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

ProspectNet: Weighted Conditional Attention for Future Interaction Modeling in Behavior Prediction

Many machine learning applications involve jointly predicting multiple
mutually dependent output variables. Learning to search is a family of methods
where the complex decision problem is cast into a sequence of decisions via a
search space. Although these methods have shown promise both in theory and in
practice, implementing them has been burdensomely awkward. In this paper, we
show the search space can be defined by an arbitrary imperative program,
turning learning to search into a credit assignment compiler. Altogether with
the algorithmic improvements for the compiler, we radically reduce the
complexity of programming and the running time. We demonstrate the feasibility
of our approach on multiple joint prediction tasks. In all cases, we obtain
accuracies as high as alternative approaches, at drastically reduced execution
and programming time.

本文介绍了一种新的学习搜索方法，通过将复杂的决策问题转化为搜索空间的一系列决策，从而实现多个相互依赖输出变量的联合预测任务，并将其定义为任意命令程序的搜索空间，通过算法改进和编译器将编程和运行时间的复杂度大大降低，其效果在多个预测任务中获得了与替代方法相当的准确性。