A control system (10) is suitable for use in one's own motor vehicle (12) and is configured and intended to use the environmental data provided to determine a position and a speed of a first motor vehicle (28) which is traveling directly ahead of one's own motor vehicle (12) in a first Lane (36), wherein one's own motor vehicle (12) is in the said lane (36). Furthermore, the control system is at least configured and intended to determine a position and a speed of a second motor vehicle (30) which is traveling in a lane (38) adjacent to the first lane (36) from the environmental data provided. Furthermore, the control system is configured and intended to detect from the environmental data provided whether there is a zipper situation. The control system is configured and intended to increase a target distance of one's own motor vehicle (12) to the first motor vehicle, if an amount of a relative speed of the second motor vehicle (30) relative to one's own motor vehicle (12) or relative to the first motor vehicle (28) is less than a predetermined first value, if the second motor vehicle (30) is located between one's own motor vehicle (12) and the first motor vehicle (28) in a longitudinal direction which extends along the adjacent lane (38), and if it was detected that the zipper situation applies.

A path planning device configured to plan a traveling path on a future route of a vehicle includes a lane evaluation unit, a score accumulation unit, and a lane selection unit. The lane evaluation unit is configured to estimate a risk score of each chronological section of each traveling lane parallel traveling lanes, the risk score representing a traveling risk. The score accumulation unit is configured to accumulate the risk scores for each traveling lane. The lane selection unit is configured to select the traveling lane for the traveling path based on evaluation accumulated values. The lane evaluation unit is configured to estimate a base score based on static information. The static information contains a traveling difficulty for the vehicle at a change node. The lane evaluation unit is configured to estimate the base score such that the traveling risk is higher as the traveling difficulty is higher.

A method for the automatic control of the longitudinal dynamics of a vehicle is provided by which vehicles traveling ahead are detected. If an upcoming traffic jam is detected, the vehicle is decelerated until a predefined distance behind the tail end of the traffic jam is reached. When the predefined distance from the traffic jam tail end has been reached, the vehicle automatically controlled in its longitudinal dynamics is able to close the remaining, predefined distance to the traffic jam tail end at a low differential velocity in comparison to the velocity of the traffic jam tail end. Using an additional rear sensor system that senses trailing vehicles, the controlled vehicle is made to close the distance to the traffic jam tail end only if a trailing vehicle was detected.

Embodiments of this application disclose a vehicle-based data processing method performed by a computer device. The method includes: determining at least two predicted offsets of a first vehicle, a first traveling state of the first vehicle, and a second traveling state of a second vehicle; determining, according to the first traveling state and the second traveling state, first lane change payoffs of the predicted offsets when the second vehicle is in a yielding prediction state, and determining second lane change payoffs when the second vehicle is in a non-yielding prediction state; and determining a predicted yielding probability of the second vehicle, generating target lane change payoffs of the predicted offsets according to the predicted yielding probability and the first lane change payoffs and the second lane change payoffs of the predicted offsets, and determining a predicted offset having a maximum target lane change payoff as a target predicted offset.

The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

Autonomous driving of a vehicle in which computerized perception by the vehicle, including of its environment and of itself (e.g., its egomotion), is used to autonomously drive the vehicle and, additionally, can also be used to provide feedback to enhance performance, safety, and/or other attributes of autonomous driving of the vehicle (e.g., when certain conditions affecting the vehicle are determined to exist by detecting patterns in or otherwise analyzing what is perceived by the vehicle), such as by adjusting autonomous driving of the vehicle, conveying messages regarding the vehicle, and/or performing other actions concerning the vehicle.

A method may include determining time-variable risk profiles for plural separate vehicle systems that are remotely controlled by operators that are located off-board the separate vehicle systems. The time-variable risk profiles represent one or more risks to travel of the separate vehicle systems. The method may include assigning the operators to remotely monitor or control the separate vehicle systems during the trips based on the time-variable risk profiles. The operator assigned changes with respect to time while the one or more separate vehicle systems is moving along one or more routes during the trip. A system may include one or more processors that may determine time-variable risk profiles for plural separate vehicle systems that are remotely controlled and assign the operators to remotely monitor or control the separate vehicle systems during the trips based on the time-variable risk profiles.

A method for operating a driving assistant for the automated lateral guidance of a motor vehicle. The method includes: executing an automated lane guidance of the motor vehicle in a first traffic lane; ascertaining a hindrance situation of a further motor vehicle due to the motor vehicle; decision for the automated lane opening of the first traffic lane taking the hindrance situation into account; executing an automated lane change into a second traffic lane for opening the lane; executing an automated lane guidance in the second traffic lane after opening the lane. A device for carrying out the method is also described.

A system and method automatically sets vehicle functions of a vehicle, while taking account of external influencing factors. The system includes at least one back-end server which is configured to receive vehicle data of the vehicle and, while taking account of at least part of the vehicle data, to determine data with regard to external influencing factors. The back-end server is configured to determine optimal settings of the vehicle functions of the vehicle, while taking account of the vehicle data and the data with regard to the external influencing factors, and to transmit the optimum settings of the vehicle functions to the vehicle. The vehicle is configured to control the vehicle functions such that the optimum vehicle functions determined by the back-end server are adopted.

A stopping position control device according to an embodiment includes a sitting position specifying unit configured to specify a sitting position of a user who gets off a vehicle next, the vehicle being subjected to automatic driving control, and a stopping position determination unit configured to determine a stopping position of the vehicle, at which the user gets off the vehicle, corresponding to the sitting position of the user.