Abstract: A method, a computer program code, an apparatus for processing environmental data of an environment of a vehicle, a driver assistance system, which makes use of such a method or apparatus, and an autonomous or semi-autonomous vehicle comprising such a driver assistance system. Depth data of the environment of the vehicle is received from at least one depth sensor of the vehicle. Furthermore, thermal data of the environment of the vehicle is received from at least one thermal sensor of the vehicle. The depth data and the thermal data are then fused to generate fused environmental data.

Abstract: This disclosure is related to a method, a computer program code, and an apparatus for training a convolutional neural network for an autonomous driving system. The disclosure is further related to a convolutional neural network, to an autonomous driving system comprising a neural network, and to an autonomous or semi-autonomous vehicle comprising such an autonomous driving system. For training the convolutional neural network, in a first step real-world driving data are selected as training data. Furthermore, synthetic driving data are generated as training data. The convolutional neural network is then trained on the selected real-world driving data and the generated synthetic driving data using a genetic algorithm.

Abstract: To determine a driving context of a vehicle, in a first step, sensor data of one or more sensors of the vehicle are received. Then an occupancy grid is determined based on the sensor data. Finally, the occupancy grid is parsed with a convolutional neural network for determining the driving context.

Abstract: An autonomous driving controller predicts future sensory observations of a distance ranging device of an autonomous or semi-autonomous vehicle having such an autonomous driving controller. In a first step, a sequence of previous sensory observations and a sequence of control actions are received. The sequence of previous sensory observations and the sequence of control actions are then processed with a temporal neural network to generate a sequence of predicted future sensory observations. Finally, the sequence of predicted future sensory observations is output for further use.

Abstract: A controller for an agent of a group of agents, in particular for a group of autonomous or semi-autonomous vehicles, and to a computer program implementing such a controller. A temporal deep network for such a controller and to a method, a computer program and an apparatus for training the temporal deep network. The controller includes a temporal deep network designed to calculate a desired trajectory for the agent, a nonlinear model predictive controller designed to calculate commands for the agent based on the desired trajectory and desired trajectories of the other agents of the group of agents, and an augmented memory designed to integrate historic system states of the group of agents for the temporal deep network.

Abstract: A controller for an autonomous or semi-autonomous vehicle, a computer program implementing such a controller, an autonomous or semi-autonomous vehicle having such a controller a method, a computer program, and an apparatus for training the controller. Furthermore, a nonlinear approximator for an automatic estimation of an optimal desired trajectory and to a computer program implementing such a nonlinear approximator. The controller is configured to use an a priori process model in combination with a behavioral model and a disturbance model. The behavioral model is responsible for estimating a behavior of a controlled system in different operating conditions and to calculate a desired trajectory for a constrained nonlinear model predictive controller. The disturbance model is used for compensating disturbances.

Abstract: A method, a computer program code, an apparatus for processing environmental data of an environment of a vehicle, a driver assistance system, which makes use of such a method or apparatus, and an autonomous or semi-autonomous vehicle comprising such a driver assistance system. Depth data of the environment of the vehicle is received from at least one depth sensor of the vehicle. Furthermore, thermal data of the environment of the vehicle is received from at least one thermal sensor of the vehicle. The depth data and the thermal data are then fused to generate fused environmental data.