125 results about "Traffic conflict" patented technology

The invention relates to the technical field of traffic safety, in particular to a highway risk map construction method and device fusing multi-source data, and the method comprises the following steps: S1, selecting a traffic conflict risk, a driving behavior risk and a vehicle safety risk as risk dimensions, selecting risk indexes according to the traffic conflict risk, the driving behavior risk and the vehicle safety risk, and calculating risk index data and the weight of the risk indexes; s2, calculating the total risk of the road section according to the weight of the risk dimension, the weight of the risk index and the risk index data; and S3, obtaining a risk map according to the total risk of the road section and the weight of the risk dimension. According to the method, by constructing the risk map and identifying the high-risk area, the application of multi-source data fusion in the aspect of highway risk assessment is enhanced, a scientific basis and technical support can be provided for traffic safety management, and the safety of the highway is improved.

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 a method and a system for identifying traffic conflicts in a road interleaving area, particularly relates to the field of traffic management, is used for solving the problems of low identification precision and early warning delay of the traffic conflicts in the interleaving area, and is used for accurately capturing risk events and filtering non-intentional behavior interference based on a composite trigger mechanism of turn light activation and transverse displacement rate; a behavior feature vector is matched with a conflict type template library to realize self-adaptive interception of an asymmetric time window, and risk evolution cycles of scenes such as sharp cut-in and slow lane change are completely covered; introducing a dynamic window expansion mechanism driven by a conflict energy gradient to ensure the whole-process capture of a risk accumulation stage; multi-scale spatio-temporal features are extracted in combination with a convolutional network to generate a thermodynamic diagram, and refined positioning of risk spatio-temporal distribution is achieved; an early warning signal grading mechanism and a closed loop feedback optimization system are established, the system adaptability is continuously improved, real-time and accurate decision support is provided for an intelligent traffic management system, the secondary accident risk is effectively restrained, and the road network traffic efficiency is optimized.

The invention discloses a human-vehicle traffic early warning system applied to traffic safety, which relates to the technical field of traffic safety and comprises a data acquisition unit, a data processing unit, an early warning decision unit, a user interaction unit, a safety and compliance unit and a system management and maintenance unit. The data processing unit is used for receiving the trajectory data and processing the trajectory data; the early warning decision-making unit is used for analyzing the processed trajectory data by applying a safe distance technology and a traffic conflict model so as to evaluate and predict traffic risks and generate an early warning decision; and the user interaction unit is used for displaying the generated early warning decision through a visual interface and sending out a corresponding early warning signal. Trajectory data of vehicles and pedestrians can be collected through the data acquisition unit and transmitted to the data processing unit for processing, a solid foundation is provided for subsequent early warning decisions, and timeliness and accuracy of early warning information are 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 traffic conflict risk evaluation method, system and device based on simulation modeling and a storage medium, and relates to the technical field of traffic safety evaluation method research, and the method comprises the steps: collecting road data and vehicle driving data, and constructing a traffic data set; carrying out traffic simulation modeling based on the traffic data set; carrying out traffic conflict index calculation based on traffic simulation modeling; evaluating the traffic conflict risk according to the traffic conflict index; according to the method, the traffic conflict is dynamically analyzed, the occurrence of the traffic conflict can be more accurately judged, the traffic conflict risk problem can be effectively solved, and a new thought is provided for judging the regional traffic conflict risk condition.

The invention provides a multi-road condition optimization way-finding method based on an ant colony algorithm. The method is specially designed for route planning and scheduling of an automatic driving unmanned vehicle in an actual production environment. According to the method, on the basis of a traditional heuristic algorithm, heuristic factors such as road width, total path curvature and traffic conflict probability which have great influence on vehicle driving time are added as consideration factors to be added into a way-finding method, and a driving route with good comprehensive road conditions is obtained by sacrificing part of road length. The method designs a penalty mechanism to improve the convergence speed of the algorithm aiming at the situation that the convergence speed is reduced after a traditional heuristic algorithm enters a dead path. Meanwhile, path smoothing processing is performed on an abstract driving path obtained by an algorithm based on a path smoothing technology of a road condition, so that the problem of path difference from an abstract scheduling path to actual driving of the vehicle caused by physical limitation of a vehicle walking mechanism is avoided.

