Trajectory prediction modules are key enablers for safe and efficient
planning of autonomous vehicles (AVs), particularly in highly interactive
traffic scenarios. Recently, learning-based trajectory predictors have
experienced considerable success in providing state-of-the-art performance due
to their ability to learn multimodal behaviors of other agents from data. In
this paper, we present an algorithm called multi-predictor fusion (MPF) that
augments the performance of learning-based predictors by imbuing them with
motion planners that are tasked with satisfying logic-based rules. MPF
probabilistically combines learning- and rule-based predictors by mixing
trajectories from both standalone predictors in accordance with a belief
distribution that reflects the online performance of each predictor. In our
results, we show that MPF outperforms the two standalone predictors on various
metrics and delivers the most consistent performance.

本文介绍了一种称为 MPF 的算法，它通过将遵循基于逻辑的规则的运动规划和基于学习的预测器相结合，在高度交互式交通场景中提高了学习基础预测器的性能。结果显示，在各种指标上，MPF 优于两个独立预测器，并提供最一致的性能。