Interactive trajectory prediction for autonomous driving based on Transformer

Abstract. Trajectory planning has undergone remarkable strides in recent times, especially in the behavior prediction of traffic participants. Given that strong coupling conditions such as pedestrians, vehicles, and roads restrict the interactive behavior of autonomous vehicles and other traffic participants, it has become critical to design a trajectory prediction algorithm based on traffic scenarios for autonomous-driving technology. In this paper, we propose a novel trajectory prediction algorithm based on Transformer networks, a data-driven method that ingeniously harnesses dual-input channels. The rationale underlying this approach lies in its seamless fusion of scene context modeling and multi-modal prediction within a neural network architecture. At the heart of this innovative framework resides the multi-headed attention mechanism, ingeniously deployed in both the agent attention layer and the scene attention layer. This finessing not only captures the profound interdependence between agents and their surroundings but also imbues the algorithm with a better real-time predictive prowess, enhancing computational efficiency. Eventually, substantial experiments with the Argoverse dataset will demonstrate improved trajectory accuracy, with the minimum average displacement error (MADE) and minimum final displacement error (MFDE) being reduced by 12 % and 31 %, respectively.