery. Virtual rehearsal and safety threshold verification are carried out on a power distribution strategy through a digital twinborn model, potential risks are identified before an instruction is issued, increment retraining is triggered, a closed-loop feedback mechanism is formed, the defects that a traditional deep learning scheme is insufficient in safety and rigid in a vehicle-mounted scene are overcome, and the safety of the vehicle-mounted scene is improved. The system robustness is obviously improved; and the service life of the battery is prolonged.

The invention discloses an intelligent vehicle autonomous lane changing decision-making system and method based on near-end strategy optimization. Belongs to the technical field of intelligent traffic and automatic driving. The technical problem that in the prior art, defects still exist in the aspects of state space design, security constraint modeling, training strategy optimization and the like is solved. The system comprises a traffic simulation environment module, a state observation and feature construction module, a near-end strategy optimization lane change decision module, a reward calculation and safety supervision module, a training control and data recording module and a strategy export and application module. A training environment is constructed based on a microscopic traffic simulation platform, a training scene is closer to real road traffic by configuring different traffic flow densities and speed distributions, and the strategy has better adaptability under different working conditions.

The invention discloses an intelligent vehicle collaborative scheduling method based on multi-source perception and dynamic game, and relates to the technical field of traffic control, and the method comprises the steps: extracting structured prediction data of a demand prediction thermodynamic diagram, carrying out the joint mapping of the structured prediction data and a traffic control state set, and outputting a traffic control constraint set; analyzing the state feasibility of the traffic control state set according to the traffic control constraint set, and generating a candidate traffic control action set; and evaluating the dependency degree of the candidate traffic control action set on the vehicle multi-source sensing data, generating an action dependency label set, executing joint modeling and dynamic game judgment processing on the candidate traffic control action set according to the credibility evolution sequence and the action dependency label set, and outputting a collaborative scheduling scheme. Through credibility evolution based on traffic control closed-loop evidence and dynamic game decision based on scheduling action dependency modeling, the risk of collaborative scheduling failure is reduced, and the overall collaborative scheduling reliability level in an intelligent traffic Internet of Things application service scene is improved.

The invention belongs to the field of vehicle trajectory tracking control, and discloses an intelligent vehicle trajectory tracking control method and system based on differential programming, and the method comprises the steps: constructing a vehicle dynamics model; a reference trajectory time-varying curvature is introduced, delay of a steering execution mechanism is expressed as pure delay plus a first-order inertia link, and construction of a road tracking model is completed; assuming that the system is in a steady state, calculating reference values of a state quantity and a control quantity; constructing an MPC optimization problem, converting the MPC optimization problem into a QP problem, and calculating to obtain an MPC loss value; constructing a differential programming training calculation network diagram; a hypercube is used for sampling the state quantity of a vehicle, the driving speed and the time-varying reference items of a road, and construction of a network training initial state data set is completed; carrying out MPC trajectory tracking task integration, and constructing a physical model in network training; using a full-link layer network, adding tire slip angle soft constraints, completing the construction of a strategy network, carrying out end-to-end network training, and completing the control of a turning angle instruction. According to the method, the MPC online solving optimization problem can be avoided.

An intelligent vehicle active obstacle avoidance control method based on visual perception is used for improving the driving safety of a vehicle in a complex traffic scene. The invention relates to the field of intelligent driving. The system comprises an upper layer and a lower layer, the upper layer is an environment sensing module, and the lower layer comprises a risk assessment module, a path planning module and a pure tracking algorithm. The upper-layer environment sensing module comprises RRW-YOLOv11n, multi-scale features are extracted through an RGCEBlock module, feature fusion is enhanced through a Re-calibration FPN structure, the bounding box regression precision is optimized by using a WPIOU loss function, and finally sensing information is output; a lower-layer risk assessment module performs multi-source risk assessment to obtain a multi-source risk probability, calculates a posterior risk probability through Bayesian risk fusion, and plans a path through a path planning module; path tracking control is carried out through a pure tracking algorithm, and safe driving of the vehicle in a complex traffic scene is achieved.

