281results about How to "Ease traffic congestion" patented technology

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention discloses an in-tunnel construction vehicle meeting scheduling system based on positioning equipment. The system comprises a basic service module which is used for storing basic data for business service analysis in an acquisition and collection mode; the business service module is used for carrying out detailed analysis of the corresponding service through the data acquired by the basic service, and providing an analysis result to the display end for display; and the display end module is used for displaying the analysis result of the business service module, wherein the display modes comprise a mobile phone APP, a webpage large screen, an LED panel, a tunnel loudspeaker and a tunnel traffic light. The system carries out weight grading on vehicle and personnel positioning labels by standardizing a tunnel construction process, and integrates data of avoidance points of a transverse tunnel. And in combination with an intelligent technology, efficient vehicle meeting scheduling, intelligent construction path planning and avoidance point path planning of the construction area are realized.

The invention discloses an intelligent parking space state recognition system and method based on video detection, and belongs to the technical field of parking space state recognition, and the method specifically comprises the steps: collecting the video data of a parking area, carrying out the preprocessing of the collected video data of the parking area, carrying out the modeling of a parking lot background based on a time sequence, and carrying out the real-time updating, based on a background modeling result, a foreground is detected, a foreground area is extracted, contour analysis is carried out on the detected foreground area, the occupation state of each parking space is judged, a real-time parking space distribution diagram of the parking lot is generated according to the occupation states of all the parking spaces, and the occupation or idle state of each parking space is displayed. The intelligent parking space state recognition system based on video detection can keep high accuracy in actual scenes such as complex illumination conditions, complex environments, different vehicle types and angles and the like, helps a driver to quickly find an idle parking space, and reduces traffic jam caused by parking space finding.

The invention discloses an urban traffic optimization and planning system based on reinforcement learning, and relates to the technical field of computer information, and the system comprises a traffic data acquisition unit, a reinforcement learning processing unit and a traffic planning unit. The traffic data acquisition unit is used for regarding target urban traffic as a network and regarding each intersection as a node, and constructing a nonlinear traffic flow dynamic model to update a multi-dimensional tensor at each time step; the reinforcement learning processing unit is used for taking a signal timing matrix of a signal lamp of a road section between every two nodes of target urban traffic as a state space and calculating a corresponding action space when a target reward function is a maximum value; and the traffic planning unit is used for finishing the traffic planning of the target city according to the action space of each road section. According to the method, the global optimization capability, the multi-objective optimization capability, the adaptive learning capability, the data processing capability, the safety and the interpretability of traffic signal control are remarkably improved.

The invention discloses a graph convolution recurrent neural network road traffic flow prediction method considering data missing, and relates to the technical field of intelligent traffic, and the method comprises the steps: data collection and preprocessing; constructing node features based on a Gaussian mixture model; designing a graph convolution layer considering data loss; establishing a space-time dynamic model and optimizing the space-time dynamic model; predicting a future traffic state; establishing a model, and continuously optimizing model hyper-parameters through training by using a stochastic gradient descent algorithm to obtain a traffic state prediction scheme; according to the method, the problem of data missing caused by sparse sensor deployment, data loss, hardware faults and other factors can be solved, meanwhile, the spatial-temporal correlation of traffic data can be effectively captured, and the accuracy and reliability of traffic state prediction are effectively improved.

The invention relates to the technical field of traffic control, and particularly discloses a smart traffic flow prediction system based on big data analysis, which comprises a data acquisition system and a flow prediction system, and is characterized in that the data acquisition system acquires the number of vehicles and driving direction data through multiple channels, integrates weather information and event information, ensures that the data is comprehensive and updated in real time, and transmits the data to the traffic flow prediction system; the collected data is stored in a cloud database for subsequent analysis; according to the method, the importance of different regions is calculated through the graph attention convolutional network to capture the spatial-temporal characteristics, the problem of over-extrusion of a graph neural network is solved, a deeper network model is constructed, a semantic adjacency matrix is introduced to learn the spatial correlation of local regions and semantic information in a global space, and the spatial-temporal characteristics are captured. Traffic flow prediction from a local area to a global scale is realized, a causal relationship between long and short time in traffic flow data is captured by using a time causal convolutional network, and local and global spatial-temporal correlation in the traffic data is synchronously captured.

