  To handle the two shortcomings of existing methods, (i)nearly all models rely
on high-definition (HD) maps, yet the map information is not always available
in real traffic scenes and HD map-building is expensive and time-consuming and
(ii) existing models usually focus on improving prediction accuracy at the
expense of reducing computing efficiency, yet the efficiency is crucial for
various real applications, this paper proposes an efficient trajectory
prediction model that is not dependent on traffic maps. The core idea of our
model is encoding single-agent's spatial-temporal information in the first
stage and exploring multi-agents' spatial-temporal interactions in the second
stage. By comprehensively utilizing attention mechanism, LSTM, graph
convolution network and temporal transformer in the two stages, our model is
able to learn rich dynamic and interaction information of all agents. Our model
achieves the highest performance when comparing with existing map-free methods
and also exceeds most map-based state-of-the-art methods on the Argoverse
dataset. In addition, our model also exhibits a faster inference speed than the
baseline methods.
