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.
翻译:预测环绕车辆的动力,以便进行自我驾驶规划至关重要,尤其是以社会兼容和灵活的方式进行。然而,由于驾驶行为的互动性和不确定性,未来预测具有挑战性。我们提出基于规划的轨迹预测(PiP),以解决多试剂环境下的预测问题。我们的方法与传统的预测方式不同,传统的预测方式仅以历史信息为基础,与规划脱钩。通过向预测过程提供自我驾驶车辆规划信息,我们的方法实现了公路数据集多试剂预测的最先进的性能。此外,我们的方法使预测和规划搭配了一条新型管道,为自用车辆的多个候选轨迹设置了PiPiP,这对互动情况下的自主驾驶非常有益。