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 discloses an intelligent targeted dredging method for an expressway confluence area, which belongs to the technical field of intelligent traffic and comprises the following steps: S1, acquiring traffic flow, vehicle speed, lane occupancy and weather parameters of a main road / ramp through a vehicle-mounted communication unit; s2, constructing a traffic capacity baseline value based on a random forest model; s3, when the traffic capacity is lower than a dynamic threshold value, calling a deep reinforcement learning model through an edge computing node; s4, a hierarchical pushing mechanism is adopted to distribute a control instruction, and variable information board speed limit values are issued to non-network-connected vehicles; and S5, when a residual error between a predicted value and an actual value of the sensor data confidence coefficient continuous time window exceeds a historical mean value standard deviation, switching to a time sequence prediction model to generate a conservative control strategy. According to the method, a multi-index feedback mechanism based on the carbon emission, the average delay time and the number of traffic conflict points is constructed, the weight is dynamically distributed through the entropy weight method, and quantitative evaluation of the dredging effect is achieved.

The invention relates to the field of intelligent traffic systems, in particular to an RF-LSTM algorithm-based expressway traffic conflict analysis and prediction method, and aims to realize accurate identification and prediction of potential conflicts in expressway traffic flow by combining the advantages of a random forest (RF) and a long-short-term memory (LSTM) network. The method comprises the following steps: step 1, deploying equipment on an expressway, and collecting operation data of a vehicle; 2, cleaning and normalizing the data to obtain a vehicle track data set; step 3, training a K-means clustering model to carry out traffic conflict clustering analysis; 4, training a random forest model to perform feature selection; step 5, training a long-short-term memory network for track prediction; and step 6, carrying out conflict prediction on the traffic flow of the expressway, and identifying potential high-risk areas and time.

The embodiment of the invention discloses a mixed driving risk identification method and system for a road interleaving area and a computer storage medium, and the method comprises the steps: respectively quantifying the contribution value of each index to a traffic accident and a traffic conflict for each index in a multi-dimensional risk identification index system; sorting the indexes according to the contribution values to obtain a traffic accident contribution value sorting result and a traffic conflict contribution value sorting result; according to the traffic accident contribution value sorting result and the traffic conflict contribution value sorting result, indexes of which the contribution values to the traffic accidents and the traffic conflicts exceed a preset threshold value are selected and combined to form a high-contribution index system; screening the indexes of which the correlation among the indexes is smaller than a threshold value from the high-contribution index system; training a double-risk identification model based on the screened high-contribution index system; and constructing an accident conflict two-dimensional risk matrix so as to detect real-time traffic flow data by using the trained double-risk identification model and the accident conflict two-dimensional risk matrix.

The invention belongs to the technical field of automatic driving test, and discloses an automatic driving vehicle chassis domain evaluation method with comprehensive intelligent measurement, which comprises the following steps: step 1, a vehicle chassis adopts a nonlinear system integrated with multivariable control, the nonlinear system is decoupled through a decoupling pre-compensator, and the decoupled nonlinear system is connected with a decoupled pre-compensator; therefore, evaluation element data in the urban high traffic conflict density scene are extracted; 2, establishing an evaluation system according to related evaluation elements and traffic flow characteristics, and selecting evaluation indexes; and step 3, synthesizing a CRITIC method, an entropy evaluation method and an order relation analysis method to obtain a weight set, and obtaining an evaluation matrix according to a fuzzy comprehensive evaluation method so as to construct an evaluation model. According to the method, extraction of chassis dynamic performance elements is realized, and objective evaluation of the vehicle chassis performance in an urban high traffic conflict density scene is also realized.

