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.

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.

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.

In a vehicle control system (1, 101), a control unit (15) is configured to execute a stop process by which the vehicle is parked in a prescribed stop position located within a permitted distance when it is detected that the control unit or the driver has become incapable of properly maintaining a traveling state of the vehicle, and, in executing the stop process, the control unit computes an agreement between an object (X) contained in the map information based on an estimated position of the vehicle and an object (Y) on the road detected by an external environment recognition device (6), the permitted distance being smaller when the agreement is below a prescribed agreement threshold than when the agreement is equal to or above the agreement threshold.

The disclosed embodiments are directed to adjusting the operational characteristics of a black box installed in a vehicle. In one embodiment a method is disclosed comprising classifying a mental state of a driver of an automobile, loading at least one setting based on the mental state, the setting defining an operational characteristic of a black box installed in the automobile, configuring the black box using the at least one setting, and recording one or more events by the black box.

A vehicle includes: a driver assistance system; an accelerator configured to perform acceleration of the vehicle; a braking device configured to perform deceleration of the vehicle; a velocity sensor configured to detect a current velocity of the vehicle; a driver status sensor configured to acquire a driver's behavioral data; and a controller. The controller is configured to identify a carelessness status of the driver based on the driver's behavioral data and to activate a velocity control mode when the carelessness status of the driver is detected in the activation status of the driver assistance system.

A system has a microcontroller with operative control of a set of vehicle lights including an ability to flash all of the set of vehicle signal lights simultaneously as hazard flashers and an ability to strobe all of the set of vehicle signal lights as high conspicuity emergency lighting. Upon receipt of an indication from a vehicle data bus that the vehicle is slowing as a result of a stopping event, the microcontroller determines a speed of the vehicle and strobes the set of vehicle signal lights as high conspicuity emergency lighting if the speed of the vehicle is below a predetermined threshold.

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records, each driving event record including phone usage by a user, wherein a driving event record is labeled as an actual distracted driving event or a passenger event, (ii) divide the driving event records into at least two clusters based at least in part upon common features and the labels of each driving event record by processing the plurality of driving event records with a semi-supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the clusters using the trained model, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device programmed to: (i) receive driving event records including phone usage by a user that occurred within a time period of a driving event, (ii) divide the driving event records into at least two clusters based at least in part upon common features of one or more driving event records of the plurality of driving event records by processing the driving event records using an unsupervised machine learning algorithm, (iii) generate a trained model based at least in part upon the at least two clusters including cluster labels, (iv) process a new driving event using the trained model, (v) assign the new driving event to one of the at least two clusters using the trained model, and (vi) based at least in part upon the cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event.