891 results about "Traffic signal" patented technology

According to the method, multi-modal data such as voice, images, texts, GIS and Internet of Things sensing are fused, and deep neural network prediction, reinforcement learning scheduling optimization and rule engine compliance check are combined; the intelligent vehicle and material dispatching method and system are applied to multiple scenes such as emergency material storage depots, fire-fighting emergency command, urban disaster response, traffic accidents and medical first aid. The system is interconnected and intercommunicated with an intelligent emergency material storage cloud platform, a city brain, Beidou navigation, intelligent fire fighting and other external platforms, and supports one-key issuing, path optimization, traffic signal linkage and whole-course return closed loop. Compared with the prior art, the method has the advantages that unification of multi-modal situation awareness, data-driven optimal scheduling and expert knowledge constraints is realized, the response time is remarkably shortened, the resource utilization rate is improved, and compliance safety is ensured.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention discloses an intelligent interpretable traffic signal adaptive control method. The method comprises the following steps: training an intelligent agent through reinforcement learning according to environment state information of an intersection; a timing decision of each phase of the intersection is generated by using the intelligent agent; guiding the first large language model to generate pre-training data by the timing decision and the cue word to perform LoRA fine tuning on the second large language model, and inputting the cue word into the fine-tuned second large language model to enable the second large language model to generate a plurality of reasoning tracks to generate positive samples and negative samples; performing all-parameter fine tuning on the second large language model to obtain a traffic control signal decision model; and inputting the constructed cue word into a traffic control signal decision model to obtain each phase timing scheme of the intersection. According to the method, the defects that an intelligent traffic signal control algorithm based on deep reinforcement learning lacks interpretability and the cross-scene generalization ability is poor are overcome, and the method has important significance in improving the decision credibility and the deployment efficiency.

The invention belongs to the technical field of urban traffic planning and intelligent traffic systems, particularly relates to an intersection phase structure optimization method based on a large language model, and aims to improve the intelligent level and operation efficiency of traffic signal control. According to the method, semantic mapping cues of traffic flow and a phase structure are constructed, and a large language model is guided to generate a phase structure scheme adapted to an actual traffic state. Compared with a traditional scheme depending on artificial experience and a fixed structure, the method can automatically generate diversified and data-driven phase structure combinations, and has higher adaptability and generalization ability. In the aspect of technical implementation, the method fuses prompt engineering and guides a large language model to generate an initial phase scheme, and performs evaluation and feedback by using a value network, so that optimization of a phase structure is realized, and the overall operation efficiency of a traffic system is improved.

The invention discloses an intelligent decision-making system construction method for traffic signal control. The method comprises a model training and deployment stage and an application and evolution stage, and specifically comprises the following steps of: S1, generating a pairing training sample set of traffic state structured data and conflict-free signal control instructions based on a pre-stored road traffic conflict rule in the model training and deployment stage; utilizing the paired training sample set to supervise and finely adjust a large language model to obtain a basic model; s2, accessing the basic model into a traffic simulation environment for reinforcement learning training; and in each training step, the basic model outputs a signal control instruction according to the current traffic state, performs safety verification on the instruction according to the road traffic conflict rule, generates a safety reward signal and the like. The traffic signal intelligent decision-making system which is safe, credible, sustainable in evolution and suitable for edge independent deployment is constructed.

The invention discloses a real-time collaborative cross-scene visual assistance and environment perception system for visually impaired people based on head-mounted equipment. According to the invention, through fusion of a multi-mode perception technology and a neural feedback mechanism, all-around environmental cognition support is provided for visually impaired people. Key targets such as curbs, steps and traffic signals in a complex scene can be accurately recognized, and a safe navigation scheme is generated in real time in combination with a dynamic path planning and obstacle avoidance algorithm. Through personalized feedback modes such as voice and vibration, the system can adjust the interaction rhythm according to the behavior habit and cognitive state of the user, ensure efficient and natural information transmission, effectively reduce the cognitive load of the user, help the visually impaired people to travel autonomously in different environments, improve the independent living ability, and improve the user experience. Real-time response and calculation efficiency are balanced through a collaborative architecture, so that a high-performance auxiliary function is not limited by heavy hardware equipment any more. The universal design reduces the use threshold, and is helpful for more visually impaired people to enjoy the convenience brought by science and technology.

