Feed-forwards meet recurrent networks in vehicle trajectory prediction

For an autonomous car, getting surprised is the worst thing that can happen. To prevent that, plenty of studies are trying to forecast traffic participants’ actions especially in an urban scene using recurrent networks. Recurrent networks are used for temporal tasks in many application domains like Natural Language processing and computer vision. Despite the tendency in the literature to use recurrent neural networks for trajectory prediction, we argue that because of small dependency in trajectory sequences of a vehicle, a feed-forward neural network can be used, instead. In this paper, we will compare these two methods in vehicle trajectory prediction while considering the vanilla models or taking the scene into account. In order to have more variations in trajectories, roundabouts are used as a case study. Our results show that the proposed feed-forward network has competitive results with a recurrent network with 6 times faster processing time.