882 results about "Road junction" patented technology

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

Systems and methods for controlling traffic flow along an arterial. The systems comprise a data processing apparatus configured to: monitor traffic light control signals (TLCSs) used to control traffic lights at intersection(s) and traffic delays occurring at intersection(s) at a time when the traffic lights are being controlled by TLCSs; access a hybrid model comprising a first linear term corresponding to an instance of traffic delays, a second linear term corresponding to a concurrent instance of TLCSs, and a nonlinear function defining a nonlinear relationship between the instance of the traffic delays and a previous instance of TLCSs; use the hybrid module to predict traffic delays at intersection(s) based on the traffic control signals and the traffic delays; determine, based on TLCSs and the traffic delays, traffic control signals that cause the predicted traffic delays to decrease; and cause the traffic lights to be controlled using the traffic control signals.

The invention discloses a dynamic weight correction and path deviation probability prediction method for a vehicle track, which comprises the following steps of: acquiring vehicle data and multi-source dynamic data of the vehicle track in real time through an optical sensor, a radio wave sensor and an inertial navigation sensor, carrying out space-time calibration, extracting obstacle characteristics and road structure characteristics, and predicting the path deviation probability of the vehicle track. The obstacle movement trend is quickly captured through a space-time diagram sequence and a diagram convolutional neural network, real-time obstacle avoidance is realized in combination with dynamic weight adjustment, a driving intention is predicted by using a Bayesian neural network, a trajectory planning strategy is adjusted through weight correction, and uncertainty is reduced by using multi-source data fusion and probabilistic prediction. A closed-loop feedback mechanism continuously optimizes the model, and efficient operation is kept in complex scenes such as intersections and roundabout through real-time weight adjustment and closed-loop feedback, so that the purpose of quickly responding to dynamic obstacles or driving behavior changes can be achieved, and the precision of the path deviation prediction probability is improved.

The invention relates to the technical field of camera layout, in particular to a modeling camera layout method and system. The method comprises the following steps: performing laser radar scanning on an intersection area to obtain intersection three-dimensional point cloud data; performing static element identification on the intersection three-dimensional point cloud data to obtain an intersection base layer data graph; traffic flow and people flow collection is carried out on the intersection area to obtain an intersection dynamic layer data graph; performing hydrodynamic modeling on the intersection area based on the intersection basic layer data graph and the intersection dynamic layer data graph to obtain intersection traffic flow field data; performing flow field feature recognition on the intersection traffic flow field data to obtain an intersection flow field feature point distribution map; and performing speed density field simulation on the intersection area based on the intersection flow field feature point distribution diagram to obtain an intersection flow field dynamic distribution model. According to the invention, the accuracy and effectiveness of the camera layout scheme are ensured.

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 relates to the technical field of intelligent network connection vehicles, in particular to an intersection signal lamp and intelligent network connection vehicle trajectory collaborative optimization method, which comprises the following steps: acquiring state information of all vehicles within a certain range of an intersection; obtaining mathematical expressions of signal lamp parameters of all lanes based on the entrance lanes of the intersection and a pre-established adaptive traffic signal model; generalized delays and vehicle tracks corresponding to different signal lamp parameters are obtained based on a pre-constructed vehicle delay estimation model; and solving an optimal signal lamp parameter and a corresponding vehicle track based on a heuristic search algorithm. Therefore, the problems that in the related technology, the real-time state of the traffic flow is not considered, the mixed traffic condition of mixed driving of the intelligent network connection vehicle and the human driving vehicle and the interaction relation between the signal lamp and the vehicle are not considered, so that the signal lamp parameter and the track of the intelligent network connection vehicle cannot be collaboratively optimized, the intersection traffic efficiency is low, and the traffic efficiency is poor are solved. And actual requirements cannot be met.

The invention discloses an urban traffic signal space-time adaptive flexible regulation and control method and system. The method comprises the steps of collecting traffic data such as the number of vehicles, the vehicle speed, the number of pedestrians and the pedestrian speed at an intersection in real time; performing cleaning, normalization and feature extraction on the acquired traffic data to generate standardized traffic parameters; based on the standardized traffic parameters, a signal lamp timing scheme is generated through a space-time diagram convolutional network and lightweight deep reinforcement learning hybrid algorithm; adjusting the traffic signal lamp in real time according to the signal lamp timing scheme; the method comprises the steps of storing traffic data and remotely monitored intersection states, integrating information of adjacent intersections to perform regional strategy optimization, integrating strategy data of each intersection through federal learning, and generating a global model to optimize a collaborative penalty coefficient; the method can quickly respond to the dynamic change of the traffic flow, optimizes the signal lamp timing scheme, and remarkably improves the data processing speed and decision-making efficiency.

