1168 results about "Traffic signal" patented technology

The invention belongs to the technical field of vehicle control, and particularly relates to a vehicle-mounted image recognition and target detection system based on deep learning, and the system comprises a distributed monitoring module which collects the operation, obstacle and traffic signal information of a target vehicle through multi-modal classification and scene matching, completes the marking of a shielding region and the matching of information through the combination of shared data, and achieves the recognition of the target vehicle. Forming an enhanced monitoring set; the label planning module constructs an enhanced topological space based on the enhanced monitoring set, and adjusts moving tracks in different scenes by combining with vehicle and pedestrian track probability distribution fed back by dynamic intention recognition; the action recognition module predicts trajectory parameters and collision probabilities of non-target vehicles and pedestrians by using Bayesian and multi-modal algorithms; the decision-making module generates a real-time control instruction through particle swarm optimization and fuzzy control, and optimal control parameters are fed back through simulation; according to the invention, intelligent track planning and real-time control in a complex scene are realized, and the detection precision and control robustness of the shielded and label-free area are improved.

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 provides a method and a device for determining an end-to-end perception decision regulation and control architecture, and belongs to the technical field of automatic driving. A first multi-modal data set is constructed based on a basic traffic signal image of an automatic driving scene, road map data and a basic driving rule; the second multi-modal data set is constructed based on dynamic traffic element image sequence data of an automatic driving scene, complex environment element image sequence data and complex driving rules, and the third multi-modal data set is constructed based on an automatic driving scene image, an automatic driving target and sensor time sequence data; and performing staged training on the multi-modal large model to obtain an end-to-end perception decision regulation and control architecture suitable for an automatic driving scene. A multi-modal large model is trained through a staged training strategy, basic element recognition, complex element recognition and decision training are gradually realized, the real-time performance of the model is enhanced, and the requirement of an automatic driving task for the real-time performance is met.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, the methods addresses traffic sign recognition as a language model-based image reasoning task by utilizing a multi-modal transformer architecture that combines the strength of vision and language machine learning (ML) models. The transformer architecture recognizes traffic signs based on visual features and associated taxonomy of the traffic signs. In a second aspect, the methods leverage context surrounding an autonomous vehicle through a graph-based modeling framework that fuses outputs from multiple perception modules to construct a semantic scene graph representation of an intersection, which consolidates processing diverse data types for traffic light relevancy detection. Other aspects and features are also claimed and described.

The invention discloses a multi-intersection traffic signal cooperative control method driven by a cross attention neural network, and the method employs a local cooperative Transform architecture, integrates a decision converter and a shared memory mechanism, and achieves the efficient modeling of a space-time dependence relation of a multi-intersection traffic state. The method comprises the following steps: firstly, through a memory head module, extracting a hidden state of each agent in a sequence modeling process, and updating global shared memory for supporting information interaction and strategy collaboration among multiple agents; and then, a cross attention module is adopted to carry out cross calculation on the local representation and the shared memory of each agent, so that dynamic perception and efficient modeling of the global state of the traffic system are realized. A backbone network of the model is based on Transform, and the understanding ability of time and space traffic characteristics is enhanced through position coding, self-attention and cross attention mechanisms. In the fine tuning stage, only the inserted Adapter module and the output layer are subjected to parameter updating.

The invention discloses an AI-based urban traffic flow prediction and dynamic signal optimization method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining real-time traffic data, historical flow data and external environment data of a target region, obtaining multi-source data, and constructing a space-time matrix corresponding to the target region according to the multi-source data; learning the space-time matrix by using a preset space-time fusion model, and outputting a prediction result of the traffic flow in a future preset time period through the space-time fusion model obtained by learning; wherein the space-time fusion model is a combined architecture of a graph convolutional network and a Transform network; and dynamically adjusting traffic signal control parameters in the target area through a multi-agent learning algorithm based on a prediction result. According to the method, closed-loop regulation and control of'data perception-prediction modeling-autonomous decision 'are formed, so that the limitation of spatial feature modeling staticization and time correlation analysis fragmentation of a traditional method is broken through, and the problem of regional imbalance caused by single-point optimization is effectively solved.

