243 results about "Ground vehicles" patented technology

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention provides a modular and multifunctional karst collapse physical simulation experiment platform. Comprising a rigid frame, a bearing device, a moving device, a loading device, a lifting device, a pile foundation construction dynamic load simulation device, a ground traveling dynamic load simulation device, a rainfall simulation device, a computer vision monitoring device, an underground water pumping and draining simulation device and an underground space excavation disturbance simulation device. The moving device is arranged on the bearing device, the lifting device, the loading device and the pile foundation construction dynamic load simulation device are arranged on the moving device, the travelling crane dynamic load simulation device is connected with a traction device of the moving trolley, and the camera shooting monitoring device and the rainfall simulation device are arranged on the rigid frame through a support. According to the method, key disaster-causing conditions in the urban environment are fully considered, the working condition scene coverage is wide, and the applicability is high; modularized design is adopted as a whole, all the modules can be flexibly loaded according to requirements, the real environment can be simulated more truly, and the catastrophe evolution process of surface collapse can be reproduced.

The invention discloses an aerial unmanned aerial vehicle configuration number prediction method based on ground vehicle trajectory data, and belongs to the technical field of unmanned aerial vehicle control. In order to solve the problem of predicting the configuration number of aerial unmanned aerial vehicles based on a ground traffic state, the method comprises the steps of constructing a vehicle trajectory set; constructing a traffic density thermal field; constructing an average speed trend vector field; constructing a direction consistency index; a traffic density thermal field is used as a basic load, the modulus length of an average speed trend vector field represents mobility, a direction consistency index represents a traffic order, weighted combination is carried out to construct distribution pressure intensity, the distribution pressure intensity is converted into the demand intensity of air resources, and a space vector from a vehicle position to a nearest unmanned aerial vehicle take-off and landing point and a terrain complexity factor are considered. Constructing a preliminary unmanned aerial vehicle demand estimation field; and introducing a task urgency weighting function, the current task backlog amount and the weather flight risk to construct a final unmanned aerial vehicle deployment suggestion function, and completing prediction of the air unmanned aerial vehicle configuration number based on the ground vehicle trajectory data.

The invention discloses an expressway intelligent scheduling system based on air-ground collaborative data, and relates to the technical field of intelligent traffic, and the system comprises a low-altitude aircraft module which is used for monitoring the traffic state of an expressway in real time through a sensor, and generating a multi-sensor fusion data set through a data fusion algorithm; the ground vehicle module is used for communicating with the management and control center module through a vehicle-mounted terminal and receiving traffic guidance information, accident early warning and navigation instructions; the road side unit module is used for monitoring data of lanes and traffic nodes, generating a traffic flow initial data set and carrying out relay transmission on communication data among different modules; and the management and control center module is used for generating traffic guidance information, accident early warning and navigation instructions by using an artificial intelligence algorithm, and issuing the traffic guidance information, accident early warning and navigation instructions to ground vehicles by using a communication network. According to the invention, through cooperation of the low-altitude aircraft, the ground vehicle, the road side unit and the management and control center module, fusion analysis and intelligent scheduling of multi-source data are realized.

Autonomous ground vehicles that are outfitted with radar sensors and inertial measurement units accurately determine states of the autonomous ground vehicles, e.g., estimates of the vehicles' positions, orientations, or velocities or accelerations along or about one or more axes, based on data captured by the radar sensors and the inertial measurement units. Where objects are detected in radar scans, the objects are determined to be static (or fixed), or dynamic (or moving), and landmarks representing static objects are identified. Constraints on estimates of states may be calculated based on doppler effects, inertial measurement unit effects, or locations of landmarks, and the states may be determined as solutions to optimization problems based on the data and the calculated constraints. Odometry messages representing the determined states may be generated and stored or utilized for any purpose.

