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Long-term situation prediction plays a crucial role in the development ofintelligent vehicles. A major challenge still to overcome is the prediction ofcomplex downtown scenarios with multiple road users, e.g., pedestrians, bikes,and motor vehicles, interacting with each other. This contribution tackles thischallenge by combining a Bayesian filtering technique for environmentrepresentation, and machine learning as long-term predictor. More specifically,a dynamic occupancy grid map is utilized as input to a deep convolutionalneural network. This yields the advantage of using spatially distributedvelocity estimates from a single time step for prediction, rather than a rawdata sequence, alleviating common problems dealing with input time series ofmultiple sensors. Furthermore, convolutional neural networks have the inherentcharacteristic of using context information, enabling the implicit modeling ofroad user interaction. Pixel-wise balancing is applied in the loss functioncounteracting the extreme imbalance between static and dynamic cells. One ofthe major advantages is the unsupervised learning character due to fullyautomatic label generation. The presented algorithm is trained and evaluated onmultiple hours of recorded sensor data and compared to Monte-Carlo simulation.

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling