论文标题
前景网：行为预测中未来互动建模的加权有条件注意
ProspectNet: Weighted Conditional Attention for Future Interaction Modeling in Behavior Prediction
论文作者
论文摘要
行为预测在集成的自主驾驶软件解决方案中起着重要作用。在行为预测研究中，与单一代理行为预测相比，交互行为预测是一个易于探索的领域。预测互动剂的运动需要启动新的机制来捕获交互式对的关节行为。在这项工作中，我们将端到端的关节预测问题作为边际学习和车辆行为联合学习的顺序学习过程。我们提出了ProspectNet，这是一个采用加权注意分数的联合学习块，以模拟交互式剂对之间的相互影响。联合学习块首先称重多模式预测的候选轨迹，然后通过交叉注意来更新自我代理的嵌入。此外，我们将每个交互式代理的个人未来预测播放到一个优率评分模块中，以选择顶部的$ K $预测对。我们表明，ProspectNet优于两个边缘预测的笛卡尔产品，并且在Waymo交互式运动预测基准上实现了可比的性能。
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.