The invention discloses an air-ground cooperative path planning method for multi-type target reconnaissance, and the method comprises the steps: obtaining the position information of a base and all targets, carrying out the clustering, obtaining a plurality of target sub-regions, taking each clustering center as a virtual point corresponding to the target sub-region, and enabling each target sub-region to have at least one target; generating a ground vehicle path based on the coordinates of the base and each virtual point, and optimizing the ground vehicle path; feature points of various types of reconnaissance targets are obtained, unmanned aerial vehicle paths of various target sub-regions are obtained based on improved adaptive large neighborhood search, each unmanned aerial vehicle path takes a virtual point of the target sub-region as a starting point and an ending point of the unmanned aerial vehicle path, and the feature points of the targets are connected; and integrating the ground vehicle path and the unmanned aerial vehicle path of each target sub-region to complete air-ground cooperative path planning. The method is applied to the field of path planning, full-coverage reconnaissance of various types of targets such as points, lines and planes can be efficiently completed, and the charging frequency of the unmanned aerial vehicle is reduced.

The invention relates to the technical field of artificial intelligence security, in particular to a physical domain robust adversarial attack method oriented to unmanned aerial vehicle aerial photography target detection, and aims at challenges of various environmental factors, the method comprises the following steps: initializing a learnable pixel tensor to generate an adversarial patch; inputting the training samples, the adversarial patches and label files of the adversarial patches into a patch conversion module to generate adversarial samples, inputting the adversarial samples into a YOLO target detector, performing weighted summation on each loss item based on the performance of the adversarial patches on each target detection network, constructing a unified optimization target, solving the overall loss for a detection model, and constructing an optimal target; according to the method, the adversarial patch is installed on the top of the vehicle or around the vehicle, so that the ground vehicle can avoid the unmanned aerial vehicle target detection system, and therefore, robustness detection of the aerial photography target detection neural network can be realized.

A system for controlling unmanned vehicles such as drones, ground vehicles and robots includes multiple human-to-machine interface components to manage interactions efficiently and effectively between humans and robots with respect to control mechanisms (i.e., inputs such as voice-commands or joystick use) and monitoring (i.e., outputs such as camera or other sensor feeds, geolocation overlay, or audio feedbacks). A system according to the principles of the invention includes numerous modalities for control (e.g., voice-control, gesture-control, touch screen, joystick or other hand-held controllers) as well as monitoring (visual screen display, heads-up-display, audio feedbacks, geolocation overlay, haptic feedback, wearable sensor devices). The system is preferably expandable and may include additional modalities in the future, such as eye-tracking as compared to voice or gesture control.

The invention provides a multi-unmanned aerial vehicle cooperative reasoning optimization method based on DAG topology generation and dynamic DNN model partitioning, and the method comprises the steps: dynamically adjusting the position of an unmanned aerial vehicle based on a potential field method when a task request of a ground vehicle is received, and constructing a topology network in combination with greedy clustering and a DAG topology generation strategy; performing dynamic DNN model partitioning based on a discontinuous layer allocation mechanism and a dynamic programming algorithm DPA, and dynamically allocating DNN discontinuous layers to different unmanned aerial vehicles; performing DNN reasoning according to the topology network and the partitioning scheme; performing cache updating based on the priority and the energy perception strategy; and calculating time delay and overhead. Task allocation and load balancing can be intelligently performed according to the complexity of tasks and the computing power of each unmanned aerial vehicle, so that the overall efficiency of the system is improved, and the processing time delay is reduced; the task and data allocation strategy can be flexibly adjusted according to the network state and the environment change, and the real-time performance and the high efficiency of the task are ensured.

The invention discloses an air-ground task intersection prediction method for emergency rescue, and belongs to the technical field of unmanned aerial vehicle control. In order to solve the problem of accurately predicting an air-ground task intersection with reachability and energy efficiency requirements, the method comprises the following steps: predicting an unmanned aerial vehicle path; predicting a ground vehicle path; generating candidate intersection points; calculating the time cost of the rescue task, the energy consumption cost of flight and the environmental risk cost; constructing a comprehensive cost function; constructing a confidence factor through the terrain height difference, the path node complexity and the communication shielding score, and carrying out risk correction on the comprehensive cost function to obtain a corrected comprehensive cost function; and introducing factors such as a ground slope angle, a wind speed value, a ground trafficability score and an unmanned aerial vehicle flight distance to carry out punishment correction, and extracting an optimal air-ground task intersection point coordinate for emergency rescue. The method is suitable for various emergency rescue, multi-platform cooperation and intelligent task scheduling, and has wide application prospect and popularization value.

