15 results about "Traffic simulator" patented technology

The invention provides a distributed multi-agent traffic signal cooperative control method, and belongs to the field of intelligent traffic control. The method comprises the steps of performing initialization operation on a road network environment, constructing an adjacency list according to a topological structure of an urban road network, and regarding each traffic signal lamp control intersection in the urban road network as an independent intelligent agent; for each intelligent agent, acquiring a local observation state of the intersection where the intelligent agent is located; performing action selection through sampling according to the action probability distribution; sending the selected action to the traffic simulator by using the intelligent agent, updating the state of the traffic signal lamp, and calculating and returning the intelligent agent information of the next time step by using the traffic simulator; sending the action probability distribution to all neighbor agents; and parameter updating is carried out until a preset training round is reached, and cooperative control of the traffic signals is completed. By adopting the distributed multi-agent reinforcement learning framework, the exploration efficiency is improved, and the collaboration among the agents is promoted.

The invention discloses a novel Internet of Vehicles congestion simulation method oriented to a 5G V2X Uu interface, a semi-physical simulation test platform oriented to the 5G V2X Uu interface is included, the test platform architecture comprises a traffic simulator, data packet software, a receiving end OBU, a sending end OBU and a 5G comprehensive tester, and the problem that the semi-physical simulation is limited by the number of terminals and is difficult to simulate a congestion scene is solved. By limiting available resources of the network, the requirement of semi-physical simulation on the number of terminals is reduced, and meanwhile, the increase of the number of vehicles in the network is simulated by increasing the network traffic load. The adjustment of the traffic load is realized by setting a data packet sending period and the size of a data packet. And selecting a required channel model to calculate fading according to a traffic simulation scene and vehicle time-position information, and then adding the fading into a corresponding data packet. And for a V2N2V link, respectively adding uplink fading and downlink fading for a data packet according to different environments and positions of receiving and transmitting end vehicles.

The invention discloses an Internet of Vehicles trust management method based on an improved LSTM neural network, and the method comprises the steps: carrying out urban traffic simulation through employing a traffic simulator SUMO and a discrete event simulation platform OMNet + +, collecting message data transmitted between vehicles in a simulation process to construct a data set, and dividing the data set into a training set and a test set; training and testing the improved LSTM neural network by using the training set and the test set, and deploying the neural network with the optimal performance to a vehicle server; and using the improved LSTM neural network to carry out authenticity judgment on the received message, and updating the trust value of the vehicle so as to realize trust management in the Internet of Vehicles environment. Based on a deep learning technology, the method can be effectively applied to credibility evaluation of vehicles in an Internet of Vehicles environment and establishment of long-term trust management, so that detection of malicious or untrusted vehicles in a global range is realized, and the method has a wide application prospect.

The invention belongs to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle path planning method based on offline reinforcement learning, and the method comprises the steps: building a communication time delay model and an unmanned aerial vehicle energy consumption model of an unmanned aerial vehicle and a vehicle, carrying out the simulation of a traffic flow through employing an SUMO traffic simulator, recording the state data of the unmanned aerial vehicle and the vehicle, and carrying out the calculation of the unmanned aerial vehicle path. Generating an off-line training data set for off-line reinforcement learning training, designing an unmanned aerial vehicle state space, an action space and a reward function for deep reinforcement learning according to the off-line training data set, training an off-line reinforcement learning model, and combining with the initial position of the unmanned aerial vehicle to obtain an unmanned aerial vehicle state space, an action space and a reward function; and outputting the flight path of the unmanned aerial vehicle based on the trained offline reinforcement learning model. The method can effectively reduce the communication time delay while reducing the energy consumption of the unmanned aerial vehicle, thereby remarkably improving the task completion rate of the unmanned aerial vehicle in a complex dynamic environment.

