Abstract: A traffic congestion determination device includes an acquisition unit configured to acquire a total number of automobiles for each mesh obtained by virtually dividing a determination target region of traffic congestion and for each unit time, and a determination unit configured to determine whether occurrence of traffic congestion is sudden for each of the meshes based on the acquired total number of automobiles for each of the meshes and unit time.

Abstract: The traffic flow estimation apparatus acquires images and position information and speed information, the images being captured at different timings by a first moving object that is in motion in a target area and including a second moving object around the first moving object, the position information and speed information being of the first moving object at the timings; estimates a lane in which the second moving object is in motion; and estimates a traffic flow for each lane in the target area based on the speed information of the first moving object, the information indicative of a change over time of positions of the second moving object, and the lanes in which the first moving object and the second moving object are in motion.

Abstract: Provided is a highly reliable technology for optimizing an action for controlling an environment in a target space.

Abstract: An estimation method includes acquiring sensor information acquired by a sensor mounted on a first vehicle belonging to a vehicle line, acquiring position information on a location of the first vehicle, and estimating a cause of the vehicle line by using the sensor information and the position information. The estimating of the cause includes estimating the cause by using information on a facility or a feature present near the first vehicle, information indicating a state of the first vehicle, information on an object present ahead of the first vehicle included in the sensor information, or information on a road that affects traveling. The information on the facility or the feature is searched based on the position information, the information on the object is estimated based on the sensor information, and the information on the road is searched based on the position information.

Abstract: A position estimation unit 25 estimates, for each movement in a predetermined section, a plurality of position candidates of a position estimation target on the basis of position information and sensor information. A position determination unit 26 evaluates, for each movement in the predetermined section, on the basis of the plurality of position candidates of the position estimation target obtained by the movement in the predetermined section, whether the position information used to estimate the position candidates has an error caused by any of a position of a GPS satellite, a position of a mobile body in a real space, or the mobile body or does not have any error caused thereby.

Abstract: A state estimation method executed by a state estimation apparatus including a processor and a memory storing program instructions that cause the processor to estimate a state related to congestion on a subject lane, includes acquiring a state related to congestion of a monitoring vehicle traveling on a non-subject lane, counting a number of vehicles traveling on the subject lane, the vehicles being overtaken by the monitoring vehicle or being oncoming vehicles passed by the monitoring vehicle, and estimating the state related to the congestion on the subject lane, using the state related to the congestion of the monitoring vehicle and the number of vehicles. In the estimating, when the number of the vehicles is equal to or greater than a threshold value according to the state related to the congestion of the monitoring vehicle, the state related to the congestion on the subject lane is estimated as traffic congestion.

Abstract: A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.

Abstract: A vehicle condition estimation method performed by an information processing device including a processor and a memory connected to the processor to estimate a position or a condition of a vehicle, includes: acquiring an image including a vehicle to be estimated; and estimating the position or the condition of the vehicle to be estimated, using a line segment connecting at least two points selected from a region in which the vehicle to be estimated is captured in the image, with reference to an imaging device that has captured the image.

Abstract: A people flow prediction device includes a training data selection unit configured to select, in accordance with a prediction condition including a target prediction period subject to prediction, training data related to people flow data of a plurality of dates and times corresponding to the target prediction period, a prediction model creation unit configured to train, in accordance with the training data being selected, and store, in a model storage unit, a prediction model having a predetermined feature and for predicting people flow data of a predetermined date and time, and a prediction unit configured to select the prediction model from the model storage unit in accordance with the prediction condition and a permission condition related to a feature of the prediction model, and to predict people flow data under the prediction condition in accordance with the prediction model being selected.

Abstract: Sufficient data extension can be achieved even if the starting date/time and ending date/time of the learning execution data have been uniquely defined. Based on entire learning data that is a set of time-series data, wherein the entire learning data is a set of minimum constitution unit data, each of which is time-series data having a first time interval that is a time interval required for learning, wherein the time-series data having the first time interval is each assigned a first label that indicates a feature in a time series of the first interval; a generation unit (103) generates learning execution data that is a set of time-series data to be used in learning, by combining the minimum constitution unit data included in the entire learning data such that regularity of the first label in a time series of the entire learning data is maintained.

Abstract: The invention provides a waterless offset lithographic printing plate precursor that is mountable on a magnet type printing cylinder and has high durability and halftone dot reproducibility. The waterless offset lithographic printing plate precursor contains at least a heat-sensitive layer and a silicone rubber layer disposed on a substrate, the substrate being of a ferromagnet material.

Abstract: Provided is a learning support device capable of providing highly reliable parameter estimation. A learning support device for supporting user learning acquires correct/incorrect answer data indicating whether each of answers from a plurality of users to a plurality of items to be learned is correct or incorrect, and calculates, based on the acquired correct/incorrect answer data, a proficiency level indicating an ability level of each user, a difficulty level indicating a difficulty of each item, and reliabilities indicating reliability levels of the proficiency level and the difficulty level.

Abstract: The traffic flow estimation apparatus acquires images and position information and speed information, the images being captured at different timings by a first moving object that is in motion in a target area and including a second moving object around the first moving object, the position information and speed information being of the first moving object at the timings; estimates a lane in which the second moving object is in motion; and estimates a traffic flow for each lane in the target area based on the speed information of the first moving object, the information indicative of a change over time of positions of the second moving object, and the lanes in which the first moving object and the second moving object are in motion.

Abstract: A driving assistance device according to an embodiment includes a hardware processor and a memory, in which the hardware processor is configured to execute: acquiring image data captured in a vehicle and sensor data relating to a driving operation; converting an image in each frame included in the image data into an image feature amount indicated by the image; combining the image feature amount and the acquired sensor data; performing, based on the combined data, a learning process for a driving assistance information learning model used when a target value relating to the driving operation by a driver of the vehicle is generated from the image feature amount included in the combined data; and generating, when new image data captured in the running vehicle is acquired, the target value relating to the driving operation based on the image feature amount converted from the acquired new image data and the driving assistance information learning model.

Abstract: A technique of estimating, based on a camera image captured from a mobile object, the position coordinates of an ambient object or the mobile object itself is offered. A plurality of camera images that are captured at different geographical locations and contain at least one identical stationary object are acquired; position coordinates of the imaging locations of the corresponding camera images are acquired; and the position coordinates of the stationary object are estimated based on the position coordinates of the imaging locations and gaze vectors oriented from a camera to the stationary object in each of the camera images.

Abstract: A vehicle control apparatus includes a first ECU electrically connected to a wheel speed sensor and a second ECU electrically connected to the wheel speed sensor.

Abstract: A lane pricing technique for charging a mobile body traveling on a road for use of a lane is provided. A lane pricing device according to one embodiment is configured to: acquire from a mobile body traveling on a road, at least one of position information of a mobile body measured based on a positioning signal, and/or a camera image that is acquired by capturing an image from the mobile body and includes a part of a lane in which the mobile body is moving; specify, based on at least one of the position information and/or the camera image, a lane in which the mobile body is moving; acquire lane identification information for identifying a specified lane along with time information indicative of time when the mobile body is moving; and determine and output a toll to be charged to the mobile body in units of lanes based on the lane identification information and the time information.

Abstract: Provided is a highly reliable technology for optimizing an action for controlling an environment in a target space.

Abstract: A learning schedule of a user is generated on the basis of information indicating a learning history of the user. A question answer data acquisition section (111) acquires the information.

Abstract: A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.