The invention relates to a traffic signal management technology, in particular to an intelligent traffic signal control system, which comprises a traffic data acquisition module for acquiring data of a traffic sensor in real time; the data processing module is used for analyzing the data collected by the sensor and generating an optimized signal switching strategy; the signal control module is used for controlling the state of the traffic signal lamp according to the instruction of the data processing module and automatically adjusting the signal time according to the change of the traffic flow; the cloud computing platform module is used for remotely monitoring and managing the plurality of signal control units; and the fault diagnosis and maintenance module automatically monitors the operation state of each component of the system. By acquiring the traffic flow and the pedestrian state in real time, the signal switching strategy can be intelligently analyzed and optimized, the vehicle waiting time is shortened, the traffic capacity of the intersection is improved, and the system can adjust the state of the signal lamp in time according to flow analysis and behavior pattern recognition, so that the traffic jam in the peak period is effectively relieved.

The invention relates to a lockage ship reservation scheduling optimization method. The method comprises the following steps: S1, obtaining ship information of a water transportation junction, attribute information of a ship lock and ship remote lockage declaration information; s2, taking the minimization of the ship lock waiting time and the ship reservation adjustment rate as optimization objectives, constructing a ship reservation scheduling double-objective optimization model, and establishing constraint conditions satisfied by lockage ship reservation scheduling; and S3, performing iterative solution on the ship reservation scheduling dual-objective optimization model based on a fast non-dominated sorting genetic algorithm to obtain an optimized lockage ship scheduling scheme. The technical problems that existing ship lock resources are not enough in utilization and the ship dam passing efficiency is not high are solved, the waterway traffic jam problem can be effectively relieved, the overall ship lock passing efficiency can be improved, the ship waiting time is shortened by optimizing the matching relation between the ship dam passing number and the ship lock service capacity, and the ship dam passing efficiency is improved. And the ship reservation adjustment rate is reduced, so that the ship lockage efficiency is improved.

The invention relates to a traffic signal control method based on multi-agent deep reinforcement learning. The method comprises the following steps: setting an intelligent agent for an intersection, deploying a fitting network at a cloud end or a server, and performing prediction according to a traffic road network structure and historical traffic flow data to obtain a traffic flow prediction result Y; training each intelligent agent and a fitting network by using the traffic flow prediction result and randomly acquired real-time traffic information of each intersection to obtain each optimized intelligent agent and an optimized fitting network; each optimized intelligent agent performs independent observation according to a traffic flow prediction result Y and respective current real-time traffic information, transmits the result to the optimized fitting network, calculates a global reward and feeds back the global reward to each intelligent agent, and each intelligent agent executes the next action according to the respective current observation result and reward. And forming a current optimal traffic signal control strategy of the target area. According to the invention, the traffic signal coordination control of multiple intersections is realized, and the travel efficiency is obviously improved.

The invention discloses a processing method for dynamically adjusting a traffic light signal scheme according to traffic flow, which comprises the following steps of: deploying sensors including a ground induction coil, a camera and a pedestrian button at each traffic intersection, and preprocessing collected traffic flow and pedestrian flow data; and predicting trends of traffic flow, non-motor vehicle flow and pedestrian flow of each intersection in a short period, modeling traffic light states of each intersection into a dynamic system, calculating an optimal traffic light adjustment strategy, and adjusting the traffic light states in real time. By dynamically adjusting the traffic light signals, the system can more effectively respond to the actual on-site conditions including the flow of pedestrians and non-motor vehicles, more reasonable signal duration distribution is achieved, waiting time is shortened, traffic efficiency is improved, meanwhile, the pedestrians and the non-motor vehicles are considered, the safety of the system can be improved through an optimized signal control strategy, and the traffic light control method is suitable for popularization and application. The signal lamp is adjusted to provide more transition time for pedestrians and non-motor vehicles, so that the accident risk is reduced.