A method and system for cloud-based V2X for providing real-time broadcast of signal phase and timing (SPaT) data. An example method includes receiving SPaT data from one or more traffic signal controllers, processing the SPaT data by converting the SPaT data from an original format into one or more different formats, where the processing includes distributing a processing load over a plurality of docker containers using a grouping or clustering algorithm that takes into account a raw data arrival sequence from the traffic signal controllers, determining one or more nearest intersections based on geolocation of a client device, and transmitting processed SPaT data related to at least one of the one or more nearest intersections to the client device.

The invention provides a traffic signal lamp data quality detection method, a traffic signal lamp data quality correction method and related equipment, and the method comprises the steps: receiving traffic signal lamp data reported by traffic signal lamp equipment in real time based on a distributed stream processing architecture, carrying out the structural processing, and extracting equipment identification, timestamp, signal state, phase information and other fields; in a distributed stream processing architecture, anomaly detection tasks of multiple dimensions of data timeliness, signal logic consistency and equipment online state are executed in parallel, data quality marks including anomaly types and corresponding anomaly parameter values are generated, and traffic signal lamp data with quality labels are formed. And for the detected abnormal data, executing corresponding data correction strategies according to different abnormal types, and issuing the corrected traffic signal lamp data to a downstream service module for use. According to the invention, high-concurrency and low-delay data processing is realized in a scene of dense deployment of multi-source traffic signal lamp equipment, and the real-time performance and accuracy of the traffic signal lamp data are improved.

The method is mainly applied to the technical field of vehicle engineering. The invention discloses a combined driving assistance vehicle driving control method, system and device and a medium. The method comprises the steps that running state information of a vehicle and traffic state information of the position where the vehicle is located are obtained; generating a driving path for guiding the vehicle to pass through the traffic signal prompt area according to the traffic state information, and determining the speed of the vehicle when the vehicle runs on the driving path; inputting the running state information and the vehicle speed into a preset energy consumption characteristic model, and determining the energy consumption of the vehicle when the vehicle runs on the running path through the energy consumption characteristic model; and generating a control instruction set based on the driving path, the vehicle speed and the energy consumption, and controlling the vehicle to execute driving operation according to the control instruction set. According to the invention, through the combined decision and the driving assistance control operation, the driving intelligence is obviously improved.

Techniques are described herein for determining whether a traffic light signal is flashing. The technique comprises collating data representing a time-ordered sequence of classifications indicative of a likelihood that a traffic light signal is active at respective times, thereby to create collated data. output data is generated, based on the collated data, using a convolutional neural network (CNN) arranged to provide an indication of a likelihood that a traffic light signal is flashing based on an input time-ordered sequence of classifications. It is then determined whether the traffic light signal is flashing based on the output data.

Provided are a reinforcement learning (RL)-based traffic signal control (TSC) method and apparatus, a device, a medium, and a product. The TSC method includes: obtaining traffic state data of a target intersection at a current time point and a road network graph, where the traffic state data includes a quantity of lanes at the target intersection and a traffic flow of each of the lanes; inputting the traffic state data and the road network graph into a preset traffic signal prediction model, and obtaining a target phase action output by the traffic signal prediction model, where the traffic signal prediction model includes a spatiotemporal encoder and a return-based action decoder, and the traffic signal prediction model is obtained through training based on return-based contrastive learning; and controlling, based on the target phase action, a traffic light at the target intersection to execute the target phase action.

