Learning When to Search: Adaptive Test-Time Computation for Generative Motion Planning

In autonomous driving, generative motion planners often use candidate generation and scoring to improve the final trajectory, but running this search process at every planning step introduces additional computation. In this study, we investigate when search is actually needed. Our analysis shows that the benefit of search varies greatly across planning steps and is related to both the current planning information and recent history. We therefore develop a lightweight history-aware model to decide whether search should be performed after the base trajectory is generated. If search is needed, the original search module generates and evaluates additional candidates; otherwise, the base trajectory is used directly. Closed-loop experiments on nuPlan show that our method achieves an average score of 86.07 with search on 56.81% of planning steps, comparable to Full Search at 85.64 with 100% calls. It also performs better than fixed search schedules at similar search rates and reduces mean planning inference time by 32.0%.