A Lightweight Goal-Based model for Trajectory Prediction

Abstract

We present a lightweight goal-based model for multimodal, probabilistic trajectory prediction for urban driving. Previous conditioned-on-goal methods have used map information in order to establish a set of potential goals and then complete the corresponding full trajectory for each goal. We instead propose two original representations, based on the agent's states and its kinematics, to extract the potential goals. In this paper, we conduct a comparative study between the two representations. We also evaluate our approach on the nuScenes dataset, and show that it outperforms a wide array of state-ofthe-art methods.