71 results about "Traffic conflict" patented technology

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

The invention provides a multi-AGV bidirectional channel dynamic avoidance method and system based on conflict prediction, and relates to the field of industrial logistics automation control, when a vehicle-mounted laser radar of a first AGV detects an opposite second AGV in a preset fan-shaped early warning interval, double-AGV priority quantification is carried out, avoidance role distribution is carried out according to a quantification priority comparison result, and an avoidance AGV is positioned; and the way-giving AGV receives the standard driving track of the non-way-giving AGV, calculates a smooth way-giving track, and carries out two-way traffic conflict dynamic resolution. The technical problem that the logistics efficiency of a high-beat precise assembly production line is seriously restricted due to the fact that the accuracy of collision conflict prediction and avoidance processing and the response timeliness are insufficient due to the fact that the autonomous collaboration of multi-AGV passing control is weak in a narrow industrial channel in the prior art is solved. Full-autonomous cooperative passage control of multiple AGV clusters in a narrow industrial channel is realized, and zero-interruption execution of high-value logistics tasks is ensured.

The invention discloses an interpretable high-speed ramp intelligent driving vehicle confluence control method. The method comprises the steps that a state space is constructed, wherein the state space comprises the position and speed of a vehicle, lane information and the relative position and speed of adjacent vehicles; human-like decision guidance: training a human-like behavior discriminator based on real driving data by using a random forest model, screening action selection of reinforcement learning, and constructing a sample set containing state vectors and human-like action labels; reinforcement learning control; and carrying out interpretability analysis. Through an SHAP analysis method, an explainable DQN longitudinal acceleration control model in an automatic driving vehicle ramp merging process is provided, and an explainable automatic driving vehicle ramp merging control model is constructed in combination with an merging decision-making model constructed based on a random forest. According to the explainable high-speed ramp automatic driving vehicle confluence control method based on the confluence urgency degree and the environmental historical information, traffic conflicts during highway confluence can be relieved, and therefore the road passing efficiency is effectively improved.

This invention discloses a lane-level conflict prediction method for expressway weaving areas based on real-time traffic flow data, comprising the following steps: S1, selecting a suitable expressway weaving area as the survey location and conducting on-site investigation; S2, collecting traffic flow data and traffic conflict data for each lane, and defining thresholds for different conflict types; S3, building a traffic simulation model using the traffic parameters obtained from the survey, defining different conflict types according to the conflict thresholds obtained in step S2, and obtaining traffic conflict data for different types; S4, extracting feature variables from the data obtained in step S3, and constructing datasets for prediction models of different conflict types; S5, constructing a conflict analysis model; S6, constructing a conflict prediction model. This invention can achieve real-time lane-level traffic conflict prediction in expressway weaving areas, thereby providing more accurate lane-level conflict risk information for vehicles in each lane, helping drivers choose safer lanes and routes.

The invention discloses a visual constraint-based vehicle safety domain modeling and conflict risk assessment method, which comprises the following steps of: analyzing and selecting a non-signalized intersection with frequent traffic accidents by using accident black spots, and acquiring multi-dimensional traffic information of the intersection through an unmanned aerial vehicle; building three vehicle safety domain models of straight running, right turning and left turning; by predicting a vehicle trajectory equation, determining an intersection conflict position, dividing intersection conflicts into two categories of cross conflicts and confluence conflicts according to a trajectory interaction mode, and in different conflict scenes, according to a constructed vehicle safety domain, performing quantitative calculation on a conflict risk of vehicles in a non-signalized intersection to obtain traffic conflict exposure time; and based on the traffic conflict exposure time data, determining an optimal clustering number K of a K-means algorithm through an elbow rule, clustering results of the traffic conflict exposure time by using the K-means algorithm, and grading conflict risks. According to the invention, the accuracy of vehicle conflict detection at the non-signalized intersection is improved.

