4879results about "Road vehicles traffic control" patented technology

A system and method of calculating an estimated time of arrival for a traveler is provided, along with a notification of this estimated time that is sent to a third party. The traveler is equipped with a navigation unit having a global positioning sensor and a wireless communications device. The navigation unit communicates with a service center equipped with a server capable of generating a preferred navigational route for the traveler. The service center also includes a traffic database for maintaining current traffic information. Once the traveler has selected a predetermined time and frequency of notifications based upon a selected criterion, the navigation unit transmits the traveler's position and velocity to the service center. The service center then calculates the estimated time of arrival based upon the traveler's position, velocity, distance to be traveled and traffic and weather conditions. Once this time is calculated, the service center sends a notification to the third party.

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The present disclosure relates to configured artificial intelligence methods and systems and related transportation systems and methods, including software-defined vehicles, for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

The invention provides an Internet of Vehicles key information classification early warning method and a vehicle-mounted device. The Internet of Vehicles key information classification early warning method comprises the steps: acquiring Internet of Vehicles information of a target vehicle; determining current driving state information of the target vehicle according to the Internet of Vehicles information; and determining the type of information sent to the target vehicle according to the driving state information. According to the invention, the information with different emergency degrees can be sent to the user according to the current driving state information, multi-channel grading early warning is realized according to the information type, and the effects of assisting driving, promoting safety and improving user experience are achieved.

The invention relates to a data processing method for iterative sensor fusion. The method comprises receiving sensor data from a first data source type and a second data source type. The first data source type is adapted for providing sensor data for exclusively determining an entity and the second data source type is adapted for providing sensor data of a tracked entity. The method further comprises generating a data source identifier for each of the sensor data. The data source identifier comprises a data source type identifier corresponding to a data source type from which the sensor data is received. A first data source type identifier corresponds to the first data source type and a second data source type identifier corresponds to the second data source type. The method further comprises compiling a dataset that comprises newest sensor data received, and iteratively performing a data association step on the dataset.

Disclosed is route planning using a worst-case risk analysis and, if needed, a best-case risk analysis of GNSS coverage. The worst-case risk analysis identifies cuboids or 2d regions through which a vehicle can be routed with assurance that adequate GNSS coverage will be available regardless of the time of day that the vehicle travels. The best-case risk analysis identifies cuboids or 2d regions through which there is adequate coverage at some times during the day. In case path finding using the worst-case risk analysis fails, a best-case risk analysis can be requested and used to find alternate potential path(s). Time dependent forecast data that covers regions along the alternate potential path(s) can be requested and used to route vehicles, including autonomous drones, from starting points to destinations. This includes generation, distribution and use of risk analysis data, implemented as methods, systems and articles of manufacture.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention discloses a method for dynamically monitoring and automatically processing abnormity of an entrance and an exit of a parking lot, relates to the technical field of intelligent parking lot management, and aims to solve the problems that existing unattended parking lot entrance and exit management depends on manpower, abnormity processing is passive, and the misjudgment rate is high. The method is based on an information acquisition and execution layer-edge processing layer-cloud platform three-layer architecture, the core realizes high-precision scene understanding through a multi-algorithm fused visual perception module, multi-modal data weighted fusion is combined with a ground induction coil, a radar and the like, and the misjudgment rate is reduced; a sensing result is converted into an automatic processing action through a configurable intelligent rule engine; and meanwhile, the cloud platform stores passing data and an evidence chain, optimizes an AI model through incremental training and returns the AI model to an edge layer, so that closed-loop evolution is realized.

The invention discloses an automatic driving safety operation system integrating environmental perception and decision reasoning. The system generates and dynamically updates a security risk map covering an operation area by fusing real-time environment perception, historical operation data and traffic management information. On the basis, the collaborative safety decision-making module further carries out behavior modeling and intention prediction on other traffic participants, and in combination with map risks and traffic instructions, a driving strategy is actively adjusted under a dynamic game framework. According to the invention, the safety control is improved from passive response to an active mode of behavior pre-judgment and game dominance, and the safety and reliability of the operating vehicle in a complex environment and the cooperative capability of the operating vehicle and traffic management are obviously enhanced.

A method and system accurately identify vehicles during transit and may be used for tracking the vehicle. Images of discrete distinguishing characteristics of a vehicle, respectively, are captured at an unmanned monitoring station. The images are collectively analyzed to produce an individualistic identifier. The individualistic identifier is correlated with data representative of a particular vehicle stored as part of a database of vehicles. Accordingly, the particular vehicle present at the monitoring station can be identified so that the vehicle can be tracked. Furthermore, key assets of the vehicle, such as the tractor, chassis, and cargo box of a truck, can be identified during the process and by the system so that they can be tracked independently of one another.

