507 results about "Road junction" patented technology

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.

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 method for realizing holographic traffic at an intersection. The method comprises the following steps: acquiring a real-time video stream at the intersection; motor vehicles, non-motor vehicles and pedestrians in the video are identified based on a deep learning target detection algorithm, a continuous motion track of each target is generated through a multi-target tracking algorithm, identity association is carried out on cross-camera targets in combination with a re-identification algorithm, and structured traffic data including target types, real-time speeds and motion directions are output; obtaining coordinate points of each lane of the intersection based on a geographic information system, and constructing a lane network topological relation; combining with the structured traffic data to generate target trajectory data; and constructing an intersection three-dimensional model based on an unreal engine, generating a virtual target in the three-dimensional model according to the target trajectory data, and driving the virtual target to move in real time. According to the method, a holographic traffic implementation scheme is provided for a business scene which is free of radar and only monitored by a video.

The invention belongs to the field of visual tracking, and relates to a multi-target visual tracking method based on cooperation of a dynamic neural field and a neural network, which takes a cross-modal cooperation architecture as a core and comprises a dynamic neural field module based on multi-target trajectory maintenance and shielding matching and an improved MoESDQ neural network module. Meanwhile, a collaborative decision-making mechanism is designed, when the activation peak value of the dynamic neural field is attenuated to a preset threshold value, neural network feature matching is triggered, and disappearance target reproduction correlation is achieved based on cosine similarity. The objective of the invention is to solve the visual tracking capability under the condition of scene and target motion change in a monitoring range, for example, under an intelligent traffic intersection scene. The problems of high ID switching rate, multi-target misassociation and low tracking precision under a real-time tracking background caused by scene change or frequent shielding of vehicles and pedestrians, similar target appearances, transient disappearance and reproduction of the targets and sudden illumination change are solved.

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.

Provided is a driving support device including: a side sensor configured to detect a target object existing on a lateral side of an own vehicle; and a control device configured to notify a driver of the own vehicle of existence of a target object approaching the own vehicle from the lateral side of the own vehicle when, at a time of entry of the own vehicle into an intersection, the target object exists and the target object is positioned in a notification target region. The notification target region is a region having a strip shape which is included in a road crossing a road on which the own vehicle is traveling or stopped, and which extends from the intersection in an extending direction of the road. The control device is configured to acquire a size of the intersection, and to determine a width of the notification target region in accordance with the size of the intersection.

The invention discloses a dynamic planning method and system for smart city traffic, and relates to the technical field of smart cities and intelligent traffic, and the method comprises the steps: collecting multi-source traffic data, and carrying out the fusion preprocessing; constructing a dynamic weight model to calculate a priority weight; a signal lamp timing scheme is generated based on the weight and is adjusted through a rolling optimization mechanism; outputting an optimization strategy by combining historical and real-time feedback evaluation effects; and a manager is notified through the acousto-optic prompt device and a report is generated. According to the invention, through the dynamic weight model and the rolling optimization mechanism, the overall delay time of the intersection is reduced, the traffic capacity of the traffic flow is improved, the Kalman filter is introduced to fuse multi-source data, and the data processing precision is improved; the LSTM network is adopted to predict the future traffic flow, a scientific basis is provided for a signal lamp timing scheme, and the workload of traffic management personnel is reduced through the acousto-optic prompt device and the automatic report generation module.

The invention discloses an automatic driving decision-making method and system for a port mixed traffic intersection based on deep reinforcement learning, and relates to the technical field of automatic driving. Comprising the following steps: acquiring real-time state information of automatic driving transportation equipment in a port mixed traffic intersection environment, and fusing the real-time state information into an automatic driving transportation equipment state vector in a heterogeneous manner; constructing a reinforcement learning decision model based on the DDPG; building a port traffic environment simulation platform; the reinforcement learning decision model based on the DDPG is trained; and solidifying and deploying the trained reinforcement learning decision model based on the DDPG in a vehicle-mounted calculation unit of the automatic driving transportation equipment, and controlling the driving of the automatic driving transportation equipment. The traffic efficiency, stability and safety of the automatic driving transportation equipment at the port intersection can be improved, the unmanned transportation process of the port mixed traffic scene is promoted, and the wharf transformation and operation cost is reduced.

