492 results about "Intelligent transportation system its" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention relates to the technical field of intelligent traffic systems, and discloses a hard shoulder driving and variable speed limit cooperative control method and system.The method comprises the steps that traffic data are collected, and the traffic data comprise traffic information and speed limit values; according to the hard shoulder lane-borrowing driving motivation, the lane-borrowing safety condition and the emergency or rescue vehicle requirement of the networked autonomous vehicle, whether the networked autonomous vehicle can run through the hard shoulder lane or not is judged; inputting the collected traffic data into a pre-constructed DDPG algorithm to determine a state, an action space and a reward function of the DDPG algorithm, and outputting an optimal speed limit value; and the optimal speed limit value of each lane of the control road section is generated through a DDPG algorithm, and the network-connected automatic driving vehicle adjusts the vehicle speed according to the optimal speed limit value. The method can be used for highway entrance ramp sections with serious congestion, the average travel time and carbon dioxide emission of the road network can be reduced, and a new method is provided for efficient and low-carbon targets of smart highways in China.

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention discloses an unmanned aerial vehicle highway intelligent patrol method and system based on patrol demands, and belongs to the field of unmanned aerial vehicle technologies, intelligent traffic systems and artificial intelligence, and the method comprises the steps: obtaining a natural language instruction, and carrying out the semantic analysis to generate a structured task vector; constructing and generating a self-adaptive inspection strategy based on the task vector and fusing real-time data; distributing the strategy to the unmanned aerial vehicle for autonomous inspection, and identifying and generating a classification event alarm; and finally responding to an alarm and triggering a processing mode to generate a closed-loop inspection task report. According to the method, a technical path of combining multi-level reasoning based on a large language model and dynamic behavior strategy synthesis is adopted, and a complete cognition and action framework from semantic intention understanding, behavior decision making to closed-loop response is constructed, so that autonomous generation and intelligent execution of a complex and fuzzy inspection task can be realized; and the automation level, the response speed and the decision-making intelligence of highway inspection are obviously improved.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively improved.

The invention relates to the technical field of expressway monitoring, and provides an expressway abnormal state detection method and system based on a support vector machine, which processes vehicle trajectory data and ETC gantry data through Kalman filtering and adaptive weighted average to improve the data accuracy, introduces a Gaussian kernel function to construct an SVM model, and improves the detection accuracy of the expressway abnormal state. An original feature space is mapped to a high-dimensional space to deal with a nonlinear problem, samples with labels are used to train a multi-classification SVM model, and accurate detection of various abnormal states such as traffic congestion, accidents and vehicle faults is realized through a voting mechanism. According to the method, the accuracy of abnormal state detection is improved, powerful decision support is provided for expressway managers, the traffic flow is optimized, the road traffic capacity is improved, and the development of an intelligent traffic system is promoted.

The invention relates to the technical field of intelligent high-speed vehicle passing control, and provides a method for predicting a vehicle driving track under highway passing control based on digital twin tracking simulation, and the method comprises the steps: collecting highway digital twin system, target vehicle and environment data in real time, and carrying out the integration to form an input data set; a future driving track of a vehicle is predicted by using a pre-trained LSTM deep neural network model, and an actual track is collected through devices such as a GPS and the like for verification. And when the prediction deviation exceeds the preset range, the model is dynamically adjusted to improve the accuracy. Prediction results and deviation analysis are transmitted to a background control terminal and a driver mobile terminal in real time, and traffic control, accident early warning and driving aid decision making such as front road condition prediction and suggested driving speed providing are supported. The highway traffic efficiency and safety are effectively improved, the driver experience is enhanced, the development of an intelligent traffic system is promoted, the operation cost is reduced, and remarkable social and economic benefits are achieved.

The invention belongs to the technical field of intelligent transportation, and particularly relates to a track representation pre-training system based on deep learning. The invention discloses a track representation pre-training system based on deep learning. The method comprises the following steps: constructing a deep learning model oriented to a multi-scale track structure; the model fully combines static and dynamic features of a road network, and learns feature representation of a moving track from fine granularity to coarse granularity by mixing attention mechanisms of different scales and a self-supervised pre-training normal form, so that richer track space-time semantic information is obtained. The method specifically comprises a road network static feature construction module, a road network dynamic feature construction module, a multi-scale trajectory attention encoder and a travel trajectory self-supervised learning module. The whole pre-training system can enable track features to perceive more spatio-temporal information on different scales, and track vector representation obtained through self-supervised learning in massive tracks can be widely applied to various intelligent traffic system tasks such as travel time estimation and traffic condition prediction.

