Expand-and-Prune: Test-Time Candidate-Space Adaptation for End-to-End Autonomous Driving
Candidate-based end-to-end driving planners reason over a finite set of trajectory hypotheses. However, for sparse planners, a limited candidate set may provide insufficient support for scene-relevant maneuvers, while fixed downstream candidate budgets do not account for prediction uncertainty. To address these challenges, we introduce Expand-and-Prune, a test-time framework that jointly adapts candidate support and capacity without updating learned parameters. The framework expands the native candidate set using semantic driving intent and temporally propagated predictions and then uses uncertainty in intermediate trajectory scores to adaptively prune the resulting candidates. Across fou...
| Authors | Fabian Schmidt, Karol Fedurko, Markus Enzweiler et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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