A control system for use in a motor vehicle and configured to monitor a current driving situation of the motor vehicle on the basis of surrounding data of the motor vehicle acquired from at least one surrounding sensor arranged on the motor vehicle in a current driving situation is disclosed. The control system is configured to determine information relating to a current driving situation of the motor vehicle on the basis of the provided surrounding data, to determine information relating to a current driving situation of the motor vehicle and to determine a component of a future driving maneuver for the motor vehicle on the basis of the information relating to the current driving situation of the motor vehicle. Furthermore, the control system is configured to determine a multiplicity of model trajectories for the motor vehicle on the basis of the determined component of the future driving maneuver for the motor vehicle and to determine from the multiplicity of model trajectories a trajectory for the motor vehicle which the motor vehicle is to follow in the further course of its travel. The control system is also configured to update the information relating to the current driving situation of the motor vehicle and/or the supplied surrounding data and to adapt the trajectory for the motor vehicle on the basis of a target function and on the basis of the updated supplied surrounding data and/or on the basis of the updated information relating to the current driving situation of the motor vehicle.

The present disclosure provides a method for generating road topology information performed by at least one processor. With respect to coordinate data of a road, boundary conditions between segments including topology information may be calculated to make the connection between segments flexible, and a midpoint of a curve of a segment may be detected to directly calculate control points of a B?zier curve, thereby generating a parametric B?zier curve. Accordingly, more precise road map may be generated even when the coordinate data of the segment of the road includes an error.

In various example embodiments, a system and method for determining a gross combined weight of an electric or hybrid vehicle and its load is disclosed. A method includes: providing predetermined calibration settings relating engine drive force to engine speed and electric motor drive force to motor speed for a vehicle travelling at various speeds, altering accelerometer data based on filtered vehicle pitch and roll data, determining that one or more vehicle performance parameters fall within a threshold range, storing a plurality of data pairs that include longitudinal acceleration and drive force, and determining a slope of a line that linearly approximates the plurality of data pairs, the slope indicating a total weight, the total weight comprising a weight of the vehicle and a weight being hauled by the vehicle; and transmitting the total weight to a display.

Method and control unit for predicting a path of a vehicle are provided. The method comprises measuring velocity of the vehicle; measuring steering wheel angle (?.sub.sw); measuring steering wheel angle rate (?.sub.sw); calculating a future steering wheel angle (?.sub.sw), based on the measured steering wheel angle (?.sub.sw) and the measured steering wheel angle rate (?.sub.sw); calculating a future yaw rate (?) of the vehicle based on the measured velocity of the vehicle and the calculated future steering wheel angle (?.sub.sw); extrapolating a vehicle position of the vehicle in a set of future time frames, based on the calculated future yaw rate (?) and the vehicle velocity; and predicting the path of the vehicle based on the extrapolated vehicle positions in the set of future time frames.

In order to control a technical system, a user control variable is read in and a plurality of control variable variants of the user control variable are generated. A respective trajectory of the technical system is extrapolated for the user control variable and for the control variable variants, for which a respective reliability is evaluated. Furthermore, a respective distance of each control variable variant to the user control variable is determined. The user control variable is then selected as a control signal for the technical system in the event that the trajectory extrapolated for the user control variable is evaluated as reliable. Otherwise, a control variable variant with an extrapolated trajectory evaluated as reliable is selected from the control variable variants as a control signal, wherein a control variable variant with a low distance is preferably selected. Finally, the control signal for controlling the technical system is emitted.

A method for determining an incline in a road that is some distance ahead of a motor vehicle relative to the section of road that is currently being driven on by the motor vehicle, wherein images of the road that is ahead of the motor vehicle are recorded by a camera, road path lines are identified ahead of the motor vehicle in the recorded images and the relative incline is calculated with reference to the differences in the courses of the road path lines at different distances.

In various example embodiments, a system and method for determining a gross combined weight of an electric or hybrid vehicle and its load is disclosed. A method includes: providing predetermined calibration settings relating engine drive force to engine speed and electric motor drive force to motor speed for a vehicle travelling at various speeds, altering accelerometer data based on filtered vehicle pitch and roll data, determining that one or more vehicle performance parameters fall within a threshold range, storing a plurality of data pairs that include longitudinal acceleration and drive force, and determining a slope of a line that linearly approximates the plurality of data pairs, the slope indicating a total weight, the total weight comprising a weight of the vehicle and a weight being hauled by the vehicle; and transmitting the total weight to a display.

A system includes a computer programmed to identify, from a first vehicle, one or more second vehicles within a specified distance to the first vehicle. The computer is further programmed to receive data about operations of each of the second vehicles, including trajectory data. Based on the data, the computer is programmed to identify, for each of the second vehicles, a distribution of probabilities of each of a set of potential planned trajectories. The computer is further programmed to determine a planned trajectory for the first vehicle, based on the respective distributions of probabilities of each of the set of potential planned trajectories for each of the second vehicles. The computer is further programmed to provide an instruction to at least one controller associated with the first vehicle based on the determined planned trajectory.

An autonomous driving apparatus and a method for generating a precise map using the autonomous driving apparatus are configured to vary an application ratio of interpolation to precise map data to be stored depending on an accident risk on a corresponding road and a distance from a host vehicle during driving so as to optimize the precise map data to be stored. The autonomous driving apparatus includes an accident risk classification unit configured to vary the application ratio of interpolation to data and whether or not to store data acquired by applying interpolation to the data depending on the accident risk on the corresponding road and the distance from the host vehicle during driving, and an autonomous driving controller configured to generate the precise map data depending on the accident risk classification unit.

A system includes a computer programmed to identify, from a first vehicle, one or more second vehicles within a specified distance to the first vehicle. The computer is further programmed to receive data about operations of each of the second vehicles, including trajectory data. Based on the data, the computer is programmed to identify, for each of the second vehicles, a distribution of probabilities of each of a set of potential planned trajectories. The computer is further programmed to determine a planned trajectory for the first vehicle, based on the respective distributions of probabilities of each of the set of potential planned trajectories for each of the second vehicles. The computer is further programmed to provide an instruction to at least one controller associated with the first vehicle based on the determined planned trajectory.