The invention discloses a traffic signal lamp intelligent control method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting traffic operation and environment data through deploying a sensor group in a target traffic area signal lamp; a self-adaptive analysis model including anomaly detection and congestion prediction is obtained through a traffic scheduling platform, traffic conditions are analyzed, and congestion parameters are obtained. And starting an emergency response mechanism according to the parameters, analyzing and determining signal lamp control strategy parameters, and finally realizing adaptive regulation and control of the traffic signal lamp. The technical problems that the real-time response capability of an existing traffic signal lamp control system is insufficient, and traffic abnormity and congestion cannot be effectively predicted are solved, and through real-time traffic data analysis and intelligent prediction, the real-time response capability of the traffic signal lamp system is enhanced, and the traffic safety is improved. And traffic abnormity and congestion can be effectively predicted and dealt with.

A method by which a first device performs maneuver driving in an intelligent transportation system (ITS) according to various embodiments may comprise: receiving a vehicle-to-everything (V2X) message related to a maneuver sharing and coordinating service (MSCS) from a second device; and determining whether to perform cooperative driving with the second device on the basis of the V2X message.

The invention provides a sensing equipment communication method, system and equipment for an intelligent traffic system, and belongs to the technical field of traffic intelligent control. The method comprises the following steps: acquiring an initial communication message of sensing equipment based on a preset dual-mode detection mechanism; according to the initial communication message, determining a corresponding message protocol feature vector, and dynamically weighting each feature dimension through an information entropy weighting algorithm; and matching the weighted message protocol feature vector with a preset protocol fingerprint database to determine whether the initial communication message has a matching protocol. According to a protocol conversion rule corresponding to the matching result, the initial communication message is converted into first protocol data, and the initial communication message is compressed into second protocol data, so that the first protocol data and the second protocol data are synchronously sent to the management terminal; wherein the protocol conversion rule comprises: converting the initial communication message without the matching protocol based on the temporary protocol mapping rule, and converting the initial communication message based on the matching protocol.

The invention discloses an RFID multi-label information fusion and unmanned vehicle path real-time correction method, system and device based on improved whale optimization, and a storage medium, and belongs to the technical field of the crossing field of the Internet of Things and an intelligent traffic system, and the method comprises the steps: collecting received signal strength indication information and label position information, constructing a label signal propagation model and a positioning probability distribution model; performing fusion processing of multi-label positioning information, adopting a dynamic weighting strategy to adjust signal contribution, obtaining an unmanned vehicle position estimation value, taking the unmanned vehicle position estimation value as state input of path optimization, constructing a path cost function, introducing a whale optimization search mechanism based on dynamic update parameters, performing iterative optimization on a path candidate solution, and obtaining a path optimization result; outputting a path correction result; and a real-time path instruction is issued to the vehicle control module, and the unmanned vehicle is guided to complete path correction and navigation control in a dynamic environment. According to the method, the problems of poor path adaptability, low fusion precision and local optimum in the existing method are solved.

The invention discloses a trajectory prediction method and system based on a CNN-LSTM-Attention neural network, belongs to the technical field of machine learning and data analysis, and solves the problem that the precision and reliability of trajectory prediction are reduced due to noise and accumulative errors existing in IMU (Inertial Measurement Unit) data. The method comprises the steps of collecting sensor data, performing preprocessing, dividing the sensor data into a training set and a test set, and performing standardization processing on input features and target output; constructing a hybrid neural network architecture, setting structures and parameters of each layer in the hybrid neural network architecture, and defining a loss function and an optimizer; training set data is adopted to train the hybrid neural network architecture, and a back propagation algorithm is adopted to optimize model parameters; calculating an error index between the predicted trajectory and the real trajectory, and evaluating the prediction performance of the hybrid neural network architecture according to the error index; and selecting the neural network with the best prediction performance to carry out real-time trajectory prediction. The method is suitable for application scenes such as intelligent traffic systems, unmanned driving and route planning.

The invention relates to a highway vehicle trajectory reconstruction method based on a car-following model and dynamic time warping, belongs to the technical field of trajectory reconstruction, and is particularly suitable for networking and automatic driving vehicle (CAV) environments. The method comprises the following steps: firstly, extracting CAV and detected motion characteristics of front and back common vehicles, and reconstructing a candidate track set by using a car-following model and an inverse car-following model; then, calculating the similarity between the candidate trajectory and the known trajectory by adopting a DTW algorithm; and finally, performing weighted fusion according to similarity scores, and finally generating a more accurate reconstruction trajectory. Experiments show that the method can effectively improve the trajectory reconstruction precision under the condition that the CAV permeability is low, and further reduce errors along with the increase of the permeability, thereby providing data support for the application of automatic driving and intelligent traffic systems.