The invention discloses an intelligent positioning system based on NDT and odometer fusion, and relates to the technical field of intelligent vehicle positioning. The data preprocessing module is used for realizing time synchronization, coordinate transformation and filtering processing of sensor data; the NDT matching module is used for providing a local positioning result based on point cloud matching; the fusion filtering module is used for fusing NDT and odometer data by adopting a covariance-based Kalman filtering framework; and a state management module. According to the intelligent positioning system based on NDT and odometer fusion, through innovative designs such as multi-sensor space-time synchronization, dynamic anomaly detection based on mahalanobis distance, three-dimensional motion model and covariance propagation, adaptive zero offset estimation and drift correction, multi-state machine management and the like, centimeter-level positioning precision is realized; the method has the advantages of strong robustness, high adaptive ability and good real-time performance, is easy to integrate based on an ROS framework, and can meet the positioning requirements of scenes such as automatic driving.

The invention relates to a tunnel scene-oriented vehicle-tunnel collaborative innovation mode and a safety operation and maintenance management and control system and method thereof. The system comprises a vehicle-tunnel collaborative subsystem, a tunnel safety operation and maintenance and intelligent disaster prevention subsystem and a vehicle-tunnel digital base. The vehicle-tunnel cooperation subsystem is composed of an intelligent vehicle and an intelligent tunnel side subsystem, multi-source sensing fusion, road condition prediction and cooperative decision making are achieved, and a low-delay vehicle-tunnel integrated data flow is provided for the vehicle-tunnel digital base. The tunnel safety operation and maintenance and intelligent disaster prevention subsystem collects the operation state and environment data of electromechanical equipment and uploads the operation state and environment data to the vehicle tunnel digital base. The vehicle and tunnel digital base carries out unified processing, analysis and decision making on vehicle and tunnel data based on a full space-time digital twinborn model and an artificial intelligence algorithm, distributes road resources for an automatic driving vehicle and issues an electromechanical equipment control instruction. According to the system, tunnel traffic safety is improved, operation and maintenance difficulty and cost are reduced, and safe operation and intelligent management of the whole life cycle of the tunnel are supported.

The invention relates to the technical field of intelligent vehicle control, in particular to a multi-agent model parameter initialization method and device based on imitation learning and a storage medium, and the method comprises the steps: S1, constructing an imitation learning framework based on a single-vehicle expert strategy, training a single-agent strategy network through employing an iterative online strategy learning normal form, and obtaining a single-agent model; obtaining an initial strategy network model with basic driving ability; s2, a controlled random expert distillation mechanism is introduced in the training process, augmented state vectors aligned with the multi-agent environment are constructed through virtual other vehicle state injection, expert action tendency distribution is dynamically adjusted through a strategy focusing factor, and enhanced pre-training strategy network parameters are obtained; and S3, copying the enhanced pre-training strategy network parameters and initializing each agent strategy network in the multi-agent reinforcement learning environment. According to the method, the problems of slow convergence and easy local optimum in a multi-vehicle cooperative task are solved, hot start is realized through imitation learning, and the training efficiency and the final performance are improved.

The invention provides an intelligent parking space distribution method and system for a new energy charging station. The method comprises the following steps: acquiring vehicle parameter information of each entering vehicle and state information of each charging pile in the charging station in real time; based on the vehicle parameter information and the state information, a multi-dimensional decision model is constructed, and the multi-dimensional decision model at least comprises a member level sub-model, a power matching sub-model and a time optimization sub-model; and calculating a comprehensive priority score of each entering vehicle corresponding to each idle charging pile based on the multi-dimensional decision model, and carrying out parking space distribution based on the comprehensive priority score. According to the method, the priority rights and interests of different member levels are guaranteed, the power matching degree between the entering vehicle and the idle charging pile, the predicted waiting time of the entering vehicle and the like are fully considered, the parking space distribution is more accurate, and the resource utilization rate and the charging efficiency in the charging station are improved.