A method for speed control of a ground-based vehicle, a computer-implemented method for speed control of a ground-based vehicle, and a control device for speed control of a ground-based vehicle. A speed planner is provided and includes a macro-planner for rapid forward-backward speed planning and a meso-planner for optimizing driving time and energy consumption using an optimization solver. A distance planner is provided and includes a micro-planner for distance control of distance to a vehicle driving ahead. A speed setpoint value is initially calculated by the macro-planner or the meso-planner, the speed setpoint value then optimized by the micro-planner to generate a final speed setpoint value. The ground-based vehicle is then controlled by operating it using the final speed setpoint value.

Systems and methods for causing a first autonomous vehicle (e.g., a drone or other aerial vehicle) to utilize a LIDAR pattern being emitted by a LIDAR device of a second autonomous vehicle (e.g., a ground vehicle) to determine one or more characteristics of the second vehicle. The one or more characteristics may comprise, for example, (i) a make and / or model of a manufacturer of the LIDAR device and / or the second vehicle; and (ii) a particular unit of the LIDAR device and / or the second vehicle. The first vehicle may further utilize the LIDAR pattern of the second vehicle to perform other operations, such as mapping a 3-D model of the environment being traversed by the second vehicle.

A method for navigating an unmanned ground vehicle (UGV) includes obtaining, by a sensor spaced apart from the UGV and at least temporarily fixed relative to a stretch of land on which the UGV is to operate, a three-dimensional map of the stretch of land. A navigation signal is generated based on the three-dimensional map and a user-specified task. The navigation signal is transmitted to a controller operatively coupled to the UGV, and configured to receive a navigation signal and operate the UGV in accordance with the navigation signal.

The invention discloses an unmanned aerial vehicle assisted Internet of Vehicles trajectory planning method and system based on traffic flow prediction, and the method comprises the steps: S1, employing multi-source historical traffic data collected by a distributed sensor network as input, and building a traffic flow space-time prediction model based on a deep learning algorithm; s2, a vehicle networking model composed of a plurality of vehicles and a plurality of unmanned aerial vehicles is constructed, the unmanned aerial vehicles fly at a fixed height and serve as air base stations to provide services for ground vehicles, and only one unmanned aerial vehicle provides services for each vehicle; and S3, constructing a multi-unmanned aerial vehicle assisted Internet of Vehicles communication system model, clustering ground vehicles by using a clustering algorithm in combination with traffic flow prediction data, and further jointly optimizing trajectory planning and resource allocation strategies of the unmanned aerial vehicles by taking a clustering center as a pre-deployment position of the unmanned aerial vehicles for each vehicle cluster. The corresponding system comprises the sensor network, the cloud platform and the unmanned aerial vehicle cluster carrying the base station, and the communication quality and the energy efficiency of the Internet of Vehicles can be effectively improved.

The invention relates to an optimal data transmission method for an unmanned ground vehicle, belongs to the technical field of unmanned vehicle control, and solves the problems of limitation of a static defense strategy and insufficient resource optimization of the unmanned ground vehicle in the aspect of safety protection. The method comprises the steps that a Stackelberg dynamic game model with an unmanned ground vehicle as a leader and an attacker as a follower is constructed, a defense target function and an attack target function are constructed respectively, iterative optimization is carried out, and when Stackelberg equilibrium is achieved, an optimal attack and defense strategy structural form is obtained; based on the optimal attack and defense strategy structural form, after the unmanned ground vehicle executes the optimal defense strategy at the current moment for data transmission, an attacker obtains the optimal attack strategy at the current moment based on the optimal defense strategy; and the unmanned ground vehicle adjusts the defense strategy of the unmanned ground vehicle according to the optimal attack strategy at the current moment, and obtains the optimal defense strategy at the next moment for data transmission.

A agricultural vision system including an aerial and a land vehicle equipped with imaging and geolocation units to generate lateral and overhead maps of crops. These maps fused into a multidimensional model for object tracking, yield estimation, and growth analysis. AI-driven algorithms provide actionable insights and enable automation, including pruning, harvesting, and augmented reality visualization for precision agriculture.

