A driving assistance system for automated driving of a vehicle includes at least one processor unit, which is designed to perform the following when the driving assistance system is carrying out a maneuver of turning onto a street: to determine, on the basis of environment data of an environment sensor system of the vehicle, whether another, on-coming road user is blocking a lane of the street which corresponds to the vehicle; and when it is determined that the other road user is blocking the lane of the street which corresponds to the vehicle to carry out a situation evaluation in order to determine whether the vehicle should back up in order to allow the other road user to exit the street or whether the other road user should be requested to clear the lane by backing up.

An assistance system for a vehicle capable of automated operation has a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The vehicle is located on a first lane in a vicinity of one or more neighboring vehicles, the first lane merging with a second lane at a merging trajectory location. The controller is adapted to selectively execute a leader determination module when a distance of the vehicle to the merge starting point is less than a threshold value. This includes determining an estimated arrival time of the vehicle to a merge starting point of the merging trajectory location. The controller is adapted to select a leader vehicle from the neighboring vehicles based in part on their respective estimated arrival times to the merge starting point. Operation of the vehicle is controlled based in part on the leader vehicle.

Provided is a method and apparatus with driving control. A method includes estimating a predicted route of a vehicle based on odometry information of the vehicle and location information of the vehicle, based on road map information, determining a target zone corresponding to the predicted route, and generating driving control information of the vehicle based on an object detection result for the determined target zone.

A control system of a vehicle includes: a target speed module configured to, using a parametric driver model and based on first driver parameters, second driver parameters, and vehicle parameters, determine a target vehicle speed trajectory for a future predetermined period; a driver parameters module configured to determine the first driver parameters based on conditions within a predetermined distance in front of the vehicle; and a control module configured to adjust at least one actuator of the vehicle based on the target vehicle speed trajectory and a present vehicle speed.

Systems and methods for characterizing a driving style of an autonomous vehicle are presented. A system may include one or more sensors configured to collect information concerning driving characteristics; a memory containing computer-readable instructions for evaluating the driving characteristics for a pattern(s) correlatable with a driving style of the autonomous vehicle and for characterizing aspects of driving style based on the one or more patterns; and a processor configured to evaluate the driving characteristics for the one or more patterns correlatable with the driving style, and characterize aspects of the driving style based on the pattern(s). Corresponding methods and non-transitory media are disclosed.

This application is directed to on-vehicle behavior modeling of vehicles. A vehicle has one or more processors, memory, a plurality of sensors, and a vehicle control system. The vehicle collects training data via the plurality of sensors, and the training data include data for one or more vehicles during a collection period. The vehicle locally applies machine learning to train a vehicle driving behavior model using the collected training data. The vehicle driving behavior model is configured to predict a behavior of one or more vehicles. The vehicle subsequently collecting sensor data from the plurality of sensors and drives the vehicle by applying the vehicle driving behavior model to predict vehicle behavior based on the collected sensor data. The vehicle driving behavior model is configured to predict behavior of an ego vehicle and/or a distinct vehicle that appears near the ego vehicle.

A processor unit (3) is configured for accessing speed data of a second vehicle (18), the speed data generated by a sensor of a first vehicle (1). The processor unit is also configured for creating a driving behavior profile of the second vehicle (18) based on the speed data and making a prediction about the future driving behavior of the second vehicle (18) based on the driving behavior profile of the second vehicle (18). Moreover, the processor unit is configured for determining a trajectory for the first vehicle (1) by executing an MPC algorithm, which includes a longitudinal dynamic model of the first vehicle and a cost function, such that the cost function is minimized. The prediction about the future driving behavior of the second vehicle (18) is taken into account in the determination of the trajectory.

The disclosure relates to a control method for driver assistance. The method includes: receiving positioning information of a vehicle; obtaining information about a road structure ahead of the vehicle based on the positioning information; and determining, based on the information about the road structure, whether an alarm for a sharp bend is needed. The disclosure further relates to a control device for driver assistance, a computer storage medium, and a vehicle.

Technologies and techniques for carrying out an assisted lane change onto a deceleration lane, during which an intervention in an acceleration device may be carried out in an at least partially assisted manner as a function of a driver input on a driving lane of the roadway. The lane change from the driving lane onto the deceleration lane may be carried out in an at least partially assisted manner, wherein swarm data are received from at least one further motor vehicle, which carried out a lane change onto the deceleration lane at a crossing, and a change position for the lane change of the motor vehicle is determined as a function of the received crossing position. The lane change is carried out at the determined change position. Other aspects relate to a computer program product and to an assistance system.

A method that includes obtaining vehicle sensed environment information by at least one sensor of a vehicle; determining, by an initial location estimate module of the vehicle, an initial location estimate of the vehicle; obtaining, by processor of the vehicle, aerial map segment information related to a segment of an aerial map, the segment comprises an environment of the initial location estimate of the vehicle; determining, based on the vehicle sensed information and on the aerial map segment information, to perform the driving related operation within at least the environment of the initial location estimate of the vehicle; and performing the driving related operation.