374 results about "Vehicle behavior" patented technology

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention belongs to the technical field of automatic driving, and relates to a virtual simulation dynamic path test method based on a fixed trajectory of an accident scene, which comprises the following steps: S1, collecting and preprocessing multi-source data, analyzing the data to generate point cloud information and visual information, and extracting vehicle parameter information, vehicle behavior information, environment information and traffic participant abnormal behavior information; the vehicle behavior information comprises a fixed track; s2, a virtual simulation scene is constructed, a virtual three-dimensional scene model is constructed according to the point cloud data and the visual information, and parameters of the virtual three-dimensional scene model are set according to the vehicle parameter information, the vehicle behavior information, the environment information, the traffic participant abnormal behavior information and the traffic rules; s3, testing by taking the fixed track as a reference track, and performing dynamic path adjustment by utilizing a path planning algorithm in an automatic driving system during testing; and S4, analyzing the test data, evaluating from multiple dimensions, generating an analysis report, and optimizing suggestions. The problem of inaccurate test results is solved.

The invention discloses an unmanned aerial vehicle behavior recognition method based on multi-modal fusion and a space-time enhancement network, and relates to the technical field of computer vision and artificial intelligence, and the method comprises the steps: extracting human body joint point coordinates frame by frame from an input unmanned aerial vehicle video through employing a lightweight attitude estimation model, and generating an initial skeleton sequence; performing enhancement processing on the initial skeleton sequence, and inputting the enhanced skeleton sequence into a double-branch space-time enhancement network to perform space-time feature extraction; the method comprises the following steps: detecting and segmenting objects in a scene from an unmanned aerial vehicle video by utilizing a grouping visual model, and obtaining a pixel-level object mask and a semantic category thereof; constructing a space-time dynamic graph network based on the detected object; performing cross-modal fusion on the spatio-temporal features and the spatio-temporal dynamic graph; and classifying the fused features, and outputting a final recognition result. According to the method, the behavior recognition robustness in the low-quality video is remarkably improved, interference caused by shielding and multi-view change is effectively relieved, and the ability of understanding human-object interaction semantics is enhanced.

The invention relates to the technical field of parking resource scheduling, in particular to an intelligent parking resource scheduling system based on multi-source data analysis, which realizes full-scale digital mapping from vehicle individual behaviors and parking lot flow fields to a regional traffic network by constructing microscopic-mesoscopic-macroscopic three-level parking twin bodies and fusing dynamic and static multi-source data. Through scale conversion and data interaction among the three-level model, dynamic characteristics of parking resources in space and time dimensions are accurately captured, a high-fidelity and fine-grained digital twinborn base is provided for scheduling decision making, splitting of local optimization and global collaboration in traditional scheduling is broken through, the accuracy and foresight of resource allocation are supported, and the scheduling efficiency is improved. Based on dynamic construction of an algorithm strategy library and twinborn simulation evaluation, the scheme takes a multi-dimensional scheduling value as a core to realize closed-loop optimization of algorithm combination-effect rehearsal-strategy optimization-dynamic adjustment.

The invention provides a trajectory prediction method and device based on behavior intention of an unmanned aerial vehicle, a device and a medium, and the method comprises the steps: employing a random forest model to screen multi-source state information of the unmanned aerial vehicle, obtaining flight state key features, and completing feature importance evaluation and precise screening; building a multi-classification maneuvering intention recognition model by adopting a support vector machine based on the flight state key features, and obtaining probability distribution of various maneuvering actions of the unmanned aerial vehicle based on the multi-classification maneuvering intention recognition model; a neural network trajectory prediction model of a coding-decoding structure with a bidirectional gating circulation unit is constructed, probability distribution of various maneuvering actions of the unmanned aerial vehicle is introduced, the probability distribution is used as key semantic constraints to be embedded into a trajectory prediction algorithm, a technical link of multi-link collaborative optimization is formed, and a prediction result of a future trajectory of the unmanned aerial vehicle is obtained. And high-precision prediction of the track of the unmanned aerial vehicle in a complex environment is effectively realized.