The invention discloses a ground vehicle adaptive path planning method and device based on fuzzy reasoning, and relates to the field of path planning, and the method comprises the steps: constructing a terrain grid map based on a satellite image map and satellite elevation data; calculating the ground type coefficient, gradient and flatness of the target position; taking the cross-country performance of the target vehicle as an input variable, taking the gradient weight and the flatness weight of the target position as output variables, fuzzifying the input variable and the output variable, establishing a fuzzy rule by using a membership function, performing fuzzy reasoning, and converting a fuzzy quantity after reasoning into an accurate quantity for output; and using an A * algorithm to take the lowest comprehensive passing cost as a target, performing adaptive path retrieval on the surrounding position of the target position, updating the target position, and continuing path retrieval until the target position arrives at the final position to obtain an optimal path. According to the invention, the adaptability of the vehicle cross-country performance to the path is improved, the rationality and adaptability of path planning are enhanced, and the driving efficiency is improved.

The invention discloses a satellite-ground vehicle networking task unloading and resource allocation joint optimization method and system, and relates to the technical field of resource allocation, and the specific steps are as follows: constructing a three-layer cooperative computing network composed of a vehicle layer, an RSU layer and an LEO satellite layer; constructing a dynamic link available time constraint model based on a three-layer cooperative computing network; if the tasks are unloaded to the LEO satellites, whether migration triggering conditions are met or not is judged based on a task migration mechanism, and if yes, the tasks are unloaded to the adjacent LEO satellites through inter-satellite links; an ATPER-MATD3 algorithm is adopted, optimization is carried out by taking minimization of task execution total time delay and energy consumption as an optimization target based on a three-layer cooperative computing network, a dynamic link available time constraint model and a task migration mechanism, and an unloading strategy and a resource allocation strategy are generated. According to the invention, through dynamic unloading decision and multi-agent collaborative optimization, the task processing time delay is significantly reduced, the resource allocation efficiency is improved, and the system robustness and expandability under the scene of fusion of the Internet of Vehicles and the satellite network are enhanced.

Systems and methods for simulating aircraft systems are provided. A system includes simulation entity agents defined to represent the behavior of multi-modal ride-sharing agents (e.g., aerial vehicle, ground vehicle, air traffic controllers, etc.) and world data representing a world environment within which the simulation entity agents may operate. The system obtains agent data indicative of a simulation aerial vehicle and world data indicative of a particular simulated world environment. The system determines scenario data including a scenario definition defining a simulation scenario and generates a simulation instance based on the scenario data. The simulation instance includes the simulation aerial vehicle within the simulated world environment. The simulation aerial vehicle can have access, within the simulation, to one or more backend services of a transportation services system configured to facilitate an aerial transportation service. The system can initiate the simulation instance and obtain data while the simulation is running.

The invention relates to the field of multi-mode collaborative transportation and logistics, in particular to a collaborative distribution optimization method and system for taking off, landing and synchronous recovery of a vehicle-mounted unmanned aerial vehicle on the way (in the advancing process). According to the method, a layered decoupling framework is adopted; in the first layer, subsection division is performed on a road network, taking-off and landing positions are continuously optimized in candidate taking-off and landing subsection pairs based on a Lagrange multiplier method and geometric synchronization constraint, and a client cluster candidate set is formed; the second layer performs joint optimization on selection of a single take-off and landing sub-segment pair in each client cluster and a global path of a ground vehicle to construct a generalized traveling salesman variant, and designs a sub-segment distance measurement and an overlapping deduction strategy to deal with any continuous take-off and landing distance calculation and multi-unmanned aerial vehicle cooperation problem. Experiments show that compared with a fixed stop point or discretization in-transit strategy, the method can significantly reduce the total delivery cost and the completion time, and has robustness and expandability for customer scale, distribution, unmanned aerial vehicle speed and road network density change.

