The present disclosure relates to a method for controlling at least one drive assistance system of a motor vehicle, a device for carrying out the steps of this method and a system including such a device. The disclosure also relates to a motor vehicle including such a device or such a system.

A vehicle control device includes a first control unit that executes, when an abnormality of a driver of a vehicle is detected, stop control, a second control unit that executes, when the vehicle is determined to have a risk of collision, deceleration control, a determination unit that identifies an object around the vehicle as a target candidate of the collision and determines whether or not there is the risk of the collision with the identified target candidate, and a setting unit that sets, when the abnormality is detected, an operation mode of the deceleration control to a special mode from a normal mode, the normal mode provided for cases in which the abnormality is undetected. The determination unit expands a range for identifying the object around the vehicle as the target candidate of the collision in the special mode as compared with the range in the normal mode.

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.

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

Systems and methods for distracted driving detection are described. A method includes receiving proximate vehicle data about a proximate vehicle proximate to the host vehicle. The method also includes estimating one or more baselines for a predetermined future time for the proximate vehicle from the proximate vehicle data. The method further includes comparing current kinematic data of the proximate vehicle data for the predetermined future time to the one or more baselines. The method includes generating distraction flags associated with the proximate vehicle based on the comparison. The method also includes controlling one or more vehicle systems of the host vehicle based on the generated distraction flags.

Systems and methods are provided for predictive assessment of driver perception abilities based on driving behavior personalized to the driver in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with on embodiment, a method comprises receiving first vehicle operating data and associated first gaze data of a driver operating a vehicle; training a model for the driver based on the first vehicle operating data and the first gaze data, the model indicating driving behavior of the driver; receiving second vehicle operating data and associated second gaze data of the driver; and determining that an ability of the driver to perceive hazards is impaired based on applying the model to the second vehicle operating data and associated second gaze data.

A transport device with an occupant compartment includes a control apparatus, a transport device door, a transport device roof, a safety belt, a transport device seat, a headset, a headrest, a rearview mirror, a sun visor, and/or a dashboard, as well as a sensor for breath gas analysis, a position locating device for determining the distance between the sensor and the front side of the head of a compartment occupant, a data processing device, and in some cases a sensor for measuring at least one vital sign. The sensor or sensors are present on or in the control apparatus, the transport device door, the transport device roof, the safety belt, the transport device seat, the headset, the headrest, the rearview mirror, the sun visor, and/or the dashboard, and the data processing device is in operative connection with a device for blocking the transport device and/or with an information output unit.

A driver monitor includes a processor configured to: detect the posture of a driver of a vehicle from an image of the interior of the vehicle generated by a camera provided on the vehicle, determine that the driver's condition is abnormal, when the detected posture satisfies an abnormality determining condition, detect an unusual sound made by the driver of the vehicle, based on a voice signal of the interior of the vehicle obtained by a microphone provided on the vehicle, and make the abnormality determining condition for the case where the unusual sound is detected less strict than an abnormality determining condition for the case where the unusual sound is not detected.

An interactive system (1) for interacting with a user is disclosed. The interactive system (1) includes at least one interface (3), a measuring device (5), and a processing unit (7) comprising an interpretation module for receiving a physiological parameter obtained by the measuring device (5) and for defining, based on the physiological parameter, a datum representative of the physiological state of the user (U). The processing unit (7) comprises a data communication module for communicating data with a server (11) storing a library of applications, a data processing module for selecting an application on the basis of the datum representative of the physiological state, an information module for notifying the user (U) of the selected application, and detecting a validation action from the user (U) validating the notified application, and a download module for downloading the validated application.

The disclosed technologies relate to reducing audible distractions for a driver of a vehicle. A method includes obtaining audio data based on sound detected inside the vehicle, identifying an audio event based on the audio data, determining a distraction rating for the audio event, the distraction rating indicating an estimated level of distraction caused by the audio event, and generating an alert when the distraction rating exceeds a threshold.