The invention discloses a high-adaptability road network level traffic signal control method capable of complementing missing observation in real time, and the method comprises the following steps: selecting a real road network to be subjected to traffic signal control, and taking the real road network as a subsequent training and testing scene; constructing a traffic scene as a training environment of a simulation stage; designing a DRL signal controller by using an MAT encoder-decoder architecture, and training the DRL signal controller in the SUMO traffic simulator to obtain an MAT controller; building a real-time observation completion module based on a feature propagation structure FP; and combining the trained MAT controller and the constructed real-time observation completion module FP to form an FP-MAT combined framework, and putting the FP-MAT combined framework into a corresponding road network to execute a signal control task. According to the method, missing observation in a global state can be automatically identified, real-time and accurate complementation can be carried out, normal work of the signal controller is ensured, and the method has environmental adaptability and high efficiency.

To provide traffic simulator adjustment.SOLUTION: The method can include determining a number of vehicles depicted in one or more images of at least a portion of a transportation network. The method may include determining, from the one or more images, respective lengths of segments in the transportation network that correspond to individual ones of the vehicles depicted in the one or more images. The method may also include determining an observed traffic density and an observed network flow corresponding to the transportation network based on the number of vehicles and the respective lengths of the segments. The method may include determining, using a traffic simulator, a traffic density estimate and a network flow estimate. The method may further include adjusting the traffic simulator based on the observed traffic density, the traffic density estimate, the observed network flow, and the network flow estimate.SELECTED DRAWING: Figure 1A

A data control device (1) successively executes logic based on action logic that describes action of an object. When the fact that the object has moved to a predetermined position is notified from a traffic simulator (3A) in accordance with execution of specific logic, the data control device (1) determines to which of a first shop simulator (3B) and a second shop simulator (3B) data of the object is to be transmitted using an attribute of the object stored in advance. The data control device (1) transmits the data of the object to the shop simulator (3B) to which the data of the object is determined to be transmitted. Such processing of the data control device (1) can be applied to social simulations used in a local government or a transportation infrastructure, for example.

A method may include determining a number of vehicles depicted in one or more images of at least a portion of a transportation network. The method may include determining, from the one or more images, respective lengths of segments in the transportation network corresponding to individual vehicles of the vehicles depicted in the one or more images. The method may also include determining an observed traffic density and an observed network flow corresponding to the transportation network based on the number of vehicles and the respective lengths of segments. The method may include determining a traffic density estimation and a network flow estimation, using a traffic simulator. The method may additionally include adjusting the traffic simulator based on the observed traffic density, the traffic density estimation, the observed network flow, and the network flow estimation.

This invention relates to an autonomous driving testing system in a strongly game-theoretic interactive environment, comprising: a traffic simulator for simulating a traffic system; an interactive game-theoretic decision-maker that takes road traffic information output by the traffic simulator as input, embeds a game-theoretic decision-making algorithm based on logical rules, classifies vehicle operation modes into five categories: cruising, lane changing, returning to the lane, traffic lights, and conflict zones, constructs a decision-making mechanism for each operation mode, determines the operation mode for the next simulation step, and outputs the corresponding vehicle state; an interactive game-theoretic controller that outputs control quantities for controlling the lateral and longitudinal behavior of the autonomous vehicle based on the vehicle state output by the interactive game-theoretic decision-maker; and a vehicle dynamics model that simulates the real response of the vehicle's mechanical system to the control quantities. Compared with existing technologies, this invention has the advantages of clearly depicting vehicle decision-making behavior, meeting real-time simulation requirements, and providing high reliability in simulation testing.

This invention discloses a novel vehicle-to-everything (V2X) congestion simulation method for the 5G V2X Uu interface. It includes a hardware-in-the-loop (HIL) simulation test platform for the 5G V2X Uu interface. The platform architecture comprises a traffic simulator, data packet software, a receiving OBU, a transmitting OBU, and a 5G comprehensive test instrument. This invention overcomes the problem of HIL being limited by the number of terminals, making it difficult to simulate congestion scenarios. By limiting available network resources, the requirement for the number of terminals in HIL is reduced, while increasing network traffic load simulates an increase in the number of vehicles in the network. Adjustments to the traffic load are achieved by setting the data packet transmission period and data packet size. The required channel model is selected based on the traffic simulation scenario and vehicle time-location information to calculate fading, which is then added to the corresponding data packets. For V2N2V links, uplink and downlink fading are added to the data packets according to the different environments and locations of the transmitting and receiving vehicles.

