Abstract: An embodiment apparatus for predicting traffic flow on a new road includes a memory, an input device, and a controller configured to input traffic data corresponding to a default context of the new road received by the input device to a prediction model stored in the memory, training of which is completed, and to predict a traffic flow corresponding to various contexts of the new road based on the prediction model.

Abstract: In a traffic speed prediction apparatus and a method therefor, the traffic speed prediction apparatus includes: a processor configured for estimating a path navigation demand based on current path navigation data for each of road sections, and to predict a future traffic speed using the path navigation demand and past speed data from a current time point for each of the road sections; and a storage configured to store algorithms and data driven by the processor.

Abstract: An apparatus for estimating a traffic volume based on demand for route search, includes a processor and a storage medium configured to record one or more programs configured to be executable by the processor. The processor is configured to collect a plurality of pieces of route search data, generate route search demand data based on the collected plurality of pieces of route search data, correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume, and estimate an actual traffic volume for each road by applying the corrected route search demand data to a pre-trained learning model.

Abstract: An apparatus for forecasting a traffic flow of a road includes storage that stores traffic information by date for a preset time period, and a controller that divides dates into a specified number of classes, determines a target class corresponding to a target date among the classes, and forecasts a traffic flow of the target date based on the traffic information by date included in the target class.

Abstract: A traffic speed prediction apparatus may include: a communication device configured to receive probe data from a probe vehicle driving on a road; a storage configured to store data and algorithms for predicting a traffic speed; and at least one processor electrically connected to the communication device and the storage. The processor may be configured to predict the traffic speed on the road using traffic speed data, traffic volume data, and congestion data obtained based on probe data, and to obtain the congestion data by creating a target road network including a target collection section where the probe data is collected and determining a congestion matrix of the target road network.

Abstract: Disclosed herein is a shell simulated shooting simulation system for performing a shell simulated shooting similar to a live shell shooting includes an execution module loading and aiming a shell to perform a shell simulated shooting, a display module visually displaying shooting of a shell according to loading and aiming through the execution module, and a server module collecting information to visually display a result of the live shell shooting on the display module based on the information on the loading and aiming of the shell of the execution module.

Abstract: In a simulator for emergency treatment training, a chest compression training module measures chest compression training information, an artificial respiration training module measures respiration training information, an automated external defibrillator training module measures pad attachment training information, and a compression training module measures hemostatic compression training information. A control unit is installed in a trunk of a manikin and obtains pieces of training information from the chest compression training module, the artificial respiration training module, the automated external defibrillator training module, and the hemostatic compression training module. A monitoring apparatus is connected to the control unit in a wired or wireless manner, receives the pieces of the training information from the control unit, executes an evaluation program, generates an evaluation result, and displays the generated evaluation result on a screen.

Abstract: A vehicle speed prediction apparatus includes a data preprocessor that performs preprocessing of correct answer data and input data for deep learning, and a deep learning modeling device that is configured to learn a relationship of a lane speed for each exit lane, based on a representative speed per link, the number of collected probes, and a collection time.

Abstract: Disclosed are an apparatus and method for generating a link for each lane when there is a plurality of exit lanes. The apparatus may acquire information about a speed of a probe vehicle from the probe vehicle, and determine a section for generating a divided link along a lane, based on a speed difference of the probe vehicle in an exit direction. Accordingly, it is possible to improve the accuracy and reliability of the speed of each lane.

Abstract: An embodiment apparatus for predicting traffic flow on a new road includes a memory, an input device, and a controller configured to input traffic data corresponding to a default context of the new road received by the input device to a prediction model stored in the memory, training of which is completed, and to predict a traffic flow corresponding to various contexts of the new road based on the prediction model.

Abstract: An apparatus for predicting a traffic speed and a method thereof are provided. The apparatus includes an input device that receives traffic speed sequences of a plurality of links and a controller that detects a spatio-temporal relationship between traffic speeds of the plurality of links and predicts a future traffic speed of a target link based on the spatio-temporal relationship between the traffic speeds of the plurality of links.

Abstract: In an embodiment a traffic speed prediction device includes at least one processor is configured to determine a second section connected with a first section, wherein the second section is a target road section, and wherein the first section includes a road section in front of the second section, to output first output data using traffic speed data during a first time, the traffic speed data being obtained based on probe data collected in the first section and the second section, to output second output data using traffic volume data during a second time, the traffic volume data being obtained based on the probe data collected in the first section and the second section, and to predict a traffic speed of the road including the second section using the first output data and the second output data.

Abstract: An apparatus of predicting the congestion time point may include a first deep learning device that outputs first output data using traffic speed data during a first time, a second deep learning device that outputs second output data using traffic volume data during a second time, and a congestion time point prediction model that predicts the congestion time point using at least a portion of the first output data and the second output data.

Abstract: An apparatus and a method for predicting a traffic speed are disclosed. The apparatus includes a controller that learns a deep auto-encoder to output a past traffic speed and a future traffic speed by inputting the past traffic speed, and predicts the future traffic speed based on the deep auto-encoder which completes the learning, and storage that stores the deep auto-encoder which completes the learning.

Abstract: In a simulator for emergency treatment training, a chest compression training module measures chest compression training information. An artificial, an artificial respiration training module measures respiration training information, an automated external defibrillator training module measures pad attachment training information, and a compression training module measures hemostatic compression training information. A control unit is installed in a trunk of a manikin and obtains pieces of training information from the chest compression training module, the artificial respiration training module, the automated external defibrillator training module, and the hemostatic compression training module. A monitoring apparatus is connected to the control unit in a wired or wireless manner, receives the pieces of the training information from the control unit, executes an evaluation program, generates an evaluation result, and displays the generated evaluation result on a screen.

Abstract: An apparatus and a method for providing traffic information are provided. The apparatus includes a traffic data database (DB) that stores traffic data and a processor connected to the traffic data DB. The processor generates a prediction model to predict a traffic flow and determines an appropriate data input range for a target time point in the future using the prediction model. Additionally, the processor extracts past traffic data from the traffic data DB based on the determined appropriate data input range, predicts a traffic flow at the target time point based on the extracted past traffic data and provides the predicted traffic flow as traffic information.

Abstract: Disclosed are an apparatus and method for generating a link for each lane when there is a plurality of exit lanes. The apparatus may acquire information about a speed of a probe vehicle from the probe vehicle, and determine a section for generating a divided link along a lane, based on a speed difference of the probe vehicle in an exit direction. Accordingly, it is possible to improve the accuracy and reliability of the speed of each lane.

Abstract: Provided is a sensing axis variable wheel speed sensor apparatus for an autonomous vehicle, and more particularly, a sensing axis variable wheel speed sensor apparatus for an autonomous vehicle, which includes a plurality of sensors and in which the position of a sensing axis is selectively varied.

Abstract: A device and a method for predicting traffic information are provided to improve a traffic information prediction accuracy. The device includes a storage that stores a plurality of probe data generation models based on characteristic of a road and a communication device that receives probe data from a probe vehicle traveling on a target road. A controller detects a probe data generation model corresponding to a characteristic of the target road among the plurality of probe data generation models, generates a preset number of probe data based on the detected probe data generation model, and predicts traffic information of the target road based on the generated probe data and the received probe data.

Abstract: A vehicle speed prediction apparatus includes a data preprocessor that performs preprocessing of correct answer data and input data for deep learning, and a deep learning modeling device that is configured to learn a relationship of a lane speed for each exit lane, based on a representative speed per link, the number of collected probes, and a collection time.