The invention relates to the technical field of intelligent parking management, and discloses a curb parking dynamic charging and fee evasion prevention and control method based on multi-dimensional data fusion, which comprises the following steps: a data fusion reconstruction step: receiving a video stream, sensing a parking space state and reporting data through mobile inspection, performing fusion processing through space-time alignment and a confidence decision algorithm, and obtaining a data fusion result; outputting a standard parking event sequence; a dynamic charging decision-making step of matching a charging strategy model including time-phased elastic rate parameters according to the time-space attribute of the event sequence, calculating the cost and verifying the parking rights and interests in real time, and generating a parking order; and a fee evasion risk real-time prevention and control step: constructing a vehicle behavior portrait feature vector based on the event sequence and the historical order, obtaining a real-time risk level through a pre-trained risk grading model, and triggering a cascade response disposal action according to the real-time risk level, including exit interception and recovery of a high-risk vehicle. According to the invention, the charging accuracy is improved, and the rate dynamic adjustment and the active and accurate prevention and control of the fee evasion behavior are realized.

The invention relates to an unmanned aerial vehicle threat intention prediction method and device and a storage medium, and is applied to the technical field of anti-unmanned aerial vehicles. The method specifically comprises the steps that through multi-modal deep feature fusion and deep adversarial learning, the system can extract deeper and more abstract unmanned aerial vehicle behavior mode features from multi-source heterogeneous data, so that accurate prediction of the real threat intention of the unmanned aerial vehicle is achieved, the limitation that a traditional method is only based on surface feature judgment is overcome, and the accuracy of the unmanned aerial vehicle threatening intention prediction is improved. The transformation of threat assessment from post-event analysis to pre-event prediction is realized; in the deep adversarial learning framework, various complex unmanned aerial vehicle threat behavior modes including disguise, interference and novel attack strategies can be simulated, and meanwhile, the recognition capability is continuously improved in adversarial training, so that the system can still keep high accuracy and low false alarm rate when facing unknown or variable threats, and the safety of the system is improved. And the robustness and adaptability of the system are obviously enhanced.

The invention provides an expressway lane dynamic optimization management method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: firstly, collecting traffic flow, environment and vehicle credit data through high-speed key node multi-source sensing equipment and a data center, and fusing to generate a time-space fusion data set; inputting the data into a space-time diagram convolution network model, dynamically mapping a road topology into a variable weight directed graph, and generating future lane-level traffic flow, vehicle speed and occupancy rate prediction data through convolution operation; inputting the prediction data into a hierarchical reinforcement learning model, generating a single-vehicle differential management and control instruction in combination with vehicle credit data, and determining an optimal strategy through digital twinborn simulation; then, a modularized lane controller is used for executing a strategy and collecting feedback data; and finally, inputting feedback data into a causal inference engine to analyze the effect, optimizing the reinforcement learning model, and dynamically updating credit data according to vehicle behavior data.

The invention provides a vehicle trajectory prediction system embedded with a spatial-temporal feature fusion strategy and a control method thereof. The system comprises a data time sequence decomposition processing module; a future feature reasoning module; a cross-time motion mixer module; a future interactive attention predictor module; time sequence decomposition is carried out on vehicle trajectory data through time sequence coding, short-term dynamic features and long-term trend features in the vehicle trajectory data are obtained, obtained combined feature information provides input for historical and future trajectory feature interactive modeling and trajectory prediction, and meanwhile a model is helped to achieve long-term prediction of future trajectories; the interaction of complex spaces is considered, a dynamic shuffling mechanism and a channel attention mechanism are combined, the channel attention mechanism helps a model pay attention to important interaction features, the dynamic shuffling mechanism enhances information exchange between channels, local space features and global space features are fused through deep convolution and global pooling, and the dynamic shuffling mechanism and the channel attention mechanism are combined. The generated future interaction features help the model to accurately simulate future vehicle behaviors.

