844 results about "Vehicle behavior" patented technology

The invention provides an intelligent traffic vehicle behavior modeling method and system based on collaborative learning, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining traffic data in a target region; performing data calibration processing according to the traffic data to obtain a collaborative perception data set; a vehicle space-time trajectory function is generated according to the collaborative perception data set, and a conflict domain space-time distribution diagram is obtained; according to the vehicle space-time trajectory function and the conflict domain space-time distribution diagram, probability distribution characteristics of the vehicle driving intention and a multi-workshop competition cooperation relation matrix are extracted, and a multi-dimensional interaction characteristic vector is obtained; and behavior modeling is carried out according to the multi-dimensional interaction feature vectors, and a vehicle behavior model representing interaction behavior rules and decision logic between vehicles is obtained. According to the method, through multi-vehicle cooperative sensing data calibration and dynamic fusion of road geometric constraints, the problems of single-vehicle sensor shielding and cross-vehicle data heterogeneity are effectively solved, and the global consistency of vehicle position and motion state estimation is remarkably improved.

The invention discloses a digital human emotion recognition and feedback system based on a large model, and relates to the technical field of large models.During operation of the system, multi-modal data of a driver are collected in real time through an embedded sensor network in various driving environments, and the emotion, driving behaviors and driving environment parameters of the driver are obtained; the method comprises the steps of constructing fatigue, anger and other multi-dimensional emotion features of a driver to form an emotion feature vector, analyzing an emotion change trend through threshold comparison, obtaining an evaluation state, dynamically generating a feedback strategy for optimizing a driving state based on an emotion state and a driving scene of the driver, and embedding a generative large model into a module. Diversified feedback responses including audio reminding, driving mode adjustment and entertainment content recommendation are automatically generated, feedback strength and form are adaptively adjusted through multi-dimensional parameters, a feedback generation effect is dynamically adjusted, and an emotion state analysis result and vehicle behavior monitoring data are integrated, so that a user experience is improved. And evaluating the potential dangerous state of the driver in real time.

The invention discloses an AR-fused remote driving vehicle virtual-real interaction hardware-in-the-loop test system, particularly relates to the technical field of automatic driving test, and is used for solving the problems of inaccurate coupling between a virtual scene and a real vehicle behavior and lack of AR prompt response evaluation. The method comprises the following steps: firstly, constructing a dynamic obstacle intention-driven prediction model based on time series data of a multi-modal sensor, and generating a trajectory probability distribution and risk thermodynamic diagram; then, space-time alignment of the virtual accident scene and the real environment is achieved through a dynamic binding algorithm, and the virtual-real shielding priority of an AR interface is dynamically adjusted; by simulating abnormal disturbance of a vehicle actuator, synchronously collecting control and watching responses of a driver, and extracting obstacle avoidance path deviation degree and takeover timeliness parameters; and finally, separating and compensating virtual and actual residual errors based on a path deviation index, realizing online correction of a virtual scene attitude and a dynamic trajectory, constructing a closed-loop optimization mechanism, and improving the precision and stability of a test system.

The invention provides a remote identity recognition method and system based on unmanned aerial vehicle management and control requirements. Wherein the preparation state of the unmanned aerial vehicle is determined by generating an inquiry code, sending the inquiry code to all online unmanned aerial vehicles in a target airspace, and receiving an identity response signal comprising a unique identifier, a task brief description and a flight path. A multi-dimensional data fusion algorithm is combined with historical flight data, meteorological conditions and geographic information to predict an unmanned aerial vehicle behavior mode, and a behavior prediction report is formed. The block chain technology is used for cross verification, and the real identity and authorization state of the unmanned aerial vehicle are verified through the dual key encryption technology and the consensus algorithm. And according to a verification result, using machine learning to construct a temporary virtual fence and plan an optimized route, and automatically adjusting an air corridor to form a traffic management scheme. And finally, analyzing actual flight parameters by applying a self-adaptive control algorithm, and further optimizing route setting. According to the technical scheme provided by the embodiment of the invention, the safety and efficiency of air traffic management of the unmanned aerial vehicle are improved.

