A system includes an interface and a processor. The interface is configured to receive one or more images of an interior of a vehicle from a camera. The processor is configured to determine whether the one or more images indicates to initiate communication to a selected group of receivers from a driver; receive a message; and provide the message to the selected group of receivers.

An abnormality detection device is provided to detect an abnormal posture of a driver of a vehicle based on information of an image captured by an imaging unit for which an imaging region is defined to image a head of the driver. The abnormality detection device is configured to define at least one premonition region in a range in which the head is supposed to be positioned in process of transitioning from a normal posture to the abnormal posture in the imaging region. When the head stays in a premonition region, the abnormality detection device determines that the driver is in a state of the abnormal posture.

Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

Systems and methods are disclosed for determining a distraction level of a driver. A real-time driver analysis computer may receive sensor data from one or more driver analysis sensors. The real-time driver analysis computer may analyze the sensor data to determine a distraction level of a driver. Based on the distraction level, the real-time driver analysis computer may send one or more control signal to the vehicle and output one or more alerts to a mobile device associated with the driver.

Systems and methods for adjusting one or more component of a vehicle are disclosed herein. In an embodiment, a system for adjusting one or more component of a vehicle includes at least one adjustable vehicle component, an authentication device, an audio device, and a controller. The authentication device is configured to detect a vehicle user outside of the vehicle and generate corresponding authentication data. The audio device is configured to receive an audible command from the vehicle user from outside of the vehicle and generate corresponding command data. The controller is programmed to cause an adjustment of the at least one adjustable vehicle component based on the command data when the vehicle user has been authenticated based on the authentication data.

A system for detecting and mitigating an unsafe condition in a vehicle includes an image sensor configured to generate and output image data of one or more seats in a cabin of the vehicle and a processing system operably connected to the image sensor and including at least one processor. The processing system is configured to receive the image data from the image sensor, process the image data to determine a location of at least one passenger in the cabin, detect that the at least one passenger is located outside of the one or more seats based on the determined location of the at least one passenger in the cabin, and operate at least one component of the vehicle in a predefined manner in response to detecting that the at least one passenger is located outside of the one or more seats.

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.

An apparatus and a method are configured to control a steering wheel of an autonomous driving vehicle. A state of a driver may be monitored during autonomous driving, and at least one of an upward/downward location, a leftward/rightward tilting angle, or an axial movement of the steering wheel of the autonomous driving vehicle may be controlled based on the state of the driver. As a result, danger information may be delivered to the driver through a dual system during autonomous driving.

An HMI control device includes an automation level acquisition portion and a display control unit. The automation level acquisition portion acquires an autonomous driving level determined by a driving control device. The display control portion controls an image display operation of an HMI device according to the autonomous driving level acquired by the automation level acquisition portion. When terminating a high autonomous driving level as the autonomous driving level, the display control portion controls the HMI device to provide an action instruction display that instructs the driver to take a low-level associated state so as to handle a low autonomous driving level. The low autonomous driving level is the autonomous driving level under which an in-vehicle system including the driving control device is prohibited from performing at least one of a lateral motion control realized by performing steering and a longitudinal motion control realized by performing acceleration/deceleration.

The present teaching relates to method, system, and medium, for switching a mode of a vehicle. Real-time data related to the vehicle are received, which include intrinsic/extrinsic capability parameters, based on which a set of tasks to switch from a current mode to a different mode is determined. A first duration of time required for the switch is determined based on a first risk evaluated with respect to the current mode and the real-time data. A task duration time needed by a driver to complete the task is estimated for each of the set of tasks. A second risk for the switching is estimated based on the required first duration of time and a total task duration times needed to complete the set of tasks. The switch is carried out when the second risk satisfying a criterion.