A method and device for optimized scheduling of massive transport vehicles during flood disasters, relating to the field of transportation resource scheduling is provided. The method includes determining flood inundation ranges during various time periods of a flood based on watershed precipitation and river cross-section structural data and marking accessible roads within a flood-affected area with double truncation. A road information matrix and a resettlement zone information matrix is determined and model information and location information of transport vehicles within the flood-affected area are also determined. A transport vehicle dataset is also determined. With consideration of road emergencies, time-related variations of a road matrix, road collapse incidents, and mud-covered roads, a time-varying dynamic-planning traffic scheduling model is established with a goal of minimizing an arrival time of a last evacuated transport vehicle, and an optimal scheduling plan is determined to perform optimized scheduling on transport vehicles within the flood-affected area.

The invention discloses a self-supervised traffic flow prediction method based on multi-scale space-time-frequency fusion. The method comprises the following steps: acquiring enhanced data; performing multi-scale spatial-temporal feature coding; performing frequency domain residual filtering; generating a traffic flow prediction result; and carrying out joint target optimization. According to the invention, the multi-scale space-time frequency encoder is designed, local and global time features are captured at the same time through the mixed time encoding module in the time dimension, the multi-scale space encoding module aggregates space features under different distances in the space dimension, and the prediction precision is significantly improved. A frequency domain residual filtering module is embedded in the encoder to adaptively purify frequency domain features in an end-to-end mode, enhance key periodic features and suppress irrelevant noise, space-time features are fused through residual connection, space-time-frequency three-dimension collaborative modeling is achieved, meanwhile, frequency domain consistency loss is introduced in the optimization stage, and time-frequency three-dimension collaborative modeling is achieved. And the method is more robust when facing real traffic data containing noise.

The invention relates to the technical field of path planning, in particular to a multi-agent dynamic nuclear emergency evacuation path planning method and system based on deep reinforcement learning. In particular to a multi-agent optimization path generation and dynamic adjustment method based on a deep reinforcement learning technology, and aims to improve the real-time performance, the safety and the execution efficiency of a public evacuation path in an emergency nuclear event. According to the scheme, the optimal evacuation path is automatically explored through deep reinforcement learning, a path model does not need to be manually preset, complex factors such as pollution dynamic diffusion and traffic jam under the nuclear accident scene can be dealt with, and the efficiency and safety of emergency evacuation path planning are greatly improved. The system is suitable for public emergency response systems in high-risk environments such as nuclear accidents, earthquakes, fire disasters and urban explosions.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

In some implementations, the techniques described herein relate to a method including: receiving a data log entry associated with a driver that includes a service provider location and a timestamp; identifying a vehicle associated with the driver based on the data log entry by identifying the vehicle includes applying a machine learning model to the data log entry and a vehicle database; loading a vehicle location log associated with the identified vehicle, the vehicle location log including a plurality of location data points and associated timestamps; computing an alternate data log entry based on the data log entry and the vehicle location log, wherein computing the alternate data log entry includes applying a rule-based optimization algorithm to a historical service provider database; and transmitting a recommendation based on the alternate data log entry, wherein the recommendation includes a geospatial visualization of the alternate data log entry.

A time series data prediction method and apparatus, and a storage medium are provided. The method includes: obtaining current time series data collected in a current time window that is adjacent to and precedes a prediction time window in a current time period, and obtaining a plurality of groups of historical time series data separately collected in a same target time window of a plurality of historical time periods; encoding the plurality of groups of historical time series data by using a plurality of encoders respectively, to obtain a plurality of historical time series features, where each historical time series feature represents relative location information and change trend information of each group of historical time series data in the target time window; and determining, predicted time series data corresponding to a target object in the prediction time window.

The invention provides a driving route recognition system and method, electronic equipment and a readable medium, and belongs to the field of intelligent driving. According to the processed original image and preset navigation information, navigation and road condition information guidance are provided for a driver or intelligent driving is provided for the driver, and according to the intelligent driving technology provided by the patent, an intelligent driving auxiliary system with the core target of solving driver navigation confusion under complex road conditions is constructed. Through deep fusion of high-precision visual perception and map navigation information, strong road identification judgment logic is utilized, when a driver actively requests or a system pre-judges needs, an intelligent driving module provides accurate vehicle control assistance, and clear navigation prompts are combined to jointly ensure that a vehicle safely and correctly passes through a complex road scene, so that the driving safety of the vehicle is improved. And the driving convenience and safety are obviously improved. Voice input is a key link of man-machine cooperation.