The invention discloses a vehicle speed prediction method and system in a dynamic vehicle following scene considering a traffic environment, and the method comprises the steps: extracting vehicle following case data through setting a standard and a threshold value of a vehicle following process in a natural driving data set; using a target detection algorithm to obtain traffic environment characteristics related to the vehicle following case data, extracting vehicle dynamics parameters in the vehicle following case data, fusing the vehicle dynamics parameters and the traffic environment characteristics, and performing normalization processing to obtain fusion characteristics; removing redundant variables in the fused features by adopting a combined feature selection method; and dividing the fusion features from which the redundant variables are removed into a data set, training a pre-established Informer-FDR vehicle speed prediction model, and obtaining a vehicle speed prediction result by using the trained vehicle speed prediction model. The method effectively reflects the dynamic interaction characteristics of the driver and the traffic environment and potential traffic conflicts, enhances the understanding of the model for the complex driving environment, and improves the real-time performance and accuracy of vehicle speed prediction.

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 discloses an automatic driving non-signalized intersection traffic conflict control method based on a space-time network, and the method comprises the steps: carrying out the preprocessing: determining an intersection traffic conflict control range, and carrying out the grid division, after the vehicle arrives at the control area, the speed, the position, the acceleration, the lane entering time, the intentional steering time and the intersection control area time of the vehicle are reported to the central manager; a request is sent when the vehicle arrives, and the central manager judges whether the vehicle is allowed to enter; solving the space-time network control model and the trajectory reconstruction model, and feeding back the specific driving trajectory to the vehicle; the vehicle runs according to the running track fed back by the central manager and safely passes through the intersection; according to the invention, the path of the autonomous vehicle at the intersection is changed based on the path of the unconventional intersection, and the traffic conflict control of the non-signalized intersection in the pure automatic driving environment is explored, so that the traffic efficiency of the intersection is better improved.

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 discloses a method for identifying the driving style of an automobile driver. The method comprises the steps that driving feature data and environment feature data in the automobile driving process are acquired; preprocessing the driving feature data and the environment feature data, and extracting time sequence feature data; performing model training on the time sequence feature data to establish a driver style identification model; identifying the driving style of the driver in real time according to a driver style identification model; and the driving style of the driver is visualized outside the automobile. Through the mode, the identification precision of the driving style can be remarkably improved, the system can dynamically adjust the identification logic, it is ensured that the real style of a driver can still be accurately reflected in different scenes, and the robustness and practicability of the method are enhanced; according to the method, the driving style of the driver is visualized outside the automobile, the information transparency between traffic participants can be remarkably improved, traffic conflicts are reduced, and finally collaborative optimization of road traffic efficiency and driving safety is achieved.

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 present invention belongs to the technical field of intelligent remote monitoring and dispatching of smart transportation, and relates to an intelligent remote monitoring and dispatching system for smart transportation. The present invention reduces vehicle waiting time, improves road traffic efficiency, and reduces traffic conflicts and congestion by obtaining traffic signal light dispatching information of each time period at each traffic intersection in the target area. By dynamically adjusting the traffic signal light dispatching information of each traffic signal light in the target area to change the traffic signal light dispatching information of the traffic intersection, it helps to adapt to the traffic demand of the current time period and current road conditions, and reduces the risk of traffic conflicts and collisions caused by the inconsistency between the traffic data of the current time period and the historical data. By obtaining the traffic signal light dispatching information of each traffic signal light guiding the intersection in the target area, it helps to achieve coordinated control between signal lights, forming a "green wave" effect, so that vehicles can pass through multiple intersections continuously, reduce the number of stops and starts, and improve traffic fluency.

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 invention belongs to the technical field of urban traffic control, and particularly relates to an intersection far-leading U-turn and shift left-turn cooperative control method, which comprises the steps of S1, intersection channelization design; s2, designing intersection signals, including designing an intersection main signal and an intersection pre-signal; s3, designing a traffic organization; s4, designing a phase sequence of an intersection phase; step S5, determining full red time; s6, determining pre-signals, including determining a turning-around pre-signal and a shifting pre-signal; and step S7, determining the length from the opening to the intersection, including determining the minimum value of the length of the shift lane and the minimum value of the length of the turn-round lane. By optimizing signal timing and space resource allocation, cooperative operation of far-guiding turning-around of left-turning vehicles on the secondary road and tailgating left-turning of the main road is achieved, the passing efficiency of the intersection is improved, and traffic conflicts are eliminated.