This application discloses a method, system, device, and storage medium for determining data transmission performance, relating to the field of communication technology, and used to improve the accuracy and efficiency of determining the transmission performance of terminal devices. The method includes: constructing a virtual traffic scenario based on a traffic simulator, comprising multiple first terminal devices and at least one second terminal device; obtaining multiple target data packets transmitted between the multiple first terminal devices and at least one second terminal device from the traffic simulator; calculating the signal fading corresponding to each first terminal device based on the information included in the multiple target data packets using a preset target channel model, and adding the signal fading corresponding to each first terminal device to the corresponding target data packet; sending the multiple target data packets from the first real terminal devices corresponding to the multiple first terminal devices to the second real terminal devices corresponding to at least one second terminal device; and obtaining the transmission performance corresponding to each first terminal device from the second real terminal device.

The invention provides a non-signalized intersection vehicle cooperative hierarchical control architecture based on ROS communication, and the architecture comprises the following steps: high-level control: employing a 5G communication technology, and after a high-level server layer receives the state information of all road side units (RSU) in an area, dynamically adjusting the priority of RSU lanes in the area based on dynamic density-waiting time weighted road right distribution; middle-layer control: the RSU establishes an uplink communication link with a high-layer server layer through a 5G network, and senses the state of a vehicle in an area in real time based on a V2X direct communication network; based on dynamic priority calculation realized by a high-level server layer, the middle layer generates a safe passing time slot in combination with multi-dimensional parameters so as to avoid conflicts; and low-layer control: the vehicle, namely the low layer, adopts V2X to realize communication with a road side unit (RSU), generates a passing track time curve based on the safety gap of the middle layer, and uses a sliding mode to control and track. According to the invention, the three-layer architecture is creatively and deeply integrated with the SUMO traffic simulator and the ROS2 operating system.

This data processing method comprises: a step in which conditions including a road section which includes a series of a plurality of intersections, and including a traveling direction of a vehicle in the road section and a time slot, and simulation results from a traffic simulator conforming to a designated signal control parameter related to the plurality of intersections are acquired; a step in which the simulation results are output; a step in which, after an adjustment of the signal control parameter, the simulation results are updated using the adjustment, and the updated simulation results are used to evaluate the adjustment for a change in situation with respect to the conditions; and a step in which data related to the results of the evaluation are output.

A wide-area traffic credible charging supporting method based on a pressure test comprises the steps of generating mass trajectory data covering a target area through a traffic simulator, executing a credible navigation map matching algorithm on the trajectory data, identifying map matching easy-to-fail road sections based on spatial error and morphological trajectory consistency, and outputting a thermodynamic diagram. A road side unit deployment effect is simulated according to a failure scene result, a lane-level charging verification system is constructed, space-time and vehicle type charging coefficient dynamic combination is supported, a function closed loop of pressure test, failure detection, road side unit correction and credible charging is realized, a full-process simulation support system is provided for credible charging, the verification cost is reduced, the verification period is shortened, and the credible charging efficiency is improved. And a basis is provided for scientific deployment of vehicle-road cooperation facilities, and a technical support is provided for policy making and assessment.

The present invention provides a distributed multi-agent traffic signal cooperative control method, belonging to the field of intelligent traffic control. The method includes performing an initialization operation on the road network environment, constructing an adjacency list according to the topological structure of the urban road network, and treating each traffic signal control intersection in the urban road network as an independent agent; for each agent, obtaining the local observation state of the intersection where it is located; making an action selection through sampling according to the action probability distribution; using the agent to send the selected action to the traffic simulator and updating the state of the traffic signal, using the traffic simulator to calculate and return the agent information at the next time step; sending the action probability distribution to all its neighbor agents; performing parameter update until a preset number of training rounds is reached, and completing the cooperative control of the traffic signal. The present invention adopts a distributed multi-agent reinforcement learning framework to improve the exploration efficiency and promote the cooperative cooperation among agents.