The invention relates to a method for operating an autonomously controlled vehicle (2), comprising at least one automated driving system (8) for controlling a vehicle journey, a vehicle interior (4) for a vehicle occupant (6), with at least one comfort system (10) for providing a comfort function for the vehicle occupant (6), and with at least one safety system (12) for protecting the vehicle occupant (6), an interior sensor (14) for detecting an interior situation (16) of the vehicle occupant (6), and an environment sensor (18) for detecting an environment situation (20), wherein a current actual driving situation is detected on the basis of system data (36) of the driving system (8), the comfort system (10) and / or the safety system (12) as well as sensor data (38) of the interior sensor (14) and the environment sensor (18).a key parameter (40) relating to the vehicle occupants (6) for a future driving situation is predicted and evaluated based on the system data (36) and the sensor data (38), based on the evaluation a vehicle behavior to realize a target driving situation is determined, the specific vehicle behavior is adapted taking into account an interaction between the interior situation (16) and / or the exterior situation (20), and the adapted vehicle behavior (42) is set by controlling the driving system (8), the comfort system (10) and / or the safety system (12).

The invention discloses a non-motor vehicle violation and illegal behavior intelligent identification method based on multiple features, relates to the technical field of non-motor vehicle behavior identification, and solves the problem that in the prior art, non-motor vehicle violation behavior identification systems mostly depend on a single feature for identification, so that misjudgment or missed judgment is easily caused in a complex traffic scene. According to the method, the global semantic understanding capability of a large model is introduced, so that deep fusion of various characteristics such as appearance, movement, behaviors and environment is realized. Therefore, the system can understand the traffic scene more comprehensively, so that the traffic violation behavior of the non-motor vehicle can be identified more accurately. In addition, the method further improves the recognition capability of the key information through the attention mechanism and region enhanced feature extraction. The multi-feature fusion mode not only improves the recognition accuracy, but also enhances the robustness and generalization ability of the system, so that the system can better adapt to different traffic scenes and environmental conditions.

The invention discloses an expressway dynamic vehicle behavior trajectory tracking induction monitoring system, which relates to the technical field of intelligent traffic monitoring, and comprises a multi-source data acquisition module used for responding to a trigger signal of a vehicle entering a monitoring area, acquiring multi-source data, and sending the multi-source data to the monitoring area; the multi-source data comprises three-dimensional point cloud data, a thermal infrared image, a multi-view visible light image, vehicle speed and lane data and a license plate image; and the preprocessing and calibration module is used for registering and fusing the thermal infrared image and the visible light image. According to the expressway dynamic vehicle behavior trajectory tracking induction monitoring system provided by the invention, through multi-source sensor fusion and intelligent algorithm innovation, high-precision tracking and real-time monitoring of vehicle behavior trajectories are realized, road safety and traffic efficiency are effectively improved, and the expressway dynamic vehicle behavior trajectory tracking induction monitoring system is suitable for popularization and application. Multi-dimensional sensors such as a laser radar, an infrared thermal inductance instrument and a visible light camera are adopted for cooperative work, and the capability of capturing vehicle information in a weak light environment is enhanced.

The invention relates to the technical field of vehicles, in particular to an automatic driving behavior decision-making method and device of a vehicle, the vehicle and a storage medium, and the method comprises the steps: generating a plurality of automatic driving behavior decision-making sequences of the vehicle in a preset action space based on current state information, and carrying out the screening to obtain an effective candidate decision-making sequence set, generating an automatic driving candidate track corresponding to each effective candidate decision sequence based on the effective candidate decision sequence set by using a preset kinematic model; based on the environment prediction information, after safety evaluation is conducted on each automatic driving candidate track, the total cost of the safety candidate tracks is calculated through a multi-dimensional cost function, and the automatic driving behavior decision corresponding to the safety candidate track with the minimum total cost serves as the optimal automatic driving behavior decision of the vehicle. Therefore, the problems of single decision dimension, incapability of effectively combining a global path planning target to ensure that the vehicle behavior is consistent with the macroscopic driving route and the like in related technologies are solved.

The invention discloses a blind area risk defense control method based on adjacent vehicle behavior inference. The blind area risk defense control method comprises the steps that a known environment risk potential energy field reflecting current known traffic constraints is constructed; a composite risk potential energy field of the view angle of the adjacent vehicles is deduced by resolving a decision sequence of the adjacent vehicles in traffic interaction; fitting and optimizing a driving style weight coefficient in a cost function through historical observation data of an adjacent vehicle; according to the driving style weight coefficient and the actually measured motion state of the adjacent vehicle, calculating an actually predicted energy residual error, and according to the actually predicted energy residual error, inferring the position information of the blind area traffic participant under the own vehicle coordinate system; non-dynamic features are extracted, and a spatial gain mask is generated according to the non-dynamic features and the position information of the blind area traffic participants under the own vehicle coordinate system; and calculating a residual risk potential energy field according to the known environmental risk potential energy field and the composite risk potential energy field, and calculating a corrected residual risk potential energy field according to the residual risk potential energy field and the spatial gain mask so as to perform blind area risk defense control.