The application relates to the technical field of blind area monitoring, and relates to a low-speed blind area monitoring method and system based on multi-view camera shooting, which comprises the following steps: registering multiple cameras in a vehicle to obtain a target vehicle and a vehicle coordinate system; confirming multiple early warning coordinates based on the vehicle coordinate system, a vehicle bird's eye view and the vehicle; confirming a candidate window based on the vehicle bird's eye view and the early warning coordinates; performing target detection on the candidate window by using a pre-constructed target detection model to obtain a detection result; if the detection result is a first result, taking a pre-constructed safety instruction as a target instruction; otherwise, confirming relative positions and relative speeds based on target bounding boxes in a second result and multiple radars in the target vehicle; calculating an expected collision time according to the relative positions and the relative speeds; confirming the target instruction based on the expected collision time; and summarizing the target instruction to obtain multiple target instructions, thereby completing low-speed blind area monitoring. The application can improve the precision of low-speed blind area monitoring.

The invention discloses a mixed traffic flow-oriented automatic driving taxi dynamic scheduling system and a collaborative decision-making method, belongs to the field of intelligent traffic systems, and solves the problem that in the coexistence environment of manual driving vehicles and automatic driving taxies, the automatic driving taxies cannot be automatically scheduled. The technical problem of how to efficiently and cooperatively dispatch vehicles, accurately predict demands, optimize energy management and improve the overall operation efficiency of the system is solved. The system comprises a scheduling server which is connected with a road side sensing unit, a vehicle-mounted control unit and a charging station management platform. The scheduling server comprises a traffic flow analysis module; a demand prediction module; a dynamic scheduling module; and an energy collaboration module. The system is mainly used for realizing real-time, dynamic and intelligent scheduling and energy management of the automatic driving taxis in the mixed traffic flow, improving the operation efficiency, relieving the traffic jam and optimizing the charging resource utilization.

The invention provides a non-motor vehicle overspeed identification method and device based on an image sequence, and relates to the technical field of image analysis, and the method comprises the steps: obtaining image sequence data collected by a non-motor vehicle in a low-illumination curve channel; feature parameter extraction is carried out based on the image sequence data, mapping processing is carried out based on the extracted feature parameters, and an embedded track point set of the non-motor vehicle in the curve channel is obtained; performing direction sensitive tensor graph construction and multi-scale steering perception graph modeling processing to obtain a multi-dimensional graph structure; a speed estimation vector sequence is obtained through processing of an angular graph attention mechanism and a convolution trajectory transformation network; carrying out acceleration filtering and compensation processing to obtain a speed sequence without turning inertia errors; and performing curve overspeed behavior analysis to obtain a judgment result of whether the non-motor vehicle has the curve overspeed behavior or not. According to the method, fine reconstruction of the non-motor vehicle track, inertial error filtering and speed behavior analysis are realized by utilizing multi-stage algorithm combination.

The invention discloses a multi-model fusion target vehicle identification analysis method and system based on machine vision, and the method comprises the steps: A, capturing and collecting a road vehicle video / image through an unmanned plane, and transmitting the video / image to a streaming media server in a wireless manner; b, an improved target vehicle detection model is adopted, a multi-task cooperative detection framework is constructed, and vehicle illegal parking is judged by applying a multi-model fusion detection mechanism; c, designing a lightweight detection network, reducing the problems of high reasoning delay and large resource occupation in the multi-model cooperative detection process, and reducing the complexity of a detection algorithm; and D, carrying out intelligent alarm and event pushing. According to the invention, the method can improve the recognition accuracy of vehicle illegal parking behaviors in a complex scene, and improves the real-time performance and stability of a single-model monitoring system for vehicle target detection.

The invention provides a bridge health monitoring and intelligent control method and system based on machine learning, and the method comprises the steps: carrying out the distributed sensing data collection of a bridge structure, generating a structure monitoring data set, carrying out the spatial-temporal feature extraction, and obtaining a vibration response feature sequence and a traffic load feature sequence of the bridge structure; performing correlation analysis on the two sequences, generating a structural damage sensitive feature matrix, performing damage evolution trend prediction based on the structural damage sensitive feature matrix, generating damage development probability distribution of the bridge key component, and generating a dynamic traffic control instruction according to the damage development probability distribution. The dynamic traffic control instruction comprises a vehicle passing parameter dynamically adjusted based on a damage degree quantitative index, and sending the dynamic traffic control instruction to a traffic management system. According to the invention, the accuracy of bridge health monitoring and the dynamic adaptability of control measures can be improved.

The present invention discloses a multi-layer attention-based graph fusion traffic flow prediction method, medium and device that can capture the complex traffic flow patterns of different roads and achieve high-precision traffic flow prediction. [Solution] Historical traffic flow data for all roads in a target road network is sampled at different sampling intervals to obtain multiple historical flow sequences representing different data periods for each road. A road spatial relationship graph and a road functional similarity relationship graph corresponding to each data period are then constructed for all roads in the target road network. The road spatial relationship graph is then subjected to adjacency matrix attention fusion with the road functional similarity relationship graph corresponding to each data period. Finally, the fused adjacency matrix and vertex feature matrix for each data period are input into a graph attention network, and weighted fusion is performed on the output results to obtain future flow prediction results for the target road network.