The invention provides a smart city traffic abnormity monitoring method and system based on an Internet of Things large model, and relates to the field of Internet of Things and smart city traffic management. The system comprises an abnormity judgment module and a diversion module. The abnormity judgment module is configured to perform abnormity judgment on the target area according to the multi-source data and determine a plurality of hidden danger hot areas; the diversion module is configured to determine a plurality of standby routes according to the judgment result and the regional road network topological map; determining a plurality of main routes according to the plurality of standby routes and the position information of the plurality of variable information boards, generating a diversion instruction, and sending the diversion instruction to the emergency supervision object platform; and based on the diversion instruction, controlling a plurality of variable information boards to display a sketch of a corresponding main route, and controlling traffic lights on a plurality of standby routes to perform green light signal display according to a passing period. According to the method, the main pushing route of the variable information boards can be reasonably determined and controlled, potential traffic accident hidden dangers are prevented, and traffic safety is guaranteed.

The invention discloses a multi-agent traffic signal control method based on traffic flow dynamic prediction, and the method comprises the steps: constructing an independent reinforcement learning agent for each intersection, precisely depicting a causal mechanism of signal decision to congestion propagation through an intersection-level queue evolution model, and carrying out the prediction of a traffic signal on the basis of a simple traffic state index. According to the method, the comprehensive traffic state fusing the traffic capacity of vehicles at the intersection and the future queue length is established, the method is used for dynamic prediction of each intersection based on the traffic flow state, an intelligent agent can select the action enabling the queue length to be reduced fastest, and therefore a better control effect is achieved. And a neural network model is constructed based on transformer, and optimal traffic phase control based on a reinforcement learning algorithm is realized by using time sequence characteristics and spatial characteristics of multi-intersection traffic flow. The method can effectively utilize the current signal phase and the vehicle state and distribution to predict the queue change after phase switching, and can be used for traffic agent design of a large-scale road network.

The invention discloses a smart city traffic planning method and system based on deep learning, and relates to the technical field of city traffic management, and the method comprises the steps: recognizing an activity region of a traffic disturbance activity, constructing a disturbance region graph Ga, collecting vehicle driving related data and lamp control cycle data, and constructing a standardized data set H; coupling modeling is carried out on path length change and direction deviation of the traffic driving path before and after the traffic disturbance activity so as to quantify the traffic state of the activity area, a path disturbance deviation coefficient Py is acquired, the traffic state of the activity area is evaluated in combination with a path disturbance threshold Pyyz, and when the traffic state is in an abnormal state, the traffic disturbance deviation coefficient Py is calculated. According to the method, signal lamp cycle rhythm analysis is carried out, a phase dislocation propagation index Xw is obtained, and coupling modeling is carried out to obtain a comprehensive rhythm adaptation index Zh, so that the risk level of a traffic signal control system is divided, a system adjustment instruction is generated, intelligent adjustment is carried out, and the intelligent, precise and real-time level of smart city traffic planning is improved.

The invention provides a gradient induction control method. The method comprises the steps of obtaining traffic flow data of a target intersection at a current moment and operation data of a corresponding traffic signal lamp; according to the running duration of the target stage and the corresponding relation between the different running duration intervals of the lamp sets in the preset target stage and the vehicle number threshold values, gradient vehicle number threshold values corresponding to the lamp sets at present in the target stage are determined; according to the operation data, the traffic flow data and a gradient vehicle number threshold value, determining a stage vehicle state value and a stage pedestrian state value of the target stage at the current moment; according to the stage vehicle state value and the stage pedestrian state value of the target stage at the current moment, determining a stage signal value of the target stage at the current moment; determining a stage decision value of the target stage at the current moment according to the stage signal value of the target stage at the current moment; and according to the stage decision value of the target stage at the current moment, generating a control instruction for controlling the traffic signal lamp of the target intersection.