The invention discloses a distributed motorcade collaborative shockwave absorption method based on multi-objective optimization, and the method comprises the steps: S1, obtaining a scene of a mixed flow motorcade passing through a shockwave region, dividing a long motorcade, and determining key parameters; s2, constructing an optimization control model to solve an optimal motorcade splitting point, and splitting the hybrid motorcade; and S3, based on an improved AS-OCD distributed trajectory optimization algorithm, performing synchronous control on each sub-motorcade so as to completely absorb traffic shockwaves. The problem that traffic shockwaves frequently occur in a high-saturation traffic flow or an urban expressway is solved, state sharing and boundary coordination among the sub-motorcades are achieved through distributed collaborative optimization, the adaptability and robustness of the system in a high-density complex traffic environment are enhanced, speed coordination and safe distance between the motorcades are guaranteed, the risk of traffic conflicts is reduced, and the traffic safety is improved. And the stability and the operation efficiency of the whole traffic system are improved.

The invention provides a mine laneway traffic scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a continuous vibration signal monitored by a distributed optical fiber sound wave sensing system in a mine laneway; performing filtering and signal identification on the continuous vibration signal to identify a vehicle driving signal; monitoring the triggering time of the calibration point in the mine laneway according to the vehicle driving signal, and determining the position and the driving direction of the vehicle; according to the vehicle driving signal, the position of the vehicle and the driving direction, determining whether vehicle early warning needs to be carried out in the mine laneway; and in response to the need of vehicle early warning, generating a vehicle scheduling and early warning instruction so as to perform vehicle scheduling. Therefore, by analyzing the vibration signals monitored by the distributed optical fiber sound wave sensing system in the mine laneway in real time, the position, the speed and the vehicle driving direction of the vehicle are accurately recognized, so that before potential traffic conflicts occur, dispatching and early warning instructions are sent to related vehicles, and the passing efficiency and the safety of the mine laneway are effectively improved.

The invention discloses a traffic risk analysis method and system based on multi-source perception data, and relates to the technical field of traffic risk management. The traffic risk analysis method based on the multi-source perception data comprises the following steps of performing noise interference optimization judgment; quantifying the timeliness of traffic risk capture; traffic conflict probability prediction; and carrying out traffic risk decision response analysis. According to the method, whether noise interference optimization is carried out or not is judged through the obtained high-frequency signal amplitude, whether timeliness optimization is carried out or not is judged based on the obtained capture timeliness quantification result, and when the timeliness is qualified, whether interaction parameter step length optimization is carried out or not is judged based on the predicted traffic conflict probability; meanwhile, whether decision response optimization is carried out or not is judged based on the decision response analysis result, and the effect of improving the accuracy of dynamic traffic jam risk assessment in the road transformation area in the urban road transformation process is achieved; the problem that the accuracy of dynamic traffic jam risk assessment is not high in the urban road transformation process in the prior art is solved.

The invention provides a low-altitude and ground traffic intelligent cooperative control method based on multi-agent reinforcement learning. The method comprises the steps that traffic state information is acquired, and scene classification is performed through a multi-agent reinforcement learning model; if the scene is a low-altitude congestion scene, executing unmanned aerial vehicle path dynamic planning, airspace resource allocation and conflict resolution, and fusing to generate a low-altitude control instruction; if the scene is a ground conflict scene, vehicle priority ranking, signal lamp optimization and road right distribution are executed, and a ground control instruction is generated through secondary fusion after a conflict resolution weight matrix is constructed; and finally, fusing low-altitude and ground instructions through a double-layer attention mechanism, and outputting a global cooperative scheduling scheme. According to the method, the airspace resource utilization rate and the road traffic efficiency can be improved, the traffic conflict risk is reduced, and a global integrated optimal scheduling scheme is formed.

The invention belongs to the technical field of intelligent traffic safety and risk prediction, and particularly relates to a traffic conflict risk spatio-temporal evolution prediction method based on a vehicle interaction relationship, and the method comprises the steps: taking a target vehicle as a main vehicle, and extracting the positions and speeds of the main vehicle and surrounding vehicles in a unified coordinate system through multi-source data; the method comprises the following steps: calculating a relative position and a relative speed, forming interaction relationship representation data, constructing a vehicle risk potential field on a space-time continuous domain on the basis, mapping a discrete interaction state into continuous and differentiable risk intensity distribution, further forming a sequence by the risk potential fields at each moment, introducing a space-time coding module based on Transform, and coding the vehicle risk potential fields at each moment by using a time-space coding module based on the Transform. And combining position coding, self-attention and graph attention to represent time sequence dependence and multi-vehicle risk propagation coupling, and outputting future risk field evolution distribution. According to the method, the problems of information loss and insufficient spatio-temporal expression of a traditional single time interval index and fixed time window method are solved, conflict risk dynamic prediction is realized, and a reliable basis is provided for traffic safety management and active intervention.