The invention relates to a multi-sensor data fusion method and device, equipment and a medium, and the method comprises the steps: obtaining data and GPS information of a plurality of sensors such as a binocular camera and a radar, taking the data and GPS information as input data, and carrying out the preprocessing, including one or more of image color space conversion, filtering and denoising, and data density reduction; further processing the preprocessed data, wherein the processing comprises one or more of target detection and tracking, coordinate system conversion, target UID distribution and aerial view angle projection; a precision time protocol (PTP) is adopted to synchronize processed data of a plurality of sensors, and the time is kept consistent; a central point of a road intersection is selected, GPS information is recorded, a fusion coordinate system is established, sensor and target position information is calculated according to the GPS information of a sensor, the information is mapped to the fusion coordinate system, and target, association speed, distance and other information and UID are screened and merged according to an overlap ratio threshold. According to the invention, modeling display of the whole intersection is realized, and a reliable basis is provided for traffic management decisions.

The invention relates to the field of urban traffic management and supervision and the field of traffic simulation, and provides a human-vehicle interactive simulation method and system based on an improved social force model and application. The method comprises the following steps: S1, constructing an improved social force model; s2, constructing a decision-making model, wherein the decision-making model is used for determining a behavior mode when the pedestrian and the vehicle meet; and S3, giving initial basic data of pedestrians and vehicles, and performing interactive simulation on human-vehicle movement conditions by using the improved social force model and the decision model. The method can be applied to scenes where vehicles and pedestrians are crossed and mixed, such as intersections without traffic lights, including crossroads and multi-directional intersections. According to the method, the trueness of the model in a complex traffic environment is remarkably improved, and behavior decisions of pedestrians in an actual scene can be more accurately simulated, especially under the condition that potential conflicts occur between the pedestrians and vehicles; through the introduction of modular design and transfer learning, the model can quickly adapt to a dynamically changing environment.

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.

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera, at least one captured image representative of features in an environment of the vehicle. The processor may identify an intersection and a pedestrian in a vicinity of the intersection represented in the image. The processor may determine a navigational action for the vehicle relative to the intersection based on routing information for the vehicle; and determine a predicted path for the vehicle relative to the intersection based on the determined navigational action and a predicted path for the pedestrian based on analysis of the image. The processor may further determine whether the vehicle is projected to collide with the pedestrian based on the projected paths; and, in response, cause a system associated with the vehicle to implement a collision mitigation action.

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.

Disclosed are techniques for traffic flow control. In an aspect, an on-board computer (OBC) of a vehicle of a secondary traffic flow determines that a primary traffic flow is passing through an intersection associated with a traffic light, wherein the determination that the primary traffic flow is passing through the intersection is based on sensor data from one or more sensors of the vehicle, vehicle-to-vehicle (V2V) communication, vehicle-to-everything (V2X) communication, or any combination thereof, and reduces a speed of the vehicle to prevent the vehicle from tripping a traffic light sensor associated with the traffic light until after the primary traffic flow has passed through the intersection.

Embodiments disclosed herein are directed to a system for controlling a vehicle through an intersection. The system includes a server-computing device, a sensing system, and an electronic control unit that has a processing device and a memory device that stores logic, when executed by the at least one processing device, causes the electronic control unit to produce a rumble strip haptic feedback at a first zone prior to the intersection, slow the vehicle down at a predetermined deceleration rate when the sensing system indicates a driver is not paying attention, autonomously navigate the intersection if the driver is determined to be paying attention in a second zone when the driver was not paying attention in the first zone, the second zone is prior to the intersection, and autonomously stop the vehicle when the driver was not paying attention in the second zone after not paying attention in the first zone.

The invention belongs to the technical field of vehicle-road cooperation, discloses a vehicle cooperative control method for an emergency vehicle, and solves the problem that local traffic jam is easily caused due to the lack of precise linkage because the driving state of social vehicles around the emergency vehicle is not considered in a traditional scheme. Therefore, the influence on other social vehicles is reduced while the emergency vehicle passing efficiency is guaranteed. According to the scheme, the traffic data information of the emergency vehicle and the social vehicles around the emergency vehicle is obtained in real time, and when it is judged that the emergency vehicle is currently driven on the lane, the dynamic interval of the emergency vehicle is established according to the current lane where the emergency vehicle is located and the current traffic data information of the emergency vehicle; when other social vehicles in the dynamic interval are judged to meet the lane changing condition, lane changing guiding information is sent to the corresponding social vehicles; when the emergency vehicle arrives at the intersection detection area, traffic light control is executed according to the current traffic data of the emergency vehicle, the length of the intersection detection area and the length of the intersection in combination with the current traffic light state; and finally, after the emergency vehicle arrives at a designated position, ending the vehicle-road cooperative control algorithm.

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 discloses a vehicle anti-collision method and device for a mining area intersection based on the C-V2X technology in the technical field of intelligent traffic, and the method comprises the steps: obtaining the vehicle information of a vehicle, and judging whether the vehicle enters an area around the intersection or an intersection area or not according to the vehicle information of the vehicle; after the vehicle drives into the surrounding area of the intersection, vehicle information of a nearby vehicle is obtained through V2V communication; after the vehicle is driven into the intersection area, judging whether a nearby vehicle is driven into the intersection area or not according to the vehicle information of the nearby vehicle; if there is an adjacent vehicle entering the intersection area, predicting driving tracks of the own vehicle and the adjacent vehicle in the intersection area according to the vehicle information of the own vehicle and the adjacent vehicle; and performing anti-collision processing according to the driving tracks of the own vehicle and the adjacent vehicle in the intersection area. According to the invention, the traffic safety of the intersection of the mining area can be obviously improved.

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.

An introduction support apparatus (10) of pedestrian-vehicle separated signal control according to the present disclosure includes: a data acquisition unit (11) configured to acquire data regarding movements of vehicles and pedestrians within a measurement zone of an intersection that is a candidate for introduction of pedestrian-vehicle separated signal control; a first calculation unit (12) configured to calculate a first evaluation index indicating a degree to which the movements of the vehicles in the intersection are impeded by the pedestrians based on the data acquired from the data acquisition unit (11); and an evaluation unit (13) configured to obtain an evaluation value for evaluating whether or not to introduce the pedestrian-vehicle separated signal control based on the first evaluation index.

A mixed traffic flow-oriented vehicle eco-driving control method and an electronic device are provided. The method includes: calculating a gathering wave speed and a dissipation wave speed at an intersection, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time, acquiring predicted trajectories of the manual-driving vehicle; constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, obtaining an acceleration curve of the connected and autonomous vehicle, and acquiring an ecological reference trajectory of the connected and autonomous vehicle; and setting a risk factor based on the predicted trajectories of the manual-driving vehicles, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle.

A vehicular control system includes a sensor disposed at a vehicle and capturing sensor data. The system, as the vehicle is approaching an intersection and responsive to processing by a processor of sensor data captured by the sensor, detects a cross-traffic threat approaching the intersection and maintains a buffer to store a trajectory of the cross-traffic threat. The system, using the trajectory, determines an intersection point between the vehicle and the cross-traffic threat, determines an arrival time at the intersection point for both the vehicle and the cross-traffic threat, and determines a difference between the arrival time of the vehicle at the intersection and the cross-traffic threat. The system, responsive to determining that the difference between the arrival time of the equipped vehicle at the intersection and the arrival time of the cross-traffic threat is less than a threshold amount, controls a safety system of the vehicle.

An information processing device includes a communication unit and a control unit. The communication unit is capable of communicating with a plurality of vehicles. The control unit calculates the switching timing of the hand signal based on the traffic amount in the vicinity of the intersection, identifies the head vehicle stopped at the intersection, and controls the in-vehicle device capable of recognizing the operation from the outside of the head vehicle provided in the head vehicle via the communication unit based on the switching timing.

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.

The invention discloses a traffic signal lamp control method based on road monitoring, and belongs to the technical field of signal control, and the method specifically comprises the steps: obtaining the road information of all traffic road sections, selecting any intersection, monitoring the congestion coefficients of lanes in all directions in the intersection, and determining the traffic information of all traffic road sections; if the congestion coefficient of the lane in any direction is greater than or equal to a preset threshold value, the lane in the direction is calibrated as a target lane; counting the total number of target lanes of the road junction, calculating a congestion coefficient mean value of the target lanes, calculating priority values W of all the road junctions, and sorting all the road junctions in the current traffic network according to a sequence of the priority values from large to small to generate a priority sequence; the congestion coefficients of adjacent upstream and downstream lanes in the corresponding directions of any target lane of each road junction are sequentially obtained, the green light passing time of the road junction is controlled, intelligent regulation and control of the traffic signal lamps are achieved, and therefore traffic pressure is effectively relieved.

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.