Toyota Jidosha Kabushiki Kaisha

Other

Nagoya University

Pacific Industrial Company, Ltd.

Kurusugawa Computer Inc.

Yusuke Nakano

Eric Martinson

Emrah Akin Sisbot

Kentaro Oguchi

David Kim

Yutaka Takaoka

Rui Guo

Yoshio Kudo

Toru Takashima

Hiroshi Murase

Takeshi Kitahara

Daisaku Honda

Rio Suda

Masayuki Itoh

Norimasa Kobori

Veeraganesh Yalla

Tetsuya Morino

Suguru Watanabe

Manabu Nagai

Aya Hamajima

Akansel Cosgun

Daisuke Deguchi

Koutarou Ichikawa

Toshiya Furukura

Kengo Kurosawa

Halit Bener Suay

Satoshi Komorita

Yasutomo Kawanishi

Youhei Yamaguchi

Nik Mohd Zarifie Bin Hashim

Katsunori Nakanishi

Jose Capriles Grane

Joseph Djugash

Yukio Ishizaka

Takanari Hamada

A model generation method includes specifying image coordinates that fall within a two-dimensional image obtained by capturing at least a mobile object and that correspond to at least one point among vertexes of a rectangular shape formed when an outer shape of the mobile object viewed from above is projected on a road, as a key point of the mobile object, and creating the two-dimensional image, to which information on the key point is added, as training data, and generating a machine learning model that outputs the key point from a two-dimensional image obtained by capturing at least a mobile object, by performing machine learning using the training data.

Model generation method, model generation apparatus, non-transitory storage medium, mobile object posture estimation method, and mobile object posture estimation apparatus

An estimation result determination unit determines, for each of the objects, a correct estimation result or one of a plurality of false estimation types, which indicate types of false estimation results using ground truth data that corresponds to a video image and output data indicating the result of the estimation made on the video image by the algorithm. The evaluation value calculation unit adds false estimation coefficients that correspond to the plurality of respective false estimation types and are provided so as to become higher in accordance with a degree of impact of the false estimation type for a number of objects that correspond to the false estimation type and thus calculates an evaluation value of the algorithm based on the total value of the added values of the false estimation coefficients obtained for each of the plurality of false estimation types.

Evaluation system, evaluation method, and program

A key point correction apparatus detects a plurality of key points of a target object from each of a plurality of captured images. The key point correction apparatus decides whether or not a plurality of key points of the target object detected from the captured image satisfy an appropriacy condition. When the key points of the target object satisfy the appropriacy condition, the key point correction apparatus determines a correction parameter regarding the target object using the key points. When a correction parameter has been determined for the target object detected from the captured image, the key point correction apparatus corrects the key points of the target object using a correction parameter.

Key point correction system, key point correction method, and nontransitory computer readable medium

An image processing system includes an image acquisition unit that acquires a plurality of images including a moving object image, an image capturing direction data calculation unit that calculates image capturing direction data indicating an image capturing direction in which an imaging device captures an image of a moving object at a time when the images is captured, a feature amount calculation unit that calculates a feature amount of the moving object image extracted from the images, and an associating unit that associates the moving objects in the images with each other based on the image capturing direction data and the feature amount.

Image processing system, image processing method, and storage medium for associating moving objects in images

A calibration system includes: a storage unit that stores a reference trajectory of a mobile body in an image of a predetermined traffic environment photographed by an imaging sensor and a reference position in the image; an acquisition unit that acquires a plurality of the images of the traffic environment that are sequentially photographed; a generation unit that generates an estimated trajectory of the mobile body based on position information of the mobile body detected from the plurality of the images; a calculation unit that calculates an amount of positional deviation of the imaging sensor based on the reference trajectory and the estimated trajectory; a correction unit that corrects the reference position by using the amount of positional deviation; and an update unit that updates a position transformation model for transforming a two-dimensional position in the image into a three-dimensional position by using the corrected reference position.

Calibration system, calibration method, and calibration program

A vehicle running control system includes: a steering apparatus that includes a turning device being mechanically separated from a steering wheel; a vehicle drive unit; and a control device. The control device is configured to execute: a turning processing that controls the turning device such that an actual turning angle of the wheel approaches a target turning angle; a vehicle driving processing that determines a target vehicle driving force according to a required vehicle driving force based on a vehicle driving request and that controls the vehicle drive unit so as to cause an actual vehicle driving force to the target vehicle driving force determined; and where a turning angle difference is greater than a turning angle threshold value in a low vehicle speed condition, a driving force limiting processing that limits the target vehicle driving force so as to become smaller than the required vehicle driving force.

Vehicle running control system

A steering system is configured such that a ratio of a turning angle change amount with respect to a steering angle change amount in a first steering angle area is larger than the ratio of the turning angle change amount with respect to the steering angle change amount in a second steering angle area, and a steering angle in the second steering angle is smaller than the steering angle of the first steering angle area. An electronic control unit of the steering system sets a delay degree of a change in turning angle with respect to a change in steering angle in the first steering angle area to be larger than the delay degree of the change in turning angle with respect to the change in steering angle in the second steering angle area regardless of a turning-over operation or a turning-back operation.

