18results about How to "Ease traffic congestion" patented technology

The application discloses a charging control method and system of an electric vehicle, applied to the technical field of charging control, and comprises the following steps: obtaining historical charging data of each electric vehicle to be charged; extracting charging behavior features from each historical charging data, and determining the loss degree of each electric vehicle to be charged based on the charging behavior features; obtaining a corresponding power consumption influence coefficient according to the loss degree of each electric vehicle to be charged; generating an initial charging scheme based on the geographical distribution data of charging facilities and the real-time position information, trip information and power consumption influence coefficient of each electric vehicle to be charged; optimizing the initial charging scheme to obtain a charging control scheme of all electric vehicles to be charged; and sending a control instruction to each electric vehicle to be charged, wherein the control instruction is obtained from the charging control scheme. The charging control method and system of the electric vehicle provided by the application effectively improve the reliability of charging control.

The application discloses a multi-terminal positioning intelligent cooperative management system based on Beidou satellites and relates to the technical field of operation and maintenance data intelligent management. The application collects crossroad vehicle information, sets a collection period and a monitoring range, counts vehicle information, calls historical data, extracts historical reference vehicle data, calculates historical traffic demand, analyzes the influence degree of traffic prediction, obtains traffic demand of the next monitoring period, monitors congestion crossroads, analyzes green light adjustment demand, adjusts the green light duration, monitors the congestion of adjacent crossroads after adjusting the congestion crossroads, adjusts the green light duration of the adjacent crossroads, and is used for synchronously and reversely adjusting the red light duration while adjusting the green light duration. The application accurately analyzes the traffic demand of each direction, monitors the traffic conditions of adjacent crossroads, and is adaptively adjusted to avoid the transfer of traffic pressure, so that the traffic operation of the whole region is more smooth.

The application provides a low-carbon travel carbon emission reduction amount verification method and system based on travel chain big data, which comprises the following steps: step 1) obtaining sample data of travel data; step 2) when the sample data is walking data, going to step 3); when the sample data is bicycle data, going to step 4); when the sample data is electric bicycle data, going to step 5); step 3) verifying the sample data by using a walking travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of walking according to the final effective travel distance; step 4) verifying the sample data by using a bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; calculating the carbon emission of the bicycle according to the final effective travel distance; step 5) verifying the sample data by using an electric bicycle travel distance verification method and removing duplicates to obtain a final effective travel distance; and calculating the carbon emission of the electric bicycle.

The invention discloses an expressway collaborative management and control method, device and equipment and a storage medium. Comprising the steps that constraint conditions and a tool set issued by an operation side are acquired, the constraint conditions are used for constraining behavior boundaries of management and control measures of the operation side, and the tool set comprises management and control measures of economic dimensions; obtaining a constraint condition and a management and control measure set of the operation side, fusing the constraint condition of the operation side with the constraint condition of the operation side to obtain a collaborative constraint condition, and fusing the tool set of the operation side with the management and control measure set of the operation side to obtain a collaborative management and control measure set; in response to a to-be-handled event occurring on the highway, performing optimization solution based on event related information, a collaborative constraint condition and a collaborative management and control measure set, and generating a joint optimization decision; and executing a joint optimization decision, and handling the event. According to the invention, real-time cooperation of operation management and control and operation targets is realized, and the overall consistency and comprehensive benefits of disposal decisions are improved.

The application relates to the technical field of traffic signal control, and particularly discloses an adaptive traffic signal control method based on reinforcement learning and a self-attention mechanism, which comprises the following steps: establishing a Markov decision process model and a policy neural network based on a deep reinforcement learning algorithm; building a deep Q network and a self-attention mechanism based on the Markov decision process model; evaluating the policy and updating the neural network of the policy neural network, an encoder and a decoder; performing multiple iterative training on the neural network; obtaining a trained neural network model; and generating real-time traffic signal control signals according to the trained neural network model. The method can gradually learn an optimal policy in a complex traffic flow. In addition, the algorithm adopts an encoder-decoder structure, stores historical trajectories in a replay buffer, and uses proximal policy optimization to update parameters. The method solves the problem that the existing adaptive traffic signal control method needs a large amount of data for training and requires a large amount of computing resources.