An intelligent vehicle charging system for charging a fleet of autonomous vehicles throughout a network of charging stations dispersed throughout a geographic area. The intelligent vehicle charging system includes a remote control system that is in operative communication with each of the autonomous vehicles in the fleet and each of the charging stations in the network. When an autonomous vehicle is in need of a power charge, or as directed by the remote control system, the remote control system will identify an available charging station, guide the autonomous vehicle to the charging station, verify that the autonomous vehicle has arrived at the charging station, initiate the power charging process, account and bill appropriate fees for the charging process, and log all associated activity. The remote control system is also capable of remotely and instantaneously terminating the power charging process to dynamically return a vehicle back to service.

The invention discloses an intelligent vehicle-mounted interaction system and method based on multi-sensor fusion, and relates to the technical field of multi-source data fusion. According to the intelligent vehicle-mounted interaction system and method based on the multi-sensor fusion, interaction records with characteristic action codes are obtained, the difference value of timestamps between continuous image frames and recognition actions is calculated, and based on characteristic light scores, movement scores and the difference value of the timestamps, the interaction records are obtained; establishing a time synchronization model; according to real-time data, whether light is abnormal or not is judged, if light is abnormal, a real-time timestamp difference value is calculated, and continuous image frames and recognition actions are synchronized, so that the robustness and response speed of gesture recognition in a complex environment are improved.

The invention provides a tennis ball receiving and dispatching intelligent vehicle based on machine vision and a working method of the tennis ball receiving and dispatching intelligent vehicle. The intelligent vehicle is composed of a visual recognition module, an automatic picking module, a motion control module and an intelligent ball serving module. The visual identification part adopts a double-strategy fusion case, and obtains the accurate three-dimensional pose of the tennis ball through contour and height feature matching; the automatic picking module is of a belt transmission integrated structure, and continuous rolling-in, separation and warehousing of the tennis balls are achieved. The motion control module is based on a crawler-type differential chassis and a PID algorithm, and precise control over paths and postures is achieved. The intelligent serving module adopts a double-friction-wheel differential serving structure and is combined with a pitch angle and horizontal angle adjusting mechanism, so that multiple types of serving such as upward rotation, downward rotation and flat striking are realized, and the serving mode can be dynamically adjusted according to action characteristics of a trainer. The system can automatically complete the whole process of tennis ball recognition, picking, storage, moving and serving, the training efficiency and the automation level are improved, the labor burden is reduced, and the system is suitable for multi-scene and multi-intensity tennis training.

Directed to implementing Internet of Things (“IoT”) functionality, and, in particular embodiments, implementing added services for OBD2 connection for IoT-capable vehicles. In various embodiments, a portable device (when connected to an OBD2 DLC port of a vehicle) might monitor wireless communications between a vehicle computing system(s) and an external device(s), might monitor vehicle sensor data from vehicular sensors tracking operational conditions of the vehicle, and might monitor operator input sensor data from operator input sensors tracking input by a vehicle operator. The portable device (or a server) might analyze either the monitored wireless communications or a combination of the monitored vehicle sensor data and the monitored operator input sensor data, to determine whether vehicle operation has been compromised. If so, the portable device (or the server) might alert the operator of the vehicle via a user interface and might initiate one or more remediation operations.

A control apparatus and an intelligent driving device. The control apparatus comprises: a first computing unit group, a second computing unit group, and a control unit, wherein first and second interfaces of the first computing unit group are used for respectively communicating with a first group of sensors and a second group of sensors, which first and second groups of sensors are from a first-type sensor group; third and fourth interfaces of the second computing unit group are used for respectively communicating with the first group of sensors and the second group of sensors; the first to fourth interfaces are first-type interfaces; the first computing unit group and the second computing unit group communicate with each other by means of second-type interfaces, and the first computing unit group and the second computing unit group communicate with the control unit by means of third-type interfaces and fourth-type interfaces, respectively; and the first computing unit group and / or the second computing unit group generate / generates, on the basis of signals received from sensors, control information for controlling a vehicle. When no fault occurs in the control unit, the control unit sends control information. When the present solution is applied to the field of intelligent vehicles, the high reliability of an intelligent driving system can be achieved at a low cost.