Techniques are provided which may be implemented using various methods and / or apparatuses in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capabilities and external CV2X input to determine, send, receive and utilize data elements to determine inter-vehicle spacing, intersection priority, lane change behavior and spacing and other autonomous vehicle behavior.

The invention discloses an intelligent parking system and a vehicle parking management method, and relates to the technical field of intelligent traffic and Internet of Things, and the method comprises the steps: S1, collecting vehicle position signals and vehicle image features, and carrying out the data fusion, and obtaining the real-time coordinates of a vehicle with direction information; s2, constructing a continuous track chain based on the real-time coordinates of the vehicle, and recognizing abnormal behaviors of the vehicle based on a preset track feature threshold value; and S3, generating and executing a corresponding barrier gate control instruction according to the abnormal behavior of the vehicle. According to the method, geomagnetism and video sensing data are fused in a dynamic self-adaptive mode, a vehicle continuous track chain is constructed, abnormal behaviors such as vehicle retention and turning around can be accurately recognized, real-time linkage with barrier gate control is achieved, and therefore the vehicle smashing prevention safety, the accuracy of illegal behavior recognition and the real-time performance of system response are effectively improved.

The invention provides a safety analysis method and system for an automatic driving Transform model, a product and a vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: systematically recognizing the specific safety risk of the Transform model in the whole life cycle of an automatic driving system, and constructing a structured safety demonstration model and verifiable safety demands on the basis; further embedding the model into a vehicle dynamic control link, and positioning an unsafe control action caused by abnormal output of the model by using a system theoretical analysis method, so as to design and deploy a safety relief mechanism adaptive to a Transform architecture in a targeted manner; meanwhile, a multi-level verification system covering the internal mechanism of the model and the vehicle behavior is established, an empirical evidence supporting safety demonstration is generated, and continuous performance monitoring and closed-loop optimization are implemented in the operation stage, so that the long-term safety, reliability and compliance of the automatic driving system in a complex open environment are effectively guaranteed.

The embodiment of the invention discloses a traffic condition identification method and device and a computer program product. The method comprises the following steps: acquiring trajectory data of a vehicle in a target area; determining vehicle behavior characteristics of each road section unit in the target area based on the trajectory data; performing spatial-temporal clustering on abnormal behaviors in the vehicle behavior characteristics, and determining candidate road section units where traffic events occur in the target area based on a spatial-temporal clustering result; and determining a detection interval based on the candidate road section units, and identifying the traffic condition of the detection interval based on the vehicle behavior characteristics of each road section unit in the detection interval. According to the invention, accurate identification of the traffic condition can be realized, and the timeliness and the coverage degree are significantly improved.

The invention discloses a multi-modal data fusion cabin safety monitoring method and system, and relates to the technical field of automobile safety driving. The method comprises the following steps: obtaining personnel, vehicle and environment data in a cabin; the data of personnel in the cabin comprises a driver and passengers, the vehicle data comprises a vehicle speed, an acceleration, a steering angle, a brake pressure and a light state, and the data of environment in the cabin comprises an audio signal in the cabin; performing depth feature extraction and cross-modal feature fusion on the acquired personnel, vehicle and environment data in the cockpit to obtain a cockpit scene feature vector; performing deep semantic analysis and risk assessment on the cockpit scene feature vector to obtain a security situation portrait; a corresponding intervention strategy is generated through a hierarchical decision-making mechanism according to the security situation portrait, the strategy is converted into a specific control instruction, and then the control instruction is converted into actual cabin and vehicle behaviors; a complete safety protection system is formed, and the safety of personnel in the cabin is guaranteed.

