Modern neural trajectory predictors in autonomous driving are developed using imitation learning (IL) from driving logs. Although IL benefits from its ability to glean nuanced and multi-modal human driving behaviors from large datasets, the resulting predictors often struggle with out-of-distribution (OOD) scenarios and with traffic rule compliance. On the other hand, classical rule-based predictors, by design, can predict traffic rule satisfying behaviors while being robust to OOD scenarios, but these predictors fail to capture nuances in agent-to-agent interactions and human driver's intent. In this paper, we present RuleFuser, a posterior-net inspired evidential framework that combines neural predictors with classical rule-based predictors to draw on the complementary benefits of both, thereby striking a balance between performance and traffic rule compliance. The efficacy of our approach is demonstrated on the real-world nuPlan dataset where RuleFuser leverages the higher performance of the neural predictor in in-distribution (ID) scenarios and the higher safety offered by the rule-based predictor in OOD scenarios.

翻译：现代自动驾驶中的神经轨迹预测器通过模仿学习（IL）从驾驶日志中开发。尽管IL能够从大规模数据集中获取细微且多模态的人类驾驶行为，但生成的预测器通常在分布外（OOD）场景和交通规则遵守方面表现不佳。另一方面，经典基于规则的预测器通过设计能够预测符合交通规则的行为，同时对OOD场景具有鲁棒性，但这些预测器无法捕捉智能体间交互的细微之处以及人类驾驶员的意图。在本文中，我们提出了RuleFuser，一种基于后验网络启发的证据框架，它将神经预测器与经典基于规则的预测器相结合，以利用两者的互补优势，从而在性能与交通规则遵守之间取得平衡。我们在真实世界的nuPlan数据集上展示了该方法的有效性，其中RuleFuser在分布内（ID）场景下利用了神经预测器的高性能，在分布外（OOD）场景下利用了基于规则预测器提供的高安全性。