Smart vehicle-to-everything (V2X) preemptive connections for emergency and / or potential accident scenarios. A V2X-enabled vehicle may determine a current route of the vehicle and high-risk areas through which the vehicle may travel (e.g., a hospital zone, high pedestrian traffic area, etc.). The vehicle may determine a distance (d) from a beginning of the high-risk area and begin establishing a V2X connection upon reaching the determined distance. The V2X-enabled vehicle may also, or alternatively, pre-emptively establish a V2X connection upon detecting an event initiative of an emergency, a hazardous situation, or a potential emergency. Examples of such events may include a sharp turn, a vehicle speed exceeding a speed threshold, a sudden acceleration or declaration, etc. A preemptive V2X connection may, for example, enable vehicles to begin communicating with one another faster in case of a threat, emergency, or unsafe conditions for vehicles in the area.

The embodiment of the application discloses a kind of diagnostic process abnormality monitoring method, device, medium, OBD module and vehicle;Based on abnormality monitoring method, the leakage failure that can occur in fault diagnosis process is included in the main object of monitoring, by the signal monitoring of the object to be monitored, especially the signal monitoring of the evaporation system of fuel vehicle, on the basis of analyzing signal characteristics, with existing hardware structure, the abnormality monitoring of fault diagnosis system is realized;In addition, the related scheme is also applicable to the development of computer storage medium, OBD module and intelligent vehicle, and the upgrading of original system can be conveniently realized by the upgrading of control software.

The present disclosure provides a system and a method (200) for detecting an agent vehicle entering a traffic lane. The method includes the use of a sensor for collecting (202) data from nearby vehicles and a control unit for extracting (204) features from this data. These features are then inputted (206) into a Long Short Memory (LSTM) model, which generates an n×1 output vector (208). A user-defined loss function is applied (210) to balance class imbalances in the output vector. The processor predicts (212) the probability of an entry maneuver at several future intervals based on the outputs of the LSTM model. If an entry maneuver is predicted, the processor adjusts the navigation or steering strategy of the agent vehicle accordingly (214).

The invention discloses a control method and device and a vehicle, the method comprises the steps that obstacle information and motion state information of the vehicle are acquired, the obstacle information indicates the position of a first obstacle relative to the vehicle, the speed of the first obstacle and the motion direction of the first obstacle, and the motion state information indicates the speed and the motion direction of the vehicle; determining an obstacle avoidance strategy for the vehicle to avoid the first obstacle according to the obstacle information and the motion state information; when the obstacle avoidance strategy meets a first condition, a first light device is controlled to prompt first information, and / or a first sound production device is controlled to prompt second information; wherein the first light device is a light device outside a cabin of the vehicle, and the first sound production device is a sound production device outside the cabin of the vehicle. The technical scheme can be applied to the field of vehicle safety associated with intelligent vehicles such as electric vehicles and new energy vehicles, the risk that the vehicle collides with the obstacle is reduced under the condition that vehicle obstacle avoidance fails, and therefore the obstacle avoidance success rate of the vehicle is increased.