The invention provides an airport flying area crossing traffic command system based on an A-SMGCS system, and belongs to the technical field of intelligent traffic, and the system comprises a sensor layer which is used for collecting aircraft dynamic data, to-be-passed authorized object data, environment meteorological data, and dynamic obstacle data; the data processing and decision-making layer is used for carrying out standardization processing and space-time mapping processing on the collected data, judging existing potential conflicts in combination with a dynamic threshold adjustment mechanism, and generating an issuing instruction based on a multi-target optimization decision; and the execution layer is used for controlling crossing traffic management and control equipment according to the issued instruction and feeding back an execution result to the data processing and decision-making layer in real time to form closed-loop control. The traffic conflict risk of the airport flight area crossing is effectively reduced, the crossing passing efficiency is improved, the vehicle waiting time and the aircraft taxiing delay are reduced, the fine scheduling requirement for scene movement is met, and reliable support is provided for safe and efficient traffic operation of the airport flight area.

The invention discloses a motor vehicle chain conflict identification method based on a rear vehicle avoidance characteristic, and the method comprises the steps: 1, collecting the driving track data of motor vehicles on an urban road, calculating the collision time, and identifying a single traffic conflict between the motor vehicles on the same longitudinal lane and between the motor vehicles on adjacent transverse lanes; 2, on the basis of a stimulation-reaction model, determining a vehicle avoidance reaction threshold by combining a signal detection theory, and identifying an avoidance behavior of a vehicle after the conflict; and 3, merging conflicts with continuous avoidance behaviors, dynamically delimiting the influence duration of the conflicts, and constructing a complete chain conflict chain based on causal association between the conflicts. According to the method, the defect that space-time proximity is equivalent to causal relevance in the prior art is overcome, the accuracy of chain conflict recognition is improved, and reliable support is provided for road traffic safety early warning and management and control.

The invention discloses an intersection state sensing and traffic safety evaluation method based on an unmanned aerial vehicle and a large model, and belongs to the field of traffic safety evaluation. Comprising the following steps: constructing a target detection and tracking model based on an unmanned aerial vehicle visual angle, collecting intersection state and traffic flow data by using an unmanned aerial vehicle aerial video, counting traffic conflict data, constructing intersection potential conflict indexes, constructing a comprehensive grade evaluation model, improving an initial model by using a large model, and outputting an intersection comprehensive safety evaluation grade. Using an improved RT-DETR algorithm and a Deepsort model to improve the detection precision of the traffic participants and carrying out training; the intersection state and traffic flow data are extracted, and the overall risk quantification of the intersection is realized through a plurality of potential conflict indexes and by adjusting the weight of each index through a large model. According to the method, dynamic regulation and control are carried out on the safety index weight in combination with a large model, comprehensive evaluation of potential risks, abnormal behaviors and the overall safety level of the intersection is achieved, and the method has important significance.

This invention relates to an AEB (Autonomous Emergency Braking) triggering method, device, and medium based on traffic conflict, comprising the following steps: identifying at least one target vehicle in the environment; determining the vehicle type of the target vehicle based on a preset target detection model; matching a preset target vehicle mass based on the vehicle type; calculating the difference in braking distance based on the acquired relative distance between the vehicle and the target vehicle and the target vehicle speed; calculating the collision loss momentum based on the target vehicle speed, the target vehicle mass, the acquired vehicle speed, and preset vehicle mass information; calculating the risk level based on the difference in braking distance and the collision loss momentum, and according to the vehicle type; and triggering AEB when the risk level meets predetermined conditions. Compared with the prior art, this invention ensures vehicle driving safety while also considering comfort.

