  Predicting the future motion of surrounding road users is a crucial and
challenging task for autonomous driving (AD) and various advanced
driver-assistance systems (ADAS). Planning a safe future trajectory heavily
depends on understanding the traffic scene and anticipating its dynamics. The
challenges do not only lie in understanding the complex driving scenarios but
also the numerous possible interactions among road users and environments,
which are practically not feasible for explicit modeling. In this work, we
tackle the above challenges by jointly learning and predicting the motion of
all road users in a scene, using a novel convolutional neural network (CNN) and
recurrent neural network (RNN) based architecture. Moreover, by exploiting
grid-based input and output data structures, the computational cost is
independent of the number of road users and multi-modal predictions become
inherent properties of our proposed method. Evaluation on the nuScenes dataset
shows that our approach reaches state-of-the-art results in the prediction
benchmark.