The invention discloses a traffic transportation emergency command and dispatch system based on big data resources, which is constructed based on the big data resources and mainly comprises a data acquisition module, a big data processing and analysis module, an event early warning and prediction module, an emergency command and dispatch module, an information display and interaction module and the like. Various devices (a sensor, a camera, a GPS and the like) are utilized to comprehensively collect data such as traffic flow, road conditions, weather, events and the like. Acquisition has the characteristics of high frequency and high precision, supports a plurality of data transmission modes and protocols, and can integrate and format the acquired data to guarantee data quality and convenience of subsequent processing. According to the system, effective utilization of traffic data is realized through collaborative operation of all the modules, and the traffic transportation emergency management capability is improved from multiple aspects, including improvement of management efficiency, enhancement of emergency response capability, improvement of decision support, enhancement of information transparency, optimization of resource allocation, reduction of economic loss and improvement of public safety.

The invention relates to a vehicle track and traffic flow prediction method based on checkpoint data, and belongs to the field of intelligent traffic, the checkpoint data is adopted and combined with GCN, GRU and KAN models to carry out vehicle track prediction, and the predicted vehicle tracks are superposed to obtain a flow statistical result of each checkpoint. Therefore, the purposes of accurately reflecting the real traffic condition, enhancing the road traffic safety and relieving the traffic jam problem are achieved. Comprising the steps of (1) definition, (2) data preprocessing, (3) checkpoint data information extraction, (4) vehicle track construction and division, (5) selection of vehicles meeting conditions, (6) vehicle track prediction, (7) traffic flow prediction, and superposition of the predicted vehicle tracks to obtain a traffic flow prediction value of each checkpoint in the future.

The invention provides a smart city traffic information system based on a 5G network, and relates to the technical field of data processing, and the system comprises a digital traffic processing module which is used for collecting and analyzing the operation data of various traffic facilities and vehicles in real time so as to recognize a traffic flow mode, predict a traffic state, obtain an analysis result, and according to the analysis result, determine the traffic flow mode; traffic signals are automatically adjusted, and traffic flow is optimized; and the digital government affair cooperation module is used for automatically acquiring and distributing update information of traffic policies and laws and regulations, automatically processing traffic violation behaviors, and collecting and analyzing feedback information of the public for the traffic policies to obtain an analysis report. According to the system, a plurality of digital modules are integrated, real-time collection, intelligent analysis and efficient management of traffic information are realized, the traffic operation efficiency and safety are improved, the public participation degree and satisfaction degree are enhanced, and intelligent, collaborative and sustainable development of urban traffic is effectively promoted.

The invention discloses a vehicle path planning method in a V2X environment, and particularly relates to an improved RRT path planning method based on a dynamic search step size of tabu search. The method mainly comprises the following steps: initializing a starting point and an ending point, taking the starting point as a root node of a tree, and generating a search map according to real-time road and obstacle information by using a V2X technology; randomly generating points within a limited angle, generating a new node according to a dynamic search step length, detecting whether the new node intersects with an obstacle in the map, and if so, abandoning the node; if the current node is not intersected with the new node, adding the new node into the RRT spanning tree, adding a path between the current node and the new node into the path RRT spanning tree, and adding the accessed nodes into a taboo list to avoid re-accessing the nodes in subsequent search; checking whether the new node is close to the target point, if so, stopping searching, and finishing path planning; and repeating the above steps until the path connecting the starting point and the target point is found or the maximum number of iterations is reached.

The invention discloses a BIM-based municipal road construction method and system, and the method comprises the steps: collecting traffic flow data, accident information and weather conditions, and generating original traffic data and traffic state data; collecting construction data; cleaning and processing the collected original traffic data and traffic state data to obtain traffic data and traffic information characteristics; generating traffic flow prediction data and an initial model framework based on the traffic data and the traffic information features; generating a construction scheme and a stage schedule by using an optimization algorithm in combination with the predicted traffic flow data and construction data; and filling the construction scheme and the stage schedule into the initial model framework to generate an initial BIM model, and generating the BIM model according to the construction data through dynamic traffic simulation. Through collection, processing and analysis of comprehensive traffic data and construction data, a construction scheme is optimized, and dynamic traffic simulation is carried out, so that the construction efficiency is remarkably improved, traffic congestion is reduced, and the construction safety is improved.