The invention discloses an intelligent traffic regulation and control method and system for cities, relates to the technical field of intelligent traffic, and realizes high-precision real-time sensing of complex traffic states of urban intersections by constructing a multi-source traffic data acquisition system. A multi-modal feature fusion and semantic tag matching mechanism is adopted to recognize overlapping and dynamic changes of traffic states, a multi-strategy linkage signal timing scheme is generated in time, and the problems that a traditional signal control system is lagged in response and single in state recognition are solved. According to the method, commuting and logistics interference indexes are introduced, a release sequence adjustment and channel optimization strategy is intelligently generated according to a people flow peak and logistics concurrence situation, and the method aims at solving the problem of intersection signal timing regulation and control under the condition of complex multi-mode traffic flow superposition. And meanwhile, an execution result is collected in a closed-loop manner, a strategy deviation value is calculated in real time, error tolerance is set, adaptive iterative optimization is supported, and stable convergence of the strategy and achievement of a regulation target are ensured.

The invention relates to a method for operating a motor vehicle (2), wherein the motor vehicle (2) is automatically guided longitudinally. An intersection (6) arranged in the direction of travel (X) in front of the motor vehicle (2) is detected, at which the traffic rule "right before left" applies and at which there is a road (8) from the right. Furthermore, visibility (E) is ensured. a , E b ) of the intersection (6) is determined, and a first velocity (v a , v b ) for at least partial passage of the intersection (6), in particular only depending on the certain visibility (E a , E b ) is determined. The speed of the motor vehicle (v KFZ ) is adjusted to the first speed (v a , v b ) set.

A method for calculating and comparing evaluation indices for a signal system of an intersection includes: a traffic statistical information generation step of receiving image information in real time through cameras to derive traffic object information including traffic volume for each driving direction and traffic volume for each lane on a corresponding road at a preset first cycle, and to generate traffic statistical information including the traffic object information and intersection environment information; a digital twin generation step of inputting the traffic statistical information to a simulator, and implementing a traffic situation of the intersection as a graphical digital twin; and a composite congestion index derivation step of deriving a composite congestion index from each of an actual digital twin and a virtual digital twin, in which the evaluation indices are derived based on a congestion time of a predetermined number of vehicles at the corresponding intersection.

The invention discloses a vehicle state sensing method and system in a vehicle-road cooperation environment, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting the real-time traffic data of a target intersection; establishing a temporary V2X communication link; receiving real-time traffic data and K pieces of real-time vehicle state information of K running vehicles; if the one-dimensional risk detection results of the roadside edge computing device on the real-time traffic data and the K pieces of real-time vehicle state information are set to be 0, compensating the K pieces of real-time vehicle state information by adopting the real-time traffic data to obtain K pieces of compensated vehicle state information, constructing a global dynamic traffic model, and performing track conflict prediction and potential collision risk positioning; and according to the risk avoidance priority, broadcasting the potential collision risk to K running vehicles through a V2X communication link. The technical problems of inaccurate traffic modeling and untimely collision risk identification caused by incomplete vehicle-mounted sensing information in the prior art are solved, and the technical effects of improving the vehicle state sensing accuracy and the collision risk early warning timeliness are achieved.

Technical problems and their solution are disclosed regarding the location of mobile devices requesting services near a site from a server. Embodiments adapt and / or configure the transmitting device near the site, the mobile device communicating with the transmitting device using a short haul wireless communications protocol to deliver a token based upon a key shared with the server but invisible to the mobile device. The server can determine the proximity of the mobile device to the site to control actuation of the requested service or disable the service request, and possibly flushing the service request from the server. Solutions are disclosed for traffic intersections involving one or more traffic lights, elevators in buildings, fire alarms in buildings and valet parking facilities.

The present disclosure provides a system and method for jointly controlling one or more networked automated vehicles (CAVs) and one or more human driven vehicles (HDVs) subject to integer constraints for crossing each of a plurality of intersections on a road. The method includes collecting a digital representation of a state of each of CAV, HDV, and traffic signs, solving an optimization problem of joint optimization of traffic flow based on a macroscopic traffic flow model in a convex relaxation centralized traffic controller (CTC) subject to integer constraints, solving a multivariable mixed integer programming (MIP) problem in each of a plurality of intersection traffic controllers (ITCs) that optimizes a cost function and minimizes tracking errors of traffic flow values of the microscopic traffic flow model with respect to relaxed traffic flow values from the CTCs, and transmitting optimized values of the control commands to corresponding CAVs and corresponding traffic signs.

A vehicle control device autonomously controls driving of a subject vehicle. The vehicle control device is configured to grasp information; and determine autonomous driving control of the subject vehicle. The vehicle control device grasps information about an environment of an intersection where the subject vehicle is scheduled to travel and information about an emergency vehicle present around the subject vehicle. The vehicle control device decides whether to execute evacuation control for evacuating the subject vehicle from the emergency vehicle. The vehicle control device searches for an evacuation position that is deviated from a prediction track of the emergency vehicle and from which the subject vehicle is configured to return to the traveling direction, and decides to evacuate the subject vehicle to the evacuation position.

