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.

翻译：轨迹预测模块是实现自动驾驶汽车安全高效规划的关键使能技术，特别是在高度交互的交通场景中。近年来，基于学习的轨迹预测器因能够从数据中学习其他智能体的多模态行为而取得了显著成功，达到了当前最优性能。本文提出了一种名为多预测器融合（MPF）的算法，该算法通过引入专门执行基于逻辑规则的运动规划器，来增强基于学习的预测器的性能。MPF通过结合两个独立预测器生成的轨迹，并依据反映每个预测器在线性能的信念分布进行概率性融合，从而将基于学习与基于规则的预测方法相结合。实验结果表明，MPF在多项指标上均优于两个独立预测器，并展现出最稳定的性能表现。