The invention discloses an over-height vehicle management and control method based on traffic signal linkage, and the method comprises the following steps: setting an over-height vehicle detection section in a first lane group where an over-height vehicle is located, installing an over-height vehicle detection facility, carrying out the recognition of the over-height vehicle, and judging whether an over-height vehicle forbidding event is triggered or not; when an over-high vehicle forbidding event occurs, a first lane group signal lamp is regulated and controlled, and a normal state is switched to a first emergency state; regulating and controlling a second lane group signal lamp conflicting with the bypass dispersion of the ultrahigh vehicle, switching to a second emergency state, synchronously regulating and controlling the first lane group signal lamp, and switching to a third emergency state; and regulating and controlling the first lane group signal lamp and the second lane group signal lamp at the end of the detouring guidance, and switching to a normal state. The invention further discloses an over-height vehicle management and control system based on traffic signal linkage. The invention solves the technical problem that the prior art cannot actively control the vehicles intruding into the height-limited road section.

The invention discloses a locomotive depot turnout state recognition and linkage warning method based on visual tracking, relates to the technical field of rail traffic signal processing, is applied to a locomotive depot station track automation system, and comprises a station track automation manipulator and an algorithm server which are connected through a local area network. The method comprises the following steps: S1, receiving and analyzing a route selection and arrangement command, generating a starting command according to a constructed turnout-camera mapping table to trigger a target turnout to act, and executing an image recognition task by a target camera; s2, the target turnout performs corresponding actions according to the route selecting and arranging command, and meanwhile, a target camera is triggered to dynamically track the actions of the turnout and collect a video stream; s3, performing multi-dimensional image intelligent identification according to the collected video stream data, wherein the identification specifically comprises turnout state identification, personnel and obstacle detection and crossing safety behavior identification; and S4, performing intelligent warning and bidirectional feedback according to an image intelligent identification result. According to the invention, the turnout state identification accuracy and efficiency are improved.

The invention provides a vehicle track collaborative prediction method based on a global interaction map, and the method comprises the steps: S1, obtaining environment data in a preset range with a controlled vehicle as a center, and the environment data comprises the historical track information of surrounding vehicles, map lane line information and traffic signal lamp information; s2, encoding the environment data into a global real-time interaction map, wherein the global real-time interaction map dynamically presents vehicle positions, lane lines and traffic lights in a space thermodynamic diagram form; s3, processing the global real-time interaction map through a ViT trajectory prediction model, and extracting multi-vehicle interaction features by using an attention mechanism; and S4, based on the extracted multi-vehicle interaction features, outputting prediction trajectories of a plurality of surrounding vehicles in the global real-time interaction map, so as to solve the problems that an existing prediction model is difficult to distinguish the vehicles actually interacting with the control vehicle and needs to consume relatively large computing power, improve the prediction accuracy and reduce the computing power.

The invention discloses a fusion analysis and quality evaluation method based on multi-source data elements, and relates to the technical field of traffic data processing. In order to solve the problems that traditional traffic multi-source data fusion is weak in space-time relevance and quality evaluation is not combined with traffic scene characteristics, the method comprises the following steps: firstly, constructing a traffic multi-source data space-time alignment model to realize space-time unification of road condition images, vehicle GPS tracks, traffic signal lamp states and citizen reported data; secondly, proposing a dynamic weight fusion algorithm based on traffic congestion association degree, and dynamically adjusting the weight according to the association strength of data and congestion assessment; and finally, a multi-dimensional quality evaluation system containing traffic scene exclusive dimensions is established, quality dynamic tracking and early warning are realized in combination with a traffic flow time sequence prediction model, and according to the method, in a smart city traffic scene, the traffic jam prediction accuracy and the data quality evaluation and traffic decision adaptation degree are improved, and the method is significantly superior to a traditional method.