The invention discloses an intelligent road cone and a vehicle dynamic guiding method, and belongs to the technical field of intelligent traffic facilities, the intelligent road cone comprises a cone structure, a counterweight base, a sensing unit module, a microcontroller, a communication module and a warning module.The inclination state is monitored through a posture detection assembly, a sensing unit collects multi-dimensional data, and the warning module sends the multi-dimensional data to the microcontroller; according to the guiding method based on the road cone, coherent electromagnetic waves are emitted through the intelligent road cones on the two sides of a road to form an interference enhancement area, a repulsive force-gravitational force virtual force field model is constructed in combination with multi-channel signal detection, and the interference enhancement area and the repulsive force-gravitational force field model are combined. And guiding the vehicle to drive towards a safe area by using the control logic that the gravitation weight is dynamically higher than the repulsive force. According to the invention, hot plug expansion is realized by adopting a modular architecture, the structural stability and the function expansibility are enhanced, the vehicle guiding precision is improved through dual-source electromagnetic wave interference and an intelligent control algorithm, the traffic conflict risk in a construction area is effectively reduced, and the road construction safety and the management efficiency are improved.

The application discloses a kind of dangerous event chain extraction method and system based on complex traffic scene, including obtaining test scene data;Based on the key factors of dynamic attribute and interaction between vehicle and environment determined by preset natural driving data and sensor performance information, and the uncertainty of key factors is quantified;Based on the risk estimation multidimensional feature set and uncertainty information preset constructs driving risk estimation model for test scene;The driving risk estimation model is optimized, and the optimized risk estimation model for test is obtained;Based on the time evolution characteristics of dangerous event, a dangerous time chain model is constructed according to the optimized risk estimation model;Dangerous time chain model is solved, and dangerous event chain is obtained.The application uses traffic accident data and traffic conflict data to reveal the space-time evolution law of complex traffic scene, and improves the effect of later test scene reproduction and reconstruction by solving the dangerous time chain model.

This invention provides a short-term traffic conflict prediction method and early warning system based on RSU group collaboration and spatiotemporal graph neural network. It acquires vehicle trajectory and status data collected collaboratively by roadside unit groups, using the coverage area of ​​each RSU as a spatial analysis unit, and constructs an initial energy field distribution map of traffic conflict based on the spatial density of vehicle distribution and the intensity of speed temporal fluctuations. A pre-constructed spatiotemporal graph neural network is invoked to capture energy transfer paths between units, and a temporal cyclic layer is used to capture the energy change inertia within units, predicting and generating a dynamic evolution map of traffic conflict energy containing an energy evolution path network and peak times. Based on the map, potential conflict outbreak sources and expected affected unit sequences are identified, generating conflict propagation situation prediction information including spatial coordinates and propagation time sequences. Based on this information, differentiated collaborative working mode adjustment instructions are generated and distributed to each RSU. This invention achieves short-term prediction of traffic conflicts and collaborative scheduling of roadside resources.

The invention provides an open block traffic flow intelligent distribution method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the processing of a basic triangular unit, so as to define the interaction range of multi-mode traffic flow; according to the interaction range, mapping the point cloud data on the virtual analysis plane to a corresponding actual road network area so as to extract mutual interference parameters of multimode traffic flows in the branch network, and generating a Gaussian mixture model initial parameter set; and based on the initial parameter set of the Gaussian mixture model, carrying out iterative optimization distribution on the open block OD pair, dynamically adjusting the path selection probability to obtain a final flow distribution scheme, and outputting the real-time flow distribution proportion of each path. According to the method, key features can be extracted from mass trajectory data, traffic conflict areas are accurately positioned in space, and interference intensity is quantified.

The application provides a traffic conflict identification method for full linear road sections based on collision conditions, collects high-precision trajectory data based on a multi-source heterogeneous sensor network, and then uses a denoising method of adaptive Kalman filtering (AKF) to extract more accurate trajectory data. Based on the processed trajectory data, a multi-factor fusion transformer model is used to predict the future vehicle trajectory, and then a TTCA model under Frenet is constructed, without pre-judging the conflict type and identifying the traffic conflict of the driving road section, and the vector cross product method is used to consider the collision conditions between vehicles. The application fills the gap of the existing conflict identification technology in multi-type conflict identification in complex scenes, and improves the accuracy of the conflict identification technology.

