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 driver monitor system and method for predicting impairment of a user of a vehicle. The system includes video cameras, an input device for inputting a list of medications being taken by the driver. Processing circuitry predicts side effects of the medications based on the half-life of the medication, detecting eye gaze movement, eye lid position, and facial expression of the user using images from the video camera, predicting whether the user is transitioning into an impaired physical state that is a side effect of the medications, verifying the side effect of the medications, determining whether the user is fit to drive using the verified side effects of the medications, and outputting to the vehicle an instruction to operate the vehicle in a level of automation that makes up for the at least one side effect or to perform a safe pull over operation of the vehicle.

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.

Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

Managing vehicle operations according to driver behavior by: defining a multi-dimensional preemptive driver daily behavior (PDDB) data structure; defining a PDDB abnormal behavior criteria; and defining a PDDB abnormal behavior algorithm. Further, by: collecting PDDB data; acquiring a PDDB assessment according to the PDDB analysis; and preemptively communicating driver vehicle-access instructions according to the PDDB assessment.

Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to monitoring driver and/or driving behaviors in view of an exhaustive list of variables to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

A method for adjusting a driving control authority of a vehicle includes: generating stimulation for a driver using a stimulation generator; measuring a driver reaction signal in response to the generated stimulation using a measurement device; processing the measured driver reaction signal using a signal processor; determining a driver state based on the processed driver reaction signal using a determiner; and adjusting the driving control authority of the vehicle according to the determined driver state using a vehicle controller.

An evacuation driving assistance apparatus capable of evacuation driving toward an appropriate evacuation location is provided. In the evacuation driving assistance apparatus, a driver determination unit determines whether or not a driver of an own vehicle is in a normal-driving enabled state. If it is determined by the driver determination unit that the driver of the own vehicle is in a normal-driving disabled state, a notification unit provides a notification of location information of the own vehicle to a center. The center is capable of determining an evacuation location based on the location information of the own vehicle, and transmitting the evacuation location. A reception unit receives the evacuation location transmitted by the center. An evacuation driving execution unit executes evacuation driving toward the evacuation location received by the reception unit.

Managing vehicle operations according to driver behavior by: defining a multi-dimensional preemptive driver daily behavior (PDDB) data structure; defining a PDDB abnormal behavior criteria; and defining a PDDB abnormal behavior algorithm. Further, by: collecting PDDB data; acquiring a PDDB assessment according to the PDDB analysis; and preemptively communicating driver vehicle-access instructions according to the PDDB assessment.

Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to monitoring driver and/or driving behaviors in view of an exhaustive list of variables to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.