10 results about "Traffic simulator" patented technology

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.

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.