Multi-Modal Simultaneous Forecasting of Vehicle Position Sequences using Social Attention

Figure 1: A driving scene top view representation with superposed forecast probability density functions represented in blue shades in log scale. The forcasting model uses the past trajectories plotted in gray as input. Abstract -- V ehicle trajectory forecasting models use a wide variety of frameworks for interaction and multi-modality. They rely on various representations of the road scene and definitions of maneuvers. In this paper we present a simple model that simultaneously forecasts each vehicle position on a road scene as a sequence of multi-modal probability density functions. This relies solely on vehicle position tracks and does not define maneuvers. We produce an easily extendable model that combines these predictive capabilities while surpassing state-of-the-art results. Its architecture uses multi-head attention to account for complete interactions between all vehicles, and long short-term memory (LSTM) layers for encoding and forecasting. I. INTRODUCTION Automation of driving tasks aims for safety and comfort improvements. For that purpose, most Autonomous Driving (AD) system relies on the anticipation of the traffic scene movements.