Abstract: An information provision system includes a design data management unit to manage device design data regarding design of a target device, including one or more items of data out of machine design data as data regarding machine design of the target device, electric design data as data regarding electric design of the target device, and control design data as data regarding control design of the target device, by handling each of a plurality of elements constituting the device design data as a unit and linking relevance information indicating relevance between elements in the plurality of elements, with each element, a design data extraction unit to generate disclosure design data by extracting a part corresponding to a designated disclosure range from the device design data in units of elements and by referring to the relevance information, and a design data issuance unit to issue the disclosure design data.

Abstract: A course prediction device (1) includes a blind spot calculation unit (11) and a prediction unit (12). The blind spot calculation unit (11) acquires a sensing result sequentially from a sensor which is arranged in a mobile object and which detects whether an object exists, and calculates, based on sensor information, a blind spot area expressing such an area that an object existing therein cannot be detected by the sensor. The blind spot calculation unit (11) also detects an object entering the blind spot area based on the sensor information. The prediction unit (12) predicts a course in the blind spot area of the detected object based on the sensing result.

Abstract: An information processing device includes an acquisition unit that acquires mobile object information, parcel information, pickup delivery information as information regarding at least one of pickup and delivery, map information, weather information, and traffic information, a calculation control unit that calculates a delivery company burden, as a cost defrayment by a delivery company, in regard to each combination of target parcels and a mobile object by using the mobile object information, the parcel information, the pickup delivery information, the map information, the weather information and the traffic information, a determination unit that determines an optimum combination out of a plurality of combinations based on the delivery company burden calculated in regard to each combination of target parcels and a mobile object, and an output unit that outputs the optimum combination.

Abstract: A driving monitoring device (100) decides an applicable collision pattern that applies to a collision pattern of a case where a vehicle (200) collides with a mobile object, based on a velocity vector of the vehicle, a velocity vector of the mobile object, and so on. Subsequently, the driving monitoring device calculates a time until collision, which is a time taken until the vehicle collides with the mobile object, in the applicable collision pattern. Then, the driving monitoring device calculates a danger level of an accident in which the vehicle collides with the mobile object, based on the applicable collision pattern and the time until collision.

Abstract: An information processing device includes an acquisition unit that acquires user identification information as information identifying a user and position information as at least one item of information out of present position information as information indicating the user's present position and destination information as information indicating a destination, acquires purchase information indicating a purchased item purchased by the user in the past by using the user identification information, and acquires a learned model, an estimation unit that estimates an advertisement to be provided to the user by using the position information, the purchase information and the learned model, and a provision control unit that executes a process for providing the advertisement to the user.

Abstract: A delivery management device includes a communication unit and a control unit. The control unit acquires vehicle position information and user position information when a request for delivery of a parcel is received, selects one vehicle among a plurality of vehicles based on contents of the request, information acquired from a storage device, the vehicle position information and the user position information, makes the selected vehicle as a responsible vehicle move to the user present position, and receives a pickup completion notification indicating that the responsible vehicle moved to the user present position and a shipment parcel has been loaded onto the responsible vehicle or a completion notification indicating that the responsible vehicle arrived at the user present position and a reception parcel has been taken out of the responsible vehicle.

Abstract: A failure detection apparatus (10) acquires, as target data, sensor data output in a past reference period by a sensor (31), such as a millimeter wave radar or LiDAR (Light Detection And Ranging), mounted on a moving body (100). The failure detection apparatus (10) determines whether detected data indicating a characteristic of a detected object indicated by normal data, which is sensor data output when the sensor (31) is normal, is included in the acquired detected data in the past reference period. In this way, the failure detection apparatus (10) determines whether a failure has occurred in the sensor (31).

Abstract: A first image-data acquisition unit (101) acquires a plurality of pieces of image data in which different areas in a facility in which an autonomous driving vehicle conducts autonomous traveling are shown. A priority setting unit (102) analyzes a status shown in each of the plurality of pieces of image data and sets a priority to each image data. A first image-data display unit (103) decides a display mode at a time of displaying the plurality of pieces of image data according to the priority set by the priority setting unit (102).

Abstract: An acquisition unit of a falling object detection apparatus installed and used in a first vehicle acquires a depth image of a second vehicle, on which a load is mounted and which is traveling in front of the first vehicle, and of the area around the second vehicle. A determination unit of the falling object detection apparatus determines whether the load has not made a movement different from that of the second vehicle, using the depth image acquired by the acquisition unit. A detection unit of the falling object detection apparatus detects a fall of the load based on a result of determination by the determination unit.

Abstract: A detected data acquisition unit (21) acquires detected data outputted during a past reference period by a sensor (31) mounted on a moving body (100). A peripheral data acquisition unit (22) acquires peripheral data detected by a peripheral body existing on peripheral of the moving body (100). A failure determination unit (23) determines whether or not there is a failure in the sensor (31) based on whether or not the detected data includes detected data that identifies a detected content of which difference from a peripheral content identified from the peripheral data is within a reference scope.

