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).

翻译：自动驾驶中的车辆运动预测需要深入理解智能体交互，并保持运动在欧几里得几何变换下的等变性。传统模型往往缺乏应对自动驾驶场景中复杂动力学特性及智能体间交互关系的能力，导致模型容量不足，进而造成预测误差升高、训练效率降低。本研究采用EqMotion——一种先进的等变粒子与人体运动预测模型（同时兼顾智能体间不变性交互特性），并将其应用于多智能体车辆运动预测任务。此外，我们引入多模态预测机制，以概率化方式实现多未来轨迹的预测。通过结合EqMotion，本模型在仅有120万参数和不到2小时训练时间的条件下，实现了最先进的预测性能。