The application provides a method and system for detecting camouflaged targets by unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The system ensures the synchronization of UAVs and UGVs through the global positioning system (GPS), and uses the infrared cameras, binocular cameras, and radar sensors carried by both to efficiently collect data in the target area. The system further preprocesses and analyzes the collected data, and uses improved SSD algorithms and pattern recognition techniques to identify potential camouflaged targets, and performs pattern matching and information fusion to improve the accuracy of the identification results. The SE attention module is introduced innovatively, which optimizes the feature fusion process, enhances the network's spatial correlation capture ability, and enables the system to use global information more effectively, improving the detection accuracy of camouflaged targets. The application solves the problem of poor reliability in traditional methods for detecting camouflaged targets, improves the real-time performance and effectiveness of the cooperative operation of UAVs and UGVs, and significantly improves the accuracy and response speed of camouflaged target identification in battlefield environments. It has important theoretical value and application prospect in various military crisis response.

Provided is an antenna integrated structure in which an antenna is inserted into ground vehicle, marine ship and aircraft structure, and more particularly, a sandwich panel type antenna integrated latticecore having a structure for improving communication performance of an antenna module.

The present invention discloses an airborne lightning detection system, which belongs to the technical fields of lightning early warning for aircraft and lightning monitoring and early warning for high-speed ground vehicles. It addresses the problem that existing technologies cannot achieve high-precision single-station measurements, making them unsuitable for use in mobile devices. The system includes vertical and horizontal antennas for acquiring vertical and horizontal electric field signals from a space lightning radiation source; an integration circuit for converting the electric field signals into voltages; a high-pass amplifier circuit for filtering out clutter from the integrated voltage signal, performing waveform identification and matching, and amplifying the radiation signal; a sampling analog-to-digital conversion circuit for sampling the signal output by the high-pass amplifier circuit and converting the analog signal into a digital signal; a radio positioning antenna for acquiring lightning direction information from the lightning radiation source; and an embedded Linux system that combines the digital signal and lightning direction information to calculate the distance from the lightning radiation source. The system is used to calculate the distance between the radiation source and an observation point.

The application discloses a method for predicting the number of air unmanned aerial vehicles (UAVs) based on ground vehicle trajectory data, and belongs to the technical field of UAV control. In order to solve the problem of predicting the number of air unmanned aerial vehicles based on ground traffic state, the application comprises the following steps: constructing a vehicle trajectory set; constructing a traffic density thermal field; constructing an average speed trend vector field; constructing a direction consistency index; taking the traffic density thermal field as a basic load, the modulus of the average speed trend vector field representing fluidity, and the direction consistency index representing traffic order, and performing weighted combination to construct a demand intensity of air resources converted from a distribution pressure intensity; considering a spatial vector from a vehicle position to a nearest UAV take-off point and a terrain complexity factor, and constructing a preliminary UAV demand estimation field; introducing a task urgency weighting function, a current task backlog and a weather flight risk to construct a final UAV deployment suggestion function, and completing the prediction of the number of air unmanned aerial vehicles based on ground vehicle trajectory data.

An automated cruise control system is disclosed. Sensors detect driving parameters associated with the ground vehicle as the ground vehicle maneuvers along a segment of the roadway. A ground vehicle control detector detects ground vehicle control inputs associated with an operation of the ground vehicle. The ground vehicle control inputs are generated from a longitudinal operation of the ground vehicle. A energy consumption cruise controller automatically adjusts the operation of the ground vehicle as the ground vehicle maneuvers along the segment of the roadway to maintain the operation of the ground vehicle within an operation threshold based on the detected driving parameters and ground vehicle control inputs. The operation threshold is the operation of the ground vehicle that decreases an amount of overall energy consumed by the ground vehicle and maintains a longitudinal speed of the ground vehicle within a longitudinal speed threshold.