The invention discloses a global traffic signal control method based on a regional context enhanced large language model, and the method comprises the steps: dividing a target region into K traffic signal lamp control regions according to a traffic network structure of the target region; global real-time traffic information of the target area is collected and converted into global traffic text information; and inputting the global traffic text information into the large language model after LoRA fine tuning, and outputting a global traffic signal lamp control strategy of the target area, namely a traffic signal lamp phase of each intersection. In the fine tuning process of the large language model, an optimal phase decision is generated by using a reinforcement learning agent, a decision result is converted into text data, a region-level optimal phase decision data set is constructed according to an adjacent region division strategy, fine tuning is performed on the model by using the data set, region loss is calculated, and the large language model is optimized through the loss. According to the invention, optimization of global traffic signal lamp control can be realized, the overall traffic delay is reduced, and the traffic efficiency is improved.

The invention relates to the technical field of image processing, and discloses a traffic behavior real-time identification method based on multi-modal spatial-temporal feature fusion. Traffic scene videos, images and traffic signal lamp state information are collected through a road monitoring camera, and target detection and tracking are carried out on video frames to obtain a target spatial-temporal trajectory; respectively extracting a target visual feature sequence and a signal feature sequence after signal lamp state coding by using a time sequence-space perception module, inputting the two sequences into a multi-modal semantic coupling and fusion network, and generating a fusion feature vector through feature alignment; and based on the vector, target behaviors are discriminated in real time through a behavior classifier, and a normal or abnormal detection result is output and an alarm is given. According to the method, multi-modal information is fused, semantic understanding and dynamic expression are enhanced, recognition robustness is improved, real-time performance and flexibility are achieved, misjudgment and missed judgment can be effectively reduced, and the method has great significance in improvement of the efficiency and the safety level of an intelligent traffic system.

The invention discloses an urban traffic signal real-time collaborative optimization system and method based on a space-time diagram convolutional network and reinforcement learning, and relates to the technical field of intelligent traffic control. In order to overcome the defects of traffic signal fixed period control, the technical scheme adopted by the invention comprises edge computing equipment which is deployed beside an intersection camera and is used for acquiring video stream data in real time through a built-in local model, extracting traffic flow state characteristics and realizing dynamic phase timing optimization through cross-intersection collaborative decision, meanwhile, local model parameters are generated and uploaded to the cloud federated learning platform; the cloud federated learning platform is used for aggregating and optimizing the local model parameters of the edge computing devices, and regularly issuing global update parameters to the edge computing devices; and the traffic signal control equipment is deployed at the intersection and is used for adjusting the display state of the traffic signal lamp in real time according to the dynamic phase timing instruction. The traffic efficiency of the urban road network can be obviously improved, and the traffic control cost is reduced.

The invention discloses a traffic signal global collaborative prediction method based on a quantum entanglement state, and the method comprises the steps: building a quantization model of a dynamic evolution path of a target traffic network in a time window in the future, and constructing a space-time diagram model; mapping the signal phase state of the intersection into a time-space integrated quantum state by adopting a layered quantum coding scheme; on the basis of a preset traffic optimization target, constructing Hamiltonian including spatial coupling, time evolution and a cost item, solving a ground state of the Hamiltonian through a mixed quantum-classical calculation method, and determining an optimal dynamic evolution path; coding the actual state of the current traffic network into an initial state, carrying out sequential measurement through an optimal evolution operator, decoding a cooperative signal control strategy of each time point in the future, and executing the cooperative signal control strategy through a rolling time domain control framework; according to the invention, global optimization is carried out by using quantum parallelism, the optimal cooperation strategy in the whole space-time range can be obtained at one time, and the overall operation efficiency, predictability and robustness of the traffic network are improved.

A traffic management system and method for operating the same includes a plurality of roadside devices associated a plurality of intersections. A control module is programmed to receive priority requests from priority connected vehicles that comprise a priority level associated with a type of vehicle. The control module generates average estimated arrival time for an intersection for the vehicles based on historical data, receives roadside device data from a plurality of roadside devices, estimates congestion and flow dynamics for the priority connected vehicles based on the average estimated arrival times and real-time data from the vehicles and data from the roadside devices, determines adapted arrival times based on the congestion and flow dynamics and adjusts a signal phase and timing for a traffic signal of the intersection for the priority connected vehicles based on the priority and adapted arrival times. A traffic signal operates with the signal phase and timing.

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 provides an intersection priority passing method, device and equipment for an emergency vehicle and a medium, and relates to the technical field of traffic signal control, and the method comprises the steps: generating a passing route of the emergency vehicle according to an emergency planning route of the emergency vehicle and the predicted passing time between every two adjacent intersections in the emergency planning route, the predicted passing time is obtained according to a current traffic state and a historical traffic state, and the passing route comprises information of a plurality of intersections through which the emergency vehicle sequentially passes; under the condition that the emergency vehicle drives into the upstream preset distance of each intersection in the passing route, the green light time of the emergency passing direction of the intersection is prolonged; according to the traffic signal when the emergency vehicle is predicted to arrive at the stop line of each intersection in the passing route, the corresponding green light extension strategy or the red light early cut-off strategy is adopted to adjust the timing parameter of the traffic signal when the emergency vehicle is predicted to arrive at the intersection, and the problem of priority passing of the emergency vehicle at the intersection can be solved.