The invention relates to the technical field of intelligent traffic systems, and provides an attention traffic signal control method and system based on a physical prior constraint time sequence diagram in order to solve the problems that in the prior art, risks cannot be recognized in advance, and direct quantification and constraint are lacked for core risks. The attention traffic signal control method based on the physical prior constraint time sequence diagram comprises the steps that the physical storage capacity of a road section is calculated, and the remaining storage space of a downstream road section is obtained in combination with the current vehicle number, namely overflow safety buffer; the overflow safety buffer is converted into overflow risk cost, and a constrained Markov decision process model with the passing efficiency as the return and the overflow risk as the cost is constructed; and solving the constrained Markov decision process model by using a pre-trained time sequence diagram attention network to obtain an optimal signal control action for balancing the traffic efficiency and the overflow risk. According to the method, the overflow risk can be proactively identified and actively avoided, so that cascade congestion caused by queue overflow can be actively avoided.

The invention provides a quantum enhanced real-time multi-mode traffic network optimization method and system, and belongs to the technical field of intelligent traffic control and optimization. Comprising the following steps: carrying out edge calculation and preprocessing on multi-source motion data in a traffic network to generate traffic state characteristic data; a quantum calculation model is adopted to generate signal schemes for different traffic control targets; constructing a digital twinborn model to verify the signal scheme; converting the verified signal scheme into a traffic signal control instruction, and issuing the traffic signal control instruction to a traffic signal controller; and continuously monitoring the traffic condition, and feeding back the monitoring result to the quantum calculation model so as to adjust the signal scheme in real time. According to the method, the multi-mode traffic flow can be coordinated efficiently in real time in a large-scale urban network, the emergency quick response and closed-loop self-adaption capabilities are achieved, and the actual requirements of the future smart city and vehicle road collaborative development can be met.

The invention discloses a traffic signal scheduling system and method based on road section congestion information, and relates to the technical field of traffic management and control. Comprising the following steps: S1, data acquisition and preprocessing: acquiring original traffic data of a road section in real time through a multi-source data acquisition unit, the original traffic data comprising traffic flow, vehicle speed, queuing length, occupancy and travel time; meanwhile, external environment data are obtained, wherein the external environment data comprise weather information, holiday and festival information and traffic event information; traffic data and external environment data are acquired in real time through the multi-source data acquisition unit, data quality is guaranteed through cleaning, denoising and standardized preprocessing, and the problem that data fusion of an existing system is insufficient is solved; by means of an adaptive weight learning model, historical traffic data, real-time data and environmental data are combined to dynamically adjust a congestion evaluation index weight, and the defect that a fixed or empirical weight is difficult to adapt to traffic dynamic changes is overcome.

The invention discloses a thermal management domain control method and system based on multi-source data fusion, and belongs to the technical field of vehicle battery thermal management. The method comprises the following steps: firstly, synchronously acquiring start-stop frequency data of a vehicle, route traffic light time sequence prediction data and battery temperature change rate data; thirdly, performing frequent start-stop thermal load characteristic analysis in combination with start-stop data and traffic light information; then, fusing the thermal load characteristics and the battery temperature change to carry out response lag early warning analysis; based on the early warning result and the thermal load characteristics, a predictive thermal management strategy is generated through an intelligent algorithm; finally, the strategy is analyzed and executed, and predictive closed-loop control over the battery thermal management domain controller is achieved. According to the method, thermal load impact caused by frequent start and stop and traffic signal changes can be dealt with prospectively, temperature fluctuation of the battery is remarkably restrained, it is ensured that the battery works in the optimal temperature interval, and therefore the safety, stability and energy efficiency of thermal management of the whole vehicle are improved.

The invention discloses a traffic signal lamp adjusting method based on queuing length. The method comprises the following steps: collecting vehicle ID, stay time and speed information of an RSU coverage area; judging whether the vehicle enters a queuing state or not based on the stop duration and the speed information; if the multiple vehicles enter the queuing state, determining an RSU section where the tail of the queue is located based on the sectional occupation conditions of the multiple RSUs, and calculating the effective queuing length of the lane by combining the occupation correction of the tail part of the section; when the queue tail position exceeds the position of the farthest RSU, an extrapolation formula based on the arrival rate, the departure rate and the congestion density is adopted to calculate the overflow length, and the overflow length is added to the effective queuing length; defining the release priority of each phase by using the normalized effective queuing length and the required saturation; and under the condition that the minimum green light time, the maximum green light time, the phase safety interval and the period length constraint are satisfied, the green light time and the phase sequence of each phase are calculated based on the release priority. The traffic efficiency is improved.