The invention is suitable for the technical field of intelligent traffic and automatic driving, and provides a training method of a vehicle path planning model, and a path planning method and device.The training method comprises the steps that a sample driving mode and sample state space data of a sample vehicle at a first moment and a sample path of the sample vehicle in a first time period are acquired; calling a sample reinforcement learning model; according to the difference between the first driving path and the sample path, updating at least one model parameter of the sample reinforcement learning model to obtain an initial path planning model; calling an initial path planning model; and according to the difference between the path score corresponding to the second driving path and a preset score threshold, updating at least one model parameter of the initial path planning model to obtain a vehicle path planning model. The driving path of the vehicle can be accurately simulated, so that the accuracy of vehicle behavior simulation is improved.

The invention discloses a vehicle transportation process detection method and system based on a cloud platform, and the method comprises the steps: remotely configuring a terminal positioning data collection strategy of a vehicle through the cloud platform, obtaining the GNSS original data of the vehicle, constructing a transportation region configuration table and a multi-dimensional triggering behavior rule, and dynamically binding a target vehicle; area attachment detection is executed in real time based on the vehicle floating data, and an area attachment event KEvent is generated; carrying out misjudgment filtering and continuous tracking research and judgment on an information source of a region attachment event KEvent, generating a KMessage message, verifying the right to verify whether a vehicle behavior meets a rule requirement or not, and outputting a detection result; and according to the detection result and the regional attribute decision, detecting service start and stop, and synchronously and remotely acquiring and storing vehicle image data. According to the method, multi-dimensional automatic monitoring and accurate behavior study and judgment in the transportation process are realized, and the real-time performance, the credibility and the traceability of process supervision are effectively improved.

The invention discloses an intelligent access control anti-collision barrier gate, and particularly relates to the technical field of access control barrier gates, the intelligent access control anti-collision barrier gate comprises a base, a case is fixed at the top end of the base, a blocking piece is arranged on one side of the case, a deceleration strip is arranged at the bottom of the blocking piece, and an automatic license plate recognizer is fixed at the top end of the base through a mounting frame and used for recognizing license plate information of a vehicle. A radar velocimeter is fixed on the mounting frame and is used for acquiring the driving speed and acceleration data of a vehicle in the process of approaching a barrier gate in real time, and a panoramic camera is fixed at the top end of the case and is used for shooting the driving track of the vehicle so as to judge whether the vehicle maliciously intrudes into a community or not. The vehicle behavior can be intelligently judged, so that the registration release or blocking interception process is triggered, the safety and the passing efficiency are both considered, the daily life of residents is facilitated, and the internal management of a community is facilitated.

The invention relates to a display device and a display method of vehicle behavior. A display device disclosed in the present specification draws, along a travel trajectory of a vehicle, a band having a width corresponding to a magnitude of a lateral acceleration at each position of the travel trajectory on a side opposite to a direction of the lateral acceleration of the vehicle at each position. When the vehicle turns on the curve, lateral acceleration towards the inner side of the curve is generated on the vehicle. In a display device disclosed in the present specification, a belt extending to the outside of a curve is drawn along a travel trajectory. The greater the lateral acceleration, the wider the width of the belt becomes.

The invention discloses a mountainous area expressway secondary accident active prevention and control method and system, and the method comprises the steps: carrying out the global monitoring of a mountainous area expressway continuous slope road section, extracting traffic scene features, and identifying an accident scene; in combination with the current traffic flow state, the road static characteristics, the environmental conditions and the vehicle behaviors, estimating the initial accident occurrence risk based on an accident risk estimation model of risk coupling; the method comprises the following steps: predicting whether a secondary accident of the continuous slope bending of the expressway occurs or not based on a Bayesian optimization random forest model, if so, predicting the occurrence time and place of the secondary accident, and then performing active prevention and control through a warning means and a forced control means. According to the invention, through air-ground integrated monitoring and intelligent control, rapid detection of a primary traffic accident and accurate assessment and active prevention control of a secondary accident risk are realized, so that the traffic accident frequency and damage degree of a high-risk road section are greatly reduced.

