It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.

中文翻译：

---

PiP：自动驾驶的规划信息轨迹预测

预测周围车辆的运动以进行自动驾驶规划至关重要，尤其是以符合社会要求和灵活的方式。然而，由于驾驶行为的相互作用和不确定性，未来的预测具有挑战性。我们提出了基于规划的轨迹预测 (PiP) 来解决多智能体设置中的预测问题。我们的方法不同于传统的预测方式，传统的预测方式仅基于历史信息，与规划脱钩。通过利用自我车辆的规划通知预测过程，我们的方法在高速公路数据集上实现了多智能体预测的最先进性能。此外，我们的方法通过在自我车辆的多个候选轨迹上调节 PiP，实现了一种将预测和规划相结合的新管道，