The invention relates to the technical field of conductive materials, and discloses ECC concrete based on carbon black doping and a preparation method and application thereof.The preparation method includes the following steps that cement, slag, quartz sand, silica fume and limestone powder are subjected to dry mixing to obtain a dry material; dispersing carbon black and a dispersing agent in water to obtain carbon black dispersion liquid; and mixing the dry material, the carbon black dispersion liquid, a polycarboxylate superplasticizer and polyethylene fibers to obtain the carbon black doped concrete. The conductivity of the ECC concrete is improved through the carbon black, the ECC concrete is applied to the field of friction nano-generators, potential difference can be formed through extrusion of an automobile in the using process, and therefore current is output to supply power to traffic lights, monitoring equipment and the like.

The invention discloses a multi-intersection traffic signal control method fusing non-neighborhood information space-time perception. Traffic signal control of multiple intersections is optimized by adopting a reinforcement learning strategy, each intersection is used as a reinforcement learning agent, and space and time sequence characteristics of each intersection are extracted as state information. Non-neighborhood information spatial-temporal characteristics of multiple intersections are realized by using a non-neighborhood spatial characteristic enhancement graph transformer and a two-way multi-head cross attention mechanism to perform action decision, optimal control of a multi-intersection traffic network is realized, and spatial-temporal association of traffic flow and global road network situation awareness are realized.

The invention discloses an urban traffic signal lamp adaptive control method based on artificial intelligence, and the method comprises the steps: firstly converting vehicle data into a fluid field matrix through employing a space grid mapping technology, calculating a virtual fluid dynamic pressure index combining kinetic energy density and acceleration divergence, so as to represent a congestion evolution direction; the system identifies a fluid infinitesimal state, generates a game weight, monitors back propagation characteristics of a dynamic pressure shock wave, sends a phase pre-wake-up request to an upstream intersection, edge computing nodes construct a game revenue function based on a node reputation value and downstream impedance, and searches an optimal timing strategy through distributed interaction. And the system triggers asynchronous signal switching according to the deviation between the optimal strategy and the current state, and executes phase difference nonlinear compensation. According to the method, through quantification of the traffic flow state and the cooperative game, potential congestion sources are effectively identified, non-cooperative behaviors of nodes are inhibited, and real-time blocking of burst shock waves and dynamic coordination of regional signals are realized.

The invention relates to the technical field of vehicle control, and discloses a vehicle scheduling method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining the driving state information of a vehicle before the vehicle is driven to an intersection without traffic lights, and obtaining the relative positions and predicted driving path information of other vehicles in a current intersection region; in each sampling period, determining a corresponding multi-objective optimization function based on the traffic interaction scene where the vehicle is located; wherein the traffic interaction scene is determined based on the driving state information of the vehicle and the relative positions and predicted driving path information of other vehicles; constructing a vehicle longitudinal dynamic model based on the driving state information of the vehicle, and constructing a model prediction control problem in combination with a multi-objective optimization function and a safety constraint condition; and solving the model prediction control problem to obtain a target control input sequence in a control time domain, and outputting the first control input quantity in the target control input sequence to an execution mechanism of the own vehicle to realize real-time regulation and control of the own vehicle.

Techniques are described herein for determining an active traffic light signal. The techniques involve, determining active signals for first and second traffic light heads of a group of traffic light heads, wherein members of the group of traffic light heads share an active signal at a point in time. A first vote is allocated for the active signal for the first traffic light head indicating a signal state of the first traffic light head and a second vote is allocated for the active signal for the second traffic light head indicating a signal state of the second traffic light head. A signal indicated by the group of traffic light heads is determined based at least in part on the first and second votes.