Methods, systems, and apparatus for a vehicle monitoring and driver support system. The vehicle monitoring and driver support system includes one or more sensors configured to capture vehicle sensor data of a vehicle and an electronic control unit in electronic communication with the sensors. When a driver is operating the vehicle, the system receives the vehicle sensor data. The system can also receive survey data indicative of a personality and/or an attitude of the driver. Based on the vehicle sensor data and/or the survey data, the system uses a first predictive model to predict a general risky driving behavior. In response to predicting the general risky driving behavior, the system uses a second predictive model to predict a specific risky driving behavior. The system can activate countermeasures to encourage the driver to respond and mitigate the risky driving behavior.

A method for producing a model (15) for automated prediction of interactions of a user with a user interface of a motor vehicle. Vehicle operating logs (11, 12, 13) are provided and each includes a record of a time sequence of user interactions with the user interface. Each of the user interactions recorded in the vehicle operating logs (11, 12, 13) is assigned context information (21, 22) that includes a functional category (21) of the user interaction and a driving state (22) of the motor vehicle at the time of the user interaction. Training data (14) are generated based on the vehicle operating logs (11, 12, 13) and the associated context information (21, 22). A context-sensitive interaction model (15) is trained by machine learning on the basis of the training data (14) to make a prediction about a future user interaction based on a time sequence of past user interactions.

A computer-implemented method of determining a torque limit for an operating state of a first vehicle combination is provided. The method includes simulating a plurality of operating states for one or more second vehicle combinations. Each operating state is based on one or more operational parameters related to physical properties of the one or more second vehicle combinations, one or more parameters related to an operating environment of the one or more second vehicle combinations, and one or more parameters related to a driving scenario of the one or more second vehicle combinations. The method includes classifying each of the simulated operating states as safe or unsafe, receiving an unsimulated operating state for the first vehicle combination, and determining a torque limit for the unsimulated operating state based on the simulated operating states.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for planning lane changes for autonomous vehicles.

Aspects of the present disclosure relate to a control system, a system, a method, a vehicle and a non-transitory computer readable medium for receiving user movement data indicative of movement of a user's head; receiving object movement data indicative of movement of an object, the object being associated with a non-driving task; determining one or more relative movement parameters indicative of the relative movement of the user's head with respect to the object based at least in part on the received user movement data and the object movement data; and determining an attention level of the user to the non-driving task based at least in part on the determined relative movement parameter.

A method for controlling a vehicle that is at least partially assisted via an ADAS control system. A planner module performs numerical optimization to achieve goals such as a short travel time and low energy consumption. The numerical optimization uses context information as input parameters, such context information including route information, road course information, and/or environmental information. An output of the planner module is used as an input parameter for the ADAS control system for operating the vehicle. A personalized driver parameter set for corresponding to a specific driver is used as a boundary condition for the numerical optimization. The personalized driver parameter set represents a personal driving style of the specific driver.

Methods, systems, and apparatus for a vehicle monitoring and driver support system. The vehicle monitoring and driver support system includes one or more sensors configured to capture vehicle sensor data of a vehicle and an electronic control unit in electronic communication with the sensors. When a driver is operating the vehicle, the system receives the vehicle sensor data. The system can also receive survey data indicative of a personality and/or an attitude of the driver. Based on the vehicle sensor data and/or the survey data, the system uses a first predictive model to predict a general risky driving behavior. In response to predicting the general risky driving behavior, the system uses a second predictive model to predict a specific risky driving behavior. The system can activate countermeasures to encourage the driver to respond and mitigate the risky driving behavior.