The invention provides a V2X-based traffic situation early warning method, system and device, and the method comprises the steps: obtaining traffic data collected by a plurality of data sources, and carrying out the preprocessing of the received traffic data; corresponding traffic indexes are determined according to the preprocessed traffic data, and the traffic indexes at least comprise the vehicle pedestrian flow, the vehicle average speed and the lane traffic situation; wherein the lane traffic situation at least comprises a corresponding grade representing a road congestion condition in a preset time period, and the corresponding grade at least comprises one of unblocked, mild congestion, moderate congestion and serious congestion; and evaluating a corresponding traffic situation condition according to the traffic index, and if the traffic situation condition is abnormal, outputting corresponding risk early warning information. Through the real-time data monitoring technology, the urban traffic management efficiency is improved, the traffic jam problem is relieved, and the comprehensiveness and accuracy of information are ensured.

The invention provides a flood season anchoring ground-wharf channel ship scheduling optimization method based on a multi-population genetic algorithm, and belongs to the field of ship scheduling, and the method comprises the steps: building a flood season anchoring ground-wharf channel ship scheduling model with the purpose of minimizing the waiting time of a ship at an anchoring ground and the departure delay time of the ship at a transfer wharf, and optimizing the ship scheduling model; establishing constraint conditions satisfied by ship scheduling of the anchorage ground-wharf channel in the flood season; respectively solving a flood season anchoring ground-wharf channel ship dispatching anchoring ground departure time decision problem, a water flow limiting channel distribution problem and a transfer wharf berth distribution problem based on the satisfied constraint conditions to obtain a flood season anchoring ground-wharf channel ship dispatching initial scheme; and based on a multi-population genetic algorithm with a hybrid coding strategy, an elitism strategy and a self-adaptive mechanism, carrying out iterative solution on a ship scheduling problem to obtain an optimized ship scheduling scheme. The ship transportation efficiency of a water-land combined transportation mode is improved, and the navigation pressure of the hydro-junction ship lock is relieved.

The invention discloses a variable curvature complex road condition mixed traffic flow-oriented CAVs car-following queue forming method. The method comprises the steps of obtaining state information of confluence CAVs and a confluence center of an initial CAVs queue; performing confluence scene division according to the state information of the confluence CAVs; for the collaborative lane-changing confluence scene, establishing a collaborative lane-changing game model considering the compromise degree of a human driver based on the confluence CAVs, the confluence center and the position state of HVs in the confluence direction, and generating a collaborative lane-changing confluence decision; for an overtaking lane-merging confluence scene, generating an initial confluence track based on road surface information by using a dynamic programming algorithm; constraining the initial confluence trajectory through a double-layer quadratic programming algorithm to generate an overtaking lane-merging confluence decision; and the confluence CAVs complete confluence through a collaborative lane changing confluence decision or an overtaking lane changing confluence decision, and join the confluence into the initial CAVs queue to form a CAVs car following queue. According to the method, the influence of HVs and road conditions (environmental information) on the formation of the CAVs car-following queue in a mixed traffic scene is considered.

The invention belongs to the technical field of intelligent traffic, and particularly relates to an expressway traffic state judgment method based on vehicle-level macro and micro evaluation. The method comprises the following steps: firstly, acquiring millimeter-wave radar data of a target road section in a target time period, and preprocessing the millimeter-wave radar data; then, according to millimeter wave radar data, evaluation indexes are calculated, and the evaluation indexes comprise a microcosmic index and a macroscopic index; the correlation between the indexes is checked, part of indexes with high correlation are removed, and the obtained microcosmic indexes comprise the vehicle drift angle variance, the lane changing behavior frequency, the time headway variance, the acceleration variance and the aggressive driving behavior variation index variance; the macroscopic indexes comprise average flow, a speed variation coefficient, a cart mixing rate and a congestion index; and finally, based on the macro and micro evaluation indexes, calculating the traffic state entropy of each road section in the target road section in the target time period, and classifying the traffic state according to the traffic state entropy. According to the method, the aggressive driving behavior variation index is provided for measuring the vehicle danger degree caused by sudden acceleration, deceleration or too high driving speed, so that the traffic state is reflected more finely from the microcosmic level, and meanwhile, the traffic state is accurately quantified by considering the mutual influence among the road sections.