The invention provides a parking lot safety operation intelligent monitoring and self-adaptive scheduling management platform based on cloud edge cooperation AI, and the platform comprises a data analysis and alarm module which carries out the analysis of the behaviors of a driver and the surrounding environment of a vehicle based on an AI model disposed on a parking lot intelligent terminal, and carries out the instant alarm when an abnormal driving behavior or a safety risk exists; the global analysis index determination module is used for uploading the behavior recognition result obtained through analysis and the time series data of the environmental risk information to the cloud platform, and aggregating and mining the time series data of each parking lot vehicle in the space region based on the cloud platform to obtain the global analysis index of the working efficiency and the safety situation of the parking lot vehicle; and the scheduling management module dynamically generates a task scheduling instruction and a path planning instruction of each parking lot based on the global analysis index, and issues the task scheduling instruction and the path planning instruction to the parking lot intelligent terminal for cooperative management of operation behaviors. The operation safety and the operation efficiency are remarkably improved, and human errors and resource waste are reduced.

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 planning method and device, a storage medium and a program product, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring vehicle driving data and a real-time traffic condition; according to the vehicle driving data and the real-time traffic condition, a plurality of particles are initialized, and each particle represents an acceleration sequence of the vehicle in a future preset time period; iteratively updating the particles until convergence, and obtaining a preliminarily optimized acceleration sequence; and a quadratic programming algorithm is applied to smooth the preliminarily optimized acceleration sequence, a target acceleration sequence is obtained, and the target acceleration sequence is used for controlling the vehicle to run. The technical problem that a rule-based speed planning method in the prior art lacks flexibility and is difficult to deal with a complex traffic environment is solved.

A driving assistance device according to an embodiment of the present invention comprises: a recognition unit that recognizes the surrounding situation of a vehicle; a driving control unit that executes lane keep control for controlling at least steering of the vehicle such that the vehicle travels within a predetermined lane, on the basis of the recognition results of the recognition unit; and a reception unit that receives a vehicle lane change intention through an operation by an occupant of the vehicle. The driving control unit stops the lane keep control when the reception unit receives the lane change intention during the execution of the lane keep control, and restarts the lane keep control at a timing earlier than completion of the lane change.

The invention discloses lane positioning determination for autonomous systems and applications. In various examples, use of bi-directional output to determine lane localization for autonomous and / or semi-autonomous systems and applications is described herein. Systems and methods described herein may determine a plurality of outputs (e.g., vectors) associated with a lane of a driving surface (e.g., a roadway), where the outputs are indexed from different locations with respect to the driving surface, and then use the plurality of outputs to determine a lane in which the machine is traveling. In some examples, the output may include a vector including a plurality of elements, where a respective element is associated with at least a lane of the driving surface and indicates a probability that the machine is located within the lane. Further, in some examples, the outputs may be indexed from different sides of the driving surface, such as the right and left sides of the driving surface.

The invention discloses a berth positioning system and method based on video recognition, and relates to the technical field of intelligent transportation, and the system comprises a calibration module which generates berth calibration data based on a berth type, and stores the data to a cloud server; the edge calculation service module is configured to judge the spatial relationship between the vehicle and the berth through a berth type adaptive algorithm based on the real-time latitude and longitude coordinates of the inspection vehicle and the berth calibration data; and the vehicle identification module records license plate information when determining that the vehicle is in the berth, and actively snapshots an empty berth image when no vehicle is in the berth and uploads the empty berth image to the cloud. According to the scheme of the invention, the accuracy, environmental adaptability and long-term operation stability of parking state recognition can be remarkably improved, and a comprehensive solution with high robustness and low misjudgment rate is provided for urban intelligent parking management.

The invention discloses a carbon flow optimization method and device for edge calculation under vehicle-road cooperation and a medium, and relates to the technical field of traffic carbon emission. The method comprises the steps that a real-time state parameter sequence and environment information of a vehicle are acquired, and at least two candidate driving strategies are generated through a built-in carbon emission digital twinborn model; according to the candidate driving strategies, the corresponding expected carbon emission amount is calculated by inquiring the emission factor mapping table of the specific power of the vehicle and integrating; packaging the candidate driving strategy and the corresponding expected carbon emission into a bidding packet, and sending the bidding packet to an edge computing node on the roadside; based on the received bidding package, carbon right reverse auction is initiated, a combined optimization problem is solved by adopting a heuristic search algorithm, and a bid winning strategy set is determined; and generating a cooperative driving strategy according to the bid winning strategy set, and issuing the cooperative driving strategy to the corresponding vehicle end node. By means of the method, information distortion is restrained, and direct and real-time optimization control over regional total carbon emission is achieved.