Deep neural networks for Markovian interactive scene prediction in highway scenarios

In this paper, we compare different deep neural network approaches for motion prediction within a highway entrance scenario. The focus of our work lies on models that operate on limited history of data in order to fulfill the Markov property1 and be usable within an integrated prediction and motion planning framework for automated vehicles. We examine different model structures and feature combinations in order to find a model with a good tradeoff between accuracy and computational performance. We evaluate all models with standard metrics like the negative log-likelihood (NLL) and evaluate the performance of each model within a closed-loop simulation. We find a neural network only operating on spatial features of the current state to have the best closed-loop prediction performance, despite the NLL suggesting otherwise.