The invention discloses a vehicle owner behavior data driving type vehicle insurance risk assessment method based on a large language model, and relates to the technical field of risk assessment. Comprehensive vehicle owner and vehicle behavior data are collected from multiple channels, and deep analysis is performed on the data through the large language model; driving behavior features, historical accident record features and vehicle use condition features highly related to the vehicle insurance risk are extracted, and the influence of various factors on the risk is accurately reflected in combination with a nonlinear conversion function; key features are converted into numerical vectors and stored in the form of a multi-dimensional feature matrix, and high-quality input data are provided for a deep learning model; a risk assessment model constructed by adopting a PyTorch framework is combined with loss function optimization and a gradient descent algorithm, so that efficient model training and prediction are realized; and the risk assessment result is adjusted in real time through dynamic data updating, so that the accuracy of vehicle insurance risk assessment and the response speed to the behavior change of the vehicle owner are remarkably improved.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention provides an unmanned aerial vehicle detection tracking method and system based on combination of a laser radar and vision, and the method comprises the steps: obtaining timestamp information through employing a distributed calculation framework, generating a multi-source data set according to the timestamp information, carrying out the data fusion based on the multi-source data set through employing a deep reinforcement learning algorithm, and obtaining multi-source heterogeneous data, the method comprises the steps of obtaining an unmanned aerial vehicle behavior mode and a predicted motion trail by using multi-source heterogeneous data, performing analysis through an intelligent task allocation protocol under a self-organizing network according to the unmanned aerial vehicle behavior mode and the predicted motion trail, generating an optimal sensing resource allocation scheme, and performing resource allocation on the basis of the optimal sensing resource allocation scheme. Processing the intelligent visual sensing node to obtain an emergency correction program, and according to the emergency correction program, integrating edge computing power to generate an unmanned aerial vehicle detection and tracking instruction; according to the technical scheme provided by the invention, the quality and accuracy of data fusion are improved, and the perception capability is enhanced.

For generating driving scenarios for testing an autonomous vehicle planner in a simulation environment, a scenario model comprises a scenario variable and a distribution associated with the scenario variable. The scenario variable is a road layout variable. Multiple sampled values of the scenario variable are computed based on the distribution associated the scenario variable. Based on the scenario model, multiple driving scenarios are generated for testing an autonomous vehicle planner in a simulation environment, each driving scenario comprising a road layout generated using a sampled value of said multiple sampled values of the scenario variable.

The invention relates to the technical field of smart parking lots, in particular to a vehicle safety management method based on a smart parking lot, which comprises the following steps: S1, multi-source data acquisition: acquiring dynamic trajectory data of a target vehicle, parking lot environment parameters and adjacent parking space state data in real time, and generating a multi-source feature vector set; s2, data space-time alignment: carrying out space-time alignment processing on the multi-source feature vector set, and outputting a standardized vehicle behavior sequence; s3, abnormal behavior recognition: inputting the standardized vehicle behavior sequence into an abnormal behavior recognition model, and generating an abnormal behavior characteristic spectrum and a confidence coefficient parameter; s4, risk level assessment: constructing a risk assessment model based on the confidence coefficient parameter and the environmental parameter, and outputting the risk level of the parking lot in the current scene; and S5, security policy triggering: triggering a corresponding security policy execution instruction according to the risk level. According to the invention, potential risks can be effectively prevented, and the safety of vehicles and personnel is ensured.

The invention relates to the technical field of intelligent traffic safety management and control, and discloses a highway high-risk road section early warning method and system, and the method comprises the steps: collecting real-time environment and vehicle data through a vehicle side and a roadside sensor, uploading the real-time environment and vehicle data to a cloud platform for data fusion, and obtaining fusion data; establishing a risk judgment model on the cloud platform, updating the risk judgment model in real time through incremental training, analyzing the fused data by using the risk judgment model, and generating a comprehensive risk score; and triggering graded early warning according to the comprehensive risk score, carrying out early warning in advance at a vehicle end, and carrying out visual display on an early warning result. According to the method, the adaptive capacity of the safety detection system to variable weather and vehicle behaviors is improved, finally, accidents are effectively reduced, and the driving experience and the traffic efficiency are improved.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to an anti-unmanned aerial vehicle detection method and system, and the method comprises the following steps: scanning a target airspace in real time through broadband cognitive radio, and constructing an environment feature matrix; based on the environment feature matrix, double-flow feature extraction is carried out, and a protocol fingerprint vector is generated; activating a corresponding group behavior analysis model according to the protocol fingerprint vector, and constructing a behavior topological graph; constructing a dynamic threat vector by adopting a game theory in combination with the behavior topological graph and the real-time motion parameters; generating a multi-dimensional interference strategy set according to the dynamic threat vector; according to the invention, an omnibearing and real-time updated threat vector is constructed, a node threat value is calculated through a non-cooperative game model, accurate evaluation is carried out in combination with spatio-temporal information, potential threats are effectively identified, accurate interference on an unmanned aerial vehicle group is realized, the perception capability of unmanned aerial vehicle behaviors in a complex scene is enhanced, and the risk of the unmanned aerial vehicle behavior in the complex scene is reduced. And the execution precision and efficiency of the interference strategy are obviously improved.

