Abstract: A computer-implemented method and system for classifying a predefined traffic situation comprised by a data record of environment data of a motor vehicle, with an application of a directed graph to the first data record. Nodes of the directed graph segment the first data record in each case into at least one segment of a movement behavior of the ego vehicle and/or the fellow vehicle relative to a vehicle environment according to a first condition satisfied in a time interval comprising edges of the directed graph symbolizing links between the respective nodes. The predefined traffic situation is classified if all of the specified segments meet a second condition of the predefined traffic situation. A class is outputted representing the predefined traffic situation and/or a respective start and end time of the second data record comprising a segment representing the predefined traffic situation.

Abstract: A computer-implemented method and system for classifying a predefined traffic situation comprised by a data record of environment data of a motor vehicle, with an application of a directed graph to the first data record. Nodes of the directed graph segment the first data record in each case into at least one segment of a movement behavior of the ego vehicle and/or the fellow vehicle relative to a vehicle environment according to a first condition satisfied in a time interval comprising edges of the directed graph symbolizing links between the respective nodes. The predefined traffic situation is classified if all of the specified segments meet a second condition of the predefined traffic situation. A class is outputted representing the predefined traffic situation and/or a respective start and end time of the second data record comprising a segment representing the predefined traffic situation.

Abstract: Upon detecting a mobile object approaching a target region, a target speed profile for a vehicle, a position and a speed for the vehicle, and a position and a speed for the mobile object are input to a first neural network that outputs an entry probability distribution of predicted entry times at which the mobile object will enter the target region. The target speed profile, the position and the speed for the vehicle, the position and the speed for the mobile object, and a predicted entry time at which the mobile object entered the target region are input to a second neural network that outputs an exit probability distribution of predicted exit times at which the mobile object will exit the target region. An optimized speed profile is determined based on the entry and exit probability distributions by executing a model predictive control algorithm in a rolling horizon. A vehicle is operated based on the optimized speed profile.

Abstract: Upon detecting a mobile object approaching a target region, a target speed profile for a vehicle, a position and a speed for the vehicle, and a position and a speed for the mobile object are input to a first neural network that outputs an entry probability distribution of predicted entry times at which the mobile object will enter the target region. The target speed profile, the position and the speed for the vehicle, the position and the speed for the mobile object, and a predicted entry time at which the mobile object entered the target region are input to a second neural network that outputs an exit probability distribution of predicted exit times at which the mobile object will exit the target region. An optimized speed profile is determined based on the entry and exit probability distributions by executing a model predictive control algorithm in a rolling horizon. A vehicle is operated based on the optimized speed profile.