The invention specifically discloses a data skeleton matching method and system based on an intelligent Internet of Vehicles environment, and the method comprises the steps: carrying out the type division of a vehicle according to the automatic driving level of the vehicle, calculating the specific data demand intensity, scene emergency degree score and communication constraint degree of the vehicle, constructing a quantitative model based on a spatial view level demand, calculating a view demand factor, and determining the spatial view level demand; global fusion is carried out on the vehicle automatic driving level, the specific data demand intensity of the vehicle, the scene emergency degree score, the communication constraint degree and the view demand, and the precision level and scale of a final data skeleton are output; and according to the precision grade and the scale of the final data skeleton, the road side unit encapsulates the sensing data and sends the sensing data to the requesting vehicle. By adopting the technical scheme, the personalized and accurate matching of the data response in the vehicle-road cooperation system is realized through a multi-level and self-adaptive decision process, and the communication resource utilization rate, the data transmission real-time performance and the vehicle driving safety are improved.

The invention discloses a multi-element perception fusion positioning system and method based on vehicle-road cooperation, and belongs to the technical field of intelligent network connection automobiles, and the method comprises the steps: collecting multi-mode sensor data at a vehicle-mounted end and a road side end, and constructing a local semantic map; cross-source alignment and fusion of semantic graphs are realized through a graph structure matching algorithm and a time window dynamic alignment mechanism, and collaborative semantic graphs are generated; on the basis of data source redundancy, observation angle difference and historical credibility distribution characteristics of nodes, a nonlinear confidence regulation and control model is constructed, and self-adaptive adjustment of multi-source data fusion weights is achieved; key semantic events are extracted on the basis of the fused semantic map, and semantic-driven position state reasoning is carried out in combination with a prior map and historical trajectory data; on the basis of real-time positioning error feedback, updating a confidence model structure and parameters by using an online sample mining and edge cloud collaborative optimization algorithm; the method has high precision and self-learning capability, and is suitable for an intelligent vehicle positioning system in a complex traffic environment.

This application provides a cross-application service scheduling and execution method and system in a vehicle environment, relating to the field of intelligent vehicle system technology. It acquires known service-related data of the vehicle system, as well as environmental perception data and user voice information of the vehicle environment; parses the user voice information to obtain target demand information, intent association information, and voice feature information; forms scene adaptation parameters based on environmental perception data; generates multiple second instruction transmission signals based on target demand information; detects each target demand information based on service-related data, intent association information, voice feature information, and scene adaptation parameters, obtains detection results, and combines these with the second instruction transmission signals to schedule and execute multiple cross-application services in the vehicle environment, generating service scheduling and execution results. This achieves dynamic coordination and orderly scheduling of cross-application services, improving the response accuracy and execution security of the vehicle system in complex driving scenarios.

The present invention discloses a vehicle fuzzy feedforward path tracking control method based on hydraulic steering leakage compensation. The method comprises: acquiring the steering wheel angle, wheel angle, and piston rod displacement; outputting desired wheel and steering wheel angles in different cycles using a path tracking algorithm; obtaining a feedforward leakage compensation parameter via fuzzy control; constructing a fuzzy control system; defuzzifying it using the centroid method to yield a fuzzy sliding mode controller; comparing the actual rear wheel angle; and controlling a compensation solenoid valve to replenish fluid each cycle. These steps are repeated for subsequent cycles until the path tracking process is complete. The method precisely determines the feedforward compensation parameter, thus accurately controlling the solenoid valve's duty cycle to timely compensate for leakage and enhance the path tracking accuracy of intelligent vehicles.

This invention provides a laser adhesive removal device and method for airport runways, belonging to the field of airport runway adhesive removal technology. It includes an intelligent vehicle and a laser cleaning device. The laser cleaning device includes laser galvanometers, with at least two galvanometers. Multiple laser galvanometers are connected to the bottom of the intelligent vehicle and arranged perpendicular to the vehicle's forward direction. By arranging multiple laser galvanometers perpendicular to the forward direction, the width of a single operation is extended to the full width of the intelligent vehicle. Compared to single-head cleaning equipment, the efficiency is increased several times. Furthermore, by using lasers for adhesive removal, the entire process requires no chemical solvents or corrosive reagents, fundamentally avoiding chemical residues, environmental pollution, and potential damage to the runway structure.