Provided is a vehicle stop support system for supporting vehicle stop in an emergency condition. The vehicle stop support system sets a target time period based on physical abnormality of a driver; sets an allowable lateral acceleration, based on the abnormality; estimates a time period required to reach each of a plurality of stop point candidates; estimates a lateral acceleration to be generated during traveling of a vehicle to each of the candidates; sets a stop point; and controls the vehicle to travel to the stop point and stop at the stop point. The system is operable to set, as the stop point, one of the candidates which satisfies a condition that the lateral acceleration estimated with respect thereto is equal to or less than the allowable lateral acceleration, and the required time period estimated with respect thereto is equal to or less than the target time period.

A vehicle includes: a plurality of sensor devices that determine a driver state; a driver state determining device that receives detection results from a plurality of sensor devices and determines whether the driver state is a dangerous state; and a driving assistance device that performs lane keeping control and speed control of a vehicle and transmits a network connection request to a management server when the driver state determining device has determined the dangerous state.

A system for controlling vehicle functions based on monitored driving performance includes one or more sensors that capture performance-based data characterizing driving-performance implicating behaviors of a driving team during one or more driving campaigns. The performance-based data is analyzed according to one or more performance indicators to determine a compatibility score for the driving team that characterizes how driving team composition affects driving performance. One or more vehicle functions can then be controlled one or more vehicle functions based on compatibility scores for various driving teams.

A computer-implemented method for determining an estimate of the capability of a vehicle driver to take over control of a vehicle, wherein the method comprises: determining at least one estimation parameter, the at least one estimation parameter representing an influencing factor for the capability of the vehicle driver to take over control of the vehicle; and determining an estimate on the basis of the at least one estimation parameter by means of a predefined estimation rule, the estimate representing the capability of the vehicle driver to take over control of the vehicle.

A system may present notifications to an occupant of a vehicle and determine whether the occupant has given attention to the notification. The system oscillates a visual notification at an output frequency. The system captures images of an eye of the occupant to determine an eye pupil oscillation frequency associated with the occupant. The system compares the eye pupil oscillation frequency to the output frequency. The system determines whether attention was given to the notification based on whether the output frequency and the eye pupil oscillation frequency match. If the system determines that attention was not given, the system may cause performance of one or more follow-up operations.

The present disclosure provides a method and system to operationalize driver eye movement data analysis based on moving objects of interest. Correlation and/or regression analyses between indirect (e.g., eye-tracking) and/or direct measures of driver awareness may identify variables that feature spatial and/or temporal aspects of driver eye glance behavior relative to objects of interest. The proposed systems and methods may be further combined with computer-vision techniques such as object recognition to (e.g., fully) automate eye movement data processing as well as machine learning approaches to improve the accuracy of driver awareness estimation.

The system comprises input means for receiving user data from associated user monitoring means that each monitor at least one respective attribute of the user of the vehicle. The user data is indicative of the plurality of respective attributes. A plurality of inference modules each receive user data from the user monitoring means, and each determine a respective inference of a cognitive state of the user based on the received user data. Each inference module is arranged to output user state data indicative of the determined inference of the cognitive state of the user. An inference fusion module receives the user state data from each of the plurality of inference modules to determine an inference of an aggregated cognitive state of the user and to output cognitive state data indicative of the aggregated cognitive state for controlling the vehicle functions.

A system for increasing the safety of voice conversations between drivers and remote parties is shown. The system includes an in-vehicle subsystem and a remote subsystem. The system includes a plurality of sensors which are configured to generate monitoring data. The system includes a computing device, which may be distributed between the subsystems and is configured to calculate a risk level as a function of the monitoring data. The computing device may engage an automatic safety response as a function of the risk level, that may include suspension or termination of on-going conversations among the parties, together with notification about the status of the communication channel. The safety response may be communicated to the driver by generating an alert. The in-vehicle and the remote subsystems communicate using a wireless connection and collaborate in engaging the automatic safety response and communicating any alerts to the driver and remote party using notifications.

Various embodiments provide systems and methods for identifying impairment using measurement devices.

Aspects of adaptive trust calibration may include receiving a trust model for an occupant of an autonomous vehicle calculated based on occupant sensor data and a first scene context sensor data, and/or receiving a second scene context sensor data associated with an environment of the autonomous vehicle, determining an over trust scenario or an under trust scenario based on the trust model and a trust model threshold, and generating and implementing a human machine interface (HMI) action or a driving automation action based on the determination of the over trust scenario or the determination of the under trust scenario, and/or the second scene context sensor data.