The invention discloses a heaven and earth integrated multi-domain fused automobile high-level automatic driving system, which realizes more comprehensive and non-blind area sensing and positioning of an automatic driving automobile through multi-domain information fusion of a satellite platform, a cloud platform, an automobile end platform and the like, and promotes comprehensive upgrading of comprehensive efficiency of an intelligent traffic system. Based on cloud error broadcasting and a high and low orbit satellite fusion enhancement technology, the problem of automobile communication loss caused by the fact that single ground communication is limited by base station coverage is solved; based on a cloud reinforcement learning architecture, the communication network stability of the space-ground integrated system is improved through adaptive switching of a communication network; a road semantic map and a decision control experience pool are established based on vehicle high-precision trajectory data collected by a platform, and the limitation of the perception ability of a bicycle is made up through cloud federal learning scene experience sharing; the vehicle end dynamically updates a cloud road semantic map through local map matching; and information support is provided for realizing a chassis multi-domain fusion technology based on high-precision positioning.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention relates to the technical field of ship automatic control and intelligent traffic systems, solves the technical problem that trust evaluation is invalid or inaccurate due to the fact that a traditional trust evaluation method does not fully consider changes of a dynamic environment and a system state, and particularly relates to a shore-based driving and control inland river ship intelligent navigation man-machine interaction trust evaluation method. The objective of the invention is to improve the interaction safety and system stability between a ship and a shore-based control center through a dynamic trust calculation and risk assessment mechanism. The method specifically comprises four parts: trust index system construction, data acquisition and preprocessing, dynamic trust calculation and risk assessment and response. According to the method, the man-machine interaction trust in the intelligent navigation of the inland ship is dynamically evaluated and managed through a zero trust framework, so that the safety and stability of the ship in the navigation process are improved, and particularly, the trust degree of interaction between a shore base and the ship is evaluated in a complex and dynamic navigation environment; therefore, the safety and reliability of the system are ensured.

The invention provides a vehicle trajectory prediction method of a sparse space-time fusion module, belongs to the technical field of intelligent traffic and automatic driving, and solves the technical problem that a traditional attention mechanism lacks accurate focusing on key interaction features in a dynamic scene. According to the technical scheme, the method comprises the following steps: firstly, acquiring target information from a sensor, and constructing corresponding features by taking the target information as nodes; secondly, lane line and vehicle features are extracted through a sparse space-time attention module combining diffusion convolution and tan-sigmoid; and finally, predicting a vehicle track through a full connection layer. According to the method, the extracted features are subjected to attention enhancement in space and time dimensions by applying diffusion convolution and efficient additive attention, the precision and real-time performance of vehicle trajectory prediction are improved, the driving path of the vehicle can be mastered, and the method is suitable for automatic driving and intelligent traffic systems.

The invention relates to the technical field of image processing, and discloses a traffic behavior real-time identification method based on multi-modal spatial-temporal feature fusion. Traffic scene videos, images and traffic signal lamp state information are collected through a road monitoring camera, and target detection and tracking are carried out on video frames to obtain a target spatial-temporal trajectory; respectively extracting a target visual feature sequence and a signal feature sequence after signal lamp state coding by using a time sequence-space perception module, inputting the two sequences into a multi-modal semantic coupling and fusion network, and generating a fusion feature vector through feature alignment; and based on the vector, target behaviors are discriminated in real time through a behavior classifier, and a normal or abnormal detection result is output and an alarm is given. According to the method, multi-modal information is fused, semantic understanding and dynamic expression are enhanced, recognition robustness is improved, real-time performance and flexibility are achieved, misjudgment and missed judgment can be effectively reduced, and the method has great significance in improvement of the efficiency and the safety level of an intelligent traffic system.

The invention provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

The invention discloses an intelligent traffic system and digital twinborn technology integration method under a dual-carbon strategy. A data acquisition module, a data preprocessing module, a carbon emission model, an air quality model, a traffic flow model, a carbon fusion model, a digital twinborn visualization module and a dynamic simulation and prediction module are included. The system also comprises data synchronization and integration, and real-time monitoring and feedback. According to the invention, the monitoring and prediction precision of carbon emission, air quality and traffic flow is improved; real-time dynamic monitoring and optimization of the traffic system are realized, and the intelligent level of traffic management is enhanced; through a multi-objective optimization algorithm, traffic flow, air quality and carbon emission are comprehensively considered, and an optimized traffic management strategy and an optimized carbon emission reduction measure are provided; and by adopting a digital twinning technology, visualization and real-time feedback of data are realized, and transparency and operability of traffic system management are improved.