Steering system and control method for steering system

A merging support information (MSI) distribution apparatus includes a MSI reception unit configured to receive MSI including a speed recommended for a vehicle traveling on a branch line to merge with a main line from a merging prediction apparatus, a distribution destination information reception unit configured to receive distribution destination registration information including terminal movement information indicating a movement state of a mobile terminal mounted on the vehicle traveling on the branch line and terminal identification information identifying the mobile terminal from the mobile terminal via a mobile communication network, and an information distribution unit configured to specify the mobile terminal to be a distribution destination to which the MSI is distributed based on the branch line traveling information and the terminal movement information, and distribute the MSI to the specified mobile terminal via the mobile communication network based on the terminal identification information of the specified mobile terminal.

Merging support information distribution apparatus, merging support system, merging support information distribution method, and computer program

A viewpoint recommendation apparatus includes image feature extraction means for extracting an image feature from the acquired image at a first viewpoint, pose estimation means for calculating a first likelihood map indicating a relation between the estimated pose of the object and a likelihood of this estimated pose, second storage means for storing a second likelihood map indicating a relation between the true first viewpoint and a likelihood of this first viewpoint in the estimated pose, third storage means for storing a third likelihood map indicating a relation between the pose of the object when the object is observed at the first and the second viewpoints and a likelihood of this pose, and viewpoint estimation means for estimating the second viewpoint so that a value of an evaluation function of the first, second, and third likelihood maps becomes the maximum or minimum.

Viewpoint recommendation apparatus, recommendation method thereof, and non-transitory computer readable medium

The novel technology described in this disclosure includes an example method comprising selecting a target of interest having an obsolete appearance model, the obsolete appearance model describing a prior appearance of the target of interest, navigating a first mobile robot to a location the first mobile robot including a mechanical component providing motive force to the first mobile robot and an image sensor, and searching for the target of interest at the location. The method may include collecting, in the location by the image sensor of the first mobile robot, appearance data of the target of interest, and updating the obsolete appearance model using the appearance data of the target of interest. In some implementations, the method may, in a subsequent meeting between the target of interest and a second mobile robot at a later point in time, recognizing the target of interest using the updated appearance model.

Proactive acquisition of data for maintenance of appearance model by mobile robot

The novel technology described in this disclosure includes an example method comprising initializing an observability grid with an observation likelihood distribution for an environment being navigated by a mobile detection system, such as but not limited to a robot; searching the environment using the observability grid for an observation point; navigating, using a propulsion system, the robot to the observation point; and observing a target object from the observation point. The observability grid may include two or more spatial dimensions and an angular dimension. In some cases, the method may include sampling the environment with the robot based on the observation likelihood distribution of the observability grid.

Observability grid-based autonomous environment search

The novel technology described in this disclosure includes an example method comprising capturing sensor data using one or more sensors describing a particular environment; processing the sensor data using one or more computing devices coupled to the one or more sensors to detect a participant within the environment; determining a location of the participant within the environment; querying a feature database populated with a multiplicity of features extracted from the environment using the location of the participant for one or more features being located proximate the location of the participant; and selecting, using the one or more computing devices, a scene type from among a plurality of predetermined scene types based on association likelihood values describing probabilities of each feature of the one or more features being located within the scene types.

Autonomous human-centric place recognition

This disclosure describes, according to some implementations, a system and method for adapting object handover from robot to human using perceptual affordances. In an example method, upon receiving sensor data describing surroundings and/or operational state of a robot unit from a sensor, the method calculates a probability of a perceptual classification based on the sensor data. The perceptual classification may be one or more of an environment classification, an object classification, a human action classification, or an electro-mechanical state classification. The method further calculates an affordance of the probability of the perceptual classifier using a preference model, determines a handover action based on the affordance, executes the handover action, and updates the preference model based on feedback.

Adapting object handover from robot to human using perceptual affordances

The disclosure includes methods for determining a current location for a user in an environment; detecting obstacles within the environment; estimating one or more physical capabilities of the user based on an EHR associated with the user; generating, with a processor-based device that is programmed to perform the generating, instructions for a robot to perform a task based on the obstacles within the environment and one or more physical capabilities of the user; and instructing the robot to perform the task.

Providing personalized patient care based on electronic health record associated with a user

Technology for localized guidance of a body part of a user to specific objects within a physical environment using a vibration interface is described. An example system may include a vibration interface wearable on an extremity by a user. The vibration interface includes a plurality of motors. The system includes sensor(s) coupled to the vibrotactile system and a sensing system coupled to the sensor(s) and the vibration interface. The sensing system is configured to analyze a physical environment in which the user is located for a tangible object using the sensor(s), to generate a trajectory for navigating the extremity of the user to the tangible object based on a relative position of the extremity of the user bearing the vibration interface to a position of the tangible object within the physical environment, and to guide the extremity of the user along the trajectory by vibrating the vibration interface.

Growing community of inventors

Nagoya, Japan

Yusuke Nakano