The invention relates to the technical field of intelligent driving, and discloses a user service recommendation method for a vehicle-mounted scene, and the method comprises the steps: collecting the vehicle-using behavior data of a user, carrying out the correlation marking of the intention of the user through a multi-dimensional marking system, carrying out the vectorization, and storing the vectorized intention into a vector database; capturing real-time behavior data of a user to generate a real-time behavior vector; based on the vector database, generating an intention context corresponding to a real-time behavior vector through a hybrid retrieval strategy; and generating a prompt project based on the intention context, inputting a large model output service recommendation result, and pushing the result to the user. By integrating mechanisms such as data annotation, vector retrieval and large model reasoning, the problem of long cold start period depending on long-term buried point data learning in the prior art is solved, potential intentions are quickly matched, the system effective time is shortened, and the service recommendation accuracy based on the user intentions is effectively improved. The invention further discloses a user service recommendation device for the vehicle-mounted scene, the vehicle and a computer readable storage medium.

The invention relates to the technical field of highway traffic safety guarantee, and particularly discloses a highway early warning system and an early warning equipment layout method under ice and snow conditions. The problems that recognition lags, the early warning response has the regional splitting phenomenon and the early warning information has deviation in the common technology are solved. Comprising a data acquisition layer which is used for acquiring meteorological data, road surface data, traffic flow data and vehicle behavior data in real time and eliminating abnormal data through a cross validation mechanism; the analysis decision-making layer analyzes the fused multi-dimensional data based on a multi-factor coupling dynamic risk assessment model, and generates a blue / yellow / orange / red four-level early warning signal; and the early warning issuing layer is used for issuing early warning information according to the early warning grade matching equipment combination. Through multi-source data cross validation and feature fusion, the false report and missing report rate is remarkably reduced, and the risk identification accuracy is improved.

In order to solve the defects in the prior art, the invention discloses a mixed traffic flow cooperative control method based on double-layer parameterization deep reinforcement learning. According to the method, traffic high-dimensional spatial features are extracted from an intersection environment through a convolutional neural network, a first-layer model CNN-MPDQN is constructed in combination with a multi-channel parameterization depth Q network algorithm, and the main task of the method is to select a passing priority lane and determine passing duration. And then, on the basis of the first-layer algorithm, a second-layer DQN algorithm is adopted to optimize and coordinate the passing sequence of the vehicles on the lane, and the problem of vehicle track conflict is solved. Through continuous loop training and iteration of a two-layer deep reinforcement learning algorithm model, model parameters are optimized step by step, and finally an optimal convergence model is obtained; the model can effectively cope with various traffic conditions and vehicle behaviors, improves the traffic efficiency and safety of the intersection, and is especially suitable for a mixed flow intersection without signal control.

The disclosure relate to detecting differences indicative of non-determinism in software used to control autonomous vehicles. For instance, a simulation may be run using the software at least two times. The output of the simulations may be tracked. The output of the simulations includes messages may be generated by a decision making module of the software that affect vehicle behavior. A pair of messages generated from each of the at least two times the simulation is run may be compared. Based on the comparison, a difference indicative of non-determinism in the software may be detected.

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 discloses a tunnel entrance dynamic light and signal control system based on a visual algorithm, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of poor light control when the visual field of a tunnel entrance suddenly changes. According to the method, a dynamic sensing mechanism based on visual feature understanding and multi-strategy linkage is constructed, continuous monitoring and intelligent control over the driving visual adaptation state of the tunnel portal are achieved, multi-dimensional visual features are extracted through image collection and preprocessing, visual risk scores are generated, visual field sudden change and traffic abnormity are recognized in combination with a sliding window, and the driving visual adaptation state of the tunnel portal is obtained. A risk response instruction is output, illumination adjustment and signal linkage control are driven, then vehicle behavior continuity, parking probability and blind area shielding factors are fused, speed limiting, guiding or warning signals are dynamically issued, and visual model fine adjustment and strategy updating are performed based on execution feedback. And finally, illumination and signal adaptive optimization under cross-time and multi-environment conditions is realized in combination with a control scene library, and the traffic safety and control intelligence level are improved.