The application discloses a highway cooperative decision safety driving method, comprising the following steps: S1, acquiring vehicle end sensing information and road end sensing information and performing information fusion; S2, establishing a plane rectangular coordinate system, and establishing a speed potential field, a lane potential field, a road boundary potential field and a solid obstacle potential field on a road surface according to the fused sensing information; wherein the speed potential field enables a vehicle to travel at a set expected speed, the lane potential field enables the vehicle to travel in a current lane, the road boundary potential field enables the vehicle to travel away from a road boundary, and the solid obstacle potential field enables the vehicle to travel away from a solid obstacle; S3, a traveling vehicle is affected by a total potential field formed by superposition of the speed potential field, the lane potential field, the road boundary potential field and the solid obstacle potential field, and target accelerations of the vehicle in a driving direction and a transverse direction are obtained according to a potential field intensity gradient of the total potential field at a vehicle position in the driving direction and the transverse direction; and S4, the vehicle travels according to the target accelerations in the driving direction and the transverse direction.

This invention relates to the field of vehicle management, specifically to a data management system and method based on vehicle traffic planning. The system includes an image perception module, a data fusion module, a path guidance module, a vehicle tracking module, and a replanning module. The image perception module identifies vehicle boundaries, the data fusion module performs indoor vehicle positioning, the path guidance module plans pedestrian and vehicle routes, the vehicle tracking module establishes a kinematic model and labels matched vehicles, and the replanning module recalculates the merging path to minimize the estimated merging time or travel cost. This invention can supplement missing image details, making the identification of key features of vehicles and pedestrians clearer, improving target detection efficiency, reducing waiting time for pedestrians and vehicles, alleviating traffic congestion in complex road conditions, shortening vehicle pick-up time, improving road capacity, and achieving two-way dynamic matching and visualized path collaboration between pedestrians and vehicles.

The application provides a vehicle scheduling method and device, a medium and a quantum computer. The method comprises the following steps: obtaining scheduling reference data; determining a plurality of candidate scheduling schemes according to the scheduling reference data, and determining, in each candidate scheduling scheme, a first time required for each to-be-parked vehicle to arrive at a corresponding parking space and a second time required for each to-be-parked vehicle to park in the corresponding parking space; determining a total cost of each candidate scheduling scheme according to the first time and the second time of each to-be-parked vehicle in the candidate scheduling scheme; determining a target scheduling scheme from the plurality of candidate scheduling schemes according to the total cost of each candidate scheduling scheme; and guiding the plurality of to-be-parked vehicles to park in the corresponding parking spaces based on the target scheduling scheme. By introducing the parking time of the vehicle into the selection of the path scheme, the traffic congestion caused by the parking operation is reduced, the traffic flow in the parking lot is ensured to be smooth, and the space utilization and the turnover rate of the parking lot are improved.

The application discloses a kind of holographic traffic perception method and system based on wide-area radar, belong to vehicle traffic control technical field, for solving the technical problems of existing traffic perception system, it is difficult to provide real-time, holographic traffic information service for mass vehicle, not conducive to optimizing traffic flow space-time distribution, and relieving congestion and reducing accident rate.Method includes: the coordinate data uniform processing of initial tracking data and initial driving data about latitude and longitude offset, respectively corresponding to obtain radar tracking data and target driving data;Radar tracking data and target driving data are calculated about the trajectory topology of target reference system, respectively to obtain first state estimation information and second state estimation information;And generate minimum topology distance optimization model;Based on minimum topology distance optimization model, relative position of multi-vehicle target is distinguished and identified, to obtain vehicle holographic information;Realize holographic traffic perception to global vehicle target.

