Behavior prediction plays an important role in integrated autonomous driving software solutions. In behavior prediction research, interactive behavior prediction is a less-explored area, compared to single-agent behavior prediction. Predicting the motion of interactive agents requires initiating novel mechanisms to capture the joint behaviors of the interactive pairs. In this work, we formulate the end-to-end joint prediction problem as a sequential learning process of marginal learning and joint learning of vehicle behaviors. We propose ProspectNet, a joint learning block that adopts the weighted attention score to model the mutual influence between interactive agent pairs. The joint learning block first weighs the multi-modal predicted candidate trajectories, then updates the ego-agent's embedding via cross attention. Furthermore, we broadcast the individual future predictions for each interactive agent into a pair-wise scoring module to select the top $K$ prediction pairs. We show that ProspectNet outperforms the Cartesian product of two marginal predictions, and achieves comparable performance on the Waymo Interactive Motion Prediction benchmarks.

本文提出了ProspectNet作为一种新的机制，通过采用加权注意分数来建模交互代理对之间的相互影响和联合行为识别来解决自主驾驶软件解决方案中的行为预测问题。实验结果表明ProspectNet性能优于两个边缘预测的Cartesian乘积，并在Waymo交互运动预测基准上达到了可比较的性能。

ProspectNet：基于加权的条件式注意力机制的交互未来预测建模