Abstract: An information presentation device includes: an imager to image an area around a vehicle and generate a vehicle outside image; an imager to image an inside of the vehicle and generate a vehicle inside image; a display including multiple display portions; and circuitry to recognize one or more obstacles from the vehicle outside image, generate obstacle information indicating a result of the recognition, generate, from the vehicle inside image, line-of-sight information indicating a direction of a line of sight of a driver, make a determination regarding display on each display portion based on the obstacle information and line-of-sight information, and control display on each display portion based on the determination. The circuitry causes, for each obstacle, an image including the obstacle to be displayed on one of the display portions located in a direction of the obstacle or a direction close thereto as viewed from the driver.

Abstract: A course prediction device (1) includes a blind spot calculation unit (11) and a prediction unit (12). The blind spot calculation unit (11) acquires a sensing result sequentially from a sensor which is arranged in a mobile object and which detects whether an object exists, and calculates, based on sensor information, a blind spot area expressing such an area that an object existing therein cannot be detected by the sensor. The blind spot calculation unit (11) also detects an object entering the blind spot area based on the sensor information. The prediction unit (12) predicts a course in the blind spot area of the detected object based on the sensing result.

Abstract: A first image-data acquisition unit (101) acquires a plurality of pieces of image data in which different areas in a facility in which an autonomous driving vehicle conducts autonomous traveling are shown. A priority setting unit (102) analyzes a status shown in each of the plurality of pieces of image data and sets a priority to each image data. A first image-data display unit (103) decides a display mode at a time of displaying the plurality of pieces of image data according to the priority set by the priority setting unit (102).

Abstract: A failure detection apparatus (10) acquires, as target data, sensor data output in a past reference period by a sensor (31), such as a millimeter wave radar or LiDAR (Light Detection And Ranging), mounted on a moving body (100). The failure detection apparatus (10) determines whether detected data indicating a characteristic of a detected object indicated by normal data, which is sensor data output when the sensor (31) is normal, is included in the acquired detected data in the past reference period. In this way, the failure detection apparatus (10) determines whether a failure has occurred in the sensor (31).

Abstract: A crossing detection unit (21) detects a pedestrian who is about to cross a roadway on which a vehicle (100) travels. A behavior detection unit (22) detects a behavior of a nearby vehicle traveling around the vehicle (100). A stop determination unit (23) determines whether or not to stop the vehicle (100) in view of the detection of the pedestrian who is about to cross the roadway by the crossing detection unit (21) and the behavior of the nearby vehicle detected by the behavior detection unit (22). A vehicle control unit (24) stops the vehicle (100) before the pedestrian when the stop determination unit (23) determines to stop the vehicle.

Abstract: An information processing device is mounted on a movable body. A calculation unit acquires sensor data in which detection points are indicated, the detection points being acquired by scanning a periphery of a sensor by the sensor, the sensor being located at the periphery of the movable body, analyzes the acquired sensor data, and calculates a distribution range of the detection points. Based on the distribution range of the detection points calculated by the calculation unit, a removal unit extracts a detection point for the movable body from among the detection points indicated in the sensor data, and removes the extracted detection point for the movable body from the sensor data.

Abstract: An acquisition unit of a falling object detection apparatus installed and used in a first vehicle acquires a depth image of a second vehicle, on which a load is mounted and which is traveling in front of the first vehicle, and of the area around the second vehicle. A determination unit of the falling object detection apparatus determines whether the load has not made a movement different from that of the second vehicle, using the depth image acquired by the acquisition unit. A detection unit of the falling object detection apparatus detects a fall of the load based on a result of determination by the determination unit.

Abstract: A driving monitoring device (100) decides an applicable collision pattern that applies to a collision pattern of a case where a vehicle (200) collides with a mobile object, based on a velocity vector of the vehicle, a velocity vector of the mobile object, and so on. Subsequently, the driving monitoring device calculates a time until collision, which is a time taken until the vehicle collides with the mobile object, in the applicable collision pattern. Then, the driving monitoring device calculates a danger level of an accident in which the vehicle collides with the mobile object, based on the applicable collision pattern and the time until collision.

Abstract: A direction identification unit (21) identifies a traveling direction in which a surrounding vehicle travels in a partial region of a region indicated by image information (42) obtained by photographing using a camera (31). A feature amount acquisition unit (22) acquires a reference feature amount (41) being a feature amount computed from a reference image corresponding to the identified traveling direction. A vehicle determination unit (23) computes an image feature amount being a feature amount of the image information of the partial region and compares the computed image feature amount with the acquired reference feature amount (41), thereby determining whether the surrounding vehicle is present in the partial region.

Abstract: A detected data acquisition unit (21) acquires detected data outputted during a past reference period by a sensor (31) mounted on a moving body (100). A peripheral data acquisition unit (22) acquires peripheral data detected by a peripheral body existing on peripheral of the moving body (100). A failure determination unit (23) determines whether or not there is a failure in the sensor (31) based on whether or not the detected data includes detected data that identifies a detected content of which difference from a peripheral content identified from the peripheral data is within a reference scope.