The application provides a kind of underground mine truck scheduling method and device based on state machine and reinforcement learning, it is related to underground mine transportation system scheduling optimization technical field.The method comprises: obtaining the scene model of underground mine transportation system, constructs topological structure model, constructs the underground mine truck fleet operation problem according to topological structure model and conflict type, and then constructs the fleet scheduling decision model based on state machine and reinforcement learning;Wherein, each truck is regarded as the agent of fleet scheduling decision model, and the state machine is equipped for the agent;With the goal of maximizing transportation efficiency, reducing waiting time and reducing conflict, the fleet scheduling decision model is solved, and the scheduling result of mine truck in the underground transportation working face with bidirectional single lane driving characteristics is obtained.The application realizes the intelligent scheduling of underground mine truck fleet by combining state machine and reinforcement learning technology, to improve transportation efficiency, reduce traffic conflict and reduce energy consumption.

The application provides a kind of mixed flow environment under the confluence area vehicle initiative transverse and longitudinal coordination control method, based on wide-area time, continuous variable and discrete variable, carry out multi-system input and output coordination control.First, by intelligent network connection vehicle speed control and variable speed limit area control, adjust the speed and the distance between vehicles when traffic flow meets motion wave, effectively slow down traffic motion wave and improve the efficiency of traffic flow;Second, by the behavior control of intelligent network connection vehicle, limit the influence of the change of vehicle speed caused by motion wave and main line control strategy on ramp merging area vehicle flow and speed, so as to avoid traffic conflict in the process of confluence;In addition, transverse lane changing rules are also designed to reduce transverse disturbance in interlaced area, and the disturbance of transverse and longitudinal behavior in the area on the traffic system model is considered.The strategy of the application strives to reduce traffic conflict in the pursuit of the coordinated optimization of traffic safety and efficiency, so as to achieve the optimal goal of the system.

This invention relates to the technical fields of tunnel safety control, intelligent transportation, vehicle-road cooperation, and tunnel safety early warning. Specifically, it relates to a tunnel active safety control system and method based on vehicle-road cooperation. Key technical points include: a multi-source data fusion module: integrating data from roadside equipment (including radar, cameras, and fiber optic sensors) and vehicle-mounted terminals to construct a four-dimensional perception network of people, vehicles, roads, and the environment; a dynamic risk assessment module: quantifying the probability of an accident (P) and collision energy loss (E) based on an equivalent traffic conflict number (ECN) model to achieve risk-level early warning; and a vehicle-road cooperative intervention module: triggering early warning and control strategies from both the roadside and vehicle perspectives through two-way communication between roadside smart guidance signs, information boards, and vehicle-mounted terminals. This invention improves the ability to respond to tunnel risks.

The invention discloses a perception analysis system based on intelligent management. An operation method of the system comprises the following steps: performing information input and data processing of an intelligent network connection automobile; detecting, tracking, analyzing and processing the intelligent networked automobile; carrying out intelligent network connection automobile traffic conflict analysis, discrimination and management; and carrying out statistics on the user data information of the intelligent networked automobile. The information input and data processing of the intelligent connected automobile comprises the following steps: inputting destination information to the system through the sensing analysis system before the user of the intelligent connected automobile drives each time; distortion elimination processing is carried out on the image information collected by the intelligent network connection automobile equipment; after the laser video all-in-one machine in the intelligent networked automobile completes acquisition of driving data, coordinate system conversion processing is carried out on the driving data. The step of detecting, tracking, analyzing and processing the intelligent networked automobile comprises the step of detecting the position information of the intelligent networked automobile in real time. The system has the characteristics of intelligent analysis and management and high safety.

The invention provides a signal and vehicle cooperative intelligent control method and system based on traffic conflicts, and the method employs a double-layer cooperative optimization control architecture: an upper-layer traffic signal control model dynamically adjusts the duration of a green light; according to the lower-layer vehicle track control model, the length of a conflict area is calculated in combination with the maximum braking distance of the vehicle according to traffic conflicts caused by bus entering and exiting, conflict recognition conditions and conflict elimination constraints are provided, and on the basis, the acceleration of a vehicle team pilot vehicle is decided with the fuel consumption and comfort of the vehicle as optimization targets. According to the method, a double-layer optimization control model is solved by adopting dynamic planning, green light duration and vehicle tracks are adjusted in real time in combination with vehicle-road cooperative communication, bus priority passing and safe and orderly avoidance of common vehicles can be guaranteed, average passenger delay and travel time of buses in a conflict area are effectively reduced, and the traffic safety of the buses is improved. The overall oil consumption is reduced; and the intersection passing efficiency is improved.