The invention discloses a road transportation government affair safety supervision and management method, and relates to the technical field of road transportation safety supervision data processing. The vehicle and road safety supervision and management method comprises the following steps: collecting passing driving dynamic information from any traffic observation device; the vehicle behavior monitoring device and the road surface state monitoring device are integrated, the vehicle driving behavior data and the road safety condition data are collected in real time, and the monitoring frequency is dynamically adjusted according to the space interval, the abnormal information and the historical data, so that the utilization efficiency of monitoring resources is improved, and the monitoring cost is reduced. The system collects historical data and real-time abnormal data of vehicle driving behaviors and road safety conditions, then a primary space and a secondary space are calculated through a calculation and analysis engine, corresponding correction coefficients are determined according to intervals and historical safety condition information, then the observation frequency of the monitoring device is adjusted, and the monitoring accuracy is improved. And resource allocation is optimized while the detection coverage rate of the system is ensured.

The invention relates to the technical field of traffic management, and discloses an intelligent traffic dispersion method and system based on a smart city, and the method comprises the steps: determining a traffic facility operation and maintenance protocol corresponding to a traffic facility, and formulating a congestion evaluation factor of an urban traffic region; analyzing the traffic flow dimension state of the urban traffic area, calculating a congestion disturbance coefficient corresponding to the congestion evaluation element, and evaluating the congestion state intensity of the urban traffic area; analyzing vehicle behavior intention characteristics of vehicles in the urban traffic area, analyzing a traffic condition matrix of roads in the urban traffic area, and calculating traffic efficiency of the roads in the urban traffic area; extracting attraction element factors in the land utilization information and the real-time event information, and analyzing the traffic pressure conductivity of the surrounding area to the urban traffic area; and formulating a traffic intelligent dispersion strategy of the urban traffic area, and executing traffic dispersion work of the urban traffic area to obtain a traffic dispersion result. The intelligent traffic dispersion efficiency of the smart city can be improved.

The invention provides an artificial intelligence-based automatic driving vehicle behavior prediction method and system. The method comprises the following steps of: firstly, acquiring multi-mode driving data of a sample automatic driving vehicle and preprocessing the multi-mode driving data; feature extraction is carried out on data of different modals, and weighted fusion is carried out based on a cross-modal attention mechanism; and constructing an initial dynamic graph network model and a traffic participant behavior model, taking traffic participant behaviors as nodes, determining an interaction relationship between the nodes, and dynamically updating the weight of the edge to obtain an updated dynamic graph network model. On the basis of an attention mechanism, attention distribution of the traffic participants to surrounding obstacles is calculated, and a multi-modal feature vector is constructed and input into the dynamic graph network model to obtain updated node features; and constructing a high-level decision model according to the updated node features, and inputting the multi-modal data of the to-be-tested autonomous vehicle into the model to generate a result. According to the invention, by fusing the multi-modal data, the accuracy of behavior prediction of the automatic driving vehicle is improved.

An apparatus and method for determining road environment is provide, and the apparatus for determining road environment according to an embodiment of the present disclosure comprises: a data collector configured to obtain data on road environment information and data on vehicle behavior; a data processor configured to determine reliability of the obtained data; a data determiner configured to determine the road environment based on the reliability; and a vehicle controller configured to control a vehicle based on the determined road environment.

A suspension system performs calculations using machine learning by mapping sensor information and suspension state information. The suspension system includes: a weighting parameter storage unit; a vehicle state estimation unit; a first vehicle behavior calculation unit that calculates a first physical value based on the estimation result; a second vehicle behavior calculation unit that calculates a second physical value based on the sensor information; an estimation accuracy verification unit that outputs an estimation accuracy of the suspension state by comparing the first and second physical values; and a driving data control unit that instructs weighting parameter learning based on an output result of the estimation accuracy.

The invention relates to the technical field of traffic control, in particular to a smart city traffic guardrail anti-collision prompting system, which comprises a vehicle sensing module, which is used for detecting passing vehicles, acquiring the speed, position and direction information of the vehicles, transmitting the information to a data center in real time and storing data based on sensors mounted on two sides of a road, recording as a data set, and generating vehicle dynamic data. According to the invention, through comprehensive collection and monitoring of the vehicle driving data, accurate evaluation of vehicle behaviors is realized. The speed, the position and the direction of the vehicle are collected and analyzed in real time, and data support is provided for judging the relative position and the movement trend between the vehicle and the guardrail. And whether the vehicle may deviate from the normal driving route is effectively predicted by comparing the real-time driving route with the preset route. Through continuous tracking and immediate feedback of vehicle behaviors, the sensitivity and accuracy of the early warning system when potential collision risks are judged are remarkably improved.