The invention provides a traffic accident detection method based on deep learning, and the method comprises the following steps: S1, obtaining an accessed real-time video, and verifying whether a scene is normally shot, and does not have short-range and long-range stretching and angle deviation or not according to the scene id of the real-time video, so as to prevent a detection region under normal shooting from changing; s2, judging the vehicle parking information of the accessed real-time video, and detecting whether a static vehicle exists or not; s3, filtering out vehicles outside the detection area by loading a detection area configuration file; and S4, predicting whether a road traffic accident occurs through a region accident identification algorithm according to the vehicle stop time, the vehicle stop number and whether the frame regions of the adjacent vehicles are overlapped. According to the traffic accident detection method based on deep learning, vehicle collision accidents in two accident high-incidence scenes of urban crossroads and high-speed key road sections can be detected and analyzed, and an alarm is given in time.

The application discloses a kind of construction methods of expressway intelligent management and control preplan of double emergency lane, by determining the traffic management and control comprehensive condition index calculation formula of road to be managed and controlled, the data of road are collected in real time, traffic management and control comprehensive condition characteristics are constructed, production control preplan cluster, then by calculation and simulation verification will obtain target preplan and its preplan execution effect, execution condition are recorded into preplan library, so that the expressway of double emergency lane can quickly search and directly provide solution when coping with large flow traffic or accident in inner lane, emergency response speed is faster, and by index calculation and simulation simulation two angles respectively to a series of preplans in management and control preplan cluster are compared horizontally, avoid the error caused by unreasonable influence factor parameter value, and obtain the emergency target preplan with high traffic efficiency and low secondary accident risk, so that accident risk is fully reduced, and vehicle traffic efficiency is greatly improved.

The application relates to a road section vehicle group dissolving method based on real-time vehicle speed regulation, which comprises the following steps: S1, acquiring real-time traffic information of a road section; S2, when the headway between a host vehicle and a front vehicle in the same lane is less than a safe following headway and the headways between the host vehicle and front vehicles in adjacent lanes are both less than a safe lane-changing headway, the front vehicle in the same lane and the front vehicles in the adjacent lanes form a vehicle group; S3, judging the risks of vehicles in the vehicle group, and the vehicle with the maximum risk value R being the risk source of the vehicle group; S4, regulating the vehicle speed of the risk source vehicle of the vehicle group; and S5, when the host vehicle satisfies the condition that the headway with the front vehicle in the same lane is greater than or the headway with the front vehicle in the adjacent lane is greater than, the original vehicle group is dissolved. The application dissolves the vehicle group and reduces the driving risk of the road section by regulating the real-time vehicle speed of the risk source vehicle.

The invention discloses an urban traffic flow prediction and signal optimization method based on a space-time diagram neural network. The method specifically comprises the following steps: S1, constructing an urban road network topological graph; s2, multi-source heterogeneous data fusion and feature extraction; s3, establishing a space-time diagram neural network prediction model; s4, a signal optimization algorithm based on a prediction result; s5, model training and online strategy updating; s6, integration and real-time reasoning optimization are carried out; according to the method, through constructing topological graph representation of an urban road network, fusing multi-source heterogeneous traffic data and adopting a deep learning architecture combining a graph convolutional network and a time sequence prediction model, accurate short-term prediction of traffic flows of intersections and road sections is realized, and a dynamic signal optimization algorithm is designed based on a prediction result, so that the traffic flow prediction efficiency is improved. The adaptive adjustment of traffic signal timing is realized, so that the traffic efficiency of a road network is improved, and the vehicle delay is reduced.