The application relates to a real-time identification method and identification system for highway traffic operation risk under ice and snow conditions based on dynamic multi-layer fuzzy logic, and belongs to the field of traffic safety.The application aims to solve the problems that the existing method cannot process the relationship of various influence factors, cannot give a quantitative index of traffic operation risk, and cannot guarantee the robustness of the model under a new environment, leading to low accuracy and poor real-time performance of the identification of highway traffic operation risk under ice and snow conditions.The model prediction result has high reliability in the case of missing accident data and traffic conflict data.The application processes the quantitative problem of the operation risk index by constructing a multi-layer fuzzy logic structure.The intelligent rule generation module is added to improve the prediction accuracy and robustness of the system under different environments.

The application provides an airport flight area crossing traffic command system based on an A-SMGCS system, and belongs to the technical field of intelligent traffic, and comprises: a sensor layer, which is used for collecting aircraft dynamic data, to-be-passed authorization object data, environmental meteorological data and dynamic obstacle data; a data processing and decision-making layer, which is used for standardizing processing and space-time mapping processing of the collected data, judging existing potential conflicts in combination with a dynamic threshold adjustment mechanism, and generating and issuing instructions based on multi-objective optimization decision-making; and an execution layer, which is used for controlling crossing traffic control equipment according to the issued instructions, and feeding back execution results to the data processing and decision-making layer in real time to form a closed-loop control. The application effectively reduces the traffic conflict risk of the crossing of the airport flight area, improves the crossing efficiency, reduces the vehicle waiting time and the aircraft taxi delay, adapts to the fine scheduling demand of the airport scene movement, and provides reliable support for the safe and efficient operation of the airport flight area traffic.

The invention provides a heterogeneous traffic flow-oriented expressway deceleration lane length optimization calculation method. A deceleration lane is divided into a deceleration section, a transition section and an auxiliary lane section; designing a simulation experiment, generating a plurality of groups of heterogeneous traffic flow working condition combination sets based on orthogonal experiment design, and outputting traffic operation data of each working condition under a microscopic simulation platform; wherein the traffic volume, the shunting vehicle proportion and the automatic driving vehicle permeability are represented; constructing a reward function by taking the serious traffic conflict rate, the relative traffic efficiency and the speed variation coefficient as evaluation indexes; and calculating a reward function through the heterogeneous traffic flow working condition combination set, and carrying out optimization solution on the deceleration section, the transition section and the auxiliary lane section by adopting a search optimization algorithm to obtain optimal lane lengths of the deceleration section, the transition section and the auxiliary lane section.

The invention discloses an intersection safety evaluation method based on an unmanned aerial vehicle and a large model. The method comprises the following steps: shooting a traffic operation video of a to-be-detected intersection by using the unmanned aerial vehicle; inputting the traffic operation video into a pre-constructed target detection tracking model, obtaining intersection information and traffic flow data, and calculating motion information of traffic participants; counting traffic conflict data according to the traffic flow data and the motion information, and constructing potential conflict indexes of the intersection; constructing a comprehensive safety level evaluation model framework according to the potential conflict indexes, and improving the comprehensive safety level evaluation model framework by using a large model in combination with intersection information and traffic flow data to obtain a comprehensive safety level evaluation model; and inputting the potential conflict indexes into the comprehensive safety level evaluation model for calculation, and outputting the comprehensive safety evaluation level of the intersection. According to the method, the intersection safety level is evaluated in a multi-dimensional manner through the potential conflict indexes, and the potential risk and the overall safety level of the intersection are comprehensively reflected.

A projection control method, a data processing method, a projection system and related products are applied to the technical field of projection. The projection control method comprises the steps that a control device can obtain a first position indication or determine the first position indication based on environment information around a terminal, a projection module of the terminal is enabled to project a prompt icon through the first position indication, and the projection position of the prompt icon is fixedly located at the position indicated by the first position indication; the prompt icon is used for indicating the terminal to approach the traffic conflict area. By resolving the fixed projection position and projecting the icon at the fixed projection position, the continuous prompting capability of the terminal in the traffic conflict scene can be improved, the warning effect of the projected image is enhanced, and the safety of the terminal is improved.