The invention discloses a traffic abnormal event handling emergency degree sorting method based on unmanned aerial vehicle monitoring, and belongs to the technical field of unmanned aerial vehicle traffic application. In order to process traffic abnormal events in time based on the emergency degree, the method comprises the following steps: carrying out image processing on a traffic image obtained from a traffic video to obtain an updated traffic image; images of traffic anomalies including different types of vehicles are manually selected, and the YOLOv10 deep learning model is trained to obtain a traffic intelligent identification model for identifying the traffic anomalies; establishing a conversion relationship between an image size and an actual size for the image of which the traffic abnormal event is identified; constructing an evolution law analysis method of the influence of the traffic abnormal events on the traffic, and analyzing traffic jam length change, traffic flow change, average vehicle speed change and delay time change; and establishing a comprehensive influence index of the traffic abnormal events based on the constructed matrix, and sorting the handling emergency degrees of the traffic abnormal events based on the unmanned aerial vehicle monitoring.

The invention relates to the technical field of urban governance, in particular to a method for optimizing an urban commuting pattern by using an artificial intelligence algorithm, which comprises the following steps of: identifying urban employment center data according to mobile phone signaling data, and summarizing and preprocessing the data; utilizing a Gaussian mixture model to identify space relative distribution characteristics and influence weights of the employment places and the employment centers; identifying a space relative change rule of a residence place and an employment place through an XGBoost model, and analyzing a job-residence relationship; solving the gravity model by using an XGBoost model to identify a commuting rule between the residence place and the employment place so as to obtain a commuting structure; simulating employment center adjustment through an intelligent optimization algorithm, and predicting changes of an employment place, a residence place and a commuting flow to generate a commuting pattern; the commuting pattern is evaluated by collecting actual commuting data and user feedback. The method can provide data support and scientific guidance for urban public facility pattern, urban quality improvement and urban fine governance.

The invention discloses an expressway traffic flow control method, device and system, and the method comprises the steps: extracting a preset key feature from obtained historical road traffic data, and constructing a feature vector; sending the feature vector to a pre-trained traffic state prediction model, and predicting a current traffic state; the hyper-parameters of the traffic state prediction model are obtained through PSO algorithm optimization; sending the predicted current traffic state to a pre-trained control strategy model to obtain an optimal control strategy; the control strategy model is established and obtained by taking maximization of traffic flow efficiency, minimization of congestion degree and minimization of accident risk as targets. According to the method, the generalization ability and the prediction precision of the model can be improved, so that a solid technical support is provided for active control of the highway traffic flow.

The invention discloses a dynamic traffic distribution and signal control combined optimization method, and belongs to the field of intelligent traffic. The method specifically comprises the steps that firstly, a road section impedance function is constructed, node impedance is introduced to obtain an intersection impedance combination function, and the intersection impedance combination function is optimized through the traffic flow, density and speed relation based on dynamic adjustment parameters, vehicle type differences, weather conditions and traffic flow speed to obtain a combination optimization traffic impedance model; and solving the combinatorial optimization traffic impedance model by using a particle swarm algorithm to output a traffic distribution scheme. According to the method, dynamic traffic distribution and adaptive signal control are integrated, the road network emergency response capability is improved, traffic prediction and signal regulation and control synchronization is realized, and an accurate impedance model is introduced to dynamically adapt to driver preferences, so that congestion is relieved, and travel time and resource allocation are optimized.