The invention belongs to the technical field of automobile automatic driving, and particularly relates to an automobile track prediction method and system based on a high-definition map and a GEGNN-LSTM model, and the method comprises the steps: firstly obtaining precise road information, including a road shape, an intersection position and the like, through employing the high-definition map; and then, a GEGNN-LSTM model is adopted to learn the historical track data of the automobile, various factors such as the historical track of the automobile, traffic signal lamp sign information and road information are comprehensively considered, and the future track of the automobile is predicted. The method can be widely applied to the fields of intelligent traffic systems, automatic driving and the like, and provides powerful support for the aspects of vehicle driving safety, road traffic efficiency improvement and the like. Meanwhile, the implementation of the method also helps to promote the further development of the intelligent traffic system, and has important practical value and social benefit.

The invention provides an urban traffic flow prediction method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: receiving traffic data, and carrying out the linear transformation processing through a full connection layer; the processed traffic data are input into multiple layers of time-space modules of the same structure for feature extraction, each layer of time-space module is composed of a time convolution module and a mixed time-varying graph module, and time features output by the time convolution module and spatial features output by the mixed time-varying graph module are spliced to form output of the time-space modules; parameterized learning is carried out on the mixed time-varying graph module; performing jump connection on the outputs of all the space-time modules to generate fused space-time features; and processing the fused spatial-temporal characteristics through a ReLU activation function, and transmitting a processing result to a full connection layer to obtain a final prediction result. According to the method, the urban traffic condition and the change trend can be accurately judged, and effective decision support is provided for urban planning and traffic management.

The invention relates to the field of intelligent traffic systems, and discloses an intelligent parking management system using artificial intelligence image recognition, and the system comprises an image collection module which is used for collecting a parking space image in a parking lot in real time, and is combined with at least one sensor unit which comprises a high-definition camera, an infrared thermal imager and a laser radar sensor; the image preprocessing module is used for preprocessing the acquired parking space image, removing noise in the image and enhancing the image; the geometric transformation module is used for converting the parking space image from the current view angle into a standard parking space model and eliminating view angle distortion; and the illumination optimization module is used for carrying out illumination correction on the parking space image and reducing the influence of illumination change on the parking space image. Through an image acquisition technology based on multi-sensor data fusion and in combination with a high-definition camera, an infrared thermal imager and a laser radar sensor, a parking space image can be accurately acquired under different illumination and complex environment conditions.

The invention discloses a traffic sign recognition and lane line detection method and system for extreme weather, and relates to the technical field of computer vision and artificial intelligence. According to the method, an efficient image restoration algorithm is designed, the influence of haze, rain and snow and other weather noise on the image quality is eliminated, and key detail information of traffic signs and lane lines is reserved; secondly, a YOLOv8 target detection network is improved, the problems of missing detection of small targets and false detection of complex backgrounds are solved, and the detection accuracy of the multi-scale traffic signs is improved; furthermore, a lightweight lane line detection algorithm is designed based on an improved line anchor mechanism, and the detection precision and real-time performance of the lane line in a complex scene are improved; and finally, an image restoration module, a traffic sign recognition module and a lane line detection module are integrated, multi-modal support of images, videos and real-time camera data is realized, an engineering solution adaptive to extreme weather is formed, and practical application of an intelligent traffic system in a complex environment is promoted.

The invention discloses a multi-pedestrian target detection method based on an unmanned aerial vehicle, and the method comprises the steps: (1) collecting pedestrian video data of the unmanned aerial vehicle, carrying out the frame extraction preprocessing, and marking a pedestrian bounding box, a type, and a confidence coefficient; (2) constructing a YOLOv8 optimization network, wherein a neck network of the YOLOv8 optimization network is integrated with a feature enhancement module, a channel reweighting module and a spatial context sensing module; (3) designing a ternary loss function fusing intersection-to-union ratio, center distance and length-width ratio constraints, wherein the ternary loss function comprises position, category and confidence loss; (4) training the network to converge by adopting a gradient descent method; and (5) deploying the model to the unmanned aerial vehicle to realize real-time detection. According to the method, by improving the network structure and the loss function, the pedestrian detection precision in a complex scene is remarkably improved, efficient data support is provided for an intelligent traffic system, and pedestrian safety guarantee and traffic management efficiency are enhanced.