Provided herein is a method for assisting a driver with driving a motor vehicle. The method includes ascertaining a permissible driving speed in a route section by evaluating environmental data describing the motor vehicle environment. The method further includes calculating a target maximum speed, which is lower than the permissible driving speed, by subtracting a predefined reduction amount from the permissible driving speed and/or by multiplying the permissible driving speed by a predefined scaling factor. The method further includes controlling at least one alert device for outputting an alert to the driver when a present driving speed of the motor vehicle exceeds the target maximum speed and/or controlling the driving speed of the motor vehicle by a longitudinally guiding driver assistance system, wherein the target maximum speed is used as a maximum speed or as a target speed.

A method for controlling a vehicle includes identifying a user identity associated with a user riding in the vehicle, determining a destination associated with the user identity, creating a detection zone proximate to the vehicle when the vehicle is localized in a first drop-off location associated with the destination, causing a sensory system to identify a hazard within the detection zone and generate a localization of the hazard, determining that a first probability of realizing a risk associated with the hazard is less than a first probability threshold, and generating a vehicle door actuation that permits the user to exit the vehicle based on the first probability of realizing the risk.

A method of providing a content and advertisement customized to a passenger boarding a vehicle includes: receiving passenger information about a passenger from a user terminal of a passenger in a vehicle by a server; determining a passenger's preference based on the passenger information, and selecting a plurality of pieces of content information and a plurality of pieces of advertisement information according to the determined passenger's preference by the server; calculating an estimated travel time for the vehicle to arrive at a destination based on the passenger information by the server; creating a playlist by selecting the content information and the advertisement information such that the total sum of the replay time is at least equal to the estimated travel time among a plurality of pieces of selected content information and a plurality of pieces of selected advertisement information; and transmitting the created playlist to the passenger terminal.

Disclosed is a simulation learning-based drowsy driving simulation platform system for detecting careless driving in conjunction with deep learning, the simulation learning-based drowsy driving simulation platform system comprising: a drive state warning device configured to determine a driver's careless driving from a captured image, determine a driver's careless driving determination level, and output the determined level; a smart cruise control interworking part configured to transmit the driver's careless driving determination level outputted from the drive state warning device; and a smart cruise control processing part configured to control a vehicle according to the driver's careless driving determination level transmitted by the smart cruise control interworking part, during a smart cruise control operation.

Disclosed are a vehicle capable of controlling acceleration in consideration of a driving environment and an acceleration limit control method therefor. The acceleration limit control method of the disclosure includes determining a base value of an acceleration limit level, which is classified into a plurality of levels, determining a first correction value based on manipulation of an accelerator pedal, determining a second correction value based on a driving environment, determining a final acceleration limit level by applying the first correction value and the second correction value to the base value, and determining a limit acceleration based on the final acceleration limit level.

A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

A vehicular occupant health monitoring system includes a heartbeat sensor disposed at a vehicle and operable to capture sensor data measuring an aspect associated with the heart of an occupant of the vehicle. A control includes a processor operable to process sensor data captured by the heartbeat sensor and provided to the control. The vehicular occupant health monitoring system, responsive to processing by the processor of sensor data captured by the heartbeat sensor, determines whether the measured aspect associated with the heart of the occupant is abnormal. The vehicular occupant health monitoring system, responsive to determining the measured aspect associated with the heart of the occupant is abnormal, controls a function of the vehicle.

A pacification method based on emotion recognition, includes: acquiring at least one of a voice and an image of a user; determining whether the user has abnormal emotion, according to the at least one of the voice and the image of a user; and in response to the user having abnormal emotion, determining a pacification manner according to the emotion of the user, and performing emotional pacification on the user. An apparatus, a device and a storage medium are also provided.

An unsupervised learning-based detection method according to one technical aspect of the present disclosure relates to an unsupervised learning-based detection method using a supervised-learned model, and includes: generating a first plurality of matrix data on the basis of driving data; generating encoding information by encoding the first plurality of matrix data using a convolutional neural network; modeling a time series feature of the encoding information by using a long short-term memory (LSTM) network, so as to derive a correlation between variables according to a time series; re-implementing a second plurality of matrix data through a deconvolution calculation of the correlation between the variables according to the time series; and determining whether the driving data corresponds to a pre-supervised learned driver profile, on the basis of a difference between the first plurality of matrix data and the second plurality of matrix data.

A vehicle control system executing a voice control system for facilitating voice-based dialog with a driver to enable the driver or autonomous vehicle to control certain operational aspects of an autonomous vehicle is provided. Using environmental and sensor input, the vehicle control system can select optimal routes for operating the vehicle in an autonomous mode or choose a preferred operational mode. Occupants of the autonomous vehicle can change a destination, route or driving mode by engaging with the vehicle control system in a dialog enabled by the voice control system.