Systems and methods are described for an adaptive user interface system for a vehicle with an automatic vehicle system. The adaptive user interface system includes a display and an electronic controller. The controller is configured to generate a graphical user interface indicative of operation of the automatic vehicle system, output the graphical user interface on the display, monitor an indicia of a driver's comfort level, and determine, based on the monitored indicia, when the driver is not comfortable with the operation of the automatic vehicle system. In response to determining that the driver is not comfortable with the operation of the automatic vehicle system, the electronic controller modifies the graphical user interface to provide an increased level of detail.

A control device of a vehicle comprises: a driving plan generating part 90 configured to generate a driving plan in automated driving of the host vehicle; a package extracting part 91 configured to extract driving assistance packages packaging permissions for a plurality of driving assistance operations based on at least one of the surrounding environment information, the vehicle information, and the driver information; a package proposing part 92 configured to propose driving assistance packages to the driver of the host vehicle based on the driving assistance packages extracted by the package extracting part and the driving plan; and an automated driving executing part 93 configured to perform driving assistance operations permitted in a driving assistance package proposed by the package proposing part and approved by the driver of the host vehicle.

A vehicle is operable in a first operating mode in which the vehicle travels autonomously inside the traffic lane based on a detection of lane markings of a traffic lane and in a second operating mode in which the vehicle autonomously follows a vehicle driving in front while ignoring lane markings, and a method of its operation includes operating the vehicle in a first of the two operating modes, detecting a vehicle environment, and switching from the first operating mode to the other of the two operating modes as a function of the detected vehicle environment. A device can execute the method and a computer program can be executed by a device for performing the method.

In one embodiment, a cognitive load driving assistant increases driving safety based on cognitive loads. In operation, the cognitive load driving assistant computes a current cognitive load of a driver based on sensor data. If the current cognitive load exceeds a threshold cognitive load, then the cognitive load driving assistant modifies the driving environment to reduce the cognitive load required to perform the primary driving task and/secondary task(s), such as texting via a cellular phone. The cognitive load driving assistant may modify the driving environment indirectly via sensory feedback to the driver or directly through reducing the complexity of the primary driving task and/or secondary tasks. In particular, if the driver is exhibiting elevated cognitive loads typically associated with distracted driving, then the cognitive load driving assistant modifies the driving environment to allow the driver to devote appropriate mental resources to the primary driving task, thereby increasing driving safety.

In a drive assist system, a map data acquiring section acquires at least one of a driver's operation ability and a load of a vehicle. An adjustment section determines an assist control amount as a control parameter of drive assist for the vehicle so that a degree of the driver's operation is increased according to reduction of the driver's operation ability or increasing of the load of the vehicle. An assist control amount calculation section transmits the assist control amount to a steering motor and a notification section so as to execute the drive assist for the vehicle.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A technique for enabling efficient retrieval of a digital representation of personality data of a user (402) by a client device (406) from a server (404) is disclosed, wherein the digital representation of the personality data is processed at the client device (406) to provide a user-adapted service to the user (402). A method implementation of the technique is performed by the server (404) and comprises storing a neural network being trained to compute personality data of a user based on input obtained from the user (402), receiving, from the client device (406), a request for a digital representation of personality data for a user (402), and sending, to the client device (406), the requested digital representation of the personality data of the user (402), wherein the personality data of the user is computed using the neural network based on input obtained from the user (402).

Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

A human machine interface control unit as an information presentation control device is used in a vehicle having an autonomous driving function to perform a driving action on behalf of a driver, and controls an information presentation device configured to present information to the driver. The human machine interface control unit includes a permissible action determination unit configured to determine a permissible action that a driver is permitted to take among actions, other than a driving action, to be possibly taken by the driver when the autonomous driving function is implemented, and an information presentation content control unit configured to cause an information presentation device to present information related to a determination result of the permissible action.

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.