  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.