The application discloses a kind of rigidity strength simulation verification methods of automobile electric step structure, mainly by constructing the finite element model including vehicle body and step assembly, define the connection unit of specific degree of freedom release simulates actual assembly constraint, and based on the mapping relationship of step geometric characteristics and passenger getting on and off vehicle behavior, load-position mapping working condition matrix including cross-bearing, multi-source coupling, cantilever limit three kinds of characteristic regions is constructed;Rated load is applied on the preset rectangular loading surface of each characteristic region, six typical stepping scenarios are simulated;Stress and deformation results under each working condition are obtained by solver calculation, and compared with material performance threshold to evaluate rigidity strength performance.The application can comprehensively and accurately predict the structural response of electric step in the product design stage, guide design optimization, significantly shorten the development cycle and reduce test cost.

The application discloses a kind of single vehicle control methods based on scene style migration and SAC network cooperation, including steps: S1, using SUMO tool to build simulation platform;S2, construct single vehicle longitudinal control model, SAC network is used to obtain traffic state from simulation platform, then take action control vehicle behavior, after action execution, agent receives the reward value updated agent parameter that simulation platform feedback;S3, define policy network, output the numerical value of acceleration;Define value function network, output layer represents the estimation of state value;S4, through the vehicle control experimental platform built in call SUMO to simulate different weather conditions, and build measurement model;S5, on the basis of single vehicle longitudinal control model build migration learning framework;S6, using migration learning will train convergent single vehicle longitudinal control model to be transplanted to different weather conditions and train.The application can effectively inhibit traffic stop and go wave, ensure traffic safety, reduce fuel consumption.

The system according to this embodiment aims to monitor in-vehicle behavior, respond immediately to abnormal behavior or emergencies, and analyze driving behavior in real time to evaluate safety. [Solution] The system according to the embodiment comprises a monitoring unit, a detection unit, a response unit, an analysis unit, and an evaluation unit. The monitoring unit monitors the behavior inside the vehicle. The detection unit detects abnormal behavior or emergencies based on the behavior monitored by the monitoring unit. The response unit immediately responds to the abnormal behavior or emergencies detected by the detection unit. The analysis unit analyzes the driver's driving behavior in real time. The evaluation unit evaluates safety based on the driving behavior analyzed by the analysis unit.

The present application relates to the technical field of dynamic assessment of traffic safety risk, in particular to a method for dynamic assessment and intelligent management and control of expressway traffic safety risk. The present application utilizes the parallel computing characteristics of LightGBM, combines edge computing nodes, and realizes millisecond-level risk inference. The present application takes the three-level risk label as the basis for label generation, simultaneously utilizes SHAP to discover the blind area not covered by the rules, and reversely optimizes the rule library. Thus, the present application realizes real-time risk quantification, hierarchical early warning and dynamic intervention on the vehicle behavior in the complex traffic flow of the expressway.

Provided is a design support device capable of identifying a control parameter to be changed in response to a request in a free description format and facilitating setting.　This design support device comprises: an input unit that receives a request related to the behavior of a vehicle and described in natural language; a module selection unit that, on the basis of a first processing result obtained by natural language processing, selects a software module related to the behavior of the vehicle included in the request as a target module, with respect to the request and module description information which describes, in natural language, functions of software modules obtained by dividing vehicle control software of the vehicle for each function; a reference parameter extraction unit that extracts a reference parameter, which is a control parameter of the vehicle referenced by the target module; a parameter selection unit that, on the basis of a second processing result obtained by the natural language processing, selects a target parameter related to the behavior of the vehicle included in the request, with respect to the request and parameter description information which describes the function of the reference parameter in natural language; and an output unit that outputs the target parameter.

The invention provides a charging management method and device for a charging station in a highway service area, and the method comprises the steps: obtaining a credit score and an intentional charging time period of a charging vehicle, distributing an idle charging position for the charging vehicle according to the credit score and the intentional charging time period, obtaining the charging reservation information, and carrying out the charging management of the charging station in the reserved charging time period. Monitoring the charging vehicle to obtain monitoring information, determining a charging state of the charging vehicle according to the parking monitoring information, the charging monitoring information, the identity recognition record and the charging reservation information, determining a charging mode of the charging vehicle according to the charging state, charging the charging vehicle according to the charging mode, and charging the charging vehicle according to the charging state. Performing credit management on the charging vehicle; according to the invention, the flexibility of charging management of the charging vehicle is improved, the capability of restraining the behavior of the charging vehicle is improved, the congestion caused by charging queuing in a highway service area can be effectively relieved, and the travel service experience of a vehicle party is improved.