The invention discloses a vehicle trajectory prediction method based on physical social soft attention Transformer, and the method comprises the steps: constructing a scene attention sharing mechanism for the condition of physical environment information loss in a conventional method, and enhancing the influence of a physical environment on attention; aiming at the condition of lacking complex vehicle behavior information, constructing a social soft attention module to learn a vehicle track intention from the vehicle interaction information; constructing a Transform network of a local-global attention mechanism to capture global and local scale features in allusion to the situation that local features of the vehicle are missing; inputting the trajectory coordinates of the vehicle and the physical scene features into a Transform network to obtain high-level spatial-temporal features under multiple factors; and then, a physical social soft attention Transform network is constructed. According to the method, through learning of complex vehicle behavior information and a physical scene, the prediction precision of a vehicle track prediction task in the complex scene is enhanced, the ability of the model to extract multi-dimensional spatial-temporal characteristics of the track is enhanced, and the prediction performance of track prediction is improved.

The invention discloses an over-the-air upgrading method, device and equipment and a storage medium, relates to the technical field of vehicle remote upgrading, and discloses an over-the-air upgrading method which comprises the following steps: acquiring behavior data of a vehicle, a battery health state, an over-the-air upgrading condition, a flashing condition of an electronic control unit and an upgrading package of the electronic control unit; predicting initial upgrading time according to the behavior data, and obtaining target upgrading time based on the initial upgrading time and the upgrading package; when it is determined that the vehicle meets the upgrading condition and the current time is the target upgrading time, whether the electronic control unit can be upgraded or not is determined according to the battery health state and the flashing condition; and when it is determined that the electronic control unit can be upgraded, performing over-the-air upgrade according to the upgrade package. By combining the vehicle behavior data, the battery health state and the flashing condition, the upgrading time is predicted and dynamically adjusted, and efficient and reliable over-the-air downloading upgrading is achieved.

The embodiment of the invention discloses a processing method for generating a driving scene video, and a model training method and device, and the method comprises the steps: obtaining multi-modal scene data; based on the multi-modal scene data, generating scene two-dimensional features corresponding to each view angle and scene environment prediction information of a future preset frame number; and generating a driving scene video by using a video generation model based on the scene two-dimensional features and the scene environment prediction information corresponding to each view angle. According to the embodiment of the invention, the scene environment prediction information is used as the condition signal for constraining the generated driving scene video, so that the generated driving scene video better conforms to conditions such as road types, traffic rules and vehicle behaviors of the driving scene, and the accuracy and reliability of the driving scene video can be improved.

The invention relates to the technical field of intelligent driving, in particular to a behavior decision optimization method and system based on intelligent driving, and the method comprises the following steps: taking a multi-modal feature map for behavior decision of an intelligent driving vehicle as input, taking a behavior decision parameter as output, training a deep neural network, and obtaining a behavior decision model; and training the behavior decision model by taking the multi-modal feature map for behavior decision optimization of the intelligent driving vehicle as input and the behavior decision optimization parameters as output to obtain a decision optimization model. According to the invention, the behavior decision model is constructed, and the universal driving behavior decision is optimized according to the real-time driving environment data of the intelligent driving vehicle, so that the real-time driving behavior decision adaptive to the real-time driving environment of the intelligent driving vehicle is obtained. Therefore, the intelligent driving vehicle can quickly obtain the optimal matching to adapt to the driving behavior of the real-time driving scene in the driving process, and the accuracy and timeliness of the decision making of the intelligent driving behavior are ensured.

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.

This application is directed to a centralized system for predicting vehicle trajectory. A computer system associated with a fixed installation having one or more first sensors obtains, from the first sensors, information of a plurality of vehicles that are traveling within a zone of interest of a road. The computer system generates a first travel trajectory for a first vehicle of the plurality of vehicles based on the information obtained from the one or more first sensors. The computer system sends, to the first vehicle, the first travel trajectory such that the first vehicle is configured to at least partially autonomously drive the first vehicle based on the first travel trajectory of the first vehicle.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle guiding method and system for an unattended highway. The invention discloses a vehicle guiding method for an unattended highway. The method comprises the following steps: S1, acquiring road image information and vehicle information on the highway in real time; s2, performing road section division according to the road image information and a historical congestion area, and analyzing each branch road section by using a mobile calculation formula to obtain an abnormal road section; s3, analyzing the behaviors of vehicles in each abnormal road section to obtain the lane changing times of the expressway entrance; and S4, performing planning guidance on the buffer road section according to the road image information of the expressway entrance. According to the method, the vehicle behavior in the abnormal road section is analyzed, the switching frequency of the expressway entrance is obtained, the buffer road section is further planned and guided, and the vehicle flow in the buffer area can be efficiently guided to avoid congestion through the dynamic analysis of the vehicle behavior and the intelligent planning of the divided road sections.