Conditional behavior prediction (CBP) builds up the foundation for a coherent
interactive prediction and planning framework that can enable more efficient
and less conservative maneuvers in interactive scenarios. In CBP task, we train
a prediction model approximating the posterior distribution of target agents'
future trajectories conditioned on the future trajectory of an assigned ego
agent. However, we argue that CBP may provide overly confident anticipation on
how the autonomous agent may influence the target agents' behavior.
Consequently, it is risky for the planner to query a CBP model. Instead, we
should treat the planned trajectory as an intervention and let the model learn
the trajectory distribution under intervention. We refer to it as the
interventional behavior prediction (IBP) task. Moreover, to properly evaluate
an IBP model with offline datasets, we propose a Shapley-value-based metric to
verify if the prediction model satisfies the inherent temporal independence of
an interventional distribution. We show that the proposed metric can
effectively identify a CBP model violating the temporal independence, which
plays an important role when establishing IBP benchmarks.

该研究介绍了一种名为条件性行为预测的方法，该方法可以更有效地进行交互式场景中的规划。他们还开发了一种名为干预性行为预测的新方法，以更好地评估关于自主代理人对目标代理人行为影响的不确定性，并提出了一种基于 Shapley 值的指标来验证模型是否满足干预概率分布固有的时间独立性。