Abstract: A driver assistance system of an ego vehicle is operated. The ego vehicle has at least one surroundings sensor for detecting the surroundings of the ego vehicle. Movements of multiple vehicles are detected with the at least one surroundings sensor in the surroundings of the ego vehicle. A movement model is generated based on the detected movements of the respective vehicles. A traffic situation is ascertained and a probability of correct classification of the traffic situation on the basis of the generated movement model by a machine learning method. The traffic situation and the probability of the correct classification of the traffic situation are ascertained by the machine learning method on the basis of the learned characteristic features of the movement model. The driver assistance system of the ego vehicle is adapted to the ascertained traffic situation.

Abstract: The present disclosure relates to a method for detecting one or more objects in an environment of a vehicle, the environment being bounded by a perimeter, the method comprising: segmenting the environment into a plurality of segments such that each segment of the plurality of segments is at least partially bounded by the perimeter of the environment; detecting one or more detection points based on the one or more objects in the environment of the vehicle; combining the one or more detection points into one or more clusters based on a spatial proximity of the one or more detection points; and assigning a state to each of the segments of the plurality of segments based on the one or more detected detection points and/or based on the one or more combined clusters. The present disclosure further relates to a system for detecting one or more objects in a vehicle environment and a vehicle comprising the system.

Abstract: A driver assistance system of an ego vehicle is operated. The ego vehicle has at least one surroundings sensor for detecting the surroundings of the ego vehicle. Movements of multiple vehicles are detected with the at least one surroundings sensor in the surroundings of the ego vehicle. A movement model is generated based on the detected movements of the respective vehicles. A traffic situation is ascertained and a probability of correct classification of the traffic situation on the basis of the generated movement model by a machine learning method. The traffic situation and the probability of the correct classification of the traffic situation are ascertained by the machine learning method on the basis of the learned characteristic features of the movement model. The driver assistance system of the ego vehicle is adapted to the ascertained traffic situation.