An abnormal physical condition determination system includes: an extraction unit that extracts a plurality of feature quantities indicating a condition of a target person from an image of the target person; an accumulation unit that accumulates the plurality of feature quantities as time series data; a calculation unit that calculates a relationship between each feature quantity from the plurality of feature quantities accumulated in the accumulation unit; and a determination unit that determines an abnormal physical condition of the target person on the basis of the relationship. According to such an abnormal physical condition determination system, it is possible to appropriately determine the abnormal physical condition of the target person.

An abnormal physical condition determination system includes: an extraction unit that extracts a plurality of feature quantities indicating a condition of a target person from an image of the target person; an accumulation unit that accumulates the plurality of feature quantities as time series data; a calculation unit that calculates a relationship between each feature quantity from the plurality of feature quantities accumulated in the accumulation unit; and a determination unit that determines an abnormal physical condition of the target person on the basis of the relationship. According to such an abnormal physical condition determination system, it is possible to appropriately determine the abnormal physical condition of the target person.

An estimation apparatus of the disclosure includes a determiner that determines whether or not an attention target is visually recognizable from a driver of a vehicle based on information related to a visual field range of the driver; and an estimator that estimates a visual field abnormality of the driver based on a driving operation of the driver in a case where the determiner determines that the attention target is visually recognizable from the driver.

An estimation apparatus of the disclosure includes a determiner that determines whether or not an attention target is visually recognizable from a driver of a vehicle based on information related to a visual field range of the driver; and an estimator that estimates a visual field abnormality of the driver based on a driving operation of the driver in a case where the determiner determines that the attention target is visually recognizable from the driver.

A human machine interface control unit as an information presentation control device is used in a vehicle having an autonomous driving function to perform a driving action on behalf of a driver, and controls an information presentation device configured to present information to the driver. The human machine interface control unit includes a permissible action determination unit configured to determine a permissible action that a driver is permitted to take among actions, other than a driving action, to be possibly taken by the driver when the autonomous driving function is implemented, and an information presentation content control unit configured to cause an information presentation device to present information related to a determination result of the permissible action.

A human machine interface control unit as an information presentation control device is used in a vehicle having an autonomous driving function to perform a driving action on behalf of a driver, and controls an information presentation device configured to present information to the driver. The human machine interface control unit includes a permissible action determination unit configured to determine a permissible action that a driver is permitted to take among actions, other than a driving action, to be possibly taken by the driver when the autonomous driving function is implemented, and an information presentation content control unit configured to cause an information presentation device to present information related to a determination result of the permissible action.

In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

According the present invention, there is provided a control system (10) that includes an image acquisition unit (11) that acquires an image generated by a camera, a controlled object determination unit (12) that analyzes the image and determines at least one of a vehicle that satisfies a predetermined condition and a vehicle in which a predetermined person is riding included in the image, as a vehicle to be controlled, a control content decision unit (13) that decides a control content for the vehicle to be controlled, and an output unit (14) that outputs a control command including the control content to the vehicle to be controlled.

A vehicle control device includes an actuation unit that actuates an anomaly control switch that acknowledges execution of safety control, which improves safety during driving of a vehicle, upon detection of an occupant in an anomalous state from a monitoring result of a state of the occupant of the vehicle. The actuation unit causes the anomaly control switch to be actuated to allow the occupant to recognize a control content of the safety control. The vehicle control device further includes an operation acknowledgement unit that acknowledges operation of the anomaly control switch and a controller that executes the safety control when the operation acknowledgement unit acknowledges the operation of the anomaly control switch.