The invention relates to the technical field of smart traffic, in particular to a smart capacity expansion method and system for an expressway operation period, and the method comprises the steps: collecting real-time traffic flow and speed data, and obtaining traffic jam information through calculating the weighted average flow and speed; analyzing congestion data between adjacent radars according to the traffic congestion information, and combining continuous congestion points to obtain a congestion section; according to the congestion section, through a demand prediction algorithm and a traffic capacity prediction algorithm, calculating a current VC ratio and a future VC ratio; and according to a calculation result, carrying out prediction analysis on a congestion trend, and according to a prediction analysis result, adjusting traffic control measures in real time. According to the invention, the problems of response lag and insufficient data accuracy when a traditional traffic management system deals with real-time traffic changes of the expressway are effectively solved, accurate prediction and dynamic management of traffic congestion are realized, and the traffic congestion problem of the expressway is effectively relieved.

The embodiments of this specification disclose a method, device and medium for diversion induction control of a highway toll station, which relates to the field of intelligent transportation technology and is used to alleviate the congestion problem of highway toll stations. The method includes: obtaining the traffic data and scene data of the highway toll station area to be induced by pre-installing multiple types of acquisition equipment; establishing a digital twin model of the highway toll station area to be induced based on the traffic data and scene data; classifying the vehicles in the area based on the flow data of the gantry pre-transaction system to obtain the first vehicle and the second vehicle; determining the current traffic data and current scene data corresponding to the vehicle based on the digital twin model, and extracting the traffic flow feature data; clustering the neighborhood topology of the traffic flow feature data to obtain the cluster center; determining the traffic state estimation of the vehicle based on the cluster center, so as to perform lane induction diversion for the first vehicle and the second vehicle based on the traffic state estimation.

The invention discloses a quay crane operation queue intelligent scheduling data processing method, and belongs to the technical field of intelligent scheduling, and the method comprises the steps: 1, preparing data, processing the data including ship information, equipment information, queue information and rule configuration, and then setting constraint conditions including a preset limiting condition and a target constraint condition; 2, building a CWP algorithm framework; 3, initializing parameters, carrying out data preprocessing, initializing parameters and variables required by an algorithm, and executing scheduling; step 4, constructing a neural network, establishing a neural network model of reinforcement learning, and realizing data progressive extraction from an input layer, a processing layer, a feature fusion layer, a decision layer to an output layer; and 5, training the network and updating parameters. The defects of a traditional scheduling algorithm are overcome, scheduling mode diversification is achieved, global optimization of intelligent scheduling is achieved, and a more flexible, efficient and intelligent job scheduling solution is provided.

The invention relates to a system and method for generating a base path for autonomous vehicle motion control. In particular, a computing system can obtain a target nominal path. The computing system can determine a current attitude for the autonomous vehicle. The computing system can determine a lane change zone based at least in part on a current attitude of the autonomous vehicle and the target nominal path. The computing system can determine one or more merge points on the target nominal path. The computing system can generate, for each respective merge point of the one or more merge points, a candidate base path from a current attitude of the autonomous vehicle to the respective merge point. The computing system can generate a suitability classification for each candidate base path. The computing system can select one or more candidate base paths based on the suitability classification for each respective candidate base path of the plurality of candidate base paths.

The invention relates to an intelligent traffic equipment scheduling method and system, equipment and a medium, and the method comprises the steps: obtaining road data and traffic management information of a region where intelligent traffic equipment is located, carrying out the region division processing of the road data based on the traffic management information, and obtaining the corresponding region road information; acquiring real-time equipment data of the intelligent traffic equipment, and performing association processing on the real-time equipment data and the regional road information to obtain a corresponding real-time regional traffic group; obtaining road vehicle data collected by the intelligent traffic equipment, and performing road prediction on the regional road information according to the road vehicle data to obtain a corresponding road congestion trend; performing road prediction on the emergency vehicle information based on the road congestion trend to obtain a corresponding emergency vehicle predicted passing road section; and comprehensively analyzing the initial equipment control scheme and the predicted emergency vehicle passing road section to obtain a corresponding global adjustment strategy. According to the invention, the equipment control scheme can be flexibly adjusted according to the passing demand of the emergency vehicle.