The driving support device includes a control unit that acquires target information indicating a target on a road intersecting the intersection from the roadside device, determines a possibility of collision between the target and the vehicle at the intersection based on the target information and the own vehicle information indicating the state of the vehicle, determines to perform driving assistance to the driver of the vehicle based on the result of the determination, acquires vehicle operation information indicating a driving operation by the driver with respect to the driving assistance, and diagnoses a driving tendency of the driver based on the vehicle operation information.

Provided is a vehicle control device capable of preventing unnecessary operations in a driving assistance function for avoiding a collision between an own vehicle and a pedestrian at an intersection, for example. There is provided an operation-suppression determination unit adapted to determine whether to change the result of collision determination (whether to suppress an unnecessary operation), based on the change (temporal change) of the lap rate between a target object and an own vehicle, and based on the change (temporal change) of the lateral speed generated in the target object with respect to the own vehicle, if a collision determination unit determines a collision. Further, there is provided a control-intervention determination unit adapted to determine control intervention in the own vehicle, based on results from the collision determination unit and the operation-suppression determination unit.

The invention provides a traffic simulation analysis method for an expressway construction area based on VISSIM. The method comprises the following steps: S1, collecting point cloud data, vehicle data and traffic video data of the landform of the construction area in real time; s2, preprocessing the collected multi-source data; s3, constructing a dynamic simulation model of the construction area; s4, simulating the traffic condition of the construction area; s5, dynamically planning an optimal driving path of the vehicle according to the simulated traffic condition and construction information; and analyzing the vehicle overlapping probability of the intersection in real time, and performing early warning according to an analysis result. Through the technical means of multi-source data fusion, high-precision dynamic modeling, real-time analogue simulation, intelligent path planning and the like, accurate simulation and optimal management of the traffic influence during the blasting demolition construction of the expressway overline overbridge group are realized, and remarkable technical advantages and application values are achieved; and a scientific and efficient solution is provided for urban traffic management.

Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle includes at least one processor programmed to: receive, from a camera of the host vehicle, one or more images captured from an environment of the host vehicle; analyze the one or more images to detect an indicator of an intersection; determine, based on output received from at least one sensor of the host vehicle, a stopping location of the host vehicle relative to the detected intersection; analyze the one or more images to determine an indicator of whether one or more other vehicles are in front of the host vehicle; and send the stopping location of the host vehicle and the indicator of whether one or more other vehicles are in front of the host vehicle to a server for use in updating a road navigation model.

In various embodiments, sensor data representing a 3D environment may be collected using one or more ego-machines while the ego-machines are navigating through the 3D environment. The sensor data may be projected into a 2D representation of the ground or other surface, and this 2D representation may form a map representing some geographic region. The map may be divided into tiles, within which detected features (e.g., road lines, road markings, surface features, etc.) may be detected and used to detect demarcated regions, such as intersections, based on the geometry and proximity of the detected features. As such, new tiles may be centered around the detected regions, and the features may be detected from each resulting centered tile. The detected features may be aggregated, de-duplicated, and / or merged, and used to label the map.

Systems and method are disclosed for adaptive and / or autonomous traffic control. In one illustrative implementation, there is provided a method for processing traffic information. Moreover, the method may include receiving data regarding travel of vehicles associated with an intersection, using neural network technology to recognize types and / or states of traffic, and using the neural network technology to process / determine / memorize optimal traffic flow decisions as a function of experience information. Exemplary implementations may also include using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions.

The invention discloses an improved auction algorithm-based automatic intersection priority dynamic allocation method, which comprises the following steps of: assuming that an automatic intersection does not need to be controlled by a signal lamp in an intelligent network connection environment; each lane at the intersection has no fixed lane function attribute, and each lane can allow left turning, straight going and right turning; each vehicle is provided with a wallet, and the vehicle needs to send vehicle basic information and pre-paid fees to the signal control center when the vehicle arrives at the intersection range; establishing a priority distribution model through model hypothesis and model preparation; in a certain time range, priority passing and vehicle payment willingness of emergency vehicles are considered, the passing sequence of the vehicles at the intersection is reasonably distributed through an improved auction algorithm, and the passing priority of the vehicles is determined; according to the invention, the passing order of the vehicles at the intersection can be well arranged, the passing time of the vehicles at the intersection is reduced, the passing right of the emergency vehicles is improved, and the waiting time of the vehicles on the low-flow lane can be reduced.

