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：摘要：自动驾驶中的车辆运动预测需要深刻理解智能体交互行为，并保持运动在欧几里得几何变换下的等变性。传统模型往往缺乏处理自动驾驶车辆固有复杂动力学以及场景中智能体间交互关系所需的能力。因此，这些模型容量较低，导致预测误差增大、训练效率降低。在本研究中，我们采用领先的等变粒子与人体预测模型EqMotion，该模型同时考虑不变性智能体交互，将其应用于多智能体车辆运动预测任务。此外，我们采用多模态预测机制以概率方式处理多种可能的未来路径。通过利用EqMotion，我们的模型以更少参数（120万）和显著缩短的训练时间（不到2小时）实现了最优性能。