A vehicle control device for controlling a vehicle, includes: a recognition unit configured to recognize a surrounding situation of the vehicle; a deviation calculation unit configured to, in a case where a traffic light is recognized by the recognition unit, calculate a deviation amount of the traffic light relative to a travel path on which the vehicle travels; a determination unit configured to determine whether the traffic light is a traffic light corresponding to the travel path based on the deviation amount calculated by the deviation calculation unit; and a vehicle control unit configured to control the vehicle based on a determination result of the determination unit.

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.

The invention discloses an automatic driving control method and device and a vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: obtaining the signal lamp indication information of a traffic signal lamp in front of the vehicle and the current position of the vehicle; under the condition that the signal lamp indication information indicates the first passing instruction, in response to the situation that the current position is located in a preset scene range, extracting wearing features of personnel in the preset scene range, determining traffic management personnel based on the wearing features, and in response to successful authentication of the traffic management personnel, determining gestures of the traffic management personnel; and in response to the gesture indicating the second passing instruction and the first passing instruction and the second passing instruction being mutually exclusive, controlling the vehicle to run based on the second passing instruction. Therefore, the automatic driving vehicle is more intelligent, the congestion is effectively relieved, and the traffic efficiency is improved.

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 belongs to the technical field of traffic control systems, and particularly relates to a large language model traffic signal control method for different types of intersections, which comprises the following steps: acquiring a historical traffic opening environment set; performing uniform state and action representation to form a first matrix; training a near-end strategy optimized reinforcement learning model based on a randomly sampled state in the first matrix, and forming a traffic signal control sequence track based on the first matrix; time sequence information contained in the state data of the traffic signal control sequence track is extracted through a convolutional neural network, and submerged space features are formed; respectively processing the subsurface space features and actions and rewards of traffic signal control sequence tracks through a linear layer to obtain input features; after operation of the trained fine-tuning large language model, prediction features are formed; the fine-tuned large language model is trained by encoder update and optimization of loss function and dual simulation metric learning, with predictive features including predictive states, predictive actions, and predictive rewards.

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 invention discloses a traffic signal optimization method based on multi-agent deep reinforcement learning, and belongs to the technical field of traffic signal control, and the method comprises the following steps: carrying out the tracking and track simulation of a vehicle according to the vehicle operation data, and constructing an urban traffic simulation model and a reinforcement intelligent learning body corresponding to the traffic signal lamp of each intersection; constructing a context enhanced state space, performing normalization processing on feature parameters in the context state space, and performing combination to obtain a real-time traffic environment state vector; a congestion index self-adaptive reward is obtained through calculation; according to a heuristic reward shaping method, defining a flow matching degree index and an indication signal period position reward, and combining a congestion index adaptive reward to obtain a traffic signal optimization reward; and according to the traffic signal optimization reward, a multi-agent double-depth Q network is adopted to train and strengthen an intelligent learning body to control traffic signal phase switching. According to the invention, the problem of insufficient traffic signal control flexibility and efficiency in a complex scene is solved.

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.

The present application relates to the technical field of traffic signal and vehicle cooperative control, and discloses a traffic signal and vehicle cooperative control method based on heterogeneous layered reinforcement learning. The present application uses two types of agents to cooperatively control traffic signals and CAVs, and the two types of agents interact with each other to improve the overall traffic efficiency in a mixed traffic flow environment. The two types of agents effectively communicate information and cooperate in tasks through a layered cooperation mechanism, further improving the traffic efficiency at intersections and the safety and comfort of CAV driving. With the help of a heterogeneous multi-agent learning framework, heterogeneous multi-task learning is effectively cooperated, the problem of unstable training is solved, and the overall training effect is improved.

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.

Provided are a method, an apparatus, and a computer program for extracting signal information through image data analysis including a traffic light. The method of extracting, by a computing apparatus, signal information through image data analysis including a traffic light according to various embodiments of the present disclosure includes collecting image data obtained by capturing an image in front of a vehicle, identifying a traffic light by analyzing the collected image data, and extracting signal information from the identified traffic light.

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.