An AI-based adaptive traffic signal control system (100) for optimizing urban mobility, comprising: (a) a plurality of traffic sensors and cameras configured to collect real-time traffic data at one or more intersections; (b) a preprocessing and data management module to clean and normalize the collected data; (c) a traffic pattern recognition module configured to classify traffic conditions based on real-time and historical traffic data; (d) an AI-based signal timing optimization module that uses reinforcement learning to dynamically determine optimal signal phase sequences and green time assignments; (e) a communication and control execution module configured to transmit and implement the optimized signal timing plans to traffic signal control devices; and (f) a monitoring and system feedback module configured to evaluate traffic performance and provide feedback to the AI-based signal timing optimization module, whereby the system dynamically adjusts traffic signal phase schedules in real time to improve traffic flow and reduce congestion.

Techniques are provided which may be implemented using various methods and / or apparatuses in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capabilities and external CV2X input to determine, send, receive and utilize data elements to determine inter-vehicle spacing, intersection priority, lane change behavior and spacing and other autonomous vehicle behavior.

The invention provides an intelligent traffic scheduling method and system based on multi-modal fusion and space-time prediction, and relates to the technical field of intelligent traffic scheduling, and the method comprises the steps: collecting traffic-related multi-modal data; performing preprocessing and data-level and feature-level fusion processing on the multi-modal data to generate a fusion feature matrix; constructing an STGCN-LSTM space-time prediction model according to the fusion feature matrix, and predicting the traffic flow and queuing length of each entrance lane of the current scene to obtain a prediction result; and dynamically optimizing a signal lamp timing scheme according to the prediction result and the real-time traffic state. By accurately sensing a real-time traffic state, predicting a traffic flow change trend in a short time in the future and dynamically adjusting an intersection signal lamp timing scheme, on-demand timing is realized, the average waiting time of vehicles is shortened, the queuing length at an intersection is reduced, the traffic efficiency at the intersection is improved, and meanwhile, the traffic demands of pedestrians and non-motor vehicles are considered; and the safety and smoothness of traffic operation are ensured.

A vehicle driving assistance apparatus notifies an operator of a vehicle of a moving body when recognizing the moving body based on wireless communication information. The moving body moves toward an intersection ahead of the vehicle. The moving body satisfies a predetermined condition. The predetermined condition includes a first condition that a moving speed of the moving body is equal to or lower than a first speed and an intersection arrival margin time is within a range equal to or shorter than a first time and equal to or longer than a second time shorter than the first time. The intersection arrival margin time is the time remaining until the moving body arrives at the intersection. The apparatus notifies the operator of the presence of the moving body when recognizing the presence of the moving body satisfying the first condition.

The invention discloses an automatic generation method of an intelligent unmanned system city simulation test scene, and relates to the field of simulation scene construction and intelligent unmanned system test.The method comprises the steps that based on terrain boundary parameters, a plurality of rectangular areas with function labels are generated through Poisson disc sampling and a distance gradient algorithm; constructing a navigation grid representing the passable area; arranging a plurality of road intersections by adopting a constrained Poisson sampling algorithm; calculating the heart weight based on each intersection, and calculating the connection strength between the intersections through a gravity model; generating a global road network topology structure by adopting an improved minimum spanning tree algorithm; performing path search by adopting an algorithm A to obtain an optimal path, and automatically generating a road model and ancillary facilities; and extracting road network, road parameters and subsidiary facility information in the generated urban scene, and generating a standardized scene description file according to an OpenDRIVE standard format. And the construction efficiency of the simulation test scene, the test effectiveness and the cross-platform compatibility are improved.

The invention discloses a multi-scale fusion intersection trajectory prediction method based on symmetric cross attention, and relates to the technical field of automatic driving. Dividing the motion characteristics of the target vehicle at the intersection at the plurality of observation moments, the relative motion characteristics of the target vehicle relative to the surrounding vehicles, and the green light remaining time corresponding to the plurality of observation moments into a plurality of time scales; for any time scale, extracting embedded representation vectors of the motion features and the relative motion features, and traffic signal control information features of residual green light time corresponding to a plurality of observation moments; based on the embedded representation vector and the traffic signal control information feature, obtaining a track feature; and splicing the trajectory features of different scales, and obtaining global features of the multi-scale trajectory information according to the spliced trajectory features, so as to determine a trajectory prediction result of the target vehicle at the future moment. The method can accurately predict the vehicle track of the intersection.