The invention provides a space-air-ground integrated Internet of Vehicles resource allocation method, device and equipment, and belongs to the field of resource allocation, and the method comprises the steps: constructing an Internet of Vehicles system model comprising satellites, unmanned aerial vehicles and vehicles; the unmanned aerial vehicle broadcasts resource condition inquiry messages regularly and collects resource conditions in the coverage range of the unmanned aerial vehicle to form a resource aggregation directory; after receiving the resource request message, the unmanned aerial vehicle searches a corresponding resource in a resource aggregation directory according to the request type, executes a preset resource allocation algorithm under the condition that the corresponding resource exists, generates a task request, and sends the task request to the service vehicle or the adjacent unmanned aerial vehicle; returning a resource mismatching message under the condition that the corresponding resource does not exist; and after receiving the result feedback of the service vehicle or the unmanned aerial vehicle, the unmanned aerial vehicle returns the result to the vehicle sending the resource request message. Therefore, limited resources can be utilized more effectively, and the utilization rate of the resources is improved under the condition that the resources are rare or the communication is real.

The invention belongs to the technical field of automobiles, and particularly relates to a ground vehicle low-calculation-power path tracking control method considering a coupling gradient. The method comprises the following steps: firstly, establishing a vehicle dynamics model and a kinematics model considering the coupling gradient through coordinate transformation and dynamics analysis, and ensuring the path tracking precision of an unmanned ground vehicle on the coupling gradient; secondly, an unmanned ground vehicle path tracking system considering actuator time lag is constructed, and state space equation discretization is carried out, so that the system can be used for model prediction control; and finally, a path tracking control method is constructed, Laguerre function fitting is utilized to replace a link of solving an optimal control sequence through quadratic programming in traditional model prediction control, and the computing power demand of the control method is remarkably reduced. Through simulation verification, the provided control method can effectively reduce the computing power of the control method on the premise of ensuring the path tracking accuracy of the unmanned ground vehicle.

Wheels are arranged on the lower portion of a ground carrier in a bilateral symmetry mode, a power system and a communication and automatic driving control system are arranged in a vehicle head on the front portion of the ground carrier, and a connecting clamping groove is formed in a floor of the ground carrier. Electromagnetic spring buckles are arranged on the lower portion of the flight cabin and correspond to the four connecting clamping grooves formed in the ground carrier floor in position. Wings with upward inverted inner sections and straight outer sections are respectively arranged on the left and right sides of the upper part of the flight cabin; the inner section and the outer section of each wing are averagely divided into two sections; when the hydraulic mechanism is opened and closed, the inner section and the outer section can be folded or unfolded around the hinge connecting mechanism, and after folding, the spring positioning pins are inserted into the positioning holes. Oblique empennages are arranged on the left side and the right side of the tail portion of the upper portion of the flight cabin respectively, and deflection rudders are arranged on the rear edges of the outer ends of the oblique empennages. The hovercar can run on the ground, can take off and land vertically, can hover in the air or fly horizontally and can sail underwater; the safety and reliability are high, and the use efficiency is high.

The application discloses a kind of vehicle-mounted mechanical arm auxiliary aircraft take-off system, belong to aviation equipment technical field.System includes aircraft, vehicle-mounted mechanical arm device, vehicle-mounted power battery pack and carrying truck;The vehicle-mounted mechanical arm device is fixedly installed on truck frame, and the end of mechanical arm can be separated with the bottom of aircraft It realizes detachable docking, relies on vehicle-mounted power battery pack to provide power drive mechanical arm to stretch and lift up.In the aircraft take-off stage, mechanical arm exerts upward and forward boost force to aircraft, and the maximum energy consumption of vertical take-off is transferred to ground vehicle system in the flat flight stage, which greatly reduces the battery consumption of aircraft itself.The system is suitable for various types of vertical take-off and landing aircraft, especially for logistics distribution unmanned aerial vehicle, manned aircraft and other scenarios, effectively prolongs the aircraft range, improves the effective payload, the overall structure is flexible and convenient to deploy, with high engineering application value.

Considering that an increase in the future is expected in applications related to communication and the amount of communication in railway ground-vehicle communication, in order to solve the problem of a high cost for providing a communication facility for each application, in a railway communication system for transmitting data of a plurality of applications between a ground facility and an on-vehicle facility using the same wireless network, an on-vehicle wireless control unit included in the on-vehicle facility has a function of monitoring a communication environment, a function of setting a transmission priority of each of the plurality of applications, and a function of performing packet control of the data based on the communication environment and the transmission priority.