Among other things, information generated by sensors of a mobile phone and indicative of motion of the mobile phone and state information indicative of a state of operation of the mobile phone are monitored. Based on the monitoring, distraction by a user of the mobile phone who is a driver of a vehicle is determined.

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.

A system for monitoring a driver comprises that a first sensor configured to detect first physical condition information of the driver, a second sensor configured to detect first driving state information of a vehicle; and a controller configured to determine whether a physical condition of the driver is abnormal on the basis of the first physical condition information, and determine whether a driving pattern of the driver is abnormal on the basis of the first driving state information.

A driving assistance device 1 is provided with: three or more detection units 5a to 5d which are configured to detect a likely-to-be-dozing state in which likelihood that a driver of a vehicle is dozing is high by different indices and at prescribed intervals; a state determination unit 34 which determines that the driver is in a dozing state when the likely-to-be-dozing state is detected by at least two of the detection units; and a warning control unit 36 which causes a warning device 6 to issue a warning when the state determination unit 34 determines that the driver is in the dozing state.

A vehicle lane change control apparatus includes a condition information acquisition unit that acquires condition information of an occupant of a vehicle, a lane change rate database unit that stores a lane change rate that is determined based on a lane change pattern of a driver analyzed based on driving information when a lane of the vehicle is changed and road condition information when the lane of the vehicle is changed and indicates a speed of the lane change, and a control unit that changes the lane of the vehicle through steering control according to operation information of the vehicle, and based on the condition information of the occupant acquired by the condition information acquisition unit, controls the lane change of the vehicle by selectively using the lane change rate and a corrected lane change rate determined by increasing or decreasing the lane change rate.

The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed UVB exposure patterns, where the driver's UVB exposure levels may serve as a proxy for vitamin D levels in that driver's body. A smart ring, wearable on a user's finger, continuously monitors user's exposure to UVB light. This UVB exposure data, representing UVB exposure patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed UVB exposure patterns. The user can be warned of this risk to prevent them from driving or to encourage them to get more sunlight exposure before driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while exposed to high risk due to deteriorated psychological or physiological conditions stemming from insufficient UVB exposure.

A method and device for determining whether a user is actively driving a motor vehicle or car sick. The sensor device is provided for sensing eye movement of the user and the method includes supplying an artificial intelligence with data originating from the sensor device in order to recognize at least one frequency of eye movements of the user, a frequency of eye movements above a first threshold characterizing a visualization by the user of a passing landscape, and being distinguished from a concentration of gaze of a vehicle driver, and determining a current frequency of eye movements of the user and comparing the current frequency with the first threshold, and if the current frequency is greater than the first threshold, triggering a notification signal for the user.

The described systems and methods determine a driver's fitness to safely operate a moving vehicle based at least in part upon observed impairment patterns. A smart ring, wearable on a user's finger, continuously monitors impairment levels. This impairment data, representing impairment patterns, can be utilized, in combination with driving data, to train a machine learning model, which will predict the user's level of risk exposure based at least in part upon observed impairment patterns. The user can be warned of this risk to prevent them from driving or to encourage them to delay driving. In some instances, the disclosed smart ring system may interact with the user's vehicle to prevent it from starting while the user is in a state of impairment induced by substance intoxication.

An approach for providing assistance to an operator of a vehicle during a medical event and/or driving impairment is disclosed. The approach includes determines driving profile of a driver, monitors the driver during a trip, identifies an occurrence of the medical event associated with the driver. The approach generates an initial action list in responsive the occurrence of the medical event. The approach executes the initial action list and determines whether the driver is cognizant. The approach generates a subsequent action list in responsive to the driver is not cognizant and executes the subsequent action list.

Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.