684 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.

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 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.

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.

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.

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.

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.

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.

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.

To ensure stable vision and reduce discomfort caused by the disappearance of an own vehicle mark when approaching an intersection, display control switches from a wide-area to a narrow-area map display. When the vehicle's distance to an intersection exceeds a first threshold, a control unit shows a first map at a wide scale, displaying the vehicle's position in a bird's-eye view of the surrounding area. As the vehicle approaches the intersection and the distance falls below the threshold, the control erases the vehicle mark and switches to a second map with a larger scale, focusing on the intersection vicinity. A guide mark then indicates the vehicle's traveling direction by adjusting the map's scale.

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 a method for jointly controlling one or multiple connected and automated vehicles (CAVs) and one or multiple human-driven vehicles (HDVs) subject to integer constraints for crossing each of multiple intersections on a road. The method comprises collecting digital representation of states of each of the CAVs, HDVs, and traffic signs, solving an optimization problem jointly optimizing traffic flows based on a macroscopic traffic flow model in a centralized traffic controller (CTC) subject to convex relaxation of the integer constraints, solving a multi-variable mixed-integer programming (MIP) problem in each of multiple intersection traffic controllers (ITCs) optimizing a cost function and minimizing tracking errors in traffic flow values of a microscopic traffic flow model with respect to relaxed traffic flow values from the CTC, and transmitting the optimized values of the control commands to the corresponding CAVs and corresponding traffic signs.

The invention provides a vehicle passing planning method and system for a no-signal intersection, and relates to the field of traffic route planning, the method is based on a partially observable hidden Markov chain decision-making model, and modeling is carried out on an intersection vehicle passing order and an advancing effect thereof; an observation matrix, a state matrix, a state transition probability model and a target optimal (average shortest vehicle passing time) calculation expression of vehicle passing are constructed, the motion behavior of a single individual passing vehicle at an intersection is modeled, and the interpretability of a planning decision is improved; in addition, an optimal group target motion time sequence chain is searched based on a cost (passing time) calculation method of a Monte Carlo tree model, so that an optimal vehicle real-time passing plan is obtained, and the uncertainty of time consumption of intersection vehicle passing plan calculation is reduced within predictable algorithm time complexity.

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.

The invention discloses a hybrid reinforcement learning traffic signal hierarchical control method, and the method comprises the steps: collecting the queue length of an upstream lane and a downstream lane of each intersection, obtaining a phase pressure value through the weighted sum corresponding to the queue length of the upstream lane and the queue length of the downstream lane, and combining the queue length with the phase pressure value to obtain local traffic data. And when the congestion or emergency vehicles are not detected, the plurality of bottom layer maximum pressure controllers are utilized to obtain the optimal phases corresponding to the intersections through a maximum pressure algorithm and local traffic data so as to control the traffic signals of the intersections. And integrating local traffic data of each intersection to obtain global traffic data, encoding the global traffic data through a graph neural network, and learning a global optimal strategy through encoded features by a global near-end strategy optimization controller. And when a congestion or emergency vehicle is detected, the global near-end strategy optimization controller adjusts the current phase by using a global optimal strategy, and issues the adjusted phase to each intersection to control traffic signals.

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.