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.

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.

A simulating method for an electric vehicle (EV) includes: receiving a request from a driver of the EV for performing a simulation of a target vehicle on the EV; obtaining a plurality of configuration parameters for simulating behavior of the target vehicle on the EV, at least including information of the target vehicle and settings of the target vehicle for the driver; obtaining a plurality of vehicle parameters of the EV, at least including run-time parameters of the EV and driving conditions of the EV; and performing a simulating process to obtain one or more control parameters to control the EV to realize behaviors, actions, and/or characteristics of the target vehicle on the EV based on the plurality of vehicle parameters and the plurality of configuration parameters using a vehicle simulator model. The vehicle simulator model is a neural network.

The disclosure relates to a method for developing a vehicle function to be executed by means of hardware of a motor vehicle. The method may comprise: identifying a vehicle function to be developed; creating a design for the vehicle function; carrying out a first permissibility check on the design for the vehicle function; approving the design for the vehicle function if first permissibility criteria are met; transmitting the design for the vehicle function; carrying out a second permissibility check on the design for the vehicle function; approving the design for the vehicle function if second permissibility criteria are met; transmitting the design for the vehicle function to the motor vehicle; and implementing the vehicle function in the motor vehicle.

A method is provided for danger prediction. The method includes generating fully-annotated simulated training data for a machine learning model responsive to receiving a set of computer-selected simulator-adjusting parameters. The method further includes training the machine learning model using reinforcement learning on the fully-annotated simulated training data. The method also includes measuring an accuracy of the trained machine learning model relative to learning a discriminative function for a given task. The discriminative function predicts a given label for a given image from the fully-annotated simulated training data. The method additionally includes adjusting the computer-selected simulator-adjusting parameters and repeating said training and measuring steps responsive to the accuracy being below a threshold accuracy. The method further includes predicting a dangerous condition relative to a motor vehicle and providing a warning to an entity regarding the dangerous condition by applying the trained machine learning model to actual unlabeled data for the vehicle.

A vehicle control device includes a first inputter, a second inputter, a mode controller configured to, when the first inputter is operated by a user, determine a driving mode of a vehicle as a first mode, and when the second inputter is operated by the user in the first mode, switch the driving mode from the first mode to a second mode, and a driving controller configured to control at least one of a steering and speed of the vehicle, and the driving controller is configured to control a steering and speed of the vehicle and prohibit change of a path of the vehicle when the driving mode is the first mode, and control a steering and speed of the vehicle and change of the path of the vehicle is allowed when the driving mode is the second mode.

There is presented a method for controlling an activation threshold of a safety system of a vehicle. The method comprises receiving map data from a remote data repository, said map data comprising a geographical location of a dynamic object located in a surrounding area of an expected path of the vehicle, determining a geographical location of the vehicle by means of a localization system of the vehicle, and lowering an activation threshold value of the safety system when the geographical location of the vehicle is within a predefined distance from the dynamic object. The presented method provides for an efficient means for preparing e.g. an emergency brake assist system of a vehicle in potentially critical situations.

Evasive steering assist (ESA) systems and methods for a vehicle utilize a set of vehicle perception systems configured to detect an object in a path of the vehicle, a driver interface configured to receive steering input from a driver of the vehicle via a steering system of the vehicle, a set of steering sensors configured to measure a set of steering parameters, and a controller configured to determine a set of driver-specific threshold values for the set of steering parameters, compare the measured set of steering parameters and the set of driver-specific threshold values to determine whether to engage/enable an ESA feature of the vehicle, and in response to engaging/enabling the ESA feature of the vehicle, command the steering system to assist the driver in avoiding a collision with the detected object.

Methods and systems for tuning vehicle drivability are provided. The vehicle system includes, in one example, a vehicle control unit (VCU) that is designed to electronically communicate with a human machine interface (HMI). In the system, the VCU is designed to, in reaction to receiving an acceleration or deceleration request from the HMI, send a virtual acceleration or deceleration command to a transmission control unit (TCU), where the virtual acceleration or deceleration command correlates to the acceleration or deceleration request and the correlation is user adjustable via a user interface (UI) of the VCU.

A driving assistance processing method performed by an electronic device disposed on a vehicle terminal, and includes generating an advanced driving assistance system (ADAS) enhancement service registration request including ADAS capability information of the vehicle terminal, sending the ADAS enhancement service registration request to the cloud platform based on information about a cloud platform that provides ADAS enhancement service, and receiving an ADAS enhancement service message from the cloud platform and corresponds to the ADAS capability information. The ADAS enhancement service registration request allowing the cloud platform to learn about the ADAS capability information of the vehicle terminal.