Interaction-Aware Trajectory Prediction based on a 3D Spatio-Temporal Tensor Representation using Convolutional–Recurrent Neural Networks

Predicting the future trajectories for all vehicles relevant to the ego vehicle is a crucial, yet unsolved challenge to master automated driving. This paper proposes a combination of two lines of research for predicting all the trajectories of a group of vehicles of arbitrary size, considering the mutual interactions possible. Treating the prediction of other vehicles as a planning task for themselves enables the application of the artificial potential field approach. Modeling the driving situation as a potential field turns the trajectory prediction problem back to its original domain – utility space. Humans generate trajectories (that should be predicted) during driving by balancing costs and rewards, which lead to a total utility. The main difficulty inherent to the potential field approach is the hard problem of parameter tuning. Therefore, it is not directly used for prediction. Instead, the potential field representation is used as input for a neural network, which predicts a distribution over trajectories based on distinct maneuvers. This allows a multi-modal prediction for each vehicle and reflects the pattern recognition character.