  Forecasting vehicular motions in autonomous driving requires a deep
understanding of agent interactions and the preservation of motion equivariance
under Euclidean geometric transformations. Traditional models often lack the
sophistication needed to handle the intricate dynamics inherent to autonomous
vehicles and the interaction relationships among agents in the scene. As a
result, these models have a lower model capacity, which then leads to higher
prediction errors and lower training efficiency. In our research, we employ
EqMotion, a leading equivariant particle, and human prediction model that also
accounts for invariant agent interactions, for the task of multi-agent vehicle
motion forecasting. In addition, we use a multi-modal prediction mechanism to
account for multiple possible future paths in a probabilistic manner. By
leveraging EqMotion, our model achieves state-of-the-art (SOTA) performance
with fewer parameters (1.2 million) and a significantly reduced training time
(less than 2 hours).