The invention discloses a road state assessment method and device under optical time domain fusion perception, and a medium, belongs to the technical field of road state assessment, and aims to solve the problems that in an existing road maintenance technology, road state perception is discontinuous and inaccurate, intelligent health assessment is not easy to carry out on road states, and the road maintenance efficiency is high. And the real-time road state monitoring requirement is difficult to meet. The method comprises the following steps: carrying out feature construction processing of a road structure essential state under related load effect stripping and temperature field compensation on road traffic working condition data to obtain a road multi-dimensional feature vector; carrying out clustering processing on the road multi-dimensional feature vector in an abnormal region; carrying out quantitative evaluation processing on a road structure state in a related health state, and carrying out residual life prediction calculation on an evaluated health state grading mapping result under performance degradation; and performing maintenance decision rule knowledge graph matching and damage repair work amount accounting processing on the damage cluster to obtain a road network level maintenance plan strategy.

This invention provides a road right-of-way detection method for autonomous vehicles, relating to the field of autonomous driving technology, comprising: Step S1: sensing and continuously updating the current environment through sensors to acquire detection information; Step S2: processing the detection information and acquiring measured road right-of-way and calculated road right-of-way; Step S3: predicting the trajectories of surrounding vehicles, using the predicted trajectory results as constraints, and obtaining predicted road right-of-way based on the calculated road right-of-way; Step S4: storing the road right-of-way information in the form of a database or file; Step S5: using an onboard communication system to transmit the vehicle's position and speed information to other vehicles, realizing the sharing and management of road right-of-way; Step S6: displaying the road right-of-way information to the user in a visual form. This invention can effectively improve the safety of autonomous vehicles, increase the driving efficiency of autonomous vehicles, and provide a foundation for achieving fully autonomous driving.

Embodiments of the present application disclose a vehicle-road cooperation method and device for intelligent public buses and a medium. A monitoring and shooting device arranged on an intelligent public bus is used to obtain first road condition information corresponding to the intelligent public bus. In the case that the first road condition information belongs to an abnormal traffic condition, current road abnormal traffic information is determined based on the first road condition information and a preset road condition recognition model. A public bus to be dispatched is determined based on the current time, the abnormal traffic information and a preset bus departure schedule. A road section key point corresponding to the current road is determined, and based on the road section key point, a plurality of other traffic paths corresponding to the current road are determined, and a to-be-traveled path is determined based on second road condition information corresponding to each of the plurality of other traffic paths. Route planning is performed again based on the position of the public bus to be dispatched and the to-be-traveled path, so as to realize vehicle-road cooperation of the intelligent public bus. The driving efficiency of the public bus is improved by the above method.

This invention discloses an intelligent emergency lane opening system based on traffic flow prediction and its implementation method. The system includes a data acquisition module, a data processing module, a central control module, a power supply module, and a display module. The method includes collecting and processing traffic flow over a period of time to construct a spatiotemporal feature matrix of traffic flow; constructing a CNN-GRU-Attention prediction model; optimizing the parameters of the CNN-GRU-Attention prediction model, including the weight matrix and bias vector, using the improved Big Cane Mouse algorithm IGCRA; obtaining traffic flow prediction results based on the optimized model; and determining whether to open the emergency lane based on the obtained traffic flow prediction results. This invention can accurately predict traffic flow, promptly identify and open emergency lanes before congestion occurs, provide additional passage space for vehicles, avoid excessive traffic concentration, thereby effectively alleviating traffic congestion, reducing vehicle waiting time, and improving overall traffic efficiency.

The invention discloses a smart city multi-modal data acquisition and fusion method and system, and relates to the technical field of city management methods. The smart city multi-modal data acquisition and fusion method comprises four steps of multi-source sensing and asynchronous acquisition, cross-modal feature extraction and alignment, multi-level linkage fusion decision and automatic response and closed-loop control. According to the smart city multi-modal data acquisition and fusion method, data is acquired asynchronously through a multi-source sensor, and it is ensured that multiple dimensions of a traffic scene are covered. Characteristic extraction and alignment ensure the consistency of different modal data in time and space. And the fusion decision outputs a fusion confidence coefficient through a pre-trained neural network model, and is used for judging whether a traffic accident occurs or not. And the automatic response